# Stable Diffusion Demo
Ryzen AI 1.8.0 provides preview demos of Stable Diffusion image-generation pipelines.
The demos cover **Image-to-Image** (ControlNet: Canny, Pose, Tile, Depth; plus Segmind-Vega
i2i without ControlNet) and **Text-to-Image** for SD1.5, SD-Turbo, SDXL-base, SDXL-Turbo,
Segmind-Vega, DreamShaper XL Lightning, SSD-1B, Playground v2.5, FLUX.1-Schnell,
FLUX.2-klein-4B, SD3.0, and SD3.5, with **inpainting** where noted for SD3.0.
Models are fetched from Hugging Face on first use (see [Installation Instructions](https://ryzenai.docs.amd.com/en/latest/inst.html) for Ryzen AI and driver
setup). SD3.0 and SD3.5 models require Hugging Face authentication and might also require
acceptance of the model license before they can be downloaded.
## Installation Steps
1. Ensure the latest version of Ryzen AI and NPU drivers are installed. See [Installation Instructions](https://ryzenai.docs.amd.com/en/latest/inst.html).
2. The GenAI-SD folder is located in the RyzenAI installation tree. Navigate to the
folder and run the following command:
```powershell
conda activate ryzen-ai-1.8.0
cd "$env:RYZEN_AI_INSTALLATION_PATH\GenAI-SD"
```
3. Models are downloaded from Hugging Face on first use and cached under
`GenAI-SD\models` or `$env:RYZENAI_GENAI_SD_MODELS_ROOT`. See [supported-models]()
for the full list; primary entry points include:
- [SD1.5](https://huggingface.co/amd/stable-diffusion-1.5-amdnpu)
- [SD1.5 ControlNet Canny](https://huggingface.co/amd/sd1.5-controlnet-canny-amdnpu)
- [SD-Turbo](https://huggingface.co/stabilityai/sd-turbo-amdnpu)
- [SDXL-Turbo](https://huggingface.co/stabilityai/sdxl-turbo-amdnpu)
- [SDXL-base](https://huggingface.co/stabilityai/sdxl-base-amdnpu)
- [Segmind-Vega](https://huggingface.co/amd/segmind-vega-amdnpu)
- [DreamShaper XL Lightning](https://huggingface.co/amd/dreamshaper-xl-lightning-amdnpu)
- [SSD-1B](https://huggingface.co/amd/SSD-1B-amdnpu)
- [Playground v2.5 1024px](https://huggingface.co/amd/playground-v2.5-1024px-aesthetic-amdnpu)
- [FLUX.1-Schnell](https://huggingface.co/amd/FLUX.1-schnell-amdnpu)
- [FLUX.2-klein-4B](https://huggingface.co/amd/FLUX.2-klein-4B-amdnpu)
- [SD3.0](https://huggingface.co/stabilityai/stable-diffusion-3-medium-amdnpu)
- [SD3.5](https://huggingface.co/stabilityai/stable-diffusion-3.5-medium-amdnpu)
## Supported models
The following summarizes default application mode, typical resolution /
dynamic-resolution (DynRes) notes, and the recommended Hugging Face `model_id` on
AMD NPU-tuned repos.
| Notes | Model | App | Default resolution / DynRes | `model_id` (AMD Hub; `stabilityai` equivalent where applicable) |
|---------|--------------------------|------------------------------------------------|----------------------------------------------------|-----------------------------------------------------------------------|
| | SD1.5 | t2i | 512x512 | `amd/stable-diffusion-1.5-amdnpu` |
| New | SD1.5 | i2i-canny | 512x512 | `amd/sd1.5-controlnet-canny-amdnpu` |
| | SD-Turbo | t2i | 512x512 | `amd/sd-turbo-amdnpu` (or `stabilityai/sd-turbo-amdnpu`) |
| | SDXL-Turbo | t2i | 512x512, 5x DynRes | `amd/sdxl-turbo-amdnpu` (or `stabilityai/sdxl-turbo-amdnpu`) |
| | SDXL-base | t2i | 1024x1024, 20x DynRes | `amd/sdxl-base-amdnpu` (or `stabilityai/sdxl-base-amdnpu`) |
| | Segmind-Vega | t2i / i2i | 1024x1024 | `amd/segmind-vega-amdnpu` |
| New | DreamShaper XL Lightning | t2i | 1024x1024, 20x DynRes | `amd/dreamshaper-xl-lightning-amdnpu` |
| New | SSD-1B | t2i | 1024x1024, 20x DynRes | `amd/SSD-1B-amdnpu` |
| New | Playground v2.5 | t2i | 1024x1024, 20x DynRes | `amd/playground-v2.5-1024px-aesthetic-amdnpu` |
| New | FLUX.1-Schnell | t2i | 1024x1024, 20x DynRes | `amd/FLUX.1-schnell-amdnpu` |
| New | FLUX.2-klein-4B | t2i | 1024x1024 | `amd/FLUX.2-klein-4B-amdnpu` |
| | SD3.0 | t2i / ControlNet / Depth / Canny / Pose / Tile | 512x512 (t2i default), 20x DynRes where applicable | `stabilityai/stable-diffusion-3-medium-amdnpu` |
| New | SD3.0 | i2i-inpainting | 1024x1024 | `stabilityai/stable-diffusion-3-medium-amdnpu` |
| | SD3.5 | t2i / ControlNet | 512x512 (t2i default), 20x DynRes where applicable | `stabilityai/stable-diffusion-3.5-medium-amdnpu` |
| | SD3.5-ControlNet (Canny) | i2i-canny | 512x512, 20x DynRes | Uses SD3.5 AMD weights plus SD3.0 Canny ControlNet assets (see below) |
In the Supported models table, the **DynRes** column counts how many dynamic-resolution
presets a pipeline exposes (for example 5x or 20x). The following material spells out which
width x height pairs are in scope.
