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_reconstrcution.png', '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, left = 0, right = 0, top = 0, bottom = 0 ): torch.manual_seed(seed) # offsets=(0,0,0,0) x0 = load_512(input_image, left,right, top, bottom, device) # 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) # # # xT=wts[skip] # etas=1.0 # prompts=[tar_prompt] # cfg_scales=[cfg_scale_tar] # prog_bar=False # zs=zs[skip:] # batch_size = len(prompts) # cfg_scales_tensor = torch.Tensor(cfg_scales).view(-1,1,1,1).to(sd_pipe.device) # text_embeddings = encode_text(sd_pipe, prompts) # uncond_embedding = encode_text(sd_pipe, [""] * batch_size) # if etas is None: etas = 0 # if type(etas) in [int, float]: etas = [etas]*sd_pipe.scheduler.num_inference_steps # assert len(etas) == sd_pipe.scheduler.num_inference_steps # timesteps = sd_pipe.scheduler.timesteps.to(sd_pipe.device) # xt = xT.expand(batch_size, -1, -1, -1) # op = tqdm(timesteps[-zs.shape[0]:]) if prog_bar else timesteps[-zs.shape[0]:] # t_to_idx = {int(v):k for k,v in enumerate(timesteps[-zs.shape[0]:])} # for t in op: # idx = t_to_idx[int(t)] # ## Unconditional embedding # with torch.no_grad(): # uncond_out = sd_pipe.unet.forward(xt, timestep = t, # encoder_hidden_states = uncond_embedding) # ## Conditional embedding # if prompts: # with torch.no_grad(): # cond_out = sd_pipe.unet.forward(xt, timestep = t, # encoder_hidden_states = text_embeddings) # z = zs[idx] if not zs is None else None # z = z.expand(batch_size, -1, -1, -1) # if prompts: # ## classifier free guidance # noise_pred = uncond_out.sample + cfg_scales_tensor * (cond_out.sample - uncond_out.sample) # else: # noise_pred = uncond_out.sample # # 2. compute less noisy image and set x_t -> x_t-1 # xt = reverse_step(sd_pipe, noise_pred, t, xt, eta = etas[idx], variance_noise = z) # # interm denoised img # with autocast("cuda"), inference_mode(): # x0_dec = sd_pipe.vae.decode(1 / 0.18215 * xt).sample # if x0_dec.dim()<4: # x0_dec = x0_dec[None,:,:,:] # interm_img = image_grid(x0_dec) # yield interm_img # yield interm_img output = sample(wt, zs, wts, prompt_tar=tar_prompt, cfg_scale_tar=cfg_scale_tar, skip=skip) return output ######## # demo # ######## intro = """

Edit Friendly DDPM Inversion

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. Duplicate Space

""" with gr.Blocks() as demo: gr.HTML(intro) with gr.Row(): input_image = gr.Image(label="Input Image", interactive=True) input_image.style(height=512, width=512) # inverted_image = gr.Image(label=f"Reconstructed Image", interactive=False) # inverted_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(): # with gr.Column(scale=1, min_width=100): # invert_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): edit_button = gr.Button("Run") with gr.Row(): tar_prompt = gr.Textbox(lines=1, label="Target Prompt", interactive=True, placeholder="optional: describe the target image") 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="optional: 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) # 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) #shift with gr.Column(): 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) # gr.Markdown(help_text) # invert_button.click( # fn=edit, # inputs=[input_image, # src_prompt, # src_prompt, # steps, # cfg_scale_src, # cfg_scale_tar, # skip, # seed, # left, # right, # top, # bottom # ], # outputs = [inverted_image], # ) edit_button.click( fn=edit, inputs=[input_image, src_prompt, tar_prompt, steps, cfg_scale_src, cfg_scale_tar, skip, seed, left, right, top, bottom ], outputs=[output_image], ) gr.Examples( label='Examples', examples=get_example(), inputs=[input_image, src_prompt, tar_prompt, steps, cfg_scale_tar, skip, cfg_scale_tar, output_image ], outputs=[output_image ], # fn=edit, # cache_examples=True ) demo.queue() demo.launch(share=False)