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 modified_pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline from torch import autocast, inference_mode 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=True, 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" device = torch.device("cuda" if torch.cuda.is_available() else "cpu") sd_pipe = StableDiffusionPipeline.from_pretrained(model_id).to(device) sd_pipe.scheduler = DDIMScheduler.from_config(model_id, subfolder = "scheduler") sem_pipe = SemanticStableDiffusionPipeline.from_pretrained(sd_model_id).to(device) def edit(input_image, input_image_prompt, target_prompt, edit_prompt, guidance_scale=15, skip=36, num_diffusion_steps=100, negative_guidance = False): offsets=(0,0,0,0) x0 = load_512(input_image, *offsets, device) # invert wt, zs, wts = invert(x0 =x0 , prompt_src=input_image_prompt, num_diffusion_steps=num_diffusion_steps) latnets = wts[skip].expand(1, -1, -1, -1) eta = 1 #pure DDPM output pure_ddpm_out = sample(wt, zs, wts, prompt_tar=target_prompt, cfg_scale_tar=guidance_scale, skip=skip, eta = eta) editing_args = dict( editing_prompt = [edit_prompt], reverse_editing_direction = [negative_guidance], edit_warmup_steps=[5], edit_guidance_scale=[8], edit_threshold=[.93], edit_momentum_scale=0.5, edit_mom_beta=0.6 ) sega_out = sem_pipe(prompt=target_prompt,eta=eta, latents=latnets, num_images_per_prompt=1, guidance_scale=edit_guidance_scale, num_inference_steps=num_diffusion_steps_pure_ddpm, use_ddpm=True, wts=wts, zs=zs[skip:], **editing_args) return pure_ddpm_out,sega_out.images[0] # See the gradio docs for the types of inputs and outputs available inputs = [ gr.Image(label="input image", shape=(512, 512)), gr.Textbox(label="input prompt"), gr.Textbox(label="target prompt"), gr.Textbox(label="SEGA edit prompt"), gr.Slider(label="guidance scale", minimum=7, maximum=18, value=15), gr.Slider(label="skip", minimum=0, maximum=40, value=36), gr.Slider(label="num diffusion steps", minimum=0, maximum=300, value=100), gr.Checkbox(label="SEGA negative_guidance"), ] outputs = [gr.Image(label="DDPM"),gr.Image(label="DDPM+SEGA")] # And the minimal interface demo = gr.Interface( fn=edit, inputs=inputs, outputs=outputs, ) demo.launch() # debug=True allows you to see errors and output in Colab