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=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=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=src_cfg_scale) # 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(model, prompts) uncond_embedding = encode_text(model, [""] * 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 # # 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 # output = sample(wt, zs, wts, prompt_tar=tar_prompt) # 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(): src_prompt = gr.Textbox(lines=1, label="Source Prompt", interactive=True, placeholder="optional: describe the original image") tar_prompt = gr.Textbox(lines=1, label="Target Prompt", interactive=True, placeholder="optional: describe the target image") 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.Accordion("Advanced Options", open=False): with gr.Row(): with gr.Column(): #inversion 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, inverted_image, output_image ], outputs=[inverted_image,output_image ], # fn=edit, # cache_examples=True ) demo.queue() demo.launch(share=False)