Spaces:
Runtime error
Runtime error
File size: 6,860 Bytes
413d4d0 |
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 |
import os
from huggingface_hub import hf_hub_download, snapshot_download
import torch
from videogen_hub import MODEL_PATH
class T2VTurbo():
def __init__(self, base_model="vc2", merged=True, device="cuda"):
"""
1. Download the pretrained model and put it inside MODEL_PATH
2. Create Pipeline
Args:
device: 'cuda' or 'cpu' the device to use the model
"""
from videogen_hub.pipelines.t2v_turbo.inference_vc2 import T2VTurboVC2Pipeline1
from videogen_hub.pipelines.t2v_turbo.inference_ms import T2VTurboMSPipeline1
self.config = {
"model": {
"target": "lvdm.models.ddpm3d.LatentDiffusion",
"params": {
"linear_start": 0.00085,
"linear_end": 0.012,
"num_timesteps_cond": 1,
"timesteps": 1000,
"first_stage_key": "video",
"cond_stage_key": "caption",
"cond_stage_trainable": False,
"conditioning_key": "crossattn",
"image_size": [320, 512],
"channels": 4,
"scale_by_std": False,
"scale_factor": 0.18215,
"use_ema": False,
"uncond_type": "empty_seq",
"use_scale": True,
"scale_b": 0.7,
"unet_config": {
"target": "lvdm.modules.networks.openaimodel3d.UNetModel",
"params": {
"in_channels": 4,
"out_channels": 4,
"model_channels": 320,
"attention_resolutions": [4, 2, 1],
"num_res_blocks": 2,
"channel_mult": [1, 2, 4, 4],
"num_head_channels": 64,
"transformer_depth": 1,
"context_dim": 1024,
"use_linear": True,
"use_checkpoint": True,
"temporal_conv": True,
"temporal_attention": True,
"temporal_selfatt_only": True,
"use_relative_position": False,
"use_causal_attention": False,
"temporal_length": 16,
"addition_attention": True,
"fps_cond": True
}
},
"first_stage_config": {
"target": "lvdm.models.autoencoder.AutoencoderKL",
"params": {
"embed_dim": 4,
"monitor": "val / rec_loss",
"ddconfig": {
"double_z": True,
"z_channels": 4,
"resolution": 512,
"in_channels": 3,
"out_ch": 3,
"ch": 128,
"ch_mult": [1, 2, 4, 4],
"num_res_blocks": 2,
"attn_resolutions": [],
"dropout": 0.0
},
"lossconfig": {
"target": "torch.nn.Identity"
}
}
},
"cond_stage_config": {
"target": "lvdm.modules.encoders.condition.FrozenOpenCLIPEmbedder",
"params": {
"freeze": True,
"layer": "penultimate"
}
}
}
}
}
if base_model == "vc2" and merged:
merged_model_path = hf_hub_download(repo_id="jiachenli-ucsb/T2V-Turbo-VC2-Merged",
filename="t2v_turbo_vc2.pt",
local_dir=os.path.join(MODEL_PATH, "T2V-Turbo-VC2"))
self.pipeline = T2VTurboVC2Pipeline1(self.config, merged, device, None, merged_model_path)
elif base_model == "vc2":
base_model_path = hf_hub_download(repo_id="VideoCrafter/VideoCrafter2",
filename="model.ckpt",
local_dir=os.path.join(MODEL_PATH, "videocrafter2"))
unet_lora_path = hf_hub_download(repo_id="jiachenli-ucsb/T2V-Turbo-VC2",
filename="unet_lora.pt",
local_dir=os.path.join(MODEL_PATH, "T2V-Turbo-VC2"))
# It uses the config provided above.
self.pipeline = T2VTurboVC2Pipeline1(self.config, merged, device, unet_lora_path, base_model_path)
else:
base_model_path = snapshot_download(repo_id="ali-vilab/text-to-video-ms-1.7b",
local_dir=os.path.join(MODEL_PATH, "modelscope_1.7b"))
unet_lora_path = hf_hub_download(repo_id="jiachenli-ucsb/T2V-Turbo-MS",
filename="unet_lora.pt",
local_dir=os.path.join(MODEL_PATH, "T2V-Turbo-MS"))
# It uses the config provided by base_model.
self.pipeline = T2VTurboMSPipeline1(device, unet_lora_path, base_model_path)
def infer_one_video(
self,
prompt: str = None,
size: list = [320, 512],
seconds: int = 2,
fps: int = 8,
seed: int = 42,
):
"""
Generates a single video based on the provided prompt and parameters.
The output is of shape [frames, channels, height, width].
Args:
prompt (str, optional): The text prompt to generate the video from. Defaults to None.
seconds (int, optional): The duration of the video in seconds. Defaults to 2.
fps (int, optional): The frames per second of the video. Defaults to 8.
seed (int, optional): The seed for random number generation. Defaults to 42.
Returns:
torch.Tensor: The generated video as a tensor.
"""
output = self.pipeline.inference(prompt=prompt, height=size[0], width=size[1],
seed=seed, num_frames=seconds * fps, fps=fps, randomize_seed=False)
# [channels, frames, height, width] -> [frames, channels, height, width]
output = output.squeeze().permute(1, 0, 2, 3)
return output.cpu()
|