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from argparse import Namespace
from glob import glob
import yaml
import os
import spaces
import gradio as gr
import torch
import torchvision
import safetensors
from diffusers import AutoencoderKL
from peft import get_peft_model, LoraConfig, set_peft_model_state_dict
from huggingface_hub import snapshot_download
pretrained_model_path = snapshot_download(repo_id="revp2024/revp-censorship")
with open(glob(os.path.join(pretrained_model_path, 'hparams.yml'), recursive=True)[0]) as f:
args = Namespace(**yaml.safe_load(f))
def prepare_model():
print('Loading model ...')
vae_lora_config = LoraConfig(
r=args.rank,
lora_alpha=args.rank,
init_lora_weights="gaussian",
target_modules=["conv", "conv1", "conv2",
"to_q", "to_k", "to_v", "to_out.0"],
)
vae = AutoencoderKL.from_pretrained(
args.pretrained_model_name_or_path, subfolder="vae"
)
vae = get_peft_model(vae, vae_lora_config)
lora_weights_path = os.path.join(pretrained_model_path, f"pytorch_lora_weights.safetensors")
state_dict = {}
with safetensors.torch.safe_open(lora_weights_path, framework="pt", device="cpu") as f:
for key in f.keys():
state_dict[key] = f.get_tensor(key)
set_peft_model_state_dict(vae, state_dict)
print('Done.')
return vae.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu')
@spaces.GPU
@torch.no_grad()
def add_censorship(input_image, mode, pixelation_block_size, blur_kernel_size, soft_edges, soft_edge_kernel_size):
background, layers, _ = input_image.values()
input_images = torch.from_numpy(background).permute(2, 0, 1)[None, :3] / 255
mask = torch.from_numpy(layers[0]).permute(2, 0, 1)[None, -1:] / 255
H, W = input_images.shape[-2:]
if H > 1024 or W > 1024:
H_t, W_t = H, W
if H > W:
H, W = 1024, int(1024 * W_t / H_t)
else:
H, W = int(1024 * H_t / W_t), 1024
H_q8 = (H // 8) * 8
W_q8 = (W // 8) * 8
input_images = torch.nn.functional.interpolate(input_images, (H_q8, W_q8), mode='bilinear')
mask = torch.nn.functional.interpolate(mask, (H_q8, W_q8))
if soft_edges:
mask = torchvision.transforms.functional.gaussian_blur(mask, soft_edge_kernel_size)[0][0]
input_images = input_images.to(vae.device)
if mode == 'Pixelation':
censored = torch.nn.functional.avg_pool2d(
input_images, pixelation_block_size)
censored = torch.nn.functional.interpolate(censored, input_images.shape[-2:])
elif mode == 'Gaussian blur':
censored = torchvision.transforms.functional.gaussian_blur(
input_images, blur_kernel_size)
elif mode == 'Black':
censored = torch.zeros_like(input_images)
else:
raise ValueError("censor_mode has to be either `pixelation' or `gaussian_blur'")
mask = mask.to(input_images.device)
censored_images = input_images * (1 - mask) + censored * mask
censored_images *= 255
input_images = input_images * 2 - 1
with vae.disable_adapter():
latents = vae.encode(input_images).latent_dist.mean
images = vae.decode(latents, return_dict=False)[0]
# denormalize
images = images / 2 + 0.5
images *= 255
residuals = (images - censored_images).clamp(-args.budget, args.budget)
images = (censored_images + residuals).clamp(0, 255).to(torch.uint8)
gr.Info("Try to donwload/copy the censored image to the `Remove censorsip' tab")
return images[0].permute(1, 2, 0).cpu().numpy()
@spaces.GPU
@torch.no_grad()
def remove_censorship(input_image, x1, y1, x2, y2):
background, layers, _ = input_image.values()
images = torch.from_numpy(background).permute(2, 0, 1)[None, :3] / 255
mask = torch.from_numpy(layers[0]).permute(2, 0, 1)[None, -1:] / 255
images = images * (1 - mask)
