text stringlengths 7 328k | id stringlengths 14 166 | metadata dict | __index_level_0__ int64 0 459 |
|---|---|---|---|
<!--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/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | diffusers/docs/source/en/api/image_processor.md/0 | {
"file_path": "diffusers/docs/source/en/api/image_processor.md",
"repo_id": "diffusers",
"token_count": 447
} | 96 |
<!--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/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | diffusers/docs/source/en/api/models/transformer2d.md/0 | {
"file_path": "diffusers/docs/source/en/api/models/transformer2d.md",
"repo_id": "diffusers",
"token_count": 467
} | 97 |
<!--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/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | diffusers/docs/source/en/api/pipelines/auto_pipeline.md/0 | {
"file_path": "diffusers/docs/source/en/api/pipelines/auto_pipeline.md",
"repo_id": "diffusers",
"token_count": 714
} | 98 |
<!--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/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | diffusers/docs/source/en/api/pipelines/stable_diffusion/image_variation.md/0 | {
"file_path": "diffusers/docs/source/en/api/pipelines/stable_diffusion/image_variation.md",
"repo_id": "diffusers",
"token_count": 494
} | 99 |
<!--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/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | diffusers/docs/source/en/api/pipelines/text_to_video_zero.md/0 | {
"file_path": "diffusers/docs/source/en/api/pipelines/text_to_video_zero.md",
"repo_id": "diffusers",
"token_count": 4486
} | 100 |
<!--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/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | diffusers/docs/source/en/optimization/mps.md/0 | {
"file_path": "diffusers/docs/source/en/optimization/mps.md",
"repo_id": "diffusers",
"token_count": 1061
} | 101 |
<!--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/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | diffusers/docs/source/en/training/instructpix2pix.md/0 | {
"file_path": "diffusers/docs/source/en/training/instructpix2pix.md",
"repo_id": "diffusers",
"token_count": 4160
} | 102 |
<!--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/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | diffusers/docs/source/en/using-diffusers/callback.md/0 | {
"file_path": "diffusers/docs/source/en/using-diffusers/callback.md",
"repo_id": "diffusers",
"token_count": 2993
} | 103 |
<!--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/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | diffusers/docs/source/en/using-diffusers/inpaint.md/0 | {
"file_path": "diffusers/docs/source/en/using-diffusers/inpaint.md",
"repo_id": "diffusers",
"token_count": 14131
} | 104 |
<!--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/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | diffusers/docs/source/en/using-diffusers/shap-e.md/0 | {
"file_path": "diffusers/docs/source/en/using-diffusers/shap-e.md",
"repo_id": "diffusers",
"token_count": 2475
} | 105 |
- sections:
- local: index
title: "🧨 Diffusers"
- local: quicktour
title: "훑어보기"
- local: stable_diffusion
title: Stable Diffusion
- local: installation
title: "설치"
title: "시작하기"
- sections:
- local: tutorials/tutorial_overview
title: 개요
- local: using-diffusers/write_own_pipeline
... | diffusers/docs/source/ko/_toctree.yml/0 | {
"file_path": "diffusers/docs/source/ko/_toctree.yml",
"repo_id": "diffusers",
"token_count": 2235
} | 106 |
<!--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/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | diffusers/docs/source/ko/stable_diffusion.md/0 | {
"file_path": "diffusers/docs/source/ko/stable_diffusion.md",
"repo_id": "diffusers",
"token_count": 8949
} | 107 |
<!--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/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | diffusers/docs/source/ko/using-diffusers/contribute_pipeline.md/0 | {
"file_path": "diffusers/docs/source/ko/using-diffusers/contribute_pipeline.md",
"repo_id": "diffusers",
"token_count": 5976
} | 108 |
