repo_full_name stringlengths 6 93 | repo_url stringlengths 25 112 | repo_api_url stringclasses 28
values | owner stringclasses 28
values | repo_name stringclasses 28
values | description stringclasses 28
values | stars int64 617 98.8k | forks int64 31 355 ⌀ | watchers int64 990 999 ⌀ | license stringclasses 2
values | default_branch stringclasses 2
values | repo_created_at timestamp[s]date 2012-07-24 23:12:50 2025-06-16 08:07:28 ⌀ | repo_updated_at timestamp[s]date 2026-02-23 15:23:15 2026-05-03 18:52:12 ⌀ | repo_topics listlengths 0 13 ⌀ | repo_languages unknown | is_fork bool 1
class | open_issues int64 3 104 ⌀ | file_path stringlengths 3 208 | file_name stringclasses 509
values | file_extension stringclasses 1
value | file_size_bytes int64 101 84k ⌀ | file_url stringclasses 627
values | file_raw_url stringclasses 627
values | file_sha stringclasses 624
values | language stringclasses 8
values | parsed_at stringdate 2026-05-04 01:12:36 2026-05-04 19:41:55 | text stringlengths 100 102k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
AIDC-AI/Pixelle-MCP | https://github.com/AIDC-AI/Pixelle-MCP | null | null | null | null | 984 | null | null | mit | null | null | null | null | null | null | null | pixelle/cli/utils/command_utils.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:45.835324 | # Copyright (C) 2025 AIDC-AI
# This project is licensed under the MIT License (SPDX-License-identifier: MIT).
"""Command-line utility functions."""
from pathlib import Path
from rich.console import Console
console = Console()
def detect_config_status() -> str:
"""Detect current config status"""
from pixel... |
AIDC-AI/Pixelle-MCP | https://github.com/AIDC-AI/Pixelle-MCP | null | null | null | null | 984 | null | null | mit | null | null | null | null | null | null | null | pixelle/cli/utils/display.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:45.896368 | # Copyright (C) 2025 AIDC-AI
# This project is licensed under the MIT License (SPDX-License-identifier: MIT).
"""Display utility functions for CLI."""
from rich.console import Console
from rich.panel import Panel
from rich.table import Table
console = Console()
def show_welcome():
"""Show welcome message"""
... |
AIDC-AI/Pixelle-MCP | https://github.com/AIDC-AI/Pixelle-MCP | null | null | null | null | 984 | null | null | mit | null | null | null | null | null | null | null | pixelle/cli/utils/server_utils.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:46.073913 | # Copyright (C) 2025 AIDC-AI
# This project is licensed under the MIT License (SPDX-License-identifier: MIT).
"""Server management utility functions."""
import typer
from rich.console import Console
from rich.panel import Panel
from rich.table import Table
from pixelle.settings import settings
from pixelle.utils.pro... |
AIDC-AI/Pixelle-MCP | https://github.com/AIDC-AI/Pixelle-MCP | null | null | null | null | 984 | null | null | mit | null | null | null | null | null | null | null | pixelle/comfyui/__init__.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:46.170964 | # Copyright (C) 2025 AIDC-AI
# This project is licensed under the MIT License (SPDX-License-identifier: MIT).
from .facade import execute_workflow, get_workflow_metadata, ComfyUIClient
from .runninghub_client import RunningHubClient, get_runninghub_client
from .runninghub_executor import RunningHubExecutor
__all__ = ... |
AIDC-AI/Pixelle-MCP | https://github.com/AIDC-AI/Pixelle-MCP | null | null | null | null | 984 | null | null | mit | null | null | null | null | null | null | null | pixelle/comfyui/base_executor.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:46.204384 | # Copyright (C) 2025 AIDC-AI
# This project is licensed under the MIT License (SPDX-License-identifier: MIT).
import os
import json
import copy
import tempfile
import mimetypes
from abc import ABC, abstractmethod
from urllib.parse import urlparse
from typing import Any, Optional, Dict, List, Tuple
from contextlib impo... |
AIDC-AI/Pixelle-MCP | https://github.com/AIDC-AI/Pixelle-MCP | null | null | null | null | 984 | null | null | mit | null | null | null | null | null | null | null | pixelle/comfyui/facade.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:46.317159 | # Copyright (C) 2025 AIDC-AI
# This project is licensed under the MIT License (SPDX-License-identifier: MIT).
from typing import Dict, Any
from pixelle.comfyui.models import ExecuteResult
from pixelle.comfyui.websocket_executor import WebSocketExecutor
from pixelle.comfyui.http_executor import HttpExecutor
from pixel... |
AIDC-AI/Pixelle-MCP | https://github.com/AIDC-AI/Pixelle-MCP | null | null | null | null | 984 | null | null | mit | null | null | null | null | null | null | null | pixelle/comfyui/http_executor.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:46.416317 | # Copyright (C) 2025 AIDC-AI
# This project is licensed under the MIT License (SPDX-License-identifier: MIT).
import os
import json
import time
import uuid
import asyncio
from typing import Optional, Dict, Any
from pixelle.comfyui.base_executor import ComfyUIExecutor, COMFYUI_API_KEY, logger
from pixelle.comfyui.mode... |
AIDC-AI/Pixelle-MCP | https://github.com/AIDC-AI/Pixelle-MCP | null | null | null | null | 984 | null | null | mit | null | null | null | null | null | null | null | pixelle/comfyui/models.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:46.448036 | # Copyright (C) 2025 AIDC-AI
# This project is licensed under the MIT License (SPDX-License-identifier: MIT).
from typing import Optional, List, Dict, Any
from pydantic import BaseModel, Field
class ExecuteResult(BaseModel):
"""Execution result model"""
status: str = Field(description="Execution status")
... |
AIDC-AI/Pixelle-MCP | https://github.com/AIDC-AI/Pixelle-MCP | null | null | null | null | 984 | null | null | mit | null | null | null | null | null | null | null | pixelle/comfyui/runninghub_executor.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:46.740148 | # Copyright (C) 2025 AIDC-AI
# This project is licensed under the MIT License (SPDX-License-identifier: MIT).
import os
import json
import time
import asyncio
from typing import Dict, Any, Optional, List
from urllib.parse import urlparse
from pixelle.comfyui.base_executor import ComfyUIExecutor, MEDIA_UPLOAD_NODE_TYP... |
AIDC-AI/Pixelle-MCP | https://github.com/AIDC-AI/Pixelle-MCP | null | null | null | null | 984 | null | null | mit | null | null | null | null | null | null | null | pixelle/comfyui/runninghub_client.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:46.749902 | # Copyright (C) 2025 AIDC-AI
# This project is licensed under the MIT License (SPDX-License-identifier: MIT).
import json
import tempfile
from typing import Optional, Dict, Any, List, Literal
from pathlib import Path
import aiohttp
import asyncio
from pixelle.logger import logger
from pixelle.settings import settings... |
AIDC-AI/Pixelle-MCP | https://github.com/AIDC-AI/Pixelle-MCP | null | null | null | null | 984 | null | null | mit | null | null | null | null | null | null | null | pixelle/comfyui/websocket_executor.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:46.836740 | # Copyright (C) 2025 AIDC-AI
