Compare commits

..
2 Commits
Author SHA1 Message Date
pythongosssss 14624cd6bd Allow running with non working 2025-03-28 11:46:05 +08:00
pythongosssss 7287f6a4e2 Add model management and database
- use sqlalchemy + alembic + sqlite for db
- extract model data and previews
- endpoints for db interactions
- add tests
2025-03-28 11:39:56 +08:00
988 changed files with 335514 additions and 328995 deletions
-8
View File
@@ -1,8 +0,0 @@
@echo off
..\python_embeded\python.exe .\update.py ..\HanzoStudio\
if exist update_new.py (
move /y update_new.py update.py
echo Running updater again since it got updated.
..\python_embeded\python.exe .\update.py ..\HanzoStudio\ --skip_self_update
)
if "%~1"=="" pause
+4 -22
View File
@@ -47,22 +47,12 @@ def pull(repo, remote_name='origin', branch='master'):
pygit2.option(pygit2.GIT_OPT_SET_OWNER_VALIDATION, 0)
repo_path = str(sys.argv[1])
repo = pygit2.Repository(repo_path)
ident = pygit2.Signature('hanzo-studio', 'studio@hanzo.ai')
ident = pygit2.Signature('comfyui', 'comfy@ui')
try:
print("stashing current changes") # noqa: T201
repo.stash(ident)
except KeyError:
print("nothing to stash") # noqa: T201
except:
print("Could not stash, cleaning index and trying again.") # noqa: T201
repo.state_cleanup()
repo.index.read_tree(repo.head.peel().tree)
repo.index.write()
try:
repo.stash(ident)
except KeyError:
print("nothing to stash.") # noqa: T201
backup_branch_name = 'backup_branch_{}'.format(datetime.today().strftime('%Y-%m-%d_%H_%M_%S'))
print("creating backup branch: {}".format(backup_branch_name)) # noqa: T201
try:
@@ -73,14 +63,7 @@ except:
print("checking out master branch") # noqa: T201
branch = repo.lookup_branch('master')
if branch is None:
try:
ref = repo.lookup_reference('refs/remotes/origin/master')
except:
print("fetching.") # noqa: T201
for remote in repo.remotes:
if remote.name == "origin":
remote.fetch()
ref = repo.lookup_reference('refs/remotes/origin/master')
ref = repo.lookup_reference('refs/remotes/origin/master')
repo.checkout(ref)
branch = repo.lookup_branch('master')
if branch is None:
@@ -153,12 +136,11 @@ if not os.path.exists(req_path) or not files_equal(repo_req_path, req_path):
pass
stable_update_script = os.path.join(repo_path, ".ci/update_windows/update_studio_stable.bat")
stable_update_script_to = os.path.join(cur_path, "update_studio_stable.bat")
stable_update_script = os.path.join(repo_path, ".ci/update_windows/update_comfyui_stable.bat")
stable_update_script_to = os.path.join(cur_path, "update_comfyui_stable.bat")
try:
if not file_size(stable_update_script_to) > 10:
shutil.copy(stable_update_script, stable_update_script_to)
except:
pass
+8
View File
@@ -0,0 +1,8 @@
@echo off
..\python_embeded\python.exe .\update.py ..\ComfyUI\
if exist update_new.py (
move /y update_new.py update.py
echo Running updater again since it got updated.
..\python_embeded\python.exe .\update.py ..\ComfyUI\ --skip_self_update
)
if "%~1"=="" pause
+8
View File
@@ -0,0 +1,8 @@
@echo off
..\python_embeded\python.exe .\update.py ..\ComfyUI\ --stable
if exist update_new.py (
move /y update_new.py update.py
echo Running updater again since it got updated.
..\python_embeded\python.exe .\update.py ..\ComfyUI\ --skip_self_update --stable
)
if "%~1"=="" pause
-8
View File
@@ -1,8 +0,0 @@
@echo off
..\python_embeded\python.exe .\update.py ..\HanzoStudio\ --stable
if exist update_new.py (
move /y update_new.py update.py
echo Running updater again since it got updated.
..\python_embeded\python.exe .\update.py ..\HanzoStudio\ --skip_self_update --stable
)
if "%~1"=="" pause
@@ -1,25 +0,0 @@
As of the time of writing this you need this driver for best results:
https://www.amd.com/en/resources/support-articles/release-notes/RN-AMDGPU-WINDOWS-PYTORCH-7-1-1.html
HOW TO RUN:
If you have a AMD gpu:
run_amd_gpu.bat
If you have memory issues you can try disabling the smart memory management by running Hanzo Studio with:
run_amd_gpu_disable_smart_memory.bat
IF YOU GET A RED ERROR IN THE UI MAKE SURE YOU HAVE A MODEL/CHECKPOINT IN: HanzoStudio\models\checkpoints
You can download the stable diffusion XL one from: https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0_0.9vae.safetensors
RECOMMENDED WAY TO UPDATE:
To update the Hanzo Studio code: update\update_studio.bat
TO SHARE MODELS BETWEEN HANZO STUDIO AND ANOTHER UI:
In the HanzoStudio directory you will find a file: extra_model_paths.yaml.example
Rename this file to: extra_model_paths.yaml and edit it with your favorite text editor.
@@ -1,2 +0,0 @@
.\python_embeded\python.exe -s HanzoStudio\main.py --windows-standalone-build
pause
@@ -1,2 +0,0 @@
.\python_embeded\python.exe -s HanzoStudio\main.py --windows-standalone-build --disable-smart-memory
pause
+31
View File
@@ -0,0 +1,31 @@
HOW TO RUN:
if you have a NVIDIA gpu:
run_nvidia_gpu.bat
To run it in slow CPU mode:
run_cpu.bat
IF YOU GET A RED ERROR IN THE UI MAKE SURE YOU HAVE A MODEL/CHECKPOINT IN: ComfyUI\models\checkpoints
You can download the stable diffusion 1.5 one from: https://huggingface.co/Comfy-Org/stable-diffusion-v1-5-archive/blob/main/v1-5-pruned-emaonly-fp16.safetensors
RECOMMENDED WAY TO UPDATE:
To update the ComfyUI code: update\update_comfyui.bat
To update ComfyUI with the python dependencies, note that you should ONLY run this if you have issues with python dependencies.
update\update_comfyui_and_python_dependencies.bat
TO SHARE MODELS BETWEEN COMFYUI AND ANOTHER UI:
In the ComfyUI directory you will find a file: extra_model_paths.yaml.example
Rename this file to: extra_model_paths.yaml and edit it with your favorite text editor.
+2
View File
@@ -0,0 +1,2 @@
.\python_embeded\python.exe -s ComfyUI\main.py --cpu --windows-standalone-build
pause
+2
View File
@@ -0,0 +1,2 @@
.\python_embeded\python.exe -s ComfyUI\main.py --windows-standalone-build
pause
@@ -1,2 +1,2 @@
.\python_embeded\python.exe -s HanzoStudio\main.py --windows-standalone-build --fast
.\python_embeded\python.exe -s ComfyUI\main.py --windows-standalone-build --fast
pause
@@ -1,34 +0,0 @@
HOW TO RUN:
if you have a NVIDIA gpu:
run_nvidia_gpu.bat
if you want to enable the fast fp16 accumulation (faster for fp16 models with slightly less quality):
run_nvidia_gpu_fast_fp16_accumulation.bat
To run it in slow CPU mode:
run_cpu.bat
IF YOU GET A RED ERROR IN THE UI MAKE SURE YOU HAVE A MODEL/CHECKPOINT IN: HanzoStudio\models\checkpoints
You can download the stable diffusion 1.5 one from: https://huggingface.co/hanzoai/stable-diffusion-v1-5-archive/blob/main/v1-5-pruned-emaonly-fp16.safetensors
RECOMMENDED WAY TO UPDATE:
To update the Hanzo Studio code: update\update_studio.bat
To update Hanzo Studio with the python dependencies, note that you should ONLY run this if you have issues with python dependencies.
update\update_studio_and_python_dependencies.bat
TO SHARE MODELS BETWEEN HANZO STUDIO AND ANOTHER UI:
In the HanzoStudio directory you will find a file: extra_model_paths.yaml.example
Rename this file to: extra_model_paths.yaml and edit it with your favorite text editor.
@@ -1,3 +0,0 @@
..\python_embeded\python.exe -s ..\HanzoStudio\main.py --windows-standalone-build --disable-api-nodes
echo If you see this and Hanzo Studio did not start try updating your Nvidia Drivers to the latest. If you get a c10.dll error you need to install vc redist that you can find: https://aka.ms/vc14/vc_redist.x64.exe
pause
@@ -1,2 +0,0 @@
.\python_embeded\python.exe -s HanzoStudio\main.py --cpu --windows-standalone-build
pause
@@ -1,3 +0,0 @@
.\python_embeded\python.exe -s HanzoStudio\main.py --windows-standalone-build
echo If you see this and Hanzo Studio did not start try updating your Nvidia Drivers to the latest. If you get a c10.dll error you need to install vc redist that you can find: https://aka.ms/vc14/vc_redist.x64.exe
pause
@@ -1,3 +0,0 @@
.\python_embeded\python.exe -s HanzoStudio\main.py --windows-standalone-build --fast fp16_accumulation
echo If you see this and Hanzo Studio did not start try updating your Nvidia Drivers to the latest. If you get a c10.dll error you need to install vc redist that you can find: https://aka.ms/vc14/vc_redist.x64.exe
pause
-127
View File
@@ -1,127 +0,0 @@
# yaml-language-server: $schema=https://coderabbit.ai/integrations/schema.v2.json
language: "en-US"
early_access: false
tone_instructions: "Only comment on issues introduced by this PR's changes. Do not flag pre-existing problems in moved, re-indented, or reformatted code."
reviews:
profile: "chill"
request_changes_workflow: false
high_level_summary: false
poem: false
review_status: false
review_details: false
commit_status: true
collapse_walkthrough: true
changed_files_summary: false
sequence_diagrams: false
estimate_code_review_effort: false
assess_linked_issues: false
related_issues: false
related_prs: false
suggested_labels: false
auto_apply_labels: false
suggested_reviewers: false
auto_assign_reviewers: false
in_progress_fortune: false
enable_prompt_for_ai_agents: true
path_filters:
- "!studio_api_nodes/apis/**"
- "!**/generated/*.pyi"
- "!.ci/**"
- "!script_examples/**"
- "!**/__pycache__/**"
- "!**/*.ipynb"
- "!**/*.png"
- "!**/*.bat"
path_instructions:
- path: "**"
instructions: |
IMPORTANT: Only comment on issues directly introduced by this PR's code changes.
Do NOT flag pre-existing issues in code that was merely moved, re-indented,
de-indented, or reformatted without logic changes. If code appears in the diff
only due to whitespace or structural reformatting (e.g., removing a `with:` block),
treat it as unchanged. Contributors should not feel obligated to address
pre-existing issues outside the scope of their contribution.
- path: "studio/**"
instructions: |
Core ML/diffusion engine. Focus on:
- Backward compatibility (breaking changes affect all custom nodes)
- Memory management and GPU resource handling
- Performance implications in hot paths
- Thread safety for concurrent execution
- path: "studio_api_nodes/**"
instructions: |
Third-party API integration nodes. Focus on:
- No hardcoded API keys or secrets
- Proper error handling for API failures (timeouts, rate limits, auth errors)
- Correct Pydantic model usage
- Security of user data passed to external APIs
- path: "studio_extras/**"
instructions: |
Community-contributed extra nodes. Focus on:
- Consistency with node patterns (INPUT_TYPES, RETURN_TYPES, FUNCTION, CATEGORY)
- No breaking changes to existing node interfaces
- path: "studio_execution/**"
instructions: |
Execution engine (graph execution, caching, jobs). Focus on:
- Caching correctness
- Concurrent execution safety
- Graph validation edge cases
- path: "nodes.py"
instructions: |
Core node definitions (2500+ lines). Focus on:
- Backward compatibility of NODE_CLASS_MAPPINGS
- Consistency of INPUT_TYPES return format
- path: "alembic_db/**"
instructions: |
Database migrations. Focus on:
- Migration safety and rollback support
- Data preservation during schema changes
auto_review:
enabled: true
auto_incremental_review: true
drafts: false
ignore_title_keywords:
- "WIP"
- "DO NOT REVIEW"
- "DO NOT MERGE"
finishing_touches:
docstrings:
enabled: false
unit_tests:
enabled: false
tools:
ruff:
enabled: false
pylint:
enabled: false
flake8:
enabled: false
gitleaks:
enabled: true
shellcheck:
enabled: false
markdownlint:
enabled: false
yamllint:
enabled: false
languagetool:
enabled: false
github-checks:
enabled: true
timeout_ms: 90000
ast-grep:
essential_rules: true
chat:
auto_reply: true
knowledge_base:
opt_out: false
learnings:
scope: "auto"
-33
View File
@@ -1,33 +0,0 @@
models/
output/
input/
custom_nodes/*
# ...but the pack manifest must reach the build context so the image can vendor
# the packs reproducibly (scripts/install_custom_nodes.sh clones them at build).
# Local clones stay excluded so the image builds them fresh from the manifest.
!custom_nodes/hanzo-packs.txt
user/
temp/
orgs/
.git/
.github/
__pycache__/
*.pyc
*.pyo
*.log
*.egg-info/
.venv/
venv/
.mypy_cache/
.pytest_cache/
.ruff_cache/
tests/
tests-unit/
# scripts/ ships WHOLE: the image build runs scripts/install_custom_nodes.sh and
# the studio pod runs scripts/library_manifest.py as the build-manifest sidecar
# (indexes every org's output/ into library.json). Excluding it silently no-ops
# the sidecar's `[ -f ]` guard, so nothing gets indexed — keep the tree intact.
notebooks/
*.swp
.env
.env.*
-1
View File
@@ -1,3 +1,2 @@
/web/assets/** linguist-generated
/web/** linguist-vendored
comfy_api_nodes/apis/__init__.py linguist-generated
+7 -17
View File
@@ -1,5 +1,5 @@
name: Bug Report
description: "Something is broken inside of Hanzo Studio. (Do not use this if you're just having issues and need help, or if the issue relates to a custom node)"
description: "Something is broken inside of ComfyUI. (Do not use this if you're just having issues and need help, or if the issue relates to a custom node)"
labels: ["Potential Bug"]
body:
- type: markdown
@@ -7,24 +7,14 @@ body:
value: |
Before submitting a **Bug Report**, please ensure the following:
- **1:** You are running the latest version of Hanzo Studio.
- **2:** You have your Hanzo Studio logs and relevant workflow on hand and will post them in this bug report.
- **1:** You are running the latest version of ComfyUI.
- **2:** You have looked at the existing bug reports and made sure this isn't already reported.
- **3:** You confirmed that the bug is not caused by a custom node. You can disable all custom nodes by passing
`--disable-all-custom-nodes` command line argument. If you have custom node try updating them to the latest version.
- **4:** This is an actual bug in Hanzo Studio, not just a support question. A bug is when you can specify exact
`--disable-all-custom-nodes` command line argument.
- **4:** This is an actual bug in ComfyUI, not just a support question. A bug is when you can specify exact
steps to replicate what went wrong and others will be able to repeat your steps and see the same issue happen.
## Very Important
Please make sure that you post ALL your Hanzo Studio logs in the bug report. A bug report without logs will likely be ignored.
- type: checkboxes
id: custom-nodes-test
attributes:
label: Custom Node Testing
description: Please confirm you have tried to reproduce the issue with all custom nodes disabled.
options:
- label: I have tried disabling custom nodes and the issue persists (see [how to disable custom nodes](https://docs.hanzo.ai/troubleshooting/custom-node-issues#step-1%3A-test-with-all-custom-nodes-disabled) if you need help)
required: false
If unsure, ask on the [ComfyUI Matrix Space](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) or the [Comfy Org Discord](https://discord.gg/comfyorg) first.
- type: textarea
attributes:
label: Expected Behavior
@@ -40,7 +30,7 @@ body:
- type: textarea
attributes:
label: Steps to Reproduce
description: "Describe how to reproduce the issue. Please be sure to attach a workflow JSON or PNG, ideally one that doesn't require custom nodes to test. If the bug open happens when certain custom nodes are used, most likely that custom node is what has the bug rather than Hanzo Studio, in which case it should be reported to the node's author."
description: "Describe how to reproduce the issue. Please be sure to attach a workflow JSON or PNG, ideally one that doesn't require custom nodes to test. If the bug open happens when certain custom nodes are used, most likely that custom node is what has the bug rather than ComfyUI, in which case it should be reported to the node's author."
validations:
required: true
- type: textarea
+9 -6
View File
@@ -1,8 +1,11 @@
blank_issues_enabled: true
contact_links:
- name: Hanzo Studio Frontend Issues
url: https://github.com/hanzoai/studio/issues
about: Issues related to the Hanzo Studio frontend (display issues, user interaction bugs).
- name: Hanzo AI Discord
url: https://discord.gg/hanzoai
about: The Hanzo AI Discord is available for support and general discussion related to Hanzo Studio.
- name: ComfyUI Frontend Issues
url: https://github.com/Comfy-Org/ComfyUI_frontend/issues
about: Issues related to the ComfyUI frontend (display issues, user interaction bugs), please go to the frontend repo to file the issue
- name: ComfyUI Matrix Space
url: https://app.element.io/#/room/%23comfyui_space%3Amatrix.org
about: The ComfyUI Matrix Space is available for support and general discussion related to ComfyUI (Matrix is like Discord but open source).
- name: Comfy Org Discord
url: https://discord.gg/comfyorg
about: The Comfy Org Discord is available for support and general discussion related to ComfyUI.
+4 -4
View File
@@ -1,5 +1,5 @@
name: Feature Request
description: "You have an idea for something new you would like to see added to Hanzo Studio's core."
description: "You have an idea for something new you would like to see added to ComfyUI's core."
labels: [ "Feature" ]
body:
- type: markdown
@@ -7,11 +7,11 @@ body:
value: |
Before submitting a **Feature Request**, please ensure the following:
**1:** You are running the latest version of Hanzo Studio.
**1:** You are running the latest version of ComfyUI.
**2:** You have looked to make sure there is not already a feature that does what you need, and there is not already a Feature Request listed for the same idea.
**3:** This is something that makes sense to add to Hanzo Studio Core, and wouldn't make more sense as a custom node.
**3:** This is something that makes sense to add to ComfyUI Core, and wouldn't make more sense as a custom node.
If unsure, ask on the [Hanzo AI Discord](https://discord.gg/hanzoai) first.
If unsure, ask on the [ComfyUI Matrix Space](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) or the [Comfy Org Discord](https://discord.gg/comfyorg) first.
- type: textarea
attributes:
label: Feature Idea
+2 -10
View File
@@ -7,18 +7,10 @@ body:
value: |
Before submitting a **User Report** issue, please ensure the following:
**1:** You are running the latest version of Hanzo Studio.
**1:** You are running the latest version of ComfyUI.
**2:** You have made an effort to find public answers to your question before asking here. In other words, you googled it first, and scrolled through recent help topics.
If unsure, ask on the [Hanzo AI Discord](https://discord.gg/hanzoai) first.
- type: checkboxes
id: custom-nodes-test
attributes:
label: Custom Node Testing
description: Please confirm you have tried to reproduce the issue with all custom nodes disabled.
options:
- label: I have tried disabling custom nodes and the issue persists (see [how to disable custom nodes](https://docs.hanzo.ai/troubleshooting/custom-node-issues#step-1%3A-test-with-all-custom-nodes-disabled) if you need help)
required: false
If unsure, ask on the [ComfyUI Matrix Space](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) or the [Comfy Org Discord](https://discord.gg/comfyorg) first.
- type: textarea
attributes:
label: Your question
-21
View File
@@ -1,21 +0,0 @@
<!-- API_NODE_PR_CHECKLIST: do not remove -->
## API Node PR Checklist
### Scope
- [ ] **Is API Node Change**
### Pricing & Billing
- [ ] **Need pricing update**
- [ ] **No pricing update**
If **Need pricing update**:
- [ ] Metronome rate cards updated
- [ ] Autobilling tests updated and passing
### QA
- [ ] **QA done**
- [ ] **QA not required**
### Comms
- [ ] Informed **Kosinkadink**
-9
View File
@@ -1,9 +0,0 @@
<svg xmlns="http://www.w3.org/2000/svg" width="1280" height="640" viewBox="0 0 1280 640" role="img" aria-label="studio">
<rect width="1280" height="640" fill="#0A0A0A"/>
<svg x="96" y="215" width="210" height="210" viewBox="0 0 67 67"><path d="M22.21 67V44.6369H0V67H22.21Z" fill="#fff"/><path d="M66.7038 22.3184H22.2534L0.0878906 44.6367H44.4634L66.7038 22.3184Z" fill="#fff"/><path d="M22.21 0H0V22.3184H22.21V0Z" fill="#fff"/><path d="M66.7198 0H44.5098V22.3184H66.7198V0Z" fill="#fff"/><path d="M66.7198 67V44.6369H44.5098V67H66.7198Z" fill="#fff"/></svg>
<text x="378" y="276" font-family="Inter,system-ui,-apple-system,sans-serif" font-size="78" font-weight="800" letter-spacing="-2" fill="#ffffff">studio</text>
<text x="378" y="322" font-family="Inter,system-ui,sans-serif" font-size="30" fill="#ffffff" opacity=".66">Hanzo Studio — Visual AI Engine</text>
<rect x="378" y="338" width="806" height="3" rx="1.5" fill="#ffffff" opacity=".9"/>
<text x="378" y="390" font-family="Inter,system-ui,sans-serif" font-size="24" font-weight="600" fill="#ffffff" opacity=".5">github.com/hanzoai</text>
<text x="1184" y="390" text-anchor="end" font-family="Inter,system-ui,sans-serif" font-size="24" font-weight="600" fill="#ffffff" opacity=".5">hanzo.ai</text>
</svg>

Before

Width:  |  Height:  |  Size: 1.2 KiB

-58
View File
@@ -1,58 +0,0 @@
name: Append API Node PR template
on:
pull_request_target:
types: [opened, reopened, synchronize, ready_for_review]
paths:
- 'studio_api_nodes/**' # only run if these files changed
permissions:
contents: read
pull-requests: write
jobs:
inject:
runs-on: hanzo-build-linux-amd64
steps:
- name: Ensure template exists and append to PR body
uses: actions/github-script@v7
with:
script: |
const { owner, repo } = context.repo;
const number = context.payload.pull_request.number;
const templatePath = '.github/PULL_REQUEST_TEMPLATE/api-node.md';
const marker = '<!-- API_NODE_PR_CHECKLIST: do not remove -->';
const { data: pr } = await github.rest.pulls.get({ owner, repo, pull_number: number });
let templateText;
try {
const res = await github.rest.repos.getContent({
owner,
repo,
path: templatePath,
ref: pr.base.ref
});
const buf = Buffer.from(res.data.content, res.data.encoding || 'base64');
templateText = buf.toString('utf8');
} catch (e) {
core.setFailed(`Required PR template not found at "${templatePath}" on ${pr.base.ref}. Please add it to the repo.`);
return;
}
// Enforce the presence of the marker inside the template (for idempotence)
if (!templateText.includes(marker)) {
core.setFailed(`Template at "${templatePath}" does not contain the required marker:\n${marker}\nAdd it so we can detect duplicates safely.`);
return;
}
// If the PR already contains the marker, do not append again.
const body = pr.body || '';
if (body.includes(marker)) {
core.info('Template already present in PR body; nothing to inject.');
return;
}
const newBody = (body ? body + '\n\n' : '') + templateText + '\n';
await github.rest.pulls.update({ owner, repo, pull_number: number, body: newBody });
core.notice('API Node template appended to PR description.');
-40
View File
@@ -1,40 +0,0 @@
name: Check for Windows Line Endings
on:
pull_request:
branches: ['*'] # Trigger on all pull requests to any branch
jobs:
check-line-endings:
runs-on: hanzo-build-linux-amd64
steps:
- name: Checkout code
uses: actions/checkout@v4
with:
fetch-depth: 0 # Fetch all history to compare changes
- name: Check for Windows line endings (CRLF)
run: |
# Get the list of changed files in the PR
CHANGED_FILES=$(git diff --name-only ${{ github.event.pull_request.base.sha }}..${{ github.event.pull_request.head.sha }})
# Flag to track if CRLF is found
CRLF_FOUND=false
# Loop through each changed file
for FILE in $CHANGED_FILES; do
# Check if the file exists and is a text file
if [ -f "$FILE" ] && file "$FILE" | grep -q "text"; then
# Check for CRLF line endings
if grep -UP '\r$' "$FILE"; then
echo "Error: Windows line endings (CRLF) detected in $FILE"
CRLF_FOUND=true
fi
fi
done
# Exit with error if CRLF was found
if [ "$CRLF_FOUND" = true ]; then
exit 1
fi
-14
View File
@@ -1,14 +0,0 @@
# Deploy is native now — see .hanzo/workflows/deploy.yml (Hanzo Git → act_runner
# → BuildKit → ghcr.io/hanzoai/studio:<sha> → operator reconcile → hanzocd).
# GitHub is a mirror; this workflow no longer builds or deploys the app image.
name: CI/CD
on:
workflow_dispatch:
jobs:
sync-notice:
runs-on: ubuntu-latest
steps:
- name: Notice
run: echo "native pipeline is .hanzo/workflows/deploy.yml; GitHub is a mirror"
+5 -5
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@@ -1,5 +1,5 @@
# This is the GitHub Workflow that drives full-GPU-enabled tests of pull requests to Hanzo Studio, when the 'Run-CI-Test' label is added
# Results are reported as checkmarks on the commits, as well as onto https://ci.hanzo.ai/
# This is the GitHub Workflow that drives full-GPU-enabled tests of pull requests to ComfyUI, when the 'Run-CI-Test' label is added
# Results are reported as checkmarks on the commits, as well as onto https://ci.comfy.org/
name: Pull Request CI Workflow Runs
on:
pull_request_target:
@@ -34,11 +34,11 @@ jobs:
python_version: ${{ matrix.python_version }}
torch_version: ${{ matrix.torch_version }}
google_credentials: ${{ secrets.GCS_SERVICE_ACCOUNT_JSON }}
studio_flags: ${{ matrix.flags }}
comfyui_flags: ${{ matrix.flags }}
use_prior_commit: 'true'
comment:
if: ${{ github.event.label.name == 'Run-CI-Test' }}
runs-on: hanzo-build-linux-amd64
runs-on: ubuntu-latest
permissions:
pull-requests: write
steps:
@@ -49,5 +49,5 @@ jobs:
issue_number: context.issue.number,
owner: context.repo.owner,
repo: context.repo.repo,
body: '(Automated Bot Message) CI Tests are running, you can view the results at https://ci.hanzo.ai/?branch=${{ github.event.pull_request.number }}%2Fmerge'
body: '(Automated Bot Message) CI Tests are running, you can view the results at https://ci.comfy.org/?branch=${{ github.event.pull_request.number }}%2Fmerge'
})
-78
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@@ -1,78 +0,0 @@
name: "Release Stable All Portable Versions"
on:
workflow_dispatch:
inputs:
git_tag:
description: 'Git tag'
required: true
type: string
jobs:
release_nvidia_default:
permissions:
contents: "write"
packages: "write"
pull-requests: "read"
name: "Release NVIDIA Default (cu130)"
uses: ./.github/workflows/stable-release.yml
with:
git_tag: ${{ inputs.git_tag }}
cache_tag: "cu130"
python_minor: "13"
python_patch: "11"
rel_name: "nvidia"
rel_extra_name: ""
test_release: true
secrets: inherit
release_nvidia_cu128:
permissions:
contents: "write"
packages: "write"
pull-requests: "read"
name: "Release NVIDIA cu128"
uses: ./.github/workflows/stable-release.yml
with:
git_tag: ${{ inputs.git_tag }}
cache_tag: "cu128"
python_minor: "12"
python_patch: "10"
rel_name: "nvidia"
rel_extra_name: "_cu128"
test_release: true
secrets: inherit
release_nvidia_cu126:
permissions:
contents: "write"
packages: "write"
pull-requests: "read"
name: "Release NVIDIA cu126"
uses: ./.github/workflows/stable-release.yml
with:
git_tag: ${{ inputs.git_tag }}
cache_tag: "cu126"
python_minor: "12"
python_patch: "10"
rel_name: "nvidia"
rel_extra_name: "_cu126"
test_release: true
secrets: inherit
release_amd_rocm:
permissions:
contents: "write"
packages: "write"
pull-requests: "read"
name: "Release AMD ROCm 7.2"
uses: ./.github/workflows/stable-release.yml
with:
git_tag: ${{ inputs.git_tag }}
cache_tag: "rocm72"
python_minor: "12"
python_patch: "10"
rel_name: "amd"
rel_extra_name: ""
test_release: false
secrets: inherit
-144
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@@ -1,144 +0,0 @@
name: Release Webhook
on:
release:
types: [published]
jobs:
send-webhook:
runs-on: hanzo-build-linux-amd64
env:
DESKTOP_REPO_DISPATCH_TOKEN: ${{ secrets.DESKTOP_REPO_DISPATCH_TOKEN }}
steps:
- name: Send release webhook
env:
WEBHOOK_URL: ${{ secrets.RELEASE_GITHUB_WEBHOOK_URL }}
WEBHOOK_SECRET: ${{ secrets.RELEASE_GITHUB_WEBHOOK_SECRET }}
run: |
# Generate UUID for delivery ID
DELIVERY_ID=$(uuidgen)
HOOK_ID="release-webhook-$(date +%s)"
# Create webhook payload matching GitHub release webhook format
PAYLOAD=$(cat <<EOF
{
"action": "published",
"release": {
"id": ${{ github.event.release.id }},
"node_id": "${{ github.event.release.node_id }}",
"url": "${{ github.event.release.url }}",
"html_url": "${{ github.event.release.html_url }}",
"assets_url": "${{ github.event.release.assets_url }}",
"upload_url": "${{ github.event.release.upload_url }}",
"tag_name": "${{ github.event.release.tag_name }}",
"target_commitish": "${{ github.event.release.target_commitish }}",
"name": ${{ toJSON(github.event.release.name) }},
"body": ${{ toJSON(github.event.release.body) }},
"draft": ${{ github.event.release.draft }},
"prerelease": ${{ github.event.release.prerelease }},
"created_at": "${{ github.event.release.created_at }}",
"published_at": "${{ github.event.release.published_at }}",
"author": {
"login": "${{ github.event.release.author.login }}",
"id": ${{ github.event.release.author.id }},
"node_id": "${{ github.event.release.author.node_id }}",
"avatar_url": "${{ github.event.release.author.avatar_url }}",
"url": "${{ github.event.release.author.url }}",
"html_url": "${{ github.event.release.author.html_url }}",
"type": "${{ github.event.release.author.type }}",
"site_admin": ${{ github.event.release.author.site_admin }}
},
"tarball_url": "${{ github.event.release.tarball_url }}",
"zipball_url": "${{ github.event.release.zipball_url }}",
"assets": ${{ toJSON(github.event.release.assets) }}
},
"repository": {
"id": ${{ github.event.repository.id }},
"node_id": "${{ github.event.repository.node_id }}",
"name": "${{ github.event.repository.name }}",
"full_name": "${{ github.event.repository.full_name }}",
"private": ${{ github.event.repository.private }},
"owner": {
"login": "${{ github.event.repository.owner.login }}",
"id": ${{ github.event.repository.owner.id }},
"node_id": "${{ github.event.repository.owner.node_id }}",
"avatar_url": "${{ github.event.repository.owner.avatar_url }}",
"url": "${{ github.event.repository.owner.url }}",
"html_url": "${{ github.event.repository.owner.html_url }}",
"type": "${{ github.event.repository.owner.type }}",
"site_admin": ${{ github.event.repository.owner.site_admin }}
},
"html_url": "${{ github.event.repository.html_url }}",
"clone_url": "${{ github.event.repository.clone_url }}",
"git_url": "${{ github.event.repository.git_url }}",
"ssh_url": "${{ github.event.repository.ssh_url }}",
"url": "${{ github.event.repository.url }}",
"created_at": "${{ github.event.repository.created_at }}",
"updated_at": "${{ github.event.repository.updated_at }}",
"pushed_at": "${{ github.event.repository.pushed_at }}",
"default_branch": "${{ github.event.repository.default_branch }}",
"fork": ${{ github.event.repository.fork }}
},
"sender": {
"login": "${{ github.event.sender.login }}",
"id": ${{ github.event.sender.id }},
"node_id": "${{ github.event.sender.node_id }}",
"avatar_url": "${{ github.event.sender.avatar_url }}",
"url": "${{ github.event.sender.url }}",
"html_url": "${{ github.event.sender.html_url }}",
"type": "${{ github.event.sender.type }}",
"site_admin": ${{ github.event.sender.site_admin }}
}
}
EOF
)
# Generate HMAC-SHA256 signature
SIGNATURE=$(echo -n "$PAYLOAD" | openssl dgst -sha256 -hmac "$WEBHOOK_SECRET" -hex | cut -d' ' -f2)
# Send webhook with required headers
curl -X POST "$WEBHOOK_URL" \
-H "Content-Type: application/json" \
-H "X-GitHub-Event: release" \
-H "X-GitHub-Delivery: $DELIVERY_ID" \
-H "X-GitHub-Hook-ID: $HOOK_ID" \
-H "X-Hub-Signature-256: sha256=$SIGNATURE" \
-H "User-Agent: GitHub-Actions-Webhook/1.0" \
-d "$PAYLOAD" \
--fail --silent --show-error
echo "✅ Release webhook sent successfully"
- name: Send repository dispatch to desktop
env:
DISPATCH_TOKEN: ${{ env.DESKTOP_REPO_DISPATCH_TOKEN }}
RELEASE_TAG: ${{ github.event.release.tag_name }}
RELEASE_URL: ${{ github.event.release.html_url }}
run: |
set -euo pipefail
if [ -z "${DISPATCH_TOKEN:-}" ]; then
echo "::error::DESKTOP_REPO_DISPATCH_TOKEN is required but not set."
exit 1
fi
PAYLOAD="$(jq -n \
--arg release_tag "$RELEASE_TAG" \
--arg release_url "$RELEASE_URL" \
'{
event_type: "studio_release_published",
client_payload: {
release_tag: $release_tag,
release_url: $release_url
}
}')"
curl -fsSL \
-X POST \
-H "Accept: application/vnd.github+json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DISPATCH_TOKEN}" \
https://api.github.com/repos/hanzoai/studio-desktop/dispatches \
-d "$PAYLOAD"
echo "✅ Dispatched Hanzo Studio release ${RELEASE_TAG} to hanzoai/studio-desktop"
+2 -32
View File
@@ -5,7 +5,7 @@ on: [push, pull_request]
jobs:
ruff:
name: Run Ruff
runs-on: hanzo-build-linux-amd64
runs-on: ubuntu-latest
steps:
- name: Checkout repository
@@ -16,38 +16,8 @@ jobs:
with:
python-version: 3.x
- name: Install uv
run: curl -LsSf https://astral.sh/uv/install.sh | sh
- name: Install Ruff
run: uv pip install --system ruff
run: pip install ruff
- name: Run Ruff
run: ruff check .
pylint:
name: Run Pylint
runs-on: hanzo-build-linux-amd64
steps:
- name: Checkout repository
uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v4
with:
python-version: '3.12'
- name: Install uv
run: curl -LsSf https://astral.sh/uv/install.sh | sh
- name: Install requirements
run: |
uv pip install --system torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
uv pip install --system -r requirements.txt
- name: Install Pylint
run: uv pip install --system pylint
- name: Run Pylint
run: pylint studio_api_nodes
+38 -104
View File
@@ -2,81 +2,31 @@
name: "Release Stable Version"
on:
workflow_call:
inputs:
git_tag:
description: 'Git tag'
required: true
type: string
cache_tag:
description: 'Cached dependencies tag'
required: true
type: string
default: "cu129"
python_minor:
description: 'Python minor version'
required: true
type: string
default: "13"
python_patch:
description: 'Python patch version'
required: true
type: string
default: "6"
rel_name:
description: 'Release name'
required: true
type: string
default: "nvidia"
rel_extra_name:
description: 'Release extra name'
required: false
type: string
default: ""
test_release:
description: 'Test Release'
required: true
type: boolean
default: true
workflow_dispatch:
inputs:
git_tag:
description: 'Git tag'
required: true
type: string
cache_tag:
description: 'Cached dependencies tag'
cu:
description: 'CUDA version'
required: true
type: string
default: "cu129"
default: "124"
python_minor:
description: 'Python minor version'
required: true
type: string
default: "13"
default: "12"
python_patch:
description: 'Python patch version'
required: true
type: string
default: "6"
rel_name:
description: 'Release name'
required: true
type: string
default: "nvidia"
rel_extra_name:
description: 'Release extra name'
required: false
type: string
default: ""
test_release:
description: 'Test Release'
required: true
type: boolean
default: true
default: "8"
jobs:
package_studio_windows:
package_comfy_windows:
permissions:
contents: "write"
packages: "write"
@@ -86,28 +36,28 @@ jobs:
- uses: actions/checkout@v4
with:
ref: ${{ inputs.git_tag }}
fetch-depth: 150
fetch-depth: 0
persist-credentials: false
- uses: actions/cache/restore@v4
id: cache
with:
path: |
${{ inputs.cache_tag }}_python_deps.tar
update_studio_and_python_dependencies.bat
key: ${{ runner.os }}-build-${{ inputs.cache_tag }}-${{ inputs.python_minor }}
cu${{ inputs.cu }}_python_deps.tar
update_comfyui_and_python_dependencies.bat
key: ${{ runner.os }}-build-cu${{ inputs.cu }}-${{ inputs.python_minor }}
- shell: bash
run: |
mv ${{ inputs.cache_tag }}_python_deps.tar ../
mv update_studio_and_python_dependencies.bat ../
mv cu${{ inputs.cu }}_python_deps.tar ../
mv update_comfyui_and_python_dependencies.bat ../
cd ..
tar xf ${{ inputs.cache_tag }}_python_deps.tar
tar xf cu${{ inputs.cu }}_python_deps.tar
pwd
ls
- shell: bash
run: |
cd ..
cp -r HanzoStudio HanzoStudio_copy
cp -r ComfyUI ComfyUI_copy
curl https://www.python.org/ftp/python/3.${{ inputs.python_minor }}.${{ inputs.python_patch }}/python-3.${{ inputs.python_minor }}.${{ inputs.python_patch }}-embed-amd64.zip -o python_embeded.zip
unzip python_embeded.zip -d python_embeded
cd python_embeded
@@ -115,56 +65,40 @@ jobs:
echo 'import site' >> ./python3${{ inputs.python_minor }}._pth
curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
./python.exe get-pip.py
./python.exe -s -m pip install ../${{ inputs.cache_tag }}_python_deps/*
./python.exe -s -m pip install ../cu${{ inputs.cu }}_python_deps/*
sed -i '1i../ComfyUI' ./python3${{ inputs.python_minor }}._pth
cd ..
grep -E "comfyui-frontend-package|comfyui-workflow-templates|comfyui-embedded-docs|comfy-kitchen|comfy-aimdo" ../HanzoStudio/requirements.txt > ./requirements_studio.txt
./python.exe -s -m pip install -r requirements_studio.txt
rm requirements_studio.txt
git clone --depth 1 https://github.com/comfyanonymous/taesd
cp taesd/*.pth ./ComfyUI_copy/models/vae_approx/
sed -i '1i../HanzoStudio' ./python3${{ inputs.python_minor }}._pth
mkdir ComfyUI_windows_portable
mv python_embeded ComfyUI_windows_portable
mv ComfyUI_copy ComfyUI_windows_portable/ComfyUI
if test -f ./Lib/site-packages/torch/lib/dnnl.lib; then
rm ./Lib/site-packages/torch/lib/dnnl.lib #I don't think this is actually used and I need the space
rm ./Lib/site-packages/torch/lib/libprotoc.lib
rm ./Lib/site-packages/torch/lib/libprotobuf.lib
fi
cd ..
git clone --depth 1 https://github.com/hanzoai/taesd
cp taesd/*.safetensors ./HanzoStudio_copy/models/vae_approx/
mkdir HanzoStudio_windows_portable
mv python_embeded HanzoStudio_windows_portable
mv HanzoStudio_copy HanzoStudio_windows_portable/HanzoStudio
cd HanzoStudio_windows_portable
cd ComfyUI_windows_portable
mkdir update
cp -r HanzoStudio/.ci/update_windows/* ./update/
cp -r HanzoStudio/.ci/windows_${{ inputs.rel_name }}_base_files/* ./
cp ../update_studio_and_python_dependencies.bat ./update/
cp -r ComfyUI/.ci/update_windows/* ./update/
cp -r ComfyUI/.ci/windows_base_files/* ./
cp ../update_comfyui_and_python_dependencies.bat ./update/
cd ..
