usedgranny/illyasviel-stable-diffusion-web-ui-amd- — explained in plain English
Analysis updated 2026-05-18
Run Stable Diffusion WebUI Forge for image generation on an AMD graphics card without CUDA or ROCm.
Pick between a faster configuration for newer RX 9000-series cards and a stability focused one for older cards.
Fix black image or NaN output errors that some AMD cards hit when running image generation at lower precision.
| usedgranny/illyasviel-stable-diffusion-web-ui-amd- | 0xustaz/streamgate | a-bissell/unleash-lite | |
|---|---|---|---|
| Stars | 1 | 1 | 1 |
| Language | Python | Python | Python |
| Setup difficulty | moderate | hard | hard |
| Complexity | 3/5 | 4/5 | 4/5 |
| Audience | general | developer | researcher |
Figures from each repo's GitHub metadata at analysis time.
Requires an AMD GPU and picking the correct one of two provided configs based on your card's generation.
This repository is a modified setup of Stable Diffusion WebUI Forge, an image generation tool, that lets it run on AMD graphics cards instead of the Nvidia cards it normally expects. Standard Forge builds rely on Nvidia's CUDA technology, so AMD users usually cannot run it out of the box. This project redirects that pipeline through Microsoft's DirectML instead, which means it needs neither CUDA nor AMD's own ROCm software to work. The author describes putting this together after sharing an early version on Reddit a couple of months earlier, then asking an AI to summarize the changes because they had forgotten the details. Practically, the setup swaps in a CPU only build of the underlying PyTorch library, since the actual graphics processing is instead handled by a separate package called torch directml. A launch flag routes image generation through that DirectML backend, and a startup check that normally requires an Nvidia card is adjusted so the app keeps running without one. Memory reporting is also patched so the app does not crash when it cannot read a real AMD card's memory the way it reads an Nvidia card's, though this means Forge's automatic memory management decisions are based on a placeholder number rather than the card's true available memory. Two versions are provided for different generations of AMD hardware. Newer RX 9000-series RDNA4 cards can use a faster configuration that runs most calculations in a lighter precision format while keeping one component in a heavier format for stability. Older or lower memory AMD cards use a fully heavier precision configuration instead, which is slower and uses more memory but avoids a known failure mode on some of these cards where images come out blank or broken under the lighter precision setting. There is no formal license file mentioned in the README, and the project is presented informally as a personal fix shared for other AMD users rather than a polished product.
A modified Stable Diffusion WebUI Forge setup that lets AMD graphics cards run image generation through DirectML instead of requiring an Nvidia CUDA card.
Mainly Python. The stack also includes Python, PyTorch, DirectML.
No license is stated in the README, so default copyright applies and reuse permissions are unclear.
Setup difficulty is rated moderate, with roughly 30min to a first successful run.
Mainly general.
This repo across BitVibe Labs
Don't trust strangers blindly. Verify against the repo.