# This project is licensed under the MIT License (SPDX-License-identifier: MIT).
import os
import json
import time
import uuid
import asyncio
from typing import Optional, Dict, Any
from urllib.parse import urlparse, urlunparse
import websockets
from pixelle.comfyui.base_executor import Co... |
AIDC-AI/Pixelle-MCP | https://github.com/AIDC-AI/Pixelle-MCP | null | null | null | null | 984 | null | null | mit | null | null | null | null | null | null | null | pixelle/logger.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:46.983571 | # Copyright (C) 2025 AIDC-AI
# This project is licensed under the MIT License (SPDX-License-identifier: MIT).
import logging
class HealthCheckFilter(logging.Filter):
"""Filter health check access logs"""
def filter(self, record):
if hasattr(record, 'getMessage'):
message = record.getMess... |
AIDC-AI/Pixelle-MCP | https://github.com/AIDC-AI/Pixelle-MCP | null | null | null | null | 984 | null | null | mit | null | null | null | null | null | null | null | pixelle/comfyui/workflow_parser.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:46.984253 | # Copyright (C) 2025 AIDC-AI
# This project is licensed under the MIT License (SPDX-License-identifier: MIT).
import json
import re
from pathlib import Path
from pixelle.logger import logger
from typing import Dict, Any, Optional, List
from pydantic import BaseModel, Field
class WorkflowParam(BaseModel):
name: st... |
AIDC-AI/Pixelle-MCP | https://github.com/AIDC-AI/Pixelle-MCP | null | null | null | null | 984 | null | null | mit | null | null | null | null | null | null | null | pixelle/cli/setup/providers/gemini.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:49.615149 | # Copyright (C) 2025 AIDC-AI
# This project is licensed under the MIT License (SPDX-License-identifier: MIT).
"""Gemini provider configuration."""
from typing import Dict, Optional
import questionary
from rich.console import Console
console = Console()
def configure_gemini() -> Optional[Dict]:
"""Configure Gem... |
AIDC-AI/Pixelle-MCP | https://github.com/AIDC-AI/Pixelle-MCP | null | null | null | null | 984 | null | null | mit | null | null | null | null | null | null | null | pixelle/cli/setup/providers/deepseek.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:49.645838 | # Copyright (C) 2025 AIDC-AI
# This project is licensed under the MIT License (SPDX-License-identifier: MIT).
"""DeepSeek provider configuration."""
from typing import Dict, Optional
import questionary
from rich.console import Console
console = Console()
def configure_deepseek() -> Optional[Dict]:
"""Configure... |
AIDC-AI/Pixelle-MCP | https://github.com/AIDC-AI/Pixelle-MCP | null | null | null | null | 984 | null | null | mit | null | null | null | null | null | null | null | pixelle/cli/setup/providers/manager.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:49.692090 | # Copyright (C) 2025 AIDC-AI
# This project is licensed under the MIT License (SPDX-License-identifier: MIT).
"""LLM provider manager."""
from typing import Dict, List, Optional
import questionary
from rich.console import Console
from rich.panel import Panel
from pixelle.cli.setup.providers.openai import configure_o... |
AIDC-AI/Pixelle-MCP | https://github.com/AIDC-AI/Pixelle-MCP | null | null | null | null | 984 | null | null | mit | null | null | null | null | null | null | null | pixelle/cli/setup/providers/__init__.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:49.803986 | # Copyright (C) 2025 AIDC-AI
# This project is licensed under the MIT License (SPDX-License-identifier: MIT).
"""LLM provider configuration modules."""
|
SublimeText/CTags | https://github.com/SublimeText/CTags | null | null | null | null | 984 | null | null | mit | null | null | null | null | null | null | null | plugin.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:51.904467 | """
A ctags plugin for Sublime Text.
"""
import sublime
if int(sublime.version()) < 3143:
print("CTags requires Sublime Text 3143+")
else:
import sys
# Clear module cache to force reloading all modules of this package.
prefix = __package__ + "." # don't clear the base package
for module_name in ... |
SublimeText/CTags | https://github.com/SublimeText/CTags | null | null | null | null | 984 | null | null | mit | null | null | null | null | null | null | null | plugins/activity_indicator.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:51.916540 | import sublime
from threading import RLock
class ActivityIndicator:
"""
An animated text-based indicator to show that some activity is in progress.
The `target` argument should be a :class:`sublime.View` or :class:`sublime.Window`.
The indicator will be shown in the status bar of that view or window.... |
SublimeText/CTags | https://github.com/SublimeText/CTags | null | null | null | null | 984 | null | null | mit | null | null | null | null | null | null | null | plugins/ctags.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:51.918479 | """
A ctags wrapper, parser and sorter.
"""
import bisect
import mmap
import os
import re
import subprocess
from subprocess import check_output
#
# Contants
#
TAGS_RE = re.compile(
r"(?P<symbol>[^\t]+)\t"
r"(?P<filename>[^\t]+)\t"
r'(?P<ex_command>(/.+/|\?.+\?|\d+));"\t'
r"(?P<type>[^\t\r\n]+)"
... |
SublimeText/CTags | https://github.com/SublimeText/CTags | null | null | null | null | 984 | null | null | mit | null | null | null | null | null | null | null | plugins/ranking/parse.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:51.922889 | import re
from ..utils import *
# import spdb
# spdb.start()
class Parser:
"""
Parses tag references and tag definitions. Used for ranking
"""
@staticmethod
def extract_member_exp(line_to_symbol, source):
"""
Extract receiver object e.g. receiver.mtd()
Strip away brackets... |
SublimeText/CTags | https://github.com/SublimeText/CTags | null | null | null | null | 984 | null | null | mit | null | null | null | null | null | null | null | plugins/edit.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:51.923462 | """
Buffer editing for both ST2 and ST3 that 'just works'.
Copyright, SublimeXiki project <https://github.com/lunixbochs/SublimeXiki>
"""
import inspect
import sublime
import sublime_plugin
try:
sublime.edit_storage
except AttributeError:
sublime.edit_storage = {}
def run_callback(func, *args, **kwargs):
... |
SublimeText/CTags | https://github.com/SublimeText/CTags | null | null | null | null | 984 | null | null | mit | null | null | null | null | null | null | null | plugins/tests/__init__.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:51.929736 | import sys
from . import mock_sublime
from . import mock_sublime_plugin
sys.modules["sublime"] = mock_sublime
sys.modules["sublime_plugin"] = mock_sublime_plugin
|
SublimeText/CTags | https://github.com/SublimeText/CTags | null | null | null | null | 984 | null | null | mit | null | null | null | null | null | null | null | plugins/cmds.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:51.961834 | import functools
import locale
import os
import pprint
import re
import string
import subprocess
import threading
from collections import defaultdict
from itertools import chain
from operator import itemgetter as iget
import sublime
import sublime_plugin
from sublime import status_message, error_message
from .activi... |
SublimeText/CTags | https://github.com/SublimeText/CTags | null | null | null | null | 984 | null | null | mit | null | null | null | null | null | null | null | plugins/ranking/rank.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:52.042233 | """
Rank and Filter support for ctags plugin for Sublime Text 2/3.
