code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
values |
|---|---|---|---|---|---|---|---|
def construct_meta_info(frames_dir_path: Path) -> dict:
"""
Construct meta information for a given frames directory.
Args:
frames_dir_path (Path): The path to the frames directory.
Returns:
dict: A dictionary containing the meta information for the frames directory, or None if the requ... |
Construct meta information for a given frames directory.
Args:
frames_dir_path (Path): The path to the frames directory.
Returns:
dict: A dictionary containing the meta information for the frames directory, or None if the required files do not exist.
| construct_meta_info | python | jdh-algo/JoyHallo | scripts/extract_meta_info_stage1.py | https://github.com/jdh-algo/JoyHallo/blob/master/scripts/extract_meta_info_stage1.py | MIT |
def main():
"""
Main function to extract meta info for training.
"""
parser = argparse.ArgumentParser()
parser.add_argument("-r", "--root_path", type=str,
required=True, help="Root path of the video directories")
parser.add_argument("-n", "--dataset_name", type=str,
... |
Main function to extract meta info for training.
| main | python | jdh-algo/JoyHallo | scripts/extract_meta_info_stage1.py | https://github.com/jdh-algo/JoyHallo/blob/master/scripts/extract_meta_info_stage1.py | MIT |
def extract_meta_info(video_path: str) -> dict:
"""
Extract meta information for a given video file.
Args:
video_path (str): The path to the video file.
Returns:
dict: A dictionary containing the meta information for the video.
"""
mask_path = construct_paths(
video_pat... |
Extract meta information for a given video file.
Args:
video_path (str): The path to the video file.
Returns:
dict: A dictionary containing the meta information for the video.
| extract_meta_info | python | jdh-algo/JoyHallo | scripts/extract_meta_info_stage2.py | https://github.com/jdh-algo/JoyHallo/blob/master/scripts/extract_meta_info_stage2.py | MIT |
def main():
"""
Main function to extract meta info for training.
"""
parser = argparse.ArgumentParser()
parser.add_argument("-r", "--root_path", type=str,
required=True, help="Root path of the video files")
parser.add_argument("-n", "--dataset_name", type=str,
... |
Main function to extract meta info for training.
| main | python | jdh-algo/JoyHallo | scripts/extract_meta_info_stage2.py | https://github.com/jdh-algo/JoyHallo/blob/master/scripts/extract_meta_info_stage2.py | MIT |
def forward(
self,
noisy_latents: torch.Tensor,
timesteps: torch.Tensor,
ref_image_latents: torch.Tensor,
face_emb: torch.Tensor,
audio_emb: torch.Tensor,
mask: torch.Tensor,
full_mask: torch.Tensor,
face_mask: torch.Tensor,
lip_mask: torch... |
simple docstring to prevent pylint error
| forward | python | jdh-algo/JoyHallo | scripts/inference.py | https://github.com/jdh-algo/JoyHallo/blob/master/scripts/inference.py | MIT |
def get_attention_mask(mask: torch.Tensor, weight_dtype: torch.dtype) -> torch.Tensor:
"""
Rearrange the mask tensors to the required format.
Args:
mask (torch.Tensor): The input mask tensor.
weight_dtype (torch.dtype): The data type for the mask tensor.
Returns:
torch.Tensor: ... |
Rearrange the mask tensors to the required format.
Args:
mask (torch.Tensor): The input mask tensor.
weight_dtype (torch.dtype): The data type for the mask tensor.
Returns:
torch.Tensor: The rearranged mask tensor.
| get_attention_mask | python | jdh-algo/JoyHallo | scripts/inference.py | https://github.com/jdh-algo/JoyHallo/blob/master/scripts/inference.py | MIT |
def get_noise_scheduler(cfg: argparse.Namespace) -> Tuple[DDIMScheduler, DDIMScheduler]:
"""
Create noise scheduler for training.
Args:
cfg (argparse.Namespace): Configuration object.
Returns:
Tuple[DDIMScheduler, DDIMScheduler]: Train noise scheduler and validation noise scheduler.
... |
Create noise scheduler for training.
Args:
cfg (argparse.Namespace): Configuration object.
Returns:
Tuple[DDIMScheduler, DDIMScheduler]: Train noise scheduler and validation noise scheduler.
| get_noise_scheduler | python | jdh-algo/JoyHallo | scripts/inference.py | https://github.com/jdh-algo/JoyHallo/blob/master/scripts/inference.py | MIT |
def process_audio_emb(audio_emb: torch.Tensor) -> torch.Tensor:
"""
Process the audio embedding to concatenate with other tensors.
Parameters:
audio_emb (torch.Tensor): The audio embedding tensor to process.
Returns:
concatenated_tensors (List[torch.Tensor]): The concatenated tensor li... |
Process the audio embedding to concatenate with other tensors.
Parameters:
audio_emb (torch.Tensor): The audio embedding tensor to process.
Returns:
concatenated_tensors (List[torch.Tensor]): The concatenated tensor list.
| process_audio_emb | python | jdh-algo/JoyHallo | scripts/inference.py | https://github.com/jdh-algo/JoyHallo/blob/master/scripts/inference.py | MIT |
def log_validation(
accelerator: Accelerator,
vae: AutoencoderKL,
net: Net,
scheduler: DDIMScheduler,
width: int,
height: int,
clip_length: int = 24,
generator: torch.Generator = None,
cfg: dict = None,
save_dir: str = None,
global_step: int = 0,
times: int = None,
fa... |
Log validation video during the training process.
Args:
accelerator (Accelerator): The accelerator for distributed training.
vae (AutoencoderKL): The autoencoder model.
net (Net): The main neural network model.
scheduler (DDIMScheduler): The scheduler for noise.
width (... | log_validation | python | jdh-algo/JoyHallo | scripts/inference.py | https://github.com/jdh-algo/JoyHallo/blob/master/scripts/inference.py | MIT |
def inference_process(cfg: argparse.Namespace) -> None:
"""
Trains the model using the given configuration (cfg).
Args:
cfg (dict): The configuration dictionary containing the parameters for training.
Notes:
- This function trains the model using the given configuration.
- It i... |
Trains the model using the given configuration (cfg).
Args:
cfg (dict): The configuration dictionary containing the parameters for training.
Notes:
- This function trains the model using the given configuration.
- It initializes the necessary components for training, such as the p... | inference_process | python | jdh-algo/JoyHallo | scripts/inference.py | https://github.com/jdh-algo/JoyHallo/blob/master/scripts/inference.py | MIT |
def load_config(config_path: str) -> dict:
"""
Loads the configuration file.
Args:
config_path (str): Path to the configuration file.
Returns:
dict: The configuration dictionary.
"""
if config_path.endswith(".yaml"):
return OmegaConf.load(config_path)
if config_pat... |
Loads the configuration file.
Args:
config_path (str): Path to the configuration file.
Returns:
dict: The configuration dictionary.
| load_config | python | jdh-algo/JoyHallo | scripts/inference.py | https://github.com/jdh-algo/JoyHallo/blob/master/scripts/inference.py | MIT |
def forward(
self,
noisy_latents,
timesteps,
ref_image_latents,
face_emb,
face_mask,
uncond_fwd: bool = False,
):
"""
Forward pass of the model.
Args:
self (Net): The model instance.
noisy_latents (torch.Tensor):... |
Forward pass of the model.
Args:
self (Net): The model instance.
noisy_latents (torch.Tensor): Noisy latents.
timesteps (torch.Tensor): Timesteps.
ref_image_latents (torch.Tensor): Reference image latents.
face_emb (torch.Tensor): Face embeddi... | forward | python | jdh-algo/JoyHallo | scripts/train_stage1_alltrain.py | https://github.com/jdh-algo/JoyHallo/blob/master/scripts/train_stage1_alltrain.py | MIT |
def get_noise_scheduler(cfg: argparse.Namespace):
"""
Create noise scheduler for training
Args:
cfg (omegaconf.dictconfig.DictConfig): Configuration object.
Returns:
train noise scheduler and val noise scheduler
"""
sched_kwargs = OmegaConf.to_container(cfg.noise_scheduler_kwar... |
Create noise scheduler for training
Args:
cfg (omegaconf.dictconfig.DictConfig): Configuration object.
Returns:
train noise scheduler and val noise scheduler
| get_noise_scheduler | python | jdh-algo/JoyHallo | scripts/train_stage1_alltrain.py | https://github.com/jdh-algo/JoyHallo/blob/master/scripts/train_stage1_alltrain.py | MIT |
def log_validation(
vae,
net,
scheduler,
accelerator,
width,
height,
imageproj,
cfg,
save_dir,
global_step,
face_analysis_model_path,
):
"""
Log validation generation image.
