import spaces import torch from diffusers import StableDiffusion3InstructPix2PixPipeline import gradio as gr import PIL.Image import numpy as np from PIL import Image, ImageOps import os import transformers from transformers.utils.hub import move_cache transformers.utils.move_cache() move_cache() pipe = StableDiffusion3InstructPix2PixPipeline.from_pretrained("bpathir1/RefEdit-SD3", torch_dtype=torch.float16).to("cuda") @spaces.GPU(duration=120) def generate(image, prompt, num_inference_steps=50, image_guidance_scale=1.5, guidance_scale=7.5, seed=255): seed = int(seed) generator = torch.manual_seed(seed) img = image.convert("RGB") desired_size = (512, 512) img = ImageOps.fit(img, desired_size, method=Image.LANCZOS, centering=(0.5, 0.5)) image = pipe( prompt, image=img, mask_img=None, num_inference_steps=num_inference_steps, image_guidance_scale=image_guidance_scale, guidance_scale=guidance_scale, generator=generator ).images[0] return image # Update the example list to remove mask-related entries example_lists = [ ['UltraEdit/images/example_images/1-input.png', "Add a moon in the sky", 20, 1.5, 12.5, 255], ['UltraEdit/images/example_images/2-input.png', "Add cherry blossoms", 20, 1.5, 12.5, 255], ['UltraEdit/images/example_images/3-input.png', "Please dress her in a short purple wedding dress adorned with white floral embroidery.", 20, 1.5, 7.5, 255], ['UltraEdit/images/example_images/4-input.png', "Give her a chief's headdress.", 20, 1.5, 7.5, 24555] ] # Update the mask_ex_list to reflect the new example list structure mask_ex_list = [] for exp in example_lists: re_list = [exp[0], exp[1], exp[2], exp[3], exp[4], exp[5]] mask_ex_list.append(re_list) # Update the input for image upload to remove mask-related functionality image_input = gr.Image(type="pil", label="Input Image", transforms=()) prompt_input = gr.Textbox(label="Prompt") num_inference_steps_input = gr.Slider(minimum=0, maximum=100, value=50, label="Number of Inference Steps") image_guidance_scale_input = gr.Slider(minimum=0.0, maximum=2.5, value=1.5, label="Image Guidance Scale") guidance_scale_input = gr.Slider(minimum=0.0, maximum=17.5, value=12.5, label="Guidance Scale") seed_input = gr.Textbox(value="255", label="Random Seed") inputs = [image_input, prompt_input, num_inference_steps_input, image_guidance_scale_input, guidance_scale_input, seed_input] outputs = [gr.Image(label="Generated Image"), gr.Text(label="Editing Mode")] article_html = """

🖼️ UltraEdit for Fine-Grained Image Editing

Haozhe Zhao^1*, Xiaojian Ma^2*, Liang Chen¹, Shuzheng Si¹, Rujie Wu¹, Kaikai An¹, Peiyu Yu³, Minjia Zhang⁴, Qing Li², Baobao Chang²

¹Peking University, ²BIGAI, ³UCLA, ⁴UIUC

Dataset_4M

Dataset_500k

Dataset_500k 🔗 Page

Checkpoint

Checkpoint GitHub

GitHub

UltraEdit is a dataset designed for fine-grained, instruction-based image editing. It contains over 4 million free-form image editing samples and more than 100,000 region-based image editing samples, automatically generated with real images as anchors.

This demo allows you to perform image editing using the Stable Diffusion 3 model trained with this extensive dataset. It supports both free-form (without mask) and region-based (with mask) image editing. Use the sliders to adjust the inference steps and guidance scales, and provide a seed for reproducibility. The image guidance scale of 1.5 and text guidance scale of 7.5 / 12.5 is a good start for free-form/region-based image editing.

Usage Instructions: You need to upload the images and prompts for editing. Use the pen tool to mark the areas you want to edit. If no region is marked, it will resort to free-form editing.

""" html = '''

Limitations:

We have not conducted any NSFW checks;
Due to the bias of the generated models, the model performance is still weak when dealing with high-frequency information such as human facial expressions or text in the images;
We unified the free-form and region-based image editing by adding an extra channel of the mask image to the dataset. When doing free-form image editing, the network receives a blank mask.
The generation result is sensitive to the guidance scale. For text guidance, based on experience, free-form image editing will perform better with a relatively low guidance score (7.5 or lower), while region-based image editing will perform better with a higher guidance score.

''' demo = gr.Interface( fn=generate, inputs=inputs, outputs=outputs, description=article_html, article=html, examples=mask_ex_list, cache_examples = True, live = False ) demo.queue().launch()