"""
import os
import re
import string
import sys
from functools import reduce
from ..utils import *
def compile_definition_filters(view):
filters = []
for selector, regexes in list(get_setting("definition_filters", {}).items()):
if ... |
SublimeText/CTags | https://github.com/SublimeText/CTags | null | null | null | null | 984 | null | null | mit | null | null | null | null | null | null | null | plugins/utils.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:52.879467 | """
common utilities used by all ctags modules
"""
import re
import sublime
def get_settings():
"""
Load settings.
:returns: dictionary containing settings
"""
return sublime.load_settings("CTags.sublime-settings")
def get_setting(key, default=None):
"""
Load individual setting.
:p... |
SublimeText/CTags | https://github.com/SublimeText/CTags | null | null | null | null | 984 | null | null | mit | null | null | null | null | null | null | null | plugins/tests/mock_sublime_plugin.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:52.993348 | """
Mock module for ``sublime_plugin`` in Sublime Text.
"""
all_callbacks = {"on_load": []}
class WindowCommand(object):
pass
class TextCommand(object):
pass
class EventListener(object):
pass
|
SublimeText/CTags | https://github.com/SublimeText/CTags | null | null | null | null | 984 | null | null | mit | null | null | null | null | null | null | null | plugins/tests/test_ctags.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:53.310808 | #!/usr/bin/env python
"""
Unit tests for 'ctags.py'.
"""
import os
import tempfile
import unittest
from subprocess import CalledProcessError
from .. import ctags
class CTagsTest(unittest.TestCase):
#
# Helper functions
#
def build_python_file(self):
"""
Build a simple Python "progr... |
SublimeText/CTags | https://github.com/SublimeText/CTags | null | null | null | null | 984 | null | null | mit | null | null | null | null | null | null | null | plugins/tests/test_ctagsplugin.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:53.340140 | #!/usr/bin/env python
"""
Unit tests for 'cmds.py'.
"""
import os
import sys
import shutil
import tempfile
import unittest
from .. import cmds
from .. import ctags
class CTagsPluginTest(unittest.TestCase):
#
# Helper functions.
#
def make_tmp_directory(self, pwd=None):
"""
Make a t... |
SublimeText/CTags | https://github.com/SublimeText/CTags | null | null | null | null | 984 | null | null | mit | null | null | null | null | null | null | null | plugins/tests/mock_sublime.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:53.405121 | """
Mock module for ``sublime`` in Sublime Text.
"""
import sys
# find flags
LITERAL = 1
IGNORECASE = 2
WHOLEWORD = 4
REVERSE = 8
WRAP = 16
def arch():
return "x64"
def platform():
if sys.platform == "darwin":
return "osx"
if sys.platform == "win32":
return "windows"
return "linux"... |
fudan-zvg/4d-gaussian-splatting | https://github.com/fudan-zvg/4d-gaussian-splatting | null | null | null | null | 983 | null | null | mit | null | null | null | null | null | null | null | gaussian_renderer/__init__.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:55.706922 | #
# Copyright (C) 2023, Inria
# GRAPHDECO research group, https://team.inria.fr/graphdeco
# All rights reserved.
#
# This software is free for non-commercial, research and evaluation use
# under the terms of the LICENSE.md file.
#
# For inquiries contact george.drettakis@inria.fr
#
import torch
from torch.nn import ... |
fudan-zvg/4d-gaussian-splatting | https://github.com/fudan-zvg/4d-gaussian-splatting | null | null | null | null | 983 | null | null | mit | null | null | null | null | null | null | null | arguments/__init__.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:55.708037 | #
# Copyright (C) 2023, Inria
# GRAPHDECO research group, https://team.inria.fr/graphdeco
# All rights reserved.
#
# This software is free for non-commercial, research and evaluation use
# under the terms of the LICENSE.md file.
#
# For inquiries contact george.drettakis@inria.fr
#
from argparse import ArgumentParse... |
fudan-zvg/4d-gaussian-splatting | https://github.com/fudan-zvg/4d-gaussian-splatting | null | null | null | null | 983 | null | null | mit | null | null | null | null | null | null | null | gaussian_renderer/diff_gaussian_rasterization.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:55.709625 | #
# Copyright (C) 2023, Inria
# GRAPHDECO research group, https://team.inria.fr/graphdeco
# All rights reserved.
#
# This software is free for non-commercial, research and evaluation use
# under the terms of the LICENSE.md file.
#
# For inquiries contact george.drettakis@inria.fr
#
from typing import NamedTuple
impo... |
fudan-zvg/4d-gaussian-splatting | https://github.com/fudan-zvg/4d-gaussian-splatting | null | null | null | null | 983 | null | null | mit | null | null | null | null | null | null | null | lpipsPyTorch/modules/utils.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:55.715723 | from collections import OrderedDict
import torch
def normalize_activation(x, eps=1e-10):
norm_factor = torch.sqrt(torch.sum(x ** 2, dim=1, keepdim=True))
return x / (norm_factor + eps)
def get_state_dict(net_type: str = 'alex', version: str = '0.1'):
# build url
url = 'https://raw.githubusercontent... |
fudan-zvg/4d-gaussian-splatting | https://github.com/fudan-zvg/4d-gaussian-splatting | null | null | null | null | 983 | null | null | mit | null | null | null | null | null | null | null | lpipsPyTorch/__init__.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:55.716893 | import torch
from .modules.lpips import LPIPS
def lpips(x: torch.Tensor,
y: torch.Tensor,
net_type: str = 'alex',
version: str = '0.1'):
r"""Function that measures
Learned Perceptual Image Patch Similarity (LPIPS).
Arguments:
x, y (torch.Tensor): the input tensors t... |
fudan-zvg/4d-gaussian-splatting | https://github.com/fudan-zvg/4d-gaussian-splatting | null | null | null | null | 983 | null | null | mit | null | null | null | null | null | null | null | lpipsPyTorch/modules/lpips.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:55.718671 | import torch
import torch.nn as nn
from .networks import get_network, LinLayers
from .utils import get_state_dict
class LPIPS(nn.Module):
r"""Creates a criterion that measures
Learned Perceptual Image Patch Similarity (LPIPS).