# Textual inversion
[[open-in-colab]]
[`StableDiffusionPipeline`]은 textual-inversion을 지원하는데, 이는 몇 개의 샘플 이미지만으로 stable diffusion과 같은 모델이 새로운 컨셉을 학습할 수 있도록 하는 기법입니다. 이를 통해 생성된 이미지를 더 잘 제어하고 특정 컨셉에 맞게 모델을 조정할 수 있습니다. 커뮤니티에서 만들어진 컨셉들의 컬렉션은 [Stable Diffusion Conceptualizer](https://huggingface.co/spaces/sd-concepts-libra... | diffusers/docs/source/ko/using-diffusers/textual_inversion_inference.md/0 | {
"file_path": "diffusers/docs/source/ko/using-diffusers/textual_inversion_inference.md",
"repo_id": "diffusers",
"token_count": 2018
} | 109 |
from typing import List, Optional, Tuple, Union
import torch
from diffusers import DiffusionPipeline
from diffusers.configuration_utils import ConfigMixin
from diffusers.pipelines.pipeline_utils import ImagePipelineOutput
from diffusers.schedulers.scheduling_utils import SchedulerMixin
class IADBScheduler(Scheduler... | diffusers/examples/community/iadb.py/0 | {
"file_path": "diffusers/examples/community/iadb.py",
"repo_id": "diffusers",
"token_count": 2510
} | 110 |
import re
from copy import deepcopy
from dataclasses import asdict, dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import torch
from numpy import exp, pi, sqrt
from torchvision.transforms.functional import resize
from tqdm.auto import tqdm
from transformers import CLIPFeatu... | diffusers/examples/community/mixture_canvas.py/0 | {
"file_path": "diffusers/examples/community/mixture_canvas.py",
"repo_id": "diffusers",
"token_count": 9676
} | 111 |
# A diffuser version implementation of Zero1to3 (https://github.com/cvlab-columbia/zero123), ICCV 2023
# by Xin Kong
import inspect
from typing import Any, Callable, Dict, List, Optional, Union
import kornia
import numpy as np
import PIL.Image
import torch
from packaging import version
from transformers import CLIPFe... | diffusers/examples/community/pipeline_zero1to3.py/0 | {
"file_path": "diffusers/examples/community/pipeline_zero1to3.py",
"repo_id": "diffusers",
"token_count": 17872
} | 112 |
from typing import Any, Callable, Dict, List, Optional, Union
import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionImg... | diffusers/examples/community/stable_diffusion_mega.py/0 | {
"file_path": "diffusers/examples/community/stable_diffusion_mega.py",
"repo_id": "diffusers",
"token_count": 3885
} | 113 |
# Custom Diffusion training example
[Custom Diffusion](https://arxiv.org/abs/2212.04488) is a method to customize text-to-image models like Stable Diffusion given just a few (4~5) images of a subject.
The `train_custom_diffusion.py` script shows how to implement the training procedure and adapt it for stable diffusio... | diffusers/examples/custom_diffusion/README.md/0 | {
"file_path": "diffusers/examples/custom_diffusion/README.md",
"repo_id": "diffusers",
"token_count": 3550
} | 114 |
#!/usr/bin/env python
# coding=utf-8
# Copyright 2024 The HuggingFace Inc. 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/licenses/LI... | diffusers/examples/dreambooth/train_dreambooth_lora_sdxl.py/0 | {
"file_path": "diffusers/examples/dreambooth/train_dreambooth_lora_sdxl.py",
"repo_id": "diffusers",
"token_count": 37626
} | 115 |
# Overview
These examples show how to run [Diffuser](https://arxiv.org/abs/2205.09991) in Diffusers.
There are two ways to use the script, `run_diffuser_locomotion.py`.
The key option is a change of the variable `n_guide_steps`.
When `n_guide_steps=0`, the trajectories are sampled from the diffusion model, but not ... | diffusers/examples/reinforcement_learning/README.md/0 | {
"file_path": "diffusers/examples/reinforcement_learning/README.md",
"repo_id": "diffusers",
"token_count": 352
} | 116 |
import argparse
import itertools
import math
import os
import random
from pathlib import Path
import intel_extension_for_pytorch as ipex
import numpy as np
import PIL
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from accelerate import Accelerator
from accelerate.logging import get_logger
... | diffusers/examples/research_projects/intel_opts/textual_inversion/textual_inversion_bf16.py/0 | {
"file_path": "diffusers/examples/research_projects/intel_opts/textual_inversion/textual_inversion_bf16.py",
