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| 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 randomize_seed_fn(seed, randomize_seed): | |
| if randomize_seed: | |
| seed = random.randint(0, np.iinfo(np.int32).max) | |
| torch.manual_seed(seed) | |
| return seed | |
| 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, | |
| seed, randomize_seed, | |
| wts, zs, | |
| src_prompt ="", | |
| tar_prompt="", | |
| steps=100, | |
| src_cfg_scale = 3.5, | |
| skip=36, | |
| tar_cfg_scale=15, | |
| ): | |
| x0 = load_512(input_image, device=device) | |
| if do_inversion or randomize_seed: | |
| # 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 | |
| return wts, zs, do_inversion | |
| def edit(input_image, | |
| wts, zs, | |
| tar_prompt, | |
| steps, | |
| skip, | |
| tar_cfg_scale, | |
| edit_concept_1,edit_concept_2,edit_concept_3, | |
| guidnace_scale_1,guidnace_scale_2,guidnace_scale_3, | |
| warmup_1, warmup_2, warmup_3, | |
| neg_guidance_1, neg_guidance_2, neg_guidance_3, | |
| threshold_1, threshold_2, threshold_3 | |
| ): | |
| # SEGA | |
| # parse concepts and neg guidance | |
| editing_args = dict( | |
| editing_prompt = [edit_concept_1,edit_concept_2,edit_concept_3], | |
| reverse_editing_direction = [ neg_guidance_1, neg_guidance_2, neg_guidance_3,], | |
| edit_warmup_steps=[warmup_1, warmup_2, warmup_3,], | |
| edit_guidance_scale=[guidnace_scale_1,guidnace_scale_2,guidnace_scale_3], | |
| edit_threshold=[threshold_1, threshold_2, threshold_3], | |
| edit_momentum_scale=0.5, | |
| edit_mom_beta=0.6, | |
| eta=1, | |
| ) | |
| latnets = wts.value[skip].expand(1, -1, -1, -1) | |
| sega_out = sem_pipe(prompt=tar_prompt, 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 = """ | |
| <h1 style="font-weight: 1400; text-align: center; margin-bottom: 7px;"> | |
| Edit Friendly DDPM X Semantic Guidance | |
| </h1> | |
| <p style="font-size: 0.9rem; text-align: center; margin: 0rem; line-height: 1.2em; margin-top:1em"> | |
| <a href="https://arxiv.org/abs/2301.12247" style="text-decoration: underline;" target="_blank">An Edit Friendly DDPM Noise Space: | |
| Inversion and Manipulations </a> X | |
| <a href="https://arxiv.org/abs/2301.12247" style="text-decoration: underline;" target="_blank">SEGA: Instructing Diffusion using Semantic Dimensions</a> | |
| <p/> | |
| <p style="font-size: 0.9rem; margin: 0rem; line-height: 1.2em; margin-top:1em"> | |
| For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings. | |
| <a href="https://huggingface.co/spaces/LinoyTsaban/ddpm_sega?duplicate=true"> | |
| <img style="margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a> | |
| <p/>""" | |
| with gr.Blocks(css='style.css') as demo: | |
| def add_concept(sega_concepts_counter): | |
| if sega_concepts_counter == 1: | |
| return row2.update(visible=True), row3.update(visible=False), plus.update(visible=True), 2 | |
| else: | |
| return row2.update(visible=True), row3.update(visible=True), plus.update(visible=False), 3 | |
| def reset_do_inversion(): | |
| do_inversion = True | |
| return do_inversion | |
| gr.HTML(intro) | |
| wts = gr.State() | |
| zs = gr.State() | |
| do_inversion = gr.State(value=True) | |
| sega_concepts_counter = gr.Number(1) | |
| 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.Tabs() as tabs: | |
| with gr.TabItem('1. Describe the desired output', id=0): | |
| with gr.Row().style(mobile_collapse=False, equal_height=True): | |
| tar_prompt = gr.Textbox( | |
| label="Edit Concept", | |
| show_label=False, | |
| max_lines=1, | |
| placeholder="Enter your 1st edit prompt", | |
| ) | |
| with gr.TabItem('2. Add SEGA edit concepts', id=1): | |
| # with gr.Group(): | |
| with gr.Row().style(mobile_collapse=False, equal_height=True): | |
