9d7371ddba
The pyworker and convert-workflows.sh both unblock when ComfyUI is ready, but conversion takes a few seconds longer — without a wait, the first benchmark loses the race and silently drops to the SD1.5 fallback. Wait up to BENCHMARK_WAIT_TIMEOUT (default 30s) for the symlink before giving up. The wait fires only when we're actually about to use the well-known tier (env var / misc/ paths short-circuit), only once per process, and is skipped entirely off the base image (parent directory absent), so non-base-image deployments don't pay the timeout. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
321 lines
11 KiB
Markdown
321 lines
11 KiB
Markdown
# ComfyUI PyWorker
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This is the base PyWorker for ComfyUI. It provides a unified interface for running any ComfyUI workflow through a proxy-based architecture. See the [Serverless documentation](https://docs.vast.ai/serverless) for guides and how-to's.
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The cost for each request has a static value of `100`. ComfyUI does not handle concurrent workloads and there is no current provision to load multiple instances of ComfyUI per worker node.
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## Instance Setup
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1. Pick a template
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- [ComfyUI (Serverless)](https://cloud.vast.ai/?ref_id=62897&creator_id=62897&name=ComfyUI%20(Serverless))
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2. Follow the [getting started guide](https://docs.vast.ai/documentation/serverless/quickstart) for help with configuring your serverless setup. For testing, we recommend that you use the default options presented by the web interface.
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## Requirements
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This worker requires both [ComfyUI](https://github.com/comfyanonymous/ComfyUI) and [ComfyUI API Wrapper](https://github.com/ai-dock/comfyui-api-wrapper).
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A docker image is provided but you may use any if the above requirements are met.
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## Client
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The client demonstrates how to use the Vast Serverless SDK to generate images, save them locally, and optionally upload to S3-compatible storage.
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### Setup
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1. Clone the PyWorker repository to your local machine and install the necessary requirements for running the test client.
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```bash
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git clone https://github.com/vast-ai/pyworker
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cd pyworker
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pip install uv
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uv venv -p 3.12
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source .venv/bin/activate
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uv pip install -r requirements.txt
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```
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2. Set your API key:
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```bash
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export VAST_API_KEY=<your_api_key>
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```
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### Usage
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```bash
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# Default prompt
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python -m workers.comfyui-json.client
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# Custom prompt
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python -m workers.comfyui-json.client --prompt "a cat sitting on a rainbow"
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# With options
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python -m workers.comfyui-json.client --prompt "sunset" --width 1024 --height 1024 --steps 30
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# Using a custom workflow file
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python -m workers.comfyui-json.client --workflow my_workflow.json
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# With S3 upload
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python -m workers.comfyui-json.client --s3
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```
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### CLI Flags
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| Flag | Default | Description |
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|------|---------|-------------|
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| `--endpoint` | `my-comfyui-endpoint` | Vast endpoint name |
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| `--prompt` | (default) | Text prompt for image generation |
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| `--workflow` | (none) | Path to custom workflow JSON file |
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| `--width` | 512 | Image width in pixels |
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| `--height` | 512 | Image height in pixels |
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| `--steps` | 20 | Number of denoising steps |
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| `--seed` | (random) | Random seed for reproducibility |
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| `--s3` | (disabled) | Upload generated images to S3 |
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### Output
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Images are saved to `./generated_images/comfy_{seed}.png`.
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### S3 Upload (Optional)
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You can optionally upload generated images to an S3-compatible storage service (AWS S3, Cloudflare R2, Backblaze B2, etc.) by using the `--s3` flag.
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**1. Set environment variables:**
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```bash
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export S3_ENDPOINT_URL="https://your-account.r2.cloudflarestorage.com"
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export S3_BUCKET_NAME="my-bucket"
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export S3_ACCESS_KEY_ID="your-access-key-id"
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export S3_SECRET_ACCESS_KEY="your-secret-access-key"
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```
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**2. Run with S3 upload enabled:**
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```bash
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python -m workers.comfyui-json.client --prompt "a beautiful landscape" --s3
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```
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Images will be saved locally AND uploaded to `s3://{bucket}/comfyui/{filename}`.
