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 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 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=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', 100, 3.5, 36, 15, 'Examples/gnochi_mirror_watercolor_painting.png', ],] return case def edit(input_image, src_prompt ="", tar_prompt="", steps=100, cfg_scale_src = 3.5, cfg_scale_tar = 15, skip=36, seed = 0, wt = None, zs = None, wts = None ): torch.manual_seed(seed) # offsets=(0,0,0,0) x0 = load_512(input_image, left,right, top, bottom, device) if not wt: # invert and retrieve noise maps and latent wt, zs, wts = invert(x0 =x0 , prompt_src=src_prompt, num_diffusion_steps=steps, cfg_scale_src=cfg_scale_src) output = sample(wt, zs, wts, prompt_tar=tar_prompt, cfg_scale_tar=cfg_scale_tar, skip=skip) return output def reset_latents(): wt = gr.State(value=None) zs = gr.State(value=None) wts = gr.State(value=None) ######## # demo # ######## intro = """
An Edit Friendly DDPM Noise Space: Inversion and Manipulations
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