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Running
on
A10G
| import gradio as gr | |
| import torch | |
| from PIL import Image | |
| from torchvision import transforms | |
| from diffusers import StableDiffusionImageVariationPipeline | |
| def main( | |
| input_im, | |
| scale=3.0, | |
| n_samples=4, | |
| steps=25, | |
| seed=0, | |
| ): | |
| generator = torch.Generator(device=device).manual_seed(int(seed)) | |
| tform = transforms.Compose([ | |
| transforms.ToTensor(), | |
| transforms.Resize( | |
| (224, 224), | |
| interpolation=transforms.InterpolationMode.BICUBIC, | |
| antialias=False, | |
| ), | |
| transforms.Normalize( | |
| [0.48145466, 0.4578275, 0.40821073], | |
| [0.26862954, 0.26130258, 0.27577711]), | |
| ]) | |
| inp = tform(input_im).to(device) | |
| images_list = pipe( | |
| inp.tile(n_samples, 1, 1, 1), | |
| guidance_scale=scale, | |
| num_inference_steps=steps, | |
| generator=generator, | |
| ) | |
| images = [] | |
| for i, image in enumerate(images_list["images"]): | |
| if(images_list["nsfw_content_detected"][i]): | |
| safe_image = Image.open(r"unsafe.png") | |
| images.append(safe_image) | |
| else: | |
| images.append(image) | |
| return images | |
| description = \ | |
| """ | |
| __Now using Image Variations v2!__ | |
| Generate variations on an input image using a fine-tuned version of Stable Diffision. | |
| Trained by [Justin Pinkney](https://www.justinpinkney.com) ([@Buntworthy](https://twitter.com/Buntworthy)) at [Lambda](https://lambdalabs.com/) | |
| This version has been ported to 🤗 Diffusers library, see more details on how to use this version in the [Lambda Diffusers repo](https://github.com/LambdaLabsML/lambda-diffusers). | |
| For the original training code see [this repo](https://github.com/justinpinkney/stable-diffusion). | |
|  | |
| """ | |
| article = \ | |
| """ | |
| ## How does this work? | |
| The normal Stable Diffusion model is trained to be conditioned on text input. This version has had the original text encoder (from CLIP) removed, and replaced with | |
| the CLIP _image_ encoder instead. So instead of generating images based a text input, images are generated to match CLIP's embedding of the image. | |
| This creates images which have the same rough style and content, but different details, in particular the composition is generally quite different. | |
| This is a totally different approach to the img2img script of the original Stable Diffusion and gives very different results. | |
| The model was fine tuned on the [LAION aethetics v2 6+ dataset](https://laion.ai/blog/laion-aesthetics/) to accept the new conditioning. | |
| Training was done on 8xA100 GPUs on [Lambda GPU Cloud](https://lambdalabs.com/service/gpu-cloud). | |
| More details are on the [model card](https://huggingface.co/lambdalabs/sd-image-variations-diffusers). | |
| """ | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| pipe = StableDiffusionImageVariationPipeline.from_pretrained( | |
| "lambdalabs/sd-image-variations-diffusers", | |
| ) | |
| pipe = pipe.to(device) | |
| inputs = [ | |
| gr.Image(), | |
| gr.Slider(0, 25, value=3, step=1, label="Guidance scale"), | |
| gr.Slider(1, 4, value=1, step=1, label="Number images"), | |
| gr.Slider(5, 50, value=25, step=5, label="Steps"), | |
| gr.Number(0, label="Seed", precision=0) | |
| ] | |
| output = gr.Gallery(label="Generated variations") | |
| output.style(grid=2) | |
| examples = [ | |
| ["examples/vermeer.jpg", 3, 1, 25, 0], | |
| ["examples/matisse.jpg", 3, 1, 25, 0], | |
| ] | |
| demo = gr.Interface( | |
| fn=main, | |
| title="Stable Diffusion Image Variations", | |
| description=description, | |
| article=article, | |
| inputs=inputs, | |
| outputs=output, | |
| examples=examples, | |
| ) | |
| demo.launch() | |