Args:
vae (nn.Module): Variational Autoencoder model.
net (Net): Main mod... |
Log validation generation image.
Args:
vae (nn.Module): Variational Autoencoder model.
net (Net): Main model.
scheduler (diffusers.SchedulerMixin): Noise scheduler.
accelerator (accelerate.Accelerator): Accelerator for training.
width (int): Width of the input images.
... | log_validation | python | jdh-algo/JoyHallo | scripts/train_stage1_alltrain.py | https://github.com/jdh-algo/JoyHallo/blob/master/scripts/train_stage1_alltrain.py | MIT |
def train_stage1_process(cfg: argparse.Namespace) -> None:
"""
Trains the model using the given configuration (cfg).
Args:
cfg (dict): The configuration dictionary containing the parameters for training.
Notes:
- This function trains the model using the given configuration.
- I... |
Trains the model using the given configuration (cfg).
Args:
cfg (dict): The configuration dictionary containing the parameters for training.
Notes:
- This function trains the model using the given configuration.
- It initializes the necessary components for training, such as the p... | train_stage1_process | python | jdh-algo/JoyHallo | scripts/train_stage1_alltrain.py | https://github.com/jdh-algo/JoyHallo/blob/master/scripts/train_stage1_alltrain.py | MIT |
def load_config(config_path: str) -> dict:
"""
Loads the configuration file.
Args:
config_path (str): Path to the configuration file.
Returns:
dict: The configuration dictionary.
"""
if config_path.endswith(".yaml"):
return OmegaConf.load(config_path)
if config_pat... |
Loads the configuration file.
Args:
config_path (str): Path to the configuration file.
Returns:
dict: The configuration dictionary.
| load_config | python | jdh-algo/JoyHallo | scripts/train_stage1_alltrain.py | https://github.com/jdh-algo/JoyHallo/blob/master/scripts/train_stage1_alltrain.py | MIT |
def forward(
self,
noisy_latents: torch.Tensor,
timesteps: torch.Tensor,
ref_image_latents: torch.Tensor,
face_emb: torch.Tensor,
audio_emb: torch.Tensor,
mask: torch.Tensor,
full_mask: torch.Tensor,
face_mask: torch.Tensor,
lip_mask: torch... |
simple docstring to prevent pylint error
| forward | python | jdh-algo/JoyHallo | scripts/train_stage2.py | https://github.com/jdh-algo/JoyHallo/blob/master/scripts/train_stage2.py | MIT |
def get_attention_mask(mask: torch.Tensor, weight_dtype: torch.dtype) -> torch.Tensor:
"""
Rearrange the mask tensors to the required format.
Args:
mask (torch.Tensor): The input mask tensor.
weight_dtype (torch.dtype): The data type for the mask tensor.
Returns:
torch.Tensor: ... |
Rearrange the mask tensors to the required format.
Args:
mask (torch.Tensor): The input mask tensor.
weight_dtype (torch.dtype): The data type for the mask tensor.
Returns:
torch.Tensor: The rearranged mask tensor.
| get_attention_mask | python | jdh-algo/JoyHallo | scripts/train_stage2.py | https://github.com/jdh-algo/JoyHallo/blob/master/scripts/train_stage2.py | MIT |
def get_noise_scheduler(cfg: argparse.Namespace) -> Tuple[DDIMScheduler, DDIMScheduler]:
"""
Create noise scheduler for training.
Args:
cfg (argparse.Namespace): Configuration object.
Returns:
Tuple[DDIMScheduler, DDIMScheduler]: Train noise scheduler and validation noise scheduler.
... |
Create noise scheduler for training.
Args:
cfg (argparse.Namespace): Configuration object.
Returns:
Tuple[DDIMScheduler, DDIMScheduler]: Train noise scheduler and validation noise scheduler.
| get_noise_scheduler | python | jdh-algo/JoyHallo | scripts/train_stage2.py | https://github.com/jdh-algo/JoyHallo/blob/master/scripts/train_stage2.py | MIT |
def process_audio_emb(audio_emb: torch.Tensor) -> torch.Tensor:
"""
Process the audio embedding to concatenate with other tensors.
Parameters:
audio_emb (torch.Tensor): The audio embedding tensor to process.
Returns:
concatenated_tensors (List[torch.Tensor]): The concatenated tensor li... |
Process the audio embedding to concatenate with other tensors.
Parameters:
audio_emb (torch.Tensor): The audio embedding tensor to process.
Returns:
concatenated_tensors (List[torch.Tensor]): The concatenated tensor list.
| process_audio_emb | python | jdh-algo/JoyHallo | scripts/train_stage2.py | https://github.com/jdh-algo/JoyHallo/blob/master/scripts/train_stage2.py | MIT |
def log_validation(
accelerator: Accelerator,
vae: AutoencoderKL,
net: Net,
scheduler: DDIMScheduler,
width: int,
height: int,
clip_length: int = 24,
generator: torch.Generator = None,
cfg: dict = None,
save_dir: str = None,
global_step: int = 0,
times: int = None,
fa... |
Log validation video during the training process.
Args:
accelerator (Accelerator): The accelerator for distributed training.
vae (AutoencoderKL): The autoencoder model.
net (Net): The main neural network model.
scheduler (DDIMScheduler): The scheduler for noise.
width (... | log_validation | python | jdh-algo/JoyHallo | scripts/train_stage2.py | https://github.com/jdh-algo/JoyHallo/blob/master/scripts/train_stage2.py | MIT |
def load_config(config_path: str) -> dict:
"""
Loads the configuration file.
Args:
config_path (str): Path to the configuration file.
Returns:
dict: The configuration dictionary.
"""
if config_path.endswith(".yaml"):
return OmegaConf.load(config_path)
if config_pat... |
Loads the configuration file.
Args:
config_path (str): Path to the configuration file.
Returns:
dict: The configuration dictionary.
| load_config | python | jdh-algo/JoyHallo | scripts/train_stage2.py | https://github.com/jdh-algo/JoyHallo/blob/master/scripts/train_stage2.py | MIT |
def forward(
self,
noisy_latents: torch.Tensor,
timesteps: torch.Tensor,
ref_image_latents: torch.Tensor,
face_emb: torch.Tensor,
audio_emb: torch.Tensor,
mask: torch.Tensor,
full_mask: torch.Tensor,
face_mask: torch.Tensor,
lip_mask: torch... |
simple docstring to prevent pylint error
| forward | python | jdh-algo/JoyHallo | scripts/train_stage2_alltrain.py | https://github.com/jdh-algo/JoyHallo/blob/master/scripts/train_stage2_alltrain.py | MIT |
def get_attention_mask(mask: torch.Tensor, weight_dtype: torch.dtype) -> torch.Tensor:
"""
Rearrange the mask tensors to the required format.
Args:
mask (torch.Tensor): The input mask tensor.
weight_dtype (torch.dtype): The data type for the mask tensor.
Returns:
torch.Tensor: ... |
Rearrange the mask tensors to the required format.
Args:
mask (torch.Tensor): The input mask tensor.
weight_dtype (torch.dtype): The data type for the mask tensor.
Returns:
torch.Tensor: The rearranged mask tensor.
| get_attention_mask | python | jdh-algo/JoyHallo | scripts/train_stage2_alltrain.py | https://github.com/jdh-algo/JoyHallo/blob/master/scripts/train_stage2_alltrain.py | MIT |
def get_noise_scheduler(cfg: argparse.Namespace) -> Tuple[DDIMScheduler, DDIMScheduler]:
"""
Create noise scheduler for training.
Args:
cfg (argparse.Namespace): Configuration object.
Returns:
Tuple[DDIMScheduler, DDIMScheduler]: Train noise scheduler and validation noise scheduler.
... |
Create noise scheduler for training.
Args:
cfg (argparse.Namespace): Configuration object.
Returns:
Tuple[DDIMScheduler, DDIMScheduler]: Train noise scheduler and validation noise scheduler.
| get_noise_scheduler | python | jdh-algo/JoyHallo | scripts/train_stage2_alltrain.py | https://github.com/jdh-algo/JoyHallo/blob/master/scripts/train_stage2_alltrain.py | MIT |
def process_audio_emb(audio_emb: torch.Tensor) -> torch.Tensor:
"""
Process the audio embedding to concatenate with other tensors.