"repo_id": "diffusers",
"token_count": 10688
} | 117 |
# Script for converting a HF Diffusers saved pipeline to a Stable Diffusion checkpoint.
# *Only* converts the UNet, VAE, and Text Encoder.
# Does not convert optimizer state or any other thing.
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# ========... | diffusers/scripts/convert_diffusers_to_original_stable_diffusion.py/0 | {
"file_path": "diffusers/scripts/convert_diffusers_to_original_stable_diffusion.py",
"repo_id": "diffusers",
"token_count": 6259
} | 118 |
# Copyright 2022 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/licenses/LICENSE-2.0
#
# Unless required by applicabl... | diffusers/scripts/convert_vae_diff_to_onnx.py/0 | {
"file_path": "diffusers/scripts/convert_vae_diff_to_onnx.py",
"repo_id": "diffusers",
"token_count": 1684
} | 119 |
# THIS FILE HAS BEEN AUTOGENERATED. To update:
# 1. modify the `_deps` dict in setup.py
# 2. run `make deps_table_update`
deps = {
"Pillow": "Pillow",
"accelerate": "accelerate>=0.11.0",
"compel": "compel==0.1.8",
"datasets": "datasets",
"filelock": "filelock",
"flax": "flax>=0.4.1",
"hf-doc... | diffusers/src/diffusers/dependency_versions_table.py/0 | {
"file_path": "diffusers/src/diffusers/dependency_versions_table.py",
"repo_id": "diffusers",
"token_count": 778
} | 120 |
# 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/licenses/LICENSE-2.0
#
# Unless required by applicabl... | diffusers/src/diffusers/loaders/unet.py/0 | {
"file_path": "diffusers/src/diffusers/loaders/unet.py",
"repo_id": "diffusers",
"token_count": 22274
} | 121 |
# 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/licenses/LICENSE-2.0
#
# Unless required by applicabl... | diffusers/src/diffusers/models/controlnet.py/0 | {
"file_path": "diffusers/src/diffusers/models/controlnet.py",
"repo_id": "diffusers",
"token_count": 18856
} | 122 |
# 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/licenses/LICENSE-2.0
#
# Unless required by applicabl... | diffusers/src/diffusers/models/t5_film_transformer.py/0 | {
"file_path": "diffusers/src/diffusers/models/t5_film_transformer.py",
"repo_id": "diffusers",
"token_count": 1343
} | 123 |
# 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/licenses/LICENSE-2.0
#
# Unless required by applicabl... | diffusers/src/diffusers/models/unets/unet_1d_blocks.py/0 | {
"file_path": "diffusers/src/diffusers/models/unets/unet_1d_blocks.py",
"repo_id": "diffusers",
"token_count": 12023
} | 124 |
# 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/licenses/LICENSE-2.0
#
# Unless required by applicabl... | diffusers/src/diffusers/models/vq_model.py/0 | {
"file_path": "diffusers/src/diffusers/models/vq_model.py",
"repo_id": "diffusers",
"token_count": 3139
} | 125 |
# Copyright 2024 CVSSP, ByteDance and 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/licenses/LICENSE-2.0
#
# Unless ... | diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py",
"repo_id": "diffusers",
"token_count": 21684
} | 126 |
# 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/licenses/LICENSE-2.0
#
# Unless required by applicabl... | diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl.py",
"repo_id": "diffusers",
"token_count": 35003
} | 127 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...utils import BaseOutput
@dataclass
class IFPipelineOutput(BaseOutput):
"""
Args:
Output class for Stable Diffusion pipelines.
images (`List[PIL.Image.Image]` or `np.ndarray`)
... | diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_output.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_output.py",
"repo_id": "diffusers",
"token_count": 409
} | 128 |
from typing import TYPE_CHECKING
from ....utils import DIFFUSERS_SLOW_IMPORT, _LazyModule
_import_structure = {"pipeline_pndm": ["PNDMPipeline"]}
if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
from .pipeline_pndm import PNDMPipeline
else:
import sys
sys.modules[__name__] = _LazyModule(
__name__,
... | diffusers/src/diffusers/pipelines/deprecated/pndm/__init__.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/deprecated/pndm/__init__.py",
"repo_id": "diffusers",
"token_count": 182
} | 129 |
# Copyright 2024 Alibaba DAMO-VILAB and 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/licenses/LICENSE-2.0
#
# Unles... | diffusers/src/diffusers/pipelines/i2vgen_xl/pipeline_i2vgen_xl.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/i2vgen_xl/pipeline_i2vgen_xl.py",
"repo_id": "diffusers",
"token_count": 16560
} | 130 |
from typing import List, Optional, Union
import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModelWithProjection, CLIPTokenizer, CLIPVisionModelWithProjection
from ...models import PriorTransformer
from ...schedulers import UnCLIPScheduler
from ...utils import (
logging,
replace... | diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_prior_emb2emb.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_prior_emb2emb.py",
"repo_id": "diffusers",
"token_count": 11229
} | 131 |
# 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/licenses/LICENSE-2.0
#
# Unless required by applicabl... | diffusers/src/diffusers/pipelines/musicldm/pipeline_musicldm.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/musicldm/pipeline_musicldm.py",
"repo_id": "diffusers",
"token_count": 13298
} | 132 |
# Copyright 2024 Open AI and 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/licenses/LICENSE-2.0
#
# Unless required ... | diffusers/src/diffusers/pipelines/shap_e/camera.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/shap_e/camera.py",
"repo_id": "diffusers",
"token_count": 2274
} | 133 |
# 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/licenses/LICENSE-2.0
#
# Unless required by applicabl... | diffusers/src/diffusers/pipelines/stable_diffusion/stable_unclip_image_normalizer.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/stable_diffusion/stable_unclip_image_normalizer.py",
"repo_id": "diffusers",
"token_count": 674
} | 134 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...utils import (
BaseOutput,
)
@dataclass
class StableDiffusionSafePipelineOutput(BaseOutput):
"""
Output class for Safe Stable Diffusion pipelines.