### Dynamic resolution (DynRes)
**Original dynamic-resolution requirements**
These are the classic fixed pairs (per model class) before the expanded preset grid.
* **DynRes 5x:** 512x512, 288x512, 512x288, 384x512, 512x384.
* **DynRes 20x:**
#### New DynRes combinations (batch size 1; align to multiple of 32 as required)
| Aspect (WxH) | Base 512x512 | Base 640x640 | Base 768x768 | Base 1024x1024 |
|----------------|----------------|----------------|----------------|------------------|
| 1:1 | 512x512 | 640x640 | 768x768 | 1024x1024 |
| 4:3 | 512x384 | 640x480 | 768x576 | 1024x768 |
| 3:4 | 384x512 | 480x640 | 576x768 | 768x1024 |
| 16:9 | 512x288 | 640x384 | 768x448 | 1024x576 |
| 9:16 | 288x512 | 384x640 | 448x768 | 576x1024 |
Public model pages follow the Hugging Face model license (HF LIC) for each repo.
**SD3.5 Canny ControlNet setup:** copy the SD3.0 Canny ControlNet files into the SD3.5
model layout as required by your GenAI-SD tree (per release notes), then run the Canny
example with `--model_id amd/stable-diffusion-3.5-medium-amdnpu`.
## Running the Demos
Activate the conda environment (see also
```
`installation-steps`_
```
):
```powershell
conda activate ryzen-ai-1.8.0
```
Optionally, set the NPU to high performance mode to maximize performance:
```powershell
xrt-smi configure --pmode performance
```
Refer to the documentation on [xrt-smi configure](https://ryzenai.docs.amd.com/en/latest/xrt_smi.html#xrt-smi-configure) for additional options.
From the `GenAI-SD\test` directory unless noted otherwise. All examples use the unified
entry point `run.py` and pass `--model_id` with the Hugging Face model identifier.
For more details, refer to the `README.md` file in the `GenAI-SD` directory.
### Image-to-Image with ControlNet
The image-to-image demo generates images from a **prompt** plus a **control image**
(ControlNet types such as Canny, pose, tile, or depth for SD3.x, selected with `-C`).
SD3.x often defaults to 512x512; override resolution with `-W` and `-H` as in the
examples below. SD3.x DynRes presets are summarized in [supported-models]() and
[Dynamic resolution (DynRes)](dynres).
To run a minimal Canny example:
```powershell
python run.py -C canny --model_id stabilityai/stable-diffusion-3-medium-amdnpu
```
The demo can use `.\ref\canny.jpg` as the control image unless you override
`--control_image_path`. Outputs are written to `generated_images` by default,
unless `--output_path` is specified. You can redirect the output directory to
a location where your user account has write permissions, for example:
`--output_path C:\Users\\Documents\generated_images`.
