text stringlengths 7 318k | id stringlengths 14 166 | metadata dict | __index_level_0__ int64 0 439 |
|---|---|---|---|
# Conclusion
That’s all for today. Congrats on finishing this unit and the tutorial!
The best way to learn is to practice and try stuff. **Why not train another agent with a different configuration?**
And don’t hesitate from time to time to check the [leaderboard](https://huggingface.co/spaces/huggingface-projects/A... | deep-rl-class/units/en/unit7/conclusion.mdx/0 | {
"file_path": "deep-rl-class/units/en/unit7/conclusion.mdx",
"repo_id": "deep-rl-class",
"token_count": 117
} | 84 |
# Visualize the Clipped Surrogate Objective Function
Don't worry. **It's normal if this seems complex to handle right now**. But we're going to see what this Clipped Surrogate Objective Function looks like, and this will help you to visualize better what's going on.
<figure class="image table text-center m-0 w-full">... | deep-rl-class/units/en/unit8/visualize.mdx/0 | {
"file_path": "deep-rl-class/units/en/unit8/visualize.mdx",
"repo_id": "deep-rl-class",
"token_count": 1594
} | 85 |
# An Introduction to Unreal Learning Agents
[Learning Agents](https://dev.epicgames.com/community/learning/tutorials/8OWY/unreal-engine-learning-agents-introduction) is an Unreal Engine (UE) plugin that allows you **to train AI characters using machine learning (ML) in Unreal**.
It's an exciting new plugin where you ... | deep-rl-class/units/en/unitbonus3/learning-agents.mdx/0 | {
"file_path": "deep-rl-class/units/en/unitbonus3/learning-agents.mdx",
"repo_id": "deep-rl-class",
"token_count": 804
} | 86 |
<!--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... | diffusers/docs/source/en/api/loaders/peft.md/0 | {
"file_path": "diffusers/docs/source/en/api/loaders/peft.md",
"repo_id": "diffusers",
"token_count": 321
} | 87 |
<!--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... | diffusers/docs/source/en/api/pipelines/stable_diffusion/overview.md/0 | {
"file_path": "diffusers/docs/source/en/api/pipelines/stable_diffusion/overview.md",
"repo_id": "diffusers",
"token_count": 4282
} | 88 |
<!--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... | diffusers/docs/source/en/api/schedulers/ddim.md/0 | {
"file_path": "diffusers/docs/source/en/api/schedulers/ddim.md",
"repo_id": "diffusers",
"token_count": 1123
} | 89 |
<!--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... | diffusers/docs/source/en/api/schedulers/pndm.md/0 | {
"file_path": "diffusers/docs/source/en/api/schedulers/pndm.md",
"repo_id": "diffusers",
"token_count": 305
} | 90 |
<!--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... | diffusers/docs/source/en/installation.md/0 | {
"file_path": "diffusers/docs/source/en/installation.md",
"repo_id": "diffusers",
"token_count": 1586
} | 91 |
<!--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... | diffusers/docs/source/en/training/controlnet.md/0 | {
"file_path": "diffusers/docs/source/en/training/controlnet.md",
"repo_id": "diffusers",
"token_count": 4989
} | 92 |
<!--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... | diffusers/docs/source/en/training/wuerstchen.md/0 | {
"file_path": "diffusers/docs/source/en/training/wuerstchen.md",
"repo_id": "diffusers",
"token_count": 2905
} | 93 |
<!--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... | diffusers/docs/source/en/using-diffusers/distilled_sd.md/0 | {
"file_path": "diffusers/docs/source/en/using-diffusers/distilled_sd.md",
"repo_id": "diffusers",
"token_count": 1681
} | 94 |
<!--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... | diffusers/docs/source/en/using-diffusers/schedulers.md/0 | {
"file_path": "diffusers/docs/source/en/using-diffusers/schedulers.md",
"repo_id": "diffusers",
"token_count": 3998
} | 95 |
<!--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... | diffusers/docs/source/ja/tutorials/autopipeline.md/0 | {
"file_path": "diffusers/docs/source/ja/tutorials/autopipeline.md",
"repo_id": "diffusers",
"token_count": 4101
} | 96 |
<!--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... | diffusers/docs/source/ko/optimization/xformers.md/0 | {
"file_path": "diffusers/docs/source/ko/optimization/xformers.md",
