repo stringclasses 454
values | file_path stringlengths 5 201 | extension stringclasses 1
value | content stringlengths 8 509k | num_lines int64 3 16.9k | size_bytes int64 8 511k |
|---|---|---|---|---|---|
Open-Sora | setup.py | .py | from typing import List
from setuptools import find_packages, setup
def fetch_requirements(paths) -> List[str]:
"""
This function reads the requirements file.
Args:
path (str): the path to the requirements file.
Returns:
The lines in the requirements file.
"""
if not isinsta... | 77 | 2,148 |
Open-Sora | opensora/registry.py | .py | from copy import deepcopy
import torch.nn as nn
from mmengine.registry import Registry
def build_module(module: dict | nn.Module, builder: Registry, **kwargs) -> nn.Module | None:
"""Build module from config or return the module itself.
Args:
module (dict | nn.Module): The module to build.
b... | 42 | 1,048 |
Open-Sora | opensora/acceleration/checkpoint.py | .py | import warnings
from collections.abc import Iterable
from typing import Callable, ContextManager, Optional, Tuple
import torch
import torch.nn as nn
from colossalai.utils import get_current_device
from torch.utils.checkpoint import (
_DEFAULT_DETERMINISM_MODE,
CheckpointFunction,
_checkpoint_without_reentr... | 272 | 12,437 |
Open-Sora | opensora/acceleration/communications.py | .py | import torch
import torch.distributed as dist
# ====================
# All-To-All
# ====================
def _all_to_all(
input_: torch.Tensor,
world_size: int,
group: dist.ProcessGroup,
scatter_dim: int,
gather_dim: int,
):
input_list = [t.contiguous() for t in torch.tensor_split(input_, worl... | 189 | 5,329 |
Open-Sora | opensora/acceleration/parallel_states.py | .py | import torch.distributed as dist
_GLOBAL_PARALLEL_GROUPS = dict()
def set_data_parallel_group(group: dist.ProcessGroup):
_GLOBAL_PARALLEL_GROUPS["data"] = group
def get_data_parallel_group(get_mixed_dp_pg : bool = False):
if get_mixed_dp_pg and "mixed_dp_group" in _GLOBAL_PARALLEL_GROUPS:
return _G... | 30 | 823 |
Open-Sora | opensora/acceleration/shardformer/modeling/t5.py | .py | import torch
import torch.nn as nn
class T5LayerNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
Construct a layernorm module in the T5 style. No bias and no subtraction of mean.
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
... | 40 | 1,778 |
Open-Sora | opensora/acceleration/shardformer/policy/t5_encoder.py | .py | from colossalai.shardformer.modeling.jit import get_jit_fused_dropout_add_func
from colossalai.shardformer.modeling.t5 import get_jit_fused_T5_layer_ff_forward, get_T5_layer_self_attention_forward
from colossalai.shardformer.policies.base_policy import Policy, SubModuleReplacementDescription
class T5EncoderPolicy(Pol... | 42 | 1,495 |
Open-Sora | opensora/datasets/utils.py | .py | import math
import os
import random
import re
from typing import Any
import numpy as np
import pandas as pd
import requests
import torch
import torch.distributed as dist
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torchvision.datasets.folder import IMG_EXTENSIONS, pil_load... | 420 | 14,154 |
Open-Sora | opensora/datasets/video_transforms.py | .py | # Copyright 2024 Vchitect/Latte
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, ... | 596 | 18,574 |
Open-Sora | opensora/datasets/bucket.py | .py | from collections import OrderedDict
import numpy as np
from opensora.utils.logger import log_message
from .aspect import get_closest_ratio, get_resolution_with_aspect_ratio
from .utils import map_target_fps
class Bucket:
def __init__(self, bucket_config: dict[str, dict[int, tuple[float, int] | tuple[tuple[floa... | 140 | 5,526 |
Open-Sora | opensora/datasets/datasets.py | .py | import os
import random
import numpy as np
import pandas as pd
import torch
from PIL import ImageFile
from torchvision.datasets.folder import pil_loader
from opensora.registry import DATASETS
from .read_video import read_video
from .utils import get_transforms_image, get_transforms_video, is_img, map_target_fps, rea... | 316 | 11,134 |
Open-Sora | opensora/datasets/parallel.py | .py | import multiprocessing
from itertools import count
from multiprocessing.managers import SyncManager
from typing import Any, Callable, Dict, Tuple, Type, cast
import dill
import pandarallel
import pandas as pd
from pandarallel.data_types import DataType
from pandarallel.progress_bars import ProgressBarsType, get_progre... | 177 | 5,840 |
Open-Sora | opensora/datasets/aspect.py | .py | import math
import os
ASPECT_RATIO_LD_LIST = [ # width:height
"2.39:1", # cinemascope, 2.39
"2:1", # rare, 2
"16:9", # rare, 1.89
"1.85:1", # american widescreen, 1.85
"9:16", # popular, 1.78
"5:8", # rare, 1.6
"3:2", # rare, 1.5
"4:3", # classic, 1.33
"1:1", # square
]
... | 152 | 5,089 |
Open-Sora | opensora/datasets/pin_memory_cache.py | .py | import threading