Args:
images (`List[PIL.I... | diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_output.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_output.py",
"repo_id": "diffusers",
"token_count": 527
} | 135 |
# Copyright 2024 TencentARC and 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/licenses/LICENSE-2.0
#
# Unless requir... | diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.py",
"repo_id": "diffusers",
"token_count": 19839
} | 136 |
from typing import TYPE_CHECKING
from ...utils import (
DIFFUSERS_SLOW_IMPORT,
OptionalDependencyNotAvailable,
_LazyModule,
get_objects_from_module,
is_torch_available,
is_transformers_available,
)
_dummy_objects = {}
_import_structure = {}
try:
if not (is_transformers_available() and is... | diffusers/src/diffusers/pipelines/wuerstchen/__init__.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/wuerstchen/__init__.py",
"repo_id": "diffusers",
"token_count": 849
} | 137 |
# 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/licenses/LICENSE-2.0
#
# Unless required by applicabl... | diffusers/src/diffusers/schedulers/scheduling_consistency_models.py/0 | {
"file_path": "diffusers/src/diffusers/schedulers/scheduling_consistency_models.py",
"repo_id": "diffusers",
"token_count": 8218
} | 138 |
# Copyright 2024 Katherine Crowson and 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/licenses/LICENSE-2.0
#
# Unless... | diffusers/src/diffusers/schedulers/scheduling_edm_euler.py/0 | {
"file_path": "diffusers/src/diffusers/schedulers/scheduling_edm_euler.py",
"repo_id": "diffusers",
"token_count": 6776
} | 139 |
# Copyright 2024 Google Brain and 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/licenses/LICENSE-2.0
#
# Unless requ... | diffusers/src/diffusers/schedulers/scheduling_sde_ve.py/0 | {
"file_path": "diffusers/src/diffusers/schedulers/scheduling_sde_ve.py",
"repo_id": "diffusers",
"token_count": 5399
} | 140 |
# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends
class MidiProcessor(metaclass=DummyObject):
_backends = ["note_seq"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["note_seq"])
@classmethod
def from... | diffusers/src/diffusers/utils/dummy_note_seq_objects.py/0 | {
"file_path": "diffusers/src/diffusers/utils/dummy_note_seq_objects.py",
"repo_id": "diffusers",
"token_count": 201
} | 141 |
---
{{ card_data }}
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
{{ model_description }}
## Intended uses & limitations
#### How to use
```python
# TODO: add an ... | diffusers/src/diffusers/utils/model_card_template.md/0 | {
"file_path": "diffusers/src/diffusers/utils/model_card_template.md",
"repo_id": "diffusers",
"token_count": 138
} | 142 |
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNet2DConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
@slow
@requi... | diffusers/tests/models/unets/test_models_unet_2d_flax.py/0 | {
"file_path": "diffusers/tests/models/unets/test_models_unet_2d_flax.py",
"repo_id": "diffusers",
"token_count": 2141
} | 143 |
# coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# 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 ag... | diffusers/tests/others/test_utils.py/0 | {
"file_path": "diffusers/tests/others/test_utils.py",
"repo_id": "diffusers",
"token_count": 3326
} | 144 |
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNet2DModel,
)
from diffusers.utils.testing_utils import (
enable_full_determinism,
nightly,
require_torch_2,
... | diffusers/tests/pipelines/consistency_models/test_consistency_models.py/0 | {
"file_path": "diffusers/tests/pipelines/consistency_models/test_consistency_models.py",
"repo_id": "diffusers",
"token_count": 4999
} | 145 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, T5EncoderModel
from diffusers import DDPMScheduler, UNet2DConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing... | diffusers/tests/pipelines/deepfloyd_if/__init__.py/0 | {
"file_path": "diffusers/tests/pipelines/deepfloyd_if/__init__.py",
"repo_id": "diffusers",
"token_count": 4583
} | 146 |
# coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# 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 ag... | diffusers/tests/pipelines/kandinsky/test_kandinsky_inpaint.py/0 | {
"file_path": "diffusers/tests/pipelines/kandinsky/test_kandinsky_inpaint.py",
"repo_id": "diffusers",
"token_count": 5400
} | 147 |
import gc
import inspect
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
LatentConsistencyModelImg2ImgPipeline,
LCMScheduler,
UNet2DConditionModel,
)
from diffusers.utils.testing_... | diffusers/tests/pipelines/latent_consistency_models/test_latent_consistency_models_img2img.py/0 | {
"file_path": "diffusers/tests/pipelines/latent_consistency_models/test_latent_consistency_models_img2img.py",
"repo_id": "diffusers",
"token_count": 4960
} | 148 |
# coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# 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 ag... | diffusers/tests/pipelines/stable_diffusion/test_stable_diffusion.py/0 | {
"file_path": "diffusers/tests/pipelines/stable_diffusion/test_stable_diffusion.py",