images = images[..., y1:y2, x1:x2]
latents = vae.encode((images * 2 - 1).to(vae.device)).latent_dist.mean
with vae.disable_adapter():
images = vae.decode(latents, return_dict=False)[0]
# denormalize
images = images / 2 + 0.5
images *= 255
images = images.clamp(0, 255).to(torch.uint8)
return images[0].permute(1, 2, 0).cpu().numpy()
# @@@@@@@ Start of the program @@@@@@@@
vae = prepare_model()
css = '''
.my-disabled {
background-color: #eee;
}
.my-disabled input {
background-color: #eee;
}
'''
with gr.Blocks(css=css) as demo:
gr.Markdown('# ReVP: Reversible Visual Processing with Latent Models')
with gr.Tab('Add censorship'):
with gr.Row():
with gr.Column():
input_image = gr.ImageEditor(brush=gr.Brush(default_size=100))
with gr.Accordion('Options', open=False) as options_accord:
mode = gr.Radio(label='Mode', choices=['Pixelation', 'Gaussian blur', 'Black'],
value='Pixelation', interactive=True)
pixelation_block_size = gr.Slider(label='Block size', minimum=10, maximum=40, value=25, step=1, interactive=True)
blur_kernel_size = gr.Slider(label='Blur kernel size', minimum=21, maximum=151, value=85, step=2, interactive=True, visible=False)
def change_mode(mode):
if mode == 'Gaussian blur':
return gr.Slider(visible=False), gr.Slider(visible=True), gr.Accordion(open=True)
elif mode == 'Pixelation':
return gr.Slider(visible=True), gr.Slider(visible=False), gr.Accordion(open=True)
elif mode == 'Black':
return gr.Slider(visible=False), gr.Slider(visible=False), gr.Accordion(open=True)
else:
raise NotImplementedError
mode.select(change_mode, mode, [pixelation_block_size, blur_kernel_size, options_accord])
with gr.Row(variant='panel'):
soft_edges = gr.Checkbox(label='Soft edges', value=True, interactive=True, scale=1)
soft_edge_kernel_size = gr.Slider(label='Soft edge kernel size', minimum=21, maximum=49, value=35, step=2, interactive=True, visible=True, scale=2)
def change_soft_edges(soft_edges):
return gr.Slider(visible=True if soft_edges else False), gr.Accordion(open=True)
soft_edges.change(change_soft_edges, soft_edges, [soft_edge_kernel_size, options_accord])
submit_btn = gr.Button('Submit')
output_image = gr.Image(label='Censored', show_download_button=True)
submit_btn.click(
fn=add_censorship,
inputs=[input_image, mode, pixelation_block_size, blur_kernel_size, soft_edges, soft_edge_kernel_size],
outputs=output_image
)
with gr.Tab('Remove censorship'):
with gr.Row():
with gr.Column():
input_image = gr.ImageEditor()
with gr.Accordion('Manual cropping', open=False):
with gr.Row():
with gr.Row():
x1 = gr.Number(value=0, label='x1')
y1 = gr.Number(value=0, label='y1')
with gr.Row():
x2_ = gr.Number(value=10000, label='x2', interactive=False, elem_classes='my-disabled')
y1_ = gr.Number(value=0, label='y1', interactive=False, elem_classes='my-disabled')
with gr.Row():
with gr.Row():
x1_ =gr.Number(value=0, label='x1', elem_classes='my-disabled')
y2_ = gr.Number(value=10000, label='y2', elem_classes='my-disabled')
with gr.Row():
x2 = gr.Number(value=10000, label='x2')
y2 = gr.Number(value=10000, label='y2')
submit_btn = gr.Button('Submit')
output_image = gr.Image(label='Uncensored')
submit_btn.click(
fn=remove_censorship,
inputs=[input_image, x1, y1, x2, y2],
outputs=output_image
)
# sync coordinate on changed
x1.change(lambda x : x, x1, x1_)
x2.change(lambda x : x, x2, x2_)
y1.change(lambda x : x, y1, y1_)
y2.change(lambda x : x, y2, y2_)
if __name__ == '__main__':
demo.queue(4)
demo.launch()
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