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 modified_pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline 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" 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") sem_pipe = SemanticStableDiffusionPipeline.from_pretrained(sd_model_id).to(device) 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', '+painting', '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', '', '', '+pink drawings of muffins', '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', '+giant elephant', 'examples/ddpm_glass_walls.png', 'examples/ddpm_sega_glass_walls_gian_elephant.png' ]] return case def edit(input_image, src_prompt ="", tar_prompt="", steps=100, # src_cfg_scale, skip=36, tar_cfg_scale=15, edit_concept="", sega_edit_guidance=0, warm_up=None, # neg_guidance=False, 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) wt, zs, wts = invert(x0 =x0 , prompt_src=src_prompt, num_diffusion_steps=steps) latnets = wts[skip].expand(1, -1, -1, -1) #pure DDPM output pure_ddpm_out = sample(wt, zs, wts, prompt_tar=tar_prompt, cfg_scale_tar=tar_cfg_scale, skip=skip) if not edit_concept or not sega_edit_guidance: return pure_ddpm_out, pure_ddpm_out # SEGA # parse concepts and neg guidance edit_concepts = edit_concept.split(",") num_concepts = len(edit_concepts) neg_guidance =[] for edit_concept in edit_concepts: edit_concept=edit_concept.strip(" ") if edit_concept.startswith("-"): neg_guidance.append(True) else: neg_guidance.append(False) edit_concepts = [concept.strip("+|-") for concept in edit_concepts] # parse warm-up steps default_warm_up_steps = [1]*num_concepts if warm_up: digit_pattern = re.compile(r"^\d+$") warm_up_steps_str = warm_up.split(",") for i,num_steps in enumerate(warm_up_steps_str[:num_concepts]): if not digit_pattern.match(num_steps): raise gr.Error("Invalid value for warm-up steps, using 1 instead") else: default_warm_up_steps[i] = int(num_steps) editing_args = dict( editing_prompt = edit_concepts, reverse_editing_direction = neg_guidance, edit_warmup_steps=default_warm_up_steps, edit_guidance_scale=[sega_edit_guidance]*num_concepts, edit_threshold=[.93]*num_concepts, edit_momentum_scale=0.5, edit_mom_beta=0.6 ) sega_out = sem_pipe(prompt=tar_prompt,eta=1, latents=latnets, guidance_scale = tar_cfg_scale, num_images_per_prompt=1, num_inference_steps=steps, use_ddpm=True, wts=wts, zs=zs[skip:], **editing_args) return pure_ddpm_out,sega_out.images[0] ######## # demo # ######## intro = """

Edit Friendly DDPM X Semantic Guidance

An Edit Friendly DDPM Noise Space: Inversion and Manipulations X SEGA: Instructing Diffusion using Semantic Dimensions

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") edit_concept = gr.Textbox(lines=1, label="SEGA Edit Concepts", interactive=True, placeholder="optional: type a list of concepts to add/remove with SEGA\n (e.g. +dog,-cat,+oil painting)") with gr.Row(): input_image = gr.Image(label="Input Image", interactive=True) ddpm_edited_image = gr.Image(label=f"DDPM Reconstructed Image", interactive=False) sega_edited_image = gr.Image(label=f"DDPM + SEGA Edited Image", interactive=False) input_image.style(height=512, width=512) ddpm_edited_image.style(height=512, width=512) sega_edited_image.style(height=512, width=512) with gr.Row(): with gr.Column(scale=1, min_width=100): generate_button = gr.Button("Run") 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.Number(value=3.5, label=f"Source CFG", 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"Guidance Scale", interactive=True) with gr.Column(): sega_edit_guidance = gr.Slider(value=10, label=f"SEGA Edit Guidance Scale", interactive=True) warm_up = gr.Textbox(label=f"SEGA Warm-up Steps", interactive=True, placeholder="type #warm-up steps for each concpets (e.g. 2,7,5...") #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) # neg_guidance = gr.Checkbox(label="SEGA Negative Guidance") # gr.Markdown(help_text) generate_button.click( fn=edit, inputs=[input_image, src_prompt, tar_prompt, steps, # src_cfg_scale, skip, tar_cfg_scale, edit_concept, sega_edit_guidance, warm_up, # neg_guidance, left, right, top, bottom ], outputs=[ddpm_edited_image, sega_edited_image], ) 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, left, right, top, bottom], outputs=[ddpm_edited_image, sega_edited_image], fn=edit, cache_examples=True) demo.queue() demo.launch(share=False)