"repo_id": "diffusers",
"token_count": 1050
} | 97 |
<!--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... | diffusers/docs/source/ko/tutorials/tutorial_overview.md/0 | {
"file_path": "diffusers/docs/source/ko/tutorials/tutorial_overview.md",
"repo_id": "diffusers",
"token_count": 1211
} | 98 |
<!--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... | diffusers/docs/source/ko/using-diffusers/schedulers.md/0 | {
"file_path": "diffusers/docs/source/ko/using-diffusers/schedulers.md",
"repo_id": "diffusers",
"token_count": 6918
} | 99 |
<!---
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 a... | diffusers/examples/README.md/0 | {
"file_path": "diffusers/examples/README.md",
"repo_id": "diffusers",
"token_count": 1797
} | 100 |
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
} | 101 |
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": 9662
} | 102 |
import math
from typing import Dict, Optional
import torch
import torchvision.transforms.functional as FF
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
from diffusers import StableDiffusionPipeline
from diffusers.models import AutoencoderKL, UNet2DConditionModel
from diffusers.pipelines.... | diffusers/examples/community/regional_prompting_stable_diffusion.py/0 | {
"file_path": "diffusers/examples/community/regional_prompting_stable_diffusion.py",
"repo_id": "diffusers",
"token_count": 13641
} | 103 |
# coding=utf-8
# Copyright 2023 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/examples/consistency_distillation/test_lcm_lora.py/0 | {
"file_path": "diffusers/examples/consistency_distillation/test_lcm_lora.py",
"repo_id": "diffusers",
"token_count": 2106
} | 104 |
import warnings
from diffusers import StableDiffusionImg2ImgPipeline # noqa F401
warnings.warn(
"The `image_to_image.py` script is outdated. Please use directly `from diffusers import"
" StableDiffusionImg2ImgPipeline` instead."
)
| diffusers/examples/inference/image_to_image.py/0 | {
"file_path": "diffusers/examples/inference/image_to_image.py",
"repo_id": "diffusers",
"token_count": 84
} | 105 |
# Research projects
This folder contains various research projects using 🧨 Diffusers.
They are not really maintained by the core maintainers of this library and often require a specific version of Diffusers that is indicated in the requirements file of each folder.
Updating them to the most recent version of the libr... | diffusers/examples/research_projects/README.md/0 | {
"file_path": "diffusers/examples/research_projects/README.md",
"repo_id": "diffusers",
"token_count": 143
} | 106 |
# Diffusion Model Alignment Using Direct Preference Optimization
This directory provides LoRA implementations of Diffusion DPO proposed in [DiffusionModel Alignment Using Direct Preference Optimization](https://arxiv.org/abs/2311.12908) by Bram Wallace, Meihua Dang, Rafael Rafailov, Linqi Zhou, Aaron Lou, Senthil Puru... | diffusers/examples/research_projects/diffusion_dpo/README.md/0 | {
"file_path": "diffusers/examples/research_projects/diffusion_dpo/README.md",
"repo_id": "diffusers",
"token_count": 932
} | 107 |
import argparse
import os
import torch
from PIL import Image, ImageFilter
from transformers import CLIPTextModel
from diffusers import DPMSolverMultistepScheduler, StableDiffusionInpaintPipeline, UNet2DConditionModel
parser = argparse.ArgumentParser(description="Inference")
parser.add_argument(
"--model_path",
... | diffusers/examples/research_projects/realfill/infer.py/0 | {
"file_path": "diffusers/examples/research_projects/realfill/infer.py",
"repo_id": "diffusers",
"token_count": 984
} | 108 |
import argparse
import torch
from safetensors.torch import save_file
def convert_motion_module(original_state_dict):
converted_state_dict = {}
for k, v in original_state_dict.items():
if "pos_encoder" in k:
continue
else:
converted_state_dict[
k.replac... | diffusers/scripts/convert_animatediff_motion_lora_to_diffusers.py/0 | {
"file_path": "diffusers/scripts/convert_animatediff_motion_lora_to_diffusers.py",
"repo_id": "diffusers",
"token_count": 642
} | 109 |
import argparse
import tempfile
import torch
from accelerate import load_checkpoint_and_dispatch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import UnCLIPPipeline, UNet2DConditionModel, UNet2DModel