Arguments:
net_type (str): the network type to compare the features: ... |
fudan-zvg/4d-gaussian-splatting | https://github.com/fudan-zvg/4d-gaussian-splatting | null | null | null | null | 983 | null | null | mit | null | null | null | null | null | null | null | gaussian_renderer/network_gui.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:55.720073 | #
# Copyright (C) 2023, Inria
# GRAPHDECO research group, https://team.inria.fr/graphdeco
# All rights reserved.
#
# This software is free for non-commercial, research and evaluation use
# under the terms of the LICENSE.md file.
#
# For inquiries contact george.drettakis@inria.fr
#
import torch
import traceback
impo... |
fudan-zvg/4d-gaussian-splatting | https://github.com/fudan-zvg/4d-gaussian-splatting | null | null | null | null | 983 | null | null | mit | null | null | null | null | null | null | null | diff-gaussian-rasterization/setup.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:55.731363 | #
# Copyright (C) 2023, Inria
# GRAPHDECO research group, https://team.inria.fr/graphdeco
# All rights reserved.
#
# This software is free for non-commercial, research and evaluation use
# under the terms of the LICENSE.md file.
#
# For inquiries contact george.drettakis@inria.fr
#
from setuptools import setup
from ... |
fudan-zvg/4d-gaussian-splatting | https://github.com/fudan-zvg/4d-gaussian-splatting | null | null | null | null | 983 | null | null | mit | null | null | null | null | null | null | null | lpipsPyTorch/modules/networks.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:55.749572 | from typing import Sequence
from itertools import chain
import torch
import torch.nn as nn
from torchvision import models
from .utils import normalize_activation
def get_network(net_type: str):
if net_type == 'alex':
return AlexNet()
elif net_type == 'squeeze':
return SqueezeNet()
elif ... |
fudan-zvg/4d-gaussian-splatting | https://github.com/fudan-zvg/4d-gaussian-splatting | null | null | null | null | 983 | null | null | mit | null | null | null | null | null | null | null | pointops2/functions/test_attention_op_step1_v2.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:57.155491 | import torch
import pointops
from torch_scatter import scatter_max, scatter_mean, scatter_add, scatter_min, scatter_sum
torch.manual_seed(1)
M = 800000
N = 35000
C = 96
h = 6
query = torch.rand(N, h, C//h).cuda()
key = torch.rand(N, h, C//h).cuda()
index_0 = torch.rand(M)
index_0[index_0 < 0] = 0
index_0 = (index_0*... |
fudan-zvg/4d-gaussian-splatting | https://github.com/fudan-zvg/4d-gaussian-splatting | null | null | null | null | 983 | null | null | mit | null | null | null | null | null | null | null | pointops2/functions/test_attention_op_step2.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:57.158600 | import torch
import pointops
from torch_scatter import scatter_max, scatter_mean, scatter_add, scatter_min, scatter_sum
torch.manual_seed(1)
M = 800000
N = 35000
C = 96
h = 6
softmax_attn_flat = torch.rand(M, h).cuda()
value = torch.rand(N, h, C//h).cuda()
index_0 = torch.rand(M)
index_0[index_0 < 0] = 0
index_0 = (... |
fudan-zvg/4d-gaussian-splatting | https://github.com/fudan-zvg/4d-gaussian-splatting | null | null | null | null | 983 | null | null | mit | null | null | null | null | null | null | null | pointops2/functions/pointops2.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:57.160115 | from typing import Tuple
import torch
from torch.autograd import Function
import torch.nn as nn
import pointops2_cuda as pointops_cuda
class FurthestSampling(Function):
@staticmethod
def forward(ctx, xyz, offset, new_offset):
"""
input: xyz: (n, 3), offset: (b), new_offset: (b)
outpu... |
fudan-zvg/4d-gaussian-splatting | https://github.com/fudan-zvg/4d-gaussian-splatting | null | null | null | null | 983 | null | null | mit | null | null | null | null | null | null | null | pointops2/functions/test_attention_op_step1.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:57.161223 | import torch
import pointops
from torch_scatter import scatter_max, scatter_mean, scatter_add, scatter_min, scatter_sum
torch.manual_seed(1)
M = 800000
N = 35000
C = 96
h = 6
query = torch.rand(N, h, C//h).cuda()
key = torch.rand(N, h, C//h).cuda()
index_0 = torch.rand(M)
index_0[index_0 < 0] = 0
index_0 = (index_0*... |
fudan-zvg/4d-gaussian-splatting | https://github.com/fudan-zvg/4d-gaussian-splatting | null | null | null | null | 983 | null | null | mit | null | null | null | null | null | null | null | pointops2/functions/test_relative_pos_encoding_op_step1.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:57.162459 | import torch
import pointops
from torch_scatter import scatter_max, scatter_mean, scatter_add, scatter_min, scatter_sum
torch.manual_seed(1)
M = 80000
N = 3500
hdim = 16
h = 6
L = 31
query = torch.rand(N, h, hdim).cuda()
table = torch.rand(L, h, hdim, 3).cuda()
index = torch.rand(M)
index[index < 0] = 0
index = (ind... |
fudan-zvg/4d-gaussian-splatting | https://github.com/fudan-zvg/4d-gaussian-splatting | null | null | null | null | 983 | null | null | mit | null | null | null | null | null | null | null | pointops2/functions/pointops.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:57.163876 | '''
The part of attention operations is written by Xin Lai.
Email: xinlai@cse.cuhk.edu.hk
'''
from typing import Tuple
import torch
from torch.autograd import Function
import torch.nn as nn
import pointops2_cuda as pointops_cuda
import time
class FurthestSampling(Function):
@staticmethod
def forward(ctx, xyz... |
fudan-zvg/4d-gaussian-splatting | https://github.com/fudan-zvg/4d-gaussian-splatting | null | null | null | null | 983 | null | null | mit | null | null | null | null | null | null | null | pointops2/functions/pointops_ablation.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:57.165316 | from typing import Tuple
import torch
from torch.autograd import Function
import torch.nn as nn
import pointops2_cuda as pointops_cuda
class FurthestSampling(Function):
@staticmethod
def forward(ctx, xyz, offset, new_offset):
"""
input: xyz: (n, 3), offset: (b), new_offset: (b)
outpu... |
fudan-zvg/4d-gaussian-splatting | https://github.com/fudan-zvg/4d-gaussian-splatting | null | null | null | null | 983 | null | null | mit | null | null | null | null | null | null | null | pointops2/functions/test_relative_pos_encoding_op_step1_v2.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:58.666334 | import torch
import pointops
from torch_scatter import scatter_max, scatter_mean, scatter_add, scatter_min, scatter_sum
torch.manual_seed(1)
M = 80000
N = 3500
hdim = 16
h = 6
L = 31
query = torch.rand(N, h, hdim).cuda()
table_q = torch.rand(L, h, hdim, 3).cuda()
key = torch.rand(N, h, hdim).cuda()
table_k = torch.ra... |
fudan-zvg/4d-gaussian-splatting | https://github.com/fudan-zvg/4d-gaussian-splatting | null | null | null | null | 983 | null | null | mit | null | null | null | null | null | null | null | pointops2/functions/test_relative_pos_encoding_op_step2_v2.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:59.306859 | import torch
import pointops
from torch_scatter import scatter_max, scatter_mean, scatter_add, scatter_min, scatter_sum
torch.manual_seed(1)
M = 80000
N = 3500
hdim = 16
h = 6
L = 31
attn = torch.rand(M, h).cuda()
v = torch.rand(N, h, hdim).cuda()
table = torch.rand(L, h, hdim, 3).cuda()
index_0 = torch.rand(M)
inde... |
fudan-zvg/4d-gaussian-splatting | https://github.com/fudan-zvg/4d-gaussian-splatting | null | null | null | null | 983 | null | null | mit | null | null | null | null | null | null | null | pointops2/functions/test_relative_pos_encoding_op_step1_v3.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:59.307928 | import torch
import pointops
from torch_scatter import scatter_max, scatter_mean, scatter_add, scatter_min, scatter_sum
torch.manual_seed(1)
M = 80000
N = 3500
# M = 80
# N = 5
hdim = 16
h = 6
L = 31
query = torch.rand(N, h, hdim).cuda()
table_q = torch.rand(L, h, hdim, 3).cuda()
key = torch.rand(N, h, hdim).cuda()
t... |
fudan-zvg/4d-gaussian-splatting | https://github.com/fudan-zvg/4d-gaussian-splatting | null | null | null | null | 983 | null | null | mit | null | null | null | null | null | null | null | scene/dataset_readers.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:59.356750 | #
