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| 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, 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, | |
| num_inference_steps=num_diffusion_steps, | |
| 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 |