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(sd_model_id).to(device) sd_pipe.scheduler = DDIMScheduler.from_config(sd_model_id, subfolder = "scheduler") sem_pipe = SemanticStableDiffusionPipeline.from_pretrained(sd_model_id).to(device) def edit(input_image, src_prompt, tar_prompt, steps, # src_cfg_scale, skip, tar_cfg_scale, edit_concept, sega_edit_guidance, warm_up, neg_guidance): offsets=(0,0,0,0) x0 = load_512(input_image, *offsets, device) # invert # wt, zs, wts = invert(x0 =x0 , prompt_src=src_prompt, num_diffusion_steps=steps, cfg_scale_src=src_cfg_scale) wt, zs, wts = invert(x0 =x0 , prompt_src=src_prompt, num_diffusion_steps=steps) latnets = wts[skip].expand(1, -1, -1, -1) eta = 1 #pure DDPM output pure_ddpm_out = sample(wt, zs, wts, prompt_tar=tar_prompt, cfg_scale_tar=tar_cfg_scale, skip=skip, eta = eta) editing_args = dict( editing_prompt = [edit_concept], reverse_editing_direction = [neg_guidance], edit_warmup_steps=[warm_up], edit_guidance_scale=[sega_edit_guidance], edit_threshold=[.93], edit_momentum_scale=0.5, edit_mom_beta=0.6 ) sega_out = sem_pipe(prompt=tar_prompt,eta=eta, latents=latnets, num_images_per_prompt=1, num_inference_steps=steps, use_ddpm=True, wts=wts, zs=zs[skip:], **editing_args) return pure_ddpm_out,sega_out.images[0] #################################### intro = """

Edit Friendly DDPM X Semantic Guidance: Editing Real Images

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) ddpm_edited_image = gr.Image(label=f"Reconstructed Image", interactive=False) sega_edited_image = gr.Image(label=f"Edited Image", interactive=False) input_image.style(height=512, width=512) ddpm_edited_image.style(height=512, width=512) sega_edited_image.style(height=512, width=512) with gr.Row(): with gr.Column(scale=1, min_width=100): generate_button = gr.Button("Generate") # with gr.Column(scale=1, min_width=100): # reset_button = gr.Button("Reset") # with gr.Column(scale=3): # instruction = gr.Textbox(lines=1, label="Edit Instruction", interactive=True) with gr.Row(): src_prompt = gr.Textbox(lines=1, label="Source Prompt", interactive=True) #edit tar_prompt = gr.Textbox(lines=1, label="Target Prompt", interactive=True) with gr.Row(): #inversion steps = gr.Number(value=100, precision=0, label="Steps", interactive=True) # src_cfg_scale = gr.Number(value=3.5, label=f"Source CFG", interactive=True) # reconstruction skip = gr.Number(value=36, precision=0, label="Skip", interactive=True) tar_cfg_scale = gr.Number(value=15, label=f"Reconstruction CFG", interactive=True) # edit edit_concept = gr.Textbox(lines=1, label="Edit Concept", interactive=True) sega_edit_guidance = gr.Number(value=5, label=f"SEGA CFG", interactive=True) warm_up = gr.Number(value=5, label=f"Warm-up Steps", interactive=True) neg_guidance = gr.Checkbox(label="SEGA negative_guidance") # gr.Markdown(help_text) generate_button.click( fn=edit, inputs=[input_image, src_prompt, tar_prompt, steps, # src_cfg_scale, skip, tar_cfg_scale, edit_concept, sega_edit_guidance, warm_up, neg_guidance ], outputs=[ddpm_edited_image], ) demo.queue(concurrency_count=1) demo.launch(share=False) ###################################################### # inputs = [ # gr.Image(label="input image", shape=(512, 512)), # gr.Textbox(label="input prompt"), # gr.Textbox(label="target prompt"), # gr.Textbox(label="SEGA edit concept"), # gr.Checkbox(label="SEGA negative_guidance"), # gr.Slider(label="warmup steps", minimum=1, maximum=30, value=5), # gr.Slider(label="edit guidance scale", minimum=0, maximum=15, value=3.5), # 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) # ] # 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