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Running
on
A10G
| import gradio as gr | |
| import torch | |
| import random | |
| import requests | |
| from io import BytesIO | |
| from diffusers import StableDiffusionPipeline | |
| from diffusers import DDIMScheduler | |
| from utils import * | |
| from inversion_utils import * | |
| 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=False, num_inference_steps=num_diffusion_steps) | |
| return zs, wts | |
| def sample(zs, wts, prompt_tar="", skip=36, cfg_scale_tar=15, 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=False, 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" | |
| # sd_model_id = "CompVis/stable-diffusion-v1-4" | |
| # sd_model_id = "stabilityai/stable-diffusion-2-base" | |
| 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") | |
| def get_example(): | |
| case = [ | |
| [ | |
| 'Examples/gnochi_mirror.jpeg', | |
| 'Watercolor painting of a cat sitting next to a mirror', | |
| 'Examples/gnochi_mirror_watercolor_painting.png', | |
| '', | |
| 100, | |
| 3.5, | |
| 36, | |
| 15, | |
| ], | |
| [ | |
| 'Examples/source_an_old_man.png', | |
| 'A bronze statue of an old man', | |
| 'Examples/ddpm_a_bronze_statue_of_an_old_man.png', | |
| '', | |
| 100, | |
| 3.5, | |
| 36, | |
| 15, | |
| ], | |
| [ | |
| 'Examples/source_a_ceramic_vase_with_yellow_flowers.jpeg', | |
| 'A pink ceramic vase with a wheat bouquet', | |
| 'Examples/ddpm_a_pink_ceramic_vase_with_a_wheat_bouquet.png', | |
| '', | |
| 100, | |
| 3.5, | |
| 36, | |
| 15, | |
| ] | |
| ] | |
| return case | |
| ######## | |
| # demo # | |
| ######## | |
| intro = """ | |
| <h1 style="font-weight: 1400; text-align: center; margin-bottom: 7px;"> | |
| Edit Friendly DDPM Inversion | |
| </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> | |
| <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 reset_do_inversion(): | |
| do_inversion = True | |
| return do_inversion | |
| def edit(input_image, | |
| do_inversion, | |
| wts, zs, | |
| src_prompt ="", | |
| tar_prompt="", | |
| steps=100, | |
| cfg_scale_src = 3.5, | |
| cfg_scale_tar = 15, | |
| skip=36, | |
| seed = 0, | |
| randomized_seed = True): | |
| if randomized_seed: | |
| seed = random.randint(0, np.iinfo(np.int32).max) | |
| torch.manual_seed(seed) | |
| # offsets=(0,0,0,0) | |
| x0 = load_512(input_image, device=device) | |
| 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=cfg_scale_src) | |
| # xt = gr.State(value=wts[skip]) | |
| # zs = gr.State(value=zs[skip:]) | |
| wts = gr.State(value=wts_tensor) | |
| zs = gr.State(value=zs_tensor) | |
| do_inversion = False | |
| # output = sample(zs.value, xt.value, prompt_tar=tar_prompt, cfg_scale_tar=cfg_scale_tar) | |
| output = sample(zs.value, wts.value, prompt_tar=tar_prompt, skip=skip, cfg_scale_tar=cfg_scale_tar) | |
| return output, wts, zs, do_inversion | |
| gr.HTML(intro) | |
| # xt = gr.State(value=False) | |
| wts = gr.State() | |
| zs = gr.State() | |
| do_inversion = gr.State(value=True) | |
| with gr.Row(): | |
| input_image = gr.Image(label="Input Image", interactive=True) | |
| input_image.style(height=512, width=512) | |
| output_image = gr.Image(label=f"Edited Image", interactive=False) | |
| output_image.style(height=512, width=512) | |
| with gr.Row(): | |
| tar_prompt = gr.Textbox(lines=1, label="Describe your desired edited output", interactive=True) | |
| with gr.Row(): | |
| with gr.Column(scale=1, min_width=100): | |
| edit_button = gr.Button("Run") | |
| 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="describe the original image") | |
| steps = gr.Number(value=100, precision=0, label="Num Diffusion Steps", interactive=True) | |
| cfg_scale_src = gr.Slider(minimum=1, maximum=15, value=3.5, label=f"Source Guidance Scale", interactive=True) | |
| with gr.Column(): | |
| # reconstruction | |
| skip = gr.Slider(minimum=0, maximum=40, value=36, precision=0, label="Skip Steps", interactive=True) | |
| cfg_scale_tar = gr.Slider(minimum=7, maximum=18,value=15, label=f"Target Guidance Scale", interactive=True) | |
| seed = gr.Number(value=0, precision=0, label="Seed", interactive=True) | |
| randomize_seed = gr.Checkbox(label='Randomize seed', value=True) | |
| edit_button.click( | |
| fn=edit, | |
| inputs=[input_image, | |
| do_inversion, wts, zs, | |
| src_prompt, | |
| tar_prompt, | |
| steps, | |
| cfg_scale_src, | |
| cfg_scale_tar, | |
| skip, | |
| seed, | |
| randomize_seed | |
| ], | |
| outputs=[output_image, wts, zs, do_inversion], | |
| ) | |
| input_image.change( | |
| fn = reset_do_inversion, | |
| outputs = [do_inversion] | |
| ) | |
| src_prompt.change( | |
| fn = reset_do_inversion, | |
| outputs = [do_inversion] | |
| ) | |
| # skip.change( | |
| # fn = reset_latents | |
| # ) | |
| gr.Examples( | |
| label='Examples', | |
| examples=get_example(), | |
| inputs=[input_image, tar_prompt,output_image, src_prompt,steps, | |
| cfg_scale_tar, | |
| skip, | |
| cfg_scale_tar | |
| ], | |
| outputs=[output_image ], | |
| ) | |
| demo.queue() | |
| demo.launch(share=False) |