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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 torch import autocast, inference_mode
import re
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"
sd_model_id = "CompVis/stable-diffusion-v1-4"
# sd_model_id = "stabilityai/stable-diffusion-2-base"
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")
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',
100,
36,
15,
'+painting',
10,
1,
'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'
],
[
'examples/source_wall_with_framed_photos.jpeg',
'',
'',
100,
36,
15,
'+pink drawings of muffins',
10,
1,
'examples/ddpm_wall_with_framed_photos.png',
'examples/ddpm_sega_plus_pink_drawings_of_muffins.png'
],
[
'examples/source_an_empty_room_with_concrete_walls.jpg',
'an empty room with concrete walls',
'glass walls',
100,
36,
17,
'+giant elephant',
10,
1,
'examples/ddpm_glass_walls.png',
'examples/ddpm_sega_glass_walls_gian_elephant.png'
]]
return case
inversion_map = dict()
def invert(input_image,
src_prompt ="",
steps=100,
src_cfg_scale = 3.5,
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)
latnets = wts[skip].expand(1, -1, -1, -1)
inversion_map['latnets'] = latnets
inversion_map['zs'] = zs
inversion_map['wts'] = wts
return
def edit(tar_prompt="",
steps=100,
skip=36,
tar_cfg_scale=15,
):
outputs = []
num_generations = 1
for i in range(num_generations):
out = sample(wt, zs, wts, prompt_tar=tar_prompt,
cfg_scale_tar=tar_cfg_scale, skip=skip)
outputs.append(out)
return outputs
def reset():
inversion_map.clear()
########
# demo #
########
intro = """
<h1 style="font-weight: 1400; text-align: center; margin-bottom: 7px;">
Edit Friendly DDPM Inversion
</h1>
<p style="font-size: 0.9rem; text-align: center; margin: 0rem; line-height: 1.2em; margin-top:1em">
<a href="https://arxiv.org/abs/2301.12247" style="text-decoration: underline;" target="_blank">An Edit Friendly DDPM Noise Space:
Inversion and Manipulations </a>
<p/>
<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, placeholder="optional: describe the original image")
tar_prompt = gr.Textbox(lines=1, label="Target Prompt", interactive=True, placeholder="optional: describe the target image to edit with DDPM")
with gr.Row():
input_image = gr.Image(label="Input Image", interactive=True)
input_image.style(height=512, width=512)
output_image = gr.Image(label=f"Edited Image", interactive=False)
output_image.style(height=512, width=512)
with gr.Row():
with gr.Column(scale=1, min_width=100):
invert_button = gr.Button("Load & Invert")
with gr.Column(scale=1, min_width=100):
edit_button = gr.Button("Sample & Edit")
with gr.Accordion("Advanced Options", open=False):
with gr.Row():
with gr.Column():
#inversion
steps = gr.Number(value=100, precision=0, label="Num Diffusion Steps", interactive=True)
src_cfg_scale = gr.Slider(minimum=1, maximum=15, value=3.5, label=f"Source Guidance Scale", 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"Target Guidance Scale", interactive=True)
#shift
with gr.Column():
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)
# gr.Markdown(help_text)
invert_button.click(
fn=invert,
inputs=[input_image,
src_prompt,
steps,
src_cfg_scale,
left,
right,
top,
bottom
],
outputs = [],
)
edit_button.click(
fn=edit,
inputs=[tar_prompt,
steps,
skip,
tar_cfg_scale,
],
outputs=[output_image],
)
input_image.change(
fn = reset
)
# gr.Examples(
# label='Examples',
# examples=get_example(),
# inputs=[input_image, src_prompt, tar_prompt, steps,
# # src_cfg_scale,
# skip,
# tar_cfg_scale,
# edit_concept,
# sega_edit_guidance,
# warm_up,
# # neg_guidance,
# ddpm_edited_image, sega_edited_image
# ],
# outputs=[ddpm_edited_image, sega_edited_image],
# # fn=edit,
# # cache_examples=True
# )
demo.queue()
demo.launch(share=False) |