Parameters:
audio_emb (torch.Tensor): The audio embedding tensor to process.
Returns:
concatenated_tensors (List[torch.Tensor]): The concatenated tensor li... |
Process the audio embedding to concatenate with other tensors.
Parameters:
audio_emb (torch.Tensor): The audio embedding tensor to process.
Returns:
concatenated_tensors (List[torch.Tensor]): The concatenated tensor list.
| process_audio_emb | python | jdh-algo/JoyHallo | scripts/train_stage2_alltrain.py | https://github.com/jdh-algo/JoyHallo/blob/master/scripts/train_stage2_alltrain.py | MIT |
def log_validation(
accelerator: Accelerator,
vae: AutoencoderKL,
net: Net,
scheduler: DDIMScheduler,
width: int,
height: int,
clip_length: int = 24,
generator: torch.Generator = None,
cfg: dict = None,
save_dir: str = None,
global_step: int = 0,
times: int = None,
fa... |
Log validation video during the training process.
Args:
accelerator (Accelerator): The accelerator for distributed training.
vae (AutoencoderKL): The autoencoder model.
net (Net): The main neural network model.
scheduler (DDIMScheduler): The scheduler for noise.
width (... | log_validation | python | jdh-algo/JoyHallo | scripts/train_stage2_alltrain.py | https://github.com/jdh-algo/JoyHallo/blob/master/scripts/train_stage2_alltrain.py | MIT |
def load_config(config_path: str) -> dict:
"""
Loads the configuration file.
Args:
config_path (str): Path to the configuration file.
Returns:
dict: The configuration dictionary.
"""
if config_path.endswith(".yaml"):
return OmegaConf.load(config_path)
if config_pat... |
Loads the configuration file.
Args:
config_path (str): Path to the configuration file.
Returns:
dict: The configuration dictionary.
| load_config | python | jdh-algo/JoyHallo | scripts/train_stage2_alltrain.py | https://github.com/jdh-algo/JoyHallo/blob/master/scripts/train_stage2_alltrain.py | MIT |
def preprocess(
self, video_path: Path | None, image_path: Path | None
) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Loads and preprocesses a video and an image.
If either path is None, no preprocessing will be done for that input.
Args:
video_path: Path to the vid... |
Loads and preprocesses a video and an image.
If either path is None, no preprocessing will be done for that input.
Args:
video_path: Path to the video file to load
image_path: Path to the image file to load
Returns:
A tuple containing:
... | preprocess | python | THUDM/CogVideo | finetune/datasets/i2v_dataset.py | https://github.com/THUDM/CogVideo/blob/master/finetune/datasets/i2v_dataset.py | Apache-2.0 |
def preprocess_image_with_resize(
image_path: Path | str,
height: int,
width: int,
) -> torch.Tensor:
"""
Loads and resizes a single image.
Args:
image_path: Path to the image file.
height: Target height for resizing.
width: Target width for resizing.
Returns:
... |
Loads and resizes a single image.
Args:
image_path: Path to the image file.
height: Target height for resizing.
width: Target width for resizing.
Returns:
torch.Tensor: Image tensor with shape [C, H, W] where:
C = number of channels (3 for RGB)
H = ... | preprocess_image_with_resize | python | THUDM/CogVideo | finetune/datasets/utils.py | https://github.com/THUDM/CogVideo/blob/master/finetune/datasets/utils.py | Apache-2.0 |
def preprocess_video_with_resize(
video_path: Path | str,
max_num_frames: int,
height: int,
width: int,
) -> torch.Tensor:
"""
Loads and resizes a single video.
The function processes the video through these steps:
1. If video frame count > max_num_frames, downsample frames evenly
... |
Loads and resizes a single video.
The function processes the video through these steps:
1. If video frame count > max_num_frames, downsample frames evenly
2. If video dimensions don't match (height, width), resize frames
Args:
video_path: Path to the video file.
max_num_frames... | preprocess_video_with_resize | python | THUDM/CogVideo | finetune/datasets/utils.py | https://github.com/THUDM/CogVideo/blob/master/finetune/datasets/utils.py | Apache-2.0 |
def preprocess_video_with_buckets(
video_path: Path,
resolution_buckets: List[Tuple[int, int, int]],
) -> torch.Tensor:
"""
Args:
video_path: Path to the video file.
resolution_buckets: List of tuples (num_frames, height, width) representing
available resolution buckets.
... |
Args:
video_path: Path to the video file.
resolution_buckets: List of tuples (num_frames, height, width) representing
available resolution buckets.
Returns:
torch.Tensor: Video tensor with shape [F, C, H, W] where:
F = number of frames
C = number of ... | preprocess_video_with_buckets | python | THUDM/CogVideo | finetune/datasets/utils.py | https://github.com/THUDM/CogVideo/blob/master/finetune/datasets/utils.py | Apache-2.0 |
def register(model_name: str, training_type: Literal["lora", "sft"], trainer_cls: Trainer):
"""Register a model and its associated functions for a specific training type.
Args:
model_name (str): Name of the model to register (e.g. "cogvideox-5b")
training_type (Literal["lora", "sft"]): Type of ... | Register a model and its associated functions for a specific training type.
Args:
model_name (str): Name of the model to register (e.g. "cogvideox-5b")
training_type (Literal["lora", "sft"]): Type of training - either "lora" or "sft"
trainer_cls (Trainer): Trainer class to register.
| register | python | THUDM/CogVideo | finetune/models/utils.py | https://github.com/THUDM/CogVideo/blob/master/finetune/models/utils.py | Apache-2.0 |
def validation_step(
self, eval_data: Dict[str, Any], pipe: CogVideoXImageToVideoPipeline
) -> List[Tuple[str, Image.Image | List[Image.Image]]]:
"""
Return the data that needs to be saved. For videos, the data format is List[PIL],
and for images, the data format is PIL
"""
... |
Return the data that needs to be saved. For videos, the data format is List[PIL],
and for images, the data format is PIL
| validation_step | python | THUDM/CogVideo | finetune/models/cogvideox_i2v/lora_trainer.py | https://github.com/THUDM/CogVideo/blob/master/finetune/models/cogvideox_i2v/lora_trainer.py | Apache-2.0 |
def validation_step(
self, eval_data: Dict[str, Any], pipe: CogVideoXPipeline
) -> List[Tuple[str, Image.Image | List[Image.Image]]]:
"""
Return the data that needs to be saved. For videos, the data format is List[PIL],
and for images, the data format is PIL
"""
promp... |
Return the data that needs to be saved. For videos, the data format is List[PIL],
and for images, the data format is PIL
| validation_step | python | THUDM/CogVideo | finetune/models/cogvideox_t2v/lora_trainer.py | https://github.com/THUDM/CogVideo/blob/master/finetune/models/cogvideox_t2v/lora_trainer.py | Apache-2.0 |
def parse_args(cls):
"""Parse command line arguments and return Args instance"""
parser = argparse.ArgumentParser()
# Required arguments
parser.add_argument("--model_path", type=str, required=True)
parser.add_argument("--model_name", type=str, required=True)
parser.add_ar... | Parse command line arguments and return Args instance | parse_args | python | THUDM/CogVideo | finetune/schemas/args.py | https://github.com/THUDM/CogVideo/blob/master/finetune/schemas/args.py | Apache-2.0 |
def cast_training_params(model: Union[torch.nn.Module, List[torch.nn.Module]], dtype=torch.float32):
"""
Casts the training parameters of the model to the specified data type.
Args:
model: The PyTorch model whose parameters will be cast.
dtype: The data type to which the model parameters wi... |
Casts the training parameters of the model to the specified data type.
Args:
model: The PyTorch model whose parameters will be cast.
dtype: The data type to which the model parameters will be cast.
| cast_training_params | python | THUDM/CogVideo | finetune/utils/torch_utils.py | https://github.com/THUDM/CogVideo/blob/master/finetune/utils/torch_utils.py | Apache-2.0 |
def generate_video(
prompt: str,
model_path: str,
output_path: str = "./output.mp4",
num_inference_steps: int = 50,
guidance_scale: float = 6.0,
num_videos_per_prompt: int = 1,
quantization_scheme: str = "fp8",
dtype: torch.dtype = torch.bfloat16,
num_frames: int = 81,
fps: int =... |
Generates a video based on the given prompt and saves it to the specified path.
Parameters:
- prompt (str): The description of the video to be generated.
- model_path (str): The path of the pre-trained model to be used.