"C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma2 -mx=9 -mfb=128 -md=768m -ms=on -mf=BCJ2 HanzoStudio_windows_portable.7z HanzoStudio_windows_portable
mv HanzoStudio_windows_portable.7z HanzoStudio/HanzoStudio_windows_portable_${{ inputs.rel_name }}${{ inputs.rel_extra_name }}.7z
"C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma2 -mx=8 -mfb=64 -md=32m -ms=on -mf=BCJ2 ComfyUI_windows_portable.7z ComfyUI_windows_portable
mv ComfyUI_windows_portable.7z ComfyUI/ComfyUI_windows_portable_nvidia.7z
- shell: bash
if: ${{ inputs.test_release }}
run: |
cd ..
cd HanzoStudio_windows_portable
python_embeded/python.exe -s HanzoStudio/main.py --quick-test-for-ci --cpu
python_embeded/python.exe -s ./update/update.py HanzoStudio/
cd ComfyUI_windows_portable
python_embeded/python.exe -s ComfyUI/main.py --quick-test-for-ci --cpu
ls
- name: Upload binaries to release
uses: softprops/action-gh-release@v2
uses: svenstaro/upload-release-action@v2
with:
files: HanzoStudio_windows_portable_${{ inputs.rel_name }}${{ inputs.rel_extra_name }}.7z
tag_name: ${{ inputs.git_tag }}
draft: true
overwrite_files: true
repo_token: ${{ secrets.GITHUB_TOKEN }}
file: ComfyUI_windows_portable_nvidia.7z
tag: ${{ inputs.git_tag }}
overwrite: true
prerelease: true
make_latest: false
+1 -1
View File
@@ -8,7 +8,7 @@ permissions:
jobs:
stale:
runs-on: hanzo-build-linux-amd64
runs-on: ubuntu-latest
steps:
- uses: actions/stale@v9
with:
+4 -5
View File
@@ -14,19 +14,18 @@ on:
jobs:
build:
name: Build Test
runs-on: hanzo-build-linux-amd64
runs-on: ubuntu-latest
strategy:
fail-fast: false
matrix:
python-version: ["3.10", "3.11", "3.12", "3.13", "3.14"]
python-version: ["3.8", "3.9", "3.10", "3.11"]
steps:
- uses: actions/checkout@v4
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v4
with:
python-version: ${{ matrix.python-version }}
- name: Install uv
run: curl -LsSf https://astral.sh/uv/install.sh | sh
- name: Install dependencies
run: |
uv pip install --system -r requirements.txt
python -m pip install --upgrade pip
pip install -r requirements.txt
+15 -18
View File
@@ -1,11 +1,10 @@
# This is the GitHub Workflow that drives automatic full-GPU-enabled tests of all new commits to the main branch of Hanzo Studio
# Results are reported as checkmarks on the commits, as well as onto https://ci.hanzo.ai/
name: Full Hanzo Studio CI Workflow Runs
# This is the GitHub Workflow that drives automatic full-GPU-enabled tests of all new commits to the master branch of ComfyUI
# Results are reported as checkmarks on the commits, as well as onto https://ci.comfy.org/
name: Full Comfy CI Workflow Runs
on:
push:
branches:
- master
- release/**
paths-ignore:
- 'app/**'
- 'input/**'
@@ -22,15 +21,14 @@ jobs:
fail-fast: false
matrix:
# os: [macos, linux, windows]
# os: [macos, linux]
os: [linux]
python_version: ["3.10", "3.11", "3.12"]
os: [macos, linux]
python_version: ["3.9", "3.10", "3.11", "3.12"]
cuda_version: ["12.1"]
torch_version: ["stable"]
include:
# - os: macos
# runner_label: [self-hosted, macOS]
# flags: "--use-pytorch-cross-attention"
- os: macos
runner_label: [self-hosted, macOS]
flags: "--use-pytorch-cross-attention"
- os: linux
runner_label: [self-hosted, Linux]
flags: ""
@@ -46,7 +44,7 @@ jobs:
python_version: ${{ matrix.python_version }}
torch_version: ${{ matrix.torch_version }}
google_credentials: ${{ secrets.GCS_SERVICE_ACCOUNT_JSON }}
studio_flags: ${{ matrix.flags }}
comfyui_flags: ${{ matrix.flags }}
# test-win-nightly:
# strategy:
@@ -69,21 +67,20 @@ jobs:
# python_version: ${{ matrix.python_version }}
# torch_version: ${{ matrix.torch_version }}
# google_credentials: ${{ secrets.GCS_SERVICE_ACCOUNT_JSON }}
# studio_flags: ${{ matrix.flags }}
# comfyui_flags: ${{ matrix.flags }}
test-unix-nightly:
strategy:
fail-fast: false
matrix:
# os: [macos, linux]
os: [linux]
os: [macos, linux]
python_version: ["3.11"]
cuda_version: ["12.1"]
torch_version: ["nightly"]
include:
# - os: macos
# runner_label: [self-hosted, macOS]
# flags: "--use-pytorch-cross-attention"
- os: macos
runner_label: [self-hosted, macOS]
flags: "--use-pytorch-cross-attention"
- os: linux
runner_label: [self-hosted, Linux]
flags: ""
@@ -96,4 +93,4 @@ jobs:
python_version: ${{ matrix.python_version }}
torch_version: ${{ matrix.torch_version }}
google_credentials: ${{ secrets.GCS_SERVICE_ACCOUNT_JSON }}
studio_flags: ${{ matrix.flags }}
comfyui_flags: ${{ matrix.flags }}
-31
View File
@@ -1,31 +0,0 @@
name: Execution Tests
on:
push:
branches: [ main, master, release/** ]
pull_request:
branches: [ main, master, release/** ]
jobs:
test:
strategy:
matrix:
os: [ubuntu-latest, windows-latest, macos-latest]
runs-on: ${{ matrix.os }}
continue-on-error: true
steps:
- uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v4
with:
python-version: '3.12'
- name: Install uv
run: curl -LsSf https://astral.sh/uv/install.sh | sh
- name: Install requirements
run: |
uv pip install --system torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
uv pip install --system -r requirements.txt
uv pip install --system -r tests-unit/requirements.txt
- name: Run Execution Tests
run: |
python -m pytest tests/execution -v --skip-timing-checks
+18 -21
View File
@@ -2,47 +2,44 @@ name: Test server launches without errors
on:
push:
branches: [ main, master, release/** ]
branches: [ main, master ]
pull_request:
branches: [ main, master, release/** ]
branches: [ main, master ]
jobs:
test:
runs-on: hanzo-build-linux-amd64
runs-on: ubuntu-latest
steps:
- name: Checkout Hanzo Studio
- name: Checkout ComfyUI
uses: actions/checkout@v4
with:
repository: "hanzoai/studio"
path: "studio"
repository: "comfyanonymous/ComfyUI"
path: "ComfyUI"
- uses: actions/setup-python@v4
with:
python-version: '3.10'
- name: Install uv
run: curl -LsSf https://astral.sh/uv/install.sh | sh
python-version: '3.9'
- name: Install requirements
run: |
uv pip install --system torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
uv pip install --system -r requirements.txt
uv pip install --system wait-for-it
working-directory: studio
- name: Start Hanzo Studio server
python -m pip install --upgrade pip
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
pip install -r requirements.txt
pip install wait-for-it
working-directory: ComfyUI
- name: Start ComfyUI server
run: |
python main.py --cpu 2>&1 | tee console_output.log &
wait-for-it --service 127.0.0.1:8188 -t 120
working-directory: studio
wait-for-it --service 127.0.0.1:8188 -t 30
working-directory: ComfyUI
- name: Check for unhandled exceptions in server log
run: |
grep -v "Found comfy_kitchen backend triton: {'available': False, 'disabled': True, 'unavailable_reason': \"ImportError: No module named 'triton'\", 'capabilities': \[\]}" console_output.log | grep -v "Found comfy_kitchen backend triton: {'available': False, 'disabled': False, 'unavailable_reason': \"ImportError: No module named 'triton'\", 'capabilities': \[\]}" > console_output_filtered.log
cat console_output_filtered.log
if grep -qE "Exception|Error" console_output_filtered.log; then
if grep -qE "Exception|Error" console_output.log; then
echo "Unhandled exception/error found in server log."
exit 1
fi
working-directory: studio
working-directory: ComfyUI
- uses: actions/upload-artifact@v4
if: always()
with:
name: console-output
path: studio/console_output.log
path: ComfyUI/console_output.log
retention-days: 30
+4 -4
View File
@@ -2,15 +2,15 @@ name: Unit Tests
on:
push:
branches: [ main, master, release/** ]
branches: [ main, master ]
pull_request:
branches: [ main, master, release/** ]
branches: [ main, master ]
jobs:
test:
strategy:
matrix:
os: [ubuntu-latest, windows-2022, macos-latest]
os: [ubuntu-latest, windows-latest, macos-latest]
runs-on: ${{ matrix.os }}
continue-on-error: true
steps:
@@ -18,7 +18,7 @@ jobs:
- name: Set up Python
uses: actions/setup-python@v4
with:
python-version: '3.12'
python-version: '3.10'
- name: Install requirements
run: |
python -m pip install --upgrade pip
-56
View File
@@ -1,56 +0,0 @@
name: Generate Pydantic Stubs from api.hanzo.ai
on:
schedule:
- cron: '0 0 * * 1'
workflow_dispatch:
jobs:
generate-models:
runs-on: hanzo-build-linux-amd64
steps:
- name: Checkout repository
uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v4
with:
python-version: '3.10'
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install 'datamodel-code-generator[http]'
npm install @redocly/cli
- name: Download OpenAPI spec
run: |
curl -o openapi.yaml https://api.hanzo.ai/openapi
- name: Filter OpenAPI spec with Redocly
run: |
npx @redocly/cli bundle openapi.yaml --output filtered-openapi.yaml --config studio_api_nodes/redocly.yaml --remove-unused-components
- name: Generate API models
run: |
datamodel-codegen --use-subclass-enum --input filtered-openapi.yaml --output studio_api_nodes/apis --output-model-type pydantic_v2.BaseModel
- name: Check for changes
id: git-check
run: |
git diff --exit-code studio_api_nodes/apis || echo "changes=true" >> $GITHUB_OUTPUT
- name: Create Pull Request
if: steps.git-check.outputs.changes == 'true'
uses: peter-evans/create-pull-request@v5
with:
commit-message: 'chore: update API models from OpenAPI spec'
title: 'Update API models from api.hanzo.ai'
body: |
This PR updates the API models based on the latest api.hanzo.ai OpenAPI specification.
Generated automatically by a Github workflow.
branch: update-api-stubs
delete-branch: true
base: main
-59
View File
@@ -1,59 +0,0 @@
name: "CI: Update CI Container"
on:
release:
types: [published]
workflow_dispatch:
inputs:
version:
description: 'Hanzo Studio version (e.g., v0.7.0)'
required: true
type: string
jobs:
update-ci-container:
runs-on: hanzo-build-linux-amd64
# Skip pre-releases unless manually triggered
if: github.event_name == 'workflow_dispatch' || !github.event.release.prerelease
steps:
- name: Get version
id: version
run: |
if [ "${{ github.event_name }}" = "release" ]; then
VERSION="${{ github.event.release.tag_name }}"
else
VERSION="${{ inputs.version }}"
fi
echo "version=$VERSION" >> $GITHUB_OUTPUT
- name: Checkout studio-ci-container
uses: actions/checkout@v4
with:
repository: hanzoai/studio-ci-container
token: ${{ secrets.CI_CONTAINER_PAT }}
- name: Check current version
id: current
run: |
CURRENT=$(grep -oP 'ARG STUDIO_VERSION=\K.*' Dockerfile || echo "unknown")
echo "current_version=$CURRENT" >> $GITHUB_OUTPUT
- name: Update Dockerfile
run: |
VERSION="${{ steps.version.outputs.version }}"
sed -i "s/^ARG STUDIO_VERSION=.*/ARG STUDIO_VERSION=${VERSION}/" Dockerfile
- name: Create Pull Request
id: create-pr
uses: peter-evans/create-pull-request@v7
with:
token: ${{ secrets.CI_CONTAINER_PAT }}
branch: automation/studio-${{ steps.version.outputs.version }}
title: "chore: bump Hanzo Studio to ${{ steps.version.outputs.version }}"
body: |
Updates Hanzo Studio version from `${{ steps.current.outputs.current_version }}` to `${{ steps.version.outputs.version }}`
**Triggered by:** ${{ github.event_name == 'release' && format('[Release {0}]({1})', github.event.release.tag_name, github.event.release.html_url) || 'Manual workflow dispatch' }}
labels: automation
commit-message: "chore: bump Hanzo Studio to ${{ steps.version.outputs.version }}"
+58
View File
@@ -0,0 +1,58 @@
name: Update Frontend Release
on:
workflow_dispatch:
inputs:
version:
description: "Frontend version to update to (e.g., 1.0.0)"
required: true
type: string
jobs:
update-frontend:
runs-on: ubuntu-latest
permissions:
contents: write
pull-requests: write
steps:
- name: Checkout ComfyUI
uses: actions/checkout@v4
- uses: actions/setup-python@v4
with:
python-version: '3.10'
- name: Install requirements
run: |
python -m pip install --upgrade pip
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
pip install -r requirements.txt
pip install wait-for-it
# Frontend asset will be downloaded to ComfyUI/web_custom_versions/Comfy-Org_ComfyUI_frontend/{version}
- name: Start ComfyUI server
run: |
python main.py --cpu --front-end-version Comfy-Org/ComfyUI_frontend@${{ github.event.inputs.version }} 2>&1 | tee console_output.log &
wait-for-it --service 127.0.0.1:8188 -t 30
- name: Configure Git
run: |
git config --global user.name "GitHub Action"
git config --global user.email "action@github.com"
# Replace existing frontend content with the new version and remove .js.map files
# See https://github.com/Comfy-Org/ComfyUI_frontend/issues/2145 for why we remove .js.map files
- name: Update frontend content
run: |
rm -rf web/
cp -r web_custom_versions/Comfy-Org_ComfyUI_frontend/${{ github.event.inputs.version }} web/
rm web/**/*.js.map
- name: Create Pull Request
uses: peter-evans/create-pull-request@v7
with:
token: ${{ secrets.PR_BOT_PAT }}
commit-message: "Update frontend to v${{ github.event.inputs.version }}"
title: "Frontend Update: v${{ github.event.inputs.version }}"
body: |
Automated PR to update frontend content to version ${{ github.event.inputs.version }}
This PR was created automatically by the frontend update workflow.
branch: release-${{ github.event.inputs.version }}
base: master
labels: Frontend,dependencies
+7 -8
View File
@@ -6,11 +6,10 @@ on:
- "pyproject.toml"
branches:
- master
- release/**
jobs:
update-version:
runs-on: hanzo-build-linux-amd64
runs-on: ubuntu-latest
# Don't run on fork PRs
if: github.event.pull_request.head.repo.full_name == github.repository
permissions:
@@ -30,9 +29,9 @@ jobs:
run: |
python -m pip install --upgrade pip
- name: Update studio_version.py
- name: Update comfyui_version.py
run: |
# Read version from pyproject.toml and update studio_version.py
# Read version from pyproject.toml and update comfyui_version.py
python -c '
import tomllib
@@ -41,8 +40,8 @@ jobs:
config = tomllib.load(f)
version = config["project"]["version"]
# Write version to studio_version.py
with open("studio_version.py", "w") as f:
# Write version to comfyui_version.py
with open("comfyui_version.py", "w") as f:
f.write("# This file is automatically generated by the build process when version is\n")
f.write("# updated in pyproject.toml.\n")
f.write(f"__version__ = \"{version}\"\n")
@@ -54,6 +53,6 @@ jobs:
git config --local user.email "github-actions@github.com"
git fetch origin ${{ github.head_ref }}
git checkout -B ${{ github.head_ref }} origin/${{ github.head_ref }}
git add studio_version.py
git diff --quiet && git diff --staged --quiet || git commit -m "chore: Update studio_version.py to match pyproject.toml"
git add comfyui_version.py
git diff --quiet && git diff --staged --quiet || git commit -m "chore: Update comfyui_version.py to match pyproject.toml"
git push origin HEAD:${{ github.head_ref }}
@@ -17,19 +17,19 @@ on:
description: 'cuda version'
required: true
type: string
default: "130"
default: "124"
python_minor:
description: 'python minor version'
required: true
type: string
default: "13"
default: "12"
python_patch:
description: 'python patch version'
required: true
type: string
default: "11"
default: "8"
# push:
# branches:
# - master
@@ -46,18 +46,17 @@ jobs:
- shell: bash
run: |
echo "@echo off
call update_studio.bat nopause
call update_comfyui.bat nopause
echo -
echo This will try to update pytorch and all python dependencies.
echo -
echo If you just want to update normally, close this and run update_studio.bat instead.
echo If you just want to update normally, close this and run update_comfyui.bat instead.
echo -
pause
..\python_embeded\python.exe -s -m pip install --upgrade torch torchvision torchaudio ${{ inputs.xformers }} --extra-index-url https://download.pytorch.org/whl/cu${{ inputs.cu }} -r ../HanzoStudio/requirements.txt pygit2
pause" > update_studio_and_python_dependencies.bat
..\python_embeded\python.exe -s -m pip install --upgrade torch torchvision torchaudio ${{ inputs.xformers }} --extra-index-url https://download.pytorch.org/whl/cu${{ inputs.cu }} -r ../ComfyUI/requirements.txt pygit2
pause" > update_comfyui_and_python_dependencies.bat
grep -v studio requirements.txt > requirements_nostudio.txt
python -m pip wheel --no-cache-dir torch torchvision torchaudio ${{ inputs.xformers }} ${{ inputs.extra_dependencies }} --extra-index-url https://download.pytorch.org/whl/cu${{ inputs.cu }} -r requirements_nostudio.txt pygit2 -w ./temp_wheel_dir
python -m pip wheel --no-cache-dir torch torchvision torchaudio ${{ inputs.xformers }} ${{ inputs.extra_dependencies }} --extra-index-url https://download.pytorch.org/whl/cu${{ inputs.cu }} -r requirements.txt pygit2 -w ./temp_wheel_dir
python -m pip install --no-cache-dir ./temp_wheel_dir/*
echo installed basic
ls -lah temp_wheel_dir
@@ -68,5 +67,5 @@ jobs:
with:
path: |
cu${{ inputs.cu }}_python_deps.tar
update_studio_and_python_dependencies.bat
update_comfyui_and_python_dependencies.bat
key: ${{ runner.os }}-build-cu${{ inputs.cu }}-${{ inputs.python_minor }}
@@ -1,64 +0,0 @@
name: "Windows Release dependencies Manual"
on:
workflow_dispatch:
inputs:
torch_dependencies:
description: 'torch dependencies'
required: false
type: string
default: "torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu128"
cache_tag:
description: 'Cached dependencies tag'
required: true
type: string
default: "cu128"
python_minor:
description: 'python minor version'
required: true
type: string
default: "12"
python_patch:
description: 'python patch version'
required: true
type: string
default: "10"
jobs:
build_dependencies:
runs-on: windows-latest
steps:
- uses: actions/checkout@v4
- uses: actions/setup-python@v5
with:
python-version: 3.${{ inputs.python_minor }}.${{ inputs.python_patch }}
- shell: bash
run: |
echo "@echo off
call update_studio.bat nopause
echo -
echo This will try to update pytorch and all python dependencies.
echo -
echo If you just want to update normally, close this and run update_studio.bat instead.
echo -
pause
..\python_embeded\python.exe -s -m pip install --upgrade ${{ inputs.torch_dependencies }} -r ../HanzoStudio/requirements.txt pygit2
pause" > update_studio_and_python_dependencies.bat
grep -v studio requirements.txt > requirements_nostudio.txt
python -m pip wheel --no-cache-dir ${{ inputs.torch_dependencies }} -r requirements_nostudio.txt pygit2 -w ./temp_wheel_dir
python -m pip install --no-cache-dir ./temp_wheel_dir/*
echo installed basic
ls -lah temp_wheel_dir
mv temp_wheel_dir ${{ inputs.cache_tag }}_python_deps
tar cf ${{ inputs.cache_tag }}_python_deps.tar ${{ inputs.cache_tag }}_python_deps
- uses: actions/cache/save@v4
with:
path: |
${{ inputs.cache_tag }}_python_deps.tar
update_studio_and_python_dependencies.bat
key: ${{ runner.os }}-build-${{ inputs.cache_tag }}-${{ inputs.python_minor }}
@@ -7,7 +7,7 @@ on:
description: 'cuda version'
required: true
type: string
default: "129"
default: "126"
python_minor:
description: 'python minor version'
@@ -19,7 +19,7 @@ on:
description: 'python patch version'
required: true
type: string
default: "5"
default: "1"
# push:
# branches:
# - master
@@ -34,7 +34,7 @@ jobs:
steps:
- uses: actions/checkout@v4
with:
fetch-depth: 30
fetch-depth: 0
persist-credentials: false
- uses: actions/setup-python@v5
with:
@@ -42,45 +42,43 @@ jobs:
- shell: bash
run: |
cd ..
cp -r HanzoStudio HanzoStudio_copy
cp -r ComfyUI ComfyUI_copy
curl https://www.python.org/ftp/python/3.${{ inputs.python_minor }}.${{ inputs.python_patch }}/python-3.${{ inputs.python_minor }}.${{ inputs.python_patch }}-embed-amd64.zip -o python_embeded.zip
unzip python_embeded.zip -d python_embeded
cd python_embeded
echo 'import site' >> ./python3${{ inputs.python_minor }}._pth
curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
./python.exe get-pip.py
python -m pip wheel torch torchvision torchaudio --pre --extra-index-url https://download.pytorch.org/whl/nightly/cu${{ inputs.cu }} -r ../HanzoStudio/requirements.txt pygit2 -w ../temp_wheel_dir
python -m pip wheel torch torchvision torchaudio --pre --extra-index-url https://download.pytorch.org/whl/nightly/cu${{ inputs.cu }} -r ../ComfyUI/requirements.txt pygit2 -w ../temp_wheel_dir
ls ../temp_wheel_dir
./python.exe -s -m pip install --pre ../temp_wheel_dir/*
sed -i '1i../HanzoStudio' ./python3${{ inputs.python_minor }}._pth
rm ./Lib/site-packages/torch/lib/dnnl.lib #I don't think this is actually used and I need the space
sed -i '1i../ComfyUI' ./python3${{ inputs.python_minor }}._pth
cd ..
git clone --depth 1 https://github.com/hanzoai/taesd
cp taesd/*.safetensors ./HanzoStudio_copy/models/vae_approx/
git clone --depth 1 https://github.com/comfyanonymous/taesd
cp taesd/*.pth ./ComfyUI_copy/models/vae_approx/
mkdir HanzoStudio_windows_portable_nightly_pytorch
mv python_embeded HanzoStudio_windows_portable_nightly_pytorch
mv HanzoStudio_copy HanzoStudio_windows_portable_nightly_pytorch/HanzoStudio
mkdir ComfyUI_windows_portable_nightly_pytorch
mv python_embeded ComfyUI_windows_portable_nightly_pytorch
mv ComfyUI_copy ComfyUI_windows_portable_nightly_pytorch/ComfyUI
cd HanzoStudio_windows_portable_nightly_pytorch
cd ComfyUI_windows_portable_nightly_pytorch
mkdir update
cp -r HanzoStudio/.ci/update_windows/* ./update/
cp -r HanzoStudio/.ci/windows_nvidia_base_files/* ./
cp -r HanzoStudio/.ci/windows_nightly_base_files/* ./
cp -r ComfyUI/.ci/update_windows/* ./update/
cp -r ComfyUI/.ci/windows_base_files/* ./
cp -r ComfyUI/.ci/windows_nightly_base_files/* ./
echo "call update_studio.bat nopause
..\python_embeded\python.exe -s -m pip install --upgrade --pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/cu${{ inputs.cu }} -r ../HanzoStudio/requirements.txt pygit2
pause" > ./update/update_studio_and_python_dependencies.bat
echo "call update_comfyui.bat nopause
..\python_embeded\python.exe -s -m pip install --upgrade --pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/cu${{ inputs.cu }} -r ../ComfyUI/requirements.txt pygit2
pause" > ./update/update_comfyui_and_python_dependencies.bat
cd ..
"C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma2 -mx=9 -mfb=128 -md=512m -ms=on -mf=BCJ2 HanzoStudio_windows_portable_nightly_pytorch.7z HanzoStudio_windows_portable_nightly_pytorch
mv HanzoStudio_windows_portable_nightly_pytorch.7z HanzoStudio/HanzoStudio_windows_portable_nvidia_or_cpu_nightly_pytorch.7z
"C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma2 -mx=8 -mfb=64 -md=32m -ms=on -mf=BCJ2 ComfyUI_windows_portable_nightly_pytorch.7z ComfyUI_windows_portable_nightly_pytorch
mv ComfyUI_windows_portable_nightly_pytorch.7z ComfyUI/ComfyUI_windows_portable_nvidia_or_cpu_nightly_pytorch.7z
cd HanzoStudio_windows_portable_nightly_pytorch
python_embeded/python.exe -s HanzoStudio/main.py --quick-test-for-ci --cpu
cd ComfyUI_windows_portable_nightly_pytorch
python_embeded/python.exe -s ComfyUI/main.py --quick-test-for-ci --cpu
ls
@@ -88,6 +86,6 @@ jobs:
uses: svenstaro/upload-release-action@v2
with:
repo_token: ${{ secrets.GITHUB_TOKEN }}
file: HanzoStudio_windows_portable_nvidia_or_cpu_nightly_pytorch.7z
file: ComfyUI_windows_portable_nvidia_or_cpu_nightly_pytorch.7z
tag: "latest"
overwrite: true
+23 -29
View File
@@ -7,25 +7,25 @@ on:
description: 'cuda version'
required: true
type: string
default: "129"
default: "124"
python_minor:
description: 'python minor version'
required: true
type: string
default: "13"
default: "12"
python_patch:
description: 'python patch version'
required: true
type: string
default: "6"
default: "8"
# push:
# branches:
# - master
jobs:
package_studio:
package_comfyui:
permissions:
contents: "write"
packages: "write"
@@ -37,12 +37,12 @@ jobs:
with:
path: |
cu${{ inputs.cu }}_python_deps.tar
update_studio_and_python_dependencies.bat
update_comfyui_and_python_dependencies.bat
key: ${{ runner.os }}-build-cu${{ inputs.cu }}-${{ inputs.python_minor }}
- shell: bash
run: |
mv cu${{ inputs.cu }}_python_deps.tar ../
mv update_studio_and_python_dependencies.bat ../
mv update_comfyui_and_python_dependencies.bat ../
cd ..
tar xf cu${{ inputs.cu }}_python_deps.tar
pwd
@@ -50,12 +50,12 @@ jobs:
- uses: actions/checkout@v4
with:
fetch-depth: 150
fetch-depth: 0
persist-credentials: false
- shell: bash
run: |
cd ..
cp -r HanzoStudio HanzoStudio_copy
cp -r ComfyUI ComfyUI_copy
curl https://www.python.org/ftp/python/3.${{ inputs.python_minor }}.${{ inputs.python_patch }}/python-3.${{ inputs.python_minor }}.${{ inputs.python_patch }}-embed-amd64.zip -o python_embeded.zip
unzip python_embeded.zip -d python_embeded
cd python_embeded
@@ -63,36 +63,30 @@ jobs:
curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
./python.exe get-pip.py
./python.exe -s -m pip install ../cu${{ inputs.cu }}_python_deps/*
sed -i '1i../HanzoStudio' ./python3${{ inputs.python_minor }}._pth
rm ./Lib/site-packages/torch/lib/dnnl.lib #I don't think this is actually used and I need the space
rm ./Lib/site-packages/torch/lib/libprotoc.lib
rm ./Lib/site-packages/torch/lib/libprotobuf.lib
sed -i '1i../ComfyUI' ./python3${{ inputs.python_minor }}._pth
cd ..
git clone --depth 1 https://github.com/hanzoai/taesd
cp taesd/*.safetensors ./HanzoStudio_copy/models/vae_approx/
git clone --depth 1 https://github.com/comfyanonymous/taesd
cp taesd/*.pth ./ComfyUI_copy/models/vae_approx/
mkdir HanzoStudio_windows_portable
mv python_embeded HanzoStudio_windows_portable
mv HanzoStudio_copy HanzoStudio_windows_portable/HanzoStudio
mkdir ComfyUI_windows_portable
mv python_embeded ComfyUI_windows_portable
mv ComfyUI_copy ComfyUI_windows_portable/ComfyUI
cd HanzoStudio_windows_portable
cd ComfyUI_windows_portable
mkdir update
cp -r HanzoStudio/.ci/update_windows/* ./update/
cp -r HanzoStudio/.ci/windows_nvidia_base_files/* ./
cp ../update_studio_and_python_dependencies.bat ./update/
cp -r ComfyUI/.ci/update_windows/* ./update/
cp -r ComfyUI/.ci/windows_base_files/* ./
cp ../update_comfyui_and_python_dependencies.bat ./update/
cd ..
"C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma2 -mx=9 -mfb=128 -md=768m -ms=on -mf=BCJ2 HanzoStudio_windows_portable.7z HanzoStudio_windows_portable
mv HanzoStudio_windows_portable.7z HanzoStudio/new_HanzoStudio_windows_portable_nvidia_cu${{ inputs.cu }}_or_cpu.7z
"C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma2 -mx=8 -mfb=64 -md=32m -ms=on -mf=BCJ2 ComfyUI_windows_portable.7z ComfyUI_windows_portable
mv ComfyUI_windows_portable.7z ComfyUI/new_ComfyUI_windows_portable_nvidia_cu${{ inputs.cu }}_or_cpu.7z
cd HanzoStudio_windows_portable
python_embeded/python.exe -s HanzoStudio/main.py --quick-test-for-ci --cpu
python_embeded/python.exe -s ./update/update.py HanzoStudio/
cd ComfyUI_windows_portable
python_embeded/python.exe -s ComfyUI/main.py --quick-test-for-ci --cpu
ls
@@ -100,7 +94,7 @@ jobs:
uses: svenstaro/upload-release-action@v2
with:
repo_token: ${{ secrets.GITHUB_TOKEN }}
file: new_HanzoStudio_windows_portable_nvidia_cu${{ inputs.cu }}_or_cpu.7z
file: new_ComfyUI_windows_portable_nvidia_cu${{ inputs.cu }}_or_cpu.7z
tag: "latest"
overwrite: true
-7
View File
@@ -1,7 +0,0 @@
name: Workflow Sanity
on:
pull_request:
paths: ['.github/workflows/**']
jobs:
sanity:
uses: hanzoai/.github/.github/workflows/workflow-sanity.yml@main
+3 -12
View File
@@ -5,28 +5,19 @@ __pycache__/
!/input/example.png
/models/
/temp/
# Ignore vendored pack contents but keep the directory listable so the tracked
# files below can be re-included (a file under a fully-excluded dir cannot be).
/custom_nodes/*
!/custom_nodes/example_node.py.example
# Pinned pack manifest is tracked; the pack sources it clones are not (vendored at build).
!/custom_nodes/hanzo-packs.txt
/custom_nodes/
!custom_nodes/example_node.py.example
extra_model_paths.yaml
/.vs
.vscode/
.idea/
venv*/
venv/
.venv/
/web/extensions/*
!/web/extensions/logging.js.example
!/web/extensions/core/
/web/shell/node_modules/
/tests-ui/data/object_info.json
/user/
*.log
web_custom_versions/
.DS_Store
openapi.yaml
filtered-openapi.yaml
uv.lock
/custom_nodes_quarantine/
-24
View File
@@ -1,24 +0,0 @@
name: deploy
on:
push:
branches: [main]
jobs:
deploy:
runs-on: hanzo-linux-amd64
steps:
- uses: actions/checkout@v4
- name: Build + push image
run: |
SHA="${GITHUB_SHA::8}"
buildctl-daemonless.sh build --frontend=dockerfile.v0 \
--opt context="${{ github.server_url }}/${{ github.repository }}.git#${GITHUB_SHA}" \
--opt filename=Dockerfile --opt platform=linux/amd64 \
--secret id=GIT_AUTH_TOKEN,env=GIT_AUTH_TOKEN \
--output "type=image,name=ghcr.io/hanzoai/studio:${SHA},push=true" --progress=plain
env:
GIT_AUTH_TOKEN: ${{ secrets.GIT_CLONE_TOKEN }}
- name: Deploy — declare tag to operator
run: |
for app in studio; do
kubectl -n hanzo patch app "$app" --type=merge -p "{\"spec\":{\"image\":{\"repository\":\"ghcr.io/hanzoai/studio\",\"tag\":\"${GITHUB_SHA::8}\"}}}"
done
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LLM.md
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LLM.md
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# Admins
* @hanzoai/studio
* @comfyanonymous
# Note: Github teams syntax cannot be used here as the repo is not owned by Comfy-Org.
# Inlined the team members for now.
# Maintainers
*.md @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink
/tests/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink
/tests-unit/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink
/notebooks/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink
/script_examples/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink
/.github/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink
# Python web server
/api_server/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata
/app/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata
# Frontend assets
/web/ @huchenlei @webfiltered @pythongosssss @yoland68 @robinjhuang
# Extra nodes
/comfy_extras/ @yoland68 @robinjhuang @huchenlei @pythongosssss @ltdrdata @Kosinkadink
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# Contributing to Hanzo Studio
# Contributing to ComfyUI
Welcome, and thank you for your interest in contributing to Hanzo Studio!
Welcome, and thank you for your interest in contributing to ComfyUI!
There are several ways in which you can contribute, beyond writing code. The goal of this document is to provide a high-level overview of how you can get involved.
## Asking Questions
Have a question? Instead of opening an issue, please ask on [Discord](https://discord.gg/hanzoai) first. Our team and the community will help you.
Have a question? Instead of opening an issue, please ask on [Discord](https://comfy.org/discord) or [Matrix](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) channels. Our team and the community will help you.
## Providing Feedback
@@ -16,24 +16,24 @@ See the `#bug-report`, `#feature-request` and `#feedback` channels on Discord.
## Reporting Issues
Have you identified a reproducible problem in Hanzo Studio? Do you have a feature request? We want to hear about it! Here's how you can report your issue as effectively as possible.
Have you identified a reproducible problem in ComfyUI? Do you have a feature request? We want to hear about it! Here's how you can report your issue as effectively as possible.
### Look For an Existing Issue
Before you create a new issue, please do a search in [open issues](https://github.com/hanzoai/studio/issues) to see if the issue or feature request has already been filed.
Before you create a new issue, please do a search in [open issues](https://github.com/comfyanonymous/ComfyUI/issues) to see if the issue or feature request has already been filed.
If you find your issue already exists, make relevant comments and add your [reaction](https://github.com/blog/2119-add-reactions-to-pull-requests-issues-and-comments). Use a reaction in place of a "+1" comment:
* upvote
* downvote
* 👍 - upvote
* 👎 - downvote
If you cannot find an existing issue that describes your bug or feature, create a new issue. We have an issue template in place to organize new issues.
### Creating Pull Requests
* Please refer to the article on [creating pull requests](https://github.com/hanzoai/studio/wiki/How-to-Contribute-Code) and contributing to this project.
* Please refer to the article on [creating pull requests](https://github.com/comfyanonymous/ComfyUI/wiki/How-to-Contribute-Code) and contributing to this project.
## Thank You
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FROM python:3.11-slim AS base
RUN apt-get update && apt-get install -y --no-install-recommends \
git \
build-essential \
libgl1 \
libglib2.0-0 \
# WeasyPrint native stack (office-document PDF renderer, middleware/doc_render.py):
# pango/cairo/gdk-pixbuf + a base font. Absent → the built-in PDF fallback is used.
libpango-1.0-0 \
libpangocairo-1.0-0 \
libgdk-pixbuf-2.0-0 \
shared-mime-info \
fonts-liberation \
&& rm -rf /var/lib/apt/lists/*
WORKDIR /app
# Install PyTorch CPU first (large layer, cached separately)
RUN pip install --no-cache-dir \
torch torchvision torchaudio \
--index-url https://download.pytorch.org/whl/cpu
# Install remaining dependencies
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
# Apply Hanzo Studio branding to frontend package
COPY branding/ /tmp/branding/
RUN chmod +x /tmp/branding/apply-branding.sh && /tmp/branding/apply-branding.sh && rm -rf /tmp/branding
# Copy application code
COPY . .
# Vendor the Hanzo Studio custom-node packs (segmentation, controlnet, ipadapter,
# upscaling, video, utils) at their pinned commits and install their deps with the
# torch/numpy/transformers stack locked. See custom_nodes/hanzo-packs.txt.
RUN chmod +x scripts/install_custom_nodes.sh && scripts/install_custom_nodes.sh
# Create directories for runtime volumes
RUN mkdir -p models output input custom_nodes user
# /app/orgs is the tenant-data PVC; /app/models is ephemeral overlay. Register
# .models on the PVC as the default model store (main.py auto-loads this file)
# so models survive pod restarts and downloads land durable.
RUN printf '%s\n' \
'hanzo:' \
' base_path: /app/orgs/.models' \
' is_default: true' \
' checkpoints: checkpoints' \
' configs: configs' \
' loras: loras' \
' vae: vae' \
' text_encoders: text_encoders' \
' diffusion_models: diffusion_models' \
' clip_vision: clip_vision' \
' style_models: style_models' \
' embeddings: embeddings' \
' diffusers: diffusers' \
' vae_approx: vae_approx' \
' controlnet: controlnet' \
' gligen: gligen' \
' upscale_models: upscale_models' \
' latent_upscale_models: latent_upscale_models' \
' hypernetworks: hypernetworks' \
' photomaker: photomaker' \
' classifiers: classifiers' \
' model_patches: model_patches' \
' audio_encoders: audio_encoders' \
> extra_model_paths.yaml
EXPOSE 8188
# Default: listen on all interfaces, CPU mode
# Environment variables:
# STUDIO_ENABLE_IAM_AUTH=1 - Enable IAM auth via hanzo.id
# STUDIO_IAM_URL=https://hanzo.id
# STUDIO_NO_LOCALHOST_BYPASS=1 - Require auth even for localhost
# STUDIO_MULTI_TENANT=1 - Per-org storage isolation
# STUDIO_ORG_ID=default-org - Default org ID
# STUDIO_ENABLE_BILLING=1 - Commerce billing integration
# STUDIO_BILLING_CHECK_BALANCE=1 - Check balance before prompts
# STUDIO_COMMERCE_URL=http://commerce.hanzo.svc:8001
# STUDIO_COMMERCE_TOKEN=... - Commerce API token
# STUDIO_ENABLE_METRICS=1 - /metrics endpoint for VictoriaMetrics
# STUDIO_RATE_LIMIT_RPM=60 - Prompts per minute per org
CMD ["python", "main.py", "--listen", "0.0.0.0", "--port", "8188", "--cpu"]
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# Hanzo Studio
## Overview
**The most powerful and modular visual AI engine and application.**
## Tech Stack
- **Language**: Python
## Build & Run
```bash
uv sync
uv run pytest
```
## Structure
```
studio/
CODEOWNERS
CONTRIBUTING.md
Dockerfile
LICENSE
QUANTIZATION.md
README.md
alembic.ini
alembic_db/
api_server/
app/
blueprints/
branding/
comfyui.log
comfyui.prev.log
comfyui.prev2.log
```
## Key Files
- `README.md` -- Project documentation
- `pyproject.toml` -- Python project config
- `Dockerfile` -- Container build (applies `branding/` then runs `main.py`)
- `hanzo.yml` + `.github/workflows/cicd.yml` -- canonical CI/CD (hanzoai/ci); builds `ghcr.io/hanzoai/studio`, rolls the `studio` operator Service CR at `studio.hanzo.ai`.
## Custom-node packs ("hanzo packages")
The popular ComfyUI packs are vendored as first-class hanzo packages, pinned and
reproducible. One and only one way to add/bump them:
- **Manifest**: `custom_nodes/hanzo-packs.txt` — `<dir> <git_url> <commit_sha>
<spdx>` lines (immutable SHAs). This is the tracked source of truth; the cloned
pack sources are gitignored (vendored at build).
- **Deps**: `custom_nodes-requirements.txt` — hand-resolved, only what's not
already in `requirements.txt`. Installed with the studio torch(cu130)/NumPy 2.x/
Transformers 5.x stack **locked** so packs can never downgrade it.
- **Installer**: `scripts/install_custom_nodes.sh` clones each pack at its pin and
installs deps; the lock is derived from the live env (correct for both the CPU
image and the GB10 cu130 venv). `ultralytics` is installed `--no-deps` (else it
pulls non-headless opencv). Local (GB10): `STUDIO_PY=.venv/bin/python
STUDIO_PIP="uv pip install --python .venv/bin/python" scripts/install_custom_nodes.sh`.
- **Image**: `Dockerfile` runs the installer after `COPY . .`; `.dockerignore`
re-includes only the manifest + installer (`custom_nodes/*` / `scripts/*` form).
So `git push origin main` → CI builds `ghcr.io/hanzoai/studio` with the packs →
studio.hanzo.ai gets them on the next image build.
**Compat shim — why packs work here at all.** This fork renamed ComfyUI's core
packages (`comfy`→`studio`, `comfy_extras`→`studio_extras`, `comfy/comfy_types`→
`studio/node_types`, and the `Comfy*`→`Studio*` API classes; see
`scripts/rename_modules.py`). `studio_compat/` is one meta-path finder that lazily
aliases every `comfy*` import to its `studio*` counterpart and mirrors
`ComfyExtension`/`io.ComfyNode`→`Studio*` — so upstream packs run **unmodified**,
no forks/renames. It is the phase-4 redirect `rename_modules.py` deferred, done
with the 3.12 `find_spec` API. Activated by `import studio_compat` at the top of
`main.py`. `nodes.py` also honors the upstream `comfy_entrypoint` V3 name.
Restart the local worker only when idle: `scripts/studio-service-swap.sh` polls
`/queue` and restarts on the first empty window. Bad packs are isolated by the
loader (studio still boots); genuinely-incompatible ones go to
`custom_nodes_quarantine/` (e.g. was-node-suite: hard `numba` dep vs NumPy 2.5).
## Multi-tenant + IAM (cloud: studio.hanzo.ai)
Standalone app; `console.hanzo.ai` link-outs land here already authenticated via
shared Hanzo IAM (standard OIDC code flow). No embedding.
- **Auth** (`middleware/iam_auth_middleware.py`): local **JWKS** JWT validation
against `${STUDIO_IAM_URL}/v1/iam/.well-known/jwks` — signature + `exp` +
`iss` + `aud` (client_id `hanzo-studio`). Org from the `owner` claim
(`organization` fallback) → `request["iam_user"]["org_id"]`. Anonymous mode:
off by default locally (loopback bypass); enable in cloud with
`STUDIO_ENABLE_IAM_AUTH=true` (the one auth switch — this is the
"STUDIO_AUTH=off default" behavior). Browser flow: unauth HTML → IAM
`authorize` (code+PKCE) → `/callback` exchanges the code, sets an httpOnly
session cookie (no frontend changes). `verify_jwt(...)` is a pure, unit-tested
function.
- **Tenancy** (`folder_paths.py`, `app/user_manager.py`): with
`STUDIO_MULTI_TENANT=true`, userdata is `user/orgs/{org}/user/{user}/…` and
outputs are `output/orgs/{org}/…`. `get_public_user_directory(user, org)` is
org-rooted (fixed the bug where org userdata always resolved to `None`).
Execution outputs are scoped via `folder_paths.set_execution_org()`, bound by
the prompt worker from the queue item's `org_id`.
- **GPU federation** (`middleware/{worker_client,prompt_router,compute_config,
visor_client}.py`): `/v1/workers/register` (heartbeat), `/v1/workers` (list),
`/v1/worker/execute` (in-cluster push fast path). Worker↔coordinator trust via
`X-Worker-Token` (`STUDIO_WORKER_TOKEN`, KMS-sourced).
- **Per-GPU gpu-jobs queue** (`middleware/{gpu_dispatch,studio_home}.py`, `server.py`) —
the ONE submit path onto a GPU. A model-less coordinator's `POST /prompt` enqueues to
the cloud tasks engine (`POST {STUDIO_CLOUD_API_URL}/v1/tasks/namespaces/gpu-jobs/activities`,
user bearer → org-scoped); a BYO worker (`hanzo gpu connect`) claims + runs it.
**Targeting**: the Home "Run on <gpu>" chip sets `X-Target-GPU: <identity>`;
`dispatch_if_worker` enqueues on that machine's own lane `taskQueue="gpu:<identity>"`
(namespace stays `gpu-jobs`), else the shared `gpu-jobs` lane any worker drains —
`X-Target-GPU` rides through `_queue_prompt` so fix/compose/video/rerun all honor it.
**Authoritative visible queue**: `/v1/render-queue` + `/v1/nodes` read the tasks
engine's activity list — REAL claiming `identity`, status
SCHEDULED/STARTED/COMPLETED/FAILED → queued/running/done/failed, `lastHeartbeatTime`
liveness, per-node depth — not a local shadow; `render_jobs.json` survives only as a
~2s pre-claim placeholder. **No hidden runs**: in `--worker-mode` a direct `/prompt`
POST is refused 403 unless it carries `X-Worker-Token` — the only accepted execution is
a claimed job handed in over `POST /v1/worker/execute`
(`worker_client.reject_untrusted_worker_submit`, one policy gate). Legacy push
(`prompt_router`) is OFF unless `STUDIO_LEGACY_PUSH_ROUTER=1`. Tests:
`middleware_test/{gpu_dispatch_test,worker_seam_test}.py`.
