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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=False, 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/gnochi_mirror.jpeg',
'',
'watercolor painting of a cat sitting next to a mirror',
100,
3.5,
36,
15,
'Examples/gnochi_mirror_reconstrcution.png',
'Examples/gnochi_mirror_watercolor_painting.png',
],]
return case
def edit(input_image,
src_prompt ="",
tar_prompt="",
steps=100,
src_cfg_scale = 3.5,
skip=36,
seed = 0,
left = 0,
right = 0,
top = 0,
bottom = 0
):
torch.manual_seed(seed)
# offsets=(0,0,0,0)
x0 = load_512(input_image, left,right, top, bottom, device)
# invert and retrieve noise maps and latent
wt, zs, wts = invert(x0 =x0 , prompt_src=src_prompt, num_diffusion_steps=steps, cfg_scale_src=src_cfg_scale)
#
xT=wts[skip]
etas=eta
prompts=[prompt_tar]
cfg_scales=[cfg_scale_tar]
prog_bar=False
zs=zs[skip:]
batch_size = len(prompts)
cfg_scales_tensor = torch.Tensor(cfg_scales).view(-1,1,1,1).to(sd_pipe.device)
text_embeddings = encode_text(model, prompts)
uncond_embedding = encode_text(model, [""] * batch_size)
if etas is None: etas = 0
if type(etas) in [int, float]: etas = [etas]*sd_pipe.scheduler.num_inference_steps
assert len(etas) == sd_pipe.scheduler.num_inference_steps
timesteps = sd_pipe.scheduler.timesteps.to(sd_pipe.device)
xt = xT.expand(batch_size, -1, -1, -1)
op = tqdm(timesteps[-zs.shape[0]:]) if prog_bar else timesteps[-zs.shape[0]:]
t_to_idx = {int(v):k for k,v in enumerate(timesteps[-zs.shape[0]:])}
for t in op:
idx = t_to_idx[int(t)]
## Unconditional embedding
with torch.no_grad():
uncond_out = sd_pipe.unet.forward(xt, timestep = t,
encoder_hidden_states = uncond_embedding)
## Conditional embedding
if prompts:
with torch.no_grad():
cond_out = sd_pipe.unet.forward(xt, timestep = t,
encoder_hidden_states = text_embeddings)
z = zs[idx] if not zs is None else None
z = z.expand(batch_size, -1, -1, -1)
if prompts:
## classifier free guidance
noise_pred = uncond_out.sample + cfg_scales_tensor * (cond_out.sample - uncond_out.sample)
else:
noise_pred = uncond_out.sample
# 2. compute less noisy image and set x_t -> x_t-1
xt = reverse_step(sd_pipe, noise_pred, t, xt, eta = etas[idx], variance_noise = z)
# interm denoised img
with autocast("cuda"), inference_mode():
x0_dec = sd_pipe.vae.decode(1 / 0.18215 * xt).sample
if x0_dec.dim()<4:
x0_dec = x0_dec[None,:,:,:]
interm_img = image_grid(x0_dec)
yield interm_img
return interm_img
# # 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
# output = sample(wt, zs, wts, prompt_tar=tar_prompt)
# return output
########
# 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")
with gr.Row():
input_image = gr.Image(label="Input Image", interactive=True)
input_image.style(height=512, width=512)
inverted_image = gr.Image(label=f"Reconstructed Image", interactive=False)
inverted_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("Invert")
with gr.Column(scale=1, min_width=100):
edit_button = gr.Button("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)
seed = gr.Number(value=0, precision=0, label="Seed", 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=edit,
inputs=[input_image,
src_prompt,
src_prompt,
steps,
src_cfg_scale,
skip,
seed,
left,
right,
top,
bottom
],
outputs = [inverted_image],
)
edit_button.click(
fn=edit,
inputs=[input_image,
src_prompt,
tar_prompt,
steps,
src_cfg_scale,
skip,
seed,
left,
right,
top,
bottom
],
outputs=[output_image],
)
gr.Examples(
label='Examples',
examples=get_example(),
inputs=[input_image, src_prompt, tar_prompt, steps,
src_cfg_scale,
skip,
tar_cfg_scale,
inverted_image, output_image
],
outputs=[inverted_image,output_image ],
# fn=edit,
# cache_examples=True
)
demo.queue()
demo.launch(share=False) |