from diffusers.models.transformers.prior_transformer import PriorTransformer
from... | diffusers/scripts/convert_kakao_brain_unclip_to_diffusers.py/0 | {
"file_path": "diffusers/scripts/convert_kakao_brain_unclip_to_diffusers.py",
"repo_id": "diffusers",
"token_count": 18242
} | 110 |
import argparse
import tempfile
import torch
from accelerate import load_checkpoint_and_dispatch
from diffusers.models.transformers.prior_transformer import PriorTransformer
from diffusers.pipelines.shap_e import ShapERenderer
"""
Example - From the diffusers root directory:
Download weights:
```sh
$ wget "https:... | diffusers/scripts/convert_shap_e_to_diffusers.py/0 | {
"file_path": "diffusers/scripts/convert_shap_e_to_diffusers.py",
"repo_id": "diffusers",
"token_count": 22932
} | 111 |
__version__ = "0.26.0.dev0"
from typing import TYPE_CHECKING
from .utils import (
DIFFUSERS_SLOW_IMPORT,
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_k_diffusion_available,
is_librosa_available,
is_note_seq_available,
is_onnx_available,
is_scipy_available,
... | diffusers/src/diffusers/__init__.py/0 | {
"file_path": "diffusers/src/diffusers/__init__.py",
"repo_id": "diffusers",
"token_count": 13685
} | 112 |
# 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 applicabl... | diffusers/src/diffusers/loaders/ip_adapter.py/0 | {
"file_path": "diffusers/src/diffusers/loaders/ip_adapter.py",
"repo_id": "diffusers",
"token_count": 4546
} | 113 |
from .autoencoder_asym_kl import AsymmetricAutoencoderKL
from .autoencoder_kl import AutoencoderKL
from .autoencoder_kl_temporal_decoder import AutoencoderKLTemporalDecoder
from .autoencoder_tiny import AutoencoderTiny
from .consistency_decoder_vae import ConsistencyDecoderVAE
| diffusers/src/diffusers/models/autoencoders/__init__.py/0 | {
"file_path": "diffusers/src/diffusers/models/autoencoders/__init__.py",
"repo_id": "diffusers",
"token_count": 99
} | 114 |
from dataclasses import dataclass
from ..utils import BaseOutput
@dataclass
class AutoencoderKLOutput(BaseOutput):
"""
Output of AutoencoderKL encoding method.
Args:
latent_dist (`DiagonalGaussianDistribution`):
Encoded outputs of `Encoder` represented as the mean and logvar of `Diag... | diffusers/src/diffusers/models/modeling_outputs.py/0 | {
"file_path": "diffusers/src/diffusers/models/modeling_outputs.py",
"repo_id": "diffusers",
"token_count": 178
} | 115 |
# 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 applicabl... | diffusers/src/diffusers/models/unet_1d.py/0 | {
"file_path": "diffusers/src/diffusers/models/unet_1d.py",
"repo_id": "diffusers",
"token_count": 411
} | 116 |
# 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 applicabl... | diffusers/src/diffusers/models/unets/unet_kandinsky3.py/0 | {
"file_path": "diffusers/src/diffusers/models/unets/unet_kandinsky3.py",
"repo_id": "diffusers",
"token_count": 9648
} | 117 |
# 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 applicabl... | diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.py",
"repo_id": "diffusers",
"token_count": 20384
} | 118 |
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
logger = logging.get_logger(__name__)
class MultiControlN... | diffusers/src/diffusers/pipelines/controlnet/multicontrolnet.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/controlnet/multicontrolnet.py",
"repo_id": "diffusers",
"token_count": 3924
} | 119 |
import html
import inspect
import re
import urllib.parse as ul
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, T5EncoderModel, T5Tokenizer
from ...loaders import LoraLoaderMixin
from ...models import UNet2DConditionModel
from ...schedulers import... | diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if.py",
"repo_id": "diffusers",
"token_count": 16560
} | 120 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ....utils import (
BaseOutput,
)
@dataclass
# Copied from diffusers.pipelines.stable_diffusion.pipeline_output.StableDiffusionPipelineOutput with Stable->Alt
class AltDiffusionPipelineOutput(BaseO... | diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_output.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_output.py",
