Spaces:
Running
on
Zero
Running
on
Zero
File size: 17,330 Bytes
94bafa8 ed933a7 94bafa8 b1e71c1 94bafa8 4051d56 94bafa8 ed933a7 94bafa8 b1e71c1 94bafa8 b1e71c1 94bafa8 b1e71c1 94bafa8 b1e71c1 94bafa8 4051d56 94bafa8 b1e71c1 94bafa8 b1e71c1 94bafa8 b1e71c1 4051d56 b1e71c1 4051d56 94bafa8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 |
import gradio as gr
import os
import spaces
import torch
import argparse
import torchvision
from diffusers.schedulers import (DDIMScheduler, DDPMScheduler, PNDMScheduler,
EulerDiscreteScheduler, DPMSolverMultistepScheduler,
HeunDiscreteScheduler, EulerAncestralDiscreteScheduler,
DEISMultistepScheduler, KDPM2AncestralDiscreteScheduler)
from diffusers.schedulers.scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
from diffusers.models import AutoencoderKL, AutoencoderKLTemporalDecoder
from omegaconf import OmegaConf
from transformers import T5EncoderModel, T5Tokenizer, BitsAndBytesConfig
import os, sys
sys.path.append(os.path.split(sys.path[0])[0])
from sample.pipeline_latte import LattePipeline
from models import get_models
import imageio
from torchvision.utils import save_image
parser = argparse.ArgumentParser()
parser.add_argument("--config", type=str, default="./configs/t2x/t2v_sample.yaml")
args = parser.parse_args()
args = OmegaConf.load(args.config)
torch.set_grad_enabled(False)
device = "cuda" if torch.cuda.is_available() else "cpu"
transformer_model = get_models(args).to(device, dtype=torch.float16)
if args.enable_vae_temporal_decoder:
vae = AutoencoderKLTemporalDecoder.from_pretrained(args.pretrained_model_path, subfolder="vae_temporal_decoder", torch_dtype=torch.float16).to(device)
else:
vae = AutoencoderKL.from_pretrained(args.pretrained_model_path, subfolder="vae", torch_dtype=torch.float16).to(device)
tokenizer = T5Tokenizer.from_pretrained(args.pretrained_model_path, subfolder="tokenizer")
text_encoder = T5EncoderModel.from_pretrained(args.pretrained_model_path,
subfolder="text_encoder",
quantization_config=BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_compute_dtype=torch.float16),
device_map="auto",
)
# set eval mode
transformer_model.eval()
vae.eval()
text_encoder.eval()
@spaces.GPU
def gen_video(text_input, sample_method, scfg_scale, seed, height, width, video_length, diffusion_step):
torch.manual_seed(seed)
if sample_method == 'DDIM':
scheduler = DDIMScheduler.from_pretrained(args.pretrained_model_path,
subfolder="scheduler",
beta_start=args.beta_start,
beta_end=args.beta_end,
beta_schedule=args.beta_schedule,
variance_type=args.variance_type,
clip_sample=False)
elif sample_method == 'EulerDiscrete':
scheduler = EulerDiscreteScheduler.from_pretrained(args.pretrained_model_path,
subfolder="scheduler",
beta_start=args.beta_start,
beta_end=args.beta_end,
beta_schedule=args.beta_schedule,
variance_type=args.variance_type)
elif sample_method == 'DDPM':
scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_path,
subfolder="scheduler",
beta_start=args.beta_start,
beta_end=args.beta_end,
beta_schedule=args.beta_schedule,
variance_type=args.variance_type,
clip_sample=False)
elif sample_method == 'DPMSolverMultistep':
scheduler = DPMSolverMultistepScheduler.from_pretrained(args.pretrained_model_path,
subfolder="scheduler",
beta_start=args.beta_start,
beta_end=args.beta_end,
beta_schedule=args.beta_schedule,
variance_type=args.variance_type)
