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| import gradio | |
| import torch | |
| import numpy | |
| from PIL import Image | |
| from torchvision import transforms | |
| #from torchvision import transforms | |
| from diffusers import StableDiffusionInpaintPipeline | |
| #from diffusers import StableDiffusionUpscalePipeline | |
| #from transformers import SegformerFeatureExtractor, SegformerForSemanticSegmentation | |
| from diffusers import DPMSolverMultistepScheduler | |
| deviceStr = "cuda" if torch.cuda.is_available() else "cpu" | |
| device = torch.device(deviceStr) | |
| if deviceStr == "cuda": | |
| pipeline = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting", | |
| revision="fp16", | |
| torch_dtype=torch.float16, | |
| safety_checker=lambda images, **kwargs: (images, False)) | |
| pipeline.to(device) | |
| pipeline.enable_xformers_memory_efficient_attention() | |
| else: | |
| pipeline = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting", | |
| safety_checker=lambda images, **kwargs: (images, False)) | |
| #superresolutionPipe = StableDiffusionUpscalePipeline.from_pretrained("stabilityai/stable-diffusion-x4-upscaler") | |
| #pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config) | |
| #generator = torch.Generator(device).manual_seed(seed) | |
| latents = torch.randn((1, 4, 64, 64), device=device) | |
| schedulers = [ | |
| "DDIMScheduler", "LMSDiscreteScheduler", "PNDMScheduler" | |
| ] | |
| latentNoiseInputs = [ | |
| "Uniform", "Low Discrepency Sequence" | |
| ] | |
| imageSize = (512, 512, 3) | |
| imageSize2 = (512, 512) | |
| #lastImage = Image.new(mode="RGB", size=(imageSize[0], imageSize[1])) | |
| def diffuse(prompt, negativePrompt, inputImage, mask, guidanceScale, numInferenceSteps, seed, noiseScheduler, latentNoise): | |
| #width = inputImage.size[1] | |
| #height = 512 | |
| #print(inputImage.size) | |
| #image = numpy.resize(inputImage, imageSize) | |
| #pilImage.thumbnail(imageSize2) | |
| #transforms.Resize(imageSize2)(inputImage) | |
| #pilImage = Image.fromarray(inputImage) | |
| #pilImage.resize(imageSize2) | |
| #imageArray = numpy.asarray(pilImage) | |
| #inputImage = torch.nn.functional.interpolate(inputImage, size=imageSize) | |
| if mask is None: | |
| return inputImage | |
| generator = torch.Generator(device).manual_seed(seed) | |
| newImage = pipeline(prompt=prompt, | |
| negative_prompt=negativePrompt, | |
| image=inputImage, | |
| mask_image=mask, | |
| guidance_scale=guidanceScale, | |
| num_inference_steps=numInferenceSteps, | |
| generator=generator).images[0] | |
| return newImage | |
| 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") | |
| inputImage = gradio.Image(label="Input Feed", source="webcam", shape=[512,512], 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=25) | |
| seed = gradio.Slider(label="Generator Seed", maximum=1000, value=512) | |
| noiseScheduler = gradio.Dropdown(schedulers, label="Noise Scheduler", value="DDIMScheduler") | |
| latentNoise = gradio.Dropdown(latentNoiseInputs, label="Latent Noise", value="Iniform") | |
| inputs=[prompt, negativePrompt, inputImage, mask, guidanceScale, numInferenceSteps, seed, noiseScheduler, latentNoise] | |
| ux = gradio.Interface(fn=diffuse, title="View Diffusion", inputs=inputs, outputs=outputImage, live=True) | |
| ux.launch() |