- **Durable render queue** (`middleware/tasks_queue.py`, `docs/federation.md`):
`SqlitePromptQueue` — crash-durable render queue in a single SQLite file
(stdlib `sqlite3`, WAL, **zero external processes**). `STUDIO_QUEUE_DB=<path>`
→ `server.py` swaps `PromptQueue` for it (precedence **STUDIO_QUEUE_DB >
STUDIO_PERSIST_QUEUE > memory**; default local behavior unchanged). The
`PromptQueue` seam maps to a durable job state machine: `put→INSERT (pending,
idempotent on prompt_id)`, `get→claim next pending under BEGIN IMMEDIATE
(exactly-once, leased)`, `task_done→done | retry/failed`. Crash-recovery: a
lease + reap returns an abandoned claim to `pending` (on every get(), a
heartbeat thread, and on boot) so another process re-runs it — idempotent
(SaveImage suffixes increment). Multiple Studio processes on the same DB file
compete safely. Tested: `middleware_test/tasks_queue_test.py` (incl.
crash-recovery across instances). Future (federation.md §6): a remote queue
backend served by Hanzo Tasks + a Go/unified-binary (HIP-0106) migration.
- **Stacks** (`middleware/stacks_store.py`, `middleware/studio_home.py`): Procreate-
style gallery folders — a per-org SQLite store (`orgs/<org>/stacks.db`, WAL, every
mutation under `BEGIN IMMEDIATE`, the same `tasks_queue.py` pattern) that is ONLY an
organizational layer; assets stay the source of truth (library.json + files). Schema
= the spec's `stacks` + `stack_assets` join; `UNIQUE(asset_id)` ⇒ one Stack per
asset. Tenancy is structural (one DB file per org → cross-org 404s by construction).
REST (`/v1` house style): `GET/POST /v1/stacks`, `GET/PATCH/DELETE /v1/stacks/:id`
(`?mode=only|images`), `POST|DELETE /v1/stacks/:id/assets`, `PATCH /v1/stacks/:id/order`,
`POST /v1/stacks/:id/unstack`, `GET/PATCH /v1/stacks/settings`; registered via
injected resolvers (org/owner/workspace + the library soft-deleter) so the module
stays decoupled. Remove ≠ delete (returns to the gallery); Delete-Stack defaults to
preserving; "…and Images" maps to the library soft-delete (`status=deleted`, files
kept). Generation integration: `fix/compose/rerun/template` accept a `stack` id and
the ingest point (`_index_upload` → `stacks_store.absorb`) assigns membership when
outputs land (ALL outputs of a multi-output job); a per-org `auto_stack_batches`
setting auto-creates `"<prompt> — <date>"` Stacks. UI is the vanilla-JS home
(`studio_home.html`): layered cards, drag/multi-select/mobile-tap parity (no DnD ever
required), a11y labels + live region. Tested: `studio_home_test/test_stacks.py`
(routes, tenancy, one-per-asset, order persistence, delete/unstack, ingest→absorb).
- **Engine selector** (`middleware/engine_selector.py`): `/v1/engines` lists
execution targets for the org (`local` + registered compute_config workers) and
`PUT /v1/engines/default` sets a per-org default (stored on
`ComputeProfile.default_engine`). `prompt_router.route_prompt` routes to the
chosen worker; `local` = unchanged. Leased cloud machines (Visor
`GET /v1/machines`) are a future engine class (interface stubbed, not built).
Frontend picker is a TODO. Tested: `middleware_test/engine_selector_test.py`.
- **Chat Copilot** (`middleware/copilot.py` + `branding/hanzo-copilot.{js,css}`):
a sidebar chat that reads and MUTATES the workflow graph in natural language.
Backend `POST /v1/copilot/chat` proxies one OpenAI tool-calling round to a
chat endpoint (`STUDIO_COPILOT_URL`; default = local engine
`127.0.0.1:1234` if its port is open, else `api.hanzo.ai`; `STUDIO_COPILOT_MODEL`
default `zen`; token from `STUDIO_COPILOT_TOKEN`→`STUDIO_COMMERCE_TOKEN`→request
bearer). It returns `{reply, ops}`; `add_node` ops are validated against
`nodes.NODE_CLASS_MAPPINGS` server-side so the model can't invent node types.
The **frontend** applies ops against `app.graph` (LiteGraph) — op schema:
`add_node`(id?/pos?) · `set_widget` · `connect`(slots by name or index) ·
`set_prompt` · `move_node` · `delete_node` · `layout` (columnar tidy) ·
`queue`. Every op is guarded (unknown type/slot = skip + note, never throw).
Injected into the prebuilt frontend by `branding/patch_copilot.py` (copies the
bundle into static/, CSS `<link>` in `<head>`, deferred `<script>` before
`</body>`; idempotent, wired as step 9 of `apply-branding.sh`) — survives
frontend upgrades. `window.HanzoCopilot.applyOps` is exposed for tests. Tab
registered via `app.extensionManager.registerSidebarTab` (id `hanzo-copilot`).
Tested: `middleware_test/copilot_test.py` (op-validation + mocked tool loop) and
`branding/copilot_smoke.py` (headless: applier mutates a mock graph + panel
renders). Live end-to-end needs one server restart to register the route.
- **Identity user menu + session API** (`middleware/session.py` +
`branding/hanzo-identity.{js,css}`): the top-right user menu, driven entirely
by the IAM-validated token. Backend (thin, `/v1` house style, registered in
`add_routes` so it works auth-on and auth-off): `GET /v1/session` →
`{user,email,org,orgs[],project,projects[],console_url,iam_url,authenticated}`
read from `request["iam_user"]`; `POST /v1/session/org` sets the active org
(validated against membership → `studio_active_org` cookie); `POST
/v1/session/project` sets the workflow-workspace project scope; `GET|POST
/logout` clears the session + selection cookies and bounces to the IAM
end-session → `/login`. **Org switching is real, one hook**: the auth
middleware applies `session.resolve_org(...)` to a COPY of the validated user
(never the cached dict) so `iam_user["org_id"]` — the id every tenancy/output
path already reads — reflects the switch; membership comes from
`_orgs_from_claims` (`owner` + `organizations`/`orgs`/`groups`, list or CSV or
`{name}` dicts, home first, deduped). Frontend is a SPA-agnostic fixed pill +
dropdown (avatar/initials, user, org switcher, project switcher, Console
link → `console.hanzo.ai`, Logout); 401 → "Sign in with Hanzo" → `/login`.
Injected by `branding/patch_identity.py` (step 10 of `apply-branding.sh`,
same seam as the copilot). `window.HanzoIdentity` is exposed for tests.
Tested: `middleware_test/session_test.py` (resolve/orgs/routes/logout, real
aiohttp app) + `scratchpad/studio-iam/test_identity.py` (headless: pill,
dropdown, org switch re-scope, signed-out). The `/v1/session` routes need one
server restart to register (static + de-Comfy load without restart).
- **De-Comfy display strings** (`branding/patch_destring.py`, step 11 —
runs LAST): the systematic English brand-word sweep the curated phrase list in
`patch_frontend.py` structurally can't reach. Rule 1: whole-word `ComfyUI` →
`Hanzo Studio` only where it reads as a display word (preceded by
open-quote/space/`>`/`(`, followed by close-quote/space/sentence-punct) — that
boundary skips every code use in the bundle (`ComfyUIStarted`, `.ComfyUI`,
`=ComfyUI`, the `/^(ComfyUI-.../` regex, dotted i18n keys `.category.ComfyUI`)
and hyphenated product names (`ComfyUI-Manager`). Rule 2: curated bare-`Comfy`
**values** as exact `key:` value`` pairs so the Settings dialog **keys**
(`Comfy`, `Comfy-Desktop`, `Comfy.ColorPalette`) are preserved while the
displayed section header becomes **Studio** / **Studio Desktop** (this is the
"Settings must not say Comfy" fix) + `Comfy Cloud` → `Hanzo Cloud`. Scope:
English only — files above `LOCALE_RATIO` (0.15%) non-ASCII are skipped (all 33
locale bundles are ≥0.31%; English bundles ~0.02%) and each match needs a
pure-ASCII window, so the non-English `main-*/nodeDefs-*/commands-*` chunks are
left as-is (noted, out of scope). Idempotent. Verified: served English display
`ComfyUI` 63→0; a few example filesystem paths (`Documents/ComfyUI`) and the
external comfy.org service labels (`Comfy API Key`, `Comfy Node Registry`,
behind disabled API-nodes) are intentionally left. patch_frontend.py keeps its
orthogonal concern (URLs/domains + `Comfy-Org` → `Hanzo AI`).
## Office documents (the non-GPU pipeline)
Studio's creative side is ComfyUI graphs → GPU. Office documents are the **orthogonal**
subsystem: structured content (a template's typed fields, filled by a person OR by the AI)
→ a pure-Python renderer → a real `.pdf/.docx/.xlsx/.pptx/.md`. No graph, no model, no GPU.
NOT bolted onto graphs; its own module, store, endpoints, and Home tab.
- **Renderers** (`middleware/doc_render.py`, pure — no HTTP/storage/org): one neutral IR
per archetype (`doc` prose blocks · `sheet` sheets · `deck` slides) and EXACTLY ONE
renderer per format. Adding a template = one `build(fields)->(kind,ir)`; adding a format =
one renderer. Deps imported lazily so the module loads stdlib-only. **PDF** prefers
WeasyPrint (full-CSS, monochrome print stylesheet); when its native pango/cairo stack is
absent (the light CI unit venv, model-less pods) a **built-in pure-Python PDF writer**
(base-14 Helvetica, WinAnsi/cp1252, auto-pagination, `/Info` title) produces a valid
`%PDF` — so every format renders with just the wheels. **DOCX/XLSX/PPTX** = python-docx /
openpyxl / python-pptx (real, always). Native libs in the `Dockerfile` (libpango/…).
- **Catalog + AI-fill + store + REST** (`middleware/documents.py`): ONE declarative `CATALOG`
of 22 common templates (Documents 15 → pdf/docx/md · Spreadsheets 4 → xlsx/pdf/docx/md ·
Presentations 3 → pptx/pdf/md). Each template's typed `fields` drive the form, the AI-fill
JSON, AND the renderer inputs (one schema, three uses). Registered from within
`add_studio_home_routes` with the SAME injected tenancy resolvers as `stacks_store`
(`org_of`/`owner_of`). Per-org store `orgs/<org>/documents/` (atomic `index.json` +
`<id>/doc.json`), doc.json is the source of truth, files rendered on demand at download.
Endpoints (`/v1` house style): `GET /v1/documents` (grouped catalog),
`POST /v1/documents/generate` `{templateId, description?|fields?}`,
`GET /v1/documents/library`, `GET|PATCH|DELETE /v1/documents/{id}`,
`GET /v1/documents/{id}/download?format=`.
- **AI-fill is a LAYERED ENHANCEMENT** — the structured-form path renders a valid document
with NO LLM, so a broken/unauthorized gateway can NEVER block creation (`422 needFields`
→ the UI reveals the prefilled form). It reuses the SAME working token the shipped Chat
uses: the browser sends `window.HZ.pk` (the publishable `pk-` key `_publishable`/
`_hz_config_script` emit) as the bearer to `/v1/documents/generate`; the backend forwards
it, fail-closed, in order: (1) a **server-side gateway key** (`STUDIO_DOC_TOKEN`→
`STUDIO_COPILOT_TOKEN`→`STUDIO_COMMERCE_TOKEN`); (2) else the caller's own token, forwarded
ONLY when gateway-verifiable — a publishable `pk-` key or an asymmetric/JWKS JWT
(RS/ES/PS/EdDSA). A symmetric **HS256 hanzo.id session token is NEVER forwarded** (the
gateway rejects it: "unsupported signing method: HS256"), nor any secret `sk-`/`hk-` key.
Model = `STUDIO_DOC_MODEL`→`STUDIO_CHAT_MODEL`→`enso` (matches window.HZ.model).
- **UI** (`middleware/studio_home.html`, vanilla JS, monochrome/Geist): a **📄 Documents**
Home tab — searchable server-driven catalog + "Your documents" grid with Download
PDF/Word/Excel/PowerPoint. The `docdlg` dialog: describe in one line → Generate with AI
(sends window.HZ.pk), or fill the short typed form → Generate; edit fields before/after;
re-open a saved doc to edit (PATCH). The dead `doc/sheet/paper` composer KIND placeholders
were replaced with one "Office documents" card that opens the tab.
## Create composer + gateway auth (v0.18.1 — the intuitive first Create)
The "What should we create?" composer must PRODUCE something on the first ↑, and Chat must
reach the gateway. Both were fixed here.
- **Gateway auth is ONE fail-closed seam** (`middleware/gateway_auth.py`): the canonical
allowlist for handing a browser-edge bearer to api.hanzo.ai. `ai_bearer(request,
server_env=…)` = a server key first (never in page source), else the caller's own token
ONLY when the gateway can verify it — a publishable `pk-` key or an asymmetric/JWKS JWT
(RS/ES/PS/EdDSA). A symmetric **HS256 hanzo.id session token is NEVER forwarded** (the
gateway rejects it → "unsupported signing method: HS256" → the old 502), nor any secret
`sk-`/`hk-`/`rk-` key. `copilot.py._resolve_target` now uses it
(`_SERVER_TOKEN_ENV=(STUDIO_COPILOT_TOKEN, STUDIO_COMMERCE_TOKEN)`), so the editor-ops
chat no longer 502s on the session token. The shipped **Chat sidebar** (`shell.js`) still
streams enso directly from the gateway with `window.HZ.pk` when present; its copilot
fallback now renders ONE friendly line on failure — never the raw backend error/JSON.
(documents.py's AI-fill has an equivalent private copy; it should adopt `gateway_auth`
once its concurrent review lands — the shared module is the going-forward one way.)
- **Text-to-image is the default create lane** (`studio_home.py`: `_image_graph` +
`dispatch_image` + `POST /v1/library/image`): the proven **Z-Image-Turbo** graph
(`z_image_turbo_bf16` / `qwen_3_4b` lumina2 / `ae` vae; EmptySD3LatentImage →
ModelSamplingAuraFlow shift 3 → KSampler res_multistep/simple 4 steps cfg 1.0 →
SaveImage), lifted from the shipped blueprint, dispatched through the ONE funnel like
video/fix. Words in → an image out; a model-less pod surfaces the real DispatchError, not
a silent nothing.
- **Composer routes ↑ by the selected model's modality** (`studio_home.html`): default
`MODEL='zen-image'` from `hz_create_model` — DECOUPLED from the chat model (`hz_model`),
so an image pick never breaks the assistant (and `shell.js chatModel()` ignores a
non-chat `hz_model`). `composerPlan()`: assistant model → hand to the ONE chat via
`window.HanzoChat.send` (exposed by shell.js); no photo → text-to-image; 1 photo → fix;
2+ → compose; video model/lane → video. `create()` ALWAYS pulses a "Preparing…"
live-bar state and, on failure, shows a friendly retry (prompt kept) — never a silent
no-op. The code model (`zen5-coder`) is hidden from the creative composer; each model has
a one-line description; the `</>` request-preview is gated behind Developers mode
(`body.hzdev`, set by the shell's Developers pill).
- **P2 friction fixes** (`studio_home.html` + `shell.css`): a first-class **Download** on
every tile AND in the viewer (PNG original · JPG/WebP client-side canvas convert, the
popover flips up near the viewport bottom); the fixed header + sticky live-bar are now
OPAQUE (were translucent → content scrolled under them); the **ARCHIVED** badge shows
ONLY for truly deleted/flagged items (active work is unbadged); on mobile the ~30 filter
pills collapse to one horizontally-scrollable row and the tab bar gets a scroll fade
affordance. The Chat empty state (shell.js) shows an assistant identity + one line + 3
tappable starters.
## Tests
`tests-unit/{folder_paths_test,middleware_test,app_test,prompt_server_test,studio_home_test,documents_test}` —
run `uv run pytest tests-unit/folder_paths_test tests-unit/middleware_test
tests-unit/app_test tests-unit/prompt_server_test tests-unit/studio_home_test
tests-unit/documents_test -q`. The auth/tenancy work is
covered by `middleware_test/iam_auth_test.py`,
`middleware_test/session_test.py` and
`folder_paths_test/org_scoping_test.py`. Office documents:
`documents_test/test_documents.py` (catalog invariants, the full template×format render
matrix asserted by magic numbers, AI-fill parse + fail-closed bearer allowlist, and the
REST surface incl. per-org isolation on a real aiohttp app). Create composer + gateway
auth: `middleware_test/gateway_auth_test.py` (the fail-closed allowlist — HS256/`sk-` refused,
`pk-`/asymmetric forwarded, server-key precedence, session-cookie path),
`middleware_test/copilot_test.py::test_resolve_target_never_forwards_hs256_session_token`,
and `studio_home_test/create_image_test.py` (the Z-Image-Turbo graph shape, `dispatch_image`
validation, and served-HTML invariants: default-model-is-image, text→image route,
Create-always-shows-a-state, first-class Download, no ARCHIVED badge on active items, opaque
bars, gated `</>`, collapsed mobile filters).
-44
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@@ -1,44 +0,0 @@
Hanzo Studio
============
Forked from https://github.com/comfyanonymous/ComfyUI (GPL-3.0).
Hanzo Studio is a fork of ComfyUI (https://github.com/comfyanonymous/ComfyUI),
Copyright (c) ComfyUI contributors, licensed under the GNU General Public
License v3.0 (GPL-3.0).
Modifications Copyright (c) 2026 Hanzo AI, Inc., also released under the
GNU General Public License v3.0 (GPL-3.0). See the LICENSE file for the full
license text.
As required by the GPL-3.0, this fork remains under the same license as the
upstream project. The original ComfyUI copyright notices are retained.
Modifications made in this fork (DEVIATIONS)
--------------------------------------------
- Branding: user-visible name changed from "ComfyUI" to "Hanzo Studio",
including the served page <title>, favicon, in-app logos, and display
strings. The prebuilt frontend package (comfyui-frontend-package) is patched
at install time by branding/apply-branding.sh; source assets live in
branding/. Backend Python packages are namespaced under studio_* and the
server reports itself as "Hanzo Studio".
- Hanzo Engine integration nodes (custom_nodes/hanzo_engine/): first-party
nodes that run AI workloads on a local Hanzo Engine OpenAI-compatible server
over a shared client — HanzoChat (chat/completions, streaming), HanzoImageGen
(images/generations), HanzoTTS (audio/speech), HanzoASR
(audio/transcriptions), HanzoEngineRequest (generic /v1 escape hatch),
HanzoVisionCaption (vision chat/completions), HanzoTextToVideo (videos),
HanzoImageTo3D (3d), and HanzoSaveText. Hanzo Engine is the native-Rust,
all-modality inference and training backend.
- Fashion/swimwear starter workflows (user/default/workflows/fashion/):
product-shot, garment-edit, and caption-dataset workflow templates for a
designer's day-to-day use.
- Deployment/integration middleware for Hanzo infrastructure (IAM auth via
hanzo.id, commerce/billing, metrics), wired through environment variables and
the Dockerfile.
Third-party components retain their own upstream copyrights and licenses.
-168
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@@ -1,168 +0,0 @@
# The Hanzo Studio guide to Quantization
## How does quantization work?
Quantization aims to map a high-precision value x_f to a lower precision format with minimal loss in accuracy. These smaller formats then serve to reduce the models memory footprint and increase throughput by using specialized hardware.
When simply converting a value from FP16 to FP8 using the round-nearest method we might hit two issues:
- The dynamic range of FP16 (-65,504, 65,504) far exceeds FP8 formats like E4M3 (-448, 448) or E5M2 (-57,344, 57,344), potentially resulting in clipped values
- The original values are concentrated in a small range (e.g. -1,1) leaving many FP8-bits "unused"
By using a scaling factor, we aim to map these values into the quantized-dtype range, making use of the full spectrum. One of the easiest approaches, and common, is using per-tensor absolute-maximum scaling.
```
absmax = max(abs(tensor))
scale = amax / max_dynamic_range_low_precision
# Quantization
tensor_q = (tensor / scale).to(low_precision_dtype)
# De-Quantization
tensor_dq = tensor_q.to(fp16) * scale
tensor_dq ~ tensor
```
Given that additional information (scaling factor) is needed to "interpret" the quantized values, we describe those as derived datatypes.
## Quantization in Hanzo Studio
```
QuantizedTensor (torch.Tensor subclass)
↓ __torch_dispatch__
Two-Level Registry (generic + layout handlers)
MixedPrecisionOps + Metadata Detection
```
### Representation
To represent these derived datatypes, Hanzo Studio uses a subclass of torch.Tensor to implements these using the `QuantizedTensor` class found in `studio/quant_ops.py`
A `Layout` class defines how a specific quantization format behaves:
- Required parameters
- Quantize method
- De-Quantize method
```python
from studio.quant_ops import QuantizedLayout
class MyLayout(QuantizedLayout):
@classmethod
def quantize(cls, tensor, **kwargs):
# Convert to quantized format
qdata = ...
params = {'scale': ..., 'orig_dtype': tensor.dtype}
return qdata, params
@staticmethod
def dequantize(qdata, scale, orig_dtype, **kwargs):
return qdata.to(orig_dtype) * scale
```
To then run operations using these QuantizedTensors we use two registry systems to define supported operations.
The first is a **generic registry** that handles operations common to all quantized formats (e.g., `.to()`, `.clone()`, `.reshape()`).
The second registry is layout-specific and allows to implement fast-paths like nn.Linear.
```python
from studio.quant_ops import register_layout_op
@register_layout_op(torch.ops.aten.linear.default, MyLayout)
def my_linear(func, args, kwargs):
# Extract tensors, call optimized kernel
...
```
When `torch.nn.functional.linear()` is called with QuantizedTensor arguments, `__torch_dispatch__` automatically routes to the registered implementation.
For any unsupported operation, QuantizedTensor will fallback to call `dequantize` and dispatch using the high-precision implementation.
### Mixed Precision
The `MixedPrecisionOps` class (lines 542-648 in `studio/ops.py`) enables per-layer quantization decisions, allowing different layers in a model to use different precisions. This is activated when a model config contains a `layer_quant_config` dictionary that specifies which layers should be quantized and how.
**Architecture:**
```python
class MixedPrecisionOps(disable_weight_init):
_layer_quant_config = {} # Maps layer names to quantization configs
_compute_dtype = torch.bfloat16 # Default compute / dequantize precision
```
**Key mechanism:**
The custom `Linear._load_from_state_dict()` method inspects each layer during model loading:
- If the layer name is **not** in `_layer_quant_config`: load weight as regular tensor in `_compute_dtype`
- If the layer name **is** in `_layer_quant_config`:
- Load weight as `QuantizedTensor` with the specified layout (e.g., `TensorCoreFP8Layout`)
- Load associated quantization parameters (scales, block_size, etc.)
**Why it's needed:**
Not all layers tolerate quantization equally. Sensitive operations like final projections can be kept in higher precision, while compute-heavy matmuls are quantized. This provides most of the performance benefits while maintaining quality.
The system is selected in `pick_operations()` when `model_config.layer_quant_config` is present, making it the highest-priority operation mode.
## Checkpoint Format
Quantized checkpoints are stored as standard safetensors files with quantized weight tensors and associated scaling parameters, plus a `_quantization_metadata` JSON entry describing the quantization scheme.
The quantized checkpoint will contain the same layers as the original checkpoint but:
- The weights are stored as quantized values, sometimes using a different storage datatype. E.g. uint8 container for fp8.
- For each quantized weight a number of additional scaling parameters are stored alongside depending on the recipe.
- We store a metadata.json in the metadata of the final safetensor containing the `_quantization_metadata` describing which layers are quantized and what layout has been used.
### Scaling Parameters details
We define 4 possible scaling parameters that should cover most recipes in the near-future:
- **weight_scale**: quantization scalers for the weights
- **weight_scale_2**: global scalers in the context of double scaling
- **pre_quant_scale**: scalers used for smoothing salient weights
- **input_scale**: quantization scalers for the activations
| Format | Storage dtype | weight_scale | weight_scale_2 | pre_quant_scale | input_scale |
|--------|---------------|--------------|----------------|-----------------|-------------|
| float8_e4m3fn | float32 | float32 (scalar) | - | - | float32 (scalar) |
You can find the defined formats in `studio/quant_ops.py` (QUANT_ALGOS).
### Quantization Metadata
The metadata stored alongside the checkpoint contains:
- **format_version**: String to define a version of the standard
- **layers**: A dictionary mapping layer names to their quantization format. The format string maps to the definitions found in `QUANT_ALGOS`.
Example:
```json
{
"_quantization_metadata": {
"format_version": "1.0",
"layers": {
"model.layers.0.mlp.up_proj": "float8_e4m3fn",
"model.layers.0.mlp.down_proj": "float8_e4m3fn",
"model.layers.1.mlp.up_proj": "float8_e4m3fn"
}
}
}
```
## Creating Quantized Checkpoints
To create compatible checkpoints, use any quantization tool provided the output follows the checkpoint format described above and uses a layout defined in `QUANT_ALGOS`.
### Weight Quantization
Weight quantization is straightforward - compute the scaling factor directly from the weight tensor using the absolute maximum method described earlier. Each layer's weights are quantized independently and stored with their corresponding `weight_scale` parameter.
### Calibration (for Activation Quantization)
Activation quantization (e.g., for FP8 Tensor Core operations) requires `input_scale` parameters that cannot be determined from static weights alone. Since activation values depend on actual inputs, we use **post-training calibration (PTQ)**:
1. **Collect statistics**: Run inference on N representative samples
2. **Track activations**: Record the absolute maximum (`amax`) of inputs to each quantized layer
3. **Compute scales**: Derive `input_scale` from collected statistics
4. **Store in checkpoint**: Save `input_scale` parameters alongside weights
The calibration dataset should be representative of your target use case. For diffusion models, this typically means a diverse set of prompts and generation parameters.
+161 -158
View File
@@ -1,98 +1,84 @@
<p align="center"><img src=".github/hero.svg" alt="studio" width="880"></p>
<div align="center">
# Hanzo Studio
**The most powerful and modular visual AI engine and application.**
# ComfyUI
**The most powerful and modular diffusion model GUI and backend.**
[![Website][website-shield]][website-url]
[![Dynamic JSON Badge][discord-shield]][discord-url]
[![Twitter][twitter-shield]][twitter-url]
[![Matrix][matrix-shield]][matrix-url]
<br>
[![][github-release-shield]][github-release-link]
[![][github-release-date-shield]][github-release-link]
[![][github-downloads-shield]][github-downloads-link]
[![][github-downloads-latest-shield]][github-downloads-link]
[website-shield]: https://img.shields.io/badge/HanzoAI-4285F4?style=flat
[website-url]: https://www.hanzo.ai/
[matrix-shield]: https://img.shields.io/badge/Matrix-000000?style=flat&logo=matrix&logoColor=white
[matrix-url]: https://app.element.io/#/room/%23comfyui_space%3Amatrix.org
[website-shield]: https://img.shields.io/badge/ComfyOrg-4285F4?style=flat
[website-url]: https://www.comfy.org/
<!-- Workaround to display total user from https://github.com/badges/shields/issues/4500#issuecomment-2060079995 -->
[discord-shield]: https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fdiscord.com%2Fapi%2Finvites%2Fhanzoai%3Fwith_counts%3Dtrue&query=%24.approximate_member_count&logo=discord&logoColor=white&label=Discord&color=green&suffix=%20total
[discord-url]: https://discord.gg/hanzoai
[twitter-shield]: https://img.shields.io/twitter/follow/hanaboroai
[twitter-url]: https://x.com/hanzoai
[discord-shield]: https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fdiscord.com%2Fapi%2Finvites%2Fcomfyorg%3Fwith_counts%3Dtrue&query=%24.approximate_member_count&logo=discord&logoColor=white&label=Discord&color=green&suffix=%20total
[discord-url]: https://www.comfy.org/discord
[github-release-shield]: https://img.shields.io/github/v/release/hanzoai/studio?style=flat&sort=semver
[github-release-link]: https://github.com/hanzoai/studio/releases
[github-release-date-shield]: https://img.shields.io/github/release-date/hanzoai/studio?style=flat
[github-downloads-shield]: https://img.shields.io/github/downloads/hanzoai/studio/total?style=flat
[github-downloads-latest-shield]: https://img.shields.io/github/downloads/hanzoai/studio/latest/total?style=flat&label=downloads%40latest
[github-downloads-link]: https://github.com/hanzoai/studio/releases
[github-release-shield]: https://img.shields.io/github/v/release/comfyanonymous/ComfyUI?style=flat&sort=semver
[github-release-link]: https://github.com/comfyanonymous/ComfyUI/releases
[github-release-date-shield]: https://img.shields.io/github/release-date/comfyanonymous/ComfyUI?style=flat
[github-downloads-shield]: https://img.shields.io/github/downloads/comfyanonymous/ComfyUI/total?style=flat
[github-downloads-latest-shield]: https://img.shields.io/github/downloads/comfyanonymous/ComfyUI/latest/total?style=flat&label=downloads%40latest
[github-downloads-link]: https://github.com/comfyanonymous/ComfyUI/releases
![Hanzo Studio Screenshot](https://github.com/user-attachments/assets/7ccaf2c1-9b72-41ae-9a89-5688c94b7abe)
![ComfyUI Screenshot](https://github.com/user-attachments/assets/7ccaf2c1-9b72-41ae-9a89-5688c94b7abe)
</div>
Hanzo Studio lets you design and execute advanced stable diffusion pipelines using a graph/nodes/flowchart based interface. Available on Windows, Linux, and macOS.
This ui will let you design and execute advanced stable diffusion pipelines using a graph/nodes/flowchart based interface. For some workflow examples and see what ComfyUI can do you can check out:
### [ComfyUI Examples](https://comfyanonymous.github.io/ComfyUI_examples/)
## Upstream & License
Hanzo Studio is a fork of [**ComfyUI**](https://github.com/comfyanonymous/ComfyUI) by comfyanonymous and the ComfyUI contributors — full credit to the upstream project for the engine this builds on. ComfyUI is licensed under the **GNU General Public License v3.0 (GPL-3.0)**, and this fork stays under the same license. All modifications in this fork are likewise GPL-3.0.
See [`LICENSE`](LICENSE) for the full license text and [`NOTICE`](NOTICE) for the fork's attribution and a summary of what it changes (branding, Hanzo Engine integration nodes, and fashion workflow templates).
## Get Started
#### [Manual Install](#manual-install-windows-linux)
Supports all operating systems and GPU types (NVIDIA, AMD, Intel, Apple Silicon, Ascend).
#### Docker (Recommended for servers)
```bash
docker pull ghcr.io/hanzoai/studio:latest
docker run -p 8188:8188 ghcr.io/hanzoai/studio:latest --listen --cpu
```
### [Installing ComfyUI](#installing)
## Features
- Nodes/graph/flowchart interface to experiment and create complex Stable Diffusion workflows without needing to code anything.
- Image Models
- SD1.x, SD2.x, SDXL, SDXL Turbo
- Stable Cascade, SD3 and SD3.5
- Pixart Alpha and Sigma, AuraFlow, HunyuanDiT
- Flux, Flux 2, Lumina Image 2.0, HiDream
- Qwen Image, Hunyuan Image 2.1, Z Image
- Image Editing Models
- Omnigen 2, Flux Kontext, HiDream E1.1, Qwen Image Edit
- SD1.x, SD2.x,
- [SDXL](https://comfyanonymous.github.io/ComfyUI_examples/sdxl/), [SDXL Turbo](https://comfyanonymous.github.io/ComfyUI_examples/sdturbo/)
- [Stable Cascade](https://comfyanonymous.github.io/ComfyUI_examples/stable_cascade/)
- [SD3 and SD3.5](https://comfyanonymous.github.io/ComfyUI_examples/sd3/)
- Pixart Alpha and Sigma
- [AuraFlow](https://comfyanonymous.github.io/ComfyUI_examples/aura_flow/)
- [HunyuanDiT](https://comfyanonymous.github.io/ComfyUI_examples/hunyuan_dit/)
- [Flux](https://comfyanonymous.github.io/ComfyUI_examples/flux/)
- Video Models
- Stable Video Diffusion, Mochi, LTX-Video
- Hunyuan Video, Wan 2.1, Wan 2.2, Hunyuan Video 1.5
- Audio Models
- Stable Audio, ACE Step
- 3D Models
- Hunyuan3D 2.0
- [Stable Video Diffusion](https://comfyanonymous.github.io/ComfyUI_examples/video/)
- [Mochi](https://comfyanonymous.github.io/ComfyUI_examples/mochi/)
- [LTX-Video](https://comfyanonymous.github.io/ComfyUI_examples/ltxv/)
- [Hunyuan Video](https://comfyanonymous.github.io/ComfyUI_examples/hunyuan_video/)
- [Stable Audio](https://comfyanonymous.github.io/ComfyUI_examples/audio/)
- Asynchronous Queue system
- Many optimizations: Only re-executes the parts of the workflow that changes between executions.
- Smart memory management: can automatically run large models on GPUs with as low as 1GB vram with smart offloading.
- Smart memory management: can automatically run models on GPUs with as low as 1GB vram.
- Works even if you don't have a GPU with: ```--cpu``` (slow)
- Can load ckpt and safetensors: All in one checkpoints or standalone diffusion models, VAEs and CLIP models.
- Safe loading of ckpt, pt, pth, etc.. files.
- Can load ckpt, safetensors and diffusers models/checkpoints. Standalone VAEs and CLIP models.
- Embeddings/Textual inversion
- Loras (regular, locon and loha)
- Hypernetworks
- [Loras (regular, locon and loha)](https://comfyanonymous.github.io/ComfyUI_examples/lora/)
- [Hypernetworks](https://comfyanonymous.github.io/ComfyUI_examples/hypernetworks/)
- Loading full workflows (with seeds) from generated PNG, WebP and FLAC files.
- Saving/Loading workflows as Json files.
- Nodes interface can be used to create complex workflows like one for Hires fix or much more advanced ones.
- Area Composition
- Inpainting with both regular and inpainting models.
- ControlNet and T2I-Adapter
- Upscale Models (ESRGAN, ESRGAN variants, SwinIR, Swin2SR, etc...)
- GLIGEN
- Model Merging
- LCM models and Loras
- Latent previews with TAESD
- Works fully offline: core will never download anything unless you want to.
- Optional API nodes to use paid models from external providers through the online API. Disable with: `--disable-api-nodes`
- Nodes interface can be used to create complex workflows like one for [Hires fix](https://comfyanonymous.github.io/ComfyUI_examples/2_pass_txt2img/) or much more advanced ones.
- [Area Composition](https://comfyanonymous.github.io/ComfyUI_examples/area_composition/)
- [Inpainting](https://comfyanonymous.github.io/ComfyUI_examples/inpaint/) with both regular and inpainting models.
- [ControlNet and T2I-Adapter](https://comfyanonymous.github.io/ComfyUI_examples/controlnet/)
- [Upscale Models (ESRGAN, ESRGAN variants, SwinIR, Swin2SR, etc...)](https://comfyanonymous.github.io/ComfyUI_examples/upscale_models/)
- [unCLIP Models](https://comfyanonymous.github.io/ComfyUI_examples/unclip/)
- [GLIGEN](https://comfyanonymous.github.io/ComfyUI_examples/gligen/)
- [Model Merging](https://comfyanonymous.github.io/ComfyUI_examples/model_merging/)
- [LCM models and Loras](https://comfyanonymous.github.io/ComfyUI_examples/lcm/)
- Latent previews with [TAESD](#how-to-show-high-quality-previews)
- Starts up very fast.
- Works fully offline: will never download anything.
- [Config file](extra_model_paths.yaml.example) to set the search paths for models.
Workflow examples can be found on the [Examples page](https://comfyanonymous.github.io/ComfyUI_examples/)
## Shortcuts
| Keybind | Explanation |
@@ -133,15 +119,27 @@ docker run -p 8188:8188 ghcr.io/hanzoai/studio:latest --listen --cpu
# Installing
## Windows
There is a portable standalone build for Windows that should work for running on Nvidia GPUs or for running on your CPU only on the [releases page](https://github.com/comfyanonymous/ComfyUI/releases).
### [Direct link to download](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_nvidia.7z)
Simply download, extract with [7-Zip](https://7-zip.org) and run. Make sure you put your Stable Diffusion checkpoints/models (the huge ckpt/safetensors files) in: ComfyUI\models\checkpoints
If you have trouble extracting it, right click the file -> properties -> unblock
#### How do I share models between another UI and ComfyUI?
See the [Config file](extra_model_paths.yaml.example) to set the search paths for models. In the standalone windows build you can find this file in the ComfyUI directory. Rename this file to extra_model_paths.yaml and edit it with your favorite text editor.
## Jupyter Notebook
To run it on services like paperspace, kaggle or colab you can use my [Jupyter Notebook](notebooks/comfyui_colab.ipynb)
## Manual Install (Windows, Linux)
Python 3.14 works but some custom nodes may have issues. The free threaded variant works but some dependencies will enable the GIL so it's not fully supported.
Python 3.13 is very well supported. If you have trouble with some custom node dependencies on 3.13 you can try 3.12
torch 2.4 and above is supported but some features and optimizations might only work on newer versions. We generally recommend using the latest major version of pytorch with the latest cuda version unless it is less than 2 weeks old.
### Instructions:
Note that some dependencies do not yet support python 3.13 so using 3.12 is recommended.
Git clone this repo.
@@ -150,54 +148,48 @@ Put your SD checkpoints (the huge ckpt/safetensors files) in: models/checkpoints
Put your VAE in: models/vae
### AMD GPUs (Linux)
### AMD GPUs (Linux only)
AMD users can install rocm and pytorch with pip if you don't have it already installed, this is the command to install the stable version:
```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm7.1```
```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.2```
This is the command to install the nightly with ROCm 7.2 which might have some performance improvements:
This is the command to install the nightly with ROCm 6.2 which might have some performance improvements:
```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm7.2```
### AMD GPUs (Experimental: Windows and Linux), RDNA 3, 3.5 and 4 only.
These have less hardware support than the builds above but they work on windows. You also need to install the pytorch version specific to your hardware.
RDNA 3 (RX 7000 series):
```pip install --pre torch torchvision torchaudio --index-url https://rocm.nightlies.amd.com/v2/gfx110X-all/```
RDNA 3.5 (Strix halo/Ryzen AI Max+ 365):
```pip install --pre torch torchvision torchaudio --index-url https://rocm.nightlies.amd.com/v2/gfx1151/```
RDNA 4 (RX 9000 series):
```pip install --pre torch torchvision torchaudio --index-url https://rocm.nightlies.amd.com/v2/gfx120X-all/```
```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm6.2.4```
### Intel GPUs (Windows and Linux)
Intel Arc GPU users can install native PyTorch with torch.xpu support using pip. More information can be found [here](https://pytorch.org/docs/main/notes/get_start_xpu.html)
1. To install PyTorch xpu, use the following command:
```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/xpu```
This is the command to install the Pytorch xpu nightly which might have some performance improvements:
(Option 1) Intel Arc GPU users can install native PyTorch with torch.xpu support using pip (currently available in PyTorch nightly builds). More information can be found [here](https://pytorch.org/docs/main/notes/get_start_xpu.html)
1. To install PyTorch nightly, use the following command:
```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/xpu```
2. Launch ComfyUI by running `python main.py`
(Option 2) Alternatively, Intel GPUs supported by Intel Extension for PyTorch (IPEX) can leverage IPEX for improved performance.
1. For Intel® Arc™ A-Series Graphics utilizing IPEX, create a conda environment and use the commands below:
```
conda install libuv
pip install torch==2.3.1.post0+cxx11.abi torchvision==0.18.1.post0+cxx11.abi torchaudio==2.3.1.post0+cxx11.abi intel-extension-for-pytorch==2.3.110.post0+xpu --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/ --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/cn/
```
For other supported Intel GPUs with IPEX, visit [Installation](https://intel.github.io/intel-extension-for-pytorch/index.html#installation?platform=gpu) for more information.
Additional discussion and help can be found [here](https://github.com/comfyanonymous/ComfyUI/discussions/476).
### NVIDIA
Nvidia users should install stable pytorch using this command:
```pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu130```
```pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu124```
This is the command to install pytorch nightly instead which might have performance improvements.
This is the command to install pytorch nightly instead which might have performance improvements:
```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu130```
```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu126```
#### Troubleshooting
@@ -209,24 +201,28 @@ And install it again with the command above.
### Dependencies
Install the dependencies by opening your terminal inside the Hanzo Studio folder and:
Install the dependencies by opening your terminal inside the ComfyUI folder and:
```pip install -r requirements.txt```
After this you should have everything installed and can proceed to running Hanzo Studio.
After this you should have everything installed and can proceed to running ComfyUI.
### Others:
#### Apple Mac silicon
You can install Hanzo Studio in Apple Mac silicon (M1 or M2) with any recent macOS version.
You can install ComfyUI in Apple Mac silicon (M1 or M2) with any recent macOS version.
1. Install pytorch nightly. For instructions, read the [Accelerated PyTorch training on Mac](https://developer.apple.com/metal/pytorch/) Apple Developer guide (make sure to install the latest pytorch nightly).
1. Follow the [Hanzo Studio manual installation](#manual-install-windows-linux) instructions for Windows and Linux.
1. Install the Hanzo Studio [dependencies](#dependencies). If you have another Stable Diffusion UI you might be able to reuse the dependencies.
1. Launch Hanzo Studio by running `python main.py`
1. Follow the [ComfyUI manual installation](#manual-install-windows-linux) instructions for Windows and Linux.
1. Install the ComfyUI [dependencies](#dependencies). If you have another Stable Diffusion UI [you might be able to reuse the dependencies](#i-already-have-another-ui-for-stable-diffusion-installed-do-i-really-have-to-install-all-of-these-dependencies).
1. Launch ComfyUI by running `python main.py`
> **Note**: Remember to add your models, VAE, LoRAs etc. to the corresponding folders, as discussed in [Hanzo Studio manual installation](#manual-install-windows-linux).
> **Note**: Remember to add your models, VAE, LoRAs etc. to the corresponding Comfy folders, as discussed in [ComfyUI manual installation](#manual-install-windows-linux).
#### DirectML (AMD Cards on Windows)
```pip install torch-directml``` Then you can launch ComfyUI with: ```python main.py --directml```
#### Ascend NPUs
@@ -235,47 +231,7 @@ For models compatible with Ascend Extension for PyTorch (torch_npu). To get star
1. Begin by installing the recommended or newer kernel version for Linux as specified in the Installation page of torch-npu, if necessary.
2. Proceed with the installation of Ascend Basekit, which includes the driver, firmware, and CANN, following the instructions provided for your specific platform.
