Edits / app.py
Linoy Tsaban
Update app.py
a93910d
raw
history blame
6.57 kB
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=100,
# src_cfg_scale,
skip=36,
tar_cfg_scale=15,
edit_concept="",
sega_edit_guidance=0,
warm_up=7,
neg_guidance=False):
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, guidance_scale = tar_cfg_scale,
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]
# demo
intro = """
<h1 style="font-weight: 1200; text-align: center; margin-bottom: 7px;">
Edit Friendly DDPM X Semantic Guidance: Editing Real Images
</h1>
<br/>
<p style="font-size: 0.9rem; margin: 0rem; line-height: 1.2em; margin-top:1em">
For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings.
<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():
input_image = gr.Image(label="Input Image", interactive=True)
ddpm_edited_image = gr.Image(label=f"DDPM Reconstructed Image", interactive=False)
sega_edited_image = gr.Image(label=f"DDPM + SEGA 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("Run")
# 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)
edit_concept = gr.Textbox(lines=1, label="SEGA Edit Concept", interactive=True)
# with gr.Row():
with gr.Accordion("Advanced Options"):
with gr.Row():
#inversion
steps = gr.Number(value=100, precision=0, label="Num Diffusion 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 Steps", interactive=True)
tar_cfg_scale = gr.Number(value=15, label=f"Guidance Scale", interactive=True)
# edit
sega_edit_guidance = gr.Number(value=5, label=f"SEGA Edit Guidance Scale", interactive=True)
warm_up = gr.Number(value=5, label=f"SEGA 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, sega_edited_image],
)
demo.queue()
demo.launch(share=False)