elif sample_method == 'DPMSolverSinglestep':
scheduler = DPMSolverSinglestepScheduler.from_pretrained(args.pretrained_model_path,
subfolder="scheduler",
beta_start=args.beta_start,
beta_end=args.beta_end,
beta_schedule=args.beta_schedule,
variance_type=args.variance_type)
elif sample_method == 'PNDM':
scheduler = PNDMScheduler.from_pretrained(args.pretrained_model_path,
subfolder="scheduler",
beta_start=args.beta_start,
beta_end=args.beta_end,
beta_schedule=args.beta_schedule,
variance_type=args.variance_type)
elif sample_method == 'HeunDiscrete':
scheduler = HeunDiscreteScheduler.from_pretrained(args.pretrained_model_path,
subfolder="scheduler",
beta_start=args.beta_start,
beta_end=args.beta_end,
beta_schedule=args.beta_schedule,
variance_type=args.variance_type)
elif sample_method == 'EulerAncestralDiscrete':
scheduler = EulerAncestralDiscreteScheduler.from_pretrained(args.pretrained_model_path,
subfolder="scheduler",
beta_start=args.beta_start,
beta_end=args.beta_end,
beta_schedule=args.beta_schedule,
variance_type=args.variance_type)
elif sample_method == 'DEISMultistep':
scheduler = DEISMultistepScheduler.from_pretrained(args.pretrained_model_path,
subfolder="scheduler",
beta_start=args.beta_start,
beta_end=args.beta_end,
beta_schedule=args.beta_schedule,
variance_type=args.variance_type)
elif sample_method == 'KDPM2AncestralDiscrete':
scheduler = KDPM2AncestralDiscreteScheduler.from_pretrained(args.pretrained_model_path,
subfolder="scheduler",
beta_start=args.beta_start,
beta_end=args.beta_end,
beta_schedule=args.beta_schedule,
variance_type=args.variance_type)
pipe_tmp = LattePipeline.from_pretrained(
args.pretrained_model_path,
transformer=None,
text_encoder=text_encoder,
device_map="balanced",)
prompt_embeds, negative_prompt_embeds = pipe_tmp.encode_prompt(text_input, negative_prompt="")
videogen_pipeline = LattePipeline(vae=vae,
# text_encoder=text_encoder,
text_encoder=None,
tokenizer=tokenizer,
scheduler=scheduler,
transformer=transformer_model).to(device)
# videogen_pipeline.enable_xformers_memory_efficient_attention()
videos = videogen_pipeline(
# text_input,
prompt_embeds=prompt_embeds,
negative_prompt=None,
negative_prompt_embeds=negative_prompt_embeds,
video_length=video_length,
height=height,
width=width,
num_inference_steps=diffusion_step,
guidance_scale=scfg_scale,
enable_temporal_attentions=args.enable_temporal_attentions,
num_images_per_prompt=1,
mask_feature=True,
enable_vae_temporal_decoder=args.enable_vae_temporal_decoder
).video
save_path = args.save_img_path + 'temp' + '.mp4'
# torchvision.io.write_video(save_path, videos[0], fps=8)
imageio.mimwrite(save_path, videos[0], fps=8, quality=7)
return save_path
if not os.path.exists(args.save_img_path):
os.makedirs(args.save_img_path)
intro = """
<div style="display: flex;align-items: center;justify-content: center">
<h1 style="display: inline-block;margin-left: 10px;margin-top: 6px;font-weight: 500">Latte: Latent Diffusion Transformer for Video Generation</h1>
</div>
"""
with gr.Blocks() as demo:
# gr.HTML(intro)
# with gr.Accordion("README", open=False):
# gr.HTML(
# """
# <p style="font-size: 0.95rem;margin: 0rem;line-height: 1.2em;margin-top:1em;display: inline-block">
# <a href="https://maxin-cn.github.io/latte_project/" target="_blank">project page</a> | <a href="https://arxiv.org/abs/2401.03048" target="_blank">paper</a>
