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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) | |
| latents, wts, zs = None, None, None | |
| def invert_and_reconstruct(input_image, | |
| src_prompt, | |
| tar_prompt, | |
| steps, | |
| # src_cfg_scale, | |
| skip, | |
| tar_cfg_scale): | |
| offsets=(0,0,0,0) | |
| x0 = load_512(input_image, *offsets, 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) | |
| eta = 1 | |
| #pure DDPM output | |
| pure_ddpm_out = sample(wt, zs, wts, prompt_tar=tar_prompt, | |
| cfg_scale_tar=tar_cfg_scale, skip=skip, | |
| eta = eta) | |
| return pure_ddpm_out | |
| def edit( tar_prompt, | |
| steps, | |
| edit_concept, | |
| sega_edit_guidance, | |
| warm_up, | |
| neg_guidance): | |
| editing_args = dict( | |
| editing_prompt = [edit_concept], | |
| reverse_editing_direction = [neg_guidance], | |
| edit_warmup_steps=[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=eta, latents=latnets, | |
| num_images_per_prompt=1, | |
| num_inference_steps=steps, | |
| use_ddpm=True, wts=wts, zs=zs[skip:], **editing_args) | |
| return sega_out.images[0] | |
| # def edit(input_image, | |
| # src_prompt, | |
| # tar_prompt, | |
| # steps, | |
| # # src_cfg_scale, | |
| # skip, | |
| # tar_cfg_scale, | |
| # edit_concept, | |
| # sega_edit_guidance, | |
| # warm_up, | |
| # neg_guidance): | |
| # offsets=(0,0,0,0) | |
| # x0 = load_512(input_image, *offsets, 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) | |
| # eta = 1 | |
| # #pure DDPM output | |
| # pure_ddpm_out = sample(wt, zs, wts, prompt_tar=tar_prompt, | |
| # cfg_scale_tar=tar_cfg_scale, skip=skip, | |
| # eta = eta) | |
| # editing_args = dict( | |
| # editing_prompt = [edit_concept], | |
| # reverse_editing_direction = [neg_guidance], | |
| # edit_warmup_steps=[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=eta, latents=latnets, | |
| # 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] | |
| #################################### | |
| intro = """<h1 style="font-weight: 900; margin-bottom: 7px;"> | |
| Edit Friendly DDPM X Semantic Guidance: Editing Real Images | |
| </h1> | |
| <p>For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings. | |
| <br/> | |
| <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(): | |
| input_image = gr.Image(label="Input Image",interactive=False) | |
| ddpm_edited_image = gr.Image(label=f"Reconstructed Image", interactive=False) | |
| sega_edited_image = gr.Image(label=f"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("Invert") | |
| with gr.Column(scale=1, min_width=100): | |
| edit_button = gr.Button("Edit") | |
| # with gr.Column(scale=1, min_width=100): | |
| # reset_button = gr.Button("Reset") | |
| # with gr.Column(scale=3): | |
| # instruction = gr.Textbox(lines=1, label="Edit Instruction", interactive=True) | |
| with gr.Row(): | |
| src_prompt = gr.Textbox(lines=1, label="Source Prompt", interactive=True) | |
| #edit | |
| tar_prompt = gr.Textbox(lines=1, label="Target Prompt", interactive=True) | |
| with gr.Row(): | |
| #inversion | |
| steps = gr.Number(value=100, precision=0, label="Steps", interactive=True) | |
| # src_cfg_scale = gr.Number(value=3.5, label=f"Source CFG", interactive=True) | |
| # reconstruction | |
| skip = gr.Number(value=36, precision=0, label="Skip", interactive=True) | |
| tar_cfg_scale = gr.Number(value=15, label=f"Reconstruction CFG", interactive=True) | |
| # edit | |
| edit_concept = gr.Textbox(lines=1, label="Edit Concept", interactive=True) | |
| sega_edit_guidance = gr.Number(value=5, label=f"SEGA CFG", interactive=True) | |
| warm_up = gr.Number(value=5, label=f"Warm-up Steps", interactive=True) | |
| neg_guidance = gr.Checkbox(label="SEGA negative_guidance") | |
| # gr.Markdown(help_text) | |
| generate_button.click( | |
| fn=invert_and_reconstruct, | |
| inputs=[input_image, | |
| src_prompt, | |
| tar_prompt, | |
| steps, | |
| src_cfg_scale, | |
| skip, | |
| tar_cfg_scale | |
| ], | |
| outputs=[ddpm_edited_image], | |
| ) | |
| edit_button.click( | |
| fn=edit, | |
| inputs=[tar_prompt, | |
| steps, | |
| edit_concept, | |
| sega_edit_guidance, | |
| warm_up, | |
| neg_guidance | |
| ], | |
| outputs=[sega_edited_image], | |
| ) | |
| demo.queue(concurrency_count=1) | |
| demo.launch(share=False) | |
| ###################################################### | |
| # inputs = [ | |
| # gr.Image(label="input image", shape=(512, 512)), | |
| # gr.Textbox(label="input prompt"), | |
| # gr.Textbox(label="target prompt"), | |
| # gr.Textbox(label="SEGA edit concept"), | |
| # gr.Checkbox(label="SEGA negative_guidance"), | |
| # gr.Slider(label="warmup steps", minimum=1, maximum=30, value=5), | |
| # gr.Slider(label="edit guidance scale", minimum=0, maximum=15, value=3.5), | |
| # gr.Slider(label="guidance scale", minimum=7, maximum=18, value=15), | |
| # gr.Slider(label="skip", minimum=0, maximum=40, value=36), | |
| # gr.Slider(label="num diffusion steps", minimum=0, maximum=300, value=100) | |
| # ] | |
| # outputs = [gr.Image(label="DDPM"),gr.Image(label="DDPM+SEGA")] | |
| # # And the minimal interface | |
| # demo = gr.Interface( | |
| # fn=edit, | |
| # inputs=inputs, | |
| # outputs=outputs, | |
| # ) | |
| # demo.launch() # debug=True allows you to see errors and output in Colab | |