# Copyright (C) 2023, Inria
# GRAPHDECO research group, https://team.inria.fr/graphdeco
# All rights reserved.
#
# This software is free for non-commercial, research and evaluation use
# under the terms of the LICENSE.md file.
#
# For inquiries contact george.drettakis@inria.fr
#
import os
import sys
from PIL impo... |
fudan-zvg/4d-gaussian-splatting | https://github.com/fudan-zvg/4d-gaussian-splatting | null | null | null | null | 983 | null | null | mit | null | null | null | null | null | null | null | scene/colmap_loader.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:59.408224 | #
# Copyright (C) 2023, Inria
# GRAPHDECO research group, https://team.inria.fr/graphdeco
# All rights reserved.
#
# This software is free for non-commercial, research and evaluation use
# under the terms of the LICENSE.md file.
#
# For inquiries contact george.drettakis@inria.fr
#
import numpy as np
import collecti... |
fudan-zvg/4d-gaussian-splatting | https://github.com/fudan-zvg/4d-gaussian-splatting | null | null | null | null | 983 | null | null | mit | null | null | null | null | null | null | null | scene/cameras.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:59.502018 | #
# Copyright (C) 2023, Inria
# GRAPHDECO research group, https://team.inria.fr/graphdeco
# All rights reserved.
#
# This software is free for non-commercial, research and evaluation use
# under the terms of the LICENSE.md file.
#
# For inquiries contact george.drettakis@inria.fr
#
import torch
from torch import nn
... |
fudan-zvg/4d-gaussian-splatting | https://github.com/fudan-zvg/4d-gaussian-splatting | null | null | null | null | 983 | null | null | mit | null | null | null | null | null | null | null | scene/__init__.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:59.581865 | #
# Copyright (C) 2023, Inria
# GRAPHDECO research group, https://team.inria.fr/graphdeco
# All rights reserved.
#
# This software is free for non-commercial, research and evaluation use
# under the terms of the LICENSE.md file.
#
# For inquiries contact george.drettakis@inria.fr
#
import os
import torch
import rand... |
fudan-zvg/4d-gaussian-splatting | https://github.com/fudan-zvg/4d-gaussian-splatting | null | null | null | null | 983 | null | null | mit | null | null | null | null | null | null | null | train.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:59.866549 | #
# Copyright (C) 2023, Inria
# GRAPHDECO research group, https://team.inria.fr/graphdeco
# All rights reserved.
#
# This software is free for non-commercial, research and evaluation use
# under the terms of the LICENSE.md file.
#
# For inquiries contact george.drettakis@inria.fr
#
import os
import random
import tor... |
fudan-zvg/4d-gaussian-splatting | https://github.com/fudan-zvg/4d-gaussian-splatting | null | null | null | null | 983 | null | null | mit | null | null | null | null | null | null | null | scripts/n3v2blender.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:59.887517 | import os
import argparse
import glob
import numpy as np
import json
import sys
import math
import shutil
import sqlite3
IS_PYTHON3 = sys.version_info[0] >= 3
MAX_IMAGE_ID = 2**31 - 1
CREATE_CAMERAS_TABLE = """CREATE TABLE IF NOT EXISTS cameras (
camera_id INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL,
model IN... |
fudan-zvg/4d-gaussian-splatting | https://github.com/fudan-zvg/4d-gaussian-splatting | null | null | null | null | 983 | null | null | mit | null | null | null | null | null | null | null | simple-knn/setup.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:59.900098 | #
# Copyright (C) 2023, Inria
# GRAPHDECO research group, https://team.inria.fr/graphdeco
# All rights reserved.
#
# This software is free for non-commercial, research and evaluation use
# under the terms of the LICENSE.md file.
#
# For inquiries contact george.drettakis@inria.fr
#
from setuptools import setup
from ... |
fudan-zvg/4d-gaussian-splatting | https://github.com/fudan-zvg/4d-gaussian-splatting | null | null | null | null | 983 | null | null | mit | null | null | null | null | null | null | null | utils/camera_utils.py | null | null | null | null | null | null | Python | 2026-05-04T01:34:59.959183 | #
# Copyright (C) 2023, Inria
# GRAPHDECO research group, https://team.inria.fr/graphdeco
# All rights reserved.
#
# This software is free for non-commercial, research and evaluation use
# under the terms of the LICENSE.md file.
#
# For inquiries contact george.drettakis@inria.fr
#
from scene.cameras import Camera
i... |
fudan-zvg/4d-gaussian-splatting | https://github.com/fudan-zvg/4d-gaussian-splatting | null | null | null | null | 983 | null | null | mit | null | null | null | null | null | null | null | utils/data_utils.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:00.000441 | import os
import torch
from torchvision.utils import save_image
from torch.utils.data import Dataset
from torchvision import datasets
from utils.general_utils import PILtoTorch
from PIL import Image
import numpy as np
class CameraDataset(Dataset):
def __init__(self, viewpoint_stack, white_background):
... |
fudan-zvg/4d-gaussian-splatting | https://github.com/fudan-zvg/4d-gaussian-splatting | null | null | null | null | 983 | null | null | mit | null | null | null | null | null | null | null | pointops2/setup.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:00.104880 | import os
from setuptools import setup
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
from distutils.sysconfig import get_config_vars
# (opt,) = get_config_vars('OPT')
# os.environ['OPT'] = " ".join(
# flag for flag in opt.split() if flag != '-Wstrict-prototypes'
# )
src = 'src'
sources = [os... |
fudan-zvg/4d-gaussian-splatting | https://github.com/fudan-zvg/4d-gaussian-splatting | null | null | null | null | 983 | null | null | mit | null | null | null | null | null | null | null | utils/general_utils.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:00.125564 | #