# </p>
# We will continue update Latte.
# """
# )
gr.Markdown("<font color=red size=10><center>Latte: Latent Diffusion Transformer for Video Generation</center></font>")
gr.Markdown(
"""<div style="display: flex;align-items: center;justify-content: center">
<h2 style="display: inline-block;margin-left: 10px;margin-top: 6px;font-weight: 500">Latte supports both T2I and T2V, and will be continuously updated, so stay tuned!</h2></div>
"""
)
gr.Markdown(
"""<div style="display: flex;align-items: center;justify-content: center">
[<a href="https://arxiv.org/abs/2401.03048">Arxiv Report</a>] | [<a href="https://maxin-cn.github.io/latte_project/">Project Page</a>] | [<a href="https://github.com/Vchitect/Latte">Github</a>]</div>
"""
)
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="Prompt")
with gr.Row():
with gr.Column(scale=0.5):
sample_method = gr.Dropdown(choices=["DDIM", "EulerDiscrete", "PNDM"], label="Sample Method", value="DDIM")
with gr.Column(scale=0.5):
video_length = gr.Dropdown(choices=[1, 16], label="Video Length (1 for T2I and 16 for T2V)", value=16)
with gr.Row():
with gr.Column(scale=1.0):
scfg_scale = gr.Slider(
minimum=1,
maximum=50,
value=7.5,
step=0.1,
interactive=True,
label="Guidence Scale",
)
with gr.Row():
with gr.Column(scale=1.0):
seed = gr.Slider(
minimum=1,
maximum=2147483647,
value=100,
step=1,
interactive=True,
label="Seed",
)
with gr.Row():
with gr.Column(scale=0.5):
height = gr.Slider(
minimum=256,
maximum=768,
value=512,
step=16,
interactive=False,
label="Height",
)
# with gr.Row():
with gr.Column(scale=0.5):
width = gr.Slider(
minimum=256,
maximum=768,
value=512,
step=16,
interactive=False,
label="Width",
)
with gr.Row():
with gr.Column(scale=1.0):
diffusion_step = gr.Slider(
minimum=20,
maximum=250,
value=50,
step=1,
interactive=True,
label="Sampling Step",
)
with gr.Column(scale=0.6, visible=True) as video_upload:
output = gr.Video(interactive=False, include_audio=True, elem_id="输出的视频") #.style(height=360)
with gr.Row():
with gr.Column(scale=1.0, min_width=0):
run = gr.Button(value="Generate", variant='primary')
EXAMPLES = [
["3D animation of a small, round, fluffy creature with big, expressive eyes explores a vibrant, enchanted forest. The creature, a whimsical blend of a rabbit and a squirrel, has soft blue fur and a bushy, striped tail. It hops along a sparkling stream, its eyes wide with wonder. The forest is alive with magical elements: flowers that glow and change colors, trees with leaves in shades of purple and silver, and small floating lights that resemble fireflies. The creature stops to interact playfully with a group of tiny, fairy-like beings dancing around a mushroom ring. The creature looks up in awe at a large, glowing tree that seems to be the heart of the forest.", "DDIM", 7.5, 100, 512, 512, 16, 50],
["A grandmother with neatly combed grey hair stands behind a colorful birthday cake with numerous candles at a wood dining room table, expression is one of pure joy and happiness, with a happy glow in her eye. She leans forward and blows out the candles with a gentle puff, the cake has pink frosting and sprinkles and the candles cease to flicker, the grandmother wears a light blue blouse adorned with floral patterns, several happy friends and family sitting at the table can be seen celebrating, out of focus. The scene is beautifully captured, cinematic, showing a 3/4 view of the grandmother and the dining room. Warm color tones and soft lighting enhance the mood.", "DDIM", 7.5, 100, 512, 512, 16, 50],
["A wizard wearing a pointed hat and a blue robe with white stars casting a spell that shoots lightning from his hand and holding an old tome in his other hand.", "DDIM", 7.5, 100, 512, 512, 16, 50],
["A young man at his 20s is sitting on a piece of cloud in the sky, reading a book.", "DDIM", 7.5, 100, 512, 512, 16, 50],
["Cinematic trailer for a group of samoyed puppies learning to become chefs.", "DDIM", 7.5, 100, 512, 512, 16, 50],
["Drone view of waves crashing against the rugged cliffs along Big Sur’s garay point beach. The crashing blue waters create white-tipped waves, while the golden light of the setting sun illuminates the rocky shore. A small island with a lighthouse sits in the distance, and green shrubbery covers the cliff’s edge. The steep drop from the road down to the beach is a dramatic feat, with the cliff’s edges jutting out over the sea. This is a view that captures the raw beauty of the coast and the rugged landscape of the Pacific Coast Highway.", "DDIM", 7.5, 100, 512, 512, 16, 50],
["A cyborg koala dj in front of aturntable, in heavy raining futuristic tokyo rooftop cyberpunk night, sci-f, fantasy, intricate, neon light, soft light smooth, sharp focus, illustration.", "DDIM", 7.5, 100, 512, 512, 16, 50],
]
examples = gr.Examples(
examples = EXAMPLES,
fn = gen_video,
inputs=[text_input, sample_method, scfg_scale, seed, height, width, video_length, diffusion_step],
outputs=[output],
cache_examples=True,
# cache_examples="lazy",
)
run.click(gen_video, [text_input, sample_method, scfg_scale, seed, height, width, video_length, diffusion_step], [output])
demo.launch(debug=False, share=True)
|