import gradio as gr import torch import numpy as np import requests import random 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 zs, wts def sample(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_cat_sitting_next_to_a_mirror.jpeg', 'a cat sitting next to a mirror', 'watercolor painting of a cat sitting next to a mirror', 100, 36, 15, '+Schnauzer dog, -cat', 5.5, 1, 'examples/ddpm_watercolor_painting_a_cat_sitting_next_to_a_mirror.png', 'examples/ddpm_sega_watercolor_painting_a_cat_sitting_next_to_a_mirror_plus_dog_minus_cat.png' ], [ '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 def invert_and_reconstruct( input_image, do_inversion, wts, zs, seed, randomize_seed, src_prompt ="", tar_prompt="", steps=100, src_cfg_scale = 3.5, skip=36, tar_cfg_scale=15, # neg_guidance=False, ): x0 = load_512(input_image, device=device) randomize_seed_fn(seed, randomize_seed) if do_inversion: # invert and retrieve noise maps and latent zs_tensor, wts_tensor = invert(x0 =x0 , prompt_src=src_prompt, num_diffusion_steps=steps, cfg_scale_src=src_cfg_scale) wts = gr.State(value=wts_tensor) zs = gr.State(value=zs_tensor) do_inversion = False output = sample(zs.value, wts.value, prompt_tar=tar_prompt, skip=skip, cfg_scale_tar=tar_cfg_scale) return output, wts, zs, do_inversion def edit(input_image, do_inversion, wts, zs, seed, src_prompt ="", tar_prompt="", steps=100, skip=36, tar_cfg_scale=15, edit_concept="", sega_edit_guidance=10, warm_up=None, # neg_guidance=False, ): # 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=[.95]*num_concepts, edit_momentum_scale=0.5, edit_mom_beta=0.6 ) latnets = wts.value[skip].expand(1, -1, -1, -1) 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.value, zs=zs.value[skip:], **editing_args) return 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(css='style.css') as demo: def reset_do_inversion(): do_inversion = True return do_inversion def randomize_seed_fn(seed, randomize_seed): if randomize_seed: seed = random.randint(0, np.iinfo(np.int32).max) torch.manual_seed(seed) # return seed gr.HTML(intro) wts = gr.State() zs = gr.State() do_inversion = gr.State(value=True) with gr.Row(): input_image = gr.Image(label="Input Image", interactive=True) ddpm_edited_image = gr.Image(label=f"DDPM Reconstructed Image", interactive=False, visible=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(): tar_prompt = gr.Textbox(lines=1, label="Target Prompt", interactive=True, placeholder="") with gr.Accordion("SEGA Concepts", open=False, visible=False): # with gr.Column(scale=1): edit_concept = gr.Textbox(lines=1, label="SEGA Edit Concepts", visible = True, interactive=True) concepts = gr.Dropdown( [], value=[], multiselect=True, label="Concepts" ) with gr.Row(): with gr.Column(scale=1, min_width=100): invert_button = gr.Button("Invert") with gr.Column(scale=1, min_width=100): edit_button = gr.Button("Edit") with gr.Accordion("Advanced Options", open=False): with gr.Row(): with gr.Column(): #inversion src_prompt = gr.Textbox(lines=1, label="Source Prompt", interactive=True, placeholder="") steps = gr.Number(value=100, precision=0, label="Num Diffusion Steps", interactive=True) src_cfg_scale = gr.Number(value=3.5, label=f"Source Guidance Scale", interactive=True) seed = gr.Number(value=0, precision=0, label="Seed", interactive=True) randomize_seed = gr.Checkbox(label='Randomize seed', value=True) with gr.Column(): # reconstruction skip = gr.Slider(minimum=0, maximum=40, value=36, label="Skip Steps", interactive=True) tar_cfg_scale = gr.Slider(minimum=7, maximum=18,value=15, label=f"Guidance Scale", interactive=True) 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...") # neg_guidance = gr.Checkbox(label="SEGA Negative Guidance") # gr.Markdown(help_text) invert_button.click( fn=invert_and_reconstruct, inputs=[input_image, do_inversion, wts, zs, seed, randomize_seed src_prompt, tar_prompt, steps, src_cfg_scale, skip, tar_cfg_scale, ], outputs=[ddpm_edited_image, wts, zs, do_inversion], ) edit_button.click( fn=edit, inputs=[input_image, do_inversion, wts, zs, seed, src_prompt, tar_prompt, steps, skip, tar_cfg_scale, edit_concept, sega_edit_guidance, warm_up, # neg_guidance, ], outputs=[sega_edited_image], ) input_image.change( fn = reset_do_inversion, outputs = [do_inversion] ) 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)