"repo_id": "diffusers",
"token_count": 26455
} | 149 |
# coding=utf-8
# Copyright 2022 HuggingFace Inc.
#
# 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 ag... | diffusers/tests/pipelines/stable_diffusion_adapter/test_stable_diffusion_adapter.py/0 | {
"file_path": "diffusers/tests/pipelines/stable_diffusion_adapter/test_stable_diffusion_adapter.py",
"repo_id": "diffusers",
"token_count": 18684
} | 150 |
# coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# 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 ag... | diffusers/tests/pipelines/stable_diffusion_sag/test_stable_diffusion_sag.py/0 | {
"file_path": "diffusers/tests/pipelines/stable_diffusion_sag/test_stable_diffusion_sag.py",
"repo_id": "diffusers",
"token_count": 3475
} | 151 |
# coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# 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 ag... | diffusers/tests/pipelines/test_pipelines_combined.py/0 | {
"file_path": "diffusers/tests/pipelines/test_pipelines_combined.py",
"repo_id": "diffusers",
"token_count": 2384
} | 152 |
# coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# 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 ag... | diffusers/tests/pipelines/wuerstchen/test_wuerstchen_decoder.py/0 | {
"file_path": "diffusers/tests/pipelines/wuerstchen/test_wuerstchen_decoder.py",
"repo_id": "diffusers",
"token_count": 2630
} | 153 |
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils.testing_utils import torch_device
from .test_schedulers import SchedulerCommonTest
class EulerDiscreteSchedulerTest(SchedulerCommonTest):
scheduler_classes = (EulerDiscreteScheduler,)
num_inference_steps = 10
def get_schedul... | diffusers/tests/schedulers/test_scheduler_euler.py/0 | {
"file_path": "diffusers/tests/schedulers/test_scheduler_euler.py",
"repo_id": "diffusers",
"token_count": 3093
} | 154 |
# coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# 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 ag... | diffusers/tests/schedulers/test_schedulers.py/0 | {
"file_path": "diffusers/tests/schedulers/test_schedulers.py",
"repo_id": "diffusers",
"token_count": 17208
} | 155 |
# Copyright 2024 The HuggingFace Team, the AllenNLP library authors. 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/licenses/LICENSE-2.0
#
... | diffusers/utils/stale.py/0 | {
"file_path": "diffusers/utils/stale.py",
"repo_id": "diffusers",
"token_count": 996
} | 156 |
<jupyter_start><jupyter_text>DreamBooth Hackathon 🏆 Welcome to the DreamBooth Hackathon! In this competition, you'll **personalise a Stable Diffusion model by fine-tuning it on a handful of your own images.** To do so, we'll use a technique called [_DreamBooth_](https://arxiv.org/abs/2208.12242), which allows one to i... | diffusion-models-class/hackathon/dreambooth.ipynb/0 | {
"file_path": "diffusion-models-class/hackathon/dreambooth.ipynb",
"repo_id": "diffusion-models-class",
"token_count": 9167
} | 157 |
<jupyter_start><jupyter_text>IntroductionThis notebook is going to cover the basics of how to use Stable Diffusion to create and modify images using existing pipelines. We'll also take a brief look at the key components within the pipeline, while leaving further exploration of them to the deep dive notebook. Specifical... | diffusion-models-class/units/en/unit3/stable_diffusion_introduction.ipynb/0 | {
"file_path": "diffusion-models-class/units/en/unit3/stable_diffusion_introduction.ipynb",
"repo_id": "diffusion-models-class",
"token_count": 6232
} | 158 |
<jupyter_start><jupyter_text>Bias et limitations Installez la bibliothèque 🤗 *Transformers* pour exécuter ce *notebook*.<jupyter_code>!pip install transformers[sentencepiece]
from transformers import pipeline
unmasker = pipeline("fill-mask", model="camembert-base")
result = unmasker("Cet homme travaille comme <mask>.... | notebooks/course/fr/chapter1/section8.ipynb/0 | {
"file_path": "notebooks/course/fr/chapter1/section8.ipynb",
"repo_id": "notebooks",
"token_count": 173
} | 159 |
<jupyter_start><jupyter_text>Finetuner un modèle avec Keras Installez les bibliothèques 🤗 Transformers et 🤗 Datasets pour exécuter ce notebook.<jupyter_code>!pip install datasets transformers[sentencepiece]
from datasets import load_dataset
from transformers import AutoTokenizer, DataCollatorWithPadding
import numpy ... | notebooks/course/fr/chapter3/section3_tf.ipynb/0 | {
"file_path": "notebooks/course/fr/chapter3/section3_tf.ipynb",
"repo_id": "notebooks",
"token_count": 1107
} | 160 |
<jupyter_start><jupyter_text>Fast tokenizers in the QA pipeline (PyTorch) Installez les bibliothèques 🤗 *Transformers* et 🤗 *Datasets* pour exécuter ce *notebook*.<jupyter_code>!pip install datasets transformers[sentencepiece]
from transformers import pipeline
question_answerer = pipeline("question-answering", model... | notebooks/course/fr/chapter6/section3b_pt.ipynb/0 | {
"file_path": "notebooks/course/fr/chapter6/section3b_pt.ipynb",
"repo_id": "notebooks",
"token_count": 2740