| edit_concept_1 = gr.Textbox( | |
| label="Edit Concept", | |
| show_label=False, | |
| max_lines=1, | |
| placeholder="Enter your 1st edit prompt", | |
| ) | |
| # tar_prompt = gr.Textbox(lines=1, label="Target Prompt", interactive=True, placeholder="") | |
| neg_guidance_1 = gr.Checkbox( | |
| label='Negative Guidance') | |
| warmup_1 = gr.Slider(label='Warmup', minimum=0, maximum=50, value=10, step=1, interactive=True) | |
| guidnace_scale_1 = gr.Slider(label='Scale', minimum=1, maximum=10, value=5, step=0.25, interactive=True) | |
| threshold_1 = gr.Slider(label='Threshold', minimum=0.5, maximum=0.99, value=0.95, steps=0.01, interactive=True) | |
| with gr.Row(visible=False) as row2: | |
| edit_concept_2 = gr.Textbox( | |
| label="Edit Concept", | |
| show_label=False,visible=True, | |
| max_lines=1, | |
| placeholder="Enter your 2st edit prompt", | |
| ) | |
| # tar_prompt = gr.Textbox(lines=1, label="Target Prompt", interactive=True, placeholder="") | |
| neg_guidance_2 = gr.Checkbox( | |
| label='Negative Guidance',visible=True) | |
| warmup_2 = gr.Slider(label='Warmup', minimum=0, maximum=50, value=10, step=1, visible=True,interactive=True) | |
| guidnace_scale_2 = gr.Slider(label='Scale', minimum=1, maximum=10, value=5, step=0.25,visible=True, interactive=True) | |
| threshold_2 = gr.Slider(label='Threshold', minimum=0.5, maximum=0.99, value=0.95, steps=0.01,visible=True, interactive=True) | |
| with gr.Row(visible=False) as row3: | |
| edit_concept_3 = gr.Textbox( | |
| label="Edit Concept", | |
| show_label=False,visible=True, | |
| max_lines=1, | |
| placeholder="Enter your 3rd edit prompt", | |
| ) | |
| # tar_prompt = gr.Textbox(lines=1, label="Target Prompt", interactive=True, placeholder="") | |
| neg_guidance_3 = gr.Checkbox( | |
| label='Negative Guidance',visible=True) | |
| warmup_3 = gr.Slider(label='Warmup', minimum=0, maximum=50, value=10, step=1, visible=True,interactive=True) | |
| guidnace_scale_3 = gr.Slider(label='Scale', minimum=1, maximum=10, value=5, step=0.25,visible=True, interactive=True) | |
| threshold_3 = gr.Slider(label='Threshold', minimum=0.5, maximum=0.99, value=0.95, steps=0.01,visible=True, interactive=True) | |
| with gr.Row().style(mobile_collapse=False, equal_height=True): | |
| plus = gr.Button("+") | |
| with gr.Row(): | |
| with gr.Column(scale=1, min_width=100): | |
| run_button = gr.Button("Run") | |
| # 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(): | |
| 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=False) | |
| with gr.Column(): | |
| 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) | |
| # gr.Markdown(help_text) | |
| plus.click(fn = add_concept, inputs=sega_concepts_counter, | |
| outputs= [row2, row3, plus, sega_concepts_counter]) | |
| run_button.click( | |
| fn = randomize_seed_fn, | |
| inputs = [seed, randomize_seed], | |
| outputs = [seed], | |
| queue = False).then( | |
| fn=invert_and_reconstruct, | |
| inputs=[input_image, | |
| do_inversion, | |
| seed, randomize_seed, | |
| wts, zs, | |
| src_prompt, | |
| tar_prompt, | |
| steps, | |
| src_cfg_scale, | |
| skip, | |
| tar_cfg_scale, | |
| ], | |
| # outputs=[ddpm_edited_image, wts, zs, do_inversion], | |
| outputs=[wts, zs, do_inversion], | |
| ).success( | |
| fn=edit, | |
| inputs=[input_image, | |
| wts, zs, | |
| tar_prompt, | |
| steps, | |
| skip, | |
| tar_cfg_scale, | |
| edit_concept_1,edit_concept_2,edit_concept_3, | |
| guidnace_scale_1,guidnace_scale_2,guidnace_scale_3, | |
| warmup_1, warmup_2, warmup_3, | |
| neg_guidance_1, neg_guidance_2, neg_guidance_3, | |
| threshold_1, threshold_2, threshold_3 | |
| ], | |
| 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) | |