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import torch
import sys
try:
import utils
from diffusion import create_diffusion
except:
sys.path.append(os.path.split(sys.path[0])[0])
import utils
import gradio as gr
from gradio.themes.utils import colors, fonts, sizes
import argparse
from omegaconf import OmegaConf
import os
from models import get_models
from diffusers.utils.import_utils import is_xformers_available
from vlogger.STEB.model_transform import tca_transform_model, ip_scale_set, ip_transform_model
from diffusers.models import AutoencoderKL
from models.clip import TextEmbedder
sys.path.append("..")
from datasets import video_transforms
from torchvision import transforms
from utils import mask_generation_before
from einops import rearrange
import torchvision
from PIL import Image
from transformers import CLIPVisionModelWithProjection, CLIPImageProcessor
from transformers.image_transforms import convert_to_rgb
import spaces
@spaces.GPU
def auto_inpainting(video_input, masked_video, mask, prompt, image, vae, text_encoder, image_encoder, diffusion, model, device, cfg_scale, img_cfg_scale, negative_prompt=""):
global use_fp16
image_prompt_embeds = None
if prompt is None:
prompt = ""
if image is not None:
clip_image = clip_image_processor(images=image, return_tensors="pt").pixel_values
clip_image_embeds = image_encoder(clip_image.to(device)).image_embeds
uncond_clip_image_embeds = torch.zeros_like(clip_image_embeds).to(device)
image_prompt_embeds = torch.cat([clip_image_embeds, uncond_clip_image_embeds], dim=0)
image_prompt_embeds = rearrange(image_prompt_embeds, '(b n) c -> b n c', b=2).contiguous()
model = ip_scale_set(model, img_cfg_scale)
if use_fp16:
image_prompt_embeds = image_prompt_embeds.to(dtype=torch.float16)
b, f, c, h, w = video_input.shape
latent_h = video_input.shape[-2] // 8
latent_w = video_input.shape[-1] // 8
if use_fp16:
z = torch.randn(1, 4, 16, latent_h, latent_w, dtype=torch.float16, device=device) # b,c,f,h,w
masked_video = masked_video.to(dtype=torch.float16)
mask = mask.to(dtype=torch.float16)
else:
z = torch.randn(1, 4, 16, latent_h, latent_w, device=device) # b,c,f,h,w
masked_video = rearrange(masked_video, 'b f c h w -> (b f) c h w').contiguous()
masked_video = vae.encode(masked_video).latent_dist.sample().mul_(0.18215)
masked_video = rearrange(masked_video, '(b f) c h w -> b c f h w', b=b).contiguous()
mask = torch.nn.functional.interpolate(mask[:,:,0,:], size=(latent_h, latent_w)).unsqueeze(1)
masked_video = torch.cat([masked_video] * 2)
mask = torch.cat([mask] * 2)
z = torch.cat([z] * 2)
prompt_all = [prompt] + [negative_prompt]
text_prompt = text_encoder(text_prompts=prompt_all, train=False)
model_kwargs = dict(encoder_hidden_states=text_prompt,
class_labels=None,
cfg_scale=cfg_scale,
use_fp16=use_fp16,
ip_hidden_states=image_prompt_embeds)
# Sample images:
samples = diffusion.ddim_sample_loop(
model.forward_with_cfg, z.shape, z, clip_denoised=False, model_kwargs=model_kwargs, progress=True, device=device, \
mask=mask, x_start=masked_video, use_concat=True
)
samples, _ = samples.chunk(2, dim=0) # [1, 4, 16, 32, 32]
if use_fp16:
samples = samples.to(dtype=torch.float16)
video_clip = samples[0].permute(1, 0, 2, 3).contiguous() # [16, 4, 32, 32]
video_clip = vae.decode(video_clip / 0.18215).sample # [16, 3, 256, 256]
return video_clip
@spaces.GPU
def auto_inpainting_temp_split(video_input, masked_video, mask, prompt, image, vae, text_encoder, image_encoder, diffusion, model, device, scfg_scale, tcfg_scale, img_cfg_scale, negative_prompt=""):
global use_fp16
image_prompt_embeds = None
if prompt is None:
prompt = ""