} | 161 |
<jupyter_start><jupyter_text>Entraîner un modèle de langage causal de zéro (TensorFlow)Ici nous entraînons un modèle à générer du code Python. Le Python utilisant des fonctions basées sur des mots anglais, nous gardons un gpt-2 anglais dans l'optique d'obtenir de meilleures performances que ce que l'on pourrait s'atten... | notebooks/course/fr/chapter7/section6_tf.ipynb/0 | {
"file_path": "notebooks/course/fr/chapter7/section6_tf.ipynb",
"repo_id": "notebooks",
"token_count": 2527
} | 162 |
<jupyter_start><jupyter_text>Fine-tuning Stable Diffusion XL with DreamBooth and LoRA on a free-tier Colab Notebook 🧨In this notebook, we show how to fine-tune [Stable Diffusion XL (SDXL)](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/stable_diffusion_xl) with [DreamBooth](https://huggin... | notebooks/diffusers/SDXL_DreamBooth_LoRA_.ipynb/0 | {
"file_path": "notebooks/diffusers/SDXL_DreamBooth_LoRA_.ipynb",
"repo_id": "notebooks",
"token_count": 4896
} | 163 |
<jupyter_start><jupyter_text>Stable Conceptualizer - Stable Diffusion using learned conceptsThe Stable Conceptualizer enables you to use pre-learned concepts on Stable Diffusion via textual-inversion using 🤗 Hugging Face [🧨 Diffusers library](https://github.com/huggingface/diffusers). Navigate the [library of pre-lea... | notebooks/diffusers/stable_conceptualizer_inference.ipynb/0 | {
"file_path": "notebooks/diffusers/stable_conceptualizer_inference.ipynb",
"repo_id": "notebooks",
"token_count": 949
} | 164 |
# this is a demo of inference of IDEFICS-9B using 4bit-quantization which needs about 7GB of GPU memory
# which makes it possible to run even on Google Colab
import torch
from transformers import IdeficsForVisionText2Text, AutoProcessor, BitsAndBytesConfig
device = "cuda" if torch.cuda.is_available() else "cpu"
chec... | notebooks/examples/idefics/inference_4bit.py/0 | {
"file_path": "notebooks/examples/idefics/inference_4bit.py",
"repo_id": "notebooks",
"token_count": 396
} | 165 |
<jupyter_start><jupyter_text>If you're opening this Notebook on colab, you will probably need to install 🤗 Transformers and 🤗 Datasets. Uncomment the following cell and run it.<jupyter_code>#! pip install transformers
#! pip install datasets
#! pip install huggingface_hub<jupyter_output><empty_output><jupyter_text>If... | notebooks/examples/language_modeling-tf.ipynb/0 | {
"file_path": "notebooks/examples/language_modeling-tf.ipynb",
"repo_id": "notebooks",
"token_count": 8823
} | 166 |
<jupyter_start><jupyter_text>If you're opening this Notebook on colab, you will probably need to install 🤗 Transformers as well as some other libraries. Uncomment the following cell and run it.<jupyter_code>#! pip install transformers evaluate datasets requests pandas sklearn<jupyter_output><empty_output><jupyter_text... | notebooks/examples/protein_language_modeling.ipynb/0 | {
"file_path": "notebooks/examples/protein_language_modeling.ipynb",
"repo_id": "notebooks",
"token_count": 7787
} | 167 |
<jupyter_start><jupyter_text>Quantizing a model with ONNX Runtime for text classification tasks This notebook shows how to apply different post-training quantization approaches such as static and dynamic quantization using [ONNX Runtime](https://onnxruntime.ai), for any tasks of the GLUE benchmark. This is made possibl... | notebooks/examples/text_classification_quantization_ort.ipynb/0 | {
"file_path": "notebooks/examples/text_classification_quantization_ort.ipynb",
"repo_id": "notebooks",
"token_count": 4607
} | 168 |
<jupyter_start><jupyter_text>Huggingface Sagemaker-sdk - Getting Started Demo Binary Classification with `Trainer` and `imdb` dataset 1. [Introduction](Introduction) 2. [Development Environment and Permissions](Development-Environment-and-Permissions) 1. [Installation](Installation) 2. [Development environment... | notebooks/sagemaker/01_getting_started_pytorch/sagemaker-notebook.ipynb/0 | {
"file_path": "notebooks/sagemaker/01_getting_started_pytorch/sagemaker-notebook.ipynb",
"repo_id": "notebooks",
"token_count": 3851
} | 169 |
<jupyter_start><jupyter_text>Huggingface Sagemaker-sdk - Deploy 🤗 Transformers for inference Welcome to this getting started guide, we will use the new Hugging Face Inference DLCs and Amazon SageMaker Python SDK to deploy a transformer model for inference. In this example we deploy a trained Hugging Face Transformer m... | notebooks/sagemaker/10_deploy_model_from_s3/deploy_transformer_model_from_s3.ipynb/0 | {
"file_path": "notebooks/sagemaker/10_deploy_model_from_s3/deploy_transformer_model_from_s3.ipynb",
"repo_id": "notebooks",
"token_count": 1178
} | 170 |
import argparse
import logging
import os
import random
import sys
import numpy as np
import torch
from datasets import load_from_disk, load_metric
from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments
from transformers.trainer_utils import get_last_checkpoint
if __name... | notebooks/sagemaker/15_training_compiler/scripts/train.py/0 | {