from typing import Dict, List, Optional
import torch
class PinMemoryCache:
force_dtype: Optional[torch.dtype] = None
min_cache_numel: int = 0
pre_alloc_numels: List[int] = []
def __init__(self):
self.cache: Dict[int, torch.Tensor] = {}
self.output_to_cache: Dict[int,... | 77 | 3,263 |
Open-Sora | opensora/datasets/dataloader.py | .py | import collections
import functools
import os
import queue
import random
import threading
import numpy as np
import torch
import torch.multiprocessing as multiprocessing
from torch._utils import ExceptionWrapper
from torch.distributed import ProcessGroup
from torch.utils.data import DataLoader, _utils
from torch.utils... | 403 | 14,667 |
Open-Sora | opensora/datasets/read_video.py | .py | import gc
import math
import os
import re
import warnings
from fractions import Fraction
import av
import cv2
import numpy as np
import torch
from torchvision import get_video_backend
from torchvision.io.video import _check_av_available
MAX_NUM_FRAMES = 2500
def read_video_av(
filename: str,
start_pts: floa... | 258 | 9,449 |
Open-Sora | opensora/datasets/sampler.py | .py | from collections import OrderedDict, defaultdict
from typing import Iterator
import numpy as np
import torch
import torch.distributed as dist
from torch.utils.data import Dataset, DistributedSampler
from opensora.utils.logger import log_message
from opensora.utils.misc import format_numel_str
from .aspect import get... | 394 | 15,870 |
Open-Sora | opensora/utils/ckpt.py | .py | import functools
import json
import operator
import os
import re
import shutil
from glob import glob
from typing import Dict, Optional
import torch
import torch.distributed as dist
import torch.nn as nn
from colossalai.booster import Booster
from colossalai.checkpoint_io import GeneralCheckpointIO
from colossalai.util... | 525 | 20,397 |
Open-Sora | opensora/utils/logger.py | .py | import logging
import os
import torch.distributed as dist
def is_distributed() -> bool:
"""
Check if the code is running in a distributed setting.
Returns:
bool: True if running in a distributed setting, False otherwise
"""
return os.environ.get("WORLD_SIZE", None) is not None
def is_m... | 91 | 2,348 |
Open-Sora | opensora/utils/inference.py | .py | import copy
import os
import re
from enum import Enum
import torch
from torch import nn
from opensora.datasets import save_sample
from opensora.datasets.aspect import get_image_size
from opensora.datasets.utils import read_from_path, rescale_image_by_path
from opensora.utils.logger import log_message
from opensora.ut... | 352 | 12,491 |
Open-Sora | opensora/utils/misc.py | .py | import os
import time
from collections import OrderedDict
from collections.abc import Sequence
from contextlib import nullcontext
import numpy as np
import psutil
import torch
import torch.distributed as dist
import torch.nn as nn
from colossalai.cluster.dist_coordinator import DistCoordinator
from torch.utils.tensorb... | 439 | 13,863 |
Open-Sora | opensora/utils/prompt_refine.py | .py | import base64
import os
from mimetypes import guess_type
from openai import OpenAI
sys_prompt_t2v = """You are part of a team of bots that creates videos. The workflow is that you first create a caption of the video, and then the assistant bot will generate the video based on the caption. You work with an assistant b... | 235 | 15,254 |
Open-Sora | opensora/utils/train.py | .py | import random
import warnings
from collections import OrderedDict
from datetime import timedelta
import torch
import torch.distributed as dist
import torch.nn.functional as F
from colossalai.booster.plugin import HybridParallelPlugin, LowLevelZeroPlugin
from colossalai.cluster import DistCoordinator
from colossalai.ut... | 459 | 20,616 |
Open-Sora | opensora/utils/sampling.py | .py | import math
import os
import random
from abc import ABC, abstractmethod
from dataclasses import dataclass, replace
import torch
from einops import rearrange, repeat
from mmengine.config import Config
from peft import PeftModel
from torch import Tensor, nn
from opensora.datasets.aspect import get_image_size
from opens... | 727 | 22,674 |
Open-Sora | opensora/utils/cai.py | .py | import colossalai
import torch
import torch.distributed as dist
from colossalai.booster import Booster
from colossalai.cluster import DistCoordinator
from opensora.acceleration.parallel_states import (
get_sequence_parallel_group,
get_tensor_parallel_group,
set_sequence_parallel_group,
)
from opensora.mode... | 92 | 2,951 |
Open-Sora | opensora/utils/config.py | .py | import argparse
import ast
import json
import os
from datetime import datetime
import torch
from mmengine.config import Config
from .logger import is_distributed, is_main_process
def parse_args() -> tuple[str, argparse.Namespace]:
"""
This function parses the command line arguments.