if image is not None:
clip_image = clip_image_processor(images=image, return_tensors="pt").pixel_values
clip_image_embeds = image_encoder(clip_image.to(device)).image_embeds
uncond_clip_image_embeds = torch.zeros_like(clip_image_embeds).to(device)
image_prompt_embeds = torch.cat([clip_image_embeds, clip_image_embeds, uncond_clip_image_embeds], dim=0)
image_prompt_embeds = rearrange(image_prompt_embeds, '(b n) c -> b n c', b=3).contiguous()
model = ip_scale_set(model, img_cfg_scale)
if use_fp16:
image_prompt_embeds = image_prompt_embeds.to(dtype=torch.float16)
b, f, c, h, w = video_input.shape
latent_h = video_input.shape[-2] // 8
latent_w = video_input.shape[-1] // 8
if use_fp16:
z = torch.randn(1, 4, 16, latent_h, latent_w, dtype=torch.float16, device=device) # b,c,f,h,w
masked_video = masked_video.to(dtype=torch.float16)
mask = mask.to(dtype=torch.float16)
else:
z = torch.randn(1, 4, 16, latent_h, latent_w, device=device) # b,c,f,h,w
masked_video = rearrange(masked_video, 'b f c h w -> (b f) c h w').contiguous()
masked_video = vae.encode(masked_video).latent_dist.sample().mul_(0.18215)
masked_video = rearrange(masked_video, '(b f) c h w -> b c f h w', b=b).contiguous()
mask = torch.nn.functional.interpolate(mask[:,:,0,:], size=(latent_h, latent_w)).unsqueeze(1)
masked_video = torch.cat([masked_video] * 3)
mask = torch.cat([mask] * 3)
z = torch.cat([z] * 3)
prompt_all = [prompt] + [prompt] + [negative_prompt]
prompt_temp = [prompt] + [""] + [""]
text_prompt = text_encoder(text_prompts=prompt_all, train=False)
temporal_text_prompt = text_encoder(text_prompts=prompt_temp, train=False)
model_kwargs = dict(encoder_hidden_states=text_prompt,
class_labels=None,
scfg_scale=scfg_scale,
tcfg_scale=tcfg_scale,
use_fp16=use_fp16,
ip_hidden_states=image_prompt_embeds,
encoder_temporal_hidden_states=temporal_text_prompt)
# Sample images:
samples = diffusion.ddim_sample_loop(
model.forward_with_cfg_temp_split, z.shape, z, clip_denoised=False, model_kwargs=model_kwargs, progress=True, device=device, \
mask=mask, x_start=masked_video, use_concat=True
)
samples, _ = samples.chunk(2, dim=0) # [1, 4, 16, 32, 32]
if use_fp16:
samples = samples.to(dtype=torch.float16)
video_clip = samples[0].permute(1, 0, 2, 3).contiguous() # [16, 4, 32, 32]
video_clip = vae.decode(video_clip / 0.18215).sample # [16, 3, 256, 256]
return video_clip
# ========================================
# Model Initialization
# ========================================
device = None
output_path = None
use_fp16 = False
model = None
vae = None
text_encoder = None
image_encoder = None
clip_image_processor = None
@spaces.GPU
def init_model():
global device
global output_path
global use_fp16
global model
global diffusion
global vae
global text_encoder
global image_encoder
global clip_image_processor
print('Initializing ShowMaker', flush=True)
parser = argparse.ArgumentParser()
parser.add_argument("--config", type=str, default="./configs/with_mask_ref_sample.yaml")
args = parser.parse_args()
args = OmegaConf.load(args.config)
device = "cuda" if torch.cuda.is_available() else "cpu"
output_path = args.save_path
# Load model:
latent_h = args.image_size[0] // 8
latent_w = args.image_size[1] // 8
args.image_h = args.image_size[0]
args.image_w = args.image_size[1]
args.latent_h = latent_h
args.latent_w = latent_w
print('loading model')
model = get_models(args).to(device)
model = tca_transform_model(model).to(device)
model = ip_transform_model(model).to(device)
if args.enable_xformers_memory_efficient_attention and device=="cuda":
if is_xformers_available():
model.enable_xformers_memory_efficient_attention()
print("xformer!")
