Spaces:
Runtime error
Runtime error
import gradio as gr | |
import torch | |
import random | |
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=False, num_inference_steps=num_diffusion_steps) | |
return zs, wts | |
def sample(zs, wts, prompt_tar="", skip=36, cfg_scale_tar=15, 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_watercolor_painting.png', | |
],] | |
return case | |
######## | |
# 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(css='style.css') as demo: | |
def reset_latents(): | |
wts = gr.State(value=False) | |
zs = gr.State(value=False) | |
return wts, zs | |
def edit(input_image, | |
wts, zs, | |
src_prompt ="", | |
tar_prompt="", | |
steps=100, | |
cfg_scale_src = 3.5, | |
cfg_scale_tar = 15, | |
skip=36, | |
seed = 0, | |
randomized_seed = True): | |
if randomized_seed: | |
seed = random.randint(0, np.iinfo(np.int32).max) | |
torch.manual_seed(seed) | |
# offsets=(0,0,0,0) | |
x0 = load_512(input_image, device=device) | |
if not wts: | |
# invert and retrieve noise maps and latent | |
zs_tensor, wts_tensor = invert(x0 =x0 , prompt_src=src_prompt, num_diffusion_steps=steps, cfg_scale_src=cfg_scale_src) | |
# xt = gr.State(value=wts[skip]) | |
# zs = gr.State(value=zs[skip:]) | |
wts = gr.State(value=wts_tensor) | |
zs = gr.State(value=zs_tensor) | |
# output = sample(zs.value, xt.value, prompt_tar=tar_prompt, cfg_scale_tar=cfg_scale_tar) | |
output = sample(zs.value, wts.value, prompt_tar=tar_prompt, skip=skip, cfg_scale_tar=cfg_scale_tar) | |
return output, wts, zs | |
gr.HTML(intro) | |
# xt = gr.State(value=False) | |
wts = gr.State(value=False) | |
zs = gr.State(value=False) | |
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(): | |
tar_prompt = gr.Textbox(lines=1, label="Describe your desired edited output", interactive=True) | |
with gr.Row(): | |
with gr.Column(scale=1, min_width=100): | |
edit_button = gr.Button("Run") | |
with gr.Accordion("Advanced Options", open=False): | |
with gr.Row(): | |
with gr.Column(): | |
#inversion | |
src_prompt = gr.Textbox(lines=1, label="Source Prompt", interactive=True, placeholder="describe the original image") | |
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) | |
with gr.Column(): | |
# 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) | |
randomize_seed = gr.Checkbox(label='Randomize seed', value=True) | |
edit_button.click( | |
fn=edit, | |
inputs=[input_image, | |
wts, zs, | |
src_prompt, | |
tar_prompt, | |
steps, | |
cfg_scale_src, | |
cfg_scale_tar, | |
skip, | |
seed, | |
randomize_seed | |
], | |
outputs=[output_image, wts, zs], | |
) | |
input_image.change( | |
fn = reset_latents, | |
outputs = [wts, zs] | |
) | |
src_prompt.change( | |
fn = reset_latents, | |
outputs = [wts, zs] | |
) | |
# skip.change( | |
# fn = reset_latents | |
# ) | |
gr.Examples( | |
label='Examples', | |
examples=get_example(), | |
inputs=[input_image, src_prompt, tar_prompt, steps, | |
cfg_scale_tar, | |
skip, | |
cfg_scale_tar, | |
output_image | |
], | |
outputs=[output_image ], | |
) | |
demo.queue() | |
demo.launch(share=False) |