"repo_id": "diffusers",
"token_count": 344
} | 121 |
# Copyright 2022 The Music Spectrogram Diffusion Authors.
# 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... | diffusers/src/diffusers/pipelines/deprecated/spectrogram_diffusion/pipeline_spectrogram_diffusion.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/deprecated/spectrogram_diffusion/pipeline_spectrogram_diffusion.py",
"repo_id": "diffusers",
"token_count": 5002
} | 122 |
from typing import TYPE_CHECKING
from ....utils import (
DIFFUSERS_SLOW_IMPORT,
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
is_transformers_available,
)
_dummy_objects = {}
_import_structure = {}
try:
if not (is_transformers_available() and is_torch_available()):
... | diffusers/src/diffusers/pipelines/deprecated/vq_diffusion/__init__.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/deprecated/vq_diffusion/__init__.py",
"repo_id": "diffusers",
"token_count": 682
} | 123 |
# 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 applicabl... | diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_controlnet.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_controlnet.py",
"repo_id": "diffusers",
"token_count": 6130
} | 124 |
from typing import TYPE_CHECKING
from ...utils import (
DIFFUSERS_SLOW_IMPORT,
OptionalDependencyNotAvailable,
_LazyModule,
get_objects_from_module,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
_dummy_objects = {}
_import_structure = {}
try:
if not (is_tr... | diffusers/src/diffusers/pipelines/musicldm/__init__.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/musicldm/__init__.py",
"repo_id": "diffusers",
"token_count": 559
} | 125 |
# Copyright 2023 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": 2275
} | 126 |
# Copyright 2023 DiffEdit Authors and Pix2Pix Zero Authors 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/license... | diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py",
"repo_id": "diffusers",
"token_count": 34833
} | 127 |
# Copyright (c) 2023 Dominic Rampas MIT License
# 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/licen... | diffusers/src/diffusers/pipelines/wuerstchen/modeling_wuerstchen_prior.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/wuerstchen/modeling_wuerstchen_prior.py",
"repo_id": "diffusers",
"token_count": 3870
} | 128 |
# Copyright 2023 ParaDiGMS authors 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_ddim_parallel.py/0 | {
"file_path": "diffusers/src/diffusers/schedulers/scheduling_ddim_parallel.py",
"repo_id": "diffusers",
"token_count": 13354
} | 129 |
# Copyright 2023 Katherine Crowson, The HuggingFace Team and hlky. 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/src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.py/0 | {
"file_path": "diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.py",
"repo_id": "diffusers",
"token_count": 9705
} | 130 |
# Copyright 2023 Microsoft 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 require... | diffusers/src/diffusers/schedulers/scheduling_vq_diffusion.py/0 | {
"file_path": "diffusers/src/diffusers/schedulers/scheduling_vq_diffusion.py",
"repo_id": "diffusers",
"token_count": 12488
} | 131 |
# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends
class OnnxStableDiffusionImg2ImgPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers", "onnx"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["... | diffusers/src/diffusers/utils/dummy_torch_and_transformers_and_onnx_objects.py/0 | {
"file_path": "diffusers/src/diffusers/utils/dummy_torch_and_transformers_and_onnx_objects.py",
"repo_id": "diffusers",
"token_count": 1270
} | 132 |
# coding=utf-8
# Copyright 2023 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/models/test_modeling_common.py/0 | {
"file_path": "diffusers/tests/models/test_modeling_common.py",
"repo_id": "diffusers",
"token_count": 13954
} | 133 |
# coding=utf-8
# Copyright 2023 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_config.py/0 | {
"file_path": "diffusers/tests/others/test_config.py",
"repo_id": "diffusers",