else:
raise ValueError("xformers is not available. Make sure it is installed correctly")
ckpt_path = args.ckpt
state_dict = state_dict = torch.load(ckpt_path, map_location=lambda storage, loc: storage)['ema']
model.load_state_dict(state_dict)
print('loading succeed')
model.eval() # important!
pretrained_model_path = args.pretrained_model_path
vae = AutoencoderKL.from_pretrained(pretrained_model_path, subfolder="vae").to(device)
text_encoder = TextEmbedder(pretrained_model_path).to(device)
image_encoder = CLIPVisionModelWithProjection.from_pretrained(args.image_encoder_path).to(device)
clip_image_processor = CLIPImageProcessor()
if args.use_fp16:
print('Warnning: using half percision for inferencing!')
vae.to(dtype=torch.float16)
model.to(dtype=torch.float16)
text_encoder.to(dtype=torch.float16)
image_encoder.to(dtype=torch.float16)
use_fp16 = True
print('Initialization Finished')
init_model()
# ========================================
# Video Generation
# ========================================
@spaces.GPU
def video_generation(text, image, scfg_scale, tcfg_scale, img_cfg_scale, diffusion):
with torch.no_grad():
print("begin generation", flush=True)
transform_video = transforms.Compose([
video_transforms.ToTensorVideo(), # TCHW
video_transforms.ResizeVideo((320, 512)),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True)
])
video_frames = torch.zeros(16, 3, 320, 512, dtype=torch.uint8)
video_frames = transform_video(video_frames)
video_input = video_frames.to(device).unsqueeze(0) # b,f,c,h,w
mask = mask_generation_before("all", video_input.shape, video_input.dtype, device)
masked_video = video_input * (mask == 0)
if image is not None:
print(image.shape, flush=True)
# image = Image.open(image)
if scfg_scale == tcfg_scale:
video_clip = auto_inpainting(video_input, masked_video, mask, text, image, vae, text_encoder, image_encoder, diffusion, model, device, scfg_scale, img_cfg_scale)
else:
video_clip = auto_inpainting_temp_split(video_input, masked_video, mask, text, image, vae, text_encoder, image_encoder, diffusion, model, device, scfg_scale, tcfg_scale, img_cfg_scale)
video_clip = ((video_clip * 0.5 + 0.5) * 255).add_(0.5).clamp_(0, 255).to(dtype=torch.uint8).cpu().permute(0, 2, 3, 1)
video_path = os.path.join(output_path, 'video.mp4')
torchvision.io.write_video(video_path, video_clip, fps=8)
return video_path
# ========================================
# Video Prediction
# ========================================
@spaces.GPU
def video_prediction(text, image, scfg_scale, tcfg_scale, img_cfg_scale, preframe, diffusion):
with torch.no_grad():
print("begin generation", flush=True)
transform_video = transforms.Compose([
video_transforms.ToTensorVideo(), # TCHW
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True)
])
preframe = torch.as_tensor(convert_to_rgb(preframe)).unsqueeze(0)
zeros = torch.zeros_like(preframe)
video_frames = torch.cat([preframe] + [zeros] * 15, dim=0).permute(0, 3, 1, 2)
H_scale = 320 / video_frames.shape[2]
W_scale = 512 / video_frames.shape[3]
scale_ = H_scale
if W_scale < H_scale:
scale_ = W_scale
video_frames = torch.nn.functional.interpolate(video_frames, scale_factor=scale_, mode="bilinear", align_corners=False)
video_frames = transform_video(video_frames)