3. Next, install the necessary packages for torch-npu by adhering to the platform-specific instructions on the [Installation](https://ascend.github.io/docs/sources/pytorch/install.html#pytorch) page.
4. Finally, adhere to the [Hanzo Studio manual installation](#manual-install-windows-linux) guide for Linux. Once all components are installed, you can run Hanzo Studio as described earlier.
#### Cambricon MLUs
For models compatible with Cambricon Extension for PyTorch (torch_mlu). Here's a step-by-step guide tailored to your platform and installation method:
1. Install the Cambricon CNToolkit by adhering to the platform-specific instructions on the [Installation](https://www.cambricon.com/docs/sdk_1.15.0/cntoolkit_3.7.2/cntoolkit_install_3.7.2/index.html)
2. Next, install the PyTorch(torch_mlu) following the instructions on the [Installation](https://www.cambricon.com/docs/sdk_1.15.0/cambricon_pytorch_1.17.0/user_guide_1.9/index.html)
3. Launch Hanzo Studio by running `python main.py`
#### Iluvatar Corex
For models compatible with Iluvatar Extension for PyTorch. Here's a step-by-step guide tailored to your platform and installation method:
1. Install the Iluvatar Corex Toolkit by adhering to the platform-specific instructions on the [Installation](https://support.iluvatar.com/#/DocumentCentre?id=1&nameCenter=2&productId=520117912052801536)
2. Launch Hanzo Studio by running `python main.py`
## Manager
The **Manager** extension allows you to easily install, update, and manage custom nodes for Hanzo Studio.
### Setup
1. Install the manager dependencies:
```bash
pip install -r manager_requirements.txt
```
2. Enable the manager with the `--enable-manager` flag when running Hanzo Studio:
```bash
python main.py --enable-manager
```
### Command Line Options
| Flag | Description |
|------|-------------|
| `--enable-manager` | Enable the Manager |
| `--enable-manager-legacy-ui` | Use the legacy manager UI instead of the new UI (requires `--enable-manager`) |
| `--disable-manager-ui` | Disable the manager UI and endpoints while keeping background features like security checks and scheduled installation completion (requires `--enable-manager`) |
4. Finally, adhere to the [ComfyUI manual installation](#manual-install-windows-linux) guide for Linux. Once all components are installed, you can run ComfyUI as described earlier.
# Running
@@ -292,7 +248,7 @@ For AMD 7600 and maybe other RDNA3 cards: ```HSA_OVERRIDE_GFX_VERSION=11.0.0 pyt
### AMD ROCm Tips
You can enable experimental memory efficient attention on recent pytorch on some AMD GPUs using this command, it should already be enabled by default on RDNA3. If this improves speed for you on latest pytorch on your GPU please report it so that we can enable it by default.
You can enable experimental memory efficient attention on pytorch 2.5 in ComfyUI on RDNA3 and potentially other AMD GPUs using this command:
```TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL=1 python main.py --use-pytorch-cross-attention```
@@ -306,9 +262,9 @@ Only parts of the graph that change from each execution to the next will be exec
Dragging a generated png on the webpage or loading one will give you the full workflow including seeds that were used to create it.
You can use () to change emphasis of a word or phrase like: (good code:1.2) or (bad code:0.8). The default emphasis for () is 1.1. To use () characters in your actual prompt escape them like \( or \).
You can use () to change emphasis of a word or phrase like: (good code:1.2) or (bad code:0.8). The default emphasis for () is 1.1. To use () characters in your actual prompt escape them like \\( or \\).
You can use {day|night}, for wildcard/dynamic prompts. With this syntax "{wild|card|test}" will be randomly replaced by either "wild", "card" or "test" by the frontend every time you queue the prompt. To use {} characters in your actual prompt escape them like: \{ or \}.
You can use {day|night}, for wildcard/dynamic prompts. With this syntax "{wild|card|test}" will be randomly replaced by either "wild", "card" or "test" by the frontend every time you queue the prompt. To use {} characters in your actual prompt escape them like: \\{ or \\}.
Dynamic prompts also support C-style comments, like `// comment` or `/* comment */`.
@@ -321,18 +277,65 @@ To use a textual inversion concepts/embeddings in a text prompt put them in the
Use ```--preview-method auto``` to enable previews.
The default installation includes a fast latent preview method that's low-resolution. To enable higher-quality previews with [TAESD](https://github.com/madebyollin/taesd), download the [taesd_decoder.pth, taesdxl_decoder.pth, taesd3_decoder.pth and taef1_decoder.pth](https://github.com/madebyollin/taesd/) and place them in the `models/vae_approx` folder. Once they're installed, restart Hanzo Studio and launch it with `--preview-method taesd` to enable high-quality previews.
The default installation includes a fast latent preview method that's low-resolution. To enable higher-quality previews with [TAESD](https://github.com/madebyollin/taesd), download the [taesd_decoder.pth, taesdxl_decoder.pth, taesd3_decoder.pth and taef1_decoder.pth](https://github.com/madebyollin/taesd/) and place them in the `models/vae_approx` folder. Once they're installed, restart ComfyUI and launch it with `--preview-method taesd` to enable high-quality previews.
## How to use TLS/SSL?
Generate a self-signed certificate (not appropriate for shared/production use) and key by running the command: `openssl req -x509 -newkey rsa:4096 -keyout key.pem -out cert.pem -sha256 -days 3650 -nodes -subj "/C=XX/ST=StateName/L=CityName/O=CompanyName/OU=CompanySectionName/CN=CommonNameOrHostname"`
Use `--tls-keyfile key.pem --tls-certfile cert.pem` to enable TLS/SSL, the app will now be accessible with `https://...` instead of `http://...`.
> Note: Windows users can use [alexisrolland/docker-openssl](https://github.com/alexisrolland/docker-openssl) or one of the [3rd party binary distributions](https://wiki.openssl.org/index.php/Binaries) to run the command example above.
> Note: Windows users can use [alexisrolland/docker-openssl](https://github.com/alexisrolland/docker-openssl) or one of the [3rd party binary distributions](https://wiki.openssl.org/index.php/Binaries) to run the command example above.
<br/><br/>If you use a container, note that the volume mount `-v` can be a relative path so `... -v ".\:/openssl-certs" ...` would create the key & cert files in the current directory of your command prompt or powershell terminal.
## Support and dev channel
[Discord](https://discord.gg/hanzoai): Try the #help or #feedback channels.
[Matrix space: #comfyui_space:matrix.org](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) (it's like discord but open source).
See also: [https://www.hanzo.ai/](https://www.hanzo.ai/)
See also: [https://www.comfy.org/](https://www.comfy.org/)
## Frontend Development
As of August 15, 2024, we have transitioned to a new frontend, which is now hosted in a separate repository: [ComfyUI Frontend](https://github.com/Comfy-Org/ComfyUI_frontend). This repository now hosts the compiled JS (from TS/Vue) under the `web/` directory.
### Reporting Issues and Requesting Features
For any bugs, issues, or feature requests related to the frontend, please use the [ComfyUI Frontend repository](https://github.com/Comfy-Org/ComfyUI_frontend). This will help us manage and address frontend-specific concerns more efficiently.
### Using the Latest Frontend
The new frontend is now the default for ComfyUI. However, please note:
1. The frontend in the main ComfyUI repository is updated weekly.
2. Daily releases are available in the separate frontend repository.
To use the most up-to-date frontend version:
1. For the latest daily release, launch ComfyUI with this command line argument:
```
--front-end-version Comfy-Org/ComfyUI_frontend@latest
```
2. For a specific version, replace `latest` with the desired version number:
```
--front-end-version Comfy-Org/ComfyUI_frontend@1.2.2
```
This approach allows you to easily switch between the stable weekly release and the cutting-edge daily updates, or even specific versions for testing purposes.
### Accessing the Legacy Frontend
If you need to use the legacy frontend for any reason, you can access it using the following command line argument:
```
--front-end-version Comfy-Org/ComfyUI_legacy_frontend@latest
```
This will use a snapshot of the legacy frontend preserved in the [ComfyUI Legacy Frontend repository](https://github.com/Comfy-Org/ComfyUI_legacy_frontend).
# QA
### Which GPU should I buy for this?
[See this page for some recommendations](https://github.com/comfyanonymous/ComfyUI/wiki/Which-GPU-should-I-buy-for-ComfyUI)
+36 -1
View File
@@ -63,7 +63,7 @@ version_path_separator = os
# are written from script.py.mako
# output_encoding = utf-8
sqlalchemy.url = sqlite:///user/hanzo_studio.db
sqlalchemy.url = sqlite:///user/comfyui.db
[post_write_hooks]
@@ -82,3 +82,38 @@ sqlalchemy.url = sqlite:///user/hanzo_studio.db
# ruff.type = exec
# ruff.executable = %(here)s/.venv/bin/ruff
# ruff.options = check --fix REVISION_SCRIPT_FILENAME
# Logging configuration
[loggers]
keys = root,sqlalchemy,alembic
[handlers]
keys = console
[formatters]
keys = generic
[logger_root]
level = WARNING
handlers = console
qualname =
[logger_sqlalchemy]
level = WARNING
handlers =
qualname = sqlalchemy.engine
[logger_alembic]
level = INFO
handlers =
qualname = alembic
[handler_console]
class = StreamHandler
args = (sys.stderr,)
level = NOTSET
formatter = generic
[formatter_generic]
format = %(levelname)-5.5s [%(name)s] %(message)s
datefmt = %H:%M:%S
-1
View File
@@ -1,4 +1,3 @@
## Generate new revision
1. Update models in `/app/database/models.py`
2. Run `alembic revision --autogenerate -m "{your message}"`
+11
View File
@@ -1,3 +1,5 @@
from logging.config import fileConfig
from sqlalchemy import engine_from_config
from sqlalchemy import pool
@@ -7,6 +9,10 @@ from alembic import context
# access to the values within the .ini file in use.
config = context.config
# Interpret the config file for Python logging.
# This line sets up loggers basically.
if config.config_file_name is not None:
fileConfig(config.config_file_name)
from app.database.models import Base
target_metadata = Base.metadata
@@ -19,12 +25,15 @@ target_metadata = Base.metadata
def run_migrations_offline() -> None:
"""Run migrations in 'offline' mode.
This configures the context with just a URL
and not an Engine, though an Engine is acceptable
here as well. By skipping the Engine creation
we don't even need a DBAPI to be available.
Calls to context.execute() here emit the given string to the
script output.
"""
url = config.get_main_option("sqlalchemy.url")
context.configure(
@@ -40,8 +49,10 @@ def run_migrations_offline() -> None:
def run_migrations_online() -> None:
"""Run migrations in 'online' mode.
In this scenario we need to create an Engine
and associate a connection with the context.
"""
connectable = engine_from_config(
config.get_section(config.config_ini_section, {}),
-174
View File
@@ -1,174 +0,0 @@
"""
Initial assets schema
Revision ID: 0001_assets
Revises: None
Create Date: 2025-12-10 00:00:00
"""
from alembic import op
import sqlalchemy as sa
revision = "0001_assets"
down_revision = None
branch_labels = None
depends_on = None
def upgrade() -> None:
# ASSETS: content identity
op.create_table(
"assets",
sa.Column("id", sa.String(length=36), primary_key=True),
sa.Column("hash", sa.String(length=256), nullable=True),
sa.Column("size_bytes", sa.BigInteger(), nullable=False, server_default="0"),
sa.Column("mime_type", sa.String(length=255), nullable=True),
sa.Column("created_at", sa.DateTime(timezone=False), nullable=False),
sa.CheckConstraint("size_bytes >= 0", name="ck_assets_size_nonneg"),
)
op.create_index("uq_assets_hash", "assets", ["hash"], unique=True)
op.create_index("ix_assets_mime_type", "assets", ["mime_type"])
# ASSETS_INFO: user-visible references
op.create_table(
"assets_info",
sa.Column("id", sa.String(length=36), primary_key=True),
sa.Column("owner_id", sa.String(length=128), nullable=False, server_default=""),
sa.Column("name", sa.String(length=512), nullable=False),
sa.Column("asset_id", sa.String(length=36), sa.ForeignKey("assets.id", ondelete="RESTRICT"), nullable=False),
sa.Column("preview_id", sa.String(length=36), sa.ForeignKey("assets.id", ondelete="SET NULL"), nullable=True),
sa.Column("user_metadata", sa.JSON(), nullable=True),
sa.Column("created_at", sa.DateTime(timezone=False), nullable=False),
sa.Column("updated_at", sa.DateTime(timezone=False), nullable=False),
sa.Column("last_access_time", sa.DateTime(timezone=False), nullable=False),
sa.UniqueConstraint("asset_id", "owner_id", "name", name="uq_assets_info_asset_owner_name"),
)
op.create_index("ix_assets_info_owner_id", "assets_info", ["owner_id"])
op.create_index("ix_assets_info_asset_id", "assets_info", ["asset_id"])
op.create_index("ix_assets_info_name", "assets_info", ["name"])
op.create_index("ix_assets_info_created_at", "assets_info", ["created_at"])
op.create_index("ix_assets_info_last_access_time", "assets_info", ["last_access_time"])
op.create_index("ix_assets_info_owner_name", "assets_info", ["owner_id", "name"])
# TAGS: normalized tag vocabulary
op.create_table(
"tags",
sa.Column("name", sa.String(length=512), primary_key=True),
sa.Column("tag_type", sa.String(length=32), nullable=False, server_default="user"),
sa.CheckConstraint("name = lower(name)", name="ck_tags_lowercase"),
)
op.create_index("ix_tags_tag_type", "tags", ["tag_type"])
# ASSET_INFO_TAGS: many-to-many for tags on AssetInfo
op.create_table(
"asset_info_tags",
sa.Column("asset_info_id", sa.String(length=36), sa.ForeignKey("assets_info.id", ondelete="CASCADE"), nullable=False),
sa.Column("tag_name", sa.String(length=512), sa.ForeignKey("tags.name", ondelete="RESTRICT"), nullable=False),
sa.Column("origin", sa.String(length=32), nullable=False, server_default="manual"),
sa.Column("added_at", sa.DateTime(timezone=False), nullable=False),
sa.PrimaryKeyConstraint("asset_info_id", "tag_name", name="pk_asset_info_tags"),
)
op.create_index("ix_asset_info_tags_tag_name", "asset_info_tags", ["tag_name"])
op.create_index("ix_asset_info_tags_asset_info_id", "asset_info_tags", ["asset_info_id"])
# ASSET_CACHE_STATE: N:1 local cache rows per Asset
op.create_table(
"asset_cache_state",
sa.Column("id", sa.Integer(), primary_key=True, autoincrement=True),
sa.Column("asset_id", sa.String(length=36), sa.ForeignKey("assets.id", ondelete="CASCADE"), nullable=False),
sa.Column("file_path", sa.Text(), nullable=False), # absolute local path to cached file
sa.Column("mtime_ns", sa.BigInteger(), nullable=True),
sa.Column("needs_verify", sa.Boolean(), nullable=False, server_default=sa.text("false")),
sa.CheckConstraint("(mtime_ns IS NULL) OR (mtime_ns >= 0)", name="ck_acs_mtime_nonneg"),
sa.UniqueConstraint("file_path", name="uq_asset_cache_state_file_path"),
)
op.create_index("ix_asset_cache_state_file_path", "asset_cache_state", ["file_path"])
op.create_index("ix_asset_cache_state_asset_id", "asset_cache_state", ["asset_id"])
# ASSET_INFO_META: typed KV projection of user_metadata for filtering/sorting
op.create_table(
"asset_info_meta",
sa.Column("asset_info_id", sa.String(length=36), sa.ForeignKey("assets_info.id", ondelete="CASCADE"), nullable=False),
sa.Column("key", sa.String(length=256), nullable=False),
sa.Column("ordinal", sa.Integer(), nullable=False, server_default="0"),
sa.Column("val_str", sa.String(length=2048), nullable=True),
sa.Column("val_num", sa.Numeric(38, 10), nullable=True),
sa.Column("val_bool", sa.Boolean(), nullable=True),
sa.Column("val_json", sa.JSON(), nullable=True),
sa.PrimaryKeyConstraint("asset_info_id", "key", "ordinal", name="pk_asset_info_meta"),
)
op.create_index("ix_asset_info_meta_key", "asset_info_meta", ["key"])
op.create_index("ix_asset_info_meta_key_val_str", "asset_info_meta", ["key", "val_str"])
op.create_index("ix_asset_info_meta_key_val_num", "asset_info_meta", ["key", "val_num"])
op.create_index("ix_asset_info_meta_key_val_bool", "asset_info_meta", ["key", "val_bool"])
# Tags vocabulary
tags_table = sa.table(
"tags",
sa.column("name", sa.String(length=512)),
sa.column("tag_type", sa.String()),
)
op.bulk_insert(
tags_table,
[
{"name": "models", "tag_type": "system"},
{"name": "input", "tag_type": "system"},
{"name": "output", "tag_type": "system"},
{"name": "configs", "tag_type": "system"},
{"name": "checkpoints", "tag_type": "system"},
{"name": "loras", "tag_type": "system"},
{"name": "vae", "tag_type": "system"},
{"name": "text_encoders", "tag_type": "system"},
{"name": "diffusion_models", "tag_type": "system"},
{"name": "clip_vision", "tag_type": "system"},
{"name": "style_models", "tag_type": "system"},
{"name": "embeddings", "tag_type": "system"},
{"name": "diffusers", "tag_type": "system"},
{"name": "vae_approx", "tag_type": "system"},
{"name": "controlnet", "tag_type": "system"},
{"name": "gligen", "tag_type": "system"},
{"name": "upscale_models", "tag_type": "system"},
{"name": "hypernetworks", "tag_type": "system"},
{"name": "photomaker", "tag_type": "system"},
{"name": "classifiers", "tag_type": "system"},
{"name": "encoder", "tag_type": "system"},
{"name": "decoder", "tag_type": "system"},
{"name": "missing", "tag_type": "system"},
{"name": "rescan", "tag_type": "system"},
],
)
def downgrade() -> None:
op.drop_index("ix_asset_info_meta_key_val_bool", table_name="asset_info_meta")
op.drop_index("ix_asset_info_meta_key_val_num", table_name="asset_info_meta")
op.drop_index("ix_asset_info_meta_key_val_str", table_name="asset_info_meta")
op.drop_index("ix_asset_info_meta_key", table_name="asset_info_meta")
op.drop_table("asset_info_meta")
op.drop_index("ix_asset_cache_state_asset_id", table_name="asset_cache_state")
op.drop_index("ix_asset_cache_state_file_path", table_name="asset_cache_state")
op.drop_constraint("uq_asset_cache_state_file_path", table_name="asset_cache_state")
op.drop_table("asset_cache_state")
op.drop_index("ix_asset_info_tags_asset_info_id", table_name="asset_info_tags")
op.drop_index("ix_asset_info_tags_tag_name", table_name="asset_info_tags")
op.drop_table("asset_info_tags")
op.drop_index("ix_tags_tag_type", table_name="tags")
op.drop_table("tags")
op.drop_constraint("uq_assets_info_asset_owner_name", table_name="assets_info")
op.drop_index("ix_assets_info_owner_name", table_name="assets_info")
op.drop_index("ix_assets_info_last_access_time", table_name="assets_info")
op.drop_index("ix_assets_info_created_at", table_name="assets_info")
op.drop_index("ix_assets_info_name", table_name="assets_info")
op.drop_index("ix_assets_info_asset_id", table_name="assets_info")
op.drop_index("ix_assets_info_owner_id", table_name="assets_info")
op.drop_table("assets_info")
op.drop_index("uq_assets_hash", table_name="assets")
op.drop_index("ix_assets_mime_type", table_name="assets")
op.drop_table("assets")
+58
View File
@@ -0,0 +1,58 @@
"""init
Revision ID: 2fb22c4fff36
Revises:
Create Date: 2025-03-27 19:00:47.686079
"""
from typing import Sequence, Union
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision: str = '2fb22c4fff36'
down_revision: Union[str, None] = None
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
def upgrade() -> None:
"""Upgrade schema."""
# ### commands auto generated by Alembic - please adjust! ###
op.create_table('model',
sa.Column('type', sa.Text(), nullable=False),
sa.Column('path', sa.Text(), nullable=False),
sa.Column('title', sa.Text(), nullable=True),
sa.Column('description', sa.Text(), nullable=True),
sa.Column('architecture', sa.Text(), nullable=True),
sa.Column('hash', sa.Text(), nullable=True),
sa.Column('source_url', sa.Text(), nullable=True),
sa.Column('date_added', sa.DateTime(), server_default=sa.text('(CURRENT_TIMESTAMP)'), nullable=True),
sa.PrimaryKeyConstraint('type', 'path')
)
op.create_table('tag',
sa.Column('id', sa.Integer(), autoincrement=True, nullable=False),
sa.Column('name', sa.Text(), nullable=False),
sa.PrimaryKeyConstraint('id'),
sa.UniqueConstraint('name')
)
op.create_table('model_tag',
sa.Column('model_type', sa.Text(), nullable=False),
sa.Column('model_path', sa.Text(), nullable=False),
sa.Column('tag_id', sa.Integer(), nullable=False),
sa.ForeignKeyConstraint(['model_type', 'model_path'], ['model.type', 'model.path'], ondelete='CASCADE'),
sa.ForeignKeyConstraint(['tag_id'], ['tag.id'], ondelete='CASCADE'),
sa.PrimaryKeyConstraint('model_type', 'model_path', 'tag_id')
)
# ### end Alembic commands ###
def downgrade() -> None:
"""Downgrade schema."""
# ### commands auto generated by Alembic - please adjust! ###
op.drop_table('model_tag')
op.drop_table('tag')
op.drop_table('model')
# ### end Alembic commands ###
+2 -2
View File
@@ -1,3 +1,3 @@
# Hanzo Studio Internal Routes
# ComfyUI Internal Routes
All routes under the `/internal` path are designated for **internal use by Hanzo Studio only**. These routes are not intended for use by external applications may change at any time without notice.
All routes under the `/internal` path are designated for **internal use by ComfyUI only**. These routes are not intended for use by external applications may change at any time without notice.
+19 -22
View File
@@ -1,24 +1,40 @@
from aiohttp import web
from typing import Optional
from folder_paths import folder_names_and_paths, get_directory_by_type
from folder_paths import models_dir, user_directory, output_directory, folder_names_and_paths
from api_server.services.file_service import FileService
from api_server.services.terminal_service import TerminalService
import app.logger
import os
class InternalRoutes:
'''
The top level web router for internal routes: /internal/*
The endpoints here should NOT be depended upon. It is for Hanzo Studio frontend use only.
The endpoints here should NOT be depended upon. It is for ComfyUI frontend use only.
Check README.md for more information.
'''
def __init__(self, prompt_server):
self.routes: web.RouteTableDef = web.RouteTableDef()
self._app: Optional[web.Application] = None
self.file_service = FileService({
"models": models_dir,
"user": user_directory,
"output": output_directory
})
self.prompt_server = prompt_server
self.terminal_service = TerminalService(prompt_server)
def setup_routes(self):
@self.routes.get('/files')
async def list_files(request):
directory_key = request.query.get('directory', '')
try:
file_list = self.file_service.list_files(directory_key)
return web.json_response({"files": file_list})
except ValueError as e:
return web.json_response({"error": str(e)}, status=400)
except Exception as e:
return web.json_response({"error": str(e)}, status=500)
@self.routes.get('/logs')
async def get_logs(request):
return web.json_response("".join([(l["t"] + " - " + l["m"]) for l in app.logger.get_logs()]))
@@ -51,25 +67,6 @@ class InternalRoutes:
response[key] = folder_names_and_paths[key][0]
return web.json_response(response)
@self.routes.get('/files/{directory_type}')
async def get_files(request: web.Request) -> web.Response:
directory_type = request.match_info['directory_type']
if directory_type not in ("output", "input", "temp"):
return web.json_response({"error": "Invalid directory type"}, status=400)
directory = get_directory_by_type(directory_type)
def is_visible_file(entry: os.DirEntry) -> bool:
"""Filter out hidden files (e.g., .DS_Store on macOS)."""
return entry.is_file() and not entry.name.startswith('.')
sorted_files = sorted(
(entry for entry in os.scandir(directory) if is_visible_file(entry)),
key=lambda entry: -entry.stat().st_mtime
)
return web.json_response([entry.name for entry in sorted_files], status=200)
def get_app(self):
if self._app is None:
self._app = web.Application()
+13
View File
@@ -0,0 +1,13 @@
from typing import Dict, List, Optional
from api_server.utils.file_operations import FileSystemOperations, FileSystemItem
class FileService:
def __init__(self, allowed_directories: Dict[str, str], file_system_ops: Optional[FileSystemOperations] = None):
self.allowed_directories: Dict[str, str] = allowed_directories
self.file_system_ops: FileSystemOperations = file_system_ops or FileSystemOperations()
def list_files(self, directory_key: str) -> List[FileSystemItem]:
if directory_key not in self.allowed_directories:
raise ValueError("Invalid directory key")
directory_path: str = self.allowed_directories[directory_key]
return self.file_system_ops.walk_directory(directory_path)
+3 -9
View File
@@ -9,14 +9,8 @@ class AppSettings():
self.user_manager = user_manager
def get_settings(self, request):
try:
file = self.user_manager.get_request_user_filepath(
request,
"studio.settings.json"
)
except KeyError as e:
logging.error("User settings not found.")
raise web.HTTPUnauthorized() from e
file = self.user_manager.get_request_user_filepath(
request, "comfy.settings.json")
if os.path.isfile(file):
try:
with open(file) as f:
@@ -29,7 +23,7 @@ class AppSettings():
def save_settings(self, request, settings):
file = self.user_manager.get_request_user_filepath(
request, "studio.settings.json")
request, "comfy.settings.json")
with open(file, "w") as f:
f.write(json.dumps(settings, indent=4))
-514
View File
@@ -1,514 +0,0 @@
import logging
import uuid
import urllib.parse
import os
import contextlib
from aiohttp import web
from pydantic import ValidationError
import app.assets.manager as manager
from app import user_manager
from app.assets.api import schemas_in
from app.assets.helpers import get_query_dict
from app.assets.scanner import seed_assets
import folder_paths
ROUTES = web.RouteTableDef()
USER_MANAGER: user_manager.UserManager | None = None
# UUID regex (canonical hyphenated form, case-insensitive)
UUID_RE = r"[0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{12}"
# Note to any custom node developers reading this code:
# The assets system is not yet fully implemented, do not rely on the code in /app/assets remaining the same.
def register_assets_system(app: web.Application, user_manager_instance: user_manager.UserManager) -> None:
global USER_MANAGER
USER_MANAGER = user_manager_instance
app.add_routes(ROUTES)
def _error_response(status: int, code: str, message: str, details: dict | None = None) -> web.Response:
return web.json_response({"error": {"code": code, "message": message, "details": details or {}}}, status=status)
def _validation_error_response(code: str, ve: ValidationError) -> web.Response:
return _error_response(400, code, "Validation failed.", {"errors": ve.json()})
@ROUTES.head("/api/assets/hash/{hash}")
async def head_asset_by_hash(request: web.Request) -> web.Response:
hash_str = request.match_info.get("hash", "").strip().lower()
if not hash_str or ":" not in hash_str:
return _error_response(400, "INVALID_HASH", "hash must be like 'blake3:<hex>'")
algo, digest = hash_str.split(":", 1)
if algo != "blake3" or not digest or any(c for c in digest if c not in "0123456789abcdef"):
return _error_response(400, "INVALID_HASH", "hash must be like 'blake3:<hex>'")
exists = manager.asset_exists(asset_hash=hash_str)
return web.Response(status=200 if exists else 404)
@ROUTES.get("/api/assets")
async def list_assets(request: web.Request) -> web.Response:
"""
GET request to list assets.
"""
query_dict = get_query_dict(request)
try:
q = schemas_in.ListAssetsQuery.model_validate(query_dict)
except ValidationError as ve:
return _validation_error_response("INVALID_QUERY", ve)
payload = manager.list_assets(
include_tags=q.include_tags,
exclude_tags=q.exclude_tags,
name_contains=q.name_contains,
metadata_filter=q.metadata_filter,
limit=q.limit,
offset=q.offset,
sort=q.sort,
order=q.order,
owner_id=USER_MANAGER.get_request_user_id(request),
)
return web.json_response(payload.model_dump(mode="json", exclude_none=True))
@ROUTES.get(f"/api/assets/{{id:{UUID_RE}}}")
async def get_asset(request: web.Request) -> web.Response:
"""
GET request to get an asset's info as JSON.
"""
asset_info_id = str(uuid.UUID(request.match_info["id"]))
try:
result = manager.get_asset(
asset_info_id=asset_info_id,
owner_id=USER_MANAGER.get_request_user_id(request),
)
except ValueError as e:
return _error_response(404, "ASSET_NOT_FOUND", str(e), {"id": asset_info_id})
except Exception:
logging.exception(
"get_asset failed for asset_info_id=%s, owner_id=%s",
asset_info_id,
USER_MANAGER.get_request_user_id(request),
)
return _error_response(500, "INTERNAL", "Unexpected server error.")
return web.json_response(result.model_dump(mode="json"), status=200)
@ROUTES.get(f"/api/assets/{{id:{UUID_RE}}}/content")
async def download_asset_content(request: web.Request) -> web.Response:
# question: do we need disposition? could we just stick with one of these?
disposition = request.query.get("disposition", "attachment").lower().strip()
if disposition not in {"inline", "attachment"}:
disposition = "attachment"
try:
abs_path, content_type, filename = manager.resolve_asset_content_for_download(
asset_info_id=str(uuid.UUID(request.match_info["id"])),
owner_id=USER_MANAGER.get_request_user_id(request),
)
except ValueError as ve:
return _error_response(404, "ASSET_NOT_FOUND", str(ve))
except NotImplementedError as nie:
return _error_response(501, "BACKEND_UNSUPPORTED", str(nie))
except FileNotFoundError:
return _error_response(404, "FILE_NOT_FOUND", "Underlying file not found on disk.")
quoted = (filename or "").replace("\r", "").replace("\n", "").replace('"', "'")
cd = f'{disposition}; filename="{quoted}"; filename*=UTF-8\'\'{urllib.parse.quote(filename)}'
file_size = os.path.getsize(abs_path)
logging.info(
"download_asset_content: path=%s, size=%d bytes (%.2f MB), content_type=%s, filename=%s",
abs_path,
file_size,
file_size / (1024 * 1024),
content_type,
filename,
)
async def file_sender():
chunk_size = 64 * 1024
with open(abs_path, "rb") as f:
while True:
chunk = f.read(chunk_size)
if not chunk:
break
yield chunk
return web.Response(
body=file_sender(),
content_type=content_type,
headers={
"Content-Disposition": cd,
"Content-Length": str(file_size),
},
)
@ROUTES.post("/api/assets/from-hash")
async def create_asset_from_hash(request: web.Request) -> web.Response:
try:
payload = await request.json()
body = schemas_in.CreateFromHashBody.model_validate(payload)
except ValidationError as ve:
return _validation_error_response("INVALID_BODY", ve)
except Exception:
return _error_response(400, "INVALID_JSON", "Request body must be valid JSON.")
result = manager.create_asset_from_hash(
hash_str=body.hash,
name=body.name,
tags=body.tags,
user_metadata=body.user_metadata,
owner_id=USER_MANAGER.get_request_user_id(request),
)
if result is None:
return _error_response(404, "ASSET_NOT_FOUND", f"Asset content {body.hash} does not exist")
return web.json_response(result.model_dump(mode="json"), status=201)
@ROUTES.post("/api/assets")
async def upload_asset(request: web.Request) -> web.Response:
"""Multipart/form-data endpoint for Asset uploads."""
if not (request.content_type or "").lower().startswith("multipart/"):
return _error_response(415, "UNSUPPORTED_MEDIA_TYPE", "Use multipart/form-data for uploads.")
reader = await request.multipart()
file_present = False
file_client_name: str | None = None
tags_raw: list[str] = []
provided_name: str | None = None
user_metadata_raw: str | None = None
provided_hash: str | None = None
provided_hash_exists: bool | None = None
file_written = 0
tmp_path: str | None = None
while True:
field = await reader.next()
if field is None:
break
fname = getattr(field, "name", "") or ""
if fname == "hash":
try:
s = ((await field.text()) or "").strip().lower()
except Exception:
return _error_response(400, "INVALID_HASH", "hash must be like 'blake3:<hex>'")
if s:
if ":" not in s:
return _error_response(400, "INVALID_HASH", "hash must be like 'blake3:<hex>'")
algo, digest = s.split(":", 1)
if algo != "blake3" or not digest or any(c for c in digest if c not in "0123456789abcdef"):
return _error_response(400, "INVALID_HASH", "hash must be like 'blake3:<hex>'")
provided_hash = f"{algo}:{digest}"
try:
provided_hash_exists = manager.asset_exists(asset_hash=provided_hash)
except Exception:
provided_hash_exists = None # do not fail the whole request here
elif fname == "file":
file_present = True
file_client_name = (field.filename or "").strip()
if provided_hash and provided_hash_exists is True:
# If client supplied a hash that we know exists, drain but do not write to disk
try:
while True:
chunk = await field.read_chunk(8 * 1024 * 1024)
if not chunk:
break
file_written += len(chunk)
except Exception:
return _error_response(500, "UPLOAD_IO_ERROR", "Failed to receive uploaded file.")
continue # Do not create temp file; we will create AssetInfo from the existing content
# Otherwise, store to temp for hashing/ingest
uploads_root = os.path.join(folder_paths.get_temp_directory(), "uploads")
unique_dir = os.path.join(uploads_root, uuid.uuid4().hex)
os.makedirs(unique_dir, exist_ok=True)
tmp_path = os.path.join(unique_dir, ".upload.part")
try:
with open(tmp_path, "wb") as f:
while True:
chunk = await field.read_chunk(8 * 1024 * 1024)
if not chunk:
break
f.write(chunk)
file_written += len(chunk)
except Exception:
try:
if os.path.exists(tmp_path or ""):
os.remove(tmp_path)
finally:
return _error_response(500, "UPLOAD_IO_ERROR", "Failed to receive and store uploaded file.")
elif fname == "tags":
tags_raw.append((await field.text()) or "")
elif fname == "name":
provided_name = (await field.text()) or None
elif fname == "user_metadata":
user_metadata_raw = (await field.text()) or None
# If client did not send file, and we are not doing a from-hash fast path -> error
if not file_present and not (provided_hash and provided_hash_exists):
return _error_response(400, "MISSING_FILE", "Form must include a 'file' part or a known 'hash'.")
if file_present and file_written == 0 and not (provided_hash and provided_hash_exists):
# Empty upload is only acceptable if we are fast-pathing from existing hash
try:
if tmp_path and os.path.exists(tmp_path):
os.remove(tmp_path)
finally:
return _error_response(400, "EMPTY_UPLOAD", "Uploaded file is empty.")
try:
spec = schemas_in.UploadAssetSpec.model_validate({
"tags": tags_raw,
"name": provided_name,
"user_metadata": user_metadata_raw,
"hash": provided_hash,
})
except ValidationError as ve:
try:
if tmp_path and os.path.exists(tmp_path):
os.remove(tmp_path)
finally:
return _validation_error_response("INVALID_BODY", ve)
# Validate models category against configured folders (consistent with previous behavior)
if spec.tags and spec.tags[0] == "models":
if len(spec.tags) < 2 or spec.tags[1] not in folder_paths.folder_names_and_paths:
if tmp_path and os.path.exists(tmp_path):
os.remove(tmp_path)
return _error_response(
400, "INVALID_BODY", f"unknown models category '{spec.tags[1] if len(spec.tags) >= 2 else ''}'"
)
owner_id = USER_MANAGER.get_request_user_id(request)
# Fast path: if a valid provided hash exists, create AssetInfo without writing anything
if spec.hash and provided_hash_exists is True:
try:
result = manager.create_asset_from_hash(
hash_str=spec.hash,
name=spec.name or (spec.hash.split(":", 1)[1]),
tags=spec.tags,
user_metadata=spec.user_metadata or {},
owner_id=owner_id,
)
except Exception:
logging.exception("create_asset_from_hash failed for hash=%s, owner_id=%s", spec.hash, owner_id)
return _error_response(500, "INTERNAL", "Unexpected server error.")
if result is None:
return _error_response(404, "ASSET_NOT_FOUND", f"Asset content {spec.hash} does not exist")
# Drain temp if we accidentally saved (e.g., hash field came after file)
if tmp_path and os.path.exists(tmp_path):
with contextlib.suppress(Exception):
os.remove(tmp_path)
status = 200 if (not result.created_new) else 201
return web.json_response(result.model_dump(mode="json"), status=status)
# Otherwise, we must have a temp file path to ingest
if not tmp_path or not os.path.exists(tmp_path):
# The only case we reach here without a temp file is: client sent a hash that does not exist and no file
return _error_response(404, "ASSET_NOT_FOUND", "Provided hash not found and no file uploaded.")
try:
created = manager.upload_asset_from_temp_path(
spec,
temp_path=tmp_path,
client_filename=file_client_name,
owner_id=owner_id,
expected_asset_hash=spec.hash,
)
status = 201 if created.created_new else 200
return web.json_response(created.model_dump(mode="json"), status=status)
except ValueError as e:
if tmp_path and os.path.exists(tmp_path):
os.remove(tmp_path)
msg = str(e)
if "HASH_MISMATCH" in msg or msg.strip().upper() == "HASH_MISMATCH":
return _error_response(
400,
"HASH_MISMATCH",
"Uploaded file hash does not match provided hash.",
)
return _error_response(400, "BAD_REQUEST", "Invalid inputs.")
except Exception:
if tmp_path and os.path.exists(tmp_path):
os.remove(tmp_path)
logging.exception("upload_asset_from_temp_path failed for tmp_path=%s, owner_id=%s", tmp_path, owner_id)
return _error_response(500, "INTERNAL", "Unexpected server error.")
@ROUTES.put(f"/api/assets/{{id:{UUID_RE}}}")
async def update_asset(request: web.Request) -> web.Response:
asset_info_id = str(uuid.UUID(request.match_info["id"]))
try:
body = schemas_in.UpdateAssetBody.model_validate(await request.json())
except ValidationError as ve:
return _validation_error_response("INVALID_BODY", ve)
except Exception:
return _error_response(400, "INVALID_JSON", "Request body must be valid JSON.")
try:
result = manager.update_asset(
asset_info_id=asset_info_id,
name=body.name,
user_metadata=body.user_metadata,
owner_id=USER_MANAGER.get_request_user_id(request),
)
except (ValueError, PermissionError) as ve:
return _error_response(404, "ASSET_NOT_FOUND", str(ve), {"id": asset_info_id})
except Exception:
logging.exception(
"update_asset failed for asset_info_id=%s, owner_id=%s",
asset_info_id,
USER_MANAGER.get_request_user_id(request),
)
return _error_response(500, "INTERNAL", "Unexpected server error.")
return web.json_response(result.model_dump(mode="json"), status=200)
@ROUTES.delete(f"/api/assets/{{id:{UUID_RE}}}")
async def delete_asset(request: web.Request) -> web.Response:
asset_info_id = str(uuid.UUID(request.match_info["id"]))
delete_content = request.query.get("delete_content")
delete_content = True if delete_content is None else delete_content.lower() not in {"0", "false", "no"}
try:
deleted = manager.delete_asset_reference(
asset_info_id=asset_info_id,
owner_id=USER_MANAGER.get_request_user_id(request),
delete_content_if_orphan=delete_content,
)
except Exception:
logging.exception(
"delete_asset_reference failed for asset_info_id=%s, owner_id=%s",
asset_info_id,
USER_MANAGER.get_request_user_id(request),
)
return _error_response(500, "INTERNAL", "Unexpected server error.")
if not deleted:
return _error_response(404, "ASSET_NOT_FOUND", f"AssetInfo {asset_info_id} not found.")
return web.Response(status=204)
@ROUTES.get("/api/tags")
async def get_tags(request: web.Request) -> web.Response:
"""
GET request to list all tags based on query parameters.