# Copyright (C) 2023, Inria
# GRAPHDECO research group, https://team.inria.fr/graphdeco
# All rights reserved.
#
# This software is free for non-commercial, research and evaluation use
# under the terms of the LICENSE.md file.
#
# For inquiries contact george.drettakis@inria.fr
#
import torch
import sys
from datet... |
fudan-zvg/4d-gaussian-splatting | https://github.com/fudan-zvg/4d-gaussian-splatting | null | null | null | null | 983 | null | null | mit | null | null | null | null | null | null | null | utils/graphics_utils.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:00.182034 | #
# Copyright (C) 2023, Inria
# GRAPHDECO research group, https://team.inria.fr/graphdeco
# All rights reserved.
#
# This software is free for non-commercial, research and evaluation use
# under the terms of the LICENSE.md file.
#
# For inquiries contact george.drettakis@inria.fr
#
import torch
import math
import nu... |
fudan-zvg/4d-gaussian-splatting | https://github.com/fudan-zvg/4d-gaussian-splatting | null | null | null | null | 983 | null | null | mit | null | null | null | null | null | null | null | pointops2/functions/test_relative_pos_encoding_op_step2.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:00.625540 | import torch
import pointops
from torch_scatter import scatter_max, scatter_mean, scatter_add, scatter_min, scatter_sum
torch.manual_seed(1)
M = 80000
N = 3500
hdim = 16
h = 6
L = 31
attn = torch.rand(M, h).cuda()
v = torch.rand(N, h, hdim).cuda()
table = torch.rand(L, h, hdim, 3).cuda()
index_0 = torch.rand(M)
inde... |
fudan-zvg/4d-gaussian-splatting | https://github.com/fudan-zvg/4d-gaussian-splatting | null | null | null | null | 983 | null | null | mit | null | null | null | null | null | null | null | utils/sh_utils.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:00.799514 | # Copyright 2021 The PlenOctree Authors.
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice,
# this list of conditions and the following discl... |
fudan-zvg/4d-gaussian-splatting | https://github.com/fudan-zvg/4d-gaussian-splatting | null | null | null | null | 983 | null | null | mit | null | null | null | null | null | null | null | utils/image_utils.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:00.830832 | #
# Copyright (C) 2023, Inria
# GRAPHDECO research group, https://team.inria.fr/graphdeco
# All rights reserved.
#
# This software is free for non-commercial, research and evaluation use
# under the terms of the LICENSE.md file.
#
# For inquiries contact george.drettakis@inria.fr
#
import torch
def mse(img1, img2):... |
fudan-zvg/4d-gaussian-splatting | https://github.com/fudan-zvg/4d-gaussian-splatting | null | null | null | null | 983 | null | null | mit | null | null | null | null | null | null | null | utils/loss_utils.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:01.550496 | #
# Copyright (C) 2023, Inria
# GRAPHDECO research group, https://team.inria.fr/graphdeco
# All rights reserved.
#
# This software is free for non-commercial, research and evaluation use
# under the terms of the LICENSE.md file.
#
# For inquiries contact george.drettakis@inria.fr
#
import torch
import torch.nn.funct... |
fudan-zvg/4d-gaussian-splatting | https://github.com/fudan-zvg/4d-gaussian-splatting | null | null | null | null | 983 | null | null | mit | null | null | null | null | null | null | null | utils/system_utils.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:01.779191 | #
# Copyright (C) 2023, Inria
# GRAPHDECO research group, https://team.inria.fr/graphdeco
# All rights reserved.
#
# This software is free for non-commercial, research and evaluation use
# under the terms of the LICENSE.md file.
#
# For inquiries contact george.drettakis@inria.fr
#
from errno import EEXIST
from os i... |
fudan-zvg/4d-gaussian-splatting | https://github.com/fudan-zvg/4d-gaussian-splatting | null | null | null | null | 983 | null | null | mit | null | null | null | null | null | null | null | scene/gaussian_model.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:04.497865 | #
# Copyright (C) 2023, Inria
# GRAPHDECO research group, https://team.inria.fr/graphdeco
# All rights reserved.
#
# This software is free for non-commercial, research and evaluation use
# under the terms of the LICENSE.md file.
#
# For inquiries contact george.drettakis@inria.fr
#
import torch
import numpy as np
fr... |
langchain-ai/mcpdoc | https://github.com/langchain-ai/mcpdoc | null | null | null | null | 982 | null | null | mit | null | null | null | null | null | null | null | mcpdoc/_version.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:06.861420 | from importlib import metadata
try:
__version__ = metadata.version(__package__)
except metadata.PackageNotFoundError:
# Case where package metadata is not available.
__version__ = ""
|
langchain-ai/mcpdoc | https://github.com/langchain-ai/mcpdoc | null | null | null | null | 982 | null | null | mit | null | null | null | null | null | null | null | mcpdoc/cli.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:06.862619 | #!/usr/bin/env python3
"""Command-line interface for mcp-llms-txt server."""
import argparse
import json
import sys
from typing import List, Dict
import yaml
from mcpdoc._version import __version__
from mcpdoc.main import create_server, DocSource
from mcpdoc.splash import SPLASH
class CustomFormatter(
argparse... |
langchain-ai/mcpdoc | https://github.com/langchain-ai/mcpdoc | null | null | null | null | 982 | null | null | mit | null | null | null | null | null | null | null | tests/unit_tests/test_imports.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:06.863412 | def test_imports():
"""Test that main modules can be imported."""
from mcpdoc import main # noqa
from mcpdoc import cli # noqa
from mcpdoc import langgraph # noqa
assert True
|
langchain-ai/mcpdoc | https://github.com/langchain-ai/mcpdoc | null | null | null | null | 982 | null | null | mit | null | null | null | null | null | null | null | mcpdoc/langgraph.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:06.864144 | """A server for just langgraph docs from langchain-ai.github.io.
This is used as a way to test the doc functionality via MCP.
"""
# /usr/bin/env python3
import httpx
from markdownify import markdownify
from mcp.server.fastmcp import FastMCP
server = FastMCP(name="llms-txt")
ALLOWED_PREFIX = "https://langchain-ai.gi... |
langchain-ai/mcpdoc | https://github.com/langchain-ai/mcpdoc | null | null | null | null | 982 | null | null | mit | null | null | null | null | null | null | null | tests/unit_tests/test_main.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:06.864595 | """Tests for mcpdoc.main module."""
import pytest
from mcpdoc.main import (
_get_fetch_description,
_is_http_or_https,
extract_domain,
)
def test_extract_domain() -> None:
"""Test extract_domain function."""
