diff --git a/.github/workflows/dev.yml b/.github/workflows/dev.yml index 64b8f80b..930833d9 100644 --- a/.github/workflows/dev.yml +++ b/.github/workflows/dev.yml @@ -13,14 +13,6 @@ jobs: - name: Checkout uses: actions/checkout@v3 - - name: Set up Python - uses: useblacksmith/setup-python@v6 - with: - python-version: "3.10" - - - name: Install dependencies - run: pip install -r requirements.txt - - name: Clear space to remove unused folders run: | rm -rf /usr/share/dotnet diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 74cfeccf..b7bf15fd 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -1,10 +1,10 @@ -name: Release +name: release on: workflow_dispatch: -# push: -# branches: -# - "main" + push: + branches: + - "main" jobs: release: @@ -21,14 +21,6 @@ jobs: with: persist-credentials: false - - name: Set up Python - uses: useblacksmith/setup-python@v6 - with: - python-version: "3.10" - - - name: Install dependencies - run: pip install -r requirements.txt - - name: Clear space to remove unused folders run: | rm -rf /usr/share/dotnet @@ -55,6 +47,7 @@ jobs: uses: codfish/semantic-release-action@v3 id: semanticrelease with: + dry-run: true additional-packages: | ['@semantic-release/git', '@semantic-release/changelog', '@semantic-release/exec'] env: diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index be42f71a..2f95b51e 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -19,15 +19,10 @@ jobs: - name: Set up Python uses: useblacksmith/setup-python@v6 with: - python-version: "3.10" + python-version: "3.11" - name: Install dependencies run: pip install -r requirements.txt - name: Run Python tests run: python -m unittest discover - - - name: Run snapshot restoration tests - run: | - chmod +x tests/test_restore_snapshot.sh - ./tests/test_restore_snapshot.sh diff --git a/.releaserc b/.releaserc index 7a113926..8a04e1ea 100644 --- a/.releaserc +++ b/.releaserc @@ -1,6 +1,7 @@ { "branches": [ - "main" + "main", + "feat/refactor" ], "tagFormat": "${version}", "plugins": [ diff --git a/.runpod/hub.json b/.runpod/hub.json index 7c879af6..4225f707 100644 --- a/.runpod/hub.json +++ b/.runpod/hub.json @@ -19,24 +19,6 @@ "description": "When enabled, the worker will stop after each finished job to have a clean state", "default": false } - }, - { - "key": "COMFY_POLLING_INTERVAL_MS", - "input": { - "name": "Polling Interval (ms)", - "type": "number", - "description": "Time to wait between poll attempts in milliseconds", - "default": 250 - } - }, - { - "key": "COMFY_POLLING_MAX_RETRIES", - "input": { - "name": "Max Polling Retries", - "type": "number", - "description": "Maximum number of poll attempts. Increase for longer workflows", - "default": 500 - } } ] } diff --git a/.runpod/tests.json b/.runpod/tests.json index 425ef06f..74bf2fbd 100644 --- a/.runpod/tests.json +++ b/.runpod/tests.json @@ -73,24 +73,11 @@ { "key": "REFRESH_WORKER", "value": "false" - }, - { - "key": "COMFY_POLLING_INTERVAL_MS", - "value": "250" - }, - { - "key": "COMFY_POLLING_MAX_RETRIES", - "value": "500" } ], "allowedCudaVersions": [ "12.7", - "12.6", - "12.5", - "12.4", - "12.3", - "12.2", - "12.1" + "12.6" ] } } diff --git a/CHANGELOG.md b/CHANGELOG.md index fe183430..2070634a 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,3 +1,16 @@ +# [4.1.0](https://github.com/runpod-workers/worker-comfyui/compare/4.0.1...4.1.0) (2025-05-02) + + +### Bug Fixes + +* moved code back into stage 1 ([d9ed145](https://github.com/runpod-workers/worker-comfyui/commit/d9ed14571308ad27481ae2dda4762d32b73b5d20)) + + +### Features + +* move stuff around ([2b2bc12](https://github.com/runpod-workers/worker-comfyui/commit/2b2bc1238dec5715092fa4b3e1418b8a443a409b)) +* removed polling; added websocket; allow multiple output images; ([79a560f](https://github.com/runpod-workers/worker-comfyui/commit/79a560f46fbd303828175d138098faebd4baa97e)) + # [3.6.0](https://github.com/blib-la/runpod-worker-comfy/compare/3.5.0...3.6.0) (2025-03-12) diff --git a/Dockerfile b/Dockerfile index 08cb3e5f..3153161a 100644 --- a/Dockerfile +++ b/Dockerfile @@ -6,7 +6,7 @@ ENV DEBIAN_FRONTEND=noninteractive # Prefer binary wheels over source distributions for faster pip installations ENV PIP_PREFER_BINARY=1 # Ensures output from python is printed immediately to the terminal without buffering -ENV PYTHONUNBUFFERED=1 +ENV PYTHONUNBUFFERED=1 # Speed up some cmake builds ENV CMAKE_BUILD_PARALLEL_LEVEL=8 @@ -30,7 +30,7 @@ RUN pip install uv RUN uv pip install comfy-cli --system # Install ComfyUI -RUN /usr/bin/yes | comfy --workspace /comfyui install --version 0.3.29 --cuda-version 12.6 --nvidia --skip-manager +RUN /usr/bin/yes | comfy --workspace /comfyui install --version 0.3.30 --cuda-version 12.6 --nvidia # Change working directory to ComfyUI WORKDIR /comfyui @@ -41,21 +41,14 @@ ADD src/extra_model_paths.yaml ./ # Go back to the root WORKDIR / -# install dependencies -RUN uv pip install runpod requests --system +# Install Python runtime dependencies for the handler +RUN uv pip install runpod requests websocket-client --system +# Add application code and scripts +ADD src/start.sh handler.py test_input.json ./ +RUN chmod +x /start.sh -# Add files -ADD src/start.sh src/restore_snapshot.sh src/rp_handler.py test_input.json ./ -RUN chmod +x /start.sh /restore_snapshot.sh - -# Optionally copy the snapshot file -ADD *snapshot*.json / - -# Restore the snapshot to install custom nodes -RUN /restore_snapshot.sh - -# Start container +# Set the default command to run when starting the container CMD ["/start.sh"] # Stage 2: Download models @@ -73,32 +66,35 @@ RUN mkdir -p models/checkpoints models/vae models/unet models/clip # Download checkpoints/vae/unet/clip models to include in image based on model type RUN if [ "$MODEL_TYPE" = "sdxl" ]; then \ - wget -O models/checkpoints/sd_xl_base_1.0.safetensors https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0.safetensors && \ - wget -O models/vae/sdxl_vae.safetensors https://huggingface.co/stabilityai/sdxl-vae/resolve/main/sdxl_vae.safetensors && \ - wget -O models/vae/sdxl-vae-fp16-fix.safetensors https://huggingface.co/madebyollin/sdxl-vae-fp16-fix/resolve/main/sdxl_vae.safetensors; \ - elif [ "$MODEL_TYPE" = "sd3" ]; then \ - wget --header="Authorization: Bearer ${HUGGINGFACE_ACCESS_TOKEN}" -O models/checkpoints/sd3_medium_incl_clips_t5xxlfp8.safetensors https://huggingface.co/stabilityai/stable-diffusion-3-medium/resolve/main/sd3_medium_incl_clips_t5xxlfp8.safetensors; \ - elif [ "$MODEL_TYPE" = "flux1-schnell" ]; then \ - wget -O models/unet/flux1-schnell.safetensors https://huggingface.co/black-forest-labs/FLUX.1-schnell/resolve/main/flux1-schnell.safetensors && \ - wget -O models/clip/clip_l.safetensors https://huggingface.co/comfyanonymous/flux_text_encoders/resolve/main/clip_l.safetensors && \ - wget -O models/clip/t5xxl_fp8_e4m3fn.safetensors https://huggingface.co/comfyanonymous/flux_text_encoders/resolve/main/t5xxl_fp8_e4m3fn.safetensors && \ - wget -O models/vae/ae.safetensors https://huggingface.co/black-forest-labs/FLUX.1-schnell/resolve/main/ae.safetensors; \ - elif [ "$MODEL_TYPE" = "flux1-dev" ]; then \ - # Full precision FLUX.1 dev - wget --header="Authorization: Bearer ${HUGGINGFACE_ACCESS_TOKEN}" -O models/unet/flux1-dev.safetensors https://huggingface.co/black-forest-labs/FLUX.1-dev/resolve/main/flux1-dev.safetensors && \ - wget -O models/clip/clip_l.safetensors https://huggingface.co/comfyanonymous/flux_text_encoders/resolve/main/clip_l.safetensors && \ - wget -O models/clip/t5xxl_fp8_e4m3fn.safetensors https://huggingface.co/comfyanonymous/flux_text_encoders/resolve/main/t5xxl_fp8_e4m3fn.safetensors && \ - wget --header="Authorization: Bearer ${HUGGINGFACE_ACCESS_TOKEN}" -O models/vae/ae.safetensors https://huggingface.co/black-forest-labs/FLUX.1-dev/resolve/main/ae.safetensors; \ - elif [ "$MODEL_TYPE" = "flux1-dev-fp8" ]; then \ - # Default model if none specified during build - wget -O models/checkpoints/flux1-dev-fp8.safetensors https://huggingface.co/Comfy-Org/flux1-dev/resolve/main/flux1-dev-fp8.safetensors; \ + wget -q -O models/checkpoints/sd_xl_base_1.0.safetensors https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0.safetensors && \ + wget -q -O models/vae/sdxl_vae.safetensors https://huggingface.co/stabilityai/sdxl-vae/resolve/main/sdxl_vae.safetensors && \ + wget -q -O models/vae/sdxl-vae-fp16-fix.safetensors https://huggingface.co/madebyollin/sdxl-vae-fp16-fix/resolve/main/sdxl_vae.safetensors; \ + fi + +RUN if [ "$MODEL_TYPE" = "sd3" ]; then \ + wget -q --header="Authorization: Bearer ${HUGGINGFACE_ACCESS_TOKEN}" -O models/checkpoints/sd3_medium_incl_clips_t5xxlfp8.safetensors https://huggingface.co/stabilityai/stable-diffusion-3-medium/resolve/main/sd3_medium_incl_clips_t5xxlfp8.safetensors; \ + fi + +RUN if [ "$MODEL_TYPE" = "flux1-schnell" ]; then \ + wget -q --header="Authorization: Bearer ${HUGGINGFACE_ACCESS_TOKEN}" -O models/unet/flux1-schnell.safetensors https://huggingface.co/black-forest-labs/FLUX.1-schnell/resolve/main/flux1-schnell.safetensors && \ + wget -q -O models/clip/clip_l.safetensors https://huggingface.co/comfyanonymous/flux_text_encoders/resolve/main/clip_l.safetensors && \ + wget -q -O models/clip/t5xxl_fp8_e4m3fn.safetensors https://huggingface.co/comfyanonymous/flux_text_encoders/resolve/main/t5xxl_fp8_e4m3fn.safetensors && \ + wget -q --header="Authorization: Bearer ${HUGGINGFACE_ACCESS_TOKEN}" -O models/vae/ae.safetensors https://huggingface.co/black-forest-labs/FLUX.1-schnell/resolve/main/ae.safetensors; \ + fi + +RUN if [ "$MODEL_TYPE" = "flux1-dev" ]; then \ + wget -q --header="Authorization: Bearer ${HUGGINGFACE_ACCESS_TOKEN}" -O models/unet/flux1-dev.safetensors https://huggingface.co/black-forest-labs/FLUX.1-dev/resolve/main/flux1-dev.safetensors && \ + wget -q -O models/clip/clip_l.safetensors https://huggingface.co/comfyanonymous/flux_text_encoders/resolve/main/clip_l.safetensors && \ + wget -q -O models/clip/t5xxl_fp8_e4m3fn.safetensors https://huggingface.co/comfyanonymous/flux_text_encoders/resolve/main/t5xxl_fp8_e4m3fn.safetensors && \ + wget -q --header="Authorization: Bearer ${HUGGINGFACE_ACCESS_TOKEN}" -O models/vae/ae.safetensors https://huggingface.co/black-forest-labs/FLUX.1-dev/resolve/main/ae.safetensors; \ + fi + +RUN if [ "$MODEL_TYPE" = "flux1-dev-fp8" ]; then \ + wget -q -O models/checkpoints/flux1-dev-fp8.safetensors https://huggingface.co/Comfy-Org/flux1-dev/resolve/main/flux1-dev-fp8.safetensors; \ fi # Stage 3: Final image FROM base AS final # Copy models from stage 2 to the final image -COPY --from=downloader /comfyui/models /comfyui/models - -# Start container -CMD ["/start.sh"] \ No newline at end of file +COPY --from=downloader /comfyui/models /comfyui/models \ No newline at end of file diff --git a/README.md b/README.md index 1a2d13b7..61a987b7 100644 --- a/README.md +++ b/README.md @@ -10,464 +10,175 @@ --- - +This project allows you to run ComfyUI workflows as a serverless API endpoint on the RunPod platform. Submit workflows via API calls and receive generated images as base64 strings or S3 URLs. + +## Table of Contents - [Quickstart](#quickstart) -- [Features](#features) -- [Config](#config) - - [Upload image to AWS S3](#upload-image-to-aws-s3) -- [Use the Docker image on RunPod](#use-the-docker-image-on-runpod) - - [Create your template (optional)](#create-your-template-optional) - - [Create your endpoint](#create-your-endpoint) - - [GPU recommendations](#gpu-recommendations) -- [API specification](#api-specification) - - [JSON Request Body](#json-request-body) - - [Fields](#fields) - - ["input.images"](#inputimages) -- [Interact with your RunPod API](#interact-with-your-runpod-api) - - [Health status](#health-status) - - [Generate an image](#generate-an-image) - - [Example request for SDXL with cURL](#example-request-for-sdxl-with-curl) -- [How to get the workflow from ComfyUI?](#how-to-get-the-workflow-from-comfyui) -- [Bring Your Own Models and Nodes](#bring-your-own-models-and-nodes) - - [Network Volume](#network-volume) - - [Custom Docker Image](#custom-docker-image) - - [Adding Custom Models](#adding-custom-models) - - [Adding Custom Nodes](#adding-custom-nodes) - - [Building the Image](#building-the-image) -- [Local testing](#local-testing) - - [Setup](#setup) - - [Setup for Windows](#setup-for-windows) - - [Testing the RunPod handler](#testing-the-runpod-handler) - - [Local API](#local-api) - - [Access the local Worker API](#access-the-local-worker-api) - - [Access local ComfyUI](#access-local-comfyui) -- [Automatically deploy to Docker hub with GitHub Actions](#automatically-deploy-to-docker-hub-with-github-actions) -- [Acknowledgments](#acknowledgments) - - +- [Available Docker Images](#available-docker-images) +- [API Specification](#api-specification) +- [Usage](#usage) +- [Getting the Workflow JSON](#getting-the-workflow-json) +- [Further Documentation](#further-documentation) --- ## Quickstart -- ๐Ÿณ Choose one of the five available images for your serverless endpoint: - - `runpod/worker-comfyui:3.6.0-base`: doesn't contain anything, just a clean ComfyUI - - `runpod/worker-comfyui:3.6.0-flux1-schnell`: contains the checkpoint, text encoders and VAE for [FLUX.1 schnell](https://huggingface.co/black-forest-labs/FLUX.1-schnell) - - `runpod/worker-comfyui:3.6.0-flux1-dev`: contains the checkpoint, text encoders and VAE for [FLUX.1 dev](https://huggingface.co/black-forest-labs/FLUX.1-dev) - - `runpod/worker-comfyui:3.6.0-sdxl`: contains the checkpoint and VAE for [Stable Diffusion XL](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) - - `runpod/worker-comfyui:3.6.0-sd3`: contains the checkpoint for [Stable Diffusion 3 medium](https://huggingface.co/stabilityai/stable-diffusion-3-medium) -- โ„น๏ธ [Use the Docker image on RunPod](#use-the-docker-image-on-runpod) -- ๐Ÿงช Pick an [example workflow](./test_resources/workflows/) & [send it to your deployed endpoint](#interact-with-your-runpod-api) - -## Features - -- Run any [ComfyUI](https://github.com/comfyanonymous/ComfyUI) workflow to generate an image -- Provide input images as base64-encoded string -- The generated image is either: - - Returned as base64-encoded string (default) - - Uploaded to AWS S3 ([if AWS S3 is configured](#upload-image-to-aws-s3)) -- There are a few different Docker images to choose from: - - `runpod/worker-comfyui:3.6.0-flux1-schnell`: contains the [flux1-schnell.safetensors](https://huggingface.co/black-forest-labs/FLUX.1-schnell) checkpoint, the [clip_l.safetensors](https://huggingface.co/comfyanonymous/flux_text_encoders/resolve/main/clip_l.safetensors) + [t5xxl_fp8_e4m3fn.safetensors](https://huggingface.co/comfyanonymous/flux_text_encoders/resolve/main/t5xxl_fp8_e4m3fn.safetensors) text encoders and [ae.safetensors](https://huggingface.co/black-forest-labs/FLUX.1-schnell/resolve/main/ae.safetensors) VAE for FLUX.1-schnell - - `runpod/worker-comfyui:3.6.0-flux1-dev`: contains the [flux1-dev.safetensors](https://huggingface.co/black-forest-labs/FLUX.1-dev) checkpoint, the [clip_l.safetensors](https://huggingface.co/comfyanonymous/flux_text_encoders/resolve/main/clip_l.safetensors) + [t5xxl_fp8_e4m3fn.safetensors](https://huggingface.co/comfyanonymous/flux_text_encoders/resolve/main/t5xxl_fp8_e4m3fn.safetensors) text encoders and [ae.safetensors](https://huggingface.co/black-forest-labs/FLUX.1-dev/resolve/main/ae.safetensors) VAE for FLUX.1-dev - - `runpod/worker-comfyui:3.6.0-sdxl`: contains the checkpoints and VAE for Stable Diffusion XL - - Checkpoint: [sd_xl_base_1.0.safetensors](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) - - VAEs: - - [sdxl_vae.safetensors](https://huggingface.co/stabilityai/sdxl-vae/) - - [sdxl-vae-fp16-fix](https://huggingface.co/madebyollin/sdxl-vae-fp16-fix/) - - `runpod/worker-comfyui:3.6.0-sd3`: contains the [sd3_medium_incl_clips_t5xxlfp8.safetensors](https://huggingface.co/stabilityai/stable-diffusion-3-medium) checkpoint for Stable Diffusion 3 medium -- [Bring your own