Returns:
tu... | 214 | 6,341 |
Open-Sora | opensora/utils/optimizer.py | .py | import torch
from colossalai.nn.lr_scheduler import CosineAnnealingWarmupLR
from colossalai.nn.optimizer import HybridAdam
from torch.optim.lr_scheduler import _LRScheduler
def create_optimizer(
model: torch.nn.Module,
optimizer_config: dict,
) -> torch.optim.Optimizer:
"""
Create an optimizer.
A... | 92 | 3,074 |
Open-Sora | opensora/models/text/conditioner.py | .py | from colossalai.shardformer import ShardConfig, ShardFormer
from torch import Tensor, nn
from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5Tokenizer
from opensora.acceleration.shardformer.policy.t5_encoder import T5EncoderPolicy
from opensora.registry import MODELS
@MODELS.register_module("tex... | 75 | 2,954 |
Open-Sora | opensora/models/mmdit/distributed.py | .py | from functools import partial
from typing import Dict, List, Optional, Tuple, Union
import torch
import torch.distributed as dist
import torch.nn as nn
from colossalai.shardformer.layer import (FusedLinear1D_Col, FusedLinear1D_Row,
Linear1D_Col, Linear1D_Row)
from colossalai.s... | 884 | 38,230 |
Open-Sora | opensora/models/mmdit/model.py | .py | # Modified from Flux
#
# Copyright 2024 Black Forest Labs
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law... | 304 | 9,698 |
Open-Sora | opensora/models/mmdit/policy.py | .py | from functools import partial
from typing import Dict, Union
import torch.nn as nn
from colossalai.shardformer.policies.base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
from opensora.models.vae.tensor_parallel import Conv3dTPCol, Conv3dTPRow, GroupNormTP
from .distributed import Co... | 156 | 6,199 |
Open-Sora | opensora/models/mmdit/math.py | .py | import torch
from einops import rearrange
from flash_attn import flash_attn_func as flash_attn_func_v2
from liger_kernel.ops.rope import LigerRopeFunction
from torch import Tensor
from typing import Tuple
try:
from flash_attn_interface import flash_attn_func as flash_attn_func_v3
SUPPORT_FA3 = True
except:
... | 118 | 4,054 |
Open-Sora | opensora/models/mmdit/layers.py | .py | # Modified from Flux
#
# Copyright 2024 Black Forest Labs
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law... | 403 | 15,544 |
Open-Sora | opensora/models/vae/utils.py | .py | import math
import numpy as np
import torch
import torch.nn.functional as F
from torch import Tensor, nn
NUMEL_LIMIT = 2**30
def ceil_to_divisible(n: int, dividend: int) -> int:
return math.ceil(dividend / (dividend // n))
def chunked_avg_pool1d(input, kernel_size, stride=None, padding=0, ceil_mode=False, cou... | 258 | 9,850 |
Open-Sora | opensora/models/vae/losses.py | .py | import torch
import torch.nn.functional as F
from einops import rearrange
from torch import Tensor, nn
from opensora.models.vae.lpips import LPIPS
def hinge_d_loss(logits_real, logits_fake):
loss_real = torch.mean(F.relu(1.0 - logits_real))
loss_fake = torch.mean(F.relu(1.0 + logits_fake))
d_loss = 0.5 *... | 224 | 7,337 |
Open-Sora | opensora/models/vae/tensor_parallel.py | .py | from typing import List, Optional, Union
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
from colossalai.device.device_mesh import DeviceMesh
from colossalai.shardformer.layer._operation import (
gather_forward_split_backward,
reduce_forward,
split_forwar... | 559 | 19,054 |
Open-Sora | opensora/models/vae/lpips.py | .py | import hashlib
import os
from collections import namedtuple
import requests
import torch
import torch.nn as nn
from torchvision import models
from tqdm import tqdm
from opensora.acceleration.checkpoint import checkpoint
URL_MAP = {"vgg_lpips": "https://heibox.uni-heidelberg.de/f/607503859c864bc1b30b/?dl=1"}
CKPT_MA... | 187 | 6,974 |
Open-Sora | opensora/models/vae/autoencoder_2d.py | .py | # Modified from Flux
#
# Copyright 2024 Black Forest Labs
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law... | 340 | 11,853 |
Open-Sora | opensora/models/vae/discriminator.py | .py | import os
import torch.nn as nn
from opensora.registry import MODELS
from opensora.utils.ckpt import load_checkpoint
def weights_init(m):
classname = m.__class__.__name__
if classname.find("Conv") != -1:
nn.init.normal_(m.weight.data, 0.0, 0.02)
elif classname.find("BatchNorm") != -1:
nn... | 110 | 3,511 |
Open-Sora | opensora/models/dc_ae/ae_model_zoo.py | .py | # Copyright 2024 MIT Han Lab
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | 85 | 2,957 |
Open-Sora | opensora/models/dc_ae/utils/list.py | .py | # Copyright 2024 MIT Han Lab
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | 69 | 1,854 |
