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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, | |
cfg_scale_src = 3.5, | |
cfg_scale_tar = 15, | |
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=1.0 | |
prompts=[tar_prompt] | |
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 | |
yield 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) | |
cfg_scale_src = 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) | |
cfg_scale_tar = 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, | |
cfg_scale_src, | |
cfg_scale_tar, | |
skip, | |
seed, | |
left, | |
right, | |
top, | |
bottom | |
], | |
outputs = [inverted_image], | |
) | |
edit_button.click( | |
fn=edit, | |
inputs=[input_image, | |
src_prompt, | |
tar_prompt, | |
steps, | |
cfg_scale_src, | |
cfg_scale_tar, | |
skip, | |
seed, | |
left, | |
right, | |
top, | |
bottom | |
], | |
outputs=[output_image], | |
) | |
gr.Examples( | |
label='Examples', | |
examples=get_example(), | |
inputs=[input_image, src_prompt, tar_prompt, steps, | |
cfg_scale_tar, | |
skip, | |
cfg_scale_tar, | |
inverted_image, output_image | |
], | |
outputs=[inverted_image,output_image ], | |
# fn=edit, | |
# cache_examples=True | |
) | |
demo.queue() | |
demo.launch(share=False) |