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**Note:** Requires `boto3` (`pip install boto3`).
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## Benchmarking
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### Custom Benchmark Workflows
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You can provide a custom ComfyUI workflow for benchmarking. This allows you to test performance using your preferred models and workflow complexity.
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**Ways to provide the benchmark file** (in resolution order — first match wins):
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1. **Fork this repository** and commit your workflow to `workers/comfyui-json/misc/benchmark.json`.
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2. **Write the file during provisioning** to a path *outside* the pyworker tree (e.g. `/workspace/benchmark.json`) and export `BENCHMARK_JSON_PATH` so the worker can find it. The pyworker repo is cloned by `start_server.sh` *after* provisioning runs, so provisioning cannot write into `misc/` directly — the destination would be clobbered, or the clone would fail.
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3. **Run on the vast.ai ComfyUI base image.** Its `convert-workflows.sh` maintains `/opt/comfyui-api-wrapper/workflows/pyworker_benchmark.json` as a symlink to the first provisioned workflow; the worker reads this automatically when neither of the above is set. No env var required.
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*Note on timing:* the symlink is created only after `convert-workflows.sh` finishes converting workflows, which races with the pyworker's own warm-up — both unblock when ComfyUI is ready, but conversion takes a few seconds longer. To avoid losing the race, the worker waits up to 30 seconds for the symlink before falling through. Override the timeout with `BENCHMARK_WAIT_TIMEOUT` if conversion is slow on your setup. The wait fires only on the well-known tier and only once per worker process; non-base-image deployments skip it (the parent directory `/opt/comfyui-api-wrapper/workflows/` doesn't exist, so there's nothing to wait for).
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If `BENCHMARK_JSON_PATH` is set but points at a missing or unreadable file, the worker logs a warning and falls through to the next tier rather than going straight to the SD1.5 fallback.
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An example workflow is provided at `workers/comfyui-json/misc/benchmark.json.example`. To ensure varied generations, use the placeholder `__RANDOM_INT__` in place of static seed values — it will be replaced with a random integer for each benchmark run.
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### Default Benchmark (Fallback)
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If `benchmark.json` is not available, a simple image generation benchmark runs when each worker initializes. This validates GPU performance and helps identify underperforming machines.
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The default benchmark uses Stable Diffusion v1.5 with ComfyUI's standard text-to-image workflow. Configure it using these environment variables:
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| Environment Variable | Default Value | Description |
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| -------------------- | ------------- | ----------- |
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| BENCHMARK_JSON_PATH | (unset) | Path to a custom workflow file outside the pyworker tree. Used if `misc/benchmark.json` is absent. Falls through to `/opt/comfyui-api-wrapper/workflows/pyworker_benchmark.json` if set but missing. |
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| BENCHMARK_WAIT_TIMEOUT | 30 | Seconds to wait for the well-known symlink before giving up (base image only). |
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| BENCHMARK_TEST_WIDTH | 512 | Fallback benchmark: image width (pixels) |
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| BENCHMARK_TEST_HEIGHT | 512 | Fallback benchmark: image height (pixels) |
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| BENCHMARK_TEST_STEPS | 20 | Fallback benchmark: number of denoising steps |
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Each benchmark run uses a random prompt from `misc/test_prompts.txt` and a random seed to ensure consistent GPU load patterns.
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#### Calibrating Fallback Benchmark Duration
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To screen for underperforming hardware, set `BENCHMARK_TEST_STEPS` to match your expected production workflow duration. This allows you to identify machines that won't meet performance requirements.
