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| import gradio | |
| import torch | |
| import PIL | |
| from torchvision import transforms | |
| from diffusers import StableDiffusionInpaintPipeline | |
| pipeline = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting", | |
| #revision="fp16", | |
| #torch_dtype=torch.float16, | |
| safety_checker=lambda images, **kwargs: (images, False)) | |
| #pipeline.to("cuda") | |
| #generator = torch.Generator(device).manual_seed(seed) | |
| def diffuse(prompt, negativePrompt, inputImage, mask, guidanceScale, numInferenceSteps): | |
| return pipeline(prompt=prompt, | |
| negative_prompt=negativePrompt, | |
| image=inputImage, | |
| mask_image=mask, | |
| guidance_scale=guidanceScale, | |
| num_inference_steps=numInferenceSteps).images[0] | |
| prompt = gradio.Textbox(label="Prompt", placeholder="A person in a room", lines=3) | |
| negativePrompt = gradio.Textbox(label="Negative Prompt", placeholder="Text", lines=3) | |
| inputImage = gradio.Image(label="Input Image", type="pil") | |
| #inputFeed = gradio.Image(label="Input Feed", source="webcam", streaming=True) | |
| mask = gradio.Image(label="Mask", type="pil") | |
| outputImage = gradio.Image(label="Extrapolated Field of View") | |
| guidanceScale = gradio.Slider(label="Guidance Scale", maximum=1, value = 0.75) | |
| numInferenceSteps = gradio.Slider(label="Number of Inference Steps", maximum=100, value = 0) | |
| ux = gradio.Interface(fn=diffuse, title="View Diffusion", inputs=[prompt, negativePrompt, inputImage, mask, guidanceScale, numInferenceSteps], outputs=outputImage, live=True) | |
| ux.launch() |