models](#bring-your-own-models) -- Based on [Ubuntu + NVIDIA CUDA](https://hub.docker.com/r/nvidia/cuda) - -## Config - -| Environment Variable | Description | Default | -| --------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------- | -| `REFRESH_WORKER` | When you want to stop the worker after each finished job to have a clean state, see [official documentation](https://docs.runpod.io/docs/handler-additional-controls#refresh-worker). | `false` | -| `COMFY_POLLING_INTERVAL_MS` | Time to wait between poll attempts in milliseconds. | `250` | -| `COMFY_POLLING_MAX_RETRIES` | Maximum number of poll attempts. This should be increased the longer your workflow is running. | `500` | -| `SERVE_API_LOCALLY` | Enable local API server for development and testing. See [Local Testing](#local-testing) for more details. | disabled | - -### Upload image to AWS S3 - -This is only needed if you want to upload the generated picture to AWS S3. If you don't configure this, your image will be exported as base64-encoded string. - -- Create a bucket in region of your choice in AWS S3 (`BUCKET_ENDPOINT_URL`) -- Create an IAM that has access rights to AWS S3 -- Create an Access-Key (`BUCKET_ACCESS_KEY_ID` & `BUCKET_SECRET_ACCESS_KEY`) for that IAM -- Configure these environment variables for your RunPod worker: - -| Environment Variable | Description | Example | -| -------------------------- | ------------------------------------------------------- | -------------------------------------------- | -| `BUCKET_ENDPOINT_URL` | The endpoint URL of your S3 bucket. | `https://.s3..amazonaws.com` | -| `BUCKET_ACCESS_KEY_ID` | Your AWS access key ID for accessing the S3 bucket. | `AKIAIOSFODNN7EXAMPLE` | -| `BUCKET_SECRET_ACCESS_KEY` | Your AWS secret access key for accessing the S3 bucket. | `wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY` | - -## Use the Docker image on RunPod - -### Create your template (optional) - -- Create a [new template](https://runpod.io/console/serverless/user/templates) by clicking on `New Template` -- In the dialog, configure: - - Template Name: `worker-comfyui` (it can be anything you want) - - Template Type: serverless (change template type to "serverless") - - Container Image: `/:tag`, in this case: `runpod/worker-comfyui:3.6.0-sd3` (or `-base` for a clean image or `-sdxl` for Stable Diffusion XL or `-flex1-schnell` for FLUX.1 schnell) - - Container Registry Credentials: You can leave everything as it is, as this repo is public - - Container Disk: `20 GB` - - (optional) Environment Variables: [Configure S3](#upload-image-to-aws-s3) - - Note: You can also not configure it, the images will then stay in the worker. In order to have them stored permanently, [we have to add the network volume](https://github.com/runpod-workers/worker-comfyui/issues/1) -- Click on `Save Template` - -### Create your endpoint - -- Navigate to [`Serverless > Endpoints`](https://www.runpod.io/console/serverless/user/endpoints) and click on `New Endpoint` -- In the dialog, configure: - - - Endpoint Name: `comfy` - - Worker configuration: Select a GPU that can run the model you have chosen (see [GPU recommendations](#gpu-recommendations)) - - Active Workers: `0` (whatever makes sense for you) - - Max Workers: `3` (whatever makes sense for you) - - GPUs/Worker: `1` - - Idle Timeout: `5` (you can leave the default) - - Flash Boot: `enabled` (doesn't cost more, but provides faster boot of our worker, which is good) - - Select Template: `worker-comfyui` (or whatever name you gave your template) - - (optional) Advanced: If you are using a Network Volume, select it under `Select Network Volume`. Otherwise leave the defaults. - -- Click `deploy` -- Your endpoint will be created, you can click on it to see the dashboard - -### GPU recommendations - -| Model | Image | Minimum VRAM Required | Container Size | -| ------------------------- | --------------- | --------------------- | -------------- | -| Stable Diffusion XL | `sdxl` | 8 GB | 15 GB | -| Stable Diffusion 3 Medium | `sd3` | 5 GB | 20 GB | -| FLUX.1 Schnell | `flux1-schnell` | 24 GB | 30 GB | -| FLUX.1 dev | `flux1-dev` | 24 GB | 30 GB | - -## API specification - -The following describes which fields exist when doing requests to the API. We only describe the fields that are sent via `input` as those are needed by the worker itself. For a full list of fields, please take a look at the [official documentation](https://docs.runpod.io/docs/serverless-usage). - -### JSON Request Body +1. ๐Ÿณ Choose one of the [available Docker images](#available-docker-images) for your serverless endpoint (e.g., `runpod/worker-comfyui:-sd3`). +2. ๐Ÿ“„ Follow the [Deployment Guide](docs/deployment.md) to set up your RunPod template and endpoint. +3. โš™๏ธ Optionally configure the worker (e.g., for S3 upload) using environment variables - see the full [Configuration Guide](docs/configuration.md). +4. ๐Ÿงช Pick an example workflow from [`test_resources/workflows/`](./test_resources/workflows/) or [get your own](#getting-the-workflow-json). +5. ๐Ÿš€ Follow the [Usage](#usage) steps below to interact with your deployed endpoint. + +## Available Docker Images + +These images are available on Docker Hub under `runpod/worker-comfyui`: + +- **`runpod/worker-comfyui:-base`**: Clean ComfyUI install with no models. +- **`runpod/worker-comfyui:-flux1-schnell`**: Includes checkpoint, text encoders, and VAE for [FLUX.1 schnell](https://huggingface.co/black-forest-labs/FLUX.1-schnell). +- **`runpod/worker-comfyui:-flux1-dev`**: Includes checkpoint, text encoders, and VAE for [FLUX.1 dev](https://huggingface.co/black-forest-labs/FLUX.1-dev). +- **`runpod/worker-comfyui:-sdxl`**: Includes checkpoint and VAEs for [Stable Diffusion XL](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0). +- **`runpod/worker-comfyui:-sd3`**: Includes checkpoint for [Stable Diffusion 3 medium](https://huggingface.co/stabilityai/stable-diffusion-3-medium). + +Replace `` with the current release tag, check the [releases page](https://github.com/runpod-workers/worker-comfyui/releases) for the latest version. + +## API Specification + +The worker exposes standard RunPod serverless endpoints (`/run`, `/runsync`, `/health`). By default, images are returned as base64 strings. You can configure the worker to upload images to an S3 bucket instead by setting specific environment variables (see [Configuration Guide](docs/configuration.md)). + +Use the `/runsync` endpoint for synchronous requests that wait for the job to complete and return the result directly. Use the `/run` endpoint for asynchronous requests that return immediately with a job ID; you'll need to poll the `/status` endpoint separately to get the result. + +### Input ```json { "input": { - "workflow": {}, + "workflow": { + "6": { + "inputs": { + "text": "a ball on the table", + "clip": ["30", 1] + }, + "class_type": "CLIPTextEncode", + "_meta": { + "title": "CLIP Text Encode (Positive Prompt)" + } + } + }, "images": [ { - "name": "example_image_name.png", - "image": "base64_encoded_string" + "name": "input_image_1.png", + "image": "data:image/png;base64,iVBOR..." } ] } } ``` -### Fields - -| Field Path | Type | Required | Description | -| ---------------- | ------ | -------- | ----------------------------------------------------------------------------------------------------------------------------------------- | -| `input` | Object | Yes | The top-level object containing the request data. | -| `input.workflow` | Object | Yes | Contains the ComfyUI workflow configuration. | -| `input.images` | Array | No | An array of images. Each image will be added into the "input"-folder of ComfyUI and can then be used in the workflow by using it's `name` | - -#### "input.images" - -An array of images, where each image should have a different name. - -๐Ÿšจ The request body for a RunPod endpoint is 10 MB for `/run` and 20 MB for `/runsync`, so make sure that your input images are not super huge as this will be blocked by RunPod otherwise, see the [official documentation](https://docs.runpod.io/docs/serverless-endpoint-urls) - -| Field Name | Type | Required | Description | -| ---------- | ------ | -------- | ---------------------------------------------------------------------------------------- | -| `name` | String | Yes | The name of the image. Please use the same name in your workflow to reference the image. | -| `image` | String | Yes | A base64 encoded string of the image. | - -## Interact with your RunPod API - -1. **Generate an API Key**: - - - In the [User Settings](https://www.runpod.io/console/serverless/user/settings), click on `API Keys` and then on the `API Key` button. - - Save the generated key somewhere safe, as you will not be able to see it again when you navigate away from the page. - -2. **Use the API Key**: - - - Use cURL or any other tool to access the API using the API key and your Endpoint ID: - - Replace `` with your key. - -3. **Use your Endpoint**: - - Replace `` with the [ID of the endpoint](https://www.runpod.io/console/serverless). (You can find the endpoint ID by clicking on your endpoint; it is written underneath the name of the endpoint at the top and also part of the URLs shown at the bottom of the first box.) - -![How to find the EndpointID](./assets/my-endpoint-with-endpointID.png) +The following tables describe the fields within the `input` object: -### Health status +| Field Path | Type | Required | Description | +| ---------------- | ------ | -------- | ------------------------------------------------------------------------------------------------------------------------------------------ | +| `input` | Object | Yes | Top-level object containing request data. | +| `input.workflow` | Object | Yes | The ComfyUI workflow exported in the [required format](#getting-the-workflow-json). | +| `input.images` | Array | No | Optional array of input images. Each image is uploaded to ComfyUI's `input` directory and can be referenced by its `name` in the workflow. | -```bash -curl -H "Authorization: Bearer " https://api.runpod.ai/v2//health -``` - -### Generate an image +#### `input.images` Object -You can either create a new job async by using `/run` or a sync by using `/runsync`. The example here is using a sync job and waits until the response is delivered. +Each object within the `input.images` array must contain: -The API expects a [JSON in this form](#json-request-body), where `workflow` is the [workflow from ComfyUI, exported as JSON](#how-to-get-the-workflow-from-comfyui) and `images` is optional. +| Field Name | Type | Required | Description | +| ---------- | ------ | -------- | --------------------------------------------------------------------------------------------------------------------------------- | +| `name` | String | Yes | Filename used to reference the image in the workflow (e.g., via a "Load Image" node). Must be unique within the array. | +| `image` | String | Yes | Base64 encoded string of the image. A data URI prefix (e.g., `data:image/png;base64,`) is optional and will be handled correctly. | -Please also take a look at the [test_input.json](./test_input.json) to see how the API input should look like. +> [!NOTE] > **Size Limits:** RunPod endpoints have request size limits (e.g., 10MB for `/run`, 20MB for `/runsync`). Large base64 input images can exceed these limits. See [RunPod Docs](https://docs.runpod.io/docs/serverless-endpoint-urls). -#### Example request for SDXL with cURL - -```bash -curl -X POST -H "Authorization: Bearer " -H "Content-Type: application/json" -d '{"input":{"workflow":{"3":{"inputs":{"seed":1337,"steps":20,"cfg":8,"sampler_name":"euler","scheduler":"normal","denoise":1,"model":["4",0],"positive":["6",0],"negative":["7",0],"latent_image":["5",0]},"class_type":"KSampler"},"4":{"inputs":{"ckpt_name":"sd_xl_base_1.0.safetensors"},"class_type":"CheckpointLoaderSimple"},"5":{"inputs":{"width":512,"height":512,"batch_size":1},"class_type":"EmptyLatentImage"},"6":{"inputs":{"text":"beautiful scenery nature glass bottle landscape, purple galaxy bottle,","clip":["4",1]},"class_type":"CLIPTextEncode"},"7":{"inputs":{"text":"text, watermark","clip":["4",1]},"class_type":"CLIPTextEncode"},"8":{"inputs":{"samples":["3",0],"vae":["4",2]},"class_type":"VAEDecode"},"9":{"inputs":{"filename_prefix":"ComfyUI","images":["8",0]},"class_type":"SaveImage"}}}}' https://api.runpod.ai/v2//runsync -``` +### Output -Example response with AWS S3 bucket configuration +> [!WARNING] > **Breaking Change in Output Format (5.0.0+)** +> Versions `< 5.0.0` returned the primary image data (S3 URL or base64 string) directly within an `output.message` field. +> Starting with `5.0.0`, the output format has changed significantly, see below ```json { - "delayTime": 2188, - "executionTime": 2297, - "id": "sync-c0cd1eb2-068f-4ecf-a99a-55770fc77391-e1", + "id": "sync-uuid-string", + "status": "COMPLETED", "output": { - "message": "https://bucket.s3.region.amazonaws.com/10-23/sync-c0cd1eb2-068f-4ecf-a99a-55770fc77391-e1/c67ad621.png", - "status": "success" + "images": [ + { + "filename": "ComfyUI_00001_.png", + "type": "base64", + "data": "iVBORw0KGgoAAAANSUhEUg..." + } + ] }, - "status": "COMPLETED" -} -``` - -Example response as base64-encoded image - -```json -{ - "delayTime": 2188, - "executionTime": 2297, - "id": "sync-c0cd1eb2-068f-4ecf-a99a-55770fc77391-e1", - "output": { "message": "base64encodedimage", "status": "success" }, - "status": "COMPLETED" + "delayTime": 123, + "executionTime": 4567 } ``` -## How to get the workflow from ComfyUI? - -- Open ComfyUI in the browser -- Open the `Settings` (gear icon in the top right of the menu) -- In the dialog that appears configure: - - `Enable Dev mode Options`: enable - - Close the `Settings` -- In the menu, click on the `Save (API Format)` button, which will download a file named `workflow_api.json` - -You can now take the content of this file and put it into your `workflow` when interacting with the API. - -## Bring Your Own Models and Nodes - -### Network Volume - -Using a Network Volume allows you to store and access custom models: - -1. **Create a Network Volume**: - - Follow the [RunPod Network Volumes guide](https://docs.runpod.io/pods/storage/create-network-volumes) to create a volume. -2. **Populate the Volume**: - - - Create a temporary GPU instance: - - Navigate to `Manage > Storage`, click `Deploy` under the volume, and deploy any GPU or CPU instance. - - Navigate to `Manage > Pods`. Under the new pod, click `Connect` to open a shell (either via Jupyter notebook or SSH). - - Populate the volume with your models: - ```bash - cd /workspace - for i in checkpoints clip clip_vision configs controlnet embeddings loras upscale_models vae; do mkdir -p models/$i; done - wget -O models/checkpoints/sd_xl_turbo_1.0_fp16.safetensors https://huggingface.co/stabilityai/sdxl-turbo/resolve/main/sd_xl_turbo_1.0_fp16.safetensors - ``` - -3. **Delete the Temporary GPU Instance**: - - - Once populated, [terminate the temporary GPU instance](https://docs.runpod.io/docs/pods#terminating-a-pod). - -4. **Configure Your Endpoint**: - - Use the Network Volume in your endpoint configuration: - - Either create a new endpoint or update an existing one. - - In the endpoint configuration, under `Advanced > Select Network Volume`, select your Network Volume. - -Note: The folders in the Network Volume are automatically available to ComfyUI when the network volume is configured and attached. - -### Custom Docker Image - -If you prefer to include your models and custom nodes directly in the Docker image, follow these steps: - -1. **Fork the Repository**: - - Fork this repository to your own GitHub account. - -#### Adding Custom Models - -To include additional models in your Docker image, edit the `Dockerfile` and add the download commands: - -```Dockerfile -RUN wget -O models/checkpoints/sd_xl_base_1.0.safetensors https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0.safetensors -``` - -#### Adding