- output_path (str): The path where the generated video will be saved.
- n... | generate_video | python | THUDM/CogVideo | inference/cli_demo_quantization.py | https://github.com/THUDM/CogVideo/blob/master/inference/cli_demo_quantization.py | Apache-2.0 |
def encode_video(model_path, video_path, dtype, device):
"""
Loads a pre-trained AutoencoderKLCogVideoX model and encodes the video frames.
Parameters:
- model_path (str): The path to the pre-trained model.
- video_path (str): The path to the video file.
- dtype (torch.dtype): The data type for... |
Loads a pre-trained AutoencoderKLCogVideoX model and encodes the video frames.
Parameters:
- model_path (str): The path to the pre-trained model.
- video_path (str): The path to the video file.
- dtype (torch.dtype): The data type for computation.
- device (str): The device to use for computat... | encode_video | python | THUDM/CogVideo | inference/cli_vae_demo.py | https://github.com/THUDM/CogVideo/blob/master/inference/cli_vae_demo.py | Apache-2.0 |
def decode_video(model_path, encoded_tensor_path, dtype, device):
"""
Loads a pre-trained AutoencoderKLCogVideoX model and decodes the encoded video frames.
Parameters:
- model_path (str): The path to the pre-trained model.
- encoded_tensor_path (str): The path to the encoded tensor file.
- dty... |
Loads a pre-trained AutoencoderKLCogVideoX model and decodes the encoded video frames.
Parameters:
- model_path (str): The path to the pre-trained model.
- encoded_tensor_path (str): The path to the encoded tensor file.
- dtype (torch.dtype): The data type for computation.
- device (str): The ... | decode_video | python | THUDM/CogVideo | inference/cli_vae_demo.py | https://github.com/THUDM/CogVideo/blob/master/inference/cli_vae_demo.py | Apache-2.0 |
def save_video(tensor, output_path):
"""
Saves the video frames to a video file.
Parameters:
- tensor (torch.Tensor): The video frames' tensor.
- output_path (str): The path to save the output video.
"""
tensor = tensor.to(dtype=torch.float32)
frames = tensor[0].squeeze(0).permute(1, 2,... |
Saves the video frames to a video file.
Parameters:
- tensor (torch.Tensor): The video frames' tensor.
- output_path (str): The path to save the output video.
| save_video | python | THUDM/CogVideo | inference/cli_vae_demo.py | https://github.com/THUDM/CogVideo/blob/master/inference/cli_vae_demo.py | Apache-2.0 |
def convert_prompt(prompt: str, retry_times: int = 3, type: str = "t2v", image_path: str = None):
"""
Convert a prompt to a format that can be used by the model for inference
"""
client = OpenAI()
## If you using with Azure OpenAI, please uncomment the below line and comment the above line
# cl... |
Convert a prompt to a format that can be used by the model for inference
| convert_prompt | python | THUDM/CogVideo | inference/convert_demo.py | https://github.com/THUDM/CogVideo/blob/master/inference/convert_demo.py | Apache-2.0 |
def read_video(
filename: str,
start_pts: Union[float, Fraction] = 0,
end_pts: Optional[Union[float, Fraction]] = None,
pts_unit: str = "pts",
output_format: str = "THWC",
) -> Tuple[torch.Tensor, torch.Tensor, Dict[str, Any]]:
"""
Reads a video from a file, returning both the video frames a... |
Reads a video from a file, returning both the video frames and the audio frames
Args:
filename (str): path to the video file
start_pts (int if pts_unit = 'pts', float / Fraction if pts_unit = 'sec', optional):
The start presentation time of the video
end_pts (int if pts_uni... | read_video | python | THUDM/CogVideo | sat/data_video.py | https://github.com/THUDM/CogVideo/blob/master/sat/data_video.py | Apache-2.0 |
def process_video(
video_path,
image_size=None,
duration=None,
num_frames=4,
wanted_fps=None,
actual_fps=None,
skip_frms_num=0.0,
nb_read_frames=None,
):
"""
video_path: str or io.BytesIO
image_size: .
duration: preknow the duration to speed up by seeking to sampled start... |
video_path: str or io.BytesIO
image_size: .
duration: preknow the duration to speed up by seeking to sampled start. TODO by_pass if unknown.
num_frames: wanted num_frames.
wanted_fps: .
skip_frms_num: ignore the first and the last xx frames, avoiding transitions.
| process_video | python | THUDM/CogVideo | sat/data_video.py | https://github.com/THUDM/CogVideo/blob/master/sat/data_video.py | Apache-2.0 |
def __init__(self, data_dir, video_size, fps, max_num_frames, skip_frms_num=3):
"""
skip_frms_num: ignore the first and the last xx frames, avoiding transitions.
"""
super(SFTDataset, self).__init__()
self.video_size = video_size
self.fps = fps
self.max_num_frame... |
skip_frms_num: ignore the first and the last xx frames, avoiding transitions.
| __init__ | python | THUDM/CogVideo | sat/data_video.py | https://github.com/THUDM/CogVideo/blob/master/sat/data_video.py | Apache-2.0 |
def log_conditionings(self, batch: Dict, n: int) -> Dict:
"""
Defines heuristics to log different conditionings.
These can be lists of strings (text-to-image), tensors, ints, ...
"""
image_h, image_w = batch[self.input_key].shape[3:]
log = dict()
for embedder in ... |
Defines heuristics to log different conditionings.
These can be lists of strings (text-to-image), tensors, ints, ...
| log_conditionings | python | THUDM/CogVideo | sat/diffusion_video.py | https://github.com/THUDM/CogVideo/blob/master/sat/diffusion_video.py | Apache-2.0 |
def get_3d_sincos_pos_embed(
embed_dim,
grid_height,
grid_width,
t_size,
cls_token=False,
height_interpolation=1.0,
width_interpolation=1.0,
time_interpolation=1.0,
):
"""
grid_size: int of the grid height and width
t_size: int of the temporal size
return:
pos_embed: ... |
grid_size: int of the grid height and width
t_size: int of the temporal size
return:
pos_embed: [t_size*grid_size * grid_size, embed_dim] or [1+t_size*grid_size * grid_size, embed_dim]
(w/ or w/o cls_token)
| get_3d_sincos_pos_embed | python | THUDM/CogVideo | sat/dit_video_concat.py | https://github.com/THUDM/CogVideo/blob/master/sat/dit_video_concat.py | Apache-2.0 |
def get_2d_sincos_pos_embed(embed_dim, grid_height, grid_width, cls_token=False, extra_tokens=0):
"""
grid_size: int of the grid height and width
return:
pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
"""
grid_h = np.arange(grid_height, dt... |
grid_size: int of the grid height and width
return:
pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
| get_2d_sincos_pos_embed | python | THUDM/CogVideo | sat/dit_video_concat.py | https://github.com/THUDM/CogVideo/blob/master/sat/dit_video_concat.py | Apache-2.0 |
def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
"""
embed_dim: output dimension for each position
pos: a list of positions to be encoded: size (M,)
out: (M, D)
"""
assert embed_dim % 2 == 0
omega = np.arange(embed_dim // 2, dtype=np.float64)
omega /= embed_dim / 2.0
omega = 1.... |
embed_dim: output dimension for each position
pos: a list of positions to be encoded: size (M,)
out: (M, D)
| get_1d_sincos_pos_embed_from_grid | python | THUDM/CogVideo | sat/dit_video_concat.py | https://github.com/THUDM/CogVideo/blob/master/sat/dit_video_concat.py | Apache-2.0 |
def is_power_of_two(n):
"""
chat.openai.com/chat
Return True if n is a power of 2, otherwise return False.
The function is_power_of_two takes an integer n as input and returns True if n is a power of 2, otherwise it returns False.
The function works by first checking if n is less than or equal to 0... |
chat.openai.com/chat
Return True if n is a power of 2, otherwise return False.
The function is_power_of_two takes an integer n as input and returns True if n is a power of 2, otherwise it returns False.
The function works by first checking if n is less than or equal to 0. If n is less than or equal to... | is_power_of_two | python | THUDM/CogVideo | sat/sgm/util.py | https://github.com/THUDM/CogVideo/blob/master/sat/sgm/util.py | Apache-2.0 |
def append_dims(x, target_dims):
"""Appends dimensions to the end of a tensor until it has target_dims dimensions."""
dims_to_append = target_dims - x.ndim
if dims_to_append < 0:
raise ValueError(f"input has {x.ndim} dims but target_dims is {target_dims}, which is less")
return x[(...,) + (None,... | Appends dimensions to the end of a tensor until it has target_dims dimensions. | append_dims | python | THUDM/CogVideo | sat/sgm/util.py | https://github.com/THUDM/CogVideo/blob/master/sat/sgm/util.py | Apache-2.0 |
def get_configs_path() -> str:
"""
Get the `configs` directory.
