Docker Compose for Stable Diffusion for ROCm

Simple docker compose for getting ComfyUI and sd-webui (Forge) up and running with minimal modification on the main system.

Requirements

• docker-compose
• AMD GPU (PRs for other cards are welcome)
• checkpoints are saved into data/checkpoints other model files in their respective subfolder, for example data/comfyui/models or data/sd-webui/models

Instructions

1. clone this repo
2. open repo directory in terminal
3. start up the docker container by typing:
   1. docker-compose up
   2. It will take a while to download all python libraries
   3. wait a while until you see the text: To see the GUI go to: http://0.0.0.0:31488
   4. After getting that message start ComfyUI by open browser with the following link: http://localhost:31488
   5. or WebUI by going to http://localhost:31489

Run locally (no Docker)

You can also run without containers using the helper script:

./run-local.sh

• On first run it will create a Python virtual environment and set up ComfyUI, ROCm PyTorch and Flash Attention.
• It creates a marker file in the data directory to skip repeated heavy setup on subsequent runs.
• By default it prompts whether to reinstall ROCm Torch and Flash Attention. To automate, use environment variables:
  • NON_INTERACTIVE=1 to disable prompts
  • REINSTALL_ROCM=yes to reinstall ROCm Torch in non-interactive mode
  • REINSTALL_FLASH=yes to reinstall Flash Attention in non-interactive mode

You can customize behavior via a .env file at the repo root (auto-loaded) or by exporting variables before running:

• PYTHON_VERSION (default 3.12)
• ROCM_VERSION (nightly, release, or cpuonly; default nightly for local run)
• ROOT_DIR (default ${PWD}/data/home-local)
• COMFYUI_PORT (default 31490)

Example non-interactive run on CPU-only:

ROCM_VERSION=cpuonly NON_INTERACTIVE=1 ./run-local.sh

Important: The provided .env in this repo is tailored for Docker and sets ROOT_DIR=/root. When running locally as a non-root user, run-local.sh will ignore ROOT_DIR=/root and use ${PWD}/data/home-local instead. To explicitly control this, set ROOT_DIR to a writable directory when invoking the script or in your own .env.local file.

Examples:

ROOT_DIR="$PWD/data/home-local" ./run-local.sh
# or
export ROOT_DIR="$PWD/data/home-local"; ./run-local.sh

Hints

To make the docker created files accessable from your own user run these commands

sudo setfacl -d -m "u:${USER}:rwX" data/
sudo setfacl -R -m "u:${USER}:rwX" data/

You can run in CPU-only mode by setting ROCM_VERSION=cpuonly and remove the lines with /dev/kfd in docker-compose.yml

Profiling (time and GPU memory)

You can enable an optional profiling mode for ComfyUI when running locally. It will:

• Continuously sample and log GPU memory usage (allocated, reserved, peak) and process RSS to a CSV file.
• Optionally run a PyTorch profiler (CPU + ROCm/CUDA) and export a Chrome trace JSON.

How to use:

1. Enable profiling for a run by setting PROFILING=1:

PROFILING=1 ./run-local.sh

2. Outputs will be stored under a timestamped folder:

${ROOT_DIR}/profiles/<YYYYMMDD-HHMMSS>/
  - memory_log.csv        # periodic memory samples
  - torch_trace.json      # Chrome trace from torch profiler

Environment variables (optional):

• PROFILING_SAMPLING_INTERVAL (default 2): seconds between memory samples.
• PROFILING_TORCH_PROFILER (default 1): set to 0 to disable the torch profiler and only collect memory samples.
• PROFILING_EXPORT_GZIP (default 1): when enabled, also writes a compressed *.json.gz version of every exported trace (recommended for Perfetto UI).
• PROFILING_RECORD_SHAPES (default 0): enable to record input shapes; increases trace size.
• PROFILING_WITH_STACK (default 0): enable to capture Python stack traces; increases trace size.

How to get torch_trace.json (step-by-step):

• Start ComfyUI with profiling enabled:
  • PROFILING=1 ./run-local.sh
  • Optional: leave the default PROFILING_TORCH_PROFILER=1 so the trace is recorded.
• Use ComfyUI as usual to reproduce the behavior you want to profile.
• Stop ComfyUI (Ctrl+C in the terminal). On shutdown, the profiler writes the trace to:
  • ${ROOT_DIR}/profiles/<YYYYMMDD-HHMMSS>/torch_trace.json
• Open the trace in one of the following tools:
  • Chrome: open chrome://tracing and load the file
  • Perfetto UI: https://ui.perfetto.dev (you can load .json or the compressed .json.gz directly)
  • Note: Firefox Profiler expects a different profile format and typically cannot import Chrome Trace JSON. Use Perfetto or Chrome for these traces.

Viewing and correlating data:

• torch_trace.json (and torch_trace.json.gz) show detailed operator timelines (CPU and, if available, ROCm GPU activity through the CUDA flag).
• memory_log.csv provides a coarse timeline of process RSS and GPU memory usage to correlate with actions in ComfyUI.

Notes:

• Profiling is opt-in and does not change the default behavior if PROFILING is not set.
• On ROCm builds, the torch profiler uses the CUDA activity flag internally but maps to HIP; it works if torch.cuda.is_available() returns true.
• The trace is written when the app shuts down (Ctrl+C). On shutdown you should see a message like: [profiler] Exported Chrome trace to: .../torch_trace.json.
• On-demand export without stopping: send SIGUSR1 to the running process (Linux/macOS). Example: pkill -USR1 -f profiler_runner.py. Each signal writes a timestamped snapshot (e.g., torch_trace-YYYYMMDD-HHMMSS-<n>.json) and automatically restarts the profiler to continue recording; on final shutdown it writes torch_trace.json.

Troubleshooting trace loading

• If Perfetto (or your browser) complains about memory or 2 GB ArrayBuffer limits, load the compressed .json.gz file instead of the raw .json.
• To reduce trace size further, keep PROFILING_RECORD_SHAPES=0 and PROFILING_WITH_STACK=0 (defaults). Enable them only when necessary.
• Firefox Profiler generally cannot import Chrome Trace JSON. Use Chrome chrome://tracing or Perfetto UI.

Notes

Big thanks for all Open Source gangs that have made this possible.

Links

• ComfyUI: https://github.com/comfyanonymous/ComfyUI
• Automatic1111: https://github.com/AUTOMATIC1111/stable-diffusion-webui
• WebUI Forge: https://github.com/lllyasviel/stable-diffusion-webui-forge