Custom Nodes - -To include custom nodes in your Docker image: - -1. [Export a snapshot from ComfyUI Manager](https://github.com/ltdrdata/ComfyUI-Manager?tab=readme-ov-file#snapshot-manager) that includes all your desired custom nodes - - 1. Open "Manager > Snapshot Manager" - 2. Create a new snapshot by clicking on "Save snapshot" - 3. Get the `*_snapshot.json` from your ComfyUI: `ComfyUI/custom_nodes/ComfyUI-Manager/snapshots` - -2. Save the snapshot file in the root directory of the project -3. The snapshot will be automatically restored during the Docker build process, see [Building the Image](#building-the-image) - -> [!NOTE] -> -> - Some custom nodes may download additional models during installation, which can significantly increase the image size -> - Having many custom nodes may increase ComfyUI's initialization time - -#### Building the Image - -Build your customized Docker image locally: +| Field Path | Type | Required | Description | +| --------------- | ---------------- | -------- | ----------------------------------------------------------------------------------------------------------- | +| `output` | Object | Yes | Top-level object containing the results of the job execution. | +| `output.images` | Array of Objects | No | Present if the workflow generated images. Contains a list of objects, each representing one output image. | +| `output.errors` | Array of Strings | No | Present if non-fatal errors or warnings occurred during processing (e.g., S3 upload failure, missing data). | -```bash -# Build the base image -docker build -t /worker-comfyui:dev-base --target base --platform linux/amd64 . - -# Build the SDXL image -docker build --build-arg MODEL_TYPE=sdxl -t /worker-comfyui:dev-sdxl --platform linux/amd64 . - -# Build the SD3 image -docker build --build-arg MODEL_TYPE=sd3 --build-arg HUGGINGFACE_ACCESS_TOKEN= -t /worker-comfyui:dev-sd3 --platform linux/amd64 . -``` +#### `output.images` -> [!NOTE] -> Ensure to specify `--platform linux/amd64` to avoid errors on RunPod, see [issue #13](https://github.com/runpod-workers/worker-comfyui/issues/13) - -## Local testing - -Both tests will use the data from [test_input.json](./test_input.json), so make your changes in there to test this properly. - -### Setup - -1. Make sure you have Python >= 3.10 -2. Create a virtual environment: - ```bash - python -m venv venv - ``` -3. Activate the virtual environment: - - **Windows**: - ```bash - .\venv\Scripts\activate - ``` - - **Mac / Linux**: - ```bash - source ./venv/bin/activate - ``` -4. Install the dependencies: - ```bash - pip install -r requirements.txt - ``` - -#### Setup for Windows - -1. Install WSL2 and a Linux distro (like Ubuntu) following [this guide](https://ubuntu.com/tutorials/install-ubuntu-on-wsl2-on-windows-11-with-gui-support#1-overview). You can skip the "Install and use a GUI package" part. -2. After installing Ubuntu, open the terminal and log in: - ```bash - wsl -d Ubuntu - ``` -3. Update the packages: - ```bash - sudo apt update - ``` -4. Install Docker in Ubuntu: - - Follow the [official Docker installation guide](https://docs.docker.com/engine/install/ubuntu/). - - Install docker-compose: - ```bash - sudo apt-get install docker-compose - ``` - - Install the NVIDIA Toolkit in Ubuntu: - Follow [this guide](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html#configuring-docker) and create the `nvidia` runtime. -5. Enable GPU acceleration on Ubuntu on WSL2: - Follow [this guide](https://canonical-ubuntu-wsl.readthedocs-hosted.com/en/latest/tutorials/gpu-cuda/). - - If you already have your GPU driver installed on Windows, you can skip the "Install the appropriate Windows vGPU driver for WSL" step. -6. Add your user to the `docker` group to use Docker without `sudo`: - ```bash - sudo usermod -aG docker $USER - ``` - -Once these steps are completed, you can either run the Docker image directly on Windows using Docker Desktop or switch to Ubuntu in the terminal to run the Docker image via WSL +Each object in the `output.images` array has the following structure: -```bash -wsl -d Ubuntu -``` +| Field Name | Type | Description | +| ---------- | ------ | ----------------------------------------------------------------------------------------------- | +| `filename` | String | The original filename assigned by ComfyUI during generation. | +| `type` | String | Indicates the format of the data. Either `"base64"` or `"s3_url"` (if S3 upload is configured). | +| `data` | String | Contains either the base64 encoded image string or the S3 URL for the uploaded image file. | > [!NOTE] +> The `output.images` field provides a list of all generated images (excluding temporary ones). > -> - Windows: Accessing the API or ComfyUI might not work when you run the Docker Image via WSL, so it is recommended to run the Docker Image directly on Windows using Docker Desktop +> - If S3 upload is **not** configured (default), `type` will be `"base64"` and `data` will contain the base64 encoded image string. +> - If S3 upload **is** configured, `type` will be `"s3_url"` and `data` will contain the S3 URL. See the [Configuration Guide](docs/configuration.md#example-s3-response) for an S3 example response. +> - Clients interacting with the API need to handle this list-based structure under `output.images`. -### Testing the RunPod handler +## Usage -- Run all tests: `python -m unittest discover` -- If you want to run a specific test: `python -m unittest tests.test_rp_handler.TestRunpodWorkerComfy.test_bucket_endpoint_not_configured` +To interact with your deployed RunPod endpoint: -You can also start the handler itself to have the local server running: `python src/rp_handler.py` -To get this to work you will also need to start "ComfyUI", otherwise the handler will not work. +1. **Get API Key:** Generate a key in RunPod [User Settings](https://www.runpod.io/console/serverless/user/settings) (`API Keys` section). +2. **Get Endpoint ID:** Find your endpoint ID on the [Serverless Endpoints](https://www.runpod.io/console/serverless/user/endpoints) page. + ![How to find the EndpointID](./assets/my-endpoint-with-endpointID.png) -### Local API +### Generate Image (Sync Example) -For enhanced local development, you can start an API server that simulates the RunPod worker environment. This feature is particularly useful for debugging and testing your integrations locally. - -Set the `SERVE_API_LOCALLY` environment variable to `true` to activate the local API server when running your Docker container. This is already the default value in the `docker-compose.yml`, so you can get it running by executing: +Send a workflow to the `/runsync` endpoint (waits for completion). Replace `` and ``. The `-d` value should contain the [JSON input described above](#input). ```bash -docker-compose up +curl -X POST \ + -H "Authorization: Bearer " \ + -H "Content-Type: application/json" \ + -d '{"input":{"workflow":{... your workflow JSON ...}}}' \ + https://api.runpod.ai/v2//runsync ``` -> [!NOTE] -> -> - This will only work on computer with an NVIDIA GPU for now, as it requires CUDA. Please open an issue if you want to use it on a CPU / Mac - -#### Access the local Worker API - -- With the local API server running, it's accessible at: [localhost:8000](http://localhost:8000) -- When you open this in your browser, you can also see the API documentation and can interact with the API directly - -> [!NOTE] -> -> - Windows: Accessing the API or ComfyUI might not work when you run the Docker Image via WSL, so it is recommended to run the Docker Image directly on Windows using Docker Desktop - -#### Access local ComfyUI - -- With the local API server running, you can access ComfyUI at: [localhost:8188](http://localhost:8188) - -> [!NOTE] -> -> - Windows: Accessing the API or ComfyUI might not work when you run the Docker Image via WSL, so it is recommended to run the Docker Image directly on Windows using Docker Desktop - -## Automatically deploy to Docker hub with GitHub Actions - -The repo contains two workflows that publish the image to Docker hub using GitHub Actions: - -- [dev.yml](.github/workflows/dev.yml): Creates the image and pushes it to Docker hub with the `dev` tag on every push to the `main` branch -- [release.yml](.github/workflows/release.yml): Creates the image and pushes it to Docker hub with the `latest` and the release tag. It will only be triggered when you create a release on GitHub +You can also use the `/run` endpoint for asynchronous jobs and then poll the `/status` to see when the job is done. Or you [add a `webhook` into your request](https://docs.runpod.io/serverless/endpoints/send-requests#webhook-notifications) to be notified when the job is done. -If you want to use this, you should add these **secrets** to your repository: +Refer to [`test_input.json`](./test_input.json) for a complete input example. -| Configuration Variable | Description | Example Value | -| -------------------------- | ----------------------------------------- | ------------------- | -| `DOCKERHUB_USERNAME` | Your Docker Hub username. | `your-username` | -| `DOCKERHUB_TOKEN` | Your Docker Hub token for authentication. | `your-token` | -| `HUGGINGFACE_ACCESS_TOKEN` | Your READ access token from Hugging Face | `your-access-token` | +## Getting the Workflow JSON -And also make sure to add these **variables** to your repository: +To get the correct `workflow` JSON for the API: -| Variable Name | Description | Example Value | -| ---------------- | ------------------------------------------------------------ | ---------------- | -| `DOCKERHUB_REPO` | The repository on Docker Hub where the image will be pushed. | `runpod` | -| `DOCKERHUB_IMG` | The name of the image to be pushed to Docker Hub. | `worker-comfyui` | +1. Open ComfyUI in your browser. +2. In the top navigation, select `Workflow > Export (API)` +3. A `workflow.json` file will be downloaded. Use the content of this file as the value for the `input.workflow` field in your API requests. -## Acknowledgments +## Further Documentation -- Thanks to [Blibla](https://github.com/blib-la) for providing the original repo (previously under blib-la/runpod-worker-comfy) to RunPod, to continue the open-source development of this worker -- Thanks to [all contributors](https://github.com/runpod-workers/worker-comfyui/graphs/contributors) for your awesome work -- Thanks to [Justin Merrell](https://github.com/justinmerrell) from RunPod for [worker-1111](https://github.com/runpod-workers/worker-a1111), which was used to get inspired on how to create this worker -- Thanks to [Ashley Kleynhans](https://github.com/ashleykleynhans) for [runpod-worker-a1111](https://github.com/ashleykleynhans/runpod-worker-a1111), which was used to get inspired on how to create this worker -- Thanks to [comfyanonymous](https://github.com/comfyanonymous) for creating [ComfyUI](https://github.com/comfyanonymous/ComfyUI), which provides such an awesome API to interact with Stable Diffusion and beyond +- **[Deployment Guide](docs/deployment.md):** Detailed steps for deploying on RunPod. +- **[Configuration Guide](docs/configuration.md):** Full list of environment variables (including S3 setup). +- **[Customization Guide](docs/customization.md):** Adding custom models and nodes (Network Volumes, Docker builds). +- **[Development Guide](docs/development.md):** Setting up a local environment for development & testing +- **[CI/CD Guide](docs/ci-cd.md):** Information about the automated Docker build and publish workflows. +- **[Acknowledgments](docs/acknowledgments.md):** Credits and thanks diff --git a/docker-bake.hcl b/docker-bake.hcl index 588a2128..3bb78e2c 100644 --- a/docker-bake.hcl +++ b/docker-bake.hcl @@ -15,7 +15,8 @@ variable "HUGGINGFACE_ACCESS_TOKEN" { } group "default" { - targets = ["base", "sdxl", "sd3", "flux1-schnell", "flux1-dev"] + // ["base", "sdxl", "sd3", "flux1-schnell", "flux1-dev"] + targets = ["base"] } target "base" { @@ -73,3 +74,12 @@ target "flux1-dev" { inherits = ["base"] } +target "flux1-dev-fp8" { + context = "." + dockerfile = "Dockerfile" + target = "final" + # No args needed, will use default MODEL_TYPE=flux1-dev-fp8 + tags = ["${DOCKERHUB_REPO}/${DOCKERHUB_IMG}:${RELEASE_VERSION}-flux1-dev-fp8"] + inherits = ["base"] +} + diff --git a/docker-compose.yml b/docker-compose.yml index 74820f45..b9f2168f 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -1,6 +1,7 @@ services: comfyui-worker: - image: runpod/worker-comfyui:dev + image: worker-comfyui:dev + pull_policy: never deploy: resources: reservations: diff --git a/docs/acknowledgments.md b/docs/acknowledgments.md new file mode 100644 index 00000000..0112281a --- /dev/null +++ b/docs/acknowledgments.md @@ -0,0 +1,7 @@ +# Acknowledgments + +- Thanks to [Blibla](https://github.com/blib-la) for providing the original repo (previously under `blib-la/runpod-worker-comfy`) to RunPod, to continue the open-source development of this worker. +- Thanks to [all contributors](https://github.com/runpod-workers/worker-comfyui/graphs/contributors) for your awesome work. +- Thanks to [Justin Merrell](https://github.com/justinmerrell) from RunPod for [worker-1111](https://github.com/runpod-workers/worker-a1111), which was used to get inspired on how to create this worker. +- Thanks to [Ashley Kleynhans](https://github.com/ashleykleynhans) for [runpod-worker-a1111](https://github.com/ashleykleynhans/runpod-worker-a1111), which was used to get inspired on how to create this worker. +- Thanks to [comfyanonymous](https://github.com/comfyanonymous) for creating [ComfyUI](https://github.com/comfyanonymous/ComfyUI), which provides such an awesome API to interact with Stable Diffusion and beyond. diff --git a/docs/ci-cd.md b/docs/ci-cd.md new file mode 100644 index 00000000..9bde6e66 --- /dev/null +++ b/docs/ci-cd.md @@ -0,0 +1,31 @@ +# CI/CD + +This project includes GitHub Actions workflows to automatically build and deploy Docker images to Docker Hub. + +## Automatic Deployment to Docker Hub with GitHub Actions + +The repository contains two workflows located in the `.github/workflows` directory: + +- [`dev.yml`](../.github/workflows/dev.yml): Creates the images (base, sdxl, sd3, flux variants) and pushes them to Docker Hub tagged as `:dev` on every push to the `main` branch. +- [`release.yml`](../.github/workflows/release.yml): Creates the images and pushes them to Docker Hub tagged as `:latest` and `:` (e.g., `worker-comfyui:3.7.0`). This workflow is triggered only when a new release is created on GitHub. + +### Configuration for Your Fork + +If you have forked this repository and want to use these actions to publish images to your own Docker Hub account, you need to configure the following in your GitHub repository settings: + +1. **Secrets** (`Settings > Secrets and variables > Actions > New repository secret`): + + | Secret Name | Description | Example Value | + | -------------------------- | -------------------------------------------------------------------------- | ------------------- | + | `DOCKERHUB_USERNAME` | Your Docker Hub username. | `your-dockerhub-id` | + | `DOCKERHUB_TOKEN` | Your Docker Hub access token with read/write permissions. | `dckr_pat_...` | + | `HUGGINGFACE_ACCESS_TOKEN` | Your READ access token from Hugging Face (required only for building SD3). | `hf_...