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| import gradio as gr | |
| import inspect | |
| import warnings | |
| from typing import List, Optional, Union | |
| import requests | |
| from io import BytesIO | |
| from PIL import Image | |
| import torch | |
| from torch import autocast | |
| from tqdm.auto import tqdm | |
| from diffusers import StableDiffusionImg2ImgPipeline | |
| from huggingface_hub import notebook_login | |
| notebook_login() | |
| device = "cuda" | |
| model_path = "CompVis/stable-diffusion-v1-4" | |
| pipe = StableDiffusionImg2ImgPipeline.from_pretrained( | |
| model_path, | |
| revision="fp16", | |
| torch_dtype=torch.float16, | |
| use_auth_token=True | |
| ) | |
| pipe = pipe.to(device) | |
| def predict(image_url, strength, seed): | |
| seed= int(seed) | |
| response = requests.get(image_url) | |
| init_img = Image.open(BytesIO(response.content)).convert("RGB") | |
| init_img = init_img.resize((768, 512)) | |
| generator = torch.Generator(device=device).manual_seed(seed) | |
| with autocast("cuda"): | |
| image = pipe(prompt="", init_image=init_img, strength=strength, guidance_scale=5, generator=generator).images[0] | |
| return image | |
| gr.Interface( | |
| predict, | |
| title = 'Image to Image using Diffusers', | |
| inputs=[ | |
| gr.Textbox(label="image_url"), | |
| gr.Slider(0, 1, value=0.05, label ="strength"), | |
| gr.Number(label = "seed") | |
| ], | |
| outputs = [ | |
| gr.Image() | |
| ] | |
| ).launch() | |