| import gradio as gr | |
| import torch | |
| from PIL import Image | |
| from diffusers import DiffusionPipeline | |
| import numpy as np | |
| multi_view_diffusion_pipeline = DiffusionPipeline.from_pretrained( | |
| "2gnak/multi-view-diffusion-demo", | |
| custom_pipeline="dylanebert/multi-view-diffusion", | |
| torch_dtype=torch.float16, | |
| trust_remote_code=True, | |
| ).to("cpu") | |
| def run(image): | |
| image = np.array(image, dtype=np.float32) / 255.0 | |
| images = multi_view_diffusion_pipeline("", image, guidance_scale=5, num_inference_steps=30, elevation=0) | |
| images = [Image.fromarray((img * 255).astype("uint8")) for img in images] | |
| width, height = images[0].size | |
| grid_img = Image.new("RGB", (2 * width, 2 * height)) | |
| grid_img.paste(images[0], (0, 0)) | |
| grid_img.paste(images[1], (width, 0)) | |
| grid_img.paste(images[2], (0, height)) | |
| grid_img.paste(images[3], (width, height)) | |
| return grid_img | |
| demo = gr.Interface(fn=run, inputs="image", outputs="image") | |
| demo.launch() |