` | + +2. **Variables** (`Settings > Secrets and variables > Actions > New repository variable`): + + | Variable Name | Description | Example Value | + | ---------------- | ---------------------------------------------------------------------------- | -------------------------- | + | `DOCKERHUB_REPO` | The target repository (namespace) on Docker Hub where images will be pushed. | `your-dockerhub-id` | + | `DOCKERHUB_IMG` | The base name for the image to be pushed to Docker Hub. | `my-custom-worker-comfyui` | + +With these secrets and variables configured, the actions will push the built images (e.g., `your-dockerhub-id/my-custom-worker-comfyui:dev`, `your-dockerhub-id/my-custom-worker-comfyui:1.0.0`, `your-dockerhub-id/my-custom-worker-comfyui:latest`) to your Docker Hub account when triggered. diff --git a/docs/configuration.md b/docs/configuration.md new file mode 100644 index 00000000..a0e2d932 --- /dev/null +++ b/docs/configuration.md @@ -0,0 +1,53 @@ +# Configuration + +This document outlines the environment variables available for configuring the `worker-comfyui`. + +## General Configuration + +| Environment Variable | Description | Default | +| -------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------- | +| `REFRESH_WORKER` | When `true`, the worker pod will stop after each completed job to ensure a clean state for the next job. See the [RunPod documentation](https://docs.runpod.io/docs/handler-additional-controls#refresh-worker) for details. | `false` | +| `SERVE_API_LOCALLY` | When `true`, enables a local HTTP server simulating the RunPod environment for development and testing. See the [Development Guide](development.md#local-api) for more details. | `false` | + +## AWS S3 Upload Configuration + +Configure these variables **only** if you want the worker to upload generated images directly to an AWS S3 bucket. If these are not set, images will be returned as base64-encoded strings in the API response. + +- **Prerequisites:** + - An AWS S3 bucket in your desired region. + - An AWS IAM user with programmatic access (Access Key ID and Secret Access Key). + - Permissions attached to the IAM user allowing `s3:PutObject` (and potentially `s3:PutObjectAcl` if you need specific ACLs) on the target bucket. + +| Environment Variable | Description | Example | +| -------------------------- | --------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------- | +| `BUCKET_ENDPOINT_URL` | The full endpoint URL of your S3 bucket. **Must be set to enable S3 upload.** | `https://.s3..amazonaws.com` | +| `BUCKET_ACCESS_KEY_ID` | Your AWS access key ID associated with the IAM user that has write permissions to the bucket. Required if `BUCKET_ENDPOINT_URL` is set. | `AKIAIOSFODNN7EXAMPLE` | +| `BUCKET_SECRET_ACCESS_KEY` | Your AWS secret access key associated with the IAM user. Required if `BUCKET_ENDPOINT_URL` is set. | `wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY` | + +**Note:** Upload uses the `runpod` Python library helper `rp_upload.upload_image`, which handles creating a unique path within the bucket based on the `job_id`. + +### Example S3 Response + +If the S3 environment variables (`BUCKET_ENDPOINT_URL`, `BUCKET_ACCESS_KEY_ID`, `BUCKET_SECRET_ACCESS_KEY`) are correctly configured, a successful job response will look similar to this: + +```json +{ + "id": "sync-uuid-string", + "status": "COMPLETED", + "output": { + "images": [ + { + "filename": "ComfyUI_00001_.png", + "type": "s3_url", + "data": "https://your-bucket-name.s3.your-region.amazonaws.com/sync-uuid-string/ComfyUI_00001_.png" + } + // Additional images generated by the workflow would appear here + ] + // The "errors" key might be present here if non-fatal issues occurred + }, + "delayTime": 123, + "executionTime": 4567 +} +``` + +The `data` field contains the presigned URL to the uploaded image file in your S3 bucket. The path usually includes the job ID. diff --git a/docs/conventions.md b/docs/conventions.md index 307d0cb2..18f4af3a 100644 --- a/docs/conventions.md +++ b/docs/conventions.md @@ -2,7 +2,7 @@ This project (`worker-comfyui`) provides a way to run [ComfyUI](https://github.com/comfyanonymous/ComfyUI) as a serverless API worker on the [RunPod](https://www.runpod.io/) platform. Its main purpose is to allow users to submit ComfyUI image generation workflows via a simple API call and receive the resulting images, either directly as base64-encoded strings or via an upload to an AWS S3 bucket. -It packages ComfyUI into Docker images, manages job handling via the `runpod` SDK, and facilitates configuration through environment variables. +It packages ComfyUI into Docker images, manages job handling via the `runpod` SDK, uses websockets for efficient communication with ComfyUI, and facilitates configuration through environment variables. # Project Conventions and Rules @@ -10,7 +10,7 @@ This document outlines the key operational and structural conventions for the `w ## 1. Configuration -- **Environment Variables:** All external configurations (e.g., AWS S3 credentials, RunPod behavior modifications like `REFRESH_WORKER`, ComfyUI polling settings) **must** be managed via environment variables. +- **Environment Variables:** All external configurations (e.g., AWS S3 credentials, RunPod behavior modifications like `REFRESH_WORKER`) **must** be managed via environment variables. - Refer to the main `README.md` sections "Config" and "Upload image to AWS S3" for details on available variables. ## 2. Docker Usage @@ -26,8 +26,10 @@ This document outlines the key operational and structural conventions for the `w ## 3. API Interaction - **Input Structure:** API calls to the `/run` or `/runsync` endpoints must adhere to the JSON structure specified in the `README.md` ("API specification"). The primary key is `input`, containing `workflow` (mandatory object) and `images` (optional array). -- **Image Encoding:** Input images provided in the `input.images` array must be base64 encoded strings. +- **Image Encoding:** Input images provided in the `input.images` array must be base64 encoded strings (optionally including a `data:[];base64,` prefix). - **Workflow Format:** The `input.workflow` object should contain the JSON exported from ComfyUI using the "Save (API Format)" option (requires enabling "Dev mode Options" in ComfyUI settings). +- **Output Structure:** Successful responses contain an `output.images` field, which is a **list of dictionaries**. Each dictionary includes `filename` (string), `type` (`"s3_url"` or `"base64"`), and `data` (string containing the URL or base64 data). Refer to the `README.md` API examples for the exact structure. +- **Internal Communication:** Job status monitoring uses the ComfyUI websocket API instead of HTTP polling for efficiency. ## 4. Testing @@ -36,7 +38,7 @@ This document outlines the key operational and structural conventions for the `w ## 5. Dependencies -- **Python:** Manage Python dependencies using `pip` and the `requirements.txt` file. Keep this file up-to-date. +- **Python:** Manage Python dependencies using `pip` (or `uv`) and the `requirements.txt` file. Keep this file up-to-date. ## 6. Code Style (General Guidance) diff --git a/docs/customization.md b/docs/customization.md new file mode 100644 index 00000000..e2604e5a --- /dev/null +++ b/docs/customization.md @@ -0,0 +1,89 @@ +# Customization + +This guide covers methods for adding your own models, custom nodes, and static input files into a custom `worker-comfyui`. + +There are two primary methods for customizing your setup: + +1. **Custom Dockerfile (recommended):** Create your own `Dockerfile` starting `FROM` one of the official `worker-comfyui` base images. This allows you to bake specific custom nodes, models, and input files directly into your image using `comfy-cli` commands. **This method does not require forking the `worker-comfyui` repository.** +2. **Network Volume:** Store models on a persistent network volume attached to your RunPod endpoint. This is useful if you frequently change models or have very large models you don't want to include in the image build process. + +## Method 1: Custom Dockerfile + +This is the most flexible and recommended approach for creating reproducible, customized worker environments. + +1. **Create a `Dockerfile`:** In your own project directory, create a file named `Dockerfile`. +2. **Start with a Base Image:** Begin your `Dockerfile` by referencing one of the official base images. Using the `-base` tag is recommended as it provides a clean ComfyUI install with necessary tools like `comfy-cli` but without pre-packaged models. + ```Dockerfile + # start from a clean base image (replace with the desired release) + FROM runpod/worker-comfyui:-base + ``` +3. **Install Custom Nodes:** Use the `comfy node install` command to add custom nodes by their repository name or URL. You can list multiple nodes. + ```Dockerfile + # install custom nodes using comfy-cli + RUN comfy node install comfyui-kjnodes comfyui-ic-light + ``` +4. **Download Models:** Use the `comfy model download` command to fetch models and place them in the correct ComfyUI directories. + + ```Dockerfile + # download models using comfy-cli + RUN comfy model download --url https://huggingface.co/KamCastle/jugg/resolve/main/juggernaut_reborn.safetensors --relative-path models/checkpoints --filename juggernaut_reborn.safetensors + ``` + + > [!INFO] + > Ensure you use the correct `--relative-path` corresponding to ComfyUI's model directory structure (starting with `models/`): + > + > checkpoints, clip, clip_vision, configs, controlnet, diffusers, embeddings, gligen, hypernetworks, loras, style_models, unet, upscale_models, vae, vae_approx, animatediff_models, animatediff_motion_lora, ipadapter, photomaker, sams, insightface, facerestore_models, facedetection, mmdets, instantid + +5. **Add Static Input Files (Optional):** If your workflows consistently require specific input images, masks, videos, etc., you can copy them directly into the image. - Create an `input/` directory in the same folder as your `Dockerfile`. - Place your static files inside this `input/` directory. - Add a `COPY` command to your `Dockerfile`: + `Dockerfile + # Copy local static input files into the ComfyUI input directory + COPY input/ /comfyui/input/ + `These files can then be referenced in your workflow using a "Load Image" (or similar) node pointing to the filename (e.g.,`my_static_image.png`). + +Once you have created your custom `Dockerfile`, refer to the [Deployment Guide](deployment.md#deploying-custom-setups) for instructions on how to build, push and deploy your custom image to RunPod. + +### Complete Custom `Dockerfile` Example + +```Dockerfile +# start from a clean base image (replace with the desired release) +FROM runpod/worker-comfyui:5.0.0-base + +# install custom nodes using comfy-cli +RUN comfy node install comfyui-kjnodes comfyui-ic-light comfyui_ipadapter_plus comfyui_essentials ComfyUI-Hangover-Nodes + +# download models using comfy-cli +# the "--filename" is what you use in your ComfyUI workflow +RUN comfy model download --url https://huggingface.co/KamCastle/jugg/resolve/main/juggernaut_reborn.safetensors --relative-path models/checkpoints --filename juggernaut_reborn.safetensors +RUN comfy model download --url https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter-plus_sd15.bin --relative-path models/ipadapter --filename ip-adapter-plus_sd15.bin +RUN comfy model download --url https://huggingface.co/shiertier/clip_vision/resolve/main/SD15/model.safetensors --relative-path models/clip_vision --filename models.safetensors +RUN comfy model download --url https://huggingface.co/lllyasviel/ic-light/resolve/main/iclight_sd15_fcon.safetensors --relative-path models/diffusion_models --filename iclight_sd15_fcon.safetensors + +# Copy local static input files into the ComfyUI input directory (delete if not needed) +# Assumes you have an 'input' folder next to your Dockerfile +COPY input/ /comfyui/input/ +``` + +## Method 2: Network Volume + +Using a Network Volume is primarily useful if you want to manage **models** separately from your worker image, especially if they are large or change often. + +1. **Create a Network Volume**: + - Follow the [RunPod Network Volumes guide](https://docs.runpod.io/pods/storage/create-network-volumes) to create a volume in the same region as your endpoint. +2. **Populate the Volume with Models**: + - Use one of the methods described in the RunPod guide (e.g., temporary Pod + `wget`, direct upload) to place your model files into the correct ComfyUI directory structure **within the volume**. The root of the volume corresponds to `/workspace` inside the container. + ```bash + # Example structure inside the Network Volume: + # /models/checkpoints/your_model.safetensors + # /models/loras/your_lora.pt + # /models/vae/your_vae.safetensors + ``` + - **Important:** Ensure models are placed in the correct subdirectories (e.g., checkpoints in `models/checkpoints`, LoRAs in `models/loras`). +3. **Configure Your Endpoint**: + - Use the Network Volume in your endpoint configuration: + - Either create a new endpoint or update an existing one (see [Deployment Guide](deployment.md)). + - In the endpoint configuration, under `Advanced > Select Network Volume`, select your Network Volume. + +**Note:** + +- When a Network Volume is correctly attached, ComfyUI running inside the worker container will automatically detect and load models from the standard directories (`/workspace/models/...`) within that volume. +- This method is **not suitable for installing custom nodes**; use the Custom Dockerfile method for that. diff --git a/docs/deployment.md b/docs/deployment.md new file mode 100644 index 00000000..28b59e38 --- /dev/null +++ b/docs/deployment.md @@ -0,0 +1,103 @@ +# Deployment + +This guide explains how to deploy the `worker-comfyui` as a serverless endpoint on RunPod, covering both pre-built official images and custom-built images. + +## Deploying Pre-Built Official Images + +This is the simplest method if the official images meet your needs. + +### Create your template (optional) + +- Create a [new template](https://runpod.io/console/serverless/user/templates) by clicking on `New Template` +- In the dialog, configure: + - Template Name: `worker-comfyui` (or your preferred name) + - Template Type: serverless (change template type to "serverless") + - Container Image: Use one of the official tags, e.g., `runpod/worker-comfyui:-sd3`. (Refer to the main [README.md](../README.md#available-docker-images) for available image tags and the current version). + - Container Registry Credentials: Leave as default (images are public). + - Container Disk: Adjust based on the chosen image tag, see [GPU Recommendations](#gpu-recommendations). + - (optional) Environment Variables: Configure S3 or other settings (see [Configuration Guide](configuration.md)). + - Note: If you don't configure S3, images are returned as base64. For persistent storage across jobs without S3, consider using a [Network Volume](customization.md#method-2-network-volume-alternative-for-models). +- Click on `Save Template` + +### Create your endpoint + +- Navigate to [`Serverless > Endpoints`](https://www.runpod.io/console/serverless/user/endpoints) and click on `New Endpoint` +- In the dialog, configure: + + - Endpoint Name: `comfy` (or your preferred name) + - Worker configuration: Select a GPU that can run the model included in your chosen image (see [GPU recommendations](#gpu-recommendations)). + - Active Workers: `0` (Scale as needed based on expected load). + - Max Workers: `3` (Set a limit based on your budget and scaling needs). + - GPUs/Worker: `1` + - Idle Timeout: `5` (Default is usually fine, adjust if needed). + - Flash Boot: `enabled` (Recommended for faster worker startup). + - Select Template: `worker-comfyui` (or the name you gave your template). + - (optional) Advanced: If you are using a Network Volume, select it under `Select Network Volume`. See the [Customization Guide](customization.md#method-2-network-volume-alternative-for-models). + +- Click `deploy` +- Your endpoint will be created. You can click on it to view the dashboard and find its ID. + +### GPU recommendations (for Official Images) + +| Model | Image Tag Suffix | Minimum VRAM Required | Recommended Container Size | +| ------------------------- | ---------------- | --------------------- | -------------------------- | +| Stable Diffusion XL | `sdxl` | 8 GB | 15 GB | +| Stable Diffusion 3 Medium | `sd3` | 5 GB | 20 GB | +| FLUX.1 Schnell | `flux1-schnell` | 24 GB | 30 GB | +| FLUX.1 dev | `flux1-dev` | 24 GB | 30 GB | +| Base (No