Open-Sora | opensora/models/dc_ae/utils/init.py | .py | # Copyright 2024 MIT Han Lab
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | 63 | 2,410 |
Open-Sora | opensora/models/dc_ae/models/dc_ae.py | .py | # Copyright 2024 MIT Han Lab
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | 816 | 31,225 |
Open-Sora | opensora/models/dc_ae/models/nn/norm.py | .py | # Copyright 2024 MIT Han Lab
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | 99 | 3,426 |
Open-Sora | opensora/models/dc_ae/models/nn/act.py | .py | # Copyright 2024 MIT Han Lab
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | 45 | 1,256 |
Open-Sora | opensora/models/dc_ae/models/nn/ops.py | .py | # Copyright 2024 MIT Han Lab
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | 979 | 30,376 |
Open-Sora | opensora/models/dc_ae/models/nn/vo_ops.py | .py | import math
from inspect import signature
from typing import Any, Callable, Optional, Union
import torch
import torch.nn.functional as F
VERBOSE = False
def pixel_shuffle_3d(x, upscale_factor):
"""
3D pixelshuffle 操作。
"""
B, C, T, H, W = x.shape
r = upscale_factor
assert C % (r * r * r) == 0... | 245 | 7,920 |
Open-Sora | opensora/models/hunyuan_vae/unet_causal_3d_blocks.py | .py | # Modified from diffusers==0.29.2 and HunyuanVideo
#
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org... | 477 | 16,246 |
Open-Sora | opensora/models/hunyuan_vae/distributed.py | .py | from typing import List, Optional, Tuple
import torch
import torch.distributed as dist
from colossalai.shardformer.layer._operation import gather_forward_split_backward, split_forward_gather_backward
from colossalai.shardformer.layer.attn import RingComm, _rescale_out_lse
from colossalai.shardformer.layer.utils import... | 581 | 22,825 |
Open-Sora | opensora/models/hunyuan_vae/autoencoder_kl_causal_3d.py | .py | # Modified from diffusers==0.29.2 and HunyuanVideo
#
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/... | 639 | 26,473 |
Open-Sora | opensora/models/hunyuan_vae/vae.py | .py | # Modified from HunyuanVideo
#
# Copyright 2024 HunyuanVideo
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from dataclasses import dataclass
from typing import Optional, Tuple
import numpy as np
import torch
import torch.nn as nn
from diffus... | 341 | 12,792 |
Open-Sora | scripts/diffusion/inference.py | .py | import os
import time
import warnings
from pprint import pformat
warnings.filterwarnings("ignore", category=FutureWarning)
warnings.filterwarnings("ignore", category=UserWarning)
import torch
import torch.distributed as dist
from colossalai.utils import set_seed
from tqdm import tqdm
from opensora.acceleration.paral... | 246 | 9,476 |
Open-Sora | scripts/diffusion/train.py | .py | import gc
import math
import os
import subprocess
import warnings
from contextlib import nullcontext
from copy import deepcopy
from pprint import pformat
warnings.filterwarnings("ignore", category=FutureWarning)
warnings.filterwarnings("ignore", category=UserWarning)
gc.disable()
import torch
import torch.distribute... | 655 | 26,337 |
Open-Sora | scripts/vae/inference.py | .py | import os
from pprint import pformat
import colossalai
import torch
from colossalai.utils import get_current_device, set_seed
from tqdm import tqdm
from opensora.acceleration.parallel_states import get_data_parallel_group
from opensora.datasets import save_sample
from opensora.datasets.dataloader import prepare_datal... | 143 | 5,182 |
Open-Sora | scripts/vae/stats.py | .py | from pprint import pformat
import colossalai
import torch
from colossalai.utils import get_current_device, set_seed
from tqdm import tqdm
from opensora.acceleration.parallel_states import get_data_parallel_group
from opensora.datasets.dataloader import prepare_dataloader
from opensora.registry import DATASETS, MODELS... | 119 | 4,129 |
Open-Sora | scripts/vae/train.py | .py | import gc
import os
import random
import subprocess
import warnings
from contextlib import nullcontext
from copy import deepcopy
from pprint import pformat
warnings.filterwarnings("ignore", category=FutureWarning)
warnings.filterwarnings("ignore", category=UserWarning)
gc.disable()
import torch
import torch.distribu... | 598 | 25,664 |
Open-Sora | scripts/cnv/shard.py | .py | import os
import pandas as pd
from tqdm import tqdm
try:
import dask.dataframe as dd
SUPPORT_DASK = True
except:
SUPPORT_DASK = False
def shard_parquet(input_path, k):
# 检查输入路径是否存在
if not os.path.exists(input_path):
raise FileNotFoundError(f"Input file {input_path} does not exist.")