video_input = video_frames.to(device).unsqueeze(0) # b,f,c,h,w
mask = mask_generation_before("first1", video_input.shape, video_input.dtype, device)
masked_video = video_input * (mask == 0)
if image is not None:
print(image.shape, flush=True)
if scfg_scale == tcfg_scale:
video_clip = auto_inpainting(video_input, masked_video, mask, text, image, vae, text_encoder, image_encoder, diffusion, model, device, scfg_scale, img_cfg_scale)
else:
video_clip = auto_inpainting_temp_split(video_input, masked_video, mask, text, image, vae, text_encoder, image_encoder, diffusion, model, device, scfg_scale, tcfg_scale, img_cfg_scale)
video_clip = ((video_clip * 0.5 + 0.5) * 255).add_(0.5).clamp_(0, 255).to(dtype=torch.uint8).cpu().permute(0, 2, 3, 1)
video_path = os.path.join(output_path, 'video.mp4')
torchvision.io.write_video(video_path, video_clip, fps=8)
return video_path
# ========================================
# Judge Generation or Prediction
# ========================================
@spaces.GPU
def gen_or_pre(text_input, image_input, scfg_scale, tcfg_scale, img_cfg_scale, preframe_input, diffusion_step):
default_step = [25, 40, 50, 100, 125, 200, 250]
difference = [abs(item - diffusion_step) for item in default_step]
diffusion_step = default_step[difference.index(min(difference))]
diffusion = create_diffusion(str(diffusion_step))
if preframe_input is None:
return video_generation(text_input, image_input, scfg_scale, tcfg_scale, img_cfg_scale, diffusion)
else:
return video_prediction(text_input, image_input, scfg_scale, tcfg_scale, img_cfg_scale, preframe_input, diffusion)
with gr.Blocks() as demo:
with gr.Row():
with gr.Column(visible=True) as input_raws:
with gr.Row():
with gr.Column(scale=1.0):
text_input = gr.Textbox(show_label=True, interactive=True, label="Text prompt")
with gr.Row():
with gr.Column(scale=0.5):
image_input = gr.Image(show_label=True, interactive=True, label="Reference image")
with gr.Column(scale=0.5):
preframe_input = gr.Image(show_label=True, interactive=True, label="First frame")
with gr.Row():
with gr.Column(scale=1.0):
scfg_scale = gr.Slider(
minimum=1,
maximum=50,
value=8,
step=0.1,
interactive=True,
label="Spatial Text Guidence Scale",
)
# with gr.Row():
# with gr.Column(scale=1.0):
# tcfg_scale = gr.Slider(
# minimum=1,
# maximum=50,
# value=6.5,
# step=0.1,
# interactive=True,
# label="Temporal Text Guidence Scale",
# )
with gr.Row():
with gr.Column(scale=1.0):
img_cfg_scale = gr.Slider(
minimum=0,
maximum=1,
value=0.3,
step=0.005,
interactive=True,
label="Image Guidence Scale",
)
with gr.Row():
with gr.Column(scale=1.0):
diffusion_step = gr.Slider(
minimum=20,
maximum=250,
value=100,
step=1,
interactive=True,
label="Diffusion Step",
)
with gr.Row():
with gr.Column(scale=0.5, min_width=0):
run = gr.Button("πSend")
with gr.Column(scale=0.5, min_width=0):
clear = gr.Button("πClearοΈ")
with gr.Column(scale=0.5, visible=True) as video_upload:
output_video = gr.Video(interactive=False, include_audio=True, elem_id="θΎεΊηθ§ι’")
clear = gr.Button("Restart")
tcfg_scale = scfg_scale
run.click(gen_or_pre, [text_input, image_input, scfg_scale, tcfg_scale, img_cfg_scale, preframe_input, diffusion_step], [output_video])
demo.queue(max_size=12).launch()
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