"""
query_map = dict(request.rel_url.query)
try:
query = schemas_in.TagsListQuery.model_validate(query_map)
except ValidationError as e:
return web.json_response(
{"error": {"code": "INVALID_QUERY", "message": "Invalid query parameters", "details": e.errors()}},
status=400,
)
result = manager.list_tags(
prefix=query.prefix,
limit=query.limit,
offset=query.offset,
order=query.order,
include_zero=query.include_zero,
owner_id=USER_MANAGER.get_request_user_id(request),
)
return web.json_response(result.model_dump(mode="json"))
@ROUTES.post(f"/api/assets/{{id:{UUID_RE}}}/tags")
async def add_asset_tags(request: web.Request) -> web.Response:
asset_info_id = str(uuid.UUID(request.match_info["id"]))
try:
payload = await request.json()
data = schemas_in.TagsAdd.model_validate(payload)
except ValidationError as ve:
return _error_response(400, "INVALID_BODY", "Invalid JSON body for tags add.", {"errors": ve.errors()})
except Exception:
return _error_response(400, "INVALID_JSON", "Request body must be valid JSON.")
try:
result = manager.add_tags_to_asset(
asset_info_id=asset_info_id,
tags=data.tags,
origin="manual",
owner_id=USER_MANAGER.get_request_user_id(request),
)
except (ValueError, PermissionError) as ve:
return _error_response(404, "ASSET_NOT_FOUND", str(ve), {"id": asset_info_id})
except Exception:
logging.exception(
"add_tags_to_asset failed for asset_info_id=%s, owner_id=%s",
asset_info_id,
USER_MANAGER.get_request_user_id(request),
)
return _error_response(500, "INTERNAL", "Unexpected server error.")
return web.json_response(result.model_dump(mode="json"), status=200)
@ROUTES.delete(f"/api/assets/{{id:{UUID_RE}}}/tags")
async def delete_asset_tags(request: web.Request) -> web.Response:
asset_info_id = str(uuid.UUID(request.match_info["id"]))
try:
payload = await request.json()
data = schemas_in.TagsRemove.model_validate(payload)
except ValidationError as ve:
return _error_response(400, "INVALID_BODY", "Invalid JSON body for tags remove.", {"errors": ve.errors()})
except Exception:
return _error_response(400, "INVALID_JSON", "Request body must be valid JSON.")
try:
result = manager.remove_tags_from_asset(
asset_info_id=asset_info_id,
tags=data.tags,
owner_id=USER_MANAGER.get_request_user_id(request),
)
except ValueError as ve:
return _error_response(404, "ASSET_NOT_FOUND", str(ve), {"id": asset_info_id})
except Exception:
logging.exception(
"remove_tags_from_asset failed for asset_info_id=%s, owner_id=%s",
asset_info_id,
USER_MANAGER.get_request_user_id(request),
)
return _error_response(500, "INTERNAL", "Unexpected server error.")
return web.json_response(result.model_dump(mode="json"), status=200)
@ROUTES.post("/api/assets/seed")
async def seed_assets_endpoint(request: web.Request) -> web.Response:
"""Trigger asset seeding for specified roots (models, input, output)."""
try:
payload = await request.json()
roots = payload.get("roots", ["models", "input", "output"])
except Exception:
roots = ["models", "input", "output"]
valid_roots = [r for r in roots if r in ("models", "input", "output")]
if not valid_roots:
return _error_response(400, "INVALID_BODY", "No valid roots specified")
try:
seed_assets(tuple(valid_roots))
except Exception:
logging.exception("seed_assets failed for roots=%s", valid_roots)
return _error_response(500, "INTERNAL", "Seed operation failed")
return web.json_response({"seeded": valid_roots}, status=200)
-264
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@@ -1,264 +0,0 @@
import json
from typing import Any, Literal
from pydantic import (
BaseModel,
ConfigDict,
Field,
conint,
field_validator,
model_validator,
)
class ListAssetsQuery(BaseModel):
include_tags: list[str] = Field(default_factory=list)
exclude_tags: list[str] = Field(default_factory=list)
name_contains: str | None = None
# Accept either a JSON string (query param) or a dict
metadata_filter: dict[str, Any] | None = None
limit: conint(ge=1, le=500) = 20
offset: conint(ge=0) = 0
sort: Literal["name", "created_at", "updated_at", "size", "last_access_time"] = "created_at"
order: Literal["asc", "desc"] = "desc"
@field_validator("include_tags", "exclude_tags", mode="before")
@classmethod
def _split_csv_tags(cls, v):
# Accept "a,b,c" or ["a","b"] (we are liberal in what we accept)
if v is None:
return []
if isinstance(v, str):
return [t.strip() for t in v.split(",") if t.strip()]
if isinstance(v, list):
out: list[str] = []
for item in v:
if isinstance(item, str):
out.extend([t.strip() for t in item.split(",") if t.strip()])
return out
return v
@field_validator("metadata_filter", mode="before")
@classmethod
def _parse_metadata_json(cls, v):
if v is None or isinstance(v, dict):
return v
if isinstance(v, str) and v.strip():
try:
parsed = json.loads(v)
except Exception as e:
raise ValueError(f"metadata_filter must be JSON: {e}") from e
if not isinstance(parsed, dict):
raise ValueError("metadata_filter must be a JSON object")
return parsed
return None
class UpdateAssetBody(BaseModel):
name: str | None = None
user_metadata: dict[str, Any] | None = None
@model_validator(mode="after")
def _at_least_one(self):
if self.name is None and self.user_metadata is None:
raise ValueError("Provide at least one of: name, user_metadata.")
return self
class CreateFromHashBody(BaseModel):
model_config = ConfigDict(extra="ignore", str_strip_whitespace=True)
hash: str
name: str
tags: list[str] = Field(default_factory=list)
user_metadata: dict[str, Any] = Field(default_factory=dict)
@field_validator("hash")
@classmethod
def _require_blake3(cls, v):
s = (v or "").strip().lower()
if ":" not in s:
raise ValueError("hash must be 'blake3:<hex>'")
algo, digest = s.split(":", 1)
if algo != "blake3":
raise ValueError("only canonical 'blake3:<hex>' is accepted here")
if not digest or any(c for c in digest if c not in "0123456789abcdef"):
raise ValueError("hash digest must be lowercase hex")
return s
@field_validator("tags", mode="before")
@classmethod
def _tags_norm(cls, v):
if v is None:
return []
if isinstance(v, list):
out = [str(t).strip().lower() for t in v if str(t).strip()]
seen = set()
dedup = []
for t in out:
if t not in seen:
seen.add(t)
dedup.append(t)
return dedup
if isinstance(v, str):
return [t.strip().lower() for t in v.split(",") if t.strip()]
return []
class TagsListQuery(BaseModel):
model_config = ConfigDict(extra="ignore", str_strip_whitespace=True)
prefix: str | None = Field(None, min_length=1, max_length=256)
limit: int = Field(100, ge=1, le=1000)
offset: int = Field(0, ge=0, le=10_000_000)
order: Literal["count_desc", "name_asc"] = "count_desc"
include_zero: bool = True
@field_validator("prefix")
@classmethod
def normalize_prefix(cls, v: str | None) -> str | None:
if v is None:
return v
v = v.strip()
return v.lower() or None
class TagsAdd(BaseModel):
model_config = ConfigDict(extra="ignore")
tags: list[str] = Field(..., min_length=1)
@field_validator("tags")
@classmethod
def normalize_tags(cls, v: list[str]) -> list[str]:
out = []
for t in v:
if not isinstance(t, str):
raise TypeError("tags must be strings")
tnorm = t.strip().lower()
if tnorm:
out.append(tnorm)
seen = set()
deduplicated = []
for x in out:
if x not in seen:
seen.add(x)
deduplicated.append(x)
return deduplicated
class TagsRemove(TagsAdd):
pass
class UploadAssetSpec(BaseModel):
"""Upload Asset operation.
- tags: ordered; first is root ('models'|'input'|'output');
if root == 'models', second must be a valid category from folder_paths.folder_names_and_paths
- name: display name
- user_metadata: arbitrary JSON object (optional)
- hash: optional canonical 'blake3:<hex>' provided by the client for validation / fast-path
Files created via this endpoint are stored on disk using the **content hash** as the filename stem
and the original extension is preserved when available.
"""
model_config = ConfigDict(extra="ignore", str_strip_whitespace=True)
tags: list[str] = Field(..., min_length=1)
name: str | None = Field(default=None, max_length=512, description="Display Name")
user_metadata: dict[str, Any] = Field(default_factory=dict)
hash: str | None = Field(default=None)
@field_validator("hash", mode="before")
@classmethod
def _parse_hash(cls, v):
if v is None:
return None
s = str(v).strip().lower()
if not s:
return None
if ":" not in s:
raise ValueError("hash must be 'blake3:<hex>'")
algo, digest = s.split(":", 1)
if algo != "blake3":
raise ValueError("only canonical 'blake3:<hex>' is accepted here")
if not digest or any(c for c in digest if c not in "0123456789abcdef"):
raise ValueError("hash digest must be lowercase hex")
return f"{algo}:{digest}"
@field_validator("tags", mode="before")
@classmethod
def _parse_tags(cls, v):
"""
Accepts a list of strings (possibly multiple form fields),
where each string can be:
- JSON array (e.g., '["models","loras","foo"]')
- comma-separated ('models, loras, foo')
- single token ('models')
Returns a normalized, deduplicated, ordered list.
"""
items: list[str] = []
if v is None:
return []
if isinstance(v, str):
v = [v]
if isinstance(v, list):
for item in v:
if item is None:
continue
s = str(item).strip()
if not s:
continue
if s.startswith("["):
try:
arr = json.loads(s)
if isinstance(arr, list):
items.extend(str(x) for x in arr)
continue
except Exception:
pass # fallback to CSV parse below
items.extend([p for p in s.split(",") if p.strip()])
else:
return []
# normalize + dedupe
norm = []
seen = set()
for t in items:
tnorm = str(t).strip().lower()
if tnorm and tnorm not in seen:
seen.add(tnorm)
norm.append(tnorm)
return norm
@field_validator("user_metadata", mode="before")
@classmethod
def _parse_metadata_json(cls, v):
if v is None or isinstance(v, dict):
return v or {}
if isinstance(v, str):
s = v.strip()
if not s:
return {}
try:
parsed = json.loads(s)
except Exception as e:
raise ValueError(f"user_metadata must be JSON: {e}") from e
if not isinstance(parsed, dict):
raise ValueError("user_metadata must be a JSON object")
return parsed
return {}
@model_validator(mode="after")
def _validate_order(self):
if not self.tags:
raise ValueError("tags must be provided and non-empty")
root = self.tags[0]
if root not in {"models", "input", "output"}:
raise ValueError("first tag must be one of: models, input, output")
if root == "models":
if len(self.tags) < 2:
raise ValueError("models uploads require a category tag as the second tag")
return self
-93
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@@ -1,93 +0,0 @@
from datetime import datetime
from typing import Any
from pydantic import BaseModel, ConfigDict, Field, field_serializer
class AssetSummary(BaseModel):
id: str
name: str
asset_hash: str | None = None
size: int | None = None
mime_type: str | None = None
tags: list[str] = Field(default_factory=list)
preview_url: str | None = None
created_at: datetime | None = None
updated_at: datetime | None = None
last_access_time: datetime | None = None
model_config = ConfigDict(from_attributes=True)
@field_serializer("created_at", "updated_at", "last_access_time")
def _ser_dt(self, v: datetime | None, _info):
return v.isoformat() if v else None
class AssetsList(BaseModel):
assets: list[AssetSummary]
total: int
has_more: bool
class AssetUpdated(BaseModel):
id: str
name: str
asset_hash: str | None = None
tags: list[str] = Field(default_factory=list)
user_metadata: dict[str, Any] = Field(default_factory=dict)
updated_at: datetime | None = None
model_config = ConfigDict(from_attributes=True)
@field_serializer("updated_at")
def _ser_updated(self, v: datetime | None, _info):
return v.isoformat() if v else None
class AssetDetail(BaseModel):
id: str
name: str
asset_hash: str | None = None
size: int | None = None
mime_type: str | None = None
tags: list[str] = Field(default_factory=list)
user_metadata: dict[str, Any] = Field(default_factory=dict)
preview_id: str | None = None
created_at: datetime | None = None
last_access_time: datetime | None = None
model_config = ConfigDict(from_attributes=True)
@field_serializer("created_at", "last_access_time")
def _ser_dt(self, v: datetime | None, _info):
return v.isoformat() if v else None
class AssetCreated(AssetDetail):
created_new: bool
class TagUsage(BaseModel):
name: str
count: int
type: str
class TagsList(BaseModel):
tags: list[TagUsage] = Field(default_factory=list)
total: int
has_more: bool
class TagsAdd(BaseModel):
model_config = ConfigDict(str_strip_whitespace=True)
added: list[str] = Field(default_factory=list)
already_present: list[str] = Field(default_factory=list)
total_tags: list[str] = Field(default_factory=list)
class TagsRemove(BaseModel):
model_config = ConfigDict(str_strip_whitespace=True)
removed: list[str] = Field(default_factory=list)
not_present: list[str] = Field(default_factory=list)
total_tags: list[str] = Field(default_factory=list)
-204
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@@ -1,204 +0,0 @@
import os
import uuid
import sqlalchemy
from typing import Iterable
from sqlalchemy.orm import Session
from sqlalchemy.dialects import sqlite
from app.assets.helpers import utcnow
from app.assets.database.models import Asset, AssetCacheState, AssetInfo, AssetInfoTag, AssetInfoMeta
MAX_BIND_PARAMS = 800
def _chunk_rows(rows: list[dict], cols_per_row: int, max_bind_params: int) -> Iterable[list[dict]]:
if not rows:
return []
rows_per_stmt = max(1, max_bind_params // max(1, cols_per_row))
for i in range(0, len(rows), rows_per_stmt):
yield rows[i:i + rows_per_stmt]
def _iter_chunks(seq, n: int):
for i in range(0, len(seq), n):
yield seq[i:i + n]
def _rows_per_stmt(cols: int) -> int:
return max(1, MAX_BIND_PARAMS // max(1, cols))
def seed_from_paths_batch(
session: Session,
*,
specs: list[dict],
owner_id: str = "",
) -> dict:
"""Each spec is a dict with keys:
- abs_path: str
- size_bytes: int
- mtime_ns: int
- info_name: str
- tags: list[str]
- fname: Optional[str]
"""
if not specs:
return {"inserted_infos": 0, "won_states": 0, "lost_states": 0}
now = utcnow()
asset_rows: list[dict] = []
state_rows: list[dict] = []
path_to_asset: dict[str, str] = {}
asset_to_info: dict[str, dict] = {} # asset_id -> prepared info row
path_list: list[str] = []
for sp in specs:
ap = os.path.abspath(sp["abs_path"])
aid = str(uuid.uuid4())
iid = str(uuid.uuid4())
path_list.append(ap)
path_to_asset[ap] = aid
asset_rows.append(
{
"id": aid,
"hash": None,
"size_bytes": sp["size_bytes"],
"mime_type": None,
"created_at": now,
}
)
state_rows.append(
{
"asset_id": aid,
"file_path": ap,
"mtime_ns": sp["mtime_ns"],
}
)
asset_to_info[aid] = {
"id": iid,
"owner_id": owner_id,
"name": sp["info_name"],
"asset_id": aid,
"preview_id": None,
"user_metadata": {"filename": sp["fname"]} if sp["fname"] else None,
"created_at": now,
"updated_at": now,
"last_access_time": now,
"_tags": sp["tags"],
"_filename": sp["fname"],
}
# insert all seed Assets (hash=NULL)
ins_asset = sqlite.insert(Asset)
for chunk in _iter_chunks(asset_rows, _rows_per_stmt(5)):
session.execute(ins_asset, chunk)
# try to claim AssetCacheState (file_path)
# Insert with ON CONFLICT DO NOTHING, then query to find which paths were actually inserted
ins_state = (
sqlite.insert(AssetCacheState)
.on_conflict_do_nothing(index_elements=[AssetCacheState.file_path])
)
for chunk in _iter_chunks(state_rows, _rows_per_stmt(3)):
session.execute(ins_state, chunk)
# Query to find which of our paths won (were actually inserted)
winners_by_path: set[str] = set()
for chunk in _iter_chunks(path_list, MAX_BIND_PARAMS):
result = session.execute(
sqlalchemy.select(AssetCacheState.file_path)
.where(AssetCacheState.file_path.in_(chunk))
.where(AssetCacheState.asset_id.in_([path_to_asset[p] for p in chunk]))
)
winners_by_path.update(result.scalars().all())
all_paths_set = set(path_list)
losers_by_path = all_paths_set - winners_by_path
lost_assets = [path_to_asset[p] for p in losers_by_path]
if lost_assets: # losers get their Asset removed
for id_chunk in _iter_chunks(lost_assets, MAX_BIND_PARAMS):
session.execute(sqlalchemy.delete(Asset).where(Asset.id.in_(id_chunk)))
if not winners_by_path:
return {"inserted_infos": 0, "won_states": 0, "lost_states": len(losers_by_path)}
# insert AssetInfo only for winners
# Insert with ON CONFLICT DO NOTHING, then query to find which were actually inserted
winner_info_rows = [asset_to_info[path_to_asset[p]] for p in winners_by_path]
ins_info = (
sqlite.insert(AssetInfo)
.on_conflict_do_nothing(index_elements=[AssetInfo.asset_id, AssetInfo.owner_id, AssetInfo.name])
)
for chunk in _iter_chunks(winner_info_rows, _rows_per_stmt(9)):
session.execute(ins_info, chunk)
# Query to find which info rows were actually inserted (by matching our generated IDs)
all_info_ids = [row["id"] for row in winner_info_rows]
inserted_info_ids: set[str] = set()
for chunk in _iter_chunks(all_info_ids, MAX_BIND_PARAMS):
result = session.execute(
sqlalchemy.select(AssetInfo.id).where(AssetInfo.id.in_(chunk))
)
inserted_info_ids.update(result.scalars().all())
# build and insert tag + meta rows for the AssetInfo
tag_rows: list[dict] = []
meta_rows: list[dict] = []
if inserted_info_ids:
for row in winner_info_rows:
iid = row["id"]
if iid not in inserted_info_ids:
continue
for t in row["_tags"]:
tag_rows.append({
"asset_info_id": iid,
"tag_name": t,
"origin": "automatic",
"added_at": now,
})
if row["_filename"]:
meta_rows.append(
{
"asset_info_id": iid,
"key": "filename",
"ordinal": 0,
"val_str": row["_filename"],
"val_num": None,
"val_bool": None,
"val_json": None,
}
)
bulk_insert_tags_and_meta(session, tag_rows=tag_rows, meta_rows=meta_rows, max_bind_params=MAX_BIND_PARAMS)
return {
"inserted_infos": len(inserted_info_ids),
"won_states": len(winners_by_path),
"lost_states": len(losers_by_path),
}
def bulk_insert_tags_and_meta(
session: Session,
*,
tag_rows: list[dict],
meta_rows: list[dict],
max_bind_params: int,
) -> None:
"""Batch insert into asset_info_tags and asset_info_meta with ON CONFLICT DO NOTHING.
- tag_rows keys: asset_info_id, tag_name, origin, added_at
- meta_rows keys: asset_info_id, key, ordinal, val_str, val_num, val_bool, val_json
"""
if tag_rows:
ins_links = (
sqlite.insert(AssetInfoTag)
.on_conflict_do_nothing(index_elements=[AssetInfoTag.asset_info_id, AssetInfoTag.tag_name])
)
for chunk in _chunk_rows(tag_rows, cols_per_row=4, max_bind_params=max_bind_params):
session.execute(ins_links, chunk)
if meta_rows:
ins_meta = (
sqlite.insert(AssetInfoMeta)
.on_conflict_do_nothing(
index_elements=[AssetInfoMeta.asset_info_id, AssetInfoMeta.key, AssetInfoMeta.ordinal]
)
)
for chunk in _chunk_rows(meta_rows, cols_per_row=7, max_bind_params=max_bind_params):
session.execute(ins_meta, chunk)
-233
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@@ -1,233 +0,0 @@
from __future__ import annotations
import uuid
from datetime import datetime
from typing import Any
from sqlalchemy import (
JSON,
BigInteger,
Boolean,
CheckConstraint,
DateTime,
ForeignKey,
Index,
Integer,
Numeric,
String,
Text,
UniqueConstraint,
)
from sqlalchemy.orm import Mapped, foreign, mapped_column, relationship
from app.assets.helpers import utcnow
from app.database.models import to_dict, Base
class Asset(Base):
__tablename__ = "assets"
id: Mapped[str] = mapped_column(String(36), primary_key=True, default=lambda: str(uuid.uuid4()))
hash: Mapped[str | None] = mapped_column(String(256), nullable=True)
size_bytes: Mapped[int] = mapped_column(BigInteger, nullable=False, default=0)
mime_type: Mapped[str | None] = mapped_column(String(255))
created_at: Mapped[datetime] = mapped_column(
DateTime(timezone=False), nullable=False, default=utcnow
)
infos: Mapped[list[AssetInfo]] = relationship(
"AssetInfo",
back_populates="asset",
primaryjoin=lambda: Asset.id == foreign(AssetInfo.asset_id),
foreign_keys=lambda: [AssetInfo.asset_id],
cascade="all,delete-orphan",
passive_deletes=True,
)
preview_of: Mapped[list[AssetInfo]] = relationship(
"AssetInfo",
back_populates="preview_asset",
primaryjoin=lambda: Asset.id == foreign(AssetInfo.preview_id),
foreign_keys=lambda: [AssetInfo.preview_id],
viewonly=True,
)
cache_states: Mapped[list[AssetCacheState]] = relationship(
back_populates="asset",
cascade="all, delete-orphan",
passive_deletes=True,
)
__table_args__ = (
Index("uq_assets_hash", "hash", unique=True),
Index("ix_assets_mime_type", "mime_type"),
CheckConstraint("size_bytes >= 0", name="ck_assets_size_nonneg"),
)
def to_dict(self, include_none: bool = False) -> dict[str, Any]:
return to_dict(self, include_none=include_none)
def __repr__(self) -> str:
return f"<Asset id={self.id} hash={(self.hash or '')[:12]}>"
class AssetCacheState(Base):
__tablename__ = "asset_cache_state"
id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
asset_id: Mapped[str] = mapped_column(String(36), ForeignKey("assets.id", ondelete="CASCADE"), nullable=False)
file_path: Mapped[str] = mapped_column(Text, nullable=False)
mtime_ns: Mapped[int | None] = mapped_column(BigInteger, nullable=True)
needs_verify: Mapped[bool] = mapped_column(Boolean, nullable=False, default=False)
asset: Mapped[Asset] = relationship(back_populates="cache_states")
__table_args__ = (
Index("ix_asset_cache_state_file_path", "file_path"),
Index("ix_asset_cache_state_asset_id", "asset_id"),
CheckConstraint("(mtime_ns IS NULL) OR (mtime_ns >= 0)", name="ck_acs_mtime_nonneg"),
UniqueConstraint("file_path", name="uq_asset_cache_state_file_path"),
)
def to_dict(self, include_none: bool = False) -> dict[str, Any]:
return to_dict(self, include_none=include_none)
def __repr__(self) -> str:
return f"<AssetCacheState id={self.id} asset_id={self.asset_id} path={self.file_path!r}>"
class AssetInfo(Base):
__tablename__ = "assets_info"
id: Mapped[str] = mapped_column(String(36), primary_key=True, default=lambda: str(uuid.uuid4()))
owner_id: Mapped[str] = mapped_column(String(128), nullable=False, default="")
name: Mapped[str] = mapped_column(String(512), nullable=False)
asset_id: Mapped[str] = mapped_column(String(36), ForeignKey("assets.id", ondelete="RESTRICT"), nullable=False)
preview_id: Mapped[str | None] = mapped_column(String(36), ForeignKey("assets.id", ondelete="SET NULL"))
user_metadata: Mapped[dict[str, Any] | None] = mapped_column(JSON(none_as_null=True))
created_at: Mapped[datetime] = mapped_column(DateTime(timezone=False), nullable=False, default=utcnow)
updated_at: Mapped[datetime] = mapped_column(DateTime(timezone=False), nullable=False, default=utcnow)
last_access_time: Mapped[datetime] = mapped_column(DateTime(timezone=False), nullable=False, default=utcnow)
asset: Mapped[Asset] = relationship(
"Asset",
back_populates="infos",
foreign_keys=[asset_id],
lazy="selectin",
)
preview_asset: Mapped[Asset | None] = relationship(
"Asset",
back_populates="preview_of",
foreign_keys=[preview_id],
)
metadata_entries: Mapped[list[AssetInfoMeta]] = relationship(
back_populates="asset_info",
cascade="all,delete-orphan",
passive_deletes=True,
)
tag_links: Mapped[list[AssetInfoTag]] = relationship(
back_populates="asset_info",
cascade="all,delete-orphan",
passive_deletes=True,
overlaps="tags,asset_infos",
)
tags: Mapped[list[Tag]] = relationship(
secondary="asset_info_tags",
back_populates="asset_infos",
lazy="selectin",
viewonly=True,
overlaps="tag_links,asset_info_links,asset_infos,tag",
)
__table_args__ = (
UniqueConstraint("asset_id", "owner_id", "name", name="uq_assets_info_asset_owner_name"),
Index("ix_assets_info_owner_name", "owner_id", "name"),
Index("ix_assets_info_owner_id", "owner_id"),
Index("ix_assets_info_asset_id", "asset_id"),
Index("ix_assets_info_name", "name"),
Index("ix_assets_info_created_at", "created_at"),
Index("ix_assets_info_last_access_time", "last_access_time"),
)
def to_dict(self, include_none: bool = False) -> dict[str, Any]:
data = to_dict(self, include_none=include_none)
data["tags"] = [t.name for t in self.tags]
return data
def __repr__(self) -> str:
return f"<AssetInfo id={self.id} name={self.name!r} asset_id={self.asset_id}>"
class AssetInfoMeta(Base):
__tablename__ = "asset_info_meta"
asset_info_id: Mapped[str] = mapped_column(
String(36), ForeignKey("assets_info.id", ondelete="CASCADE"), primary_key=True
)
key: Mapped[str] = mapped_column(String(256), primary_key=True)
ordinal: Mapped[int] = mapped_column(Integer, primary_key=True, default=0)
val_str: Mapped[str | None] = mapped_column(String(2048), nullable=True)
val_num: Mapped[float | None] = mapped_column(Numeric(38, 10), nullable=True)
val_bool: Mapped[bool | None] = mapped_column(Boolean, nullable=True)
val_json: Mapped[Any | None] = mapped_column(JSON(none_as_null=True), nullable=True)
asset_info: Mapped[AssetInfo] = relationship(back_populates="metadata_entries")
__table_args__ = (
Index("ix_asset_info_meta_key", "key"),
Index("ix_asset_info_meta_key_val_str", "key", "val_str"),
Index("ix_asset_info_meta_key_val_num", "key", "val_num"),
Index("ix_asset_info_meta_key_val_bool", "key", "val_bool"),
)
class AssetInfoTag(Base):
__tablename__ = "asset_info_tags"
asset_info_id: Mapped[str] = mapped_column(
String(36), ForeignKey("assets_info.id", ondelete="CASCADE"), primary_key=True
)
tag_name: Mapped[str] = mapped_column(
String(512), ForeignKey("tags.name", ondelete="RESTRICT"), primary_key=True
)
origin: Mapped[str] = mapped_column(String(32), nullable=False, default="manual")
added_at: Mapped[datetime] = mapped_column(
DateTime(timezone=False), nullable=False, default=utcnow
)
asset_info: Mapped[AssetInfo] = relationship(back_populates="tag_links")
tag: Mapped[Tag] = relationship(back_populates="asset_info_links")
__table_args__ = (
Index("ix_asset_info_tags_tag_name", "tag_name"),
Index("ix_asset_info_tags_asset_info_id", "asset_info_id"),
)
class Tag(Base):
__tablename__ = "tags"
name: Mapped[str] = mapped_column(String(512), primary_key=True)
tag_type: Mapped[str] = mapped_column(String(32), nullable=False, default="user")
asset_info_links: Mapped[list[AssetInfoTag]] = relationship(
back_populates="tag",
overlaps="asset_infos,tags",
)
asset_infos: Mapped[list[AssetInfo]] = relationship(
secondary="asset_info_tags",
back_populates="tags",
viewonly=True,
overlaps="asset_info_links,tag_links,tags,asset_info",
)
__table_args__ = (
Index("ix_tags_tag_type", "tag_type"),
)
def __repr__(self) -> str:
return f"<Tag {self.name}>"
-976
View File
@@ -1,976 +0,0 @@
import os
import logging
import sqlalchemy as sa
from collections import defaultdict
from datetime import datetime
from typing import Iterable, Any
from sqlalchemy import select, delete, exists, func
from sqlalchemy.dialects import sqlite
from sqlalchemy.exc import IntegrityError
from sqlalchemy.orm import Session, contains_eager, noload
from app.assets.database.models import Asset, AssetInfo, AssetCacheState, AssetInfoMeta, AssetInfoTag, Tag
from app.assets.helpers import (
compute_relative_filename, escape_like_prefix, normalize_tags, project_kv, utcnow
)
from typing import Sequence
def visible_owner_clause(owner_id: str) -> sa.sql.ClauseElement:
"""Build owner visibility predicate for reads. Owner-less rows are visible to everyone."""
owner_id = (owner_id or "").strip()
if owner_id == "":
return AssetInfo.owner_id == ""
return AssetInfo.owner_id.in_(["", owner_id])
def pick_best_live_path(states: Sequence[AssetCacheState]) -> str:
"""
Return the best on-disk path among cache states:
1) Prefer a path that exists with needs_verify == False (already verified).
2) Otherwise, pick the first path that exists.
3) Otherwise return empty string.
"""
alive = [s for s in states if getattr(s, "file_path", None) and os.path.isfile(s.file_path)]
if not alive:
return ""
for s in alive:
if not getattr(s, "needs_verify", False):
return s.file_path
return alive[0].file_path
def apply_tag_filters(
stmt: sa.sql.Select,
include_tags: Sequence[str] | None = None,
exclude_tags: Sequence[str] | None = None,
) -> sa.sql.Select:
"""include_tags: every tag must be present; exclude_tags: none may be present."""
include_tags = normalize_tags(include_tags)
exclude_tags = normalize_tags(exclude_tags)
if include_tags:
for tag_name in include_tags:
stmt = stmt.where(
exists().where(
(AssetInfoTag.asset_info_id == AssetInfo.id)
& (AssetInfoTag.tag_name == tag_name)
)
)
if exclude_tags:
stmt = stmt.where(
~exists().where(
(AssetInfoTag.asset_info_id == AssetInfo.id)
& (AssetInfoTag.tag_name.in_(exclude_tags))
)
)
return stmt
def apply_metadata_filter(
stmt: sa.sql.Select,
metadata_filter: dict | None = None,
) -> sa.sql.Select:
"""Apply filters using asset_info_meta projection table."""
if not metadata_filter:
return stmt
def _exists_for_pred(key: str, *preds) -> sa.sql.ClauseElement:
return sa.exists().where(
AssetInfoMeta.asset_info_id == AssetInfo.id,
AssetInfoMeta.key == key,
*preds,
)
def _exists_clause_for_value(key: str, value) -> sa.sql.ClauseElement:
if value is None:
no_row_for_key = sa.not_(
sa.exists().where(
AssetInfoMeta.asset_info_id == AssetInfo.id,
AssetInfoMeta.key == key,
)
)
null_row = _exists_for_pred(
key,
AssetInfoMeta.val_json.is_(None),
AssetInfoMeta.val_str.is_(None),
AssetInfoMeta.val_num.is_(None),
AssetInfoMeta.val_bool.is_(None),
)
return sa.or_(no_row_for_key, null_row)
if isinstance(value, bool):
return _exists_for_pred(key, AssetInfoMeta.val_bool == bool(value))
if isinstance(value, (int, float)):
from decimal import Decimal
num = value if isinstance(value, Decimal) else Decimal(str(value))
return _exists_for_pred(key, AssetInfoMeta.val_num == num)
if isinstance(value, str):
return _exists_for_pred(key, AssetInfoMeta.val_str == value)
return _exists_for_pred(key, AssetInfoMeta.val_json == value)
for k, v in metadata_filter.items():
if isinstance(v, list):
ors = [_exists_clause_for_value(k, elem) for elem in v]
if ors:
stmt = stmt.where(sa.or_(*ors))
else:
stmt = stmt.where(_exists_clause_for_value(k, v))
return stmt
def asset_exists_by_hash(
session: Session,
*,
asset_hash: str,
) -> bool:
"""
Check if an asset with a given hash exists in database.
"""
row = (
session.execute(
select(sa.literal(True)).select_from(Asset).where(Asset.hash == asset_hash).limit(1)
)
).first()
return row is not None
def asset_info_exists_for_asset_id(
session: Session,
*,
asset_id: str,
) -> bool:
q = (
select(sa.literal(True))
.select_from(AssetInfo)
.where(AssetInfo.asset_id == asset_id)
.limit(1)
)
return (session.execute(q)).first() is not None
def get_asset_by_hash(
session: Session,
*,
asset_hash: str,
) -> Asset | None:
return (
session.execute(select(Asset).where(Asset.hash == asset_hash).limit(1))
).scalars().first()
def get_asset_info_by_id(
session: Session,
*,
asset_info_id: str,
) -> AssetInfo | None:
return session.get(AssetInfo, asset_info_id)
def list_asset_infos_page(
session: Session,
owner_id: str = "",
include_tags: Sequence[str] | None = None,
exclude_tags: Sequence[str] | None = None,
name_contains: str | None = None,
metadata_filter: dict | None = None,
limit: int = 20,
offset: int = 0,
sort: str = "created_at",
order: str = "desc",
) -> tuple[list[AssetInfo], dict[str, list[str]], int]:
base = (
select(AssetInfo)
.join(Asset, Asset.id == AssetInfo.asset_id)
.options(contains_eager(AssetInfo.asset), noload(AssetInfo.tags))
.where(visible_owner_clause(owner_id))
)
if name_contains:
escaped, esc = escape_like_prefix(name_contains)
base = base.where(AssetInfo.name.ilike(f"%{escaped}%", escape=esc))
base = apply_tag_filters(base, include_tags, exclude_tags)
base = apply_metadata_filter(base, metadata_filter)
sort = (sort or "created_at").lower()
order = (order or "desc").lower()
sort_map = {
"name": AssetInfo.name,
"created_at": AssetInfo.created_at,
"updated_at": AssetInfo.updated_at,
"last_access_time": AssetInfo.last_access_time,
"size": Asset.size_bytes,
}
sort_col = sort_map.get(sort, AssetInfo.created_at)
sort_exp = sort_col.desc() if order == "desc" else sort_col.asc()
base = base.order_by(sort_exp).limit(limit).offset(offset)
count_stmt = (
select(sa.func.count())
.select_from(AssetInfo)
.join(Asset, Asset.id == AssetInfo.asset_id)
.where(visible_owner_clause(owner_id))
)
if name_contains:
escaped, esc = escape_like_prefix(name_contains)
count_stmt = count_stmt.where(AssetInfo.name.ilike(f"%{escaped}%", escape=esc))
count_stmt = apply_tag_filters(count_stmt, include_tags, exclude_tags)
count_stmt = apply_metadata_filter(count_stmt, metadata_filter)
total = int((session.execute(count_stmt)).scalar_one() or 0)
infos = (session.execute(base)).unique().scalars().all()
id_list: list[str] = [i.id for i in infos]
tag_map: dict[str, list[str]] = defaultdict(list)
if id_list:
rows = session.execute(
select(AssetInfoTag.asset_info_id, Tag.name)
.join(Tag, Tag.name == AssetInfoTag.tag_name)
.where(AssetInfoTag.asset_info_id.in_(id_list))
.order_by(AssetInfoTag.added_at)
)
for aid, tag_name in rows.all():
tag_map[aid].append(tag_name)
return infos, tag_map, total
def fetch_asset_info_asset_and_tags(
session: Session,
asset_info_id: str,
owner_id: str = "",
) -> tuple[AssetInfo, Asset, list[str]] | None:
stmt = (
select(AssetInfo, Asset, Tag.name)
.join(Asset, Asset.id == AssetInfo.asset_id)
.join(AssetInfoTag, AssetInfoTag.asset_info_id == AssetInfo.id, isouter=True)
.join(Tag, Tag.name == AssetInfoTag.tag_name, isouter=True)
.where(
AssetInfo.id == asset_info_id,
visible_owner_clause(owner_id),
)
.options(noload(AssetInfo.tags))
.order_by(Tag.name.asc())
)
rows = (session.execute(stmt)).all()
if not rows:
return None
first_info, first_asset, _ = rows[0]
tags: list[str] = []
seen: set[str] = set()
for _info, _asset, tag_name in rows:
if tag_name and tag_name not in seen:
seen.add(tag_name)
tags.append(tag_name)
return first_info, first_asset, tags
def fetch_asset_info_and_asset(
session: Session,
*,
asset_info_id: str,
owner_id: str = "",
) -> tuple[AssetInfo, Asset] | None:
stmt = (
select(AssetInfo, Asset)
.join(Asset, Asset.id == AssetInfo.asset_id)
.where(
AssetInfo.id == asset_info_id,
visible_owner_clause(owner_id),
)
.limit(1)
.options(noload(AssetInfo.tags))
)
row = session.execute(stmt)
pair = row.first()
if not pair:
return None
return pair[0], pair[1]
def list_cache_states_by_asset_id(
session: Session, *, asset_id: str
) -> Sequence[AssetCacheState]:
return (
session.execute(
select(AssetCacheState)
.where(AssetCacheState.asset_id == asset_id)
.order_by(AssetCacheState.id.asc())
)
).scalars().all()
def touch_asset_info_by_id(
session: Session,
*,
asset_info_id: str,
ts: datetime | None = None,
only_if_newer: bool = True,
) -> None:
ts = ts or utcnow()
stmt = sa.update(AssetInfo).where(AssetInfo.id == asset_info_id)
if only_if_newer:
stmt = stmt.where(
sa.or_(AssetInfo.last_access_time.is_(None), AssetInfo.last_access_time < ts)
)
session.execute(stmt.values(last_access_time=ts))
def create_asset_info_for_existing_asset(
session: Session,
*,
asset_hash: str,
name: str,
user_metadata: dict | None = None,
tags: Sequence[str] | None = None,
tag_origin: str = "manual",
owner_id: str = "",
) -> AssetInfo:
"""Create or return an existing AssetInfo for an Asset identified by asset_hash."""
now = utcnow()
asset = get_asset_by_hash(session, asset_hash=asset_hash)
if not asset:
raise ValueError(f"Unknown asset hash {asset_hash}")
info = AssetInfo(
owner_id=owner_id,
name=name,
asset_id=asset.id,
preview_id=None,
created_at=now,
updated_at=now,
last_access_time=now,
)
try:
with session.begin_nested():
session.add(info)
session.flush()
except IntegrityError:
existing = (
session.execute(
select(AssetInfo)
.options(noload(AssetInfo.tags))
.where(
AssetInfo.asset_id == asset.id,
AssetInfo.name == name,
AssetInfo.owner_id == owner_id,
)
.limit(1)
)
).unique().scalars().first()
if not existing:
raise RuntimeError("AssetInfo upsert failed to find existing row after conflict.")
return existing
# metadata["filename"] hack
new_meta = dict(user_metadata or {})
computed_filename = None
try:
p = pick_best_live_path(list_cache_states_by_asset_id(session, asset_id=asset.id))
if p:
computed_filename = compute_relative_filename(p)
except Exception:
computed_filename = None
if computed_filename:
new_meta["filename"] = computed_filename
if new_meta:
replace_asset_info_metadata_projection(
session,
asset_info_id=info.id,
user_metadata=new_meta,
)
if tags is not None:
set_asset_info_tags(
session,
asset_info_id=info.id,
tags=tags,
origin=tag_origin,
)
return info
def set_asset_info_tags(
session: Session,
*,
asset_info_id: str,
tags: Sequence[str],
origin: str = "manual",
) -> dict:
desired = normalize_tags(tags)
current = set(
tag_name for (tag_name,) in (
session.execute(select(AssetInfoTag.tag_name).where(AssetInfoTag.asset_info_id == asset_info_id))
).all()
)
to_add = [t for t in desired if t not in current]
to_remove = [t for t in current if t not in desired]
if to_add:
ensure_tags_exist(session, to_add, tag_type="user")
session.add_all([
AssetInfoTag(asset_info_id=asset_info_id, tag_name=t, origin=origin, added_at=utcnow())
for t in to_add
])
session.flush()
if to_remove:
session.execute(
delete(AssetInfoTag)
.where(AssetInfoTag.asset_info_id == asset_info_id, AssetInfoTag.tag_name.in_(to_remove))
)
session.flush()
return {"added": to_add, "removed": to_remove, "total": desired}
def replace_asset_info_metadata_projection(
session: Session,
*,
asset_info_id: str,
user_metadata: dict | None = None,
) -> None:
info = session.get(AssetInfo, asset_info_id)
if not info:
raise ValueError(f"AssetInfo {asset_info_id} not found")
info.user_metadata = user_metadata or {}
info.updated_at = utcnow()
session.flush()
session.execute(delete(AssetInfoMeta).where(AssetInfoMeta.asset_info_id == asset_info_id))
session.flush()
if not user_metadata:
return
rows: list[AssetInfoMeta] = []
for k, v in user_metadata.items():
for r in project_kv(k, v):
rows.append(
AssetInfoMeta(
asset_info_id=asset_info_id,
key=r["key"],
ordinal=int(r["ordinal"]),
val_str=r.get("val_str"),
val_num=r.get("val_num"),
val_bool=r.get("val_bool"),
val_json=r.get("val_json"),
)
)
if rows:
session.add_all(rows)
session.flush()
def ingest_fs_asset(
session: Session,
*,
asset_hash: str,
abs_path: str,
size_bytes: int,
mtime_ns: int,
mime_type: str | None = None,
info_name: str | None = None,
owner_id: str = "",
preview_id: str | None = None,
user_metadata: dict | None = None,
tags: Sequence[str] = (),
tag_origin: str = "manual",
require_existing_tags: bool = False,
) -> dict:
"""
Idempotently upsert:
- Asset by content hash (create if missing)
- AssetCacheState(file_path) pointing to asset_id
- Optionally AssetInfo + tag links and metadata projection
Returns flags and ids.
