from __future__ import annotations import os os.system("pip install -e git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers") os.system("pip install -e git+https://github.com/alvanli/RDM-Region-Aware-Diffusion-Model.git@main#egg=guided_diffusion") import math import random import gradio as gr import torch from PIL import Image, ImageOps from run_edit import run_model from cool_models import make_models help_text = """""" def main(): segmodel, model, diffusion, ldm, bert, clip_model, model_params = make_models() def generate( input_image: Image.Image, from_text: str, instruction: str, negative_prompt: str, randomize_seed: bool, seed: int, guidance_scale: float, clip_guidance_scale: float, cutn: int, l2_sim_lambda: float ): seed = random.randint(0, 100000) if randomize_seed else seed if instruction == "": return [seed, input_image] generator = torch.manual_seed(seed) edited_image_1 = run_model( segmodel, model, diffusion, ldm, bert, clip_model, model_params, from_text, instruction, negative_prompt, input_image.convert('RGB'), seed, guidance_scale, clip_guidance_scale, cutn, l2_sim_lambda ) return [seed, edited_image_1] def reset(): return [ "Randomize Seed", 1371, None, 5.0, 150, 16, 10000 ] with gr.Blocks() as demo: gr.HTML("""

RDM: Region-Aware Diffusion for Zero-shot Text-driven Image Editing

For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings.

""") with gr.Row(): with gr.Column(scale=1, min_width=100): generate_button = gr.Button("Generate") # with gr.Column(scale=1, min_width=100): # load_button = gr.Button("Load Example") with gr.Column(scale=1, min_width=100): reset_button = gr.Button("Reset") with gr.Column(scale=3): from_text = gr.Textbox(lines=1, label="From Text", interactive=True) instruction = gr.Textbox(lines=1, label="Edit Instruction", interactive=True) negative_prompt = gr.Textbox(lines=1, label="Negative Prompt", interactive=True) with gr.Row(): input_image = gr.Image(label="Input Image", type="pil", interactive=True) edited_image_1 = gr.Image(label=f"Edited Image", type="pil", interactive=False) # edited_image_2 = gr.Image(label=f"Edited Image", type="pil", interactive=False) input_image.style(height=512, width=512) edited_image_1.style(height=512, width=512) # edited_image_2.style(height=512, width=512) with gr.Row(): # steps = gr.Number(value=50, precision=0, label="Steps", interactive=True) seed = gr.Number(value=1371, precision=0, label="Seed", interactive=True) guidance_scale = gr.Number(value=5.0, precision=1, label="Guidance Scale", interactive=True) clip_guidance_scale = gr.Number(value=150, precision=1, label="Clip Guidance Scale", interactive=True) cutn = gr.Number(value=16, precision=1, label="Number of Cuts", interactive=True) l2_sim_lambda = gr.Number(value=10000, precision=1, label="L2 similarity to original image") randomize_seed = gr.Radio( ["Fix Seed", "Randomize Seed"], value="Randomize Seed", type="index", show_label=False, interactive=True, ) # use_ddim = gr.Checkbox(label="Use 50-step DDIM?", value=True) # use_ddpm = gr.Checkbox(label="Use 50-step DDPM?", value=True) gr.Markdown(help_text) generate_button.click( fn=generate, inputs=[ input_image, from_text, instruction, negative_prompt, randomize_seed, seed, guidance_scale, clip_guidance_scale, cutn, l2_sim_lambda ], outputs=[seed, edited_image_1], ) reset_button.click( fn=reset, inputs=[], outputs=[ randomize_seed, seed, edited_image_1, guidance_scale, clip_guidance_scale, cutn, l2_sim_lambda ], ) demo.queue(concurrency_count=1) demo.launch(share=False, server_name="0.0.0.0") if __name__ == "__main__": main()