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import torch
from diffusers import DiffusionPipeline
import huggingface_hub
import requests
from PIL import Image
from io import BytesIO
import numpy as np
import gradio as gr
multi_view_diffusion_pipeline = DiffusionPipeline.from_pretrained(
"reiirene/multi-view-diffusion",
custom_pipeline="reiirene/multi-view-diffusion",
torch_dtype=torch.float16,
trust_remote_code=True,
).to("cuda")
image_url = "https://huggingface.co/datasets/dylanebert/3d-arena/resolve/main/inputs/images/a_cat_statue.jpg"
response = requests.get(image_url)
image = Image.open(BytesIO(response.content))
image
def create_image_grid(images):
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
image = np.array(image, dtype=np.float32) / 255.0
images = multi_view_diffusion_pipeline("", image, guidance_scale=5, num_inference_steps=30, elevation=0)
create_image_grid(images)
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(debug=True) |