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Running
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A10G
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 | |
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 = "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") | |
sem_pipe = SemanticStableDiffusionPipeline.from_pretrained(sd_model_id).to(device) | |
def get_example(): | |
case = [ | |
[ | |
'examples/source_a_cat_sitting_next_to_a_mirror.jpeg', | |
'a cat sitting next to a mirror', | |
'watercolor painting of a cat sitting next to a mirror', | |
100, | |
36, | |
15, | |
'+Schnauzer dog, -cat', | |
5.5, | |
1, | |
'examples/ddpm_watercolor_painting_a_cat_sitting_next_to_a_mirror.png', | |
'examples/ddpm_sega_watercolor_painting_a_cat_sitting_next_to_a_mirror_plus_dog_minus_cat.png' | |
], | |
[ | |
'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 | |
def invert_and_reconstruct( | |
input_image, | |
src_prompt ="", | |
tar_prompt="", | |
steps=100, | |
# src_cfg_scale, | |
skip=36, | |
tar_cfg_scale=15, | |
# neg_guidance=False, | |
seed =0 | |
): | |
offsets=(0,0,0,0) | |
torch.manual_seed(seed) | |
x0 = load_512(input_image, device=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) | |
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) | |
# inversion_map['latnets'] = latnets | |
# inversion_map['zs'] = zs | |
# inversion_map['wts'] = wts | |
return pure_ddpm_out | |
def edit(input_image, | |
src_prompt ="", | |
tar_prompt="", | |
steps=100, | |
# src_cfg_scale, | |
skip=36, | |
tar_cfg_scale=15, | |
edit_concept="", | |
sega_edit_guidance=10, | |
warm_up=None, | |
# neg_guidance=False, | |
seed =0, | |
): | |
torch.manual_seed(seed) | |
# if not bool(inversion_map): | |
# raise gr.Error("Must invert before editing") | |
x0 = load_512(input_image, device=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) | |
latnets = wts[skip].expand(1, -1, -1, -1) | |
# SEGA | |
# parse concepts and neg guidance | |
edit_concepts = edit_concept.split(",") | |
num_concepts = len(edit_concepts) | |
neg_guidance =[] | |
for edit_concept in edit_concepts: | |
edit_concept=edit_concept.strip(" ") | |
if edit_concept.startswith("-"): | |
neg_guidance.append(True) | |
else: | |
neg_guidance.append(False) | |
edit_concepts = [concept.strip("+|-") for concept in edit_concepts] | |
# parse warm-up steps | |
default_warm_up_steps = [1]*num_concepts | |
if warm_up: | |
digit_pattern = re.compile(r"^\d+$") | |
warm_up_steps_str = warm_up.split(",") | |
for i,num_steps in enumerate(warm_up_steps_str[:num_concepts]): | |
if not digit_pattern.match(num_steps): | |
raise gr.Error("Invalid value for warm-up steps, using 1 instead") | |
else: | |
default_warm_up_steps[i] = int(num_steps) | |
editing_args = dict( | |
editing_prompt = edit_concepts, | |
reverse_editing_direction = neg_guidance, | |
edit_warmup_steps=default_warm_up_steps, | |
edit_guidance_scale=[sega_edit_guidance]*num_concepts, | |
edit_threshold=[.95]*num_concepts, | |
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) | |
return sega_out.images[0] | |
######## | |
# demo # | |
######## | |
intro = """ | |
<h1 style="font-weight: 1400; text-align: center; margin-bottom: 7px;"> | |
Edit Friendly DDPM X Semantic Guidance | |
</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> X | |
<a href="https://arxiv.org/abs/2301.12247" style="text-decoration: underline;" target="_blank">SEGA: Instructing Diffusion using Semantic Dimensions</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: | |
gr.HTML(intro) | |
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(): | |
tar_prompt = gr.Textbox(lines=1, label="Target Prompt", interactive=True, placeholder="") | |
# edit_concept = gr.Textbox(lines=1, label="SEGA Edit Concepts", interactive=True, placeholder="optional: type a list of concepts to add/remove with SEGA\n (e.g. +dog,-cat,+oil painting)") | |
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 | |
src_prompt = gr.Textbox(lines=1, label="Source Prompt", interactive=True, placeholder="") | |
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) | |
seed = gr.Number(value=0, precision=0, label="Seed", interactive=True) | |
with gr.Column(): | |
sega_edit_guidance = gr.Slider(value=10, label=f"SEGA Edit Guidance Scale", interactive=True) | |
warm_up = gr.Textbox(label=f"SEGA Warm-up Steps", interactive=True, placeholder="type #warm-up steps for each concpets (e.g. 2,7,5...") | |
# neg_guidance = gr.Checkbox(label="SEGA Negative Guidance") | |
# gr.Markdown(help_text) | |
invert_button.click( | |
fn=invert_and_reconstruct, | |
inputs=[input_image, | |
src_prompt, | |
tar_prompt, | |
steps, | |
# src_cfg_scale, | |
skip, | |
tar_cfg_scale, | |
# neg_guidance, | |
seed | |
], | |
outputs=[ddpm_edited_image], | |
) | |
edit_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, | |
seed | |
], | |
outputs=[sega_edited_image], | |
) | |
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) | |