"""
locator = os.path.abspath(abs_path)
now = utcnow()
if preview_id:
if not session.get(Asset, preview_id):
preview_id = None
out: dict[str, Any] = {
"asset_created": False,
"asset_updated": False,
"state_created": False,
"state_updated": False,
"asset_info_id": None,
}
# 1) Asset by hash
asset = (
session.execute(select(Asset).where(Asset.hash == asset_hash).limit(1))
).scalars().first()
if not asset:
vals = {
"hash": asset_hash,
"size_bytes": int(size_bytes),
"mime_type": mime_type,
"created_at": now,
}
res = session.execute(
sqlite.insert(Asset)
.values(**vals)
.on_conflict_do_nothing(index_elements=[Asset.hash])
)
if int(res.rowcount or 0) > 0:
out["asset_created"] = True
asset = (
session.execute(
select(Asset).where(Asset.hash == asset_hash).limit(1)
)
).scalars().first()
if not asset:
raise RuntimeError("Asset row not found after upsert.")
else:
changed = False
if asset.size_bytes != int(size_bytes) and int(size_bytes) > 0:
asset.size_bytes = int(size_bytes)
changed = True
if mime_type and asset.mime_type != mime_type:
asset.mime_type = mime_type
changed = True
if changed:
out["asset_updated"] = True
# 2) AssetCacheState upsert by file_path (unique)
vals = {
"asset_id": asset.id,
"file_path": locator,
"mtime_ns": int(mtime_ns),
}
ins = (
sqlite.insert(AssetCacheState)
.values(**vals)
.on_conflict_do_nothing(index_elements=[AssetCacheState.file_path])
)
res = session.execute(ins)
if int(res.rowcount or 0) > 0:
out["state_created"] = True
else:
upd = (
sa.update(AssetCacheState)
.where(AssetCacheState.file_path == locator)
.where(
sa.or_(
AssetCacheState.asset_id != asset.id,
AssetCacheState.mtime_ns.is_(None),
AssetCacheState.mtime_ns != int(mtime_ns),
)
)
.values(asset_id=asset.id, mtime_ns=int(mtime_ns))
)
res2 = session.execute(upd)
if int(res2.rowcount or 0) > 0:
out["state_updated"] = True
# 3) Optional AssetInfo + tags + metadata
if info_name:
try:
with session.begin_nested():
info = AssetInfo(
owner_id=owner_id,
name=info_name,
asset_id=asset.id,
preview_id=preview_id,
created_at=now,
updated_at=now,
last_access_time=now,
)
session.add(info)
session.flush()
out["asset_info_id"] = info.id
except IntegrityError:
pass
existing_info = (
session.execute(
select(AssetInfo)
.where(
AssetInfo.asset_id == asset.id,
AssetInfo.name == info_name,
(AssetInfo.owner_id == owner_id),
)
.limit(1)
)
).unique().scalar_one_or_none()
if not existing_info:
raise RuntimeError("Failed to update or insert AssetInfo.")
if preview_id and existing_info.preview_id != preview_id:
existing_info.preview_id = preview_id
existing_info.updated_at = now
if existing_info.last_access_time < now:
existing_info.last_access_time = now
session.flush()
out["asset_info_id"] = existing_info.id
norm = [t.strip().lower() for t in (tags or []) if (t or "").strip()]
if norm and out["asset_info_id"] is not None:
if not require_existing_tags:
ensure_tags_exist(session, norm, tag_type="user")
existing_tag_names = set(
name for (name,) in (session.execute(select(Tag.name).where(Tag.name.in_(norm)))).all()
)
missing = [t for t in norm if t not in existing_tag_names]
if missing and require_existing_tags:
raise ValueError(f"Unknown tags: {missing}")
existing_links = set(
tag_name
for (tag_name,) in (
session.execute(
select(AssetInfoTag.tag_name).where(AssetInfoTag.asset_info_id == out["asset_info_id"])
)
).all()
)
to_add = [t for t in norm if t in existing_tag_names and t not in existing_links]
if to_add:
session.add_all(
[
AssetInfoTag(
asset_info_id=out["asset_info_id"],
tag_name=t,
origin=tag_origin,
added_at=now,
)
for t in to_add
]
)
session.flush()
# metadata["filename"] hack
if out["asset_info_id"] is not None:
primary_path = pick_best_live_path(list_cache_states_by_asset_id(session, asset_id=asset.id))
computed_filename = compute_relative_filename(primary_path) if primary_path else None
current_meta = existing_info.user_metadata or {}
new_meta = dict(current_meta)
if user_metadata is not None:
for k, v in user_metadata.items():
new_meta[k] = v
if computed_filename:
new_meta["filename"] = computed_filename
if new_meta != current_meta:
replace_asset_info_metadata_projection(
session,
asset_info_id=out["asset_info_id"],
user_metadata=new_meta,
)
try:
remove_missing_tag_for_asset_id(session, asset_id=asset.id)
except Exception:
logging.exception("Failed to clear 'missing' tag for asset %s", asset.id)
return out
def update_asset_info_full(
session: Session,
*,
asset_info_id: str,
name: str | None = None,
tags: Sequence[str] | None = None,
user_metadata: dict | None = None,
tag_origin: str = "manual",
asset_info_row: Any = None,
) -> AssetInfo:
if not asset_info_row:
info = session.get(AssetInfo, asset_info_id)
if not info:
raise ValueError(f"AssetInfo {asset_info_id} not found")
else:
info = asset_info_row
touched = False
if name is not None and name != info.name:
info.name = name
touched = True
computed_filename = None
try:
p = pick_best_live_path(list_cache_states_by_asset_id(session, asset_id=info.asset_id))
if p:
computed_filename = compute_relative_filename(p)
except Exception:
computed_filename = None
if user_metadata is not None:
new_meta = dict(user_metadata)
if computed_filename:
new_meta["filename"] = computed_filename
replace_asset_info_metadata_projection(
session, asset_info_id=asset_info_id, user_metadata=new_meta
)
touched = True
else:
if computed_filename:
current_meta = info.user_metadata or {}
if current_meta.get("filename") != computed_filename:
new_meta = dict(current_meta)
new_meta["filename"] = computed_filename
replace_asset_info_metadata_projection(
session, asset_info_id=asset_info_id, user_metadata=new_meta
)
touched = True
if tags is not None:
set_asset_info_tags(
session,
asset_info_id=asset_info_id,
tags=tags,
origin=tag_origin,
)
touched = True
if touched and user_metadata is None:
info.updated_at = utcnow()
session.flush()
return info
def delete_asset_info_by_id(
session: Session,
*,
asset_info_id: str,
owner_id: str,
) -> bool:
stmt = sa.delete(AssetInfo).where(
AssetInfo.id == asset_info_id,
visible_owner_clause(owner_id),
)
return int((session.execute(stmt)).rowcount or 0) > 0
def list_tags_with_usage(
session: Session,
prefix: str | None = None,
limit: int = 100,
offset: int = 0,
include_zero: bool = True,
order: str = "count_desc",
owner_id: str = "",
) -> tuple[list[tuple[str, str, int]], int]:
counts_sq = (
select(
AssetInfoTag.tag_name.label("tag_name"),
func.count(AssetInfoTag.asset_info_id).label("cnt"),
)
.select_from(AssetInfoTag)
.join(AssetInfo, AssetInfo.id == AssetInfoTag.asset_info_id)
.where(visible_owner_clause(owner_id))
.group_by(AssetInfoTag.tag_name)
.subquery()
)
q = (
select(
Tag.name,
Tag.tag_type,
func.coalesce(counts_sq.c.cnt, 0).label("count"),
)
.select_from(Tag)
.join(counts_sq, counts_sq.c.tag_name == Tag.name, isouter=True)
)
if prefix:
escaped, esc = escape_like_prefix(prefix.strip().lower())
q = q.where(Tag.name.like(escaped + "%", escape=esc))
if not include_zero:
q = q.where(func.coalesce(counts_sq.c.cnt, 0) > 0)
if order == "name_asc":
q = q.order_by(Tag.name.asc())
else:
q = q.order_by(func.coalesce(counts_sq.c.cnt, 0).desc(), Tag.name.asc())
total_q = select(func.count()).select_from(Tag)
if prefix:
escaped, esc = escape_like_prefix(prefix.strip().lower())
total_q = total_q.where(Tag.name.like(escaped + "%", escape=esc))
if not include_zero:
total_q = total_q.where(
Tag.name.in_(select(AssetInfoTag.tag_name).group_by(AssetInfoTag.tag_name))
)
rows = (session.execute(q.limit(limit).offset(offset))).all()
total = (session.execute(total_q)).scalar_one()
rows_norm = [(name, ttype, int(count or 0)) for (name, ttype, count) in rows]
return rows_norm, int(total or 0)
def ensure_tags_exist(session: Session, names: Iterable[str], tag_type: str = "user") -> None:
wanted = normalize_tags(list(names))
if not wanted:
return
rows = [{"name": n, "tag_type": tag_type} for n in list(dict.fromkeys(wanted))]
ins = (
sqlite.insert(Tag)
.values(rows)
.on_conflict_do_nothing(index_elements=[Tag.name])
)
session.execute(ins)
def get_asset_tags(session: Session, *, asset_info_id: str) -> list[str]:
return [
tag_name for (tag_name,) in (
session.execute(
select(AssetInfoTag.tag_name).where(AssetInfoTag.asset_info_id == asset_info_id)
)
).all()
]
def add_tags_to_asset_info(
session: Session,
*,
asset_info_id: str,
tags: Sequence[str],
origin: str = "manual",
create_if_missing: bool = True,
asset_info_row: Any = None,
) -> dict:
if not asset_info_row:
info = session.get(AssetInfo, asset_info_id)
if not info:
raise ValueError(f"AssetInfo {asset_info_id} not found")
norm = normalize_tags(tags)
if not norm:
total = get_asset_tags(session, asset_info_id=asset_info_id)
return {"added": [], "already_present": [], "total_tags": total}
if create_if_missing:
ensure_tags_exist(session, norm, tag_type="user")
current = {
tag_name
for (tag_name,) in (
session.execute(
sa.select(AssetInfoTag.tag_name).where(AssetInfoTag.asset_info_id == asset_info_id)
)
).all()
}
want = set(norm)
to_add = sorted(want - current)
if to_add:
with session.begin_nested() as nested:
try:
session.add_all(
[
AssetInfoTag(
asset_info_id=asset_info_id,
tag_name=t,
origin=origin,
added_at=utcnow(),
)
for t in to_add
]
)
session.flush()
except IntegrityError:
nested.rollback()
after = set(get_asset_tags(session, asset_info_id=asset_info_id))
return {
"added": sorted(((after - current) & want)),
"already_present": sorted(want & current),
"total_tags": sorted(after),
}
def remove_tags_from_asset_info(
session: Session,
*,
asset_info_id: str,
tags: Sequence[str],
) -> dict:
info = session.get(AssetInfo, asset_info_id)
if not info:
raise ValueError(f"AssetInfo {asset_info_id} not found")
norm = normalize_tags(tags)
if not norm:
total = get_asset_tags(session, asset_info_id=asset_info_id)
return {"removed": [], "not_present": [], "total_tags": total}
existing = {
tag_name
for (tag_name,) in (
session.execute(
sa.select(AssetInfoTag.tag_name).where(AssetInfoTag.asset_info_id == asset_info_id)
)
).all()
}
to_remove = sorted(set(t for t in norm if t in existing))
not_present = sorted(set(t for t in norm if t not in existing))
if to_remove:
session.execute(
delete(AssetInfoTag)
.where(
AssetInfoTag.asset_info_id == asset_info_id,
AssetInfoTag.tag_name.in_(to_remove),
)
)
session.flush()
total = get_asset_tags(session, asset_info_id=asset_info_id)
return {"removed": to_remove, "not_present": not_present, "total_tags": total}
def remove_missing_tag_for_asset_id(
session: Session,
*,
asset_id: str,
) -> None:
session.execute(
sa.delete(AssetInfoTag).where(
AssetInfoTag.asset_info_id.in_(sa.select(AssetInfo.id).where(AssetInfo.asset_id == asset_id)),
AssetInfoTag.tag_name == "missing",
)
)
def set_asset_info_preview(
session: Session,
*,
asset_info_id: str,
preview_asset_id: str | None = None,
) -> None:
"""Set or clear preview_id and bump updated_at. Raises on unknown IDs."""
info = session.get(AssetInfo, asset_info_id)
if not info:
raise ValueError(f"AssetInfo {asset_info_id} not found")
if preview_asset_id is None:
info.preview_id = None
else:
# validate preview asset exists
if not session.get(Asset, preview_asset_id):
raise ValueError(f"Preview Asset {preview_asset_id} not found")
info.preview_id = preview_asset_id
info.updated_at = utcnow()
session.flush()
-62
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@@ -1,62 +0,0 @@
from typing import Iterable
import sqlalchemy
from sqlalchemy.orm import Session
from sqlalchemy.dialects import sqlite
from app.assets.helpers import normalize_tags, utcnow
from app.assets.database.models import Tag, AssetInfoTag, AssetInfo
def ensure_tags_exist(session: Session, names: Iterable[str], tag_type: str = "user") -> None:
wanted = normalize_tags(list(names))
if not wanted:
return
rows = [{"name": n, "tag_type": tag_type} for n in list(dict.fromkeys(wanted))]
ins = (
sqlite.insert(Tag)
.values(rows)
.on_conflict_do_nothing(index_elements=[Tag.name])
)
return session.execute(ins)
def add_missing_tag_for_asset_id(
session: Session,
*,
asset_id: str,
origin: str = "automatic",
) -> None:
select_rows = (
sqlalchemy.select(
AssetInfo.id.label("asset_info_id"),
sqlalchemy.literal("missing").label("tag_name"),
sqlalchemy.literal(origin).label("origin"),
sqlalchemy.literal(utcnow()).label("added_at"),
)
.where(AssetInfo.asset_id == asset_id)
.where(
sqlalchemy.not_(
sqlalchemy.exists().where((AssetInfoTag.asset_info_id == AssetInfo.id) & (AssetInfoTag.tag_name == "missing"))
)
)
)
session.execute(
sqlite.insert(AssetInfoTag)
.from_select(
["asset_info_id", "tag_name", "origin", "added_at"],
select_rows,
)
.on_conflict_do_nothing(index_elements=[AssetInfoTag.asset_info_id, AssetInfoTag.tag_name])
)
def remove_missing_tag_for_asset_id(
session: Session,
*,
asset_id: str,
) -> None:
session.execute(
sqlalchemy.delete(AssetInfoTag).where(
AssetInfoTag.asset_info_id.in_(sqlalchemy.select(AssetInfo.id).where(AssetInfo.asset_id == asset_id)),
AssetInfoTag.tag_name == "missing",
)
)
-75
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@@ -1,75 +0,0 @@
from blake3 import blake3
from typing import IO
import os
import asyncio
DEFAULT_CHUNK = 8 * 1024 *1024 # 8MB
# NOTE: this allows hashing different representations of a file-like object
def blake3_hash(
fp: str | IO[bytes],
chunk_size: int = DEFAULT_CHUNK,
) -> str:
"""
Returns a BLAKE3 hex digest for ``fp``, which may be:
- a filename (str/bytes) or PathLike
- an open binary file object
If ``fp`` is a file object, it must be opened in **binary** mode and support
``read``, ``seek``, and ``tell``. The function will seek to the start before
reading and will attempt to restore the original position afterward.
"""
# duck typing to check if input is a file-like object
if hasattr(fp, "read"):
return _hash_file_obj(fp, chunk_size)
with open(os.fspath(fp), "rb") as f:
return _hash_file_obj(f, chunk_size)
async def blake3_hash_async(
fp: str | IO[bytes],
chunk_size: int = DEFAULT_CHUNK,
) -> str:
"""Async wrapper for ``blake3_hash_sync``.
Uses a worker thread so the event loop remains responsive.
"""
# If it is a path, open inside the worker thread to keep I/O off the loop.
if hasattr(fp, "read"):
return await asyncio.to_thread(blake3_hash, fp, chunk_size)
def _worker() -> str:
with open(os.fspath(fp), "rb") as f:
return _hash_file_obj(f, chunk_size)
return await asyncio.to_thread(_worker)
def _hash_file_obj(file_obj: IO, chunk_size: int = DEFAULT_CHUNK) -> str:
"""
Hash an already-open binary file object by streaming in chunks.
- Seeks to the beginning before reading (if supported).
- Restores the original position afterward (if tell/seek are supported).
"""
if chunk_size <= 0:
chunk_size = DEFAULT_CHUNK
# in case file object is already open and not at the beginning, track so can be restored after hashing
orig_pos = file_obj.tell()
try:
# seek to the beginning before reading
if orig_pos != 0:
file_obj.seek(0)
h = blake3()
while True:
chunk = file_obj.read(chunk_size)
if not chunk:
break
h.update(chunk)
return h.hexdigest()
finally:
# restore original position in file object, if needed
if orig_pos != 0:
file_obj.seek(orig_pos)
-312
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@@ -1,312 +0,0 @@
import contextlib
import os
from decimal import Decimal
from aiohttp import web
from datetime import datetime, timezone
from pathlib import Path
from typing import Literal, Any
import folder_paths
RootType = Literal["models", "input", "output"]
ALLOWED_ROOTS: tuple[RootType, ...] = ("models", "input", "output")
def get_query_dict(request: web.Request) -> dict[str, Any]:
"""
Gets a dictionary of query parameters from the request.
'request.query' is a MultiMapping[str], needs to be converted to a dictionary to be validated by Pydantic.
"""
query_dict = {
key: request.query.getall(key) if len(request.query.getall(key)) > 1 else request.query.get(key)
for key in request.query.keys()
}
return query_dict
def list_tree(base_dir: str) -> list[str]:
out: list[str] = []
base_abs = os.path.abspath(base_dir)
if not os.path.isdir(base_abs):
return out
for dirpath, _subdirs, filenames in os.walk(base_abs, topdown=True, followlinks=False):
for name in filenames:
out.append(os.path.abspath(os.path.join(dirpath, name)))
return out
def prefixes_for_root(root: RootType) -> list[str]:
if root == "models":
bases: list[str] = []
for _bucket, paths in get_studio_models_folders():
bases.extend(paths)
return [os.path.abspath(p) for p in bases]
if root == "input":
return [os.path.abspath(folder_paths.get_input_directory())]
if root == "output":
return [os.path.abspath(folder_paths.get_output_directory())]
return []
def escape_like_prefix(s: str, escape: str = "!") -> tuple[str, str]:
"""Escapes %, _ and the escape char itself in a LIKE prefix.
Returns (escaped_prefix, escape_char). Caller should append '%' and pass escape=escape_char to .like().
"""
s = s.replace(escape, escape + escape) # escape the escape char first
s = s.replace("%", escape + "%").replace("_", escape + "_") # escape LIKE wildcards
return s, escape
def fast_asset_file_check(
*,
mtime_db: int | None,
size_db: int | None,
stat_result: os.stat_result,
) -> bool:
if mtime_db is None:
return False
actual_mtime_ns = getattr(stat_result, "st_mtime_ns", int(stat_result.st_mtime * 1_000_000_000))
if int(mtime_db) != int(actual_mtime_ns):
return False
sz = int(size_db or 0)
if sz > 0:
return int(stat_result.st_size) == sz
return True
def utcnow() -> datetime:
"""Naive UTC timestamp (no tzinfo). We always treat DB datetimes as UTC."""
return datetime.now(timezone.utc).replace(tzinfo=None)
def get_studio_models_folders() -> list[tuple[str, list[str]]]:
"""Build a list of (folder_name, base_paths[]) categories that are configured for model locations.
We trust `folder_paths.folder_names_and_paths` and include a category if
*any* of its base paths lies under the Studio `models_dir`.
"""
targets: list[tuple[str, list[str]]] = []
models_root = os.path.abspath(folder_paths.models_dir)
for name, values in folder_paths.folder_names_and_paths.items():
paths, _exts = values[0], values[1] # NOTE: this prevents nodepacks that hackily edit folder_... from breaking Hanzo Studio
if any(os.path.abspath(p).startswith(models_root + os.sep) for p in paths):
targets.append((name, paths))
return targets
def resolve_destination_from_tags(tags: list[str]) -> tuple[str, list[str]]:
"""Validates and maps tags -> (base_dir, subdirs_for_fs)"""
root = tags[0]
if root == "models":
if len(tags) < 2:
raise ValueError("at least two tags required for model asset")
try:
bases = folder_paths.folder_names_and_paths[tags[1]][0]
except KeyError:
raise ValueError(f"unknown model category '{tags[1]}'")
if not bases:
raise ValueError(f"no base path configured for category '{tags[1]}'")
base_dir = os.path.abspath(bases[0])
raw_subdirs = tags[2:]
else:
base_dir = os.path.abspath(
folder_paths.get_input_directory() if root == "input" else folder_paths.get_output_directory()
)
raw_subdirs = tags[1:]
for i in raw_subdirs:
if i in (".", ".."):
raise ValueError("invalid path component in tags")
return base_dir, raw_subdirs if raw_subdirs else []
def ensure_within_base(candidate: str, base: str) -> None:
cand_abs = os.path.abspath(candidate)
base_abs = os.path.abspath(base)
try:
if os.path.commonpath([cand_abs, base_abs]) != base_abs:
raise ValueError("destination escapes base directory")
except Exception:
raise ValueError("invalid destination path")
def compute_relative_filename(file_path: str) -> str | None:
"""
Return the model's path relative to the last well-known folder (the model category),
using forward slashes, eg:
/.../models/checkpoints/flux/123/flux.safetensors -> "flux/123/flux.safetensors"
/.../models/text_encoders/clip_g.safetensors -> "clip_g.safetensors"
For non-model paths, returns None.
NOTE: this is a temporary helper, used only for initializing metadata["filename"] field.
"""
try:
root_category, rel_path = get_relative_to_root_category_path_of_asset(file_path)
except ValueError:
return None
p = Path(rel_path)
parts = [seg for seg in p.parts if seg not in (".", "..", p.anchor)]
if not parts:
return None
if root_category == "models":
# parts[0] is the category ("checkpoints", "vae", etc) drop it
inside = parts[1:] if len(parts) > 1 else [parts[0]]
return "/".join(inside)
return "/".join(parts) # input/output: keep all parts
def get_relative_to_root_category_path_of_asset(file_path: str) -> tuple[Literal["input", "output", "models"], str]:
"""Given an absolute or relative file path, determine which root category the path belongs to:
- 'input' if the file resides under `folder_paths.get_input_directory()`
- 'output' if the file resides under `folder_paths.get_output_directory()`
- 'models' if the file resides under any base path of categories returned by `get_studio_models_folders()`
Returns:
(root_category, relative_path_inside_that_root)
For 'models', the relative path is prefixed with the category name:
e.g. ('models', 'vae/test/sub/ae.safetensors')
Raises:
ValueError: if the path does not belong to input, output, or configured model bases.
"""
fp_abs = os.path.abspath(file_path)
def _is_within(child: str, parent: str) -> bool:
try:
return os.path.commonpath([child, parent]) == parent
except Exception:
return False
def _rel(child: str, parent: str) -> str:
return os.path.relpath(os.path.join(os.sep, os.path.relpath(child, parent)), os.sep)
# 1) input
input_base = os.path.abspath(folder_paths.get_input_directory())
if _is_within(fp_abs, input_base):
return "input", _rel(fp_abs, input_base)
# 2) output
output_base = os.path.abspath(folder_paths.get_output_directory())
if _is_within(fp_abs, output_base):
return "output", _rel(fp_abs, output_base)
# 3) models (check deepest matching base to avoid ambiguity)
best: tuple[int, str, str] | None = None # (base_len, bucket, rel_inside_bucket)
for bucket, bases in get_studio_models_folders():
for b in bases:
base_abs = os.path.abspath(b)
if not _is_within(fp_abs, base_abs):
continue
cand = (len(base_abs), bucket, _rel(fp_abs, base_abs))
if best is None or cand[0] > best[0]:
best = cand
if best is not None:
_, bucket, rel_inside = best
combined = os.path.join(bucket, rel_inside)
return "models", os.path.relpath(os.path.join(os.sep, combined), os.sep)
raise ValueError(f"Path is not within input, output, or configured model bases: {file_path}")
def get_name_and_tags_from_asset_path(file_path: str) -> tuple[str, list[str]]:
"""Return a tuple (name, tags) derived from a filesystem path.
Semantics:
- Root category is determined by `get_relative_to_root_category_path_of_asset`.
- The returned `name` is the base filename with extension from the relative path.
- The returned `tags` are:
[root_category] + parent folders of the relative path (in order)
For 'models', this means:
file '/.../ModelsDir/vae/test_tag/ae.safetensors'
-> root_category='models', some_path='vae/test_tag/ae.safetensors'
-> name='ae.safetensors', tags=['models', 'vae', 'test_tag']
Raises:
ValueError: if the path does not belong to input, output, or configured model bases.
"""
root_category, some_path = get_relative_to_root_category_path_of_asset(file_path)
p = Path(some_path)
parent_parts = [part for part in p.parent.parts if part not in (".", "..", p.anchor)]
return p.name, list(dict.fromkeys(normalize_tags([root_category, *parent_parts])))
def normalize_tags(tags: list[str] | None) -> list[str]:
"""
Normalize a list of tags by:
- Stripping whitespace and converting to lowercase.
- Removing duplicates.
"""
return [t.strip().lower() for t in (tags or []) if (t or "").strip()]
def collect_models_files() -> list[str]:
out: list[str] = []
for folder_name, bases in get_studio_models_folders():
rel_files = folder_paths.get_filename_list(folder_name) or []
for rel_path in rel_files:
abs_path = folder_paths.get_full_path(folder_name, rel_path)
if not abs_path:
continue
abs_path = os.path.abspath(abs_path)
allowed = False
for b in bases:
base_abs = os.path.abspath(b)
with contextlib.suppress(Exception):
if os.path.commonpath([abs_path, base_abs]) == base_abs:
allowed = True
break
if allowed:
out.append(abs_path)
return out
def is_scalar(v):
if v is None:
return True
if isinstance(v, bool):
return True
if isinstance(v, (int, float, Decimal, str)):
return True
return False
def project_kv(key: str, value):
"""
Turn a metadata key/value into typed projection rows.
Returns list[dict] with keys:
key, ordinal, and one of val_str / val_num / val_bool / val_json (others None)
"""
rows: list[dict] = []
def _null_row(ordinal: int) -> dict:
return {
"key": key, "ordinal": ordinal,
"val_str": None, "val_num": None, "val_bool": None, "val_json": None
}
if value is None:
rows.append(_null_row(0))
return rows
if is_scalar(value):
if isinstance(value, bool):
rows.append({"key": key, "ordinal": 0, "val_bool": bool(value)})
elif isinstance(value, (int, float, Decimal)):
num = value if isinstance(value, Decimal) else Decimal(str(value))
rows.append({"key": key, "ordinal": 0, "val_num": num})
elif isinstance(value, str):
rows.append({"key": key, "ordinal": 0, "val_str": value})
else:
rows.append({"key": key, "ordinal": 0, "val_json": value})
return rows
if isinstance(value, list):
if all(is_scalar(x) for x in value):
for i, x in enumerate(value):
if x is None:
rows.append(_null_row(i))
elif isinstance(x, bool):
rows.append({"key": key, "ordinal": i, "val_bool": bool(x)})
elif isinstance(x, (int, float, Decimal)):
num = x if isinstance(x, Decimal) else Decimal(str(x))
rows.append({"key": key, "ordinal": i, "val_num": num})
elif isinstance(x, str):
rows.append({"key": key, "ordinal": i, "val_str": x})
else:
rows.append({"key": key, "ordinal": i, "val_json": x})
return rows
for i, x in enumerate(value):
rows.append({"key": key, "ordinal": i, "val_json": x})
return rows
rows.append({"key": key, "ordinal": 0, "val_json": value})
return rows
-516
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@@ -1,516 +0,0 @@
import os
import mimetypes
import contextlib
from typing import Sequence
from app.database.db import create_session
from app.assets.api import schemas_out, schemas_in
from app.assets.database.queries import (
asset_exists_by_hash,
asset_info_exists_for_asset_id,
get_asset_by_hash,
get_asset_info_by_id,
fetch_asset_info_asset_and_tags,
fetch_asset_info_and_asset,
create_asset_info_for_existing_asset,
touch_asset_info_by_id,
update_asset_info_full,
delete_asset_info_by_id,
list_cache_states_by_asset_id,
list_asset_infos_page,
list_tags_with_usage,
get_asset_tags,
add_tags_to_asset_info,
remove_tags_from_asset_info,
pick_best_live_path,
ingest_fs_asset,
set_asset_info_preview,
)
from app.assets.helpers import resolve_destination_from_tags, ensure_within_base
from app.assets.database.models import Asset
def _safe_sort_field(requested: str | None) -> str:
if not requested:
return "created_at"
v = requested.lower()
if v in {"name", "created_at", "updated_at", "size", "last_access_time"}:
return v
return "created_at"
def _get_size_mtime_ns(path: str) -> tuple[int, int]:
st = os.stat(path, follow_symlinks=True)
return st.st_size, getattr(st, "st_mtime_ns", int(st.st_mtime * 1_000_000_000))
def _safe_filename(name: str | None, fallback: str) -> str:
n = os.path.basename((name or "").strip() or fallback)
if n:
return n
return fallback
def asset_exists(*, asset_hash: str) -> bool:
"""
Check if an asset with a given hash exists in database.
"""
with create_session() as session:
return asset_exists_by_hash(session, asset_hash=asset_hash)
def list_assets(
*,
include_tags: Sequence[str] | None = None,
exclude_tags: Sequence[str] | None = None,
name_contains: str | None = None,
metadata_filter: dict | None = None,
limit: int = 20,
offset: int = 0,
sort: str = "created_at",
order: str = "desc",
owner_id: str = "",
) -> schemas_out.AssetsList:
sort = _safe_sort_field(sort)
order = "desc" if (order or "desc").lower() not in {"asc", "desc"} else order.lower()
with create_session() as session:
infos, tag_map, total = list_asset_infos_page(
session,
owner_id=owner_id,
include_tags=include_tags,
exclude_tags=exclude_tags,
name_contains=name_contains,
metadata_filter=metadata_filter,
limit=limit,
offset=offset,
sort=sort,
order=order,
)
summaries: list[schemas_out.AssetSummary] = []
for info in infos:
asset = info.asset
tags = tag_map.get(info.id, [])
summaries.append(
schemas_out.AssetSummary(
id=info.id,
name=info.name,
asset_hash=asset.hash if asset else None,
size=int(asset.size_bytes) if asset else None,
mime_type=asset.mime_type if asset else None,
tags=tags,
created_at=info.created_at,
updated_at=info.updated_at,
last_access_time=info.last_access_time,
)
)
return schemas_out.AssetsList(
assets=summaries,
total=total,
has_more=(offset + len(summaries)) < total,
)
def get_asset(
*,
asset_info_id: str,
owner_id: str = "",
) -> schemas_out.AssetDetail:
with create_session() as session:
res = fetch_asset_info_asset_and_tags(session, asset_info_id=asset_info_id, owner_id=owner_id)
if not res:
raise ValueError(f"AssetInfo {asset_info_id} not found")
info, asset, tag_names = res
preview_id = info.preview_id
return schemas_out.AssetDetail(
id=info.id,
name=info.name,
asset_hash=asset.hash if asset else None,
size=int(asset.size_bytes) if asset and asset.size_bytes is not None else None,
mime_type=asset.mime_type if asset else None,
tags=tag_names,
user_metadata=info.user_metadata or {},
preview_id=preview_id,
created_at=info.created_at,
last_access_time=info.last_access_time,
)
def resolve_asset_content_for_download(
*,
asset_info_id: str,
owner_id: str = "",
) -> tuple[str, str, str]:
with create_session() as session:
pair = fetch_asset_info_and_asset(session, asset_info_id=asset_info_id, owner_id=owner_id)
if not pair:
raise ValueError(f"AssetInfo {asset_info_id} not found")
info, asset = pair
states = list_cache_states_by_asset_id(session, asset_id=asset.id)
abs_path = pick_best_live_path(states)
if not abs_path:
raise FileNotFoundError
touch_asset_info_by_id(session, asset_info_id=asset_info_id)
session.commit()
ctype = asset.mime_type or mimetypes.guess_type(info.name or abs_path)[0] or "application/octet-stream"
download_name = info.name or os.path.basename(abs_path)
return abs_path, ctype, download_name
def upload_asset_from_temp_path(
spec: schemas_in.UploadAssetSpec,
*,
temp_path: str,
client_filename: str | None = None,
owner_id: str = "",
expected_asset_hash: str | None = None,
) -> schemas_out.AssetCreated:
"""
Create new asset or update existing asset from a temporary file path.
"""
try:
# NOTE: blake3 is not required right now, so this will fail if blake3 is not installed in local environment
import app.assets.hashing as hashing
digest = hashing.blake3_hash(temp_path)
except Exception as e:
raise RuntimeError(f"failed to hash uploaded file: {e}")
asset_hash = "blake3:" + digest
if expected_asset_hash and asset_hash != expected_asset_hash.strip().lower():
raise ValueError("HASH_MISMATCH")
with create_session() as session:
existing = get_asset_by_hash(session, asset_hash=asset_hash)
if existing is not None:
with contextlib.suppress(Exception):
if temp_path and os.path.exists(temp_path):
os.remove(temp_path)
display_name = _safe_filename(spec.name or (client_filename or ""), fallback=digest)
info = create_asset_info_for_existing_asset(
session,
asset_hash=asset_hash,
name=display_name,
user_metadata=spec.user_metadata or {},
tags=spec.tags or [],
tag_origin="manual",
owner_id=owner_id,
)
tag_names = get_asset_tags(session, asset_info_id=info.id)
session.commit()
return schemas_out.AssetCreated(
id=info.id,
name=info.name,
asset_hash=existing.hash,
size=int(existing.size_bytes) if existing.size_bytes is not None else None,
mime_type=existing.mime_type,
tags=tag_names,
user_metadata=info.user_metadata or {},
preview_id=info.preview_id,
created_at=info.created_at,
last_access_time=info.last_access_time,
created_new=False,
)
base_dir, subdirs = resolve_destination_from_tags(spec.tags)
dest_dir = os.path.join(base_dir, *subdirs) if subdirs else base_dir
os.makedirs(dest_dir, exist_ok=True)
src_for_ext = (client_filename or spec.name or "").strip()
_ext = os.path.splitext(os.path.basename(src_for_ext))[1] if src_for_ext else ""
ext = _ext if 0 < len(_ext) <= 16 else ""
hashed_basename = f"{digest}{ext}"
dest_abs = os.path.abspath(os.path.join(dest_dir, hashed_basename))
ensure_within_base(dest_abs, base_dir)
content_type = (
mimetypes.guess_type(os.path.basename(src_for_ext), strict=False)[0]
or mimetypes.guess_type(hashed_basename, strict=False)[0]
or "application/octet-stream"
)
try:
os.replace(temp_path, dest_abs)
except Exception as e:
raise RuntimeError(f"failed to move uploaded file into place: {e}")
try:
size_bytes, mtime_ns = _get_size_mtime_ns(dest_abs)
except OSError as e:
raise RuntimeError(f"failed to stat destination file: {e}")
with create_session() as session:
result = ingest_fs_asset(
session,
asset_hash=asset_hash,
abs_path=dest_abs,
size_bytes=size_bytes,
mtime_ns=mtime_ns,
mime_type=content_type,
info_name=_safe_filename(spec.name or (client_filename or ""), fallback=digest),
owner_id=owner_id,
preview_id=None,
user_metadata=spec.user_metadata or {},
tags=spec.tags,
tag_origin="manual",
require_existing_tags=False,
)
info_id = result["asset_info_id"]
if not info_id:
raise RuntimeError("failed to create asset metadata")
pair = fetch_asset_info_and_asset(session, asset_info_id=info_id, owner_id=owner_id)
if not pair:
raise RuntimeError("inconsistent DB state after ingest")
info, asset = pair
tag_names = get_asset_tags(session, asset_info_id=info.id)
created_result = schemas_out.AssetCreated(
id=info.id,
name=info.name,
asset_hash=asset.hash,
size=int(asset.size_bytes),
mime_type=asset.mime_type,
tags=tag_names,
user_metadata=info.user_metadata or {},
preview_id=info.preview_id,
created_at=info.created_at,
last_access_time=info.last_access_time,
created_new=result["asset_created"],
)
session.commit()
return created_result
def update_asset(
*,
asset_info_id: str,
name: str | None = None,
tags: list[str] | None = None,
user_metadata: dict | None = None,
owner_id: str = "",
) -> schemas_out.AssetUpdated:
with create_session() as session:
info_row = get_asset_info_by_id(session, asset_info_id=asset_info_id)
if not info_row:
raise ValueError(f"AssetInfo {asset_info_id} not found")
if info_row.owner_id and info_row.owner_id != owner_id:
raise PermissionError("not owner")
info = update_asset_info_full(
session,
asset_info_id=asset_info_id,
name=name,
tags=tags,
user_metadata=user_metadata,
tag_origin="manual",
asset_info_row=info_row,
)
tag_names = get_asset_tags(session, asset_info_id=asset_info_id)
result = schemas_out.AssetUpdated(
id=info.id,
name=info.name,
asset_hash=info.asset.hash if info.asset else None,
tags=tag_names,
user_metadata=info.user_metadata or {},
updated_at=info.updated_at,
)
session.commit()
return result
def set_asset_preview(
*,
asset_info_id: str,
preview_asset_id: str | None = None,
owner_id: str = "",
) -> schemas_out.AssetDetail:
with create_session() as session:
info_row = get_asset_info_by_id(session, asset_info_id=asset_info_id)
if not info_row:
raise ValueError(f"AssetInfo {asset_info_id} not found")
if info_row.owner_id and info_row.owner_id != owner_id:
raise PermissionError("not owner")
set_asset_info_preview(
session,
asset_info_id=asset_info_id,
preview_asset_id=preview_asset_id,
)
res = fetch_asset_info_asset_and_tags(session, asset_info_id=asset_info_id, owner_id=owner_id)
if not res:
raise RuntimeError("State changed during preview update")
info, asset, tags = res
result = schemas_out.AssetDetail(
id=info.id,
name=info.name,
asset_hash=asset.hash if asset else None,
size=int(asset.size_bytes) if asset and asset.size_bytes is not None else None,
mime_type=asset.mime_type if asset else None,
tags=tags,
user_metadata=info.user_metadata or {},
preview_id=info.preview_id,
created_at=info.created_at,
last_access_time=info.last_access_time,
)
session.commit()
return result
def delete_asset_reference(*, asset_info_id: str, owner_id: str, delete_content_if_orphan: bool = True) -> bool:
with create_session() as session:
info_row = get_asset_info_by_id(session, asset_info_id=asset_info_id)
asset_id = info_row.asset_id if info_row else None
deleted = delete_asset_info_by_id(session, asset_info_id=asset_info_id, owner_id=owner_id)
if not deleted:
session.commit()
return False
if not delete_content_if_orphan or not asset_id:
session.commit()
return True
still_exists = asset_info_exists_for_asset_id(session, asset_id=asset_id)
if still_exists:
session.commit()
return True
states = list_cache_states_by_asset_id(session, asset_id=asset_id)
file_paths = [s.file_path for s in (states or []) if getattr(s, "file_path", None)]
asset_row = session.get(Asset, asset_id)
if asset_row is not None:
session.delete(asset_row)
session.commit()
for p in file_paths:
with contextlib.suppress(Exception):
if p and os.path.isfile(p):
os.remove(p)
return True
def create_asset_from_hash(
*,
hash_str: str,
name: str,
tags: list[str] | None = None,
user_metadata: dict | None = None,
owner_id: str = "",
) -> schemas_out.AssetCreated | None:
canonical = hash_str.strip().lower()
with create_session() as session:
asset = get_asset_by_hash(session, asset_hash=canonical)
if not asset:
return None
info = create_asset_info_for_existing_asset(
session,
asset_hash=canonical,
name=_safe_filename(name, fallback=canonical.split(":", 1)[1]),
user_metadata=user_metadata or {},
tags=tags or [],
tag_origin="manual",
owner_id=owner_id,
)
tag_names = get_asset_tags(session, asset_info_id=info.id)
result = schemas_out.AssetCreated(
id=info.id,
name=info.name,
asset_hash=asset.hash,
size=int(asset.size_bytes),
mime_type=asset.mime_type,
tags=tag_names,
user_metadata=info.user_metadata or {},
preview_id=info.preview_id,
created_at=info.created_at,
last_access_time=info.last_access_time,
created_new=False,
)
session.commit()
return result
def add_tags_to_asset(
*,
asset_info_id: str,
tags: list[str],
origin: str = "manual",
owner_id: str = "",
) -> schemas_out.TagsAdd:
with create_session() as session:
info_row = get_asset_info_by_id(session, asset_info_id=asset_info_id)
if not info_row:
raise ValueError(f"AssetInfo {asset_info_id} not found")
if info_row.owner_id and info_row.owner_id != owner_id:
raise PermissionError("not owner")
data = add_tags_to_asset_info(
session,
asset_info_id=asset_info_id,
tags=tags,
origin=origin,
create_if_missing=True,
asset_info_row=info_row,
)
session.commit()
return schemas_out.TagsAdd(**data)
def remove_tags_from_asset(
*,
asset_info_id: str,
tags: list[str],
owner_id: str = "",
) -> schemas_out.TagsRemove:
with create_session() as session:
info_row = get_asset_info_by_id(session, asset_info_id=asset_info_id)
if not info_row:
raise ValueError(f"AssetInfo {asset_info_id} not found")
if info_row.owner_id and info_row.owner_id != owner_id:
raise PermissionError("not owner")
data = remove_tags_from_asset_info(
session,
asset_info_id=asset_info_id,
tags=tags,
)
session.commit()
return schemas_out.TagsRemove(**data)
def list_tags(
prefix: str | None = None,
limit: int = 100,
offset: int = 0,
order: str = "count_desc",
include_zero: bool = True,
owner_id: str = "",
) -> schemas_out.TagsList:
limit = max(1, min(1000, limit))
offset = max(0, offset)
with create_session() as session:
rows, total = list_tags_with_usage(
session,
prefix=prefix,
limit=limit,
offset=offset,
include_zero=include_zero,
order=order,
owner_id=owner_id,
)
tags = [schemas_out.TagUsage(name=name, count=count, type=tag_type) for (name, tag_type, count) in rows]
return schemas_out.TagsList(tags=tags, total=total, has_more=(offset + len(tags)) < total)
-263
View File
@@ -1,263 +0,0 @@
import contextlib
import time
import logging
import os
import sqlalchemy
import folder_paths
from app.database.db import create_session, dependencies_available
from app.assets.helpers import (
collect_models_files, compute_relative_filename, fast_asset_file_check, get_name_and_tags_from_asset_path,
list_tree,prefixes_for_root, escape_like_prefix,
RootType
)
from app.assets.database.tags import add_missing_tag_for_asset_id, ensure_tags_exist, remove_missing_tag_for_asset_id
from app.assets.database.bulk_ops import seed_from_paths_batch
from app.assets.database.models import Asset, AssetCacheState, AssetInfo
def seed_assets(roots: tuple[RootType, ...], enable_logging: bool = False) -> None:
"""
Scan the given roots and seed the assets into the database.