# Test with https URL
assert extract_domain("https://example.com/page") == "https://... |
langchain-ai/mcpdoc | https://github.com/langchain-ai/mcpdoc | null | null | null | null | 982 | null | null | mit | null | null | null | null | null | null | null | mcpdoc/splash.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:06.865284 | SPLASH = """\
███╗ ███╗ ██████╗██████╗ ██████╗ ██████╗ ██████╗
████╗ ████║██╔════╝██╔══██╗██╔══██╗██╔═══██╗██╔════╝
██╔████╔██║██║ ██████╔╝██║ ██║██║ ██║██║
██║╚██╔╝██║██║ ██╔═══╝ ██║ ██║██║ ██║██║
██║ ╚═╝ ██║╚██████╗██║ ██████╔╝╚██████╔╝╚██████╗
╚═╝ ╚═╝ ╚═════╝╚═╝ ... |
langchain-ai/mcpdoc | https://github.com/langchain-ai/mcpdoc | null | null | null | null | 982 | null | null | mit | null | null | null | null | null | null | null | mcpdoc/main.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:06.866974 | """MCP Llms-txt server for docs."""
import os
import re
from urllib.parse import urlparse, urljoin
import httpx
from markdownify import markdownify
from mcp.server.fastmcp import FastMCP
from typing_extensions import NotRequired, TypedDict
class DocSource(TypedDict):
"""A source of documentation for a library o... |
kmkurn/pytorch-crf | https://github.com/kmkurn/pytorch-crf | null | null | null | null | 981 | null | null | mit | null | null | null | null | null | null | null | setup.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:09.370475 | import os
import re
from setuptools import setup, find_packages
here = os.path.dirname(os.path.realpath(__file__))
with open(os.path.join(here, 'README.rst'), 'r', encoding='utf-8') as f:
readme = f.read()
with open(os.path.join(here, 'torchcrf', '__init__.py'), 'r', encoding='utf-8') as f:
version = re.search... |
kmkurn/pytorch-crf | https://github.com/kmkurn/pytorch-crf | null | null | null | null | 981 | null | null | mit | null | null | null | null | null | null | null | tests/test_crf.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:09.370927 | import itertools
import math
import random
import pytest
import torch
import torch.nn as nn
from packaging.version import Version
from torchcrf import CRF
RANDOM_SEED = 1478754
random.seed(RANDOM_SEED)
torch.manual_seed(RANDOM_SEED)
def compute_score(crf, emission, tag):
# emission: (seq_length, num_tags)
... |
kmkurn/pytorch-crf | https://github.com/kmkurn/pytorch-crf | null | null | null | null | 981 | null | null | mit | null | null | null | null | null | null | null | torchcrf/__init__.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:09.372821 | __version__ = '0.7.2'
from typing import List, Optional
import torch
import torch.nn as nn
class CRF(nn.Module):
"""Conditional random field.
This module implements a conditional random field [LMP01]_. The forward computation
of this class computes the log likelihood of the given sequence of tags and
... |
kmkurn/pytorch-crf | https://github.com/kmkurn/pytorch-crf | null | null | null | null | 981 | null | null | mit | null | null | null | null | null | null | null | docs/conf.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:09.374342 | # -*- coding: utf-8 -*-
#
# Configuration file for the Sphinx documentation builder.
#
# This file does only contain a selection of the most common options. For a
# full list see the documentation:
# http://www.sphinx-doc.org/en/master/config
# -- Path setup ------------------------------------------------------------... |
bytedance/MVDream | https://github.com/bytedance/MVDream | null | null | null | null | 981 | null | null | mit | null | null | null | null | null | null | null | mvdream/ldm/models/autoencoder.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:12.077096 | import torch
import torch.nn.functional as F
from contextlib import contextmanager
from ..modules.diffusionmodules.model import Encoder, Decoder
from ..modules.distributions.distributions import DiagonalGaussianDistribution
from ..util import instantiate_from_config
from ..modules.ema import LitEma
class Autoencode... |
bytedance/MVDream | https://github.com/bytedance/MVDream | null | null | null | null | 981 | null | null | mit | null | null | null | null | null | null | null | mvdream/ldm/modules/attention.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:12.079645 | from inspect import isfunction
import math
import torch
import torch.nn.functional as F
from torch import nn, einsum
from einops import rearrange, repeat
from typing import Optional, Any
from .diffusionmodules.util import checkpoint
try:
import xformers
import xformers.ops
XFORMERS_IS_AVAILBLE = True
exc... |
bytedance/MVDream | https://github.com/bytedance/MVDream | null | null | null | null | 981 | null | null | mit | null | null | null | null | null | null | null | mvdream/ldm/interface.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:12.083284 | from typing import List
from functools import partial
import numpy as np
import torch
import torch.nn as nn
from .modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
from .util import exists, default, instantiate_from_config
from .modules.distributions.distributions import Diagona... |
bytedance/MVDream | https://github.com/bytedance/MVDream | null | null | null | null | 981 | null | null | mit | null | null | null | null | null | null | null | mvdream/ldm/models/diffusion/ddim.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:12.084469 | """SAMPLING ONLY."""
import torch
import numpy as np
from tqdm import tqdm
from functools import partial
from ...modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like, extract_into_tensor
class DDIMSampler(object):
def __init__(self, model, schedule="linear", **kwar... |
bytedance/MVDream | https://github.com/bytedance/MVDream | null | null | null | null | 981 | null | null | mit | null | null | null | null | null | null | null | mvdream/camera_utils.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:12.085503 | import numpy as np
import torch
def create_camera_to_world_matrix(elevation, azimuth):
elevation = np.radians(elevation)
azimuth = np.radians(azimuth)
# Convert elevation and azimuth angles to Cartesian coordinates on a unit sphere
x = np.cos(elevation) * np.sin(azimuth)
y = np.sin(elevation)
... |
bytedance/MVDream | https://github.com/bytedance/MVDream | null | null | null | null | 981 | null | null | mit | null | null | null | null | null | null | null | mvdream/ldm/modules/encoders/modules.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:13.238487 | import torch
import torch.nn as nn
from torch.utils.checkpoint import checkpoint
from transformers import T5Tokenizer, T5EncoderModel, CLIPTokenizer, CLIPTextModel
import open_clip
from ...util import default, count_params
class AbstractEncoder(nn.Module):
def __init__(self):
super().__init__()
def... |
bytedance/MVDream | https://github.com/bytedance/MVDream | null | null | null | null | 981 | null | null | mit | null | null | null | null | null | null | null | mvdream/ldm/util.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:13.239583 | import importlib
import torch
import numpy as np
from collections import abc
from einops import rearrange
from functools import partial
import multiprocessing as mp
from threading import Thread
from queue import Queue
from inspect import isfunction
from PIL import Image, ImageDraw, ImageFont
def log_txt_as_img(wh,... |
bytedance/MVDream | https://github.com/bytedance/MVDream | null | null | null | null | 981 | null | null | mit | null | null | null | null | null | null | null | mvdream/ldm/modules/ema.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:13.240888 | import torch
from torch import nn
class LitEma(nn.Module):
def __init__(self, model, decay=0.9999, use_num_upates=True):
super().__init__()
if decay < 0.0 or decay > 1.0:
raise ValueError('Decay must be between 0 and 1')
self.m_name2s_name = {}
self.register_buffer('de... |
