AMD R9700 AI PRO (32GB) — ComfyUI Toolbox

A Fedora toolbox image with a full ROCm environment (TheRock Nightlies / ROCm 7) for image & video generation on the AMD Radeon 9700 AI PRO (32GB).

This repository provides a pre-configured Docker container to run ComfyUI with validated workflows on the AMD Radeon 9700 AI PRO (32GB).

Watch the YouTube Video

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Table of Contents

• 1. Included Workflows
• 2. Toolbox Setup
• 3. First Run Setup (Required)
• 4. Benchmarks
• 5. Kernel Log Collection
• 6. Maintainer Notes

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1. Included Workflows

The repository comes with a collection of ComfyUI workflows pre-validated on this hardware. You can find them in the workflows/API directory (mapped to /opt/comfy-workflows inside the container).

Workflow	Type	Description
HunyuanVideo 1.5	I2V / T2V	4-step LoRA, 720p resolution. Configured for 32GB.
Qwen Image	T2I	Qwen Image 2512 (FP8) & Lightning LoRA (4 steps).
Qwen Image Edit	Image Editing	Qwen Image Edit 2511 (FP8) & Lightning LoRA (4/20 steps).
Wan 2.2	I2V / T2V	14B model with 4-step Lightning LoRA.

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2. Toolbox Setup

This project uses toolbox (built on Podman) to provide a seamless development environment that integrates with your home directory.

2.1. Create the Toolbox

Run the following command on your host to create the container with GPU access:

toolbox create r9700-comfy \
  --image docker.io/kyuz0/amd-r9700-comfy:latest \
  -- --device /dev/dri --device /dev/kfd \
  --group-add video --group-add render --security-opt seccomp=unconfined

• --device /dev/dri & /dev/kfd: Exposes AMD GPU and compute devices.
• --security-opt seccomp=unconfined: Required for some ROCm/GPU operations.

2.2. Enter the Toolbox

toolbox enter r9700-comfy

Once inside, you have access to a full ROCm environment with PyTorch, ComfyUI, and helper scripts in /opt.

2.3. Updating the Toolbox

To update the container image (e.g., for newer ROCm nightly builds) without deleting your downloaded models (which should be stored in your HOME), use the provided refresh script found in the root of this repo:

./refresh-toolbox.sh

Warning

This will delete and recreate the toolbox container. Any files stored inside the container system (e.g., /opt, /usr) will be lost. Files in your home directory (~) are safe.

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3. First Run Setup (Required)

After entering the toolbox for the first time, you must configure the storage paths and download the model weights.

Step 1: Configure Persistent Paths

Run the setup script to link ComfyUI's model directories to your home folder (~/comfy-models). This ensures you don't download 100GB+ of models every time you refresh the container.

/opt/set_extra_paths.sh

Step 2: Download Models

Use the Model Manager TUI to download the required checkpoints and LoRAs for the included workflows. This tool handles the complex dependency chains (e.g., downloading base models before LoRAs).

model_manager

(Or python /opt/model_manager.py)

Select the workflow you want to run (e.g., "Wan 2.2 - Text to Video"), and the manager will download the necessary files to ~/comfy-models.

Note: The manager uses the helper scripts located in /opt/ (like get_qwen_image.sh, get_wan22.sh) under the hood. You can run these manually if you prefer CLI arguments.

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4. Benchmarks

We maintain a list of performance benchmarks for these workflows on the R9700 AI PRO.

👉 View Benchmarks: https://kyuz0.github.io/amd-r9700-comfy/

To run benchmarks yourself:

python /opt/benchmark_workflows.py

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5. Kernel Log Collection

We are working directly with AMD to improve kernel stability and performance for the R9700 AI PRO (gfx1201). If you encounter performance issues or crashes, you can help by collecting execution logs.

Tracking Issue: ROCm/TheRock#2591

How to Collect Logs

1. Make sure you are inside the toolbox.
2. Run the log collection script:

python /opt/collect_perf_logs.py

This script will:

• Run the workflows in isolation.
• Capture hipblaslt and miopen logs.
• Save them to the perf_logs/ directory in your current folder.

Please zip the perf_logs folder and attach it to the GitHub issue mentioned above, or share it with the maintainers.

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6. Maintainer Notes

Publishing Log Releases

To publish collected performance logs as a GitHub Release (for tracking historical data):

1. Zip the logs:

   zip -r perf_logs_$(date +%Y%m%d).zip perf_logs/

2. Create a Release:

   gh release create logs-$(date +%Y%m%d) perf_logs_$(date +%Y%m%d).zip \
     --title "Performance Logs $(date +%Y-%m-%d)" \
     --notes "Logs collected on R9700 AI PRO for kernel analysis."