AMD announced native ROCm 7.1.1 integration for ComfyUI on January 5, 2026, bringing professional-grade generative AI workflows to local Windows systems. The release includes three official installation methods and delivers up to 5.4x performance improvements over the previous ROCm 6.4 release, making advanced diffusion models practical on Ryzen AI and Radeon AI hardware.
What’s New in AMD ComfyUI Integration
AMD ROCm support is now natively integrated into ComfyUI, including the Desktop application version 0.7.0. The update packages ROCm 7.1.1 directly into ComfyUI, eliminating manual configuration for Windows users. AMD now offers three installation paths: an official GUI-based .exe installer for Windows, a portable archive from the ComfyUI GitHub repository, and manual setup through git for developers who need bleeding-edge access to ROCm nightlies.
The installer arrives pre-configured with tuned pipelines for popular diffusion models. Users can generate their first image within minutes on supported hardware. AMD recommends a Ryzen AI Max+ system with 128GB RAM (64GB allocated to VGM) or a Radeon AI Pro R9700 paired with 64GB system RAM for optimal performance.
Performance Gains and Hardware Acceleration
ROCm 7.1.1 builds on version 6.4 and delivers up to 5.4x faster inference speeds in ComfyUI on Windows. Testing conducted in December 2025 used the Ryzen AI Max+ 395 processor in an ASUS ROG Flow Z13 and the Radeon AI Pro R9700 with a Ryzen 9950X3D.
Beyond raw speed, the release improves stability across Ryzen AI processors and Radeon GPUs. Key improvements include:
- Better memory handling for large models
- Tuning for FP16, BF16, and FP8 mixed precision formats
- Fewer crashes during extended workflows
- Consistent behavior across AMD hardware
The ROCm 7.1.1 driver will merge into mainline AMD Software Adrenalin Edition drivers in an upcoming release. Current users must install the separate ROCm driver package alongside the ComfyUI installer.
Why Local AI Generation Matters
ComfyUI has become the standard tool for advanced diffusion workflows among power users. Its node-based interface lets creators design multi-step pipelines, chain models together, and share reusable templates. Many workflows migrated to cloud services as models grew larger, but local generation offers three advantages: full data control, faster iteration without upload delays, and predictable costs.
Early models like Stable Diffusion 1.5 (0.9 billion parameters) ran quickly but produced low-quality outputs unsuitable for professional work. Modern models like Z Image Turbo (6 billion parameters) and Flux 2 (32 billion parameters in FP8) generate photo-realistic images with accurate anatomy, lighting, and text rendering. AMD’s hardware can now handle these demanding models locally.
The Ryzen AI Max+ handles lighter prototyping and mid-tier models, while Radeon AI Pro cards accelerate heavy production renders. Teams can prototype on laptops and scale to workstations without changing software or workflows.
Installation Methods Compared
| Method | Best For | Setup Time | Control Level |
|---|---|---|---|
| Official .exe installer | Beginners, quick setup | 5-10 minutes | Low (pre-configured) |
| Portable build | Users who switch systems | 10-15 minutes | Medium (contained folder) |
| Manual git install | Developers, early access | 30+ minutes | High (full customization) |
All three methods use the same ROCm 7.1.1 backend and deliver identical performance once configured. The official installer handles dependencies automatically, while manual setup requires Python environment management and driver installation.
Model Size and Performance Trade-Offs
Older 1-billion-parameter models generate images in seconds but lack professional quality. Mid-range models like Z Image Turbo balance speed and output quality for most commercial work. Flux 2’s 32-billion-parameter architecture produces exceptional detail but requires significant VRAM and longer render times.
Video generation models like WAN 2.2 (14 billion FP8 parameters) demand even more resources but deliver high-fidelity motion and visual dynamics. Studios can now run rough cuts and experiments locally, reserving cloud resources for final production.
What’s Next for AMD and ComfyUI
AMD labels this release as the first beta version of the AMD x ComfyUI experience, with more performance and stability updates planned. Future ROCm releases will expand GPU support and optimize additional model architectures.
ComfyUI Desktop will receive continuous updates as AMD improves ROCm drivers. The integration positions AMD as a viable alternative to NVIDIA CUDA for local AI workflows, particularly for creators prioritizing data privacy and cost control.
AMD has not announced specific timelines for ROCm 7.1.1 integration into mainline Adrenalin drivers, but the company confirmed it will arrive in an upcoming release. Users running the current beta should monitor AMD’s driver update channels for production-ready versions.
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What is ComfyUI and why does AMD support matter?
ComfyUI is a node-based interface for building complex generative AI workflows using diffusion models. Native AMD ROCm support lets users run professional-quality AI image and video generation locally on Ryzen AI and Radeon hardware without cloud services, improving data control and reducing costs.
How much faster is AMD ROCm 7.1.1 compared to previous versions?
AMD ROCm 7.1.1 delivers up to 5.4x performance improvements over ROCm 6.4 in ComfyUI workflows on Windows. Actual gains vary by model size and hardware configuration, with the most significant improvements seen on Radeon AI Pro cards and Ryzen AI Max+ systems.
Which AMD GPUs work with the new ComfyUI installer?
The official installer supports AMD Ryzen AI processors (particularly Ryzen AI Max+ with 64GB VGM) and Radeon AI series GPUs, including the Radeon AI Pro R9700. Older Radeon RX GPUs may work through manual installation with compatible ROCm drivers, but AMD recommends AI-optimized hardware.
Can I run large models like Flux 2 on AMD hardware locally?
Yes, but hardware requirements are substantial. Flux 2’s 32-billion-parameter model in FP8 format requires significant VRAM and system memory. Ryzen AI Max+ systems can handle the model with longer render times, while Radeon AI Pro cards provide faster acceleration for production work.