For a working copy, this is the one in the root of the repository,
but for an installed copy, it's in the `sgm` package (see pyproject.toml).
"""
this_dir = os.path.dirname(__file__)
candidates = (
os.path.join(this_dir,... |
Get the `configs` directory.
For a working copy, this is the one in the root of the repository,
but for an installed copy, it's in the `sgm` package (see pyproject.toml).
| get_configs_path | python | THUDM/CogVideo | sat/sgm/util.py | https://github.com/THUDM/CogVideo/blob/master/sat/sgm/util.py | Apache-2.0 |
def get_nested_attribute(obj, attribute_path, depth=None, return_key=False):
"""
Will return the result of a recursive get attribute call.
E.g.:
a.b.c
= getattr(getattr(a, "b"), "c")
= get_nested_attribute(a, "b.c")
If any part of the attribute call is an integer x with current o... |
Will return the result of a recursive get attribute call.
E.g.:
a.b.c
= getattr(getattr(a, "b"), "c")
= get_nested_attribute(a, "b.c")
If any part of the attribute call is an integer x with current obj a, will
try to call a[x] instead of a.x first.
| get_nested_attribute | python | THUDM/CogVideo | sat/sgm/util.py | https://github.com/THUDM/CogVideo/blob/master/sat/sgm/util.py | Apache-2.0 |
def pytorch_worker_info(group=None): # sourcery skip: use-contextlib-suppress
"""Return node and worker info for PyTorch and some distributed environments."""
rank = 0
world_size = 1
worker = 0
num_workers = 1
try:
import torch.distributed
if torch.distributed.is_available() an... | Return node and worker info for PyTorch and some distributed environments. | pytorch_worker_info | python | THUDM/CogVideo | sat/sgm/webds.py | https://github.com/THUDM/CogVideo/blob/master/sat/sgm/webds.py | Apache-2.0 |
def pytorch_worker_seed(group=None):
"""Compute a distinct, deterministic RNG seed for each worker and node."""
rank, world_size, worker, num_workers = pytorch_worker_info(group=group)
return rank * 1000 + worker | Compute a distinct, deterministic RNG seed for each worker and node. | pytorch_worker_seed | python | THUDM/CogVideo | sat/sgm/webds.py | https://github.com/THUDM/CogVideo/blob/master/sat/sgm/webds.py | Apache-2.0 |
def tar_file_iterator_with_meta(
fileobj,
meta_names,
skip_meta=r"__[^/]*__($|/)",
suffix=None,
handler=reraise_exception,
meta_stream=None,
):
"""Iterate over tar file, yielding filename, content pairs for the given tar stream.
:param fileobj: byte stream suitable for tarfile
:para... | Iterate over tar file, yielding filename, content pairs for the given tar stream.
:param fileobj: byte stream suitable for tarfile
:param meta_names: key of different items in meta file
:param skip_meta: regexp for keys that are skipped entirely (Default value = r"__[^/]*__($|/)")
| tar_file_iterator_with_meta | python | THUDM/CogVideo | sat/sgm/webds.py | https://github.com/THUDM/CogVideo/blob/master/sat/sgm/webds.py | Apache-2.0 |
def tar_file_expander_with_meta(data, meta_names, handler=reraise_exception):
"""Expand a stream of open tar files into a stream of tar file contents.
This returns an iterator over (filename, file_contents).
"""
for source in data:
url = source["url"]
try:
assert isinstance(... | Expand a stream of open tar files into a stream of tar file contents.
This returns an iterator over (filename, file_contents).
| tar_file_expander_with_meta | python | THUDM/CogVideo | sat/sgm/webds.py | https://github.com/THUDM/CogVideo/blob/master/sat/sgm/webds.py | Apache-2.0 |
def url_opener(
data,
handler,
**kw,
):
"""Open URLs and yield a stream of url+stream pairs.
Args:
data: iterator over dict(url=...)
handler: exception handler.
kw: keyword arguments for gopen.gopen.
Yields:
a stream of url+stream pairs.
"""
for sample i... | Open URLs and yield a stream of url+stream pairs.
Args:
data: iterator over dict(url=...)
handler: exception handler.
kw: keyword arguments for gopen.gopen.
Yields:
a stream of url+stream pairs.
| url_opener | python | THUDM/CogVideo | sat/sgm/webds.py | https://github.com/THUDM/CogVideo/blob/master/sat/sgm/webds.py | Apache-2.0 |
def gopen_rclone(url, mode="rb", bufsize=1024 * 1024 * 32):
"""Open a URL with `curl`.
:param url: rclone url, e.g. data:bucket1/foo.tar. data should be configured.
:param mode: file mode
:param bufsize: buffer size
"""
url = url.replace("rclone://", "")
if mode[0] == "r":
cmd = f"r... | Open a URL with `curl`.
:param url: rclone url, e.g. data:bucket1/foo.tar. data should be configured.
:param mode: file mode
:param bufsize: buffer size
| gopen_rclone | python | THUDM/CogVideo | sat/sgm/webds.py | https://github.com/THUDM/CogVideo/blob/master/sat/sgm/webds.py | Apache-2.0 |
def gopen_boto3(url, mode="rb", bufsize=8192 * 2):
"""Open a URL with boto3 API.
:param url: boto3 url, e.g. boto3://bucket1/foo.tar. data should be configured.
:param mode: file mode
:param bufsize: buffer size
"""
import boto3
# boto3.set_stream_logger('botocore', level='DEBUG')
if u... | Open a URL with boto3 API.
:param url: boto3 url, e.g. boto3://bucket1/foo.tar. data should be configured.
:param mode: file mode
:param bufsize: buffer size
| gopen_boto3 | python | THUDM/CogVideo | sat/sgm/webds.py | https://github.com/THUDM/CogVideo/blob/master/sat/sgm/webds.py | Apache-2.0 |
def zero_module(module):
"""
Zero out the parameters of a module and return it.
"""
for p in module.parameters():
p.detach().zero_()
return module |
Zero out the parameters of a module and return it.
| zero_module | python | THUDM/CogVideo | sat/sgm/modules/attention.py | https://github.com/THUDM/CogVideo/blob/master/sat/sgm/modules/attention.py | Apache-2.0 |
def get_timestep_embedding(timesteps, embedding_dim):
"""
This matches the implementation in Denoising Diffusion Probabilistic Models:
From Fairseq.
Build sinusoidal embeddings.
This matches the implementation in tensor2tensor, but differs slightly
from the description in Section 3.5 of "Attenti... |
This matches the implementation in Denoising Diffusion Probabilistic Models:
From Fairseq.
Build sinusoidal embeddings.
This matches the implementation in tensor2tensor, but differs slightly
from the description in Section 3.5 of "Attention Is All You Need".
| get_timestep_embedding | python | THUDM/CogVideo | sat/sgm/modules/cp_enc_dec.py | https://github.com/THUDM/CogVideo/blob/master/sat/sgm/modules/cp_enc_dec.py | Apache-2.0 |
def forward(self, fmap, cond: Tensor):
"""
notation
b - batch
n - convs
o - output
i - input
k - kernel
"""
b = fmap.shape[0]
# prepare weights for modulation
weights = self.weights
# do the modulation, demodulation, as... |
notation
b - batch
n - convs
o - output
i - input
k - kernel
| forward | python | THUDM/CogVideo | sat/sgm/modules/autoencoding/magvit2_pytorch.py | https://github.com/THUDM/CogVideo/blob/master/sat/sgm/modules/autoencoding/magvit2_pytorch.py | Apache-2.0 |
def __init__(self, input_nc=3, ndf=64, n_layers=3, use_actnorm=False):
"""Construct a PatchGAN discriminator
Parameters:
input_nc (int) -- the number of channels in input images
ndf (int) -- the number of filters in the last conv layer
n_layers (int) -- the nu... | Construct a PatchGAN discriminator
Parameters:
input_nc (int) -- the number of channels in input images
ndf (int) -- the number of filters in the last conv layer
n_layers (int) -- the number of conv layers in the discriminator
norm_layer -- normalizat... | __init__ | python | THUDM/CogVideo | sat/sgm/modules/autoencoding/lpips/model/model.py | https://github.com/THUDM/CogVideo/blob/master/sat/sgm/modules/autoencoding/lpips/model/model.py | Apache-2.0 |
def quantize(self, z: Tensor) -> Tensor:
"""Quantizes z, returns quantized zhat, same shape as z."""