"file_path": "notebooks/sagemaker/15_training_compiler/scripts/train.py",
"repo_id": "notebooks",
"token_count": 1776
} | 171 |
<jupyter_start><jupyter_text>Train LLMs using QLoRA on Amazon SageMakerIn this sagemaker example, we are going to learn how to apply [QLoRA: Efficient Finetuning of Quantized LLMs](https://arxiv.org/abs/2305.14314) to fine-tune Falcon 40B. QLoRA is an efficient finetuning technique that quantizes a pretrained language ... | notebooks/sagemaker/28_train_llms_with_qlora/sagemaker-notebook.ipynb/0 | {
"file_path": "notebooks/sagemaker/28_train_llms_with_qlora/sagemaker-notebook.ipynb",
"repo_id": "notebooks",
"token_count": 3913
} | 172 |
<!--⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# Fully Sharded Data Parallel
[Fully sharded data parallel](https://pytorch.org/docs/stable/fsdp.html) (FSDP) is developed for distributed training ... | peft/docs/source/accelerate/fsdp.md/0 | {
"file_path": "peft/docs/source/accelerate/fsdp.md",
"repo_id": "peft",
"token_count": 4846
} | 173 |
<!--Copyright 2023 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/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | peft/docs/source/package_reference/auto_class.md/0 | {
"file_path": "peft/docs/source/package_reference/auto_class.md",
"repo_id": "peft",
"token_count": 470
} | 174 |
import os
import torch
from datasets import load_dataset
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, default_data_collator, get_linear_schedule_with_warmup
from peft import AdaLoraConfig, PeftConfig, PeftModel, TaskType, get_peft_model
... | peft/examples/conditional_generation/peft_adalora_seq2seq.py/0 | {
"file_path": "peft/examples/conditional_generation/peft_adalora_seq2seq.py",
"repo_id": "peft",
"token_count": 2253
} | 175 |
<jupyter_start><jupyter_text>Using PEFT with timm `peft` allows us to train any model with LoRA as long as the layer type is supported. Since `Conv2D` is one of the supported layer types, it makes sense to test it on image models.In this short notebook, we will demonstrate this with an image classification task using [... | peft/examples/image_classification/image_classification_timm_peft_lora.ipynb/0 | {
"file_path": "peft/examples/image_classification/image_classification_timm_peft_lora.ipynb",
"repo_id": "peft",
"token_count": 3067
} | 176 |
import argparse
import gc
import hashlib
import itertools
import logging
import math
import os
import threading
import warnings
from contextlib import nullcontext
from pathlib import Path
from typing import Optional
import datasets
import diffusers
import numpy as np
import psutil
import torch
import torch.nn.function... | peft/examples/lora_dreambooth/train_dreambooth.py/0 | {
"file_path": "peft/examples/lora_dreambooth/train_dreambooth.py",
"repo_id": "peft",
"token_count": 20077
} | 177 |
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from transformers import HfArgumentParser, TrainingArguments, set_seed
from trl import SFTTrainer
from utils import create_and_prepare_model, create_datasets
# Define and parse arguments.
@dataclass
class ModelArguments:
""... | peft/examples/sft/train.py/0 | {
"file_path": "peft/examples/sft/train.py",
"repo_id": "peft",
"token_count": 2380
} | 178 |
# Copyright 2023-present the HuggingFace Inc. team.
#
# 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... | peft/src/peft/import_utils.py/0 | {
"file_path": "peft/src/peft/import_utils.py",
"repo_id": "peft",
"token_count": 907
} | 179 |
# Copyright 2023-present the HuggingFace Inc. team.
#
# 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... | peft/src/peft/tuners/adaption_prompt/utils.py/0 | {
"file_path": "peft/src/peft/tuners/adaption_prompt/utils.py",
"repo_id": "peft",
"token_count": 2179
} | 180 |
# Copyright 2024-present the HuggingFace Inc. team.
#
# 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... | peft/src/peft/tuners/lora/awq.py/0 | {
"file_path": "peft/src/peft/tuners/lora/awq.py",
"repo_id": "peft",
"token_count": 1532
} | 181 |
# Copyright 2023-present the HuggingFace Inc. team.
#
# 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... | peft/src/peft/tuners/oft/model.py/0 | {
"file_path": "peft/src/peft/tuners/oft/model.py",
"repo_id": "peft",
"token_count": 1600
} | 182 |
# flake8: noqa
# There's no way to ignore "F401 '...' imported but unused" warnings in this
# module, but to preserve other warnings. So, don't check this module at all
# coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not u... | peft/src/peft/utils/__init__.py/0 | {
"file_path": "peft/src/peft/utils/__init__.py",
"repo_id": "peft",
"token_count": 722
} | 183 |
# Copyright 2023-present the HuggingFace Inc. team.
#
# 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... | peft/tests/test_decoder_models.py/0 | {
"file_path": "peft/tests/test_decoder_models.py",
"repo_id": "peft",
"token_count": 7592
} | 184 |
# Recent Changes
### Feb 7, 2023
* New inference benchmark numbers added in [results](results/) folder.