"token_count": 3987
} | 134 |
# coding=utf-8
# Copyright 2023 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/controlnet/test_flax_controlnet.py/0 | {
"file_path": "diffusers/tests/pipelines/controlnet/test_flax_controlnet.py",
"repo_id": "diffusers",
"token_count": 2142
} | 135 |
# coding=utf-8
# Copyright 2023 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/kandinsky2_2/test_kandinsky_prior.py/0 | {
"file_path": "diffusers/tests/pipelines/kandinsky2_2/test_kandinsky_prior.py",
"repo_id": "diffusers",
"token_count": 4050
} | 136 |
# coding=utf-8
# Copyright 2023 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": 25620
} | 137 |
# 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": 18685
} | 138 |
# coding=utf-8
# Copyright 2023 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": 3476
} | 139 |
# coding=utf-8
# Copyright 2023 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": 2385
} | 140 |
# coding=utf-8
# Copyright 2023 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": 2631
} | 141 |
# coding=utf-8
# Copyright 2023 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_scheduler_flax.py/0 | {
"file_path": "diffusers/tests/schedulers/test_scheduler_flax.py",
"repo_id": "diffusers",
"token_count": 18870
} | 142 |
# coding=utf-8
# Copyright 2023 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... | diffusers/utils/check_doc_toc.py/0 | {
"file_path": "diffusers/utils/check_doc_toc.py",
"repo_id": "diffusers",
"token_count": 2178
} | 143 |
# Hackathon DreamBooth 🏆
📣 **The hackathon is now over and the winners have been announced on Discord. You are still welcome to train models and submit them to the leaderboard, but we won't be offering prizes or certificates at this point in time.**
Welcome to the DreamBooth Hackathon! This is a community event wh... | diffusion-models-class/units/en/events/2.mdx/0 | {
"file_path": "diffusion-models-class/units/en/events/2.mdx",
"repo_id": "diffusion-models-class",
"token_count": 3034
} | 144 |
import wandb
import numpy as np
import torch, torchvision
import torch.nn.functional as F
from PIL import Image
from tqdm.auto import tqdm
from fastcore.script import call_parse
from torchvision import transforms
from diffusers import DDPMPipeline
from diffusers import DDIMScheduler
from datasets import load_dataset
fr... | diffusion-models-class/units/en/unit2/finetune_model.py/0 | {
"file_path": "diffusion-models-class/units/en/unit2/finetune_model.py",
"repo_id": "diffusion-models-class",
"token_count": 2007
} | 145 |
<jupyter_start><jupyter_text>Diffusion for Audio In this notebook, we're going to take a brief look at generating audio with diffusion models. What you will learn:- How audio is represented in a computer- Methods to convert between raw audio data and spectrograms- How to prepare a dataloader with a custom collate funct... | diffusion-models-class/units/en/unit4/diffusion_for_audio.ipynb/0 | {
"file_path": "diffusion-models-class/units/en/unit4/diffusion_for_audio.ipynb",
"repo_id": "diffusion-models-class",
"token_count": 4543
} | 146 |
# Diffusion pour l'audio
<CourseFloatingBanner unit={4}
classNames="absolute z-10 right-0 top-0"
notebooks={[
{label: "Diffusion pour l'audio", value: "https://colab.research.google.com/github/huggingface/diffusion-models-class/blob/main/units/fr/unit4/diffusion_for_audio.ipynb"},
{label: "Diffusion pour l'au... | diffusion-models-class/units/fr/unit4/3.mdx/0 | {
"file_path": "diffusion-models-class/units/fr/unit4/3.mdx",
"repo_id": "diffusion-models-class",
"token_count": 7898
} | 147 |
# notebooks
Notebooks using the Hugging Face libraries 🤗
| notebooks/README.md/0 | {
"file_path": "notebooks/README.md",
"repo_id": "notebooks",
"token_count": 15
} | 148 |
<jupyter_start><jupyter_text>Manipulation de plusieurs séquences (PyTorch) Installez la bibliothèque 🤗 *Transformers* pour exécuter ce *notebook*.<jupyter_code>!pip install transformers[sentencepiece]
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
checkpoint = "tblard/tf-alloc... | notebooks/course/fr/chapter2/section5_pt.ipynb/0 | {