**SD1.5 ControlNet Canny (i2i-canny)**
```powershell
python .\run.py --model_id amd/sd1.5-controlnet-canny-amdnpu
```
**Segmind-Vega (i2i, no ControlNet path)**
```powershell
python .\run.py --model_id amd/segmind-vega-amdnpu --control_image_path .\assets\controlimg_input_1024x1024.png --strength 0.95
```
**SD3.0 ControlNet (canny / pose / tile / depth)**
```powershell
python .\run.py -C canny --model_id stabilityai/stable-diffusion-3-medium-amdnpu --prompt "Anime style illustration of a girl wearing a suit. A moon in sky. In the background we see heavy rain approaching. text 'InstantX' on image" -H 1024 -W 1024 --control_image_path .\ref\canny.jpg -n 50
python .\run.py -C pose --model_id stabilityai/stable-diffusion-3-medium-amdnpu --prompt "Anime style illustration of a girl wearing a suit. A moon in sky. In the background we see heavy rain approaching. text 'InstantX' on image" -H 1024 -W 1024 --control_image_path .\ref\pose.jpg -n 50
python .\run.py -C tile --model_id stabilityai/stable-diffusion-3-medium-amdnpu --prompt "Anime style illustration of a girl wearing a suit. A moon in sky. In the background we see heavy rain approaching. text 'InstantX' on image" -H 1024 -W 1024 --control_image_path .\ref\tile.jpg -n 50
python .\run.py -C depth --model_id stabilityai/stable-diffusion-3-medium-amdnpu -H 1024 -W 1024 --control_image_path .\assets\depth.jpeg -n 50
```
**SD3.5 ControlNet Canny (i2i-canny)**
After copying SD3.0 Canny ControlNet into the SD3.5 layout as required:
```powershell
python .\run.py -C canny --model_id stabilityai/stable-diffusion-3.5-medium-amdnpu --prompt "Anime style illustration of a girl wearing a suit. A moon in sky. In the background we see heavy rain approaching. text 'InstantX' on image" -H 1024 -W 1024 --control_image_path .\ref\canny.jpg -n 50
```
For SD3 and SD3.5 models, pass `-O1` (or `--optimize_o1`) to enable a performance optimization preset that skips selected DiT denoising steps to improve inference performance.
### Text-to-Image
The text-to-image demo generates images from **text prompts only** (no control image).
It covers SD1.5 (512-class), SD-Turbo and SDXL-Turbo (512-class), SDXL-base, Segmind-Vega,
DreamShaper XL Lightning, SSD-1B, Playground v2.5, FLUX.1-Schnell, FLUX.2-klein-4B, and
SD3.0 / SD3.5 with `-C None`. Use `-H`, `-W`, and `-n` when the pipeline supports
them.
```powershell
python .\run.py --model_id amd/stable-diffusion-1.5-amdnpu
python .\run.py --model_id stabilityai/sd-turbo-amdnpu
python .\run.py --model_id stabilityai/sdxl-turbo-amdnpu
python .\run.py --model_id stabilityai/sdxl-base-amdnpu
python .\run.py --model_id amd/segmind-vega-amdnpu
python .\run.py --model_id amd/dreamshaper-xl-lightning-amdnpu
python .\run.py --model_id amd/SSD-1B-amdnpu
python .\run.py --model_id amd/playground-v2.5-1024px-aesthetic-amdnpu
python .\run.py --model_id amd/FLUX.1-schnell-amdnpu
python .\run.py --model_id amd/FLUX.2-klein-4B-amdnpu
python .\run.py -C None --model_id stabilityai/stable-diffusion-3-medium-amdnpu -H 1024 -W 1024 -n 50
python .\run.py -C None --model_id stabilityai/stable-diffusion-3.5-medium-amdnpu -H 1024 -W 1024 -n 50
```
Custom prompts can be supplied with `--prompt`. For example:
```powershell
python .\run.py --model_id stabilityai/stable-diffusion-1.5-amdnpu --prompt "Photo of an ultra realistic sailing ship, dramatic light, pale sunrise, cinematic lighting, battered, low angle, trending on artstation, 4k, hyper realistic, focused, extreme details"
```
### Inpainting
**SD3.0 (\`\`-C Inpainting\`\`)** uses a base image and `--control_mask_path` (URLs or local
paths). This extends the image-conditioned flows above with an explicit mask channel.
```powershell
python .\run.py --model_id stabilityai/stable-diffusion-3-medium-amdnpu -C Inpainting --prompt "A cat is sitting next to a puppy" --n_prompt "deformed, distorted, disfigured, poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, mutated hands and fingers, disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation, NSFW" -n 28 --controlnet_conditioning_scale 0.95 --control_image_path "https://huggingface.co/alimama-creative/SD3-Controlnet-Inpainting/resolve/main/images/dog.png" --control_mask_path "https://huggingface.co/alimama-creative/SD3-Controlnet-Inpainting/resolve/main/images/dog_mask.png" --seed 42
```
## Running with AMD Stable Diffusion Sandbox
AMD SD Sandbox is a framework for running Stable Diffusion (SD) models accelerated by
AMD Ryzen AI hardware. It provides an easy-to-use interface for evaluating, comparing,
and deploying multiple SD pipelines.
Please go to the [AMD SD Sandbox GitHub repository](https://github.com/amd/sd-sandbox)
for more information.