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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,
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)
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 = """<h1 style="font-weight: 900; margin-bottom: 7px;">
Edit Friendly DDPM X Semantic Guidance: Editing Real Images
</h1>
<p>For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings.
<br/>
<a href="https://huggingface.co/spaces/LinoyTsaban/ddpm_sega?duplicate=true">
<img style="margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
<p/>"""
with gr.Blocks() as demo:
gr.HTML(intro)
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():
input_image = gr.Image(label="Input Image", type="pil", interactive=True)
ddpm_edited_image = gr.Image(label=f"Reconstructed Image", type="pil", interactive=False)
sega_edited_image = gr.Image(label=f"Edited Image", type="pil", 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():
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=100, 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=[input_image, ddpm_edited_image, sega_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
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