... | 75 | 2,037 |
Open-Sora | scripts/cnv/meta.py | .py | import argparse
import numpy as np
import pandas as pd
from pandarallel import pandarallel
from torchvision.io.video import read_video
from tqdm import tqdm
def set_parallel(num_workers: int = None) -> callable:
if num_workers == 0:
return lambda x, *args, **kwargs: x.progress_apply(*args, **kwargs)
... | 71 | 1,965 |
Open-Sora | gradio/app.py | .py | #!/usr/bin/env python
"""
This script runs a Gradio App for the Open-Sora model.
Usage:
python demo.py <config-path>
"""
import argparse
import datetime
import importlib
import os
import subprocess
import sys
from tempfile import NamedTemporaryFile
import spaces
import torch
import gradio as gr
MODEL_TYPES = [... | 759 | 27,346 |
Open-Sora | configs/diffusion/train/stage1_i2v.py | .py | _base_ = ["stage1.py"]
# Define model components
model = dict(cond_embed=True)
condition_config = dict(
t2v=1,
i2v_head=5, # train i2v (image as first frame) with weight 5
i2v_loop=1, # train image connection with weight 1
i2v_tail=1, # train i2v (image as last frame) with weight 1
)
lr = 1e-5
opt... | 15 | 337 |
Open-Sora | configs/diffusion/train/image.py | .py | # Dataset settings
dataset = dict(
type="video_text",
transform_name="resize_crop",
fps_max=24, # the desired fps for training
vmaf=True, # load vmaf scores into text
)
grad_ckpt_settings = (8, 100) # set the grad checkpoint settings
bucket_config = {
"256px": {1: (1.0, 50)},
"768px": {1: (0... | 115 | 2,421 |
Open-Sora | configs/diffusion/train/stage2.py | .py | _base_ = ["image.py"]
# new config
grad_ckpt_settings = (100, 100)
plugin = "hybrid"
plugin_config = dict(
tp_size=1,
pp_size=1,
sp_size=4,
sequence_parallelism_mode="ring_attn",
enable_sequence_parallelism=True,
static_graph=True,
zero_stage=2,
)
bucket_config = {
"_delete_": True,
... | 95 | 1,943 |
Open-Sora | configs/diffusion/train/demo.py | .py | _base_ = ["stage1.py"]
bucket_config = {
"_delete_": True,
"256px": {
1: (1.0, 1),
33: (1.0, 1),
97: (1.0, 1),
129: (1.0, 1),
},
}
| 13 | 177 |
Open-Sora | configs/diffusion/train/stage1.py | .py | _base_ = ["image.py"]
dataset = dict(memory_efficient=False)
# new config
grad_ckpt_settings = (8, 100)
bucket_config = {
"_delete_": True,
"256px": {
1: (1.0, 45),
5: (1.0, 12),
9: (1.0, 12),
13: (1.0, 12),
17: (1.0, 12),
21: (1.0, 12),
25: (1.0, 12),
... | 57 | 1,118 |
Open-Sora | configs/diffusion/train/stage2_i2v.py | .py | _base_ = ["stage2.py"]
# Define model components
model = dict(cond_embed=True)
grad_ckpt_buffer_size = 25 * 1024**3
condition_config = dict(
t2v=1,
i2v_head=5,
i2v_loop=1,
i2v_tail=1,
)
is_causal_vae = True
bucket_config = {
"_delete_": True,
"256px": {
1: (1.0, 195),
5: (1.0,... | 88 | 1,817 |
Open-Sora | configs/diffusion/train/high_compression.py | .py | _base_ = ["image.py"]
bucket_config = {
"_delete_": True,
"768px": {
1: (1.0, 20),
16: (1.0, 8),
20: (1.0, 8),
24: (1.0, 8),
28: (1.0, 8),
32: (1.0, 8),
36: (1.0, 4),
40: (1.0, 4),
44: (1.0, 4),
48: (1.0, 4),
52: (1.0, 4),
... | 72 | 1,449 |
Open-Sora | configs/diffusion/inference/768px.py | .py | _base_ = [ # inherit grammer from mmengine
"256px.py",
"plugins/sp.py", # use sequence parallel
]
sampling_option = dict(
resolution="768px",
)
| 9 | 159 |
Open-Sora | configs/diffusion/inference/t2i2v_768px.py | .py | _base_ = [ # inherit grammer from mmengine
"768px.py",
"plugins/t2i2v.py",
]
| 5 | 86 |
Open-Sora | configs/diffusion/inference/t2i2v_256px.py | .py | _base_ = [ # inherit grammer from mmengine
"256px.py",
"plugins/t2i2v.py",