quantized = round_ste(self.bound(z))
half_width = self._levels // 2 # Renormalize to [-1, 1].
return quantized / half_width | Quantizes z, returns quantized zhat, same shape as z. | quantize | python | THUDM/CogVideo | sat/sgm/modules/autoencoding/regularizers/finite_scalar_quantization.py | https://github.com/THUDM/CogVideo/blob/master/sat/sgm/modules/autoencoding/regularizers/finite_scalar_quantization.py | Apache-2.0 |
def codes_to_indices(self, zhat: Tensor) -> Tensor:
"""Converts a `code` to an index in the codebook."""
assert zhat.shape[-1] == self.codebook_dim
zhat = self._scale_and_shift(zhat)
return (zhat * self._basis).sum(dim=-1).to(int32) | Converts a `code` to an index in the codebook. | codes_to_indices | python | THUDM/CogVideo | sat/sgm/modules/autoencoding/regularizers/finite_scalar_quantization.py | https://github.com/THUDM/CogVideo/blob/master/sat/sgm/modules/autoencoding/regularizers/finite_scalar_quantization.py | Apache-2.0 |
def forward(self, z: Tensor) -> Tensor:
"""
einstein notation
b - batch
n - sequence (or flattened spatial dimensions)
d - feature dimension
c - number of codebook dim
"""
is_img_or_video = z.ndim >= 4
# standardize image or video into (batch, se... |
einstein notation
b - batch
n - sequence (or flattened spatial dimensions)
d - feature dimension
c - number of codebook dim
| forward | python | THUDM/CogVideo | sat/sgm/modules/autoencoding/regularizers/finite_scalar_quantization.py | https://github.com/THUDM/CogVideo/blob/master/sat/sgm/modules/autoencoding/regularizers/finite_scalar_quantization.py | Apache-2.0 |
def forward(
self,
x,
inv_temperature=100.0,
return_loss_breakdown=False,
mask=None,
):
"""
einstein notation
b - batch
n - sequence (or flattened spatial dimensions)
d - feature dimension, which is also log2(codebook size)
c - ... |
einstein notation
b - batch
n - sequence (or flattened spatial dimensions)
d - feature dimension, which is also log2(codebook size)
c - number of codebook dim
| forward | python | THUDM/CogVideo | sat/sgm/modules/autoencoding/regularizers/lookup_free_quantization.py | https://github.com/THUDM/CogVideo/blob/master/sat/sgm/modules/autoencoding/regularizers/lookup_free_quantization.py | Apache-2.0 |
def get_timestep_embedding(timesteps, embedding_dim):
"""
This matches the implementation in Denoising Diffusion Probabilistic Models:
From Fairseq.
Build sinusoidal embeddings.
This matches the implementation in tensor2tensor, but differs slightly
from the description in Section 3.5 of "Attenti... |
This matches the implementation in Denoising Diffusion Probabilistic Models:
From Fairseq.
Build sinusoidal embeddings.
This matches the implementation in tensor2tensor, but differs slightly
from the description in Section 3.5 of "Attention Is All You Need".
| get_timestep_embedding | python | THUDM/CogVideo | sat/sgm/modules/autoencoding/vqvae/movq_dec_3d.py | https://github.com/THUDM/CogVideo/blob/master/sat/sgm/modules/autoencoding/vqvae/movq_dec_3d.py | Apache-2.0 |
def get_timestep_embedding(timesteps, embedding_dim):
"""
This matches the implementation in Denoising Diffusion Probabilistic Models:
From Fairseq.
Build sinusoidal embeddings.
This matches the implementation in tensor2tensor, but differs slightly
from the description in Section 3.5 of "Attenti... |
This matches the implementation in Denoising Diffusion Probabilistic Models:
From Fairseq.
Build sinusoidal embeddings.
This matches the implementation in tensor2tensor, but differs slightly
from the description in Section 3.5 of "Attention Is All You Need".
| get_timestep_embedding | python | THUDM/CogVideo | sat/sgm/modules/autoencoding/vqvae/movq_dec_3d_dev.py | https://github.com/THUDM/CogVideo/blob/master/sat/sgm/modules/autoencoding/vqvae/movq_dec_3d_dev.py | Apache-2.0 |
def get_timestep_embedding(timesteps, embedding_dim):
"""
This matches the implementation in Denoising Diffusion Probabilistic Models:
From Fairseq.
Build sinusoidal embeddings.
This matches the implementation in tensor2tensor, but differs slightly
from the description in Section 3.5 of "Attenti... |
This matches the implementation in Denoising Diffusion Probabilistic Models:
From Fairseq.
Build sinusoidal embeddings.
This matches the implementation in tensor2tensor, but differs slightly
from the description in Section 3.5 of "Attention Is All You Need".
| get_timestep_embedding | python | THUDM/CogVideo | sat/sgm/modules/autoencoding/vqvae/movq_enc_3d.py | https://github.com/THUDM/CogVideo/blob/master/sat/sgm/modules/autoencoding/vqvae/movq_enc_3d.py | Apache-2.0 |
def get_timestep_embedding(timesteps, embedding_dim):
"""
This matches the implementation in Denoising Diffusion Probabilistic Models:
From Fairseq.
Build sinusoidal embeddings.
This matches the implementation in tensor2tensor, but differs slightly
from the description in Section 3.5 of "Attenti... |
This matches the implementation in Denoising Diffusion Probabilistic Models:
From Fairseq.
Build sinusoidal embeddings.
This matches the implementation in tensor2tensor, but differs slightly
from the description in Section 3.5 of "Attention Is All You Need".
| get_timestep_embedding | python | THUDM/CogVideo | sat/sgm/modules/autoencoding/vqvae/movq_modules.py | https://github.com/THUDM/CogVideo/blob/master/sat/sgm/modules/autoencoding/vqvae/movq_modules.py | Apache-2.0 |
def get_timestep_embedding(timesteps, embedding_dim):
"""
This matches the implementation in Denoising Diffusion Probabilistic Models:
From Fairseq.
Build sinusoidal embeddings.
This matches the implementation in tensor2tensor, but differs slightly
from the description in Section 3.5 of "Attenti... |
This matches the implementation in Denoising Diffusion Probabilistic Models:
From Fairseq.
Build sinusoidal embeddings.
This matches the implementation in tensor2tensor, but differs slightly
from the description in Section 3.5 of "Attention Is All You Need".
| get_timestep_embedding | python | THUDM/CogVideo | sat/sgm/modules/autoencoding/vqvae/vqvae_blocks.py | https://github.com/THUDM/CogVideo/blob/master/sat/sgm/modules/autoencoding/vqvae/vqvae_blocks.py | Apache-2.0 |
def _find_children(
model,
search_class: List[Type[nn.Module]] = [nn.Linear],
):
"""
Find all modules of a certain class (or union of classes).
Returns all matching modules, along with the parent of those moduless and the
names they are referenced by.
"""
# For each target find every li... |
Find all modules of a certain class (or union of classes).
Returns all matching modules, along with the parent of those moduless and the
names they are referenced by.
| _find_children | python | THUDM/CogVideo | sat/sgm/modules/diffusionmodules/lora.py | https://github.com/THUDM/CogVideo/blob/master/sat/sgm/modules/diffusionmodules/lora.py | Apache-2.0 |
def _find_modules_v2(
model,
ancestor_class: Optional[Set[str]] = None,
search_class: List[Type[nn.Module]] = [nn.Linear],
exclude_children_of: Optional[List[Type[nn.Module]]] = [
LoRACompatibleLinear,
LoRACompatibleConv,
LoRALinearLayer,
LoRAConv2dLayer,
],
):
""... |
Find all modules of a certain class (or union of classes) that are direct or
indirect descendants of other modules of a certain class (or union of classes).
Returns all matching modules, along with the parent of those moduless and the
names they are referenced by.
| _find_modules_v2 | python | THUDM/CogVideo | sat/sgm/modules/diffusionmodules/lora.py | https://github.com/THUDM/CogVideo/blob/master/sat/sgm/modules/diffusionmodules/lora.py | Apache-2.0 |
def get_timestep_embedding(timesteps, embedding_dim):
"""
This matches the implementation in Denoising Diffusion Probabilistic Models:
From Fairseq.
