import gradio as gr import torch import requests from io import BytesIO from diffusers import StableDiffusionPipeline from diffusers import DDIMScheduler from utils import * from inversion_utils import * from torch import autocast, inference_mode import re def invert(x0, prompt_src="", num_diffusion_steps=100, cfg_scale_src = 3.5, eta = 1): # inverts a real image according to Algorihm 1 in https://arxiv.org/pdf/2304.06140.pdf, # based on the code in https://github.com/inbarhub/DDPM_inversion # returns wt, zs, wts: # wt - inverted latent # wts - intermediate inverted latents # zs - noise maps sd_pipe.scheduler.set_timesteps(num_diffusion_steps) # vae encode image with autocast("cuda"), inference_mode(): w0 = (sd_pipe.vae.encode(x0).latent_dist.mode() * 0.18215).float() # find Zs and wts - forward process wt, zs, wts = inversion_forward_process(sd_pipe, w0, etas=eta, prompt=prompt_src, cfg_scale=cfg_scale_src, prog_bar=True, num_inference_steps=num_diffusion_steps) return wt, zs, wts def sample(wt, zs, wts, prompt_tar="", cfg_scale_tar=15, skip=36, eta = 1): # reverse process (via Zs and wT) w0, _ = inversion_reverse_process(sd_pipe, xT=wts[skip], etas=eta, prompts=[prompt_tar], cfg_scales=[cfg_scale_tar], prog_bar=True, zs=zs[skip:]) # vae decode image with autocast("cuda"), inference_mode(): x0_dec = sd_pipe.vae.decode(1 / 0.18215 * w0).sample if x0_dec.dim()<4: x0_dec = x0_dec[None,:,:,:] img = image_grid(x0_dec) return img # load pipelines # sd_model_id = "runwayml/stable-diffusion-v1-5" sd_model_id = "CompVis/stable-diffusion-v1-4" # sd_model_id = "stabilityai/stable-diffusion-2-base" device = torch.device("cuda" if torch.cuda.is_available() else "cpu") sd_pipe = StableDiffusionPipeline.from_pretrained(sd_model_id).to(device) sd_pipe.scheduler = DDIMScheduler.from_config(sd_model_id, subfolder = "scheduler") def get_example(): case = [ [ 'examples/source_a_man_wearing_a_brown_hoodie_in_a_crowded_street.jpeg', 'a man wearing a brown hoodie in a crowded street', 'a robot wearing a brown hoodie in a crowded street', 100, 36, 15, '+painting', 10, 1, 'examples/ddpm_a_robot_wearing_a_brown_hoodie_in_a_crowded_street.png', 'examples/ddpm_sega_painting_of_a_robot_wearing_a_brown_hoodie_in_a_crowded_street.png' ], [ 'examples/source_wall_with_framed_photos.jpeg', '', '', 100, 36, 15, '+pink drawings of muffins', 10, 1, 'examples/ddpm_wall_with_framed_photos.png', 'examples/ddpm_sega_plus_pink_drawings_of_muffins.png' ], [ 'examples/source_an_empty_room_with_concrete_walls.jpg', 'an empty room with concrete walls', 'glass walls', 100, 36, 17, '+giant elephant', 10, 1, 'examples/ddpm_glass_walls.png', 'examples/ddpm_sega_glass_walls_gian_elephant.png' ]] return case inversion_map = dict() def invert(input_image, src_prompt ="", steps=100, src_cfg_scale = 3.5, left = 0, right = 0, top = 0, bottom = 0 ): # offsets=(0,0,0,0) x0 = load_512(input_image, left,right, top, bottom, device) # invert wt, zs, wts = invert(x0 =x0 , prompt_src=src_prompt, num_diffusion_steps=steps, cfg_scale_src=src_cfg_scale) latnets = wts[skip].expand(1, -1, -1, -1) inversion_map['latnets'] = latnets inversion_map['zs'] = zs inversion_map['wts'] = wts return def edit(tar_prompt="", steps=100, skip=36, tar_cfg_scale=15, ): outputs = [] num_generations = 1 for i in range(num_generations): out = sample(wt, zs, wts, prompt_tar=tar_prompt, cfg_scale_tar=tar_cfg_scale, skip=skip) outputs.append(out) return outputs def reset(): inversion_map.clear() ######## # demo # ######## intro = """

Edit Friendly DDPM Inversion

An Edit Friendly DDPM Noise Space: Inversion and Manipulations

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

""" with gr.Blocks() as demo: gr.HTML(intro) with gr.Row(): src_prompt = gr.Textbox(lines=1, label="Source Prompt", interactive=True, placeholder="optional: describe the original image") tar_prompt = gr.Textbox(lines=1, label="Target Prompt", interactive=True, placeholder="optional: describe the target image to edit with DDPM") with gr.Row(): input_image = gr.Image(label="Input Image", interactive=True) input_image.style(height=512, width=512) output_image = gr.Image(label=f"Edited Image", interactive=False) output_image.style(height=512, width=512) with gr.Row(): with gr.Column(scale=1, min_width=100): invert_button = gr.Button("Load & Invert") with gr.Column(scale=1, min_width=100): edit_button = gr.Button("Sample & Edit") with gr.Accordion("Advanced Options", open=False): with gr.Row(): with gr.Column(): #inversion steps = gr.Number(value=100, precision=0, label="Num Diffusion Steps", interactive=True) src_cfg_scale = gr.Slider(minimum=1, maximum=15, value=3.5, label=f"Source Guidance Scale", interactive=True) # reconstruction skip = gr.Slider(minimum=0, maximum=40, value=36, precision=0, label="Skip Steps", interactive=True) tar_cfg_scale = gr.Slider(minimum=7, maximum=18,value=15, label=f"Target Guidance Scale", interactive=True) #shift with gr.Column(): left = gr.Number(value=0, precision=0, label="Left Shift", interactive=True) right = gr.Number(value=0, precision=0, label="Right Shift", interactive=True) top = gr.Number(value=0, precision=0, label="Top Shift", interactive=True) bottom = gr.Number(value=0, precision=0, label="Bottom Shift", interactive=True) # gr.Markdown(help_text) invert_button.click( fn=invert, inputs=[input_image, src_prompt, steps, src_cfg_scale, left, right, top, bottom ], outputs = [], ) edit_button.click( fn=edit, inputs=[tar_prompt, steps, skip, tar_cfg_scale, ], outputs=[output_image], ) input_image.change( fn = reset ) # gr.Examples( # label='Examples', # examples=get_example(), # inputs=[input_image, src_prompt, tar_prompt, steps, # # src_cfg_scale, # skip, # tar_cfg_scale, # edit_concept, # sega_edit_guidance, # warm_up, # # neg_guidance, # ddpm_edited_image, sega_edited_image # ], # outputs=[ddpm_edited_image, sega_edited_image], # # fn=edit, # # cache_examples=True # ) demo.queue() demo.launch(share=False)