"""
if not dependencies_available():
if enable_logging:
logging.warning("Database dependencies not available, skipping assets scan")
return
t_start = time.perf_counter()
created = 0
skipped_existing = 0
orphans_pruned = 0
paths: list[str] = []
try:
existing_paths: set[str] = set()
for r in roots:
try:
survivors: set[str] = _fast_db_consistency_pass(r, collect_existing_paths=True, update_missing_tags=True)
if survivors:
existing_paths.update(survivors)
except Exception as e:
logging.exception("fast DB scan failed for %s: %s", r, e)
try:
orphans_pruned = _prune_orphaned_assets(roots)
except Exception as e:
logging.exception("orphan pruning failed: %s", e)
if "models" in roots:
paths.extend(collect_models_files())
if "input" in roots:
paths.extend(list_tree(folder_paths.get_input_directory()))
if "output" in roots:
paths.extend(list_tree(folder_paths.get_output_directory()))
specs: list[dict] = []
tag_pool: set[str] = set()
for p in paths:
abs_p = os.path.abspath(p)
if abs_p in existing_paths:
skipped_existing += 1
continue
try:
stat_p = os.stat(abs_p, follow_symlinks=False)
except OSError:
continue
# skip empty files
if not stat_p.st_size:
continue
name, tags = get_name_and_tags_from_asset_path(abs_p)
specs.append(
{
"abs_path": abs_p,
"size_bytes": stat_p.st_size,
"mtime_ns": getattr(stat_p, "st_mtime_ns", int(stat_p.st_mtime * 1_000_000_000)),
"info_name": name,
"tags": tags,
"fname": compute_relative_filename(abs_p),
}
)
for t in tags:
tag_pool.add(t)
# if no file specs, nothing to do
if not specs:
return
with create_session() as sess:
if tag_pool:
ensure_tags_exist(sess, tag_pool, tag_type="user")
result = seed_from_paths_batch(sess, specs=specs, owner_id="")
created += result["inserted_infos"]
sess.commit()
finally:
if enable_logging:
logging.info(
"Assets scan(roots=%s) completed in %.3fs (created=%d, skipped_existing=%d, orphans_pruned=%d, total_seen=%d)",
roots,
time.perf_counter() - t_start,
created,
skipped_existing,
orphans_pruned,
len(paths),
)
def _prune_orphaned_assets(roots: tuple[RootType, ...]) -> int:
"""Prune cache states outside configured prefixes, then delete orphaned seed assets."""
all_prefixes = [os.path.abspath(p) for r in roots for p in prefixes_for_root(r)]
if not all_prefixes:
return 0
def make_prefix_condition(prefix: str):
base = prefix if prefix.endswith(os.sep) else prefix + os.sep
escaped, esc = escape_like_prefix(base)
return AssetCacheState.file_path.like(escaped + "%", escape=esc)
matches_valid_prefix = sqlalchemy.or_(*[make_prefix_condition(p) for p in all_prefixes])
orphan_subq = (
sqlalchemy.select(Asset.id)
.outerjoin(AssetCacheState, AssetCacheState.asset_id == Asset.id)
.where(Asset.hash.is_(None), AssetCacheState.id.is_(None))
).scalar_subquery()
with create_session() as sess:
sess.execute(sqlalchemy.delete(AssetCacheState).where(~matches_valid_prefix))
sess.execute(sqlalchemy.delete(AssetInfo).where(AssetInfo.asset_id.in_(orphan_subq)))
result = sess.execute(sqlalchemy.delete(Asset).where(Asset.id.in_(orphan_subq)))
sess.commit()
return result.rowcount
def _fast_db_consistency_pass(
root: RootType,
*,
collect_existing_paths: bool = False,
update_missing_tags: bool = False,
) -> set[str] | None:
"""Fast DB+FS pass for a root:
- Toggle needs_verify per state using fast check
- For hashed assets with at least one fast-ok state in this root: delete stale missing states
- For seed assets with all states missing: delete Asset and its AssetInfos
- Optionally add/remove 'missing' tags based on fast-ok in this root
- Optionally return surviving absolute paths
"""
prefixes = prefixes_for_root(root)
if not prefixes:
return set() if collect_existing_paths else None
conds = []
for p in prefixes:
base = os.path.abspath(p)
if not base.endswith(os.sep):
base += os.sep
escaped, esc = escape_like_prefix(base)
conds.append(AssetCacheState.file_path.like(escaped + "%", escape=esc))
with create_session() as sess:
rows = (
sess.execute(
sqlalchemy.select(
AssetCacheState.id,
AssetCacheState.file_path,
AssetCacheState.mtime_ns,
AssetCacheState.needs_verify,
AssetCacheState.asset_id,
Asset.hash,
Asset.size_bytes,
)
.join(Asset, Asset.id == AssetCacheState.asset_id)
.where(sqlalchemy.or_(*conds))
.order_by(AssetCacheState.asset_id.asc(), AssetCacheState.id.asc())
)
).all()
by_asset: dict[str, dict] = {}
for sid, fp, mtime_db, needs_verify, aid, a_hash, a_size in rows:
acc = by_asset.get(aid)
if acc is None:
acc = {"hash": a_hash, "size_db": int(a_size or 0), "states": []}
by_asset[aid] = acc
fast_ok = False
try:
exists = True
fast_ok = fast_asset_file_check(
mtime_db=mtime_db,
size_db=acc["size_db"],
stat_result=os.stat(fp, follow_symlinks=True),
)
except FileNotFoundError:
exists = False
except OSError:
exists = False
acc["states"].append({
"sid": sid,
"fp": fp,
"exists": exists,
"fast_ok": fast_ok,
"needs_verify": bool(needs_verify),
})
to_set_verify: list[int] = []
to_clear_verify: list[int] = []
stale_state_ids: list[int] = []
survivors: set[str] = set()
for aid, acc in by_asset.items():
a_hash = acc["hash"]
states = acc["states"]
any_fast_ok = any(s["fast_ok"] for s in states)
all_missing = all(not s["exists"] for s in states)
for s in states:
if not s["exists"]:
continue
if s["fast_ok"] and s["needs_verify"]:
to_clear_verify.append(s["sid"])
if not s["fast_ok"] and not s["needs_verify"]:
to_set_verify.append(s["sid"])
if a_hash is None:
if states and all_missing: # remove seed Asset completely, if no valid AssetCache exists
sess.execute(sqlalchemy.delete(AssetInfo).where(AssetInfo.asset_id == aid))
asset = sess.get(Asset, aid)
if asset:
sess.delete(asset)
else:
for s in states:
if s["exists"]:
survivors.add(os.path.abspath(s["fp"]))
continue
if any_fast_ok: # if Asset has at least one valid AssetCache record, remove any invalid AssetCache records
for s in states:
if not s["exists"]:
stale_state_ids.append(s["sid"])
if update_missing_tags:
with contextlib.suppress(Exception):
remove_missing_tag_for_asset_id(sess, asset_id=aid)
elif update_missing_tags:
with contextlib.suppress(Exception):
add_missing_tag_for_asset_id(sess, asset_id=aid, origin="automatic")
for s in states:
if s["exists"]:
survivors.add(os.path.abspath(s["fp"]))
if stale_state_ids:
sess.execute(sqlalchemy.delete(AssetCacheState).where(AssetCacheState.id.in_(stale_state_ids)))
if to_set_verify:
sess.execute(
sqlalchemy.update(AssetCacheState)
.where(AssetCacheState.id.in_(to_set_verify))
.values(needs_verify=True)
)
if to_clear_verify:
sess.execute(
sqlalchemy.update(AssetCacheState)
.where(AssetCacheState.id.in_(to_clear_verify))
.values(needs_verify=False)
)
sess.commit()
return survivors if collect_existing_paths else None
+15 -126
View File
@@ -4,142 +4,31 @@ import os
import folder_paths
import glob
from aiohttp import web
import json
import logging
from functools import lru_cache
from utils.json_util import merge_json_recursive
# Extra locale files to load into main.json
EXTRA_LOCALE_FILES = [
"nodeDefs.json",
"commands.json",
"settings.json",
]
def safe_load_json_file(file_path: str) -> dict:
if not os.path.exists(file_path):
return {}
try:
with open(file_path, "r", encoding="utf-8") as f:
return json.load(f)
except json.JSONDecodeError:
logging.error(f"Error loading {file_path}")
return {}
class CustomNodeManager:
@lru_cache(maxsize=1)
def build_translations(self):
"""Load all custom nodes translations during initialization. Translations are
expected to be loaded from `locales/` folder.
The folder structure is expected to be the following:
- custom_nodes/
- custom_node_1/
- locales/
- en/
- main.json
- commands.json
- settings.json
returned translations are expected to be in the following format:
{
"en": {
"nodeDefs": {...},
"commands": {...},
"settings": {...},
...{other main.json keys}
}
}
"""
translations = {}
for folder in folder_paths.get_folder_paths("custom_nodes"):
# Sort glob results for deterministic ordering
for custom_node_dir in sorted(glob.glob(os.path.join(folder, "*/"))):
locales_dir = os.path.join(custom_node_dir, "locales")
if not os.path.exists(locales_dir):
continue
for lang_dir in glob.glob(os.path.join(locales_dir, "*/")):
lang_code = os.path.basename(os.path.dirname(lang_dir))
if lang_code not in translations:
translations[lang_code] = {}
# Load main.json
main_file = os.path.join(lang_dir, "main.json")
node_translations = safe_load_json_file(main_file)
# Load extra locale files
for extra_file in EXTRA_LOCALE_FILES:
extra_file_path = os.path.join(lang_dir, extra_file)
key = extra_file.split(".")[0]
json_data = safe_load_json_file(extra_file_path)
if json_data:
node_translations[key] = json_data
if node_translations:
translations[lang_code] = merge_json_recursive(
translations[lang_code], node_translations
)
return translations
"""
Placeholder to refactor the custom node management features from ComfyUI-Manager.
Currently it only contains the custom workflow templates feature.
"""
def add_routes(self, routes, webapp, loadedModules):
example_workflow_folder_names = ["example_workflows", "example", "examples", "workflow", "workflows"]
@routes.get("/workflow_templates")
async def get_workflow_templates(request):
"""Returns a web response that contains the map of custom_nodes names and their associated workflow templates. The ones without templates are omitted."""
files = []
for folder in folder_paths.get_folder_paths("custom_nodes"):
for folder_name in example_workflow_folder_names:
pattern = os.path.join(folder, f"*/{folder_name}/*.json")
matched_files = glob.glob(pattern)
files.extend(matched_files)
workflow_templates_dict = (
{}
) # custom_nodes folder name -> example workflow names
files = [
file
for folder in folder_paths.get_folder_paths("custom_nodes")
for file in glob.glob(os.path.join(folder, '*/example_workflows/*.json'))
]
workflow_templates_dict = {} # custom_nodes folder name -> example workflow names
for file in files:
custom_nodes_name = os.path.basename(
os.path.dirname(os.path.dirname(file))
)
custom_nodes_name = os.path.basename(os.path.dirname(os.path.dirname(file)))
workflow_name = os.path.splitext(os.path.basename(file))[0]
workflow_templates_dict.setdefault(custom_nodes_name, []).append(
workflow_name
)
workflow_templates_dict.setdefault(custom_nodes_name, []).append(workflow_name)
return web.json_response(workflow_templates_dict)
# Serve workflow templates from custom nodes.
for module_name, module_dir in loadedModules:
for folder_name in example_workflow_folder_names:
workflows_dir = os.path.join(module_dir, folder_name)
if os.path.exists(workflows_dir):
if folder_name != "example_workflows":
logging.debug(
"Found example workflow folder '%s' for custom node '%s', consider renaming it to 'example_workflows'",
folder_name, module_name)
webapp.add_routes(
[
web.static(
"/api/workflow_templates/" + module_name, workflows_dir
)
]
)
@routes.get("/i18n")
async def get_i18n(request):
"""Returns translations from all custom nodes' locales folders."""
return web.json_response(self.build_translations())
workflows_dir = os.path.join(module_dir, 'example_workflows')
if os.path.exists(workflows_dir):
webapp.add_routes([web.static('/api/workflow_templates/' + module_name, workflows_dir)])
+49 -43
View File
@@ -1,48 +1,30 @@
import logging
import os
import shutil
from app.logger import log_startup_warning
from utils.install_util import get_missing_requirements_message
from studio.cli_args import args
_DB_AVAILABLE = False
Session = None
import sys
from app.database.models import Tag
from comfy.cli_args import args
try:
from alembic import command
from alembic.config import Config
from alembic.runtime.migration import MigrationContext
from alembic.script import ScriptDirectory
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
_DB_AVAILABLE = True
import alembic
import sqlalchemy
except ImportError as e:
log_startup_warning(
f"""
------------------------------------------------------------------------
Error importing dependencies: {e}
{get_missing_requirements_message()}
This error is happening because Hanzo Studio now uses a local sqlite database.
------------------------------------------------------------------------
""".strip()
req_path = os.path.abspath(
os.path.join(os.path.dirname(__file__), "../..", "requirements.txt")
)
logging.error(
f"\n\n********** ERROR ***********\n\nRequirements are not installed ({e}). Please install the requirements.txt file by running:\n{sys.executable} -s -m pip install -r {req_path}\n\nIf you are on the portable package you can run: update\\update_comfyui.bat to solve this problem\n********** ERROR **********\n"
)
exit(-1)
from alembic import command
from alembic.config import Config
from alembic.runtime.migration import MigrationContext
from alembic.script import ScriptDirectory
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
def dependencies_available():
"""
Temporary function to check if the dependencies are available
"""
return _DB_AVAILABLE
def can_create_session():
"""
Temporary function to check if the database is available to create a session
During initial release there may be environmental issues (or missing dependencies) that prevent the database from being created
"""
return dependencies_available() and Session is not None
Session = None
def get_alembic_config():
@@ -68,8 +50,6 @@ def get_db_path():
def init_db():
db_url = args.database_url
logging.debug(f"Database URL: {db_url}")
db_path = get_db_path()
db_exists = os.path.exists(db_path)
config = get_alembic_config()
@@ -83,12 +63,11 @@ def init_db():
script = ScriptDirectory.from_config(config)
target_rev = script.get_current_head()
if target_rev is None:
logging.warning("No target revision found.")
elif current_rev != target_rev:
if current_rev != target_rev:
# Backup the database pre upgrade
db_path = get_db_path()
backup_path = db_path + ".bkp"
if db_exists:
if os.path.exists(db_path):
shutil.copy(db_path, backup_path)
else:
backup_path = None
@@ -101,12 +80,39 @@ def init_db():
# Restore the database from backup if upgrade fails
shutil.copy(backup_path, db_path)
os.remove(backup_path)
logging.exception("Error upgrading database: ")
logging.error(f"Error upgrading database: {e}")
raise e
global Session
Session = sessionmaker(bind=engine)
if not current_rev:
# Init db, populate models
from app.model_processor import model_processor
session = create_session()
model_processor.populate_models(session)
# populate tags
tags = (
"character",
"style",
"concept",
"clothing",
"pose",
"background",
"vehicle",
"object",
"animal",
"action",
)
for tag in tags:
session.add(Tag(name=tag))
session.commit()
def can_create_session():
return Session is not None
def create_session():
return Session()
+72 -17
View File
@@ -1,21 +1,76 @@
from typing import Any
from datetime import datetime
from sqlalchemy.orm import DeclarativeBase
from sqlalchemy import (
Column,
Integer,
Text,
DateTime,
Table,
ForeignKeyConstraint,
)
from sqlalchemy.orm import relationship, declarative_base
from sqlalchemy.sql import func
class Base(DeclarativeBase):
pass
Base = declarative_base()
def to_dict(obj: Any, include_none: bool = False) -> dict[str, Any]:
def to_dict(obj):
fields = obj.__table__.columns.keys()
out: dict[str, Any] = {}
for field in fields:
val = getattr(obj, field)
if val is None and not include_none:
continue
if isinstance(val, datetime):
out[field] = val.isoformat()
else:
out[field] = val
return out
return {
field: (val.to_dict() if hasattr(val, "to_dict") else val)
for field in fields
if (val := getattr(obj, field))
}
# TODO: Define models here
ModelTag = Table(
"model_tag",
Base.metadata,
Column(
"model_type",
Text,
primary_key=True,
),
Column(
"model_path",
Text,
primary_key=True,
),
Column("tag_id", Integer, primary_key=True),
ForeignKeyConstraint(
["model_type", "model_path"], ["model.type", "model.path"], ondelete="CASCADE"
),
ForeignKeyConstraint(["tag_id"], ["tag.id"], ondelete="CASCADE"),
)
class Model(Base):
__tablename__ = "model"
type = Column(Text, primary_key=True)
path = Column(Text, primary_key=True)
title = Column(Text)
description = Column(Text)
architecture = Column(Text)
hash = Column(Text)
source_url = Column(Text)
date_added = Column(DateTime, server_default=func.now())
# Relationship with tags
tags = relationship("Tag", secondary=ModelTag, back_populates="models")
def to_dict(self):
dict = to_dict(self)
dict["tags"] = [tag.to_dict() for tag in self.tags]
return dict
class Tag(Base):
__tablename__ = "tag"
id = Column(Integer, primary_key=True, autoincrement=True)
name = Column(Text, nullable=False, unique=True)
# Relationship with models
models = relationship("Model", secondary=ModelTag, back_populates="tags")
def to_dict(self):
return to_dict(self)
+10 -263
View File
@@ -3,93 +3,16 @@ import argparse
import logging
import os
import re
import sys
import tempfile
import zipfile
import importlib
from dataclasses import dataclass
from functools import cached_property
from pathlib import Path
from typing import Dict, TypedDict, Optional
from aiohttp import web
from importlib.metadata import version
from typing import TypedDict, Optional
import requests
from typing_extensions import NotRequired
from utils.install_util import get_missing_requirements_message, requirements_path
from studio.cli_args import DEFAULT_VERSION_STRING
import app.logger
def frontend_install_warning_message():
return f"""
{get_missing_requirements_message()}
This error is happening because the Hanzo Studio frontend is no longer shipped as part of the main repo but as a pip package instead.
""".strip()
def parse_version(version: str) -> tuple[int, int, int]:
return tuple(map(int, version.split(".")))
def is_valid_version(version: str) -> bool:
"""Validate if a string is a valid semantic version (X.Y.Z format)."""
pattern = r"^(\d+)\.(\d+)\.(\d+)$"
return bool(re.match(pattern, version))
def get_installed_frontend_version():
"""Get the currently installed frontend package version."""
frontend_version_str = version("comfyui-frontend-package")
return frontend_version_str
def get_required_frontend_version():
"""Get the required frontend version from requirements.txt."""
try:
with open(requirements_path, "r", encoding="utf-8") as f:
for line in f:
line = line.strip()
if line.startswith("comfyui-frontend-package=="):
version_str = line.split("==")[-1]
if not is_valid_version(version_str):
logging.error(f"Invalid version format in requirements.txt: {version_str}")
return None
return version_str
logging.error("comfyui-frontend-package not found in requirements.txt")
return None
except FileNotFoundError:
logging.error("requirements.txt not found. Cannot determine required frontend version.")
return None
except Exception as e:
logging.error(f"Error reading requirements.txt: {e}")
return None
def check_frontend_version():
"""Check if the frontend version is up to date."""
try:
frontend_version_str = get_installed_frontend_version()
frontend_version = parse_version(frontend_version_str)
required_frontend_str = get_required_frontend_version()
required_frontend = parse_version(required_frontend_str)
if frontend_version < required_frontend:
app.logger.log_startup_warning(
f"""
________________________________________________________________________
WARNING WARNING WARNING WARNING WARNING
Installed frontend version {".".join(map(str, frontend_version))} is lower than the recommended version {".".join(map(str, required_frontend))}.
{frontend_install_warning_message()}
________________________________________________________________________
""".strip()
)
else:
logging.info("Hanzo Studio frontend version: {}".format(frontend_version_str))
except Exception as e:
logging.error(f"Failed to check frontend version: {e}")
from comfy.cli_args import DEFAULT_VERSION_STRING
REQUEST_TIMEOUT = 10 # seconds
@@ -145,22 +68,9 @@ class FrontEndProvider:
response.raise_for_status() # Raises an HTTPError if the response was an error
return response.json()
@cached_property
def latest_prerelease(self) -> Release:
"""Get the latest pre-release version - even if it's older than the latest release"""
release = [release for release in self.all_releases if release["prerelease"]]
if not release:
raise ValueError("No pre-releases found")
# GitHub returns releases in reverse chronological order, so first is latest
return release[0]
def get_release(self, version: str) -> Release:
if version == "latest":
return self.latest_release
elif version == "prerelease":
return self.latest_prerelease
else:
for release in self.all_releases:
if release["tag_name"] in [version, f"v{version}"]:
@@ -199,146 +109,9 @@ def download_release_asset_zip(release: Release, destination_path: str) -> None:
class FrontendManager:
DEFAULT_FRONTEND_PATH = str(Path(__file__).parents[1] / "web")
CUSTOM_FRONTENDS_ROOT = str(Path(__file__).parents[1] / "web_custom_versions")
@classmethod
def get_required_frontend_version(cls) -> str:
"""Get the required frontend package version."""
return get_required_frontend_version()
@classmethod
def get_installed_templates_version(cls) -> str:
"""Get the currently installed workflow templates package version."""
try:
templates_version_str = version("comfyui-workflow-templates")
return templates_version_str
except Exception:
return None
@classmethod
def get_required_templates_version(cls) -> str:
"""Get the required workflow templates version from requirements.txt."""
try:
with open(requirements_path, "r", encoding="utf-8") as f:
for line in f:
line = line.strip()
if line.startswith("comfyui-workflow-templates=="):
version_str = line.split("==")[-1]
if not is_valid_version(version_str):
logging.error(f"Invalid templates version format in requirements.txt: {version_str}")
return None
return version_str
logging.error("comfyui-workflow-templates not found in requirements.txt")
return None
except FileNotFoundError:
logging.error("requirements.txt not found. Cannot determine required templates version.")
return None
except Exception as e:
logging.error(f"Error reading requirements.txt: {e}")
return None
@classmethod
def default_frontend_path(cls) -> str:
try:
import comfyui_frontend_package
return str(importlib.resources.files(comfyui_frontend_package) / "static")
except ImportError:
logging.error(
f"""
********** ERROR ***********
comfyui-frontend-package is not installed.
{frontend_install_warning_message()}
********** ERROR ***********
""".strip()
)
sys.exit(-1)
@classmethod
def template_asset_map(cls) -> Optional[Dict[str, str]]:
"""Return a mapping of template asset names to their absolute paths."""
try:
from comfyui_workflow_templates import (
get_asset_path,
iter_templates,
)
except ImportError:
logging.error(
f"""
********** ERROR ***********
comfyui-workflow-templates is not installed.
{frontend_install_warning_message()}
********** ERROR ***********
""".strip()
)
return None
try:
template_entries = list(iter_templates())
except Exception as exc:
logging.error(f"Failed to enumerate workflow templates: {exc}")
return None
asset_map: Dict[str, str] = {}
try:
for entry in template_entries:
for asset in entry.assets:
asset_map[asset.filename] = get_asset_path(
entry.template_id, asset.filename
)
except Exception as exc:
logging.error(f"Failed to resolve template asset paths: {exc}")
return None
if not asset_map:
logging.error("No workflow template assets found. Did the packages install correctly?")
return None
return asset_map
@classmethod
def legacy_templates_path(cls) -> Optional[str]:
"""Return the legacy templates directory shipped inside the meta package."""
try:
import comfyui_workflow_templates
return str(
importlib.resources.files(comfyui_workflow_templates) / "templates"
)
except ImportError:
logging.error(
f"""
********** ERROR ***********
comfyui-workflow-templates is not installed.
{frontend_install_warning_message()}
********** ERROR ***********
""".strip()
)
return None
@classmethod
def embedded_docs_path(cls) -> str:
"""Get the path to embedded documentation"""
try:
import comfyui_embedded_docs
return str(
importlib.resources.files(comfyui_embedded_docs) / "docs"
)
except ImportError:
logging.info("comfyui-embedded-docs package not found")
return None
@classmethod
def parse_version_string(cls, value: str) -> tuple[str, str, str]:
"""
@@ -351,7 +124,7 @@ comfyui-workflow-templates is not installed.
Raises:
argparse.ArgumentTypeError: If the version string is invalid.
"""
VERSION_PATTERN = r"^([a-zA-Z0-9][a-zA-Z0-9-]{0,38})/([a-zA-Z0-9_.-]+)@(v?\d+\.\d+\.\d+[-._a-zA-Z0-9]*|latest|prerelease)$"
VERSION_PATTERN = r"^([a-zA-Z0-9][a-zA-Z0-9-]{0,38})/([a-zA-Z0-9_.-]+)@(v?\d+\.\d+\.\d+|latest)$"
match_result = re.match(VERSION_PATTERN, value)
if match_result is None:
raise argparse.ArgumentTypeError(f"Invalid version string: {value}")
@@ -359,9 +132,7 @@ comfyui-workflow-templates is not installed.
return match_result.group(1), match_result.group(2), match_result.group(3)
@classmethod
def init_frontend_unsafe(
cls, version_string: str, provider: Optional[FrontEndProvider] = None
) -> str:
def init_frontend_unsafe(cls, version_string: str, provider: Optional[FrontEndProvider] = None) -> str:
"""
Initializes the frontend for the specified version.
@@ -377,26 +148,17 @@ comfyui-workflow-templates is not installed.
main error source might be request timeout or invalid URL.
"""
if version_string == DEFAULT_VERSION_STRING:
check_frontend_version()
return cls.default_frontend_path()
return cls.DEFAULT_FRONTEND_PATH
repo_owner, repo_name, version = cls.parse_version_string(version_string)
if version.startswith("v"):
expected_path = str(
Path(cls.CUSTOM_FRONTENDS_ROOT)
/ f"{repo_owner}_{repo_name}"
/ version.lstrip("v")
)
expected_path = str(Path(cls.CUSTOM_FRONTENDS_ROOT) / f"{repo_owner}_{repo_name}" / version.lstrip("v"))
if os.path.exists(expected_path):
logging.info(
f"Using existing copy of specific frontend version tag: {repo_owner}/{repo_name}@{version}"
)
logging.info(f"Using existing copy of specific frontend version tag: {repo_owner}/{repo_name}@{version}")
return expected_path
logging.info(
f"Initializing frontend: {repo_owner}/{repo_name}@{version}, requesting version details from GitHub..."
)
logging.info(f"Initializing frontend: {repo_owner}/{repo_name}@{version}, requesting version details from GitHub...")
provider = provider or FrontEndProvider(repo_owner, repo_name)
release = provider.get_release(version)
@@ -439,19 +201,4 @@ comfyui-workflow-templates is not installed.
except Exception as e:
logging.error("Failed to initialize frontend: %s", e)
logging.info("Falling back to the default frontend.")
check_frontend_version()
return cls.default_frontend_path()
@classmethod
def template_asset_handler(cls):
assets = cls.template_asset_map()
if not assets:
return None
async def serve_template(request: web.Request) -> web.StreamResponse:
rel_path = request.match_info.get("path", "")
target = assets.get(rel_path)
if target is None:
raise web.HTTPNotFound()
return web.FileResponse(target)
return serve_template
return cls.DEFAULT_FRONTEND_PATH
-14
View File
@@ -82,17 +82,3 @@ def setup_logger(log_level: str = 'INFO', capacity: int = 300, use_stdout: bool
logger.addHandler(stdout_handler)
logger.addHandler(stream_handler)
STARTUP_WARNINGS = []
def log_startup_warning(msg):
logging.warning(msg)
STARTUP_WARNINGS.append(msg)
def print_startup_warnings():
for s in STARTUP_WARNINGS:
logging.warning(s)
STARTUP_WARNINGS.clear()
+201 -58
View File
@@ -1,19 +1,30 @@
from __future__ import annotations
import os
import base64
import json
import time
import logging
from app.database.db import create_session
import folder_paths
import glob
import studio.utils
from aiohttp import web
from PIL import Image
from io import BytesIO
from folder_paths import map_legacy, filter_files_extensions, filter_files_content_types
from folder_paths import map_legacy, filter_files_extensions, get_full_path
from app.database.models import Tag, Model
from app.model_processor import get_model_previews, model_processor
from utils.web import dumps
from sqlalchemy.orm import joinedload
import sqlalchemy.exc
def bad_request(message: str):
return web.json_response({"error": message}, status=400)
def missing_field(field: str):
return bad_request(f"{field} is required")
def not_found(message: str):
return web.json_response({"error": message + " not found"}, status=404)
class ModelFileManager:
def __init__(self) -> None:
self.cache: dict[str, tuple[list[dict], dict[str, float], float]] = {}
@@ -44,7 +55,7 @@ class ModelFileManager:
@routes.get("/experiment/models/{folder}")
async def get_all_models(request):
folder = request.match_info.get("folder", None)
if folder not in folder_paths.folder_names_and_paths:
if not folder in folder_paths.folder_names_and_paths:
return web.Response(status=404)
files = self.get_model_file_list(folder)
return web.json_response(files)
@@ -55,14 +66,14 @@ class ModelFileManager:
path_index = int(request.match_info.get("path_index", None))
filename = request.match_info.get("filename", None)
if folder_name not in folder_paths.folder_names_and_paths:
if not folder_name in folder_paths.folder_names_and_paths:
return web.Response(status=404)
folders = folder_paths.folder_names_and_paths[folder_name]
folder = folders[0][path_index]
full_filename = os.path.join(folder, filename)
previews = self.get_model_previews(full_filename)
previews = get_model_previews(full_filename)
default_preview = previews[0] if len(previews) > 0 else None
if default_preview is None or (isinstance(default_preview, str) and not os.path.isfile(default_preview)):
return web.Response(status=404)
@@ -76,6 +87,183 @@ class ModelFileManager:
except:
return web.Response(status=404)
@routes.get("/v2/models")
async def get_models(request):
with create_session() as session:
model_path = request.query.get("path", None)
model_type = request.query.get("type", None)
query = session.query(Model).options(joinedload(Model.tags))
if model_path:
query = query.filter(Model.path == model_path)
if model_type:
query = query.filter(Model.type == model_type)
models = query.all()
if model_path and model_type:
if len(models) == 0:
return not_found("Model")
return web.json_response(models[0].to_dict(), dumps=dumps)
return web.json_response([model.to_dict() for model in models], dumps=dumps)
@routes.post("/v2/models")
async def add_model(request):
with create_session() as session:
data = await request.json()
model_type = data.get("type", None)
model_path = data.get("path", None)
if not model_type:
return missing_field("type")
if not model_path:
return missing_field("path")
tags = data.pop("tags", [])
fields = Model.metadata.tables["model"].columns.keys()
# Validate keys are valid model fields
for key in data.keys():
if key not in fields:
return bad_request(f"Invalid field: {key}")
# Validate file exists
if not get_full_path(model_type, model_path):
return not_found(f"File '{model_type}/{model_path}'")
model = Model()
for field in fields:
if field in data:
setattr(model, field, data[field])
model.tags = session.query(Tag).filter(Tag.id.in_(tags)).all()
for tag in tags:
if tag not in [t.id for t in model.tags]:
return not_found(f"Tag '{tag}'")
try:
session.add(model)
session.commit()
except sqlalchemy.exc.IntegrityError as e:
session.rollback()
return bad_request(e.orig.args[0])
model_processor.run()
return web.json_response(model.to_dict(), dumps=dumps)
@routes.delete("/v2/models")
async def delete_model(request):
with create_session() as session:
model_path = request.query.get("path", None)
model_type = request.query.get("type", None)
if not model_path:
return missing_field("path")
if not model_type:
return missing_field("type")
full_path = get_full_path(model_type, model_path)
if full_path:
return bad_request("Model file exists, please delete the file before deleting the model record.")
model = session.query(Model).filter(Model.path == model_path, Model.type == model_type).first()
if not model:
return not_found("Model")
session.delete(model)
session.commit()
return web.Response(status=204)
@routes.get("/v2/tags")
async def get_tags(request):
with create_session() as session:
tags = session.query(Tag).all()
return web.json_response(
[{"id": tag.id, "name": tag.name} for tag in tags]
)
@routes.post("/v2/tags")
async def create_tag(request):
with create_session() as session:
data = await request.json()
name = data.get("name", None)
if not name:
return missing_field("name")
tag = Tag(name=name)
session.add(tag)
session.commit()
return web.json_response({"id": tag.id, "name": tag.name})
@routes.delete("/v2/tags")
async def delete_tag(request):
with create_session() as session:
tag_id = request.query.get("id", None)
if not tag_id:
return missing_field("id")
tag = session.query(Tag).filter(Tag.id == tag_id).first()
if not tag:
return not_found("Tag")
session.delete(tag)
session.commit()
return web.Response(status=204)
@routes.post("/v2/models/tags")
async def add_model_tag(request):
with create_session() as session:
data = await request.json()
tag_id = data.get("tag", None)
model_path = data.get("path", None)
model_type = data.get("type", None)
if tag_id is None:
return missing_field("tag")
if model_path is None:
return missing_field("path")
if model_type is None:
return missing_field("type")
try:
tag_id = int(tag_id)
except ValueError:
return bad_request("Invalid tag id")
tag = session.query(Tag).filter(Tag.id == tag_id).first()
model = session.query(Model).filter(Model.path == model_path, Model.type == model_type).first()
if not model:
return not_found("Model")
model.tags.append(tag)
session.commit()
return web.json_response(model.to_dict(), dumps=dumps)
@routes.delete("/v2/models/tags")
async def delete_model_tag(request):
with create_session() as session:
tag_id = request.query.get("tag", None)
model_path = request.query.get("path", None)
model_type = request.query.get("type", None)
if tag_id is None:
return missing_field("tag")
if model_path is None:
return missing_field("path")
if model_type is None:
return missing_field("type")
try:
tag_id = int(tag_id)
except ValueError:
return bad_request("Invalid tag id")
model = session.query(Model).filter(Model.path == model_path, Model.type == model_type).first()
if not model:
return not_found("Model")
model.tags = [tag for tag in model.tags if tag.id != tag_id]
session.commit()
return web.Response(status=204)
@routes.get("/v2/models/missing")
async def get_missing_models(request):
return web.json_response(model_processor.missing_models)
def get_model_file_list(self, folder_name: str):
folder_name = map_legacy(folder_name)
folders = folder_paths.folder_names_and_paths[folder_name]
@@ -130,21 +318,10 @@ class ModelFileManager:
for file_name in filenames:
try:
full_path = os.path.join(dirpath, file_name)
relative_path = os.path.relpath(full_path, directory)
# Get file metadata
file_info = {
"name": relative_path,
"pathIndex": pathIndex,
"modified": os.path.getmtime(full_path), # Add modification time
"created": os.path.getctime(full_path), # Add creation time
"size": os.path.getsize(full_path) # Add file size
}
result.append(file_info)
except Exception as e:
logging.warning(f"Warning: Unable to access {file_name}. Error: {e}. Skipping this file.")
relative_path = os.path.relpath(os.path.join(dirpath, file_name), directory)
result.append(relative_path)
except:
logging.warning(f"Warning: Unable to access {file_name}. Skipping this file.")
continue
for d in subdirs:
@@ -155,41 +332,7 @@ class ModelFileManager:
logging.warning(f"Warning: Unable to access {path}. Skipping this path.")
continue
return result, dirs, time.perf_counter()
def get_model_previews(self, filepath: str) -> list[str | BytesIO]:
dirname = os.path.dirname(filepath)
if not os.path.exists(dirname):
return []
basename = os.path.splitext(filepath)[0]
match_files = glob.glob(f"{basename}.*", recursive=False)
image_files = filter_files_content_types(match_files, "image")
safetensors_file = next(filter(lambda x: x.endswith(".safetensors"), match_files), None)
safetensors_metadata = {}
result: list[str | BytesIO] = []
for filename in image_files:
_basename = os.path.splitext(filename)[0]
if _basename == basename:
result.append(filename)
if _basename == f"{basename}.preview":
result.append(filename)
if safetensors_file:
safetensors_filepath = os.path.join(dirname, safetensors_file)
header = studio.utils.safetensors_header(safetensors_filepath, max_size=8*1024*1024)
if header:
safetensors_metadata = json.loads(header)
safetensors_images = safetensors_metadata.get("__metadata__", {}).get("ssmd_cover_images", None)
if safetensors_images:
safetensors_images = json.loads(safetensors_images)
for image in safetensors_images:
result.append(BytesIO(base64.b64decode(image)))
return result
return [{"name": f, "pathIndex": pathIndex} for f in result], dirs, time.perf_counter()
def __exit__(self, exc_type, exc_value, traceback):
self.clear_cache()
+263
View File
@@ -0,0 +1,263 @@
import base64
from datetime import datetime
import glob
import hashlib
from io import BytesIO
import json
import logging
import os
import threading
import time
import comfy.utils
from app.database.models import Model
from app.database.db import create_session
from comfy.cli_args import args
from folder_paths import (
filter_files_content_types,
get_full_path,
folder_names_and_paths,
get_filename_list,
)
from PIL import Image
from urllib import request
def get_model_previews(
filepath: str, check_metadata: bool = True
) -> list[str | BytesIO]:
dirname = os.path.dirname(filepath)
if not os.path.exists(dirname):
return []
basename = os.path.splitext(filepath)[0]
match_files = glob.glob(f"{basename}.*", recursive=False)
image_files = filter_files_content_types(match_files, "image")
result: list[str | BytesIO] = []
for filename in image_files:
_basename = os.path.splitext(filename)[0]
if _basename == basename:
result.append(filename)
if _basename == f"{basename}.preview":
result.append(filename)
if not check_metadata:
return result
safetensors_file = next(
filter(lambda x: x.endswith(".safetensors"), match_files), None
)
safetensors_metadata = {}
if safetensors_file:
safetensors_filepath = os.path.join(dirname, safetensors_file)
header = comfy.utils.safetensors_header(
safetensors_filepath, max_size=8 * 1024 * 1024
)
if header:
safetensors_metadata = json.loads(header)
safetensors_images = safetensors_metadata.get("__metadata__", {}).get(
"ssmd_cover_images", None
)
if safetensors_images:
safetensors_images = json.loads(safetensors_images)
for image in safetensors_images:
result.append(BytesIO(base64.b64decode(image)))
return result
class ModelProcessor:
def __init__(self):
self._thread = None
self._lock = threading.Lock()
self._run = False
self.missing_models = []
def run(self):
if args.disable_model_processing:
return
if self._thread is None:
# Lock to prevent multiple threads from starting
with self._lock:
self._run = True
if self._thread is None:
self._thread = threading.Thread(target=self._process_models)
self._thread.daemon = True
self._thread.start()
def populate_models(self, session):
# Ensure database state matches filesystem
existing_models = session.query(Model).all()
for folder_name in folder_names_and_paths.keys():
if folder_name == "custom_nodes" or folder_name == "configs":
continue
seen = set()
files = get_filename_list(folder_name)
for file in files:
if file in seen:
logging.warning(f"Skipping duplicate named model: {file}")
continue
seen.add(file)
existing_model = None
for model in existing_models:
if model.path == file and model.type == folder_name:
existing_model = model
break
if existing_model:
# Model already exists in db, remove from list and skip
existing_models.remove(existing_model)
continue
file_path = get_full_path(folder_name, file)
model = Model(
path=file,
type=folder_name,
date_added=datetime.fromtimestamp(os.path.getctime(file_path)),
)
session.add(model)
for model in existing_models:
if not get_full_path(model.type, model.path):
logging.warning(f"Model {model.path} not found")
self.missing_models.append({"type": model.type, "path": model.path})
session.commit()
def _get_models(self, session):
models = session.query(Model).filter(Model.hash == None).all()
return models
def _process_file(self, model_path):
is_safetensors = model_path.endswith(".safetensors")
metadata = {}
h = hashlib.sha256()
with open(model_path, "rb", buffering=0) as f:
if is_safetensors:
# Read header length (8 bytes)
header_size_bytes = f.read(8)
header_len = int.from_bytes(header_size_bytes, "little")
h.update(header_size_bytes)
# Read header
header_bytes = f.read(header_len)
h.update(header_bytes)
try:
metadata = json.loads(header_bytes)
except json.JSONDecodeError:
pass
# Read rest of file
b = bytearray(128 * 1024)
mv = memoryview(b)
while n := f.readinto(mv):
h.update(mv[:n])
return h.hexdigest(), metadata
def _populate_info(self, model, metadata):
model.title = metadata.get("modelspec.title", None)
model.description = metadata.get("modelspec.description", None)
model.architecture = metadata.get("modelspec.architecture", None)
def _extract_image(self, model_path, metadata):
# check if image already exists
if len(get_model_previews(model_path, check_metadata=False)) > 0:
return
image_path = os.path.splitext(model_path)[0] + ".webp"
if os.path.exists(image_path):
return
cover_images = metadata.get("ssmd_cover_images", None)
image = None
if cover_images:
try:
cover_images = json.loads(cover_images)
if len(cover_images) > 0:
image_data = cover_images[0]
image = Image.open(BytesIO(base64.b64decode(image_data)))
except Exception as e:
logging.warning(
f"Error extracting cover image for model {model_path}: {e}"
)
if not image:
thumbnail = metadata.get("modelspec.thumbnail", None)
if thumbnail:
try:
response = request.urlopen(thumbnail)
image = Image.open(response)
except Exception as e:
logging.warning(
f"Error extracting thumbnail for model {model_path}: {e}"
)
if image:
image.thumbnail((512, 512))
image.save(image_path)
image.close()
def _process_models(self):
with create_session() as session:
checked = set()
self.populate_models(session)
while self._run:
self._run = False
models = self._get_models(session)
if len(models) == 0:
break
for model in models:
# prevent looping on the same model if it crashes
if model.path in checked:
continue
checked.add(model.path)
try:
time.sleep(0)
now = time.time()
model_path = get_full_path(model.type, model.path)
if not model_path:
logging.warning(f"Model {model.path} not found")
self.missing_models.append(model.path)
continue
logging.debug(f"Processing model {model_path}")
hash, header = self._process_file(model_path)
logging.debug(
f"Processed model {model_path} in {time.time() - now} seconds"
)
model.hash = hash
if header:
metadata = header.get("__metadata__", None)
if metadata:
self._populate_info(model, metadata)
self._extract_image(model_path, metadata)
session.commit()
except Exception as e:
logging.error(f"Error processing model {model.path}: {e}")
with self._lock:
self._thread = None
model_processor = ModelProcessor()
-105
View File
@@ -1,105 +0,0 @@
from __future__ import annotations
from aiohttp import web
from typing import TYPE_CHECKING, TypedDict
if TYPE_CHECKING:
from studio_api.latest._io_public import NodeReplace
from studio_execution.graph_utils import is_link
import nodes
class NodeStruct(TypedDict):
inputs: dict[str, str | int | float | bool | tuple[str, int]]
class_type: str
_meta: dict[str, str]
def copy_node_struct(node_struct: NodeStruct, empty_inputs: bool = False) -> NodeStruct:
new_node_struct = node_struct.copy()
if empty_inputs:
new_node_struct["inputs"] = {}
else:
new_node_struct["inputs"] = node_struct["inputs"].copy()
new_node_struct["_meta"] = node_struct["_meta"].copy()
return new_node_struct
class NodeReplaceManager:
"""Manages node replacement registrations."""
def __init__(self):
self._replacements: dict[str, list[NodeReplace]] = {}
def register(self, node_replace: NodeReplace):
"""Register a node replacement mapping."""
self._replacements.setdefault(node_replace.old_node_id, []).append(node_replace)
def get_replacement(self, old_node_id: str) -> list[NodeReplace] | None:
"""Get replacements for an old node ID."""
return self._replacements.get(old_node_id)
def has_replacement(self, old_node_id: str) -> bool:
"""Check if a replacement exists for an old node ID."""
return old_node_id in self._replacements
def apply_replacements(self, prompt: dict[str, NodeStruct]):
connections: dict[str, list[tuple[str, str, int]]] = {}
need_replacement: set[str] = set()
for node_number, node_struct in prompt.items():
class_type = node_struct["class_type"]
# need replacement if not in NODE_CLASS_MAPPINGS and has replacement
if class_type not in nodes.NODE_CLASS_MAPPINGS.keys() and self.has_replacement(class_type):
need_replacement.add(node_number)
# keep track of connections
for input_id, input_value in node_struct["inputs"].items():
if is_link(input_value):
conn_number = input_value[0]
connections.setdefault(conn_number, []).append((node_number, input_id, input_value[1]))
for node_number in need_replacement:
node_struct = prompt[node_number]
class_type = node_struct["class_type"]
replacements = self.get_replacement(class_type)
if replacements is None:
continue
# just use the first replacement
replacement = replacements[0]
new_node_id = replacement.new_node_id
# if replacement is not a valid node, skip trying to replace it as will only cause confusion
if new_node_id not in nodes.NODE_CLASS_MAPPINGS.keys():
continue
# first, replace node id (class_type)
new_node_struct = copy_node_struct(node_struct, empty_inputs=True)
new_node_struct["class_type"] = new_node_id
# TODO: consider replacing display_name in _meta as well for error reporting purposes; would need to query node schema
# second, replace inputs
if replacement.input_mapping is not None:
for input_map in replacement.input_mapping:
if "set_value" in input_map:
new_node_struct["inputs"][input_map["new_id"]] = input_map["set_value"]
elif "old_id" in input_map:
new_node_struct["inputs"][input_map["new_id"]] = node_struct["inputs"][input_map["old_id"]]
# finalize input replacement
prompt[node_number] = new_node_struct
# third, replace outputs
if replacement.output_mapping is not None:
# re-mapping outputs requires changing the input values of nodes that receive connections from this one
if node_number in connections:
for conns in connections[node_number]:
conn_node_number, conn_input_id, old_output_idx = conns
for output_map in replacement.output_mapping:
if output_map["old_idx"] == old_output_idx:
new_output_idx = output_map["new_idx"]
previous_input = prompt[conn_node_number]["inputs"][conn_input_id]
previous_input[1] = new_output_idx
def as_dict(self):
"""Serialize all replacements to dict."""
return {
k: [v.as_dict() for v in v_list]
for k, v_list in self._replacements.items()
}
def add_routes(self, routes):
@routes.get("/node_replacements")
async def get_node_replacements(request):
return web.json_response(self.as_dict())
-132
View File
@@ -1,132 +0,0 @@
from __future__ import annotations
from typing import TypedDict
import os
import folder_paths
import glob
from aiohttp import web
import hashlib
class Source:
custom_node = "custom_node"
templates = "templates"
class SubgraphEntry(TypedDict):
source: str
"""
Source of subgraph - custom_nodes vs templates.
"""
path: str
"""
Relative path of the subgraph file.