Build sinusoidal embeddings.
This matches the implementation in tensor2tensor, but differs slightly
from the description in Section 3.5 of "Attenti... |
This matches the implementation in Denoising Diffusion Probabilistic Models:
From Fairseq.
Build sinusoidal embeddings.
This matches the implementation in tensor2tensor, but differs slightly
from the description in Section 3.5 of "Attention Is All You Need".
| get_timestep_embedding | python | THUDM/CogVideo | sat/sgm/modules/diffusionmodules/model.py | https://github.com/THUDM/CogVideo/blob/master/sat/sgm/modules/diffusionmodules/model.py | Apache-2.0 |
def forward(self, x, emb):
"""
Apply the module to `x` given `emb` timestep embeddings.
""" |
Apply the module to `x` given `emb` timestep embeddings.
| forward | python | THUDM/CogVideo | sat/sgm/modules/diffusionmodules/openaimodel.py | https://github.com/THUDM/CogVideo/blob/master/sat/sgm/modules/diffusionmodules/openaimodel.py | Apache-2.0 |
def count_flops_attn(model, _x, y):
"""
A counter for the `thop` package to count the operations in an
attention operation.
Meant to be used like:
macs, params = thop.profile(
model,
inputs=(inputs, timestamps),
custom_ops={QKVAttention: QKVAttention.count_flo... |
A counter for the `thop` package to count the operations in an
attention operation.
Meant to be used like:
macs, params = thop.profile(
model,
inputs=(inputs, timestamps),
custom_ops={QKVAttention: QKVAttention.count_flops},
)
| count_flops_attn | python | THUDM/CogVideo | sat/sgm/modules/diffusionmodules/openaimodel.py | https://github.com/THUDM/CogVideo/blob/master/sat/sgm/modules/diffusionmodules/openaimodel.py | Apache-2.0 |
def forward(self, qkv):
"""
Apply QKV attention.
:param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.
:return: an [N x (H * C) x T] tensor after attention.
"""
bs, width, length = qkv.shape
assert width % (3 * self.n_heads) == 0
ch = width // (3 ... |
Apply QKV attention.
:param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.
:return: an [N x (H * C) x T] tensor after attention.
| forward | python | THUDM/CogVideo | sat/sgm/modules/diffusionmodules/openaimodel.py | https://github.com/THUDM/CogVideo/blob/master/sat/sgm/modules/diffusionmodules/openaimodel.py | Apache-2.0 |
def forward(self, qkv):
"""
Apply QKV attention.
:param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs.
:return: an [N x (H * C) x T] tensor after attention.
"""
bs, width, length = qkv.shape
assert width % (3 * self.n_heads) == 0
ch = width // (3 ... |
Apply QKV attention.
:param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs.
:return: an [N x (H * C) x T] tensor after attention.
| forward | python | THUDM/CogVideo | sat/sgm/modules/diffusionmodules/openaimodel.py | https://github.com/THUDM/CogVideo/blob/master/sat/sgm/modules/diffusionmodules/openaimodel.py | Apache-2.0 |
def forward(self, x, timesteps=None, context=None, y=None, **kwargs):
"""
Apply the model to an input batch.
:param x: an [N x C x ...] Tensor of inputs.
:param timesteps: a 1-D batch of timesteps.
:param context: conditioning plugged in via crossattn
:param y: an [N] Ten... |
Apply the model to an input batch.
:param x: an [N x C x ...] Tensor of inputs.
:param timesteps: a 1-D batch of timesteps.
:param context: conditioning plugged in via crossattn
:param y: an [N] Tensor of labels, if class-conditional.
:return: an [N x C x ...] Tensor of ... | forward | python | THUDM/CogVideo | sat/sgm/modules/diffusionmodules/openaimodel.py | https://github.com/THUDM/CogVideo/blob/master/sat/sgm/modules/diffusionmodules/openaimodel.py | Apache-2.0 |
def forward(self, x, timesteps):
"""
Apply the model to an input batch.
:param x: an [N x C x ...] Tensor of inputs.
:param timesteps: a 1-D batch of timesteps.
:return: an [N x K] Tensor of outputs.
"""
emb = self.time_embed(timestep_embedding(timesteps, self.mod... |
Apply the model to an input batch.
:param x: an [N x C x ...] Tensor of inputs.
:param timesteps: a 1-D batch of timesteps.
:return: an [N x K] Tensor of outputs.
| forward | python | THUDM/CogVideo | sat/sgm/modules/diffusionmodules/openaimodel.py | https://github.com/THUDM/CogVideo/blob/master/sat/sgm/modules/diffusionmodules/openaimodel.py | Apache-2.0 |
def mixed_checkpoint(func, inputs: dict, params, flag):
"""
Evaluate a function without caching intermediate activations, allowing for
reduced memory at the expense of extra compute in the backward pass. This differs from the original checkpoint function
borrowed from https://github.com/openai/guided-di... |
Evaluate a function without caching intermediate activations, allowing for
reduced memory at the expense of extra compute in the backward pass. This differs from the original checkpoint function
borrowed from https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_di... | mixed_checkpoint | python | THUDM/CogVideo | sat/sgm/modules/diffusionmodules/util.py | https://github.com/THUDM/CogVideo/blob/master/sat/sgm/modules/diffusionmodules/util.py | Apache-2.0 |
def checkpoint(func, inputs, params, flag):
"""
Evaluate a function without caching intermediate activations, allowing for
reduced memory at the expense of extra compute in the backward pass.
:param func: the function to evaluate.
:param inputs: the argument sequence to pass to `func`.
:param pa... |
Evaluate a function without caching intermediate activations, allowing for
reduced memory at the expense of extra compute in the backward pass.
:param func: the function to evaluate.
:param inputs: the argument sequence to pass to `func`.
:param params: a sequence of parameters `func` depends on bu... | checkpoint | python | THUDM/CogVideo | sat/sgm/modules/diffusionmodules/util.py | https://github.com/THUDM/CogVideo/blob/master/sat/sgm/modules/diffusionmodules/util.py | Apache-2.0 |
def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False, dtype=torch.float32):
"""
Create sinusoidal timestep embeddings.
:param timesteps: a 1-D Tensor of N indices, one per batch element.
These may be fractional.
:param dim: the dimension of the output.
:pa... |
Create sinusoidal timestep embeddings.
:param timesteps: a 1-D Tensor of N indices, one per batch element.
These may be fractional.
:param dim: the dimension of the output.
:param max_period: controls the minimum frequency of the embeddings.
:return: an [N x dim] Tensor of pos... | timestep_embedding | python | THUDM/CogVideo | sat/sgm/modules/diffusionmodules/util.py | https://github.com/THUDM/CogVideo/blob/master/sat/sgm/modules/diffusionmodules/util.py | Apache-2.0 |
def zero_module(module):
"""
Zero out the parameters of a module and return it.
"""
for p in module.parameters():
p.detach().zero_()
return module |
Zero out the parameters of a module and return it.
| zero_module | python | THUDM/CogVideo | sat/sgm/modules/diffusionmodules/util.py | https://github.com/THUDM/CogVideo/blob/master/sat/sgm/modules/diffusionmodules/util.py | Apache-2.0 |
def scale_module(module, scale):
"""
Scale the parameters of a module and return it.
"""
for p in module.parameters():
p.detach().mul_(scale)
return module |
Scale the parameters of a module and return it.
| scale_module | python | THUDM/CogVideo | sat/sgm/modules/diffusionmodules/util.py | https://github.com/THUDM/CogVideo/blob/master/sat/sgm/modules/diffusionmodules/util.py | Apache-2.0 |
def normal_kl(mean1, logvar1, mean2, logvar2):
"""
source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12
Compute the KL divergence between two gaussians.
Shapes are automatically broadcasted, so batches can be compared to
scal... |
source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12
Compute the KL divergence between two gaussians.
Shapes are automatically broadcasted, so batches can be compared to
scalars, among other use cases.
| normal_kl | python | THUDM/CogVideo | sat/sgm/modules/distributions/distributions.py | https://github.com/THUDM/CogVideo/blob/master/sat/sgm/modules/distributions/distributions.py | Apache-2.0 |
def zero_module(module):
"""
Zero out the parameters of a module and return it.