]
| 5 | 86 |
Open-Sora | configs/diffusion/inference/256px.py | .py | save_dir = "samples" # save directory
seed = 42 # random seed (except seed for z)
batch_size = 1
dtype = "bf16"
cond_type = "t2v"
# conditional inference options:
# t2v: text-to-video
# i2v_head: image-to-video (head)
# i2v_tail: image-to-video (tail)
# i2v_loop: connect images
# v2v_head_half: video extension with ... | 77 | 2,054 |
Open-Sora | configs/diffusion/inference/high_compression.py | .py | _base_ = ["t2i2v_768px.py"]
# no need for parallelism
plugin = None
plugin_config = None
plugin_ae = None
plugin_config_ae = None
# model settings
patch_size = 1
model = dict(
from_pretrained="./ckpts/Open_Sora_v2_Video_DC_AE.safetensors",
in_channels=128,
cond_embed=True,
patch_size=1,
)
# AE settin... | 36 | 704 |
Open-Sora | configs/diffusion/inference/256px_tp.py | .py | _base_ = [ # inherit grammer from mmengine
"256px.py",
"plugins/tp.py", # use tensor parallel
]
| 5 | 106 |
Open-Sora | configs/diffusion/inference/plugins/t2i2v.py | .py | use_t2i2v = True
# flux configurations
img_flux = dict(
type="flux",
from_pretrained="./ckpts/flux1-dev.safetensors",
guidance_embed=True,
# model architecture
in_channels=64,
vec_in_dim=768,
context_in_dim=4096,
hidden_size=3072,
mlp_ratio=4.0,
num_heads=24,
depth=19,
d... | 37 | 834 |
Open-Sora | configs/diffusion/inference/plugins/sp.py | .py | plugin = "hybrid"
plugin_config = dict(
tp_size=1,
pp_size=1,
sp_size=8,
sequence_parallelism_mode="ring_attn",
enable_sequence_parallelism=True,
static_graph=True,
zero_stage=2,
overlap_allgather=False,
)
plugin_ae = "hybrid"
plugin_config_ae = dict(
tp_size=8,
pp_size=1,
s... | 21 | 379 |
Open-Sora | configs/diffusion/inference/plugins/tp.py | .py | plugin = "hybrid"
plugin_config = dict(
tp_size=8,
pp_size=1,
sp_size=1,
zero_stage=2,
overlap_allgather=False,
)
plugin_ae = "hybrid"
plugin_config_ae = dict(
tp_size=8,
pp_size=1,
sp_size=1,
zero_stage=2,
overlap_allgather=False,
)
| 18 | 275 |
Open-Sora | configs/vae/train/video_dc_ae.py | .py | # ============
# model config
# ============
model = dict(
type="dc_ae",
model_name="dc-ae-f32t4c128",
from_scratch=True,
from_pretrained=None,
)
# ============
# data config
# ============
dataset = dict(
type="video_text",
transform_name="resize_crop",
data_path="datasets/pexels_45k_nec... | 75 | 1,296 |
Open-Sora | configs/vae/train/video_dc_ae_disc.py | .py | _base_ = ["video_dc_ae.py"]
discriminator = dict(
type="N_Layer_discriminator_3D",
from_pretrained=None,
input_nc=3,
n_layers=5,
conv_cls="conv3d"
)
disc_lr_scheduler = dict(warmup_steps=0)
gen_loss_config = dict(
gen_start=0,
disc_weight=0.05,
)
disc_loss_config = dict(
disc_start=0,... | 35 | 616 |
Open-Sora | configs/vae/inference/video_dc_ae.py | .py | dtype = "bf16"
batch_size = 1
seed = 42
dataset = dict(
type="video_text",
transform_name="resize_crop",
fps_max=16,
data_path="datasets/pexels_45k_necessary.csv",
)
bucket_config = {
"512px_ar1:1": {96: (1.0, 1)},
}
model = dict(
type="dc_ae",
model_name="dc-ae-f32t4c128",
from_pretra... | 33 | 632 |
Open-Sora | configs/vae/inference/hunyuanvideo_vae.py | .py | dtype = "bf16"
batch_size = 1
seed = 42
save_dir = "samples/hunyuanvideo_vae"
plugin = "zero2"
dataset = dict(
type="video_text",
transform_name="resize_crop",
fps_max=16,
data_path="datasets/pexels_45k_necessary.csv",
)
bucket_config = {
"512px_ar1:1": {97: (1.0, 1)},
}
num_workers = 24
num_bucke... | 34 | 680 |
llama3 | setup.py | .py | # Copyright (c) Meta Platforms, Inc. and affiliates.