models) | `base` | N/A | 5 GB | + +_Note: Container sizes are approximate and might vary slightly. Custom images will vary based on included models/nodes._ + +## Deploying Custom Setups + +If you have created a custom environment using the methods in the [Customization Guide](customization.md), here's how to deploy it. + +### Method 1: Manual Build, Push, and Deploy + +This method involves building your custom Docker image locally, pushing it to a registry, and then deploying that image on RunPod. + +1. **Write your Dockerfile:** Follow the instructions in the [Customization Guide](customization.md#method-1-custom-dockerfile-recommended) to create your `Dockerfile` specifying the base image, nodes, models, and any static files. +2. **Build the Docker image:** Navigate to the directory containing your `Dockerfile` and run: + ```bash + # Replace : with your desired name and tag + docker build --platform linux/amd64 -t : . + ``` + - **Crucially**, always include `--platform linux/amd64` for RunPod compatibility. +3. **Tag the image for your registry:** Replace `` and `:` accordingly. + ```bash + # Example for Docker Hub: + docker tag : /: + ``` +4. **Log in to your container registry:** + ```bash + # Example for Docker Hub: + docker login + ``` +5. **Push the image:** + ```bash + # Example for Docker Hub: + docker push /: + ``` +6. **Deploy on RunPod:** + - Follow the steps in [Create your template](#create-your-template-optional) above, but for the `Container Image` field, enter the full name of the image you just pushed (e.g., `/:`). + - If your registry is private, you will need to provide [Container Registry Credentials](https://docs.runpod.io/serverless/templates#container-registry-credentials). + - Adjust the `Container Disk` size based on your custom image contents. + - Follow the steps in [Create your endpoint](#create-your-endpoint) using the template you just created. + +### Method 2: Deploying via RunPod GitHub Integration + +RunPod offers a seamless way to deploy directly from your GitHub repository containing the `Dockerfile`. RunPod handles the build and deployment. + +1. **Prepare your GitHub Repository:** Ensure your repository contains the custom `Dockerfile` (as described in the [Customization Guide](customization.md#method-1-custom-dockerfile-recommended)) at the root or a specified path. +2. **Connect GitHub to RunPod:** Authorize RunPod to access your repository via your RunPod account settings or when creating a new endpoint. +3. **Create a New Serverless Endpoint:** In RunPod, navigate to Serverless -> `+ New Endpoint` and select the **"Start from GitHub Repo"** option. +4. **Configure:** + - Select the GitHub repository and branch you want to deploy (e.g., `main`). + - Specify the **Context Path** (usually `/` if the Dockerfile is at the root). + - Specify the **Dockerfile Path** (usually `Dockerfile`). + - Configure your desired compute resources (GPU type, workers, etc.). + - Configure any necessary [Environment Variables](configuration.md). +5. **Deploy:** RunPod will clone the repository, build the image from your specified branch and Dockerfile, push it to a temporary registry, and deploy the endpoint. + +Every `git push` to the configured branch will automatically trigger a new build and update your RunPod endpoint. For more details, refer to the [RunPod GitHub Integration Documentation](https://docs.runpod.io/serverless/github-integration). diff --git a/docs/development.md b/docs/development.md new file mode 100644 index 00000000..bdbed75a --- /dev/null +++ b/docs/development.md @@ -0,0 +1,130 @@ +# Development and Local Testing + +This guide covers setting up your local environment for developing and testing the `worker-comfyui`. + +Both tests will use the data from [`test_input.json`](../test_input.json), so make your changes in there to test different workflow inputs properly. + +## Setup + +### Prerequisites + +1. Python >= 3.10 +2. `pip` (Python package installer) +3. Virtual environment tool (like `venv`) + +### Steps + +1. **Clone the repository** (if you haven't already): + ```bash + git clone https://github.com/runpod-workers/worker-comfyui.git + cd worker-comfyui + ``` +2. **Create a virtual environment**: + ```bash + python -m venv .venv + ``` +3. **Activate the virtual environment**: + - **Windows (Command Prompt/PowerShell)**: + ```bash + .\.venv\Scripts\activate + ``` + - **macOS / Linux (Bash/Zsh)**: + ```bash + source ./.venv/bin/activate + ``` +4. **Install dependencies**: + ```bash + pip install -r requirements.txt + ``` + +### Setup for Windows (using WSL2) + +Running Docker with GPU acceleration on Windows typically requires WSL2 (Windows Subsystem for Linux). + +1. **Install WSL2 and a Linux distribution** (like Ubuntu) following [Microsoft's official guide](https://learn.microsoft.com/en-us/windows/wsl/install). You generally don't need the GUI support for this. +2. **Open your Linux distribution's terminal** (e.g., open Ubuntu from the Start menu or type `wsl` in Command Prompt/PowerShell). +3. **Update packages** inside WSL: + ```bash + sudo apt update && sudo apt upgrade -y + ``` +4. **Install Docker Engine in WSL**: + - Follow the [official Docker installation guide for your chosen Linux distribution](https://docs.docker.com/engine/install/#server) (e.g., Ubuntu). + - **Important:** Add your user to the `docker` group to avoid using `sudo` for every Docker command: `sudo usermod -aG docker $USER`. You might need to close and reopen the terminal for this to take effect. +5. **Install Docker Compose** (if not included with Docker Engine): + ```bash + sudo apt-get update + sudo apt-get install docker-compose-plugin # Or use the standalone binary method if preferred + ``` +6. **Install NVIDIA Container Toolkit in WSL**: + - Follow the [NVIDIA Container Toolkit installation guide](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html), ensuring you select the correct steps for your Linux distribution running inside WSL. + - Configure Docker to use the NVIDIA runtime as default if desired, or specify it when running containers. +7. **Enable GPU Acceleration in WSL**: + - Ensure you have the latest NVIDIA drivers installed on your Windows host machine. + - Follow the [NVIDIA guide for CUDA on WSL](https://docs.nvidia.com/cuda/wsl-user-guide/index.html). + +After completing these steps, you should be able to run Docker commands, including `docker-compose`, from within your WSL terminal with GPU access. + +> [!NOTE] +> +> - It is generally recommended to run the Docker commands (`docker build`, `docker-compose up`) from within the WSL environment terminal for consistency with the Linux-based container environment. +> - Accessing `localhost` URLs (like the local API or ComfyUI) from your Windows browser while the service runs inside WSL usually works, but network configurations can sometimes cause issues. + +## Testing the RunPod Handler + +Unit tests are provided to verify the core logic of the `handler.py`. + +- **Run all tests**: + ```bash + python -m unittest discover tests/ + ``` +- **Run a specific test file**: + ```bash + python -m unittest tests.test_handler + ``` +- **Run a specific test case or method**: + + ```bash + # Example: Run all tests in the TestRunpodWorkerComfy class + python -m unittest tests.test_handler.TestRunpodWorkerComfy + + # Example: Run a single test method + python -m unittest tests.test_handler.TestRunpodWorkerComfy.test_s3_upload + ``` + +## Local API Simulation (using Docker Compose) + +For enhanced local development and end-to-end testing, you can start a local environment using Docker Compose that includes the worker and a ComfyUI instance. + +> [!IMPORTANT] +> +> - This currently requires an **NVIDIA GPU** and correctly configured drivers + NVIDIA Container Toolkit (see Windows setup above if applicable). +> - Ensure Docker is running. + +**Steps:** + +1. **Set Environment Variable (Optional but Recommended):** + - While the `docker-compose.yml` sets `SERVE_API_LOCALLY=true` by default, you might manage environment variables externally (e.g., via a `.env` file). + - Ensure the `SERVE_API_LOCALLY` environment variable is set to `true` for the `worker` service if you modify the compose file or use an `.env` file. +2. **Start the services**: + ```bash + # From the project root directory + docker-compose up --build + ``` + - The `--build` flag ensures the image is built locally using the current state of the code and `Dockerfile`. + - This will start two containers: `comfyui` and `worker`. + +### Access the Local Worker API + +- With the Docker Compose stack running, the worker's simulated RunPod API is accessible at: [http://localhost:8000](http://localhost:8000) +- You can send POST requests to `http://localhost:8000/run` or `http://localhost:8000/runsync` with the same JSON payload structure expected by the RunPod endpoint. +- Opening [http://localhost:8000/docs](http://localhost:8000/docs) in your browser will show the FastAPI auto-generated documentation (Swagger UI), allowing you to interact with the API directly. + +### Access Local ComfyUI + +- The underlying ComfyUI instance running in the `comfyui` container is accessible directly at: [http://localhost:8188](http://localhost:8188) +- This is useful for debugging workflows or observing the ComfyUI state while testing the worker. + +### Stopping the Local Environment + +- Press `Ctrl+C` in the terminal where `docker-compose up` is running. +- To ensure containers are removed, you can run: `docker-compose down` diff --git a/docs/planning/001_websocket.md b/docs/planning/001_websocket.md new file mode 100644 index 00000000..29762482 --- /dev/null +++ b/docs/planning/001_websocket.md @@ -0,0 +1,57 @@ +# User Story: Implement Websocket API for ComfyUI Communication + +**Goal:** Replace the current HTTP polling mechanism in `handler.py` with ComfyUI's websocket API to monitor prompt execution status and retrieve generated images more efficiently and reliably. + +**Current State:** + +- `handler.py` queues a prompt via HTTP POST to `/prompt`. +- It then repeatedly polls the `/history/{prompt_id}` endpoint with a delay (`COMFY_POLLING_INTERVAL_MS`) until the job outputs appear in the history. +- Once outputs are detected, `process_output_images` retrieves image filenames from the history, constructs local file paths (assuming images are saved to `/comfyui/output`), checks for file existence, and then either uploads the file from disk to S3 or reads the file from disk to encode it as base64. This reliance on filesystem access is fragile. + +**Desired State:** + +- Use the ComfyUI websocket API (`ws:///ws?clientId=`) for real-time status updates. +- Eliminate the HTTP polling loop and associated constants. +- Retrieve final image data directly using the `/view` API endpoint instead of relying on filesystem access within the container. +- Maintain existing functionality for uploading images to S3 or returning base64 encoded images. + +**Tasks:** + +1. **Add Dependency:** Add `websocket-client` to the `requirements.txt` file. +2. **Modify `handler.py`:** + - Import `websocket` and `uuid`. + - Generate a unique `client_id` (using `uuid.uuid4()`) for each job request within the `handler` function. + - Modify `queue_workflow`: Update the function signature and implementation to accept the `client_id` and include it in the `/prompt` request payload (`{"prompt": workflow, "client_id": client_id}`). + - **Websocket Connection & Monitoring:** + - Establish a websocket connection before queuing the prompt: `ws = websocket.WebSocket()` followed by `ws.connect(f"ws://{COMFY_HOST}/ws?clientId={client_id}")`. + - After queuing the prompt and getting the `prompt_id`, implement the websocket message receiving loop (`while True: out = ws.recv()...`). Listen for the specific `executing` message indicating the prompt is finished (where `message['data']['node'] is None` and `message['data']['prompt_id'] == prompt_id`). + - Ensure the websocket connection is closed (`ws.close()`) after monitoring is complete or in case of errors (using a `try...finally` block). + - **Image Retrieval & Handling:** + - After the websocket indicates completion, call `get_history(prompt_id)` to get the final output structure (as done in the example). + - Create a new function `get_image_data(filename, subfolder, image_type)` that uses `urllib.request` (or `requests`) to fetch image _bytes_ from the `http://{COMFY_HOST}/view` endpoint. + - Replace the logic previously in `process_output_images` (or integrate into the main `handler` flow after getting history): + - Iterate through the `outputs` in the fetched history. + - For each image identified in the `outputs` dictionary: + - Call `get_image_data` to retrieve the raw image bytes. + - If S3 is configured (`BUCKET_ENDPOINT_URL` env var is set): + - Save the image bytes to a temporary file (using Python's `tempfile` module). + - Use `rp_upload.upload_image(job_id, temp_file_path)` to upload the temporary file to S3. Determine the correct file extension if possible, default to '.png'. + - Ensure the temporary file is deleted after upload. + - Store the returned S3 URL. + - If S3 is not configured: + - Base64 encode the image bytes directly. + - Store the resulting base64 string. + - Aggregate all resulting image URLs and/or base64 strings into a suitable format (e.g., a list or dictionary). + - **Cleanup:** + - Remove the old `process_output_images` function. + - Remove the polling-related constants (`COMFY_POLLING_INTERVAL_MS`, `COMFY_POLLING_MAX_RETRIES`) and the polling `while` loop. + - **Error Handling:** Add robust error handling for websocket connection establishment, message receiving/parsing, image data fetching (`/view`), temporary file operations, and S3 uploads. +3. **Testing:** + - Update unit tests in `tests/` to mock the websocket interactions and verify the new logic. + - Perform local end-to-end testing using `docker-compose up` to ensure the integration with a live ComfyUI instance works as expected for both S3 and base64 output modes. + +**Considerations:** + +- **Websocket Reliability:** Implement try/except blocks around websocket operations. Consider if simple retries are needed for connection failures. +- **Temporary Files for S3:** Using temporary files adds minor overhead but fits the current `runpod` SDK (`rp_upload.upload_image`). Ensure proper cleanup using `try...finally` or context managers. +- **Runpod Lifecycle:** Creating a new websocket connection per `handler` invocation is standard for serverless function executions and ensures isolation. diff --git a/docs/planning/002_restructure_docs.md b/docs/planning/002_restructure_docs.md new file mode 100644 index 00000000..608ad957 --- /dev/null +++ b/docs/planning/002_restructure_docs.md @@ -0,0 +1,45 @@ +# User Story: Restructure Documentation for Clarity and Maintainability + +**Goal:** Refactor the main `README.md` to focus on essential user information (deployment, configuration basics, API usage) and move detailed sections (customization, local development, CI/CD, etc.) into separate, focused documents within the `docs/` directory. Improve overall documentation structure and ease of navigation. + +**Current State:** + +- The `README.md` file is very large and covers a wide range of topics, from basic usage to advanced customization and development setup. +- It can be difficult for users to quickly find the specific information they need (e.g., just how to run the API vs. how to build a custom image). +- The release process (`.releaserc`) currently updates version numbers only within `README.md` using a `sed` command. + +**Desired State:** + +- A concise `README.md` serving as a landing page and quickstart guide, containing: + - Brief introduction/purpose. + - Quickstart guide (linking to detailed deployment). + - List of available pre-built images (with current version tags). + - Essential configuration (S3 setup, linking to full config). + - API specification (endpoints, input/output formats). + - Basic API interaction examples (linking to details if needed). + - How to get the ComfyUI workflow JSON. + - Clear links to more detailed documentation in the `docs/` directory. +- New documents created within `docs/` covering specific topics: + - `docs/deployment.md`: Detailed RunPod template/endpoint creation, GPU recommendations. + - `docs/configuration.md`: Comprehensive list and explanation of all environment variables. + - `docs/customization.md`: In-depth guide on using Network Volumes and building custom Docker images (models, nodes, snapshots). + - `docs/development.md`: Instructions for local setup (Python, WSL), running tests, using `docker-compose`, accessing local API/ComfyUI. + - `docs/ci-cd.md`: Explanation of the GitHub Actions workflows for Docker Hub deployment (secrets, variables). + - `docs/acknowledgments.md`: (Optional) Move acknowledgments here. +- Specific version numbers (e.g., `3.6.0` in image tags) should ideally only reside in the main `README.md` to avoid complicating the release script. If version numbers must exist in other files, the `.releaserc` `prepareCmd` will need modification. + +**Tasks:** + +1. **Create New Files:** Create the following new markdown files within the `docs/` directory: `deployment.md`, `configuration.md`, `customization.md`, `development.md`, `ci-cd.md`, (optionally `acknowledgments.md`). +2. **Migrate Content:** Carefully move relevant sections from the current `README.md` into the corresponding new files in `docs/`. Ensure content flows logically within each new document. +3. **Refactor `README.md`:** Rewrite and condense `README.md` to focus on the core user information identified in the "Desired State". Remove migrated content. +4. **Add Links:** Insert clear links within the refactored `README.md` pointing to the detailed information in the new `docs/` files (e.g., "For detailed deployment steps, see [Deployment Guide](docs/deployment.md)."). Also, ensure inter-linking between new docs where relevant. +5. **Review Versioning:** Scrutinize all documentation files (`README.md` and `docs/*`) to ensure specific version numbers (like image tags) are confined to `README.md` where possible. +6. **Verify Release Script:** Confirm that the existing `prepareCmd` in `.releaserc` is still sufficient (targets the right file and pattern for version replacement). If version numbers were unavoidably moved outside `README.md`, update the `sed` command accordingly to target the additional files. +7. **Review and Test:** Read through the restructured documentation to ensure clarity, accuracy, and completeness. Verify all internal links work correctly. + +**Considerations:** + +- **Discoverability:** While splitting improves focus, ensure the main `README.md` provides good entry points/links so users can find detailed information. +- **Consistency:** Maintain consistent formatting and tone across all documentation files. +- **Versioning Maintenance:** Keeping version numbers primarily in `README.md` simplifies the release automation script. diff --git a/handler.py b/handler.py new file mode 100644 index 00000000..b3c193a9 --- /dev/null +++ b/handler.py @@ -0,0 +1,631 @@ +import runpod +from runpod.serverless.utils import rp_upload +import json +import urllib.request +import urllib.parse +import time +import os +import requests +import base64 +from io import BytesIO +import websocket +import uuid +import tempfile +import socket + +# Time to wait between API check attempts in milliseconds +COMFY_API_AVAILABLE_INTERVAL_MS = 50 +# Maximum number of API check attempts +COMFY_API_AVAILABLE_MAX_RETRIES = 500 +# Websocket Reconnection Retries (when connection drops during recv) +WEBSOCKET_RECONNECT_ATTEMPTS = 2 +WEBSOCKET_RECONNECT_DELAY_S = 3 +# Host where ComfyUI is running +COMFY_HOST = "127.0.0.1:8188" +# Enforce a clean state after each job is done +# see https://docs.runpod.io/docs/handler-additional-controls#refresh-worker +REFRESH_WORKER = os.environ.get("REFRESH_WORKER", "false").lower() == "true" + + +def _attempt_websocket_reconnect(ws_url, max_attempts, delay_s, initial_error): + """ + Attempts to reconnect to the WebSocket server after a disconnect. + + Args: + ws_url (str): The WebSocket URL (including client_id). + max_attempts (int): Maximum number of reconnection attempts. + delay_s (int): Delay in seconds between attempts. + initial_error (Exception): The error that triggered the reconnect attempt. + + Returns: + websocket.WebSocket: The newly connected WebSocket object. + + Raises: + websocket.WebSocketConnectionClosedException: If reconnection fails after all attempts. + """ + print( + f"worker-comfyui - Websocket connection closed unexpectedly: {initial_error}. Attempting to reconnect..." + ) + last_reconnect_error = initial_error + for attempt in range(max_attempts): + print(f"worker-comfyui - Reconnect attempt {attempt + 1}/{max_attempts}...") + try: + # Need to create a new socket object for reconnect + new_ws = websocket.WebSocket() + new_ws.connect(ws_url, timeout=10) # Use existing ws_url + print(f"worker-comfyui - Websocket reconnected successfully.") + return new_ws # Return the new connected socket + except ( + websocket.WebSocketException, + ConnectionRefusedError, + socket.timeout, + OSError, + ) as reconn_err: + last_reconnect_error = reconn_err + print( + f"worker-comfyui - Reconnect attempt {attempt + 1} failed: {reconn_err}" + ) + if attempt < max_attempts - 1: + print( + f"worker-comfyui - Waiting {delay_s} seconds before next attempt..." + ) + time.sleep(delay_s) + else: + print(f"worker-comfyui - Max reconnection attempts reached.") + + # If loop completes without returning, raise an exception + print("worker-comfyui - Failed to reconnect websocket after connection closed.") + raise websocket.WebSocketConnectionClosedException( + f"Connection closed and failed to reconnect. Last error: {last_reconnect_error}" + ) + + +def validate_input(job_input): + """ + Validates the input for the handler function. + + Args: + job_input (dict): The input data to validate. + + Returns: + tuple: A tuple containing the validated data and an error message, if any. + The structure is (validated_data, error_message). + """ + # Validate if job_input is provided + if job_input is None: + return None, "Please provide input" + + # Check if input is a string and try to parse it as JSON + if isinstance(job_input, str): + try: + job_input = json.loads(job_input) + except json.JSONDecodeError: + return None, "Invalid JSON format in input" + + # Validate 'workflow' in input + workflow = job_input.get("workflow") + if workflow is None: + return None, "Missing 'workflow' parameter" + + # Validate 'images' in input, if provided + images = job_input.get("images") + if images is not None: + if not isinstance(images, list) or not all( + "name" in image and "image" in image for image in images + ): + return ( + None, + "'images' must be a list of objects with 'name' and 'image' keys", + ) + + # Return validated data and no error + return {"workflow": workflow, "images": images}, None + + +def check_server(url, retries=500, delay=50): + """ + Check if a server is reachable via HTTP GET request + + Args: + - url (str): The URL to check + - retries (int, optional): The number of times to attempt connecting to the server. Default is 50 + - delay (int, optional): The time in milliseconds to wait between retries. Default is 500 + + Returns: + bool: True if the server is reachable within the given number of retries, otherwise False + """ + + print(f"worker-comfyui - Checking API server at {url}...") + for i in range(retries): + try: + response = requests.get(url, timeout=5) + + # If the response status code is 200, the server is up and running + if response.status_code == 200: + print(f"worker-comfyui - API is reachable") + return True + except requests.Timeout: + pass + except requests.RequestException as e: + pass + + # Wait for the specified delay before retrying + time.sleep(delay / 1000) + + print( + f"worker-comfyui - Failed to connect to server at {url} after {retries} attempts." + ) + return False + + +def upload_images(images): + """ + Upload a list of base64 encoded images to the ComfyUI server using the /upload/image endpoint. + + Args: + images (list): A list of dictionaries, each containing the 'name' of the image and the 'image' as a base64 encoded string. + + Returns: + dict: A dictionary indicating success or error. + """ + if not images: + return {"status": "success", "message": "No images to upload", "details": []} + + responses = [] + upload_errors = [] + + print(f"worker-comfyui - Uploading {len(images)} image(s)...") + + for image in images: + try: + name = image["name"] + image_data_uri = image["image"] # Get the full string (might have prefix) + + # --- Strip Data URI prefix if present --- + if "," in image_data_uri: + # Find the comma and take everything after it + base64_data = image_data_uri.split(",", 1)[1] + else: + # Assume it's already pure base64 + base64_data = image_data_uri + # --- End strip --- + + blob = base64.b64decode(base64_data) # Decode the cleaned data + + # Prepare the form data + files = { + "image": (name, BytesIO(blob), "image/png"), + "overwrite": (None, "true"), + } + + # POST request to upload the image + response = requests.post( + f"http://{COMFY_HOST}/upload/image", files=files, timeout=30 + ) + response.raise_for_status() + + responses.append(f"Successfully uploaded {name}") + print(f"worker-comfyui - Successfully uploaded {name}") + + except base64.binascii.Error as e: + error_msg = f"Error decoding base64 for {image.get('name', 'unknown')}: {e}" + print(f"worker-comfyui - {error_msg}") + upload_errors.append(error_msg) + except requests.Timeout: + error_msg = f"Timeout uploading {image.get('name', 'unknown')}" + print(f"worker-comfyui - {error_msg}") + upload_errors.append(error_msg) + except requests.RequestException as e: + error_msg = f"Error uploading {image.get('name', 'unknown')}: {e}" + print(f"worker-comfyui - {error_msg}") + upload_errors.append(error_msg) + except Exception as e: + error_msg = ( + f"Unexpected error uploading {image.get('name', 'unknown')}: {e}" + ) + print(f"worker-comfyui - {error_msg}") + upload_errors.append(error_msg) + + if upload_errors: + print(f"worker-comfyui - image(s) upload finished with errors") + return { + "status": "error", + "message": "Some images failed to upload", + "details": upload_errors, + } + + print(f"worker-comfyui - image(s) upload complete") + return { + "status": "success", + "message": "All images uploaded successfully", + "details": responses, + } + + +def queue_workflow(workflow, client_id): + """ + Queue a workflow to be processed by ComfyUI + + Args: + workflow (dict): A dictionary containing the workflow to be processed + client_id (str): The client ID for the websocket connection + + Returns: + dict: The JSON response from ComfyUI after processing the workflow + """ + # Include client_id in the prompt payload + payload = {"prompt": workflow, "client_id": client_id} + data = json.dumps(payload).encode("utf-8") + + # Use requests for consistency and timeout + headers = {"Content-Type": "application/json"} + response = requests.post( + f"http://{COMFY_HOST}/prompt", data=data, headers=headers, timeout=30 + ) + response.raise_for_status() + return response.json() + + +def get_history(prompt_id): + """ + Retrieve the history of a given prompt using its ID + + Args: + prompt_id (str): The ID of the prompt whose history is to be retrieved + + Returns: + dict: The history of the prompt, containing all the processing steps and results + """ + # Use requests for consistency and timeout + response = requests.get(f"http://{COMFY_HOST}/history/{prompt_id}", timeout=30) + response.raise_for_status() + return response.json() + + +def get_image_data(filename, subfolder, image_type): + """ + Fetch image bytes from the ComfyUI /view endpoint. + + Args: + filename (str): The filename of the image. + subfolder (str): The subfolder where the image is stored. + image_type (str): The type of the image (e.g., 'output'). + + Returns: + bytes: The raw image data, or None if an error occurs. + """ + print( + f"worker-comfyui - Fetching image data: type={image_type}, subfolder={subfolder}, filename={filename}" + ) + data = {"filename": filename, "subfolder": subfolder, "type": image_type} + url_values = urllib.parse.urlencode(data) + try: + # Use requests for consistency and timeout + response = requests.get(f"http://{COMFY_HOST}/view?{url_values}", timeout=60) + response.raise_for_status() + print(f"worker-comfyui - Successfully fetched image data for {filename}") + return response.content + except requests.Timeout: + print(f"worker-comfyui - Timeout fetching image data for {filename}") + return None + except requests.RequestException as e: + print(f"worker-comfyui - Error fetching image data for {filename}: {e}") + return None + except Exception as e: + print( + f"worker-comfyui - Unexpected error fetching image data for {filename}: {e}" + ) + return None + + +def handler(job): + """ + Handles a job using ComfyUI via websockets for status and image retrieval. + + Args: + job (dict): A dictionary containing job details and input parameters. + + Returns: + dict: A dictionary containing either an error message or a success status with generated images. + """ + job_input = job["input"] + job_id = job["id"] + + # Make sure that the input is valid + validated_data, error_message = validate_input(job_input) + if error_message: + return {"error": error_message} + + # Extract validated data + workflow = validated_data["workflow"] + input_images = validated_data.get("images") + + # Make sure that the ComfyUI HTTP API is available before proceeding + if not check_server( + f"http://{COMFY_HOST}/", + COMFY_API_AVAILABLE_MAX_RETRIES, + COMFY_API_AVAILABLE_INTERVAL_MS, + ): + return { + "error": f"ComfyUI server ({COMFY_HOST}) not reachable after multiple retries." + } + + # Upload input images if they exist + if input_images: + upload_result = upload_images(input_images) + if upload_result["status"] == "error": + # Return upload errors + return { + "error": "Failed to upload one or more input images", + "details": upload_result["details"], + } + + ws = None + client_id = str(uuid.uuid4()) + prompt_id = None + output_data = [] + errors = [] + + try: + # Establish WebSocket connection + ws_url = f"ws://{COMFY_HOST}/ws?clientId={client_id}" + print(f"worker-comfyui - Connecting to websocket: {ws_url}") + ws = websocket.WebSocket() + ws.connect(ws_url, timeout=10) + print(f"worker-comfyui - Websocket connected") + + # Queue the workflow + try: + queued_workflow = queue_workflow(workflow, client_id) + prompt_id = queued_workflow.get("prompt_id") + if not prompt_id: + raise ValueError( + f"Missing 'prompt_id' in queue response: {queued_workflow}" + ) + print(f"worker-comfyui - Queued workflow with ID: {prompt_id}") + except requests.RequestException as e: + print(f"worker-comfyui - Error queuing workflow: {e}") + raise ValueError(f"Error queuing workflow: {e}") + except Exception as e: + print(f"worker-comfyui - Unexpected error queuing workflow: {e}") + raise ValueError(f"Unexpected error queuing workflow: {e}") + + # Wait for execution completion via WebSocket + print(f"worker-comfyui - Waiting for workflow execution ({prompt_id})...") + execution_done = False + while True: + try: + out = ws.recv() + if isinstance(out, str): + message = json.loads(out) + if message.get("type") == "status": + status_data = message.get("data", {}).get("status", {}) + print( + f"worker-comfyui - Status update: {status_data.get('exec_info', {}).get('queue_remaining', 'N/A')} items remaining in queue" + ) + elif message.get("type") == "executing": + data = message.get("data", {}) + if ( + data.get("node") is None + and data.get("prompt_id") == prompt_id + ): + print( + f"worker-comfyui - Execution finished for prompt {prompt_id}" + ) + execution_done = True + break + elif message.get("type") == "execution_error": + data = message.get("data", {}) + if data.get("prompt_id") == prompt_id: + error_details = f"Node Type: {data.get('node_type')}, Node ID: {data.get('node_id')}, Message: {data.get('exception_message')}" + print( + f"worker-comfyui - Execution error received: {error_details}" + ) + errors.append(f"Workflow execution error: {error_details}") + break + else: + continue + except websocket.WebSocketTimeoutException: + print(f"worker-comfyui - Websocket receive timed out. Still waiting...") + continue + except websocket.WebSocketConnectionClosedException as closed_err: + try: + # Attempt to reconnect + ws = _attempt_websocket_reconnect( + ws_url, + WEBSOCKET_RECONNECT_ATTEMPTS, + WEBSOCKET_RECONNECT_DELAY_S, + closed_err, + ) + + print( + "worker-comfyui - Resuming message listening after successful reconnect." + ) + continue + except ( + websocket.WebSocketConnectionClosedException + ) as reconn_failed_err: + # If _attempt_websocket_reconnect fails, it raises this exception + # Let this exception propagate to the outer handler's except block + raise reconn_failed_err + + except json.JSONDecodeError: + print(f"worker-comfyui - Received invalid JSON message via websocket.") + + if not execution_done and not errors: + raise ValueError( + "Workflow monitoring loop exited without confirmation of completion or error." + ) + + # Fetch history even if there were execution errors, some outputs might exist + print(f"worker-comfyui - Fetching history for prompt {prompt_id}...") + history = get_history(prompt_id) + + if prompt_id not in history: + error_msg = f"Prompt ID {prompt_id} not found in history after execution." + print(f"worker-comfyui - {error_msg}") + if not errors: + return {"error": error_msg} + else: + errors.append(error_msg) + return { + "error": "Job processing failed, prompt ID not found in history.", + "details": errors, + } + + prompt_history = history.get(prompt_id, {}) + outputs = prompt_history.get("outputs", {}) + + if not outputs: + warning_msg = f"No outputs found in history for prompt {prompt_id}." + print(f"worker-comfyui - {warning_msg}") + if not errors: + errors.append(warning_msg) + + print(f"worker-comfyui - Processing {len(outputs)} output nodes...") + for node_id, node_output in outputs.items(): + if "images" in node_output: + print( + f"worker-comfyui - Node {node_id} contains {len(node_output['images'])} image(s)" + ) + for image_info in node_output["images"]: + filename = image_info.get("filename") + subfolder = image_info.get("subfolder", "") + img_type = image_info.get("type") + + # skip temp images + if img_type == "temp": + print( + f"worker-comfyui - Skipping image {filename} because type is 'temp'" + ) + continue + + if not filename: + warn_msg = f"Skipping image in node {node_id} due to missing filename: {image_info}" + print(f"worker-comfyui - {warn_msg}") + errors.append(warn_msg) + continue + + image_bytes = get_image_data(filename, subfolder, img_type) + + if image_bytes: + file_extension = os.path.splitext(filename)[1] or ".png" + + if os.environ.get("BUCKET_ENDPOINT_URL"): + try: + with tempfile.NamedTemporaryFile( + suffix=file_extension, delete=False + ) as temp_file: + temp_file.write(image_bytes) + temp_file_path = temp_file.name + print( + f"worker-comfyui - Wrote image bytes to temporary file: {temp_file_path}" + ) + + print(f"worker-comfyui - Uploading {filename} to S3...") + s3_url = rp_upload.upload_image(job_id, temp_file_path) + os.remove(temp_file_path) # Clean up temp file + print( + f"worker-comfyui - Uploaded {filename} to S3: {s3_url}" + ) + # Append dictionary with filename and URL + output_data.append( + { + "filename": filename, + "type": "s3_url", + "data": s3_url, + } + ) + except Exception as e: + error_msg = f"Error uploading {filename} to S3: {e}" + print(f"worker-comfyui - {error_msg}") + errors.append(error_msg) + if "temp_file_path" in locals() and os.path.exists( + temp_file_path + ): + try: + os.remove(temp_file_path) + except OSError as rm_err: + print( + f"worker-comfyui - Error removing temp file {temp_file_path}: {rm_err}" + ) + else: + # Return as base64 string + try: + base64_image = base64.b64encode(image_bytes).decode( + "utf-8" + ) + # Append dictionary with filename and base64 data + output_data.append( + { + "filename": filename, + "type": "base64", + "data": base64_image, + } + ) + print(f"worker-comfyui - Encoded {filename} as base64") + except Exception as e: + error_msg = f"Error encoding {filename} to base64: {e}" + print(f"worker-comfyui - {error_msg}") + errors.append(error_msg) + else: + error_msg = f"Failed to fetch image data for {filename} from /view endpoint." + errors.append(error_msg) + + # Check for other output types + other_keys = [k for k in node_output.keys() if k != "images"] + if other_keys: + warn_msg = ( + f"Node {node_id} produced unhandled output keys: {other_keys}." + ) + print(f"worker-comfyui - WARNING: {warn_msg}") + print( + f"worker-comfyui - --> If this output is useful, please consider opening an issue on GitHub to discuss adding support." + ) + + except websocket.WebSocketException as e: + print(f"worker-comfyui - WebSocket Error: {e}") + return {"error": f"WebSocket communication error: {e}"} + except requests.RequestException as e: + print(f"worker-comfyui - HTTP Request Error: {e}") + return {"error": f"HTTP communication error with ComfyUI: {e}"} + except ValueError as e: + print(f"worker-comfyui - Value Error: {e}") + return {"error": str(e)} + except Exception as e: + print(f"worker-comfyui - Unexpected Handler Error: {e}") + return {"error": f"An unexpected error occurred: {e}"} + finally: + if ws and ws.connected: + print(f"worker-comfyui - Closing websocket connection.") + ws.close() + + final_result = {} + + if output_data: + final_result["images"] = output_data + + if errors: + final_result["errors"] = errors + print(f"worker-comfyui - Job completed with errors/warnings: {errors}") + + if not output_data and errors: + print(f"worker-comfyui - Job failed with no output images.") + return { + "error": "Job processing failed", + "details": errors, + } + elif not output_data and not errors: + print( + f"worker-comfyui - Job completed successfully, but the workflow produced no images." + ) + final_result["status"] = "success_no_images" + final_result["images"] = [] + + print(f"worker-comfyui - Job completed. Returning {len(output_data)} image(s).") + return final_result + + +if __name__ == "__main__": + print("worker-comfyui - Starting handler...") + runpod.serverless.start({"handler": handler}) diff --git a/requirements.txt b/requirements.txt index e079d600..d73f9a33 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1 +1,3 @@ -runpod~=1.7.9 \ No newline at end of file +runpod~=1.7.9 +websocket-client +requests \ No newline at end of file diff --git a/src/rp_handler.py b/src/rp_handler.py deleted file mode 100644 index 255753a1..00000000 --- a/src/rp_handler.py +++ /dev/null @@ -1,346 +0,0 @@ -import runpod -from runpod.serverless.utils import rp_upload -import json -import urllib.request -import urllib.parse -import time -import os -import requests -import base64 -from io import BytesIO - -# Time to wait between API check attempts in milliseconds -COMFY_API_AVAILABLE_INTERVAL_MS = 50 -# Maximum number of API check attempts -COMFY_API_AVAILABLE_MAX_RETRIES = 500 -# Time to wait between poll attempts in milliseconds -COMFY_POLLING_INTERVAL_MS = int(os.environ.get("COMFY_POLLING_INTERVAL_MS", 250)) -# Maximum number of poll attempts -COMFY_POLLING_MAX_RETRIES = int(os.environ.get("COMFY_POLLING_MAX_RETRIES", 500)) -# Host where ComfyUI is running -COMFY_HOST = "127.0.0.1:8188" -# Enforce a clean state after each job is done -# see https://docs.runpod.io/docs/handler-additional-controls#refresh-worker -REFRESH_WORKER = os.environ.get("REFRESH_WORKER", "false").lower() == "true" - - -def validate_input(job_input): - """ - Validates the input for the handler function. - - Args: - job_input (dict): The input data to validate. - - Returns: - tuple: A tuple containing the validated data and an error message, if any. - The structure is (validated_data, error_message). - """ - # Validate if job_input is provided - if job_input is None: - return None, "Please provide input" - - # Check if input is a string and try to parse it as JSON - if isinstance(job_input, str): - try: - job_input = json.loads(job_input) - except json.JSONDecodeError: - return None, "Invalid JSON format in input" - - # Validate 'workflow' in input - workflow = job_input.get("workflow") - if workflow is None: - return None, "Missing 'workflow' parameter" - - # Validate 'images' in input, if provided - images = job_input.get("images") - if images is not None: - if not isinstance(images, list) or not all( - "name" in image and "image" in image for image in images - ): - return ( - None, - "'images' must be a list of objects with 'name' and 'image' keys", - ) - - # Return validated data and no error - return {"workflow": workflow, "images": images}, None - - -def check_server(url, retries=500, delay=50): - """ - Check if a server is reachable via HTTP GET request - - Args: - - url (str): The URL to check - - retries (int, optional): The number of times to attempt connecting to the server. Default is 50 - - delay (int, optional): The time in milliseconds to wait between retries. Default is 500 - - Returns: - bool: True if the server is reachable within the given number of retries, otherwise False - """ - - for i in range(retries): - try: - response = requests.get(url) - - # If the response status code is 200, the server is up and running - if response.status_code == 200: - print(f"worker-comfyui - API is reachable") - return True - except requests.RequestException as e: - # If an exception occurs, the server may not be ready - pass - - # Wait for the specified delay before retrying - time.sleep(delay / 1000) - - print( - f"worker-comfyui - Failed to connect to server at {url} after {retries} attempts." - ) - return False - - -def upload_images(images): - """ - Upload a list of base64 encoded images to the ComfyUI server using the /upload/image endpoint. - - Args: - images (list): A list of dictionaries, each containing the 'name' of the image and the 'image' as a base64 encoded string. - server_address (str): The address of the ComfyUI server. - - Returns: - list: A list of responses from the server for each image upload. - """ - if not images: - return {"status": "success", "message": "No images to upload", "details": []} - - responses = [] - upload_errors = [] - - print(f"worker-comfyui - image(s) upload") - - for image in images: - name = image["name"] - image_data = image["image"] - blob = base64.b64decode(image_data) - - # Prepare the form data - files = { - "image": (name, BytesIO(blob), "image/png"), - "overwrite": (None, "true"), - } - - # POST request to upload the image - response = requests.post(f"http://{COMFY_HOST}/upload/image", files=files) - if response.status_code != 200: - upload_errors.append(f"Error uploading {name}: {response.text}") - else: - responses.append(f"Successfully uploaded {name}") - - if upload_errors: - print(f"worker-comfyui - image(s) upload with errors") - return { - "status": "error", - "message": "Some images failed to upload", - "details": upload_errors, - } - - print(f"worker-comfyui - image(s) upload complete") - return { - "status": "success", - "message": "All images uploaded successfully", - "details": responses, - } - - -def queue_workflow(workflow): - """ - Queue a workflow to be processed by ComfyUI - - Args: - workflow (dict): A dictionary containing the workflow to be processed - - Returns: - dict: The JSON response from ComfyUI after processing the workflow - """ - - # The top level element "prompt" is required by ComfyUI - data = json.dumps({"prompt": workflow}).encode("utf-8") - - req = urllib.request.Request(f"http://{COMFY_HOST}/prompt", data=data) - return json.loads(urllib.request.urlopen(req).read()) - - -def get_history(prompt_id): - """ - Retrieve the history of a given prompt using its ID - - Args: - prompt_id (str): The ID of the prompt whose history is to be retrieved - - Returns: - dict: The history of the prompt, containing all the processing steps and results - """ - with urllib.request.urlopen(f"http://{COMFY_HOST}/history/{prompt_id}") as response: - return json.loads(response.read()) - - -def base64_encode(img_path): - """ - Returns base64 encoded image. - - Args: - img_path (str): The path to the image - - Returns: - str: The base64 encoded image - """ - with open(img_path, "rb") as image_file: - encoded_string = base64.b64encode(image_file.read()).decode("utf-8") - return f"{encoded_string}" - - -def process_output_images(outputs, job_id): - """ - This function takes the "outputs" from image generation and the job ID, - then determines the correct way to return the image, either as a direct URL - to an AWS S3 bucket or as a base64 encoded string, depending on the - environment configuration. - - Args: - outputs (dict): A dictionary containing the outputs from image generation, - typically includes node IDs and their respective output data. - job_id (str): The unique identifier for the job. - - Returns: - dict: A dictionary with the status ('success' or 'error') and the message, - which is either the URL to the image in the AWS S3 bucket or a base64 - encoded string of the image. In case of error, the message details the issue. - - The function works as follows: - - It first determines the output path for the images from an environment variable, - defaulting to "/comfyui/output" if not set. - - It then iterates through the outputs to find the filenames of the generated images. - - After confirming the existence of the image in the output folder, it checks if the - AWS S3 bucket is configured via the BUCKET_ENDPOINT_URL environment variable. - - If AWS S3 is configured, it uploads the image to the bucket and returns the URL. - - If AWS S3 is not configured, it encodes the image in base64 and returns the string. - - If the image file does not exist in the output folder, it returns an error status - with a message indicating the missing image file. - """ - - # The path where ComfyUI stores the generated images - COMFY_OUTPUT_PATH = os.environ.get("COMFY_OUTPUT_PATH", "/comfyui/output") - - output_images = {} - - for node_id, node_output in outputs.items(): - if "images" in node_output: - for image in node_output["images"]: - output_images = os.path.join(image["subfolder"], image["filename"]) - - print(f"worker-comfyui - image generation is done") - - # expected image output folder - local_image_path = f"{COMFY_OUTPUT_PATH}/{output_images}" - - print(f"worker-comfyui - {local_image_path}") - - # The image is in the output folder - if os.path.exists(local_image_path): - if os.environ.get("BUCKET_ENDPOINT_URL", False): - # URL to image in AWS S3 - image = rp_upload.upload_image(job_id, local_image_path) - print("worker-comfyui - the image was generated and uploaded to AWS S3") - else: - # base64 image - image = base64_encode(local_image_path) - print("worker-comfyui - the image was generated and converted to base64") - - return { - "status": "success", - "message": image, - } - else: - print("worker-comfyui - the image does not exist in the output folder") - return { - "status": "error", - "message": f"the image does not exist in the specified output folder: {local_image_path}", - } - - -def handler(job): - """ - The main function that handles a job of generating an image. - - This function validates the input, sends a prompt to ComfyUI for processing, - polls ComfyUI for result, and retrieves generated images. - - Args: - job (dict): A dictionary containing job details and input parameters. - - Returns: - dict: A dictionary containing either an error message or a success status with generated images. - """ - job_input = job["input"] - - # Make sure that the input is valid - validated_data, error_message = validate_input(job_input) - if error_message: - return {"error": error_message} - - # Extract validated data - workflow = validated_data["workflow"] - images = validated_data.get("images") - - # Make sure that the ComfyUI API is available - check_server( - f"http://{COMFY_HOST}", - COMFY_API_AVAILABLE_MAX_RETRIES, - COMFY_API_AVAILABLE_INTERVAL_MS, - ) - - # Upload images if they exist - upload_result = upload_images(images) - - if upload_result["status"] == "error": - return upload_result - - # Queue the workflow - try: - queued_workflow = queue_workflow(workflow) - prompt_id = queued_workflow["prompt_id"] - print(f"worker-comfyui - queued workflow with ID {prompt_id}") - except Exception as e: - return {"error": f"Error queuing workflow: {str(e)}"} - - # Poll for completion - print(f"worker-comfyui - wait until image generation is complete") - retries = 0 - try: - while retries < COMFY_POLLING_MAX_RETRIES: - history = get_history(prompt_id) - - # Exit the loop if we have found the history - if prompt_id in history and history[prompt_id].get("outputs"): - break - else: - # Wait before trying again - time.sleep(COMFY_POLLING_INTERVAL_MS / 1000) - retries += 1 - else: - return {"error": "Max retries reached while waiting for image generation"} - except Exception as e: - return {"error": f"Error waiting for image generation: {str(e)}"} - - # Get the generated image and return it as URL in an AWS bucket or as base64 - images_result = process_output_images(history[prompt_id].get("outputs"), job["id"]) - - result = {**images_result, "refresh_worker": REFRESH_WORKER} - - return result - - -# Start the handler only if this script is run directly -if __name__ == "__main__": - runpod.serverless.start({"handler": handler}) diff --git a/src/start.sh b/src/start.sh index 1c448ed0..8c045398 100644 --- a/src/start.sh +++ b/src/start.sh @@ -10,11 +10,11 @@ if [ "$SERVE_API_LOCALLY" == "true" ]; then python /comfyui/main.py --disable-auto-launch --disable-metadata --listen & echo "worker-comfyui: Starting RunPod Handler" - python -u /rp_handler.py --rp_serve_api --rp_api_host=0.0.0.0 + python -u /handler.py --rp_serve_api --rp_api_host=0.0.0.0 else echo "worker-comfyui: Starting ComfyUI" python /comfyui/main.py --disable-auto-launch --disable-metadata & echo "worker-comfyui: Starting RunPod Handler" - python -u /rp_handler.py + python -u /handler.py fi \ No newline at end of file diff --git a/test_input.json b/test_input.json index 7fdb0f20..13384110 100644 --- a/test_input.json +++ b/test_input.json @@ -1,62 +1,118 @@ { "input": { + "images": [ + { + "name": "test.png", + "image": 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" + } + ], "workflow": { - "3": { + "6": { "inputs": { - "seed": 234234, - "steps": 20, - "cfg": 8, - "sampler_name": "euler", - "scheduler": "normal", - "denoise": 1, - "model": ["4", 0], - "positive": ["6", 0], - "negative": ["7", 0], - "latent_image": ["5", 0] + "text": "cute anime girl with massive fluffy fennec ears and a big fluffy tail blonde messy long hair blue eyes wearing a maid outfit with a long black gold leaf pattern dress and a white apron mouth open placing a fancy black forest cake with candles on top of a dinner table of an old dark Victorian mansion lit by candlelight with a bright window to the foggy forest and very expensive stuff everywhere there are paintings on the walls", + "clip": ["30", 1] }, - "class_type": "KSampler" + "class_type": "CLIPTextEncode", + "_meta": { + "title": "CLIP Text Encode (Positive Prompt)" + } }, - "4": { + "8": { "inputs": { - "ckpt_name": "sd_xl_base_1.0.safetensors" + "samples": ["31", 0], + "vae": ["30", 2] }, - "class_type": "CheckpointLoaderSimple" + "class_type": "VAEDecode", + "_meta": { + "title": "VAE Decode" + } }, - "5": { + "9": { + "inputs": { + "filename_prefix": "ComfyUI", + "images": ["8", 0] + }, + "class_type": "SaveImage", + "_meta": { + "title": "Save Image" + } + }, + "27": { "inputs": { "width": 512, "height": 512, "batch_size": 1 }, - "class_type": "EmptyLatentImage" + "class_type": "EmptySD3LatentImage", + "_meta": { + "title": "EmptySD3LatentImage" + } }, - "6": { + "30": { "inputs": { - "text": "beautiful scenery nature glass bottle landscape, purple galaxy bottle,", - "clip": ["4", 1] + "ckpt_name": "flux1-dev-fp8.safetensors" }, - "class_type": "CLIPTextEncode" + "class_type": "CheckpointLoaderSimple", + "_meta": { + "title": "Load Checkpoint" + } }, - "7": { + "31": { "inputs": { - "text": "text, watermark", - "clip": ["4", 1] + "seed": 1231231213123, + "steps": 10, + "cfg": 1, + "sampler_name": "euler", + "scheduler": "simple", + "denoise": 1, + "model": ["30", 0], + "positive": ["35", 0], + "negative": ["33", 0], + "latent_image": ["27", 0] }, - "class_type": "CLIPTextEncode" + "class_type": "KSampler", + "_meta": { + "title": "KSampler" + } }, - "8": { + "33": { "inputs": { - "samples": ["3", 0], - "vae": ["4", 2] + "text": "", + "clip": ["30", 1] }, - "class_type": "VAEDecode" + "class_type": "CLIPTextEncode", + "_meta": { + "title": "CLIP Text Encode (Negative Prompt)" + } }, - "9": { + "35": { + "inputs": { + "guidance": 3.5, + "conditioning": ["6", 0] + }, + "class_type": "FluxGuidance", + "_meta": { + "title": "FluxGuidance" + } + }, + "38": { + "inputs": { + "images": ["8", 0] + }, + "class_type": "PreviewImage", + "_meta": { + "title": "Preview Image" + } + }, + "40": { "inputs": { - "filename_prefix": "ComfyUI/test", + "filename_prefix": "ComfyUI", "images": ["8", 0] }, - "class_type": "SaveImage" + "class_type": "SaveImage", + "_meta": { + "title": "Save Image" + } } } } diff --git a/test_resources/workflows/flux_dev_checkpoint_example.json b/test_resources/workflows/flux_dev_checkpoint_example.json new file mode 100644 index 00000000..6db1b0d8 --- /dev/null +++ b/test_resources/workflows/flux_dev_checkpoint_example.json @@ -0,0 +1,120 @@ +{ + "6": { + "inputs": { + "text": "cute anime girl with massive fluffy fennec ears and a big fluffy tail blonde messy long hair blue eyes wearing a maid outfit with a long black gold leaf pattern dress and a white apron mouth open placing a fancy black forest cake with candles on top of a dinner table of an old dark Victorian mansion lit by candlelight with a bright window to the foggy forest and very expensive stuff everywhere there are paintings on the walls", + "clip": [ + "30", + 1 + ] + }, + "class_type": "CLIPTextEncode", + "_meta": { + "title": "CLIP Text Encode (Positive Prompt)" + } + }, + "8": { + "inputs": { + "samples": [ + "31", + 0 + ], + "vae": [ + "30", + 2 + ] + }, + "class_type": "VAEDecode", + "_meta": { + "title": "VAE Decode" + } + }, + "9": { + "inputs": { + "filename_prefix": "ComfyUI", + "images": [ + "8", + 0 + ] + }, + "class_type": "SaveImage", + "_meta": { + "title": "Save Image" + } + }, + "27": { + "inputs": { + "width": 512, + "height": 512, + "batch_size": 1 + }, + "class_type": "EmptySD3LatentImage", + "_meta": { + "title": "EmptySD3LatentImage" + } + }, + "30": { + "inputs": { + "ckpt_name": "flux1-dev-fp8.safetensors" + }, + "class_type": "CheckpointLoaderSimple", + "_meta": { + "title": "Load Checkpoint" + } + }, + "31": { + "inputs": { + "seed": 150959564782347, + "steps": 10, + "cfg": 1, + "sampler_name": "euler", + "scheduler": "simple", + "denoise": 1, + "model": [ + "30", + 0 + ], + "positive": [ + "35", + 0 + ], + "negative": [ + "33", + 0 + ], + "latent_image": [ + "27", + 0 + ] + }, + "class_type": "KSampler", + "_meta": { + "title": "KSampler" + } + }, + "33": { + "inputs": { + "text": "", + "clip": [ + "30", + 1 + ] + }, + "class_type": "CLIPTextEncode", + "_meta": { + "title": "CLIP Text Encode (Negative Prompt)" + } + }, + "35": { + "inputs": { + "guidance": 3.5, + "conditioning": [ + "6", + 0 + ] + }, + "class_type": "FluxGuidance", + "_meta": { + "title": "FluxGuidance" + } + } +} \ No newline at end of file diff --git a/tests/test_rp_handler.py b/tests/test_handler.py similarity index 79% rename from tests/test_rp_handler.py rename to tests/test_handler.py index 34cfaab0..33efcaa9 100644 --- a/tests/test_rp_handler.py +++ b/tests/test_handler.py @@ -5,9 +5,9 @@ import json import base64 -# Make sure that "src" is known and can be used to import rp_handler.py +# Make sure that "src" is known and can be used to import handler.py sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "src"))) -from src import rp_handler +from src import handler # Local folder for test resources RUNPOD_WORKER_COMFY_TEST_RESOURCES_IMAGES = "./test_resources/images" @@ -16,7 +16,7 @@ class TestRunpodWorkerComfy(unittest.TestCase): def test_valid_input_with_workflow_only(self): input_data = {"workflow": {"key": "value"}} - validated_data, error = rp_handler.validate_input(input_data) + validated_data, error = handler.validate_input(input_data) self.assertIsNone(error) self.assertEqual(validated_data, {"workflow": {"key": "value"}, "images": None}) @@ -25,13 +25,13 @@ def test_valid_input_with_workflow_and_images(self): "workflow": {"key": "value"}, "images": [{"name": "image1.png", "image": "base64string"}], } - validated_data, error = rp_handler.validate_input(input_data) + validated_data, error = handler.validate_input(input_data) self.assertIsNone(error) self.assertEqual(validated_data, input_data) def test_input_missing_workflow(self): input_data = {"images": [{"name": "image1.png", "image": "base64string"}]} - validated_data, error = rp_handler.validate_input(input_data) + validated_data, error = handler.validate_input(input_data) self.assertIsNotNone(error) self.assertEqual(error, "Missing 'workflow' parameter") @@ -40,7 +40,7 @@ def test_input_with_invalid_images_structure(self): "workflow": {"key": "value"}, "images": [{"name": "image1.png"}], # Missing 'image' key } - validated_data, error = rp_handler.validate_input(input_data) + validated_data, error = handler.validate_input(input_data) self.assertIsNotNone(error) self.assertEqual( error, "'images' must be a list of objects with 'name' and 'image' keys" @@ -48,46 +48,46 @@ def test_input_with_invalid_images_structure(self): def test_invalid_json_string_input(self): input_data = "invalid json" - validated_data, error = rp_handler.validate_input(input_data) + validated_data, error = handler.validate_input(input_data) self.assertIsNotNone(error) self.assertEqual(error, "Invalid JSON format in input") def test_valid_json_string_input(self): input_data = '{"workflow": {"key": "value"}}' - validated_data, error = rp_handler.validate_input(input_data) + validated_data, error = handler.validate_input(input_data) self.assertIsNone(error) self.assertEqual(validated_data, {"workflow": {"key": "value"}, "images": None}) def test_empty_input(self): input_data = None - validated_data, error = rp_handler.validate_input(input_data) + validated_data, error = handler.validate_input(input_data) self.assertIsNotNone(error) self.assertEqual(error, "Please provide input") - @patch("rp_handler.requests.get") + @patch("handler.requests.get") def test_check_server_server_up(self, mock_requests): mock_response = MagicMock() mock_response.status_code = 200 mock_requests.return_value = mock_response - result = rp_handler.check_server("http://127.0.0.1:8188", 1, 50) + result = handler.check_server("http://127.0.0.1:8188", 1, 50) self.assertTrue(result) - @patch("rp_handler.requests.get") + @patch("handler.requests.get") def test_check_server_server_down(self, mock_requests): - mock_requests.get.side_effect = rp_handler.requests.RequestException() - result = rp_handler.check_server("http://127.0.0.1:8188", 1, 50) + mock_requests.get.side_effect = handler.requests.RequestException() + result = handler.check_server("http://127.0.0.1:8188", 1, 50) self.assertFalse(result) - @patch("rp_handler.urllib.request.urlopen") + @patch("handler.urllib.request.urlopen") def test_queue_prompt(self, mock_urlopen): mock_response = MagicMock() mock_response.read.return_value = json.dumps({"prompt_id": "123"}).encode() mock_urlopen.return_value = mock_response - result = rp_handler.queue_workflow({"prompt": "test"}) + result = handler.queue_workflow({"prompt": "test"}) self.assertEqual(result, {"prompt_id": "123"}) - @patch("rp_handler.urllib.request.urlopen") + @patch("handler.urllib.request.urlopen") def test_get_history(self, mock_urlopen): # Mock response data as a JSON string mock_response_data = json.dumps({"key": "value"}).encode("utf-8") @@ -108,7 +108,7 @@ def mock_read(): mock_urlopen.return_value = mock_response # Call the function under test - result = rp_handler.get_history("123") + result = handler.get_history("123") # Assertions self.assertEqual(result, {"key": "value"}) @@ -118,12 +118,12 @@ def mock_read(): def test_base64_encode(self, mock_file): test_data = base64.b64encode(b"test").decode("utf-8") - result = rp_handler.base64_encode("dummy_path") + result = handler.base64_encode("dummy_path") self.assertEqual(result, test_data) - @patch("rp_handler.os.path.exists") - @patch("rp_handler.rp_upload.upload_image") + @patch("handler.os.path.exists") + @patch("handler.rp_upload.upload_image") @patch.dict( os.environ, {"COMFY_OUTPUT_PATH": RUNPOD_WORKER_COMFY_TEST_RESOURCES_IMAGES} ) @@ -136,12 +136,12 @@ def test_bucket_endpoint_not_configured(self, mock_upload_image, mock_exists): } job_id = "123" - result = rp_handler.process_output_images(outputs, job_id) + result = handler.process_output_images(outputs, job_id) self.assertEqual(result["status"], "success") - @patch("rp_handler.os.path.exists") - @patch("rp_handler.rp_upload.upload_image") + @patch("handler.os.path.exists") + @patch("handler.rp_upload.upload_image") @patch.dict( os.environ, { @@ -157,11 +157,15 @@ def test_bucket_endpoint_configured(self, mock_upload_image, mock_exists): mock_upload_image.return_value = "http://example.com/uploaded/image.png" # Define the outputs and job_id for the test - outputs = {"node_id": {"images": [{"filename": "ComfyUI_00001_.png", "subfolder": "test"}]}} + outputs = { + "node_id": { + "images": [{"filename": "ComfyUI_00001_.png", "subfolder": "test"}] + } + } job_id = "123" # Call the function under test - result = rp_handler.process_output_images(outputs, job_id) + result = handler.process_output_images(outputs, job_id) # Assertions self.assertEqual(result["status"], "success") @@ -170,8 +174,8 @@ def test_bucket_endpoint_configured(self, mock_upload_image, mock_exists): job_id, "./test_resources/images/test/ComfyUI_00001_.png" ) - @patch("rp_handler.os.path.exists") - @patch("rp_handler.rp_upload.upload_image") + @patch("handler.os.path.exists") + @patch("handler.rp_upload.upload_image") @patch.dict( os.environ, { @@ -195,13 +199,13 @@ def test_bucket_image_upload_fails_env_vars_wrong_or_missing( } job_id = "123" - result = rp_handler.process_output_images(outputs, job_id) + result = handler.process_output_images(outputs, job_id) # Check if the image was saved to the 'simulated_uploaded' directory self.assertIn("simulated_uploaded", result["message"]) self.assertEqual(result["status"], "success") - @patch("rp_handler.requests.post") + @patch("handler.requests.post") def test_upload_images_successful(self, mock_post): mock_response = unittest.mock.Mock() mock_response.status_code = 200 @@ -212,12 +216,12 @@ def test_upload_images_successful(self, mock_post): images = [{"name": "test_image.png", "image": test_image_data}] - responses = rp_handler.upload_images(images) + responses = handler.upload_images(images) self.assertEqual(len(responses), 3) self.assertEqual(responses["status"], "success") - @patch("rp_handler.requests.post") + @patch("handler.requests.post") def test_upload_images_failed(self, mock_post): mock_response = unittest.mock.Mock() mock_response.status_code = 400 @@ -228,7 +232,7 @@ def test_upload_images_failed(self, mock_post): images = [{"name": "test_image.png", "image": test_image_data}] - responses = rp_handler.upload_images(images) + responses = handler.upload_images(images) self.assertEqual(len(responses), 3) self.assertEqual(responses["status"], "error")