Basic Outpainting Framework

app.py

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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()