# This software may be used and distributed in accordance with the terms of the Llama 3 Community License Agreement.
from setuptools import find_packages, setup
def get_requirements(path: str):
return [l.strip() for l in open(path)]
setup(
name="llama3",... | 17 | 433 |
llama3 | example_chat_completion.py | .py | # Copyright (c) Meta Platforms, Inc. and affiliates.
# This software may be used and distributed in accordance with the terms of the Llama 3 Community License Agreement.
from typing import List, Optional
import fire
from llama import Dialog, Llama
def main(
ckpt_dir: str,
tokenizer_path: str,
temperatu... | 85 | 3,276 |
llama3 | example_text_completion.py | .py | # Copyright (c) Meta Platforms, Inc. and affiliates.
# This software may be used and distributed in accordance with the terms of the Llama 3 Community License Agreement.
from typing import List
import fire
from llama import Llama
def main(
ckpt_dir: str,
tokenizer_path: str,
temperature: float = 0.6,
... | 65 | 1,959 |
llama3 | llama/model.py | .py | # Copyright (c) Meta Platforms, Inc. and affiliates.
# This software may be used and distributed in accordance with the terms of the Llama 3 Community License Agreement.
import math
from dataclasses import dataclass
from typing import Optional, Tuple
import fairscale.nn.model_parallel.initialize as fs_init
import tor... | 303 | 10,404 |
llama3 | llama/generation.py | .py | # Copyright (c) Meta Platforms, Inc. and affiliates.
# This software may be used and distributed in accordance with the terms of the Llama 3 Community License Agreement.
import json
import os
import sys
import time
from pathlib import Path
from typing import List, Optional, Tuple, TypedDict
import torch
import torch.... | 366 | 15,393 |
llama3 | llama/test_tokenizer.py | .py | # Copyright (c) Meta Platforms, Inc. and affiliates.
# This software may be used and distributed in accordance with the terms of the Llama 3 Community License Agreement.
import os
from unittest import TestCase
from llama.tokenizer import ChatFormat, Tokenizer
# TOKENIZER_PATH=<path> python -m unittest llama/test_toke... | 89 | 2,871 |
llama3 | llama/tokenizer.py | .py | # Copyright (c) Meta Platforms, Inc. and affiliates.
# This software may be used and distributed in accordance with the terms of the Llama 3 Community License Agreement.
import os
from logging import getLogger
from pathlib import Path
from typing import (
AbstractSet,
cast,
Collection,
Dict,
Iterat... | 230 | 7,712 |
gpt-researcher | json_schema_generator.py | .py | import json
from typing import Dict, Any
from pydantic import BaseModel
class UserSchema(BaseModel):
id: int
name: str
email: str
age: int
is_active: bool
def generate_structured_json(schema: BaseModel, data: Dict[str, Any]) -> str:
"""
Generate structured JSON output based on provided sch... | 44 | 1,139 |
gpt-researcher | main.py | .py | from dotenv import load_dotenv
import logging
from pathlib import Path
# Create logs directory if it doesn't exist
logs_dir = Path("logs")
logs_dir.mkdir(exist_ok=True)
# Configure logging
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
handlers=[
... | 38 | 973 |
gpt-researcher | setup.py | .py | from setuptools import find_packages, setup
LATEST_VERSION = "0.14.7"
exclude_packages = [
"selenium",
"webdriver",
"fastapi",
"fastapi.*",
"uvicorn",
"jinja2",
"gpt-researcher",
"langgraph"
]
with open(r"README.md", "r", encoding="utf-8") as f:
long_description = f.read()
with o... | 48 | 1,452 |
gpt-researcher | cli.py | .py | """
Provides a command line interface for the GPTResearcher class.