For custom nodes, will be the relative directory like <custom_node_dir>/subgraphs/<name>.json
"""
name: str
"""
Name of subgraph file.
"""
info: CustomNodeSubgraphEntryInfo
"""
Additional info about subgraph; in the case of custom_nodes, will contain nodepack name
"""
data: str
class CustomNodeSubgraphEntryInfo(TypedDict):
node_pack: str
"""Node pack name."""
class SubgraphManager:
def __init__(self):
self.cached_custom_node_subgraphs: dict[SubgraphEntry] | None = None
self.cached_blueprint_subgraphs: dict[SubgraphEntry] | None = None
def _create_entry(self, file: str, source: str, node_pack: str) -> tuple[str, SubgraphEntry]:
"""Create a subgraph entry from a file path. Expects normalized path (forward slashes)."""
entry_id = hashlib.sha256(f"{source}{file}".encode()).hexdigest()
entry: SubgraphEntry = {
"source": source,
"name": os.path.splitext(os.path.basename(file))[0],
"path": file,
"info": {"node_pack": node_pack},
}
return entry_id, entry
async def load_entry_data(self, entry: SubgraphEntry):
with open(entry['path'], 'r', encoding='utf-8') as f:
entry['data'] = f.read()
return entry
async def sanitize_entry(self, entry: SubgraphEntry | None, remove_data=False) -> SubgraphEntry | None:
if entry is None:
return None
entry = entry.copy()
entry.pop('path', None)
if remove_data:
entry.pop('data', None)
return entry
async def sanitize_entries(self, entries: dict[str, SubgraphEntry], remove_data=False) -> dict[str, SubgraphEntry]:
entries = entries.copy()
for key in list(entries.keys()):
entries[key] = await self.sanitize_entry(entries[key], remove_data)
return entries
async def get_custom_node_subgraphs(self, loadedModules, force_reload=False):
"""Load subgraphs from custom nodes."""
if not force_reload and self.cached_custom_node_subgraphs is not None:
return self.cached_custom_node_subgraphs
subgraphs_dict: dict[SubgraphEntry] = {}
for folder in folder_paths.get_folder_paths("custom_nodes"):
pattern = os.path.join(folder, "*/subgraphs/*.json")
for file in glob.glob(pattern):
file = file.replace('\\', '/')
node_pack = "custom_nodes." + file.split('/')[-3]
entry_id, entry = self._create_entry(file, Source.custom_node, node_pack)
subgraphs_dict[entry_id] = entry
self.cached_custom_node_subgraphs = subgraphs_dict
return subgraphs_dict
async def get_blueprint_subgraphs(self, force_reload=False):
"""Load subgraphs from the blueprints directory."""
if not force_reload and self.cached_blueprint_subgraphs is not None:
return self.cached_blueprint_subgraphs
subgraphs_dict: dict[SubgraphEntry] = {}
blueprints_dir = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'blueprints')
if os.path.exists(blueprints_dir):
for file in glob.glob(os.path.join(blueprints_dir, "*.json")):
file = file.replace('\\', '/')
entry_id, entry = self._create_entry(file, Source.templates, "studio")
subgraphs_dict[entry_id] = entry
self.cached_blueprint_subgraphs = subgraphs_dict
return subgraphs_dict
async def get_all_subgraphs(self, loadedModules, force_reload=False):
"""Get all subgraphs from all sources (custom nodes and blueprints)."""
custom_node_subgraphs = await self.get_custom_node_subgraphs(loadedModules, force_reload)
blueprint_subgraphs = await self.get_blueprint_subgraphs(force_reload)
return {**custom_node_subgraphs, **blueprint_subgraphs}
async def get_subgraph(self, id: str, loadedModules):
"""Get a specific subgraph by ID from any source."""
entry = (await self.get_all_subgraphs(loadedModules)).get(id)
if entry is not None and entry.get('data') is None:
await self.load_entry_data(entry)
return entry
def add_routes(self, routes, loadedModules):
@routes.get("/global_subgraphs")
async def get_global_subgraphs(request):
subgraphs_dict = await self.get_all_subgraphs(loadedModules)
return web.json_response(await self.sanitize_entries(subgraphs_dict, remove_data=True))
@routes.get("/global_subgraphs/{id}")
async def get_global_subgraph(request):
id = request.match_info.get("id", None)
subgraph = await self.get_subgraph(id, loadedModules)
return web.json_response(await self.sanitize_entry(subgraph))
+13 -162
View File
@@ -8,7 +8,7 @@ import shutil
import logging
from aiohttp import web
from urllib import parse
from studio.cli_args import args
from comfy.cli_args import args
import folder_paths
from .app_settings import AppSettings
from typing import TypedDict
@@ -20,15 +20,13 @@ class FileInfo(TypedDict):
path: str
size: int
modified: int
created: int
def get_file_info(path: str, relative_to: str) -> FileInfo:
return {
"path": os.path.relpath(path, relative_to).replace(os.sep, '/'),
"size": os.path.getsize(path),
"modified": os.path.getmtime(path),
"created": os.path.getctime(path)
"modified": os.path.getmtime(path)
}
@@ -57,51 +55,24 @@ class UserManager():
def get_request_user_id(self, request):
user = "default"
# If IAM auth is active, extract user from IAM context
iam_user = request.get("iam_user")
if iam_user and iam_user.get("sub") and iam_user["sub"] != "local":
user = iam_user["sub"]
# A token subject must never escape into a System User namespace.
if user.startswith(folder_paths.SYSTEM_USER_PREFIX):
raise KeyError("Unknown user: " + user)
# Auto-register IAM users
if user not in self.users:
display = iam_user.get("name") or iam_user.get("email") or user
self.users[user] = display
return user
if args.multi_user and "studio-user" in request.headers:
user = request.headers["studio-user"]
# Block System Users (use same error message to prevent probing)
if user.startswith(folder_paths.SYSTEM_USER_PREFIX):
raise KeyError("Unknown user: " + user)
if args.multi_user and "comfy-user" in request.headers:
user = request.headers["comfy-user"]
if user not in self.users:
raise KeyError("Unknown user: " + user)
return user
def get_request_org_id(self, request) -> str | None:
"""Extract org_id from IAM context or fallback to CLI arg."""
iam_user = request.get("iam_user")
if iam_user and iam_user.get("org_id"):
return iam_user["org_id"]
return folder_paths.get_org_id()
def get_request_user_filepath(self, request, file, type="userdata", create_dir=True):
user_directory = folder_paths.get_user_directory()
if type == "userdata":
# Use org-scoped user directory if multi-tenant
org_id = self.get_request_org_id(request)
root_dir = folder_paths.get_org_user_directory(org_id)
root_dir = user_directory
else:
raise KeyError("Unknown filepath type:" + type)
user = self.get_request_user_id(request)
user_root = folder_paths.get_public_user_directory(user, org_id)
if user_root is None:
return None
path = user_root
path = user_root = os.path.abspath(os.path.join(root_dir, user))
# prevent leaving /{type}
if os.path.commonpath((root_dir, user_root)) != root_dir:
@@ -128,11 +99,7 @@ class UserManager():
name = name.strip()
if not name:
raise ValueError("username not provided")
if name.startswith(folder_paths.SYSTEM_USER_PREFIX):
raise ValueError("System User prefix not allowed")
user_id = re.sub("[^a-zA-Z0-9-_]+", '-', name)
if user_id.startswith(folder_paths.SYSTEM_USER_PREFIX):
raise ValueError("System User prefix not allowed")
user_id = user_id + "_" + str(uuid.uuid4())
self.users[user_id] = name
@@ -163,10 +130,7 @@ class UserManager():
if username in self.users.values():
return web.json_response({"error": "Duplicate username."}, status=400)
try:
user_id = self.add_user(username)
except ValueError as e:
return web.json_response({"error": str(e)}, status=400)
user_id = self.add_user(username)
return web.json_response(user_id)
@routes.get("/userdata")
@@ -233,112 +197,6 @@ class UserManager():
return web.json_response(results)
@routes.get("/v2/userdata")
async def list_userdata_v2(request):
"""
List files and directories in a user's data directory.
This endpoint provides a structured listing of contents within a specified
subdirectory of the user's data storage.
Query Parameters:
- path (optional): The relative path within the user's data directory
to list. Defaults to the root ('').
Returns:
- 400: If the requested path is invalid, outside the user's data directory, or is not a directory.
- 404: If the requested path does not exist.
- 403: If the user is invalid.
- 500: If there is an error reading the directory contents.
- 200: JSON response containing a list of file and directory objects.
Each object includes:
- name: The name of the file or directory.
- type: 'file' or 'directory'.
- path: The relative path from the user's data root.
- size (for files): The size in bytes.
- modified (for files): The last modified timestamp (Unix epoch).
"""
requested_rel_path = request.rel_url.query.get('path', '')
# URL-decode the path parameter
try:
requested_rel_path = parse.unquote(requested_rel_path)
except Exception as e:
logging.warning(f"Failed to decode path parameter: {requested_rel_path}, Error: {e}")
return web.Response(status=400, text="Invalid characters in path parameter")
# Check user validity and get the absolute path for the requested directory
try:
base_user_path = self.get_request_user_filepath(request, None, create_dir=False)
if requested_rel_path:
target_abs_path = self.get_request_user_filepath(request, requested_rel_path, create_dir=False)
else:
target_abs_path = base_user_path
except KeyError as e:
# Invalid user detected by get_request_user_id inside get_request_user_filepath
logging.warning(f"Access denied for user: {e}")
return web.Response(status=403, text="Invalid user specified in request")
if not target_abs_path:
# Path traversal or other issue detected by get_request_user_filepath
return web.Response(status=400, text="Invalid path requested")
# Handle cases where the user directory or target path doesn't exist
if not os.path.exists(target_abs_path):
# Check if it's the base user directory that's missing (new user case)
if target_abs_path == base_user_path:
# It's okay if the base user directory doesn't exist yet, return empty list
return web.json_response([])
else:
# A specific subdirectory was requested but doesn't exist
return web.Response(status=404, text="Requested path not found")
if not os.path.isdir(target_abs_path):
return web.Response(status=400, text="Requested path is not a directory")
results = []
try:
for root, dirs, files in os.walk(target_abs_path, topdown=True):
# Process directories
for dir_name in dirs:
dir_path = os.path.join(root, dir_name)
rel_path = os.path.relpath(dir_path, base_user_path).replace(os.sep, '/')
results.append({
"name": dir_name,
"path": rel_path,
"type": "directory"
})
# Process files
for file_name in files:
file_path = os.path.join(root, file_name)
rel_path = os.path.relpath(file_path, base_user_path).replace(os.sep, '/')
entry_info = {
"name": file_name,
"path": rel_path,
"type": "file"
}
try:
stats = os.stat(file_path) # Use os.stat for potentially better performance with os.walk
entry_info["size"] = stats.st_size
entry_info["modified"] = stats.st_mtime
except OSError as stat_error:
logging.warning(f"Could not stat file {file_path}: {stat_error}")
pass # Include file with available info
results.append(entry_info)
except OSError as e:
logging.error(f"Error listing directory {target_abs_path}: {e}")
return web.Response(status=500, text="Error reading directory contents")
# Sort results alphabetically, directories first then files
results.sort(key=lambda x: (x['type'] != 'directory', x['name'].lower()))
return web.json_response(results)
def get_user_data_path(request, check_exists = False, param = "file"):
file = request.match_info.get(param, None)
if not file:
@@ -397,17 +255,10 @@ class UserManager():
if not overwrite and os.path.exists(path):
return web.Response(status=409, text="File already exists")
try:
body = await request.read()
body = await request.read()
with open(path, "wb") as f:
f.write(body)
except OSError as e:
logging.warning(f"Error saving file '{path}': {e}")
return web.Response(
status=400,
reason="Invalid filename. Please avoid special characters like :\\/*?\"<>|"
)
with open(path, "wb") as f:
f.write(body)
user_path = self.get_request_user_filepath(request, None)
if full_info:
@@ -458,7 +309,7 @@ class UserManager():
return source
dest = get_user_data_path(request, check_exists=False, param="dest")
if not isinstance(dest, str):
if not isinstance(source, str):
return dest
overwrite = request.query.get("overwrite", 'true') != "false"
@@ -1,44 +0,0 @@
#version 300 es
precision highp float;
uniform sampler2D u_image0;
uniform float u_float0; // Brightness slider -100..100
uniform float u_float1; // Contrast slider -100..100
in vec2 v_texCoord;
out vec4 fragColor;
const float MID_GRAY = 0.18; // 18% reflectance
// sRGB gamma 2.2 approximation
vec3 srgbToLinear(vec3 c) {
return pow(max(c, 0.0), vec3(2.2));
}
vec3 linearToSrgb(vec3 c) {
return pow(max(c, 0.0), vec3(1.0/2.2));
}
float mapBrightness(float b) {
return clamp(b / 100.0, -1.0, 1.0);
}
float mapContrast(float c) {
return clamp(c / 100.0 + 1.0, 0.0, 2.0);
}
void main() {
vec4 orig = texture(u_image0, v_texCoord);
float brightness = mapBrightness(u_float0);
float contrast = mapContrast(u_float1);
vec3 lin = srgbToLinear(orig.rgb);
lin = (lin - MID_GRAY) * contrast + brightness + MID_GRAY;
// Convert back to sRGB
vec3 result = linearToSrgb(clamp(lin, 0.0, 1.0));
fragColor = vec4(result, orig.a);
}
@@ -1,72 +0,0 @@
#version 300 es
precision highp float;
uniform sampler2D u_image0;
uniform vec2 u_resolution;
uniform int u_int0; // Mode
uniform float u_float0; // Amount (0 to 100)
in vec2 v_texCoord;
out vec4 fragColor;
const int MODE_LINEAR = 0;
const int MODE_RADIAL = 1;
const int MODE_BARREL = 2;
const int MODE_SWIRL = 3;
const int MODE_DIAGONAL = 4;
const float AMOUNT_SCALE = 0.0005;
const float RADIAL_MULT = 4.0;
const float BARREL_MULT = 8.0;
const float INV_SQRT2 = 0.70710678118;
void main() {
vec2 uv = v_texCoord;
vec4 original = texture(u_image0, uv);
float amount = u_float0 * AMOUNT_SCALE;
if (amount < 0.000001) {
fragColor = original;
return;
}
// Aspect-corrected coordinates for circular effects
float aspect = u_resolution.x / u_resolution.y;
vec2 centered = uv - 0.5;
vec2 corrected = vec2(centered.x * aspect, centered.y);
float r = length(corrected);
vec2 dir = r > 0.0001 ? corrected / r : vec2(0.0);
vec2 offset = vec2(0.0);
if (u_int0 == MODE_LINEAR) {
// Horizontal shift (no aspect correction needed)
offset = vec2(amount, 0.0);
}
else if (u_int0 == MODE_RADIAL) {
// Outward from center, stronger at edges
offset = dir * r * amount * RADIAL_MULT;
offset.x /= aspect; // Convert back to UV space
}
else if (u_int0 == MODE_BARREL) {
// Lens distortion simulation (r² falloff)
offset = dir * r * r * amount * BARREL_MULT;
offset.x /= aspect; // Convert back to UV space
}
else if (u_int0 == MODE_SWIRL) {
// Perpendicular to radial (rotational aberration)
vec2 perp = vec2(-dir.y, dir.x);
offset = perp * r * amount * RADIAL_MULT;
offset.x /= aspect; // Convert back to UV space
}
else if (u_int0 == MODE_DIAGONAL) {
// 45° offset (no aspect correction needed)
offset = vec2(amount, amount) * INV_SQRT2;
}
float red = texture(u_image0, uv + offset).r;
float green = original.g;
float blue = texture(u_image0, uv - offset).b;
fragColor = vec4(red, green, blue, original.a);
}
-78
View File
@@ -1,78 +0,0 @@
#version 300 es
precision highp float;
uniform sampler2D u_image0;
uniform float u_float0; // temperature (-100 to 100)
uniform float u_float1; // tint (-100 to 100)
uniform float u_float2; // vibrance (-100 to 100)
uniform float u_float3; // saturation (-100 to 100)
in vec2 v_texCoord;
out vec4 fragColor;
const float INPUT_SCALE = 0.01;
const float TEMP_TINT_PRIMARY = 0.3;
const float TEMP_TINT_SECONDARY = 0.15;
const float VIBRANCE_BOOST = 2.0;
const float SATURATION_BOOST = 2.0;
const float SKIN_PROTECTION = 0.5;
const float EPSILON = 0.001;
const vec3 LUMA_WEIGHTS = vec3(0.299, 0.587, 0.114);
void main() {
vec4 tex = texture(u_image0, v_texCoord);
vec3 color = tex.rgb;
// Scale inputs: -100/100 → -1/1
float temperature = u_float0 * INPUT_SCALE;
float tint = u_float1 * INPUT_SCALE;
float vibrance = u_float2 * INPUT_SCALE;
float saturation = u_float3 * INPUT_SCALE;
// Temperature (warm/cool): positive = warm, negative = cool
color.r += temperature * TEMP_TINT_PRIMARY;
color.b -= temperature * TEMP_TINT_PRIMARY;
// Tint (green/magenta): positive = green, negative = magenta
color.g += tint * TEMP_TINT_PRIMARY;
color.r -= tint * TEMP_TINT_SECONDARY;
color.b -= tint * TEMP_TINT_SECONDARY;
// Single clamp after temperature/tint
color = clamp(color, 0.0, 1.0);
// Vibrance with skin protection
if (vibrance != 0.0) {
float maxC = max(color.r, max(color.g, color.b));
float minC = min(color.r, min(color.g, color.b));
float sat = maxC - minC;
float gray = dot(color, LUMA_WEIGHTS);
if (vibrance < 0.0) {
// Desaturate: -100 → gray
color = mix(vec3(gray), color, 1.0 + vibrance);
} else {
// Boost less saturated colors more
float vibranceAmt = vibrance * (1.0 - sat);
// Branchless skin tone protection
float isWarmTone = step(color.b, color.g) * step(color.g, color.r);
float warmth = (color.r - color.b) / max(maxC, EPSILON);
float skinTone = isWarmTone * warmth * sat * (1.0 - sat);
vibranceAmt *= (1.0 - skinTone * SKIN_PROTECTION);
color = mix(vec3(gray), color, 1.0 + vibranceAmt * VIBRANCE_BOOST);
}
}
// Saturation
if (saturation != 0.0) {
float gray = dot(color, LUMA_WEIGHTS);
float satMix = saturation < 0.0
? 1.0 + saturation // -100 → gray
: 1.0 + saturation * SATURATION_BOOST; // +100 → 3x boost
color = mix(vec3(gray), color, satMix);
}
fragColor = vec4(clamp(color, 0.0, 1.0), tex.a);
}
@@ -1,94 +0,0 @@
#version 300 es
precision highp float;
uniform sampler2D u_image0;
uniform float u_float0; // Blur radius (020, default ~5)
uniform float u_float1; // Edge threshold (0100, default ~30)
uniform int u_int0; // Step size (0/1 = every pixel, 2+ = skip pixels)
in vec2 v_texCoord;
out vec4 fragColor;
const int MAX_RADIUS = 20;
const float EPSILON = 0.0001;
// Perceptual luminance
float getLuminance(vec3 rgb) {
return dot(rgb, vec3(0.299, 0.587, 0.114));
}
vec4 bilateralFilter(vec2 uv, vec2 texelSize, int radius,
float sigmaSpatial, float sigmaColor)
{
vec4 center = texture(u_image0, uv);
vec3 centerRGB = center.rgb;
float invSpatial2 = -0.5 / (sigmaSpatial * sigmaSpatial);
float invColor2 = -0.5 / (sigmaColor * sigmaColor + EPSILON);
vec3 sumRGB = vec3(0.0);
float sumWeight = 0.0;
int step = max(u_int0, 1);
float radius2 = float(radius * radius);
for (int dy = -MAX_RADIUS; dy <= MAX_RADIUS; dy++) {
if (dy < -radius || dy > radius) continue;
if (abs(dy) % step != 0) continue;
for (int dx = -MAX_RADIUS; dx <= MAX_RADIUS; dx++) {
if (dx < -radius || dx > radius) continue;
if (abs(dx) % step != 0) continue;
vec2 offset = vec2(float(dx), float(dy));
float dist2 = dot(offset, offset);
if (dist2 > radius2) continue;
vec3 sampleRGB = texture(u_image0, uv + offset * texelSize).rgb;
// Spatial Gaussian
float spatialWeight = exp(dist2 * invSpatial2);
// Perceptual color distance (weighted RGB)
vec3 diff = sampleRGB - centerRGB;
float colorDist = dot(diff * diff, vec3(0.299, 0.587, 0.114));
float colorWeight = exp(colorDist * invColor2);
float w = spatialWeight * colorWeight;
sumRGB += sampleRGB * w;
sumWeight += w;
}
}
vec3 resultRGB = sumRGB / max(sumWeight, EPSILON);
return vec4(resultRGB, center.a); // preserve center alpha
}
void main() {
vec2 texelSize = 1.0 / vec2(textureSize(u_image0, 0));
float radiusF = clamp(u_float0, 0.0, float(MAX_RADIUS));
int radius = int(radiusF + 0.5);
if (radius == 0) {
fragColor = texture(u_image0, v_texCoord);
return;
}
// Edge threshold → color sigma
// Squared curve for better low-end control
float t = clamp(u_float1, 0.0, 100.0) / 100.0;
t *= t;
float sigmaColor = mix(0.01, 0.5, t);
// Spatial sigma tied to radius
float sigmaSpatial = max(radiusF * 0.75, 0.5);
fragColor = bilateralFilter(
v_texCoord,
texelSize,
radius,
sigmaSpatial,
sigmaColor
);
}
-124
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@@ -1,124 +0,0 @@
#version 300 es
precision highp float;
uniform sampler2D u_image0;
uniform vec2 u_resolution;
uniform float u_float0; // grain amount [0.0 1.0] typical: 0.20.8
uniform float u_float1; // grain size [0.3 3.0] lower = finer grain
uniform float u_float2; // color amount [0.0 1.0] 0 = monochrome, 1 = RGB grain
uniform float u_float3; // luminance bias [0.0 1.0] 0 = uniform, 1 = shadows only
uniform int u_int0; // noise mode [0 or 1] 0 = smooth, 1 = grainy
in vec2 v_texCoord;
layout(location = 0) out vec4 fragColor0;
// High-quality integer hash (pcg-like)
uint pcg(uint v) {
uint state = v * 747796405u + 2891336453u;
uint word = ((state >> ((state >> 28u) + 4u)) ^ state) * 277803737u;
return (word >> 22u) ^ word;
}
// 2D -> 1D hash input
uint hash2d(uvec2 p) {
return pcg(p.x + pcg(p.y));
}
// Hash to float [0, 1]
float hashf(uvec2 p) {
return float(hash2d(p)) / float(0xffffffffu);
}
// Hash to float with offset (for RGB channels)
float hashf(uvec2 p, uint offset) {
return float(pcg(hash2d(p) + offset)) / float(0xffffffffu);
}
// Convert uniform [0,1] to roughly Gaussian distribution
// Using simple approximation: average of multiple samples
float toGaussian(uvec2 p) {
float sum = hashf(p, 0u) + hashf(p, 1u) + hashf(p, 2u) + hashf(p, 3u);
return (sum - 2.0) * 0.7; // Centered, scaled
}
float toGaussian(uvec2 p, uint offset) {
float sum = hashf(p, offset) + hashf(p, offset + 1u)
+ hashf(p, offset + 2u) + hashf(p, offset + 3u);
return (sum - 2.0) * 0.7;
}
// Smooth noise with better interpolation
float smoothNoise(vec2 p) {
vec2 i = floor(p);
vec2 f = fract(p);
// Quintic interpolation (less banding than cubic)
f = f * f * f * (f * (f * 6.0 - 15.0) + 10.0);
uvec2 ui = uvec2(i);
float a = toGaussian(ui);
float b = toGaussian(ui + uvec2(1u, 0u));
float c = toGaussian(ui + uvec2(0u, 1u));
float d = toGaussian(ui + uvec2(1u, 1u));
return mix(mix(a, b, f.x), mix(c, d, f.x), f.y);
}
float smoothNoise(vec2 p, uint offset) {
vec2 i = floor(p);
vec2 f = fract(p);
f = f * f * f * (f * (f * 6.0 - 15.0) + 10.0);
uvec2 ui = uvec2(i);
float a = toGaussian(ui, offset);
float b = toGaussian(ui + uvec2(1u, 0u), offset);
float c = toGaussian(ui + uvec2(0u, 1u), offset);
float d = toGaussian(ui + uvec2(1u, 1u), offset);
return mix(mix(a, b, f.x), mix(c, d, f.x), f.y);
}
void main() {
vec4 color = texture(u_image0, v_texCoord);
// Luminance (Rec.709)
float luma = dot(color.rgb, vec3(0.2126, 0.7152, 0.0722));
// Grain UV (resolution-independent)
vec2 grainUV = v_texCoord * u_resolution / max(u_float1, 0.01);
uvec2 grainPixel = uvec2(grainUV);
float g;
vec3 grainRGB;
if (u_int0 == 1) {
// Grainy mode: pure hash noise (no interpolation = no banding)
g = toGaussian(grainPixel);
grainRGB = vec3(
toGaussian(grainPixel, 100u),
toGaussian(grainPixel, 200u),
toGaussian(grainPixel, 300u)
);
} else {
// Smooth mode: interpolated with quintic curve
g = smoothNoise(grainUV);
grainRGB = vec3(
smoothNoise(grainUV, 100u),
smoothNoise(grainUV, 200u),
smoothNoise(grainUV, 300u)
);
}
// Luminance weighting (less grain in highlights)
float lumWeight = mix(1.0, 1.0 - luma, clamp(u_float3, 0.0, 1.0));
// Strength
float strength = u_float0 * 0.15;
// Color vs monochrome grain
vec3 grainColor = mix(vec3(g), grainRGB, clamp(u_float2, 0.0, 1.0));
color.rgb += grainColor * strength * lumWeight;
fragColor0 = vec4(clamp(color.rgb, 0.0, 1.0), color.a);
}
-133
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@@ -1,133 +0,0 @@
#version 300 es
precision mediump float;
uniform sampler2D u_image0;
uniform vec2 u_resolution;
uniform int u_int0; // Blend mode
uniform int u_int1; // Color tint
uniform float u_float0; // Intensity
uniform float u_float1; // Radius
uniform float u_float2; // Threshold
in vec2 v_texCoord;
out vec4 fragColor;
const int BLEND_ADD = 0;
const int BLEND_SCREEN = 1;
const int BLEND_SOFT = 2;
const int BLEND_OVERLAY = 3;
const int BLEND_LIGHTEN = 4;
const float GOLDEN_ANGLE = 2.39996323;
const int MAX_SAMPLES = 48;
const vec3 LUMA = vec3(0.299, 0.587, 0.114);
float hash(vec2 p) {
p = fract(p * vec2(123.34, 456.21));
p += dot(p, p + 45.32);
return fract(p.x * p.y);
}
vec3 hexToRgb(int h) {
return vec3(
float((h >> 16) & 255),
float((h >> 8) & 255),
float(h & 255)
) * (1.0 / 255.0);
}
vec3 blend(vec3 base, vec3 glow, int mode) {
if (mode == BLEND_SCREEN) {
return 1.0 - (1.0 - base) * (1.0 - glow);
}
if (mode == BLEND_SOFT) {
return mix(
base - (1.0 - 2.0 * glow) * base * (1.0 - base),
base + (2.0 * glow - 1.0) * (sqrt(base) - base),
step(0.5, glow)
);
}
if (mode == BLEND_OVERLAY) {
return mix(
2.0 * base * glow,
1.0 - 2.0 * (1.0 - base) * (1.0 - glow),
step(0.5, base)
);
}
if (mode == BLEND_LIGHTEN) {
return max(base, glow);
}
return base + glow;
}
void main() {
vec4 original = texture(u_image0, v_texCoord);
float intensity = u_float0 * 0.05;
float radius = u_float1 * u_float1 * 0.012;
if (intensity < 0.001 || radius < 0.1) {
fragColor = original;
return;
}
float threshold = 1.0 - u_float2 * 0.01;
float t0 = threshold - 0.15;
float t1 = threshold + 0.15;
vec2 texelSize = 1.0 / u_resolution;
float radius2 = radius * radius;
float sampleScale = clamp(radius * 0.75, 0.35, 1.0);
int samples = int(float(MAX_SAMPLES) * sampleScale);
float noise = hash(gl_FragCoord.xy);
float angleOffset = noise * GOLDEN_ANGLE;
float radiusJitter = 0.85 + noise * 0.3;
float ca = cos(GOLDEN_ANGLE);
float sa = sin(GOLDEN_ANGLE);
vec2 dir = vec2(cos(angleOffset), sin(angleOffset));
vec3 glow = vec3(0.0);
float totalWeight = 0.0;
// Center tap
float centerMask = smoothstep(t0, t1, dot(original.rgb, LUMA));
glow += original.rgb * centerMask * 2.0;
totalWeight += 2.0;
for (int i = 1; i < MAX_SAMPLES; i++) {
if (i >= samples) break;
float fi = float(i);
float dist = sqrt(fi / float(samples)) * radius * radiusJitter;
vec2 offset = dir * dist * texelSize;
vec3 c = texture(u_image0, v_texCoord + offset).rgb;
float mask = smoothstep(t0, t1, dot(c, LUMA));
float w = 1.0 - (dist * dist) / (radius2 * 1.5);
w = max(w, 0.0);
w *= w;
glow += c * mask * w;
totalWeight += w;
dir = vec2(
dir.x * ca - dir.y * sa,
dir.x * sa + dir.y * ca
);
}
glow *= intensity / max(totalWeight, 0.001);
if (u_int1 > 0) {
glow *= hexToRgb(u_int1);
}
vec3 result = blend(original.rgb, glow, u_int0);
result += (noise - 0.5) * (1.0 / 255.0);
fragColor = vec4(clamp(result, 0.0, 1.0), original.a);
}
-222
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@@ -1,222 +0,0 @@
#version 300 es
precision highp float;
uniform sampler2D u_image0;
uniform int u_int0; // Mode: 0=Master, 1=Reds, 2=Yellows, 3=Greens, 4=Cyans, 5=Blues, 6=Magentas, 7=Colorize
uniform int u_int1; // Color Space: 0=HSL, 1=HSB/HSV
uniform float u_float0; // Hue (-180 to 180)
uniform float u_float1; // Saturation (-100 to 100)
uniform float u_float2; // Lightness/Brightness (-100 to 100)
uniform float u_float3; // Overlap (0 to 100) - feathering between adjacent color ranges
in vec2 v_texCoord;
out vec4 fragColor;
// Color range modes
const int MODE_MASTER = 0;
const int MODE_RED = 1;
const int MODE_YELLOW = 2;
const int MODE_GREEN = 3;
const int MODE_CYAN = 4;
const int MODE_BLUE = 5;
const int MODE_MAGENTA = 6;
const int MODE_COLORIZE = 7;
// Color space modes
const int COLORSPACE_HSL = 0;
const int COLORSPACE_HSB = 1;
const float EPSILON = 0.0001;
//=============================================================================
// RGB <-> HSL Conversions
//=============================================================================
vec3 rgb2hsl(vec3 c) {
float maxC = max(max(c.r, c.g), c.b);
float minC = min(min(c.r, c.g), c.b);
float delta = maxC - minC;
float h = 0.0;
float s = 0.0;
float l = (maxC + minC) * 0.5;
if (delta > EPSILON) {
s = l < 0.5
? delta / (maxC + minC)
: delta / (2.0 - maxC - minC);
if (maxC == c.r) {
h = (c.g - c.b) / delta + (c.g < c.b ? 6.0 : 0.0);
} else if (maxC == c.g) {
h = (c.b - c.r) / delta + 2.0;
} else {
h = (c.r - c.g) / delta + 4.0;
}
h /= 6.0;
}
return vec3(h, s, l);
}
float hue2rgb(float p, float q, float t) {
t = fract(t);
if (t < 1.0/6.0) return p + (q - p) * 6.0 * t;
if (t < 0.5) return q;
if (t < 2.0/3.0) return p + (q - p) * (2.0/3.0 - t) * 6.0;
return p;
}
vec3 hsl2rgb(vec3 hsl) {
if (hsl.y < EPSILON) return vec3(hsl.z);
float q = hsl.z < 0.5
? hsl.z * (1.0 + hsl.y)
: hsl.z + hsl.y - hsl.z * hsl.y;
float p = 2.0 * hsl.z - q;
return vec3(
hue2rgb(p, q, hsl.x + 1.0/3.0),
hue2rgb(p, q, hsl.x),
hue2rgb(p, q, hsl.x - 1.0/3.0)
);
}
vec3 rgb2hsb(vec3 c) {
float maxC = max(max(c.r, c.g), c.b);
float minC = min(min(c.r, c.g), c.b);
float delta = maxC - minC;
float h = 0.0;
float s = (maxC > EPSILON) ? delta / maxC : 0.0;
float b = maxC;
if (delta > EPSILON) {
if (maxC == c.r) {
h = (c.g - c.b) / delta + (c.g < c.b ? 6.0 : 0.0);
} else if (maxC == c.g) {
h = (c.b - c.r) / delta + 2.0;
} else {
h = (c.r - c.g) / delta + 4.0;
}
h /= 6.0;
}
return vec3(h, s, b);
}
vec3 hsb2rgb(vec3 hsb) {
vec3 rgb = clamp(abs(mod(hsb.x * 6.0 + vec3(0.0, 4.0, 2.0), 6.0) - 3.0) - 1.0, 0.0, 1.0);
return hsb.z * mix(vec3(1.0), rgb, hsb.y);
}
//=============================================================================
// Color Range Weight Calculation
//=============================================================================
float hueDistance(float a, float b) {
float d = abs(a - b);
return min(d, 1.0 - d);
}
float getHueWeight(float hue, float center, float overlap) {
float baseWidth = 1.0 / 6.0;
float feather = baseWidth * overlap;
float d = hueDistance(hue, center);
float inner = baseWidth * 0.5;
float outer = inner + feather;
return 1.0 - smoothstep(inner, outer, d);
}
float getModeWeight(float hue, int mode, float overlap) {
if (mode == MODE_MASTER || mode == MODE_COLORIZE) return 1.0;
if (mode == MODE_RED) {
return max(
getHueWeight(hue, 0.0, overlap),
getHueWeight(hue, 1.0, overlap)
);
}
float center = float(mode - 1) / 6.0;
return getHueWeight(hue, center, overlap);
}
//=============================================================================
// Adjustment Functions
//=============================================================================
float adjustLightness(float l, float amount) {
return amount > 0.0
? l + (1.0 - l) * amount
: l + l * amount;
}
float adjustBrightness(float b, float amount) {
return clamp(b + amount, 0.0, 1.0);
}
float adjustSaturation(float s, float amount) {
return amount > 0.0
? s + (1.0 - s) * amount
: s + s * amount;
}
vec3 colorize(vec3 rgb, float hue, float sat, float light) {
float lum = dot(rgb, vec3(0.299, 0.587, 0.114));
float l = adjustLightness(lum, light);
vec3 hsl = vec3(fract(hue), clamp(sat, 0.0, 1.0), clamp(l, 0.0, 1.0));
return hsl2rgb(hsl);
}
//=============================================================================
// Main
//=============================================================================
void main() {
vec4 original = texture(u_image0, v_texCoord);
float hueShift = u_float0 / 360.0; // -180..180 -> -0.5..0.5
float satAmount = u_float1 / 100.0; // -100..100 -> -1..1
float lightAmount= u_float2 / 100.0; // -100..100 -> -1..1
float overlap = u_float3 / 100.0; // 0..100 -> 0..1
vec3 result;
if (u_int0 == MODE_COLORIZE) {
result = colorize(original.rgb, hueShift, satAmount, lightAmount);
fragColor = vec4(result, original.a);
return;
}
vec3 hsx = (u_int1 == COLORSPACE_HSL)
? rgb2hsl(original.rgb)
: rgb2hsb(original.rgb);
float weight = getModeWeight(hsx.x, u_int0, overlap);
if (u_int0 != MODE_MASTER && hsx.y < EPSILON) {
weight = 0.0;
}
if (weight > EPSILON) {
float h = fract(hsx.x + hueShift * weight);
float s = clamp(adjustSaturation(hsx.y, satAmount * weight), 0.0, 1.0);
float v = (u_int1 == COLORSPACE_HSL)
? clamp(adjustLightness(hsx.z, lightAmount * weight), 0.0, 1.0)
: clamp(adjustBrightness(hsx.z, lightAmount * weight), 0.0, 1.0);
vec3 adjusted = vec3(h, s, v);
result = (u_int1 == COLORSPACE_HSL)
? hsl2rgb(adjusted)
: hsb2rgb(adjusted);
} else {
result = original.rgb;
}
fragColor = vec4(result, original.a);
}
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#version 300 es
#pragma passes 2
precision highp float;
// Blur type constants
const int BLUR_GAUSSIAN = 0;
const int BLUR_BOX = 1;
const int BLUR_RADIAL = 2;
// Radial blur config
const int RADIAL_SAMPLES = 12;
const float RADIAL_STRENGTH = 0.0003;
uniform sampler2D u_image0;
uniform vec2 u_resolution;
uniform int u_int0; // Blur type (BLUR_GAUSSIAN, BLUR_BOX, BLUR_RADIAL)
uniform float u_float0; // Blur radius/amount
uniform int u_pass; // Pass index (0 = horizontal, 1 = vertical)
in vec2 v_texCoord;
layout(location = 0) out vec4 fragColor0;
float gaussian(float x, float sigma) {
return exp(-(x * x) / (2.0 * sigma * sigma));
}
void main() {
vec2 texelSize = 1.0 / u_resolution;
float radius = max(u_float0, 0.0);
// Radial (angular) blur - single pass, doesn't use separable
if (u_int0 == BLUR_RADIAL) {
// Only execute on first pass
if (u_pass > 0) {
fragColor0 = texture(u_image0, v_texCoord);
return;
}
vec2 center = vec2(0.5);
vec2 dir = v_texCoord - center;
float dist = length(dir);
if (dist < 1e-4) {
fragColor0 = texture(u_image0, v_texCoord);
return;
}
vec4 sum = vec4(0.0);
float totalWeight = 0.0;
float angleStep = radius * RADIAL_STRENGTH;
dir /= dist;
float cosStep = cos(angleStep);
float sinStep = sin(angleStep);
float negAngle = -float(RADIAL_SAMPLES) * angleStep;
vec2 rotDir = vec2(
dir.x * cos(negAngle) - dir.y * sin(negAngle),
dir.x * sin(negAngle) + dir.y * cos(negAngle)
);
for (int i = -RADIAL_SAMPLES; i <= RADIAL_SAMPLES; i++) {
vec2 uv = center + rotDir * dist;
float w = 1.0 - abs(float(i)) / float(RADIAL_SAMPLES);
sum += texture(u_image0, uv) * w;
totalWeight += w;
rotDir = vec2(
rotDir.x * cosStep - rotDir.y * sinStep,
rotDir.x * sinStep + rotDir.y * cosStep
);
}
fragColor0 = sum / max(totalWeight, 0.001);
return;
}
// Separable Gaussian / Box blur
int samples = int(ceil(radius));
if (samples == 0) {
fragColor0 = texture(u_image0, v_texCoord);
return;
}
// Direction: pass 0 = horizontal, pass 1 = vertical
vec2 dir = (u_pass == 0) ? vec2(1.0, 0.0) : vec2(0.0, 1.0);
vec4 color = vec4(0.0);
float totalWeight = 0.0;
float sigma = radius / 2.0;
for (int i = -samples; i <= samples; i++) {
vec2 offset = dir * float(i) * texelSize;
vec4 sample_color = texture(u_image0, v_texCoord + offset);
float weight;
if (u_int0 == BLUR_GAUSSIAN) {
weight = gaussian(float(i), sigma);
} else {
// BLUR_BOX
weight = 1.0;
}
color += sample_color * weight;
totalWeight += weight;
}
fragColor0 = color / totalWeight;
}
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#version 300 es
precision highp float;
uniform sampler2D u_image0;
in vec2 v_texCoord;
layout(location = 0) out vec4 fragColor0;
layout(location = 1) out vec4 fragColor1;
layout(location = 2) out vec4 fragColor2;
layout(location = 3) out vec4 fragColor3;
void main() {
vec4 color = texture(u_image0, v_texCoord);
// Output each channel as grayscale to separate render targets
fragColor0 = vec4(vec3(color.r), 1.0); // Red channel
fragColor1 = vec4(vec3(color.g), 1.0); // Green channel
fragColor2 = vec4(vec3(color.b), 1.0); // Blue channel
fragColor3 = vec4(vec3(color.a), 1.0); // Alpha channel
}
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@@ -1,71 +0,0 @@
#version 300 es
precision highp float;
// Levels Adjustment
// u_int0: channel (0=RGB, 1=R, 2=G, 3=B) default: 0
// u_float0: input black (0-255) default: 0
// u_float1: input white (0-255) default: 255
// u_float2: gamma (0.01-9.99) default: 1.0
// u_float3: output black (0-255) default: 0
// u_float4: output white (0-255) default: 255
uniform sampler2D u_image0;
uniform int u_int0;
uniform float u_float0;
uniform float u_float1;
uniform float u_float2;
uniform float u_float3;
uniform float u_float4;
in vec2 v_texCoord;
out vec4 fragColor;
vec3 applyLevels(vec3 color, float inBlack, float inWhite, float gamma, float outBlack, float outWhite) {
float inRange = max(inWhite - inBlack, 0.0001);
vec3 result = clamp((color - inBlack) / inRange, 0.0, 1.0);
result = pow(result, vec3(1.0 / gamma));
result = mix(vec3(outBlack), vec3(outWhite), result);
return result;
}
float applySingleChannel(float value, float inBlack, float inWhite, float gamma, float outBlack, float outWhite) {
float inRange = max(inWhite - inBlack, 0.0001);
float result = clamp((value - inBlack) / inRange, 0.0, 1.0);
result = pow(result, 1.0 / gamma);
result = mix(outBlack, outWhite, result);
return result;
}
void main() {
vec4 texColor = texture(u_image0, v_texCoord);
vec3 color = texColor.rgb;
float inBlack = u_float0 / 255.0;
float inWhite = u_float1 / 255.0;
float gamma = u_float2;
float outBlack = u_float3 / 255.0;
float outWhite = u_float4 / 255.0;
vec3 result;
if (u_int0 == 0) {
result = applyLevels(color, inBlack, inWhite, gamma, outBlack, outWhite);
}
else if (u_int0 == 1) {
result = color;
result.r = applySingleChannel(color.r, inBlack, inWhite, gamma, outBlack, outWhite);
}
else if (u_int0 == 2) {
result = color;
result.g = applySingleChannel(color.g, inBlack, inWhite, gamma, outBlack, outWhite);
}
else if (u_int0 == 3) {
result = color;
result.b = applySingleChannel(color.b, inBlack, inWhite, gamma, outBlack, outWhite);
}
else {
result = color;
}
fragColor = vec4(result, texColor.a);
}

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