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Running
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A10G
| 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 | |
| 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) | |
| cache_examples = True | |
| 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' | |
| ]] | |
| 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=1, | |
| neg_guidance=False, | |
| flip=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) | |
| if flip: | |
| wt, zs, wts = torch.flip(wt, [2, 3]),torch.flip(zs, [2, 3]),torch.flip(wts, [2, 3]) | |
| 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) | |
| yield pure_ddpm_out, pure_ddpm_out | |
| # SEGA | |
| edit_concepts = edit_concept.split(",") | |
| neg_guidance =[] | |
| for edit_concept in edit_concepts: | |
| if edit_concept.startswith("-"): | |
| neg_guidance.append(True) | |
| else: | |
| neg_guidance.append(False) | |
| edit_concepts = [concept.strip("+|-") for concept in edit_concepts] | |
| default_warm_up = [1]*len(edit_concepts) | |
| editing_args = dict( | |
| editing_prompt = edit_concepts, | |
| reverse_editing_direction = neg_guidance, | |
| edit_warmup_steps=default_warm_up, | |
| edit_guidance_scale=[sega_edit_guidance], | |
| edit_threshold=[.93], | |
| 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) | |
| yield pure_ddpm_out,sega_out.images[0] | |
| ######## | |
| # demo # | |
| ######## | |
| intro = """ | |
| <h1 style="font-weight: 1400; text-align: center; margin-bottom: 7px;"> | |
| Edit Friendly DDPM X Semantic Guidance: Editing Real Images | |
| </h1> | |
| <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() as demo: | |
| gr.HTML(intro) | |
| with gr.Row(): | |
| src_prompt = gr.Textbox(lines=1, label="Source Prompt", interactive=True) | |
| tar_prompt = gr.Textbox(lines=1, label="Target Prompt", interactive=True) | |
| edit_concept = gr.Textbox(lines=1, label="SEGA Edit Concepts", interactive=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) | |
| 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(): | |
| #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) | |
| flip = gr.Checkbox(label="Flip") | |
| 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) | |
| # edit | |
| sega_edit_guidance = gr.Slider(value=10, label=f"SEGA Edit Guidance Scale", interactive=True) | |
| # warm_up = gr.Number(value=1, label=f"SEGA Warm-up Steps", 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, | |
| flip, | |
| 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, edit_concept, ddpm_edited_image, sega_edited_image], | |
| outputs=[ddpm_edited_image, sega_edited_image]) | |
| demo.queue() | |
| demo.launch(share=False) | |