Usage:
```shell
python cli.py "<query>" --report_type <report_type> --tone <tone> --query_domains <foo.com,bar.com>
```
"""
import argparse
import asyncio
import re
from argparse import RawTextHelpFormatter
from datetime import datetime
from pathlib ... | 363 | 12,555 |
gpt-researcher | docs/docs/examples/custom_prompt.py | .py | """
Custom Prompt Example for GPT Researcher
This example demonstrates how to use the custom_prompt parameter to customize report generation
based on specific formatting requirements or content needs.
"""
import asyncio
import nest_asyncio # Required for notebooks/interactive environments
# Apply nest_asyncio to al... | 74 | 3,111 |
gpt-researcher | docs/docs/examples/sample_report.py | .py | import nest_asyncio # required for notebooks
nest_asyncio.apply()
from gpt_researcher import GPTResearcher
import asyncio
async def get_report(query: str, report_type: str, custom_prompt: str = None):
researcher = GPTResearcher(query, report_type)
research_result = await researcher.conduct_research()
... | 47 | 1,577 |
gpt-researcher | docs/docs/examples/sample_sources_only.py | .py | from gpt_researcher import GPTResearcher
import asyncio
async def get_report(query: str, report_source: str, sources: list) -> str:
researcher = GPTResearcher(query=query, report_source=report_source, source_urls=sources)
research_context = await researcher.conduct_research()
return await researcher.write... | 21 | 786 |
gpt-researcher | tests/vector-store.py | .py | import asyncio
import pytest
from typing import List
from gpt_researcher import GPTResearcher
from langchain.text_splitter import CharacterTextSplitter
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import FAISS, InMemoryVectorStore
from langchain_core.documents import Document
#... | 237 | 15,727 |
gpt-researcher | tests/test_openalex_malformed_results.py | .py | import importlib.util
import sys
import types
import unittest
from pathlib import Path
from unittest.mock import MagicMock
ROOT = Path(__file__).resolve().parents[1]
MODULE_PATH = ROOT / "gpt_researcher" / "retrievers" / "openalex" / "openalex.py"
def _load():
requests_mod = types.ModuleType("requests")
cla... | 74 | 2,482 |
gpt-researcher | tests/test_multi_agents_fact_revisions.py | .py | import importlib.util
from pathlib import Path
import pytest
PATH = Path(__file__).resolve().parents[1] / "multi_agents" / "agents" / "fact_review.py"
spec = importlib.util.spec_from_file_location("fact_review", PATH)
mod = importlib.util.module_from_spec(spec)
spec.loader.exec_module(mod)
def test_accept_none_notes... | 28 | 842 |
gpt-researcher | tests/test_semantic_scholar_retriever.py | .py | """Guards for Semantic Scholar malformed payloads / openAccessPdf shapes."""
from unittest.mock import MagicMock, patch
from gpt_researcher.retrievers.semantic_scholar.semantic_scholar import (
SemanticScholarSearch,
)
def _resp(payload):
r = MagicMock()
r.raise_for_status = MagicMock()
r.json.retur... | 64 | 1,649 |
gpt-researcher | tests/test_serper_returns_list.py | .py | """Regression test: SerperSearch.search must always return a list, never None.
Sibling retrievers (serpapi/brave/bing/searx) return [] on error; callers
(`get_search_results` -> `len(search_results)`) crash on None.
"""
import sys
import types
from unittest.mock import patch
# serper.py only imports os, requests, js... | 63 | 1,598 |
gpt-researcher | tests/test_azure_document_loader.py | .py | import importlib.util
import sys
import types
import unittest
from pathlib import Path
class _FakeBlobServiceClient:
@classmethod
def from_connection_string(cls, connection_string):
return cls()
def get_container_client(self, container_name):
return None
azure_module = types.ModuleType(... | 88 | 2,653 |
gpt-researcher | tests/test_brave_retriever.py | .py | import os
import unittest
from unittest.mock import MagicMock, patch
from gpt_researcher.retrievers.brave.brave import BraveSearch
class TestBraveSearch(unittest.TestCase):
def test_missing_api_key_raises_clear_error(self):
with patch.dict(os.environ, {}, clear=True):
with self.assertRaisesRe... | 67 | 2,124 |
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