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**Example:** If your typical workflow should complete in 90 seconds on acceptable hardware:
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```bash
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# 1. Measure it/sec on your reference machine
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# RTX 4090 typically achieves ~43 it/sec with SD1.5
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# 2. Calculate required steps
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# 90 seconds × 43 it/sec = 3870 steps
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# 3. Configure benchmark
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export BENCHMARK_TEST_STEPS=3870
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# 4. Machines completing significantly slower than 90s indicate hardware issues
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```
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**Performance expectations:**
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- Benchmark duration should remain consistent across identical GPU models
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- Significant variation (>20%) may indicate thermal, power, or configuration issues
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## Endpoint
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The worker provides a single endpoint:
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- `/generate/sync`: Processes ComfyUI workflows using either predefined modifiers or custom workflow JSON
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## Request Format
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The worker accepts requests in the following format. Choose either modifier mode OR custom workflow mode:
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**Modifier Mode:**
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```json
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{
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"input": {
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"request_id": "uuid-string", // optional - UUID generated if not provided
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"modifier": "RawWorkflow",
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"modifications": {
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"prompt": "a beautiful landscape",
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"width": 1024,
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"height": 1024,
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"steps": 20,
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"seed": 123456789
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},
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"s3": { ... }, // optional
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"webhook": { ... } // optional
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}
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}
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```
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**Custom Workflow Mode:**
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```json
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{
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"input": {
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"request_id": "uuid-string", // optional - UUID generated if not provided
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"workflow_json": {
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// Complete ComfyUI workflow JSON
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},
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"s3": { ... }, // optional
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"webhook": { ... } // optional
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}
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}
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```
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## Request Fields
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### Required Fields
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- **`input`**: Contains the main workflow data
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- **`input.request_id`**: Unique identifier for the request
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### Workflow Mode (Choose One)
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You must provide either `modifier` OR `workflow_json`, but not both:
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#### Option 1: Modifier Mode
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- **`input.modifier`**: Name of the predefined workflow modifier (e.g., "Text2Image")
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- **`input.modifications`**: Parameters to pass to the modifier
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#### Option 2: Custom Workflow Mode
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- **`input.workflow_json`**: Complete ComfyUI workflow JSON
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### Optional Fields
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- **`input.s3`**: S3 configuration for file storage
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- **`input.webhook`**: Webhook configuration for notifications
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These configurations can be provided in the request JSON or via environment variables. Request-level configuration takes precedence over environment variables.
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#### S3 Configuration
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**Via Request JSON:**
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```json
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"s3": {
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"access_key_id": "your-s3-access-key",
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"secret_access_key": "your-s3-secret-access-key",
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"endpoint_url": "https://my-endpoint.backblaze.com",
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"bucket_name": "your-bucket",
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"region": "us-east-1"
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}
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```
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**Via Environment Variables:**
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```bash
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S3_ACCESS_KEY_ID=your-key
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S3_SECRET_ACCESS_KEY=your-secret
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S3_BUCKET_NAME=your-bucket
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S3_ENDPOINT_URL=https://s3.amazonaws.com
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S3_REGION=us-east-1
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```
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#### Webhook Configuration
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**Via Request JSON:**
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```json
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"webhook": {
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"url": "your-webhook-url",
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"extra_params": {
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"custom_field": "value"
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}
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}
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```
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**Via Environment Variables:**
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```bash
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WEBHOOK_URL=https://your-webhook.com # Default webhook URL
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WEBHOOK_TIMEOUT=30 # Webhook timeout in seconds
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```
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## Examples
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### Basic Text-to-Image (Modifier Mode)
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```json
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{
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"input": {
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"modifier": "Text2Image",
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"modifications": {
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"prompt": "a cat sitting on a windowsill",
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"width": 512,
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"height": 512,
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"steps": 20,
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"seed": 42
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}
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}
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}
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```
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### Custom Workflow Mode
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```json
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{
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"input": {
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"request_id": "67890", // optional - using custom ID for tracking
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"workflow_json": {
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"3": {
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"inputs": {
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"seed": 42,
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"steps": 20,
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"cfg": 8,
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"sampler_name": "euler",
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"scheduler": "normal",
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"denoise": 1,
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"model": ["4", 0],
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"positive": ["6", 0],
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"negative": ["7", 0],
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"latent_image": ["5", 0]
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},
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"class_type": "KSampler"
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}
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}
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}
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}
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```
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## Client Libraries
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See the client example for implementation details on how to integrate with the ComfyUI worker.
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---
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See Vast's serverless documentation for more details on how to use ComfyUI with autoscaler.
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