bytedance/MVDream | https://github.com/bytedance/MVDream | null | null | null | null | 981 | null | null | mit | null | null | null | null | null | null | null | mvdream/ldm/modules/distributions/distributions.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:14.780142 | import torch
import numpy as np
class AbstractDistribution:
def sample(self):
raise NotImplementedError()
def mode(self):
raise NotImplementedError()
class DiracDistribution(AbstractDistribution):
def __init__(self, value):
self.value = value
def sample(self):
retur... |
bytedance/MVDream | https://github.com/bytedance/MVDream | null | null | null | null | 981 | null | null | mit | null | null | null | null | null | null | null | mvdream/ldm/modules/diffusionmodules/util.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:14.844761 | # adopted from
# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
# and
# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
# and
# https://github.com/openai/gu... |
bytedance/MVDream | https://github.com/bytedance/MVDream | null | null | null | null | 981 | null | null | mit | null | null | null | null | null | null | null | scripts/t2i.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:14.883292 | import os
import sys
import random
import argparse
from PIL import Image
import numpy as np
from omegaconf import OmegaConf
import torch
from mvdream.camera_utils import get_camera
from mvdream.ldm.util import instantiate_from_config
from mvdream.ldm.models.diffusion.ddim import DDIMSampler
from mvdream.model_zoo imp... |
bytedance/MVDream | https://github.com/bytedance/MVDream | null | null | null | null | 981 | null | null | mit | null | null | null | null | null | null | null | mvdream/ldm/modules/diffusionmodules/openaimodel.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:14.884024 | from abc import abstractmethod
import math
import numpy as np
import torch as th
import torch.nn as nn
import torch.nn.functional as F
from .util import (
checkpoint,
conv_nd,
linear,
avg_pool_nd,
zero_module,
normalization,
timestep_embedding,
)
from ..attention import SpatialTransformer,... |
bytedance/MVDream | https://github.com/bytedance/MVDream | null | null | null | null | 981 | null | null | mit | null | null | null | null | null | null | null | scripts/gradio_app.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:15.002332 | import random
import argparse
from functools import partial
import numpy as np
import gradio as gr
from omegaconf import OmegaConf
import torch
from mvdream.camera_utils import get_camera
from mvdream.ldm.util import instantiate_from_config
from mvdream.ldm.models.diffusion.ddim import DDIMSampler
from mvdream.model_... |
bytedance/MVDream | https://github.com/bytedance/MVDream | null | null | null | null | 981 | null | null | mit | null | null | null | null | null | null | null | mvdream/ldm/modules/diffusionmodules/model.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:15.012775 | # pytorch_diffusion + derived encoder decoder
import math
import torch
import torch.nn as nn
import numpy as np
from einops import rearrange
from typing import Optional, Any
from ..attention import MemoryEfficientCrossAttention
try:
import xformers
import xformers.ops
XFORMERS_IS_AVAILBLE = True
except:
... |
bytedance/MVDream | https://github.com/bytedance/MVDream | null | null | null | null | 981 | null | null | mit | null | null | null | null | null | null | null | mvdream/model_zoo.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:15.042643 | ''' Utiliy functions to load pre-trained models more easily '''
import os
import pkg_resources
from omegaconf import OmegaConf
import torch
from huggingface_hub import hf_hub_download
from mvdream.ldm.util import instantiate_from_config
PRETRAINED_MODELS = {
"sd-v2.1-base-4view": {
"config": "sd-v2-base... |
bytedance/MVDream | https://github.com/bytedance/MVDream | null | null | null | null | 981 | null | null | mit | null | null | null | null | null | null | null | setup.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:19.242568 | from setuptools import setup, find_packages
setup(
name='mvdream',
version='0.0.1',
description='Multi-view Diffusion Models',
author="ByteDance",
packages=find_packages(),
package_data={"mvdream": ["configs/*.yaml"]} ,
install_requires=[
'torch',
'numpy',
'tqdm',
... |
dgasmith/opt_einsum | https://github.com/dgasmith/opt_einsum | null | null | null | null | 980 | null | null | mit | null | null | null | null | null | null | null | opt_einsum/backends/theano.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:21.289397 | """Required functions for optimized contractions of numpy arrays using theano."""
from opt_einsum.helpers import has_array_interface
from opt_einsum.sharing import to_backend_cache_wrap
__all__ = ["to_theano", "build_expression", "evaluate_constants"]
@to_backend_cache_wrap(constants=True)
def to_theano(array, cons... |
dgasmith/opt_einsum | https://github.com/dgasmith/opt_einsum | null | null | null | null | 980 | null | null | mit | null | null | null | null | null | null | null | opt_einsum/__init__.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:21.294147 | """Main init function for opt_einsum."""
from opt_einsum import blas, helpers, path_random, paths
from opt_einsum._version import __version__
from opt_einsum.contract import contract, contract_expression, contract_path
from opt_einsum.parser import get_symbol
from opt_einsum.path_random import RandomGreedy
from opt_ei... |
dgasmith/opt_einsum | https://github.com/dgasmith/opt_einsum | null | null | null | null | 980 | null | null | mit | null | null | null | null | null | null | null | opt_einsum/backends/tensorflow.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:21.299542 | """Required functions for optimized contractions of numpy arrays using tensorflow."""
from opt_einsum.helpers import has_array_interface
from opt_einsum.sharing import to_backend_cache_wrap
__all__ = ["to_tensorflow", "build_expression", "evaluate_constants"]
_CACHED_TF_DEVICE = None
def _get_tensorflow_and_device... |
dgasmith/opt_einsum | https://github.com/dgasmith/opt_einsum | null | null | null | null | 980 | null | null | mit | null | null | null | null | null | null | null | opt_einsum/backends/jax.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:21.302492 | """Required functions for optimized contractions of numpy arrays using jax."""
from opt_einsum.sharing import to_backend_cache_wrap
__all__ = ["build_expression", "evaluate_constants"]
_JAX = None
def _get_jax_and_to_jax():
global _JAX
if _JAX is None:
import jax # type: ignore
@to_backen... |
dgasmith/opt_einsum | https://github.com/dgasmith/opt_einsum | null | null | null | null | 980 | null | null | mit | null | null | null | null | null | null | null | opt_einsum/backends/__init__.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:21.303858 | """Compute backends for opt_einsum."""
# Backends
from opt_einsum.backends.cupy import to_cupy
from opt_einsum.backends.dispatch import (
build_expression,
evaluate_constants,
get_func,
has_backend,
has_einsum,
has_tensordot,
)
from opt_einsum.backends.tensorflow import to_tensorflow
from opt_e... |
dgasmith/opt_einsum | https://github.com/dgasmith/opt_einsum | null | null | null | null | 980 | null | null | mit | null | null | null | null | null | null | null | opt_einsum/backends/cupy.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:21.312175 | """Required functions for optimized contractions of numpy arrays using cupy."""
from opt_einsum.helpers import has_array_interface
from opt_einsum.sharing import to_backend_cache_wrap
__all__ = ["to_cupy", "build_expression", "evaluate_constants"]
@to_backend_cache_wrap
def to_cupy(array): # pragma: no cover
i... |
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