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 = """
An Edit Friendly DDPM Noise Space: Inversion and Manipulations
For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings.
""" 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)