"""
for p in module.parameters():
p.detach().zero_()
return module |
Zero out the parameters of a module and return it.
| zero_module | python | THUDM/CogVideo | sat/vae_modules/attention.py | https://github.com/THUDM/CogVideo/blob/master/sat/vae_modules/attention.py | Apache-2.0 |
def get_timestep_embedding(timesteps, embedding_dim):
"""
This matches the implementation in Denoising Diffusion Probabilistic Models:
From Fairseq.
Build sinusoidal embeddings.
This matches the implementation in tensor2tensor, but differs slightly
from the description in Section 3.5 of "Attenti... |
This matches the implementation in Denoising Diffusion Probabilistic Models:
From Fairseq.
Build sinusoidal embeddings.
This matches the implementation in tensor2tensor, but differs slightly
from the description in Section 3.5 of "Attention Is All You Need".
| get_timestep_embedding | python | THUDM/CogVideo | sat/vae_modules/cp_enc_dec.py | https://github.com/THUDM/CogVideo/blob/master/sat/vae_modules/cp_enc_dec.py | Apache-2.0 |
def is_power_of_two(n):
"""
chat.openai.com/chat
Return True if n is a power of 2, otherwise return False.
The function is_power_of_two takes an integer n as input and returns True if n is a power of 2, otherwise it returns False.
The function works by first checking if n is less than or equal to 0... |
chat.openai.com/chat
Return True if n is a power of 2, otherwise return False.
The function is_power_of_two takes an integer n as input and returns True if n is a power of 2, otherwise it returns False.
The function works by first checking if n is less than or equal to 0. If n is less than or equal to... | is_power_of_two | python | THUDM/CogVideo | sat/vae_modules/utils.py | https://github.com/THUDM/CogVideo/blob/master/sat/vae_modules/utils.py | Apache-2.0 |
def append_dims(x, target_dims):
"""Appends dimensions to the end of a tensor until it has target_dims dimensions."""
dims_to_append = target_dims - x.ndim
if dims_to_append < 0:
raise ValueError(f"input has {x.ndim} dims but target_dims is {target_dims}, which is less")
return x[(...,) + (None,... | Appends dimensions to the end of a tensor until it has target_dims dimensions. | append_dims | python | THUDM/CogVideo | sat/vae_modules/utils.py | https://github.com/THUDM/CogVideo/blob/master/sat/vae_modules/utils.py | Apache-2.0 |
def get_configs_path() -> str:
"""
Get the `configs` directory.
For a working copy, this is the one in the root of the repository,
but for an installed copy, it's in the `sgm` package (see pyproject.toml).
"""
this_dir = os.path.dirname(__file__)
candidates = (
os.path.join(this_dir,... |
Get the `configs` directory.
For a working copy, this is the one in the root of the repository,
but for an installed copy, it's in the `sgm` package (see pyproject.toml).
| get_configs_path | python | THUDM/CogVideo | sat/vae_modules/utils.py | https://github.com/THUDM/CogVideo/blob/master/sat/vae_modules/utils.py | Apache-2.0 |
def get_nested_attribute(obj, attribute_path, depth=None, return_key=False):
"""
Will return the result of a recursive get attribute call.
E.g.:
a.b.c
= getattr(getattr(a, "b"), "c")
= get_nested_attribute(a, "b.c")
If any part of the attribute call is an integer x with current o... |
Will return the result of a recursive get attribute call.
E.g.:
a.b.c
= getattr(getattr(a, "b"), "c")
= get_nested_attribute(a, "b.c")
If any part of the attribute call is an integer x with current obj a, will
try to call a[x] instead of a.x first.
| get_nested_attribute | python | THUDM/CogVideo | sat/vae_modules/utils.py | https://github.com/THUDM/CogVideo/blob/master/sat/vae_modules/utils.py | Apache-2.0 |
def checkpoint(func, inputs, params, flag):
"""
Evaluate a function without caching intermediate activations, allowing for
reduced memory at the expense of extra compute in the backward pass.
:param func: the function to evaluate.
:param inputs: the argument sequence to pass to `func`.
:param pa... |
Evaluate a function without caching intermediate activations, allowing for
reduced memory at the expense of extra compute in the backward pass.
:param func: the function to evaluate.
:param inputs: the argument sequence to pass to `func`.
:param params: a sequence of parameters `func` depends on bu... | checkpoint | python | THUDM/CogVideo | sat/vae_modules/utils.py | https://github.com/THUDM/CogVideo/blob/master/sat/vae_modules/utils.py | Apache-2.0 |
def _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters):
"""
Returns fp32 state_dict reconstructed from ds checkpoint
Args:
- ``ds_checkpoint_dir``: path to the deepspeed checkpoint folder (where the optimizer files are)
"""
print(f"Processing zero check... |
Returns fp32 state_dict reconstructed from ds checkpoint
Args:
- ``ds_checkpoint_dir``: path to the deepspeed checkpoint folder (where the optimizer files are)
| _get_fp32_state_dict_from_zero_checkpoint | python | THUDM/CogVideo | tools/convert_weight_deepspeed2hf.py | https://github.com/THUDM/CogVideo/blob/master/tools/convert_weight_deepspeed2hf.py | Apache-2.0 |
def contiguous(self):
"""
Merge partitioned weights from flat_groups into a single tensor.
"""
end_idx = self.offset + self.partitioned_numel
world_size = len(self.flat_groups)
pad_flat_param_chunks = []
for rank_i in range(world_size):
# for each ran... |
Merge partitioned weights from flat_groups into a single tensor.
| contiguous | python | THUDM/CogVideo | tools/convert_weight_deepspeed2hf.py | https://github.com/THUDM/CogVideo/blob/master/tools/convert_weight_deepspeed2hf.py | Apache-2.0 |
def to_torch_tensor(state_dict, return_empty_tensor=False):
"""
Convert state_dict of GatheredTensor to torch tensor
"""
torch_state_dict = {}
converted_tensors = {}
for name, tensor in state_dict.items():
tensor_id = id(tensor)
if tensor_id in converted_tensors: # shared tensor... |
Convert state_dict of GatheredTensor to torch tensor
| to_torch_tensor | python | THUDM/CogVideo | tools/convert_weight_deepspeed2hf.py | https://github.com/THUDM/CogVideo/blob/master/tools/convert_weight_deepspeed2hf.py | Apache-2.0 |
def convert_zero_checkpoint_to_fp32_state_dict(
checkpoint_dir,
output_dir,
max_shard_size="5GB",
safe_serialization=False,
tag=None,
exclude_frozen_parameters=False,
):
"""
Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` file that can be
loaded with ``t... |
Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` file that can be
loaded with ``torch.load(file)`` + ``load_state_dict()`` and used for training without DeepSpeed.
Args:
- ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, lik... | convert_zero_checkpoint_to_fp32_state_dict | python | THUDM/CogVideo | tools/convert_weight_deepspeed2hf.py | https://github.com/THUDM/CogVideo/blob/master/tools/convert_weight_deepspeed2hf.py | Apache-2.0 |
def setup(self) -> None:
"""Load the model into memory to make running multiple predictions efficient"""
if not os.path.exists(MODEL_CACHE):
download_weights(MODEL_URL, MODEL_CACHE)
# model_id: THUDM/CogVideoX-5b-I2V
self.pipe = CogVideoXImageToVideoPipeline.from_pretrained... | Load the model into memory to make running multiple predictions efficient | setup | python | THUDM/CogVideo | tools/replicate/predict_i2v.py | https://github.com/THUDM/CogVideo/blob/master/tools/replicate/predict_i2v.py | Apache-2.0 |
Subsets and Splits
Django Code with Docstrings
Filters Python code examples from Django repository that contain Django-related code, helping identify relevant code snippets for understanding Django framework usage patterns.
SQL Console for Shuu12121/python-treesitter-filtered-datasetsV2
Retrieves Python code examples from Django repository that contain 'django' in the code, which helps identify Django-specific code snippets but provides limited analytical insights beyond basic filtering.
SQL Console for Shuu12121/python-treesitter-filtered-datasetsV2
Retrieves specific code examples from the Flask repository but doesn't provide meaningful analysis or patterns beyond basic data retrieval.
HTTPX Repo Code and Docstrings
Retrieves specific code examples from the httpx repository, which is useful for understanding how particular libraries are used but doesn't provide broader analytical insights about the dataset.
Requests Repo Docstrings & Code
Retrieves code examples with their docstrings and file paths from the requests repository, providing basic filtering but limited analytical value beyond finding specific code samples.
Quart Repo Docstrings & Code
Retrieves code examples with their docstrings from the Quart repository, providing basic code samples but offering limited analytical value for understanding broader patterns or relationships in the dataset.