ledits / app.py
Linoy Tsaban
Update app.py
5a65206
raw
history blame
8.32 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)
cache_examples = True
def get_example():
case = [
[
'examples/source_a_man_wearing_a_brown_hoodie_in_a_crowded_street.jpeg',
'a man wearing a brown hoodie in a crowded street',
'a robot wearing a brown hoodie in a crowded street',
'painting',
'examples/ddpm_a_robot_wearing_a_brown_hoodie_in_a_crowded_street.png',
'examples/ddpm_sega_painting_of_a_robot_wearing_a_brown_hoodie_in_a_crowded_street.png'
]]
return case
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=1,
neg_guidance=False,
flip=False,
left = 0,
right = 0,
top = 0,
bottom = 0):
# offsets=(0,0,0,0)
x0 = load_512(input_image, left,right, top, bottom, 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)
if flip:
wt, zs, wts = torch.flip(wt, [2, 3]),torch.flip(zs, [2, 3]),torch.flip(wts, [2, 3])
latnets = wts[skip].expand(1, -1, -1, -1)
#pure DDPM output
pure_ddpm_out = sample(wt, zs, wts, prompt_tar=tar_prompt,
cfg_scale_tar=tar_cfg_scale, skip=skip)
yield pure_ddpm_out, pure_ddpm_out
# SEGA
edit_concepts = edit_concept.split(",")
neg_guidance =[]
for edit_concept in edit_concepts:
if edit_concept.startswith("-"):
neg_guidance.append(True)
else:
neg_guidance.append(False)
edit_concepts = [concept.strip("+|-") for concept in edit_concepts]
default_warm_up = [1]*len(edit_concepts)
editing_args = dict(
editing_prompt = edit_concepts,
reverse_editing_direction = neg_guidance,
edit_warmup_steps=default_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=1, 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)
yield pure_ddpm_out,sega_out.images[0]
########
# demo #
########
intro = """
<h1 style="font-weight: 1400; text-align: center; margin-bottom: 7px;">
Edit Friendly DDPM X Semantic Guidance: Editing Real Images
</h1>
<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():
src_prompt = gr.Textbox(lines=1, label="Source Prompt", interactive=True)
tar_prompt = gr.Textbox(lines=1, label="Target Prompt", interactive=True)
edit_concept = gr.Textbox(lines=1, label="SEGA Edit Concepts", interactive=True)
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.Accordion("Advanced Options", open=False):
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.Slider(minimum=0, maximum=40, value=36, precision=0, label="Skip Steps", interactive=True)
tar_cfg_scale = gr.Slider(minimum=7, maximum=18,value=15, label=f"Guidance Scale", interactive=True)
flip = gr.Checkbox(label="Flip")
left = gr.Number(value=0, precision=0, label="Left Shift", interactive=True)
right = gr.Number(value=0, precision=0, label="Right Shift", interactive=True)
top = gr.Number(value=0, precision=0, label="Top Shift", interactive=True)
bottom = gr.Number(value=0, precision=0, label="Bottom Shift", interactive=True)
# edit
sega_edit_guidance = gr.Slider(value=10, label=f"SEGA Edit Guidance Scale", interactive=True)
# warm_up = gr.Number(value=1, 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,
flip,
left,
right,
top,
bottom
],
outputs=[ddpm_edited_image, sega_edited_image],
)
gr.Examples(
label='Examples',
examples=get_example(),
inputs=[input_image, src_prompt, tar_prompt, edit_concept, ddpm_edited_image, sega_edited_image],
outputs=[ddpm_edited_image, sega_edited_image])
demo.queue()
demo.launch(share=False)