* Add convnext LAION CLIP trained weights and initial set of in1k fine-tunes
* `convnext_base.clip_laion2b_augreg_ft_in1k` - 86.2% @ 256x256
* `convnext_base.clip_laiona_augreg_ft_in1k_384` - 86.5% @ 384x384
*... | pytorch-image-models/docs/changes.md/0 | {
"file_path": "pytorch-image-models/docs/changes.md",
"repo_id": "pytorch-image-models",
"token_count": 29802
} | 185 |
# Dual Path Network (DPN)
A **Dual Path Network (DPN)** is a convolutional neural network which presents a new topology of connection paths internally. The intuition is that [ResNets](https://paperswithcode.com/method/resnet) enables feature re-usage while DenseNet enables new feature exploration, and both are importa... | pytorch-image-models/docs/models/.templates/models/dpn.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/dpn.md",
"repo_id": "pytorch-image-models",
"token_count": 2889
} | 186 |
# Inception v3
**Inception v3** is a convolutional neural network architecture from the Inception family that makes several improvements including using [Label Smoothing](https://paperswithcode.com/method/label-smoothing), Factorized 7 x 7 convolutions, and the use of an [auxiliary classifer](https://paperswithcode.co... | pytorch-image-models/docs/models/.templates/models/inception-v3.md/0 | {
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"repo_id": "pytorch-image-models",
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} | 187 |
# ResNeSt
A **ResNeSt** is a variant on a [ResNet](https://paperswithcode.com/method/resnet), which instead stacks [Split-Attention blocks](https://paperswithcode.com/method/split-attention). The cardinal group representations are then concatenated along the channel dimension: $V = \text{Concat}${$V^{1},V^{2},\cdots{V... | pytorch-image-models/docs/models/.templates/models/resnest.md/0 | {
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"token_count": 4643
} | 188 |
# (Tensorflow) EfficientNet Lite
**EfficientNet** is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a *compound coefficient*. Unlike conventional practice that arbitrary scales these factors, the EfficientNet scaling method uniformly... | pytorch-image-models/docs/models/.templates/models/tf-efficientnet-lite.md/0 | {
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"repo_id": "pytorch-image-models",
"token_count": 2543
} | 189 |
# Deep Layer Aggregation
Extending “shallow” skip connections, **Dense Layer Aggregation (DLA)** incorporates more depth and sharing. The authors introduce two structures for deep layer aggregation (DLA): iterative deep aggregation (IDA) and hierarchical deep aggregation (HDA). These structures are expressed through ... | pytorch-image-models/docs/models/dla.md/0 | {
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"token_count": 6755
} | 190 |
# Res2NeXt
**Res2NeXt** is an image model that employs a variation on [ResNeXt](https://paperswithcode.com/method/resnext) bottleneck residual blocks. The motivation is to be able to represent features at multiple scales. This is achieved through a novel building block for CNNs that constructs hierarchical residual-li... | pytorch-image-models/docs/models/res2next.md/0 | {
"file_path": "pytorch-image-models/docs/models/res2next.md",
"repo_id": "pytorch-image-models",
"token_count": 1708
} | 191 |
# (Tensorflow) EfficientNet CondConv
**EfficientNet** is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a *compound coefficient*. Unlike conventional practice that arbitrary scales these factors, the EfficientNet scaling method unifo... | pytorch-image-models/docs/models/tf-efficientnet-condconv.md/0 | {
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"repo_id": "pytorch-image-models",
"token_count": 3284
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# Sharing and Loading Models From the Hugging Face Hub
The `timm` library has a built-in integration with the Hugging Face Hub, making it easy to share and load models from the 🤗 Hub.
In this short guide, we'll see how to:
1. Share a `timm` model on the Hub
2. How to load that model back from the Hub
## Authent... | pytorch-image-models/hfdocs/source/hf_hub.mdx/0 | {
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"repo_id": "pytorch-image-models",
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} | 193 |
# # Ensemble Adversarial Inception ResNet v2
**Inception-ResNet-v2** is a convolutional neural architecture that builds on the Inception family of architectures but incorporates [residual connections](https://paperswithcode.com/method/residual-connection) (replacing the filter concatenation stage of the Inception arch... | pytorch-image-models/hfdocs/source/models/ensemble-adversarial.mdx/0 | {
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"repo_id": "pytorch-image-models",
"token_count": 2209
} | 194 |
# (Legacy) SENet
A **SENet** is a convolutional neural network architecture that employs [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) to enable the network to perform dynamic channel-wise feature recalibration.
The weights from this model were ported from Gluon.
## ... | pytorch-image-models/hfdocs/source/models/legacy-senet.mdx/0 | {
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"repo_id": "pytorch-image-models",
"token_count": 1605
} | 195 |
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