https://github.com/adamelliotfields/gradio-diffusion
Stable Diffusion inference in Gradio
https://github.com/adamelliotfields/gradio-diffusion
controlnet diffusers gradio img2img ip-adapter pytorch stable-diffusion txt2img
Last synced: 6 months ago
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Stable Diffusion inference in Gradio
- Host: GitHub
- URL: https://github.com/adamelliotfields/gradio-diffusion
- Owner: adamelliotfields
- License: apache-2.0
- Created: 2025-04-09T12:12:14.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-04-09T20:38:43.000Z (6 months ago)
- Last Synced: 2025-04-09T21:37:14.128Z (6 months ago)
- Topics: controlnet, diffusers, gradio, img2img, ip-adapter, pytorch, stable-diffusion, txt2img
- Language: Python
- Homepage:
- Size: 592 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# gradio-diffusion
Gradio app for Stable Diffusion 1.5 featuring:
* txt2img and img2img pipelines with IP-Adapter
* ControlNet with Canny edge detection
* FastNegative textual inversion
* Real-ESRGAN resizing up to 8x
* Compel prompt weighting support
* Multiple samplers with Karras scheduling
* DeepCache available for faster inference
## Installation
```bash
uv venv
uv pip install -r requirements.txt
uv run app.py
```
## Usage
Enter a prompt or roll the `🎲` and press `Generate`.
### Prompting
Positive and negative prompts are embedded by [Compel](https://github.com/damian0815/compel). See [syntax features](https://github.com/damian0815/compel/blob/main/doc/syntax.md) to learn more.
### Models
Some require specific parameters to get the best results, so check the model's link for more information:
* [cyberdelia/CyberRealistic_V5](https://huggingface.co/cyberdelia/CyberRealistic)
* [fluently/Fluently-v4](https://huggingface.co/fluently/Fluently-v4)
* [Lykon/dreamshaper-8](https://huggingface.co/Lykon/dreamshaper-8)
* [s6yx/ReV_Animated](https://huggingface.co/s6yx/ReV_Animated)
* [SG161222/Realistic_Vision_V5](https://huggingface.co/SG161222/Realistic_Vision_V5.1_noVAE)
* [stable-diffusion-v1-5/stable-diffusion-v1-5](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5)
* [XpucT/Deliberate_v6](https://huggingface.co/XpucT/Deliberate)
* [XpucT/Reliberate_v3](https://huggingface.co/XpucT/Reliberate) (default)
### Scale
Rescale up to 8x using [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN) with weights from [ai-forever](ai-forever/Real-ESRGAN).
### Image-to-Image
The `Image-to-Image` settings allows you to provide input images for the initial latent, ControlNet, and IP-Adapter.
#### Strength
Initial image strength (known as _denoising strength_) is essentially how much the generation will differ from the input image. A value of `0` will be identical to the original, while `1` will be a completely new image. You may want to also increase the number of inference steps.
Note that denoising strength only applies to the `Initial Image` input; it doesn't affect ControlNet or IP-Adapter.
#### ControlNet
In [ControlNet](https://github.com/lllyasviel/ControlNet), the input image is used to get a feature map from an _annotator_. These are computer vision models used for tasks like edge detection and pose estimation. ControlNet models are trained to understand these feature maps. Read the [docs](https://huggingface.co/docs/diffusers/using-diffusers/controlnet) to learn more.
Currently, the only annotator available is [Canny](https://huggingface.co/lllyasviel/control_v11p_sd15_canny) (edge detection).
#### IP-Adapter
In an image-to-image pipeline, the input image is used as the initial latent representation. With [IP-Adapter](https://github.com/tencent-ailab/IP-Adapter), the image is processed by a separate image encoder and the encoded features are used as conditioning along with the text prompt.
For capturing faces, enable `IP-Adapter Face` to use the full-face model. You should use an input image that is mostly a face and it should be high quality.
### Advanced
#### Textual Inversion
Add `` anywhere in your negative prompt to apply the [FastNegative v2](https://civitai.com/models/71961?modelVersionId=94057) textual inversion embedding. Read [An Image is Worth One Word](https://huggingface.co/papers/2208.01618) to learn more.
> 💡 Wrap in parens to weight the embedding like `()0.8`.
#### DeepCache
[DeepCache](https://github.com/horseee/DeepCache) caches lower UNet layers and reuses them every _n_ steps. Trade quality for speed:
- *1*: no caching (default)
- *2*: more quality
- *3*: balanced
- *4*: more speed