"file_path": "notebooks/course/fr/chapter2/section5_pt.ipynb",
"repo_id": "notebooks",
"token_count": 814
} | 149 |
<jupyter_start><jupyter_text>Il est temps de trancher et de découper Installez les bibliothèques 🤗 Transformers et 🤗 Datasets pour exécuter ce *notebook*.<jupyter_code>!pip install datasets evaluate transformers[sentencepiece]
!wget "https://archive.ics.uci.edu/ml/machine-learning-databases/00462/drugsCom_raw.zip"
!u... | notebooks/course/fr/chapter5/section3.ipynb/0 | {
"file_path": "notebooks/course/fr/chapter5/section3.ipynb",
"repo_id": "notebooks",
"token_count": 1912
} | 150 |
<jupyter_start><jupyter_text>Classification de token (PyTorch) Installez les bibliothèques 🤗 *Datasets*, 🤗 *Transformers* et 🤗 *Accelerate* pour exécuter ce *notebook*.<jupyter_code>!pip install datasets transformers[sentencepiece]
!pip install accelerate
# Pour exécuter l'entraînement sur TPU, vous devrez décommen... | notebooks/course/fr/chapter7/section2_pt.ipynb/0 | {
"file_path": "notebooks/course/fr/chapter7/section2_pt.ipynb",
"repo_id": "notebooks",
"token_count": 3899
} | 151 |
<jupyter_start><jupyter_text>IntroductionThis colab is design to run the pretrained models from [GeoDiff](https://github.com/MinkaiXu/GeoDiff).The visualization code is inspired by this PyMol [colab](https://colab.research.google.com/gist/iwatobipen/2ec7faeafe5974501e69fcc98c122922/pymol.ipynbscrollTo=Hm4kY7CaZSlw).The... | notebooks/diffusers/geodiff_molecule_conformation.ipynb/0 | {
"file_path": "notebooks/diffusers/geodiff_molecule_conformation.ipynb",
"repo_id": "notebooks",
"token_count": 18632
} | 152 |
<jupyter_start><jupyter_text>**How to benchmark models with Transformers**With ever-larger language models, it is no longer enough to just compare models on their performance on a specific task. One should always be aware of the computational cost that is attached to a specific model. For a given computation environmen... | notebooks/examples/benchmark.ipynb/0 | {
"file_path": "notebooks/examples/benchmark.ipynb",
"repo_id": "notebooks",
"token_count": 12105
} | 153 |
<jupyter_start><jupyter_text>**Building an Image Similarity System with 🤗 Transformers**In this notebook, you'll learn to build an image similarity system with 🤗 Transformers. Finding out the similarity between a query image and potential candidates is an important use case for information retrieval systems, reverse ... | notebooks/examples/image_similarity.ipynb/0 | {
"file_path": "notebooks/examples/image_similarity.ipynb",
"repo_id": "notebooks",
"token_count": 8098
} | 154 |
<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 datasets transformers<jupyter_output><empty_output><jupyter_text>If you're opening this notebook locally, make su... | notebooks/examples/multiple_choice.ipynb/0 | {
"file_path": "notebooks/examples/multiple_choice.ipynb",
"repo_id": "notebooks",
"token_count": 6252
} | 155 |
<jupyter_start><jupyter_text>**Fine-tuning Speech Model with 🤗 Transformers** This notebook shows how to fine-tune multi-lingual pretrained speech models for Automatic Speech Recognition. This notebook is built to run on the [TIMIT dataset](https://huggingface.co/datasets/timit) with any speech model checkpoint from t... | notebooks/examples/speech_recognition.ipynb/0 | {
"file_path": "notebooks/examples/speech_recognition.ipynb",
"repo_id": "notebooks",
"token_count": 9428
} | 156 |
<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. We also use the `sacrebleu` and `sentencepiece` libraries - you may need to install these even if you already have 🤗 Transformers!<jupyter_c... | notebooks/examples/translation-tf.ipynb/0 | {
"file_path": "notebooks/examples/translation-tf.ipynb",
"repo_id": "notebooks",
"token_count": 8046
} | 157 |
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/05_spot_instances/scripts/train.py/0 | {
"file_path": "notebooks/sagemaker/05_spot_instances/scripts/train.py",
"repo_id": "notebooks",
"token_count": 1799
} | 158 |
<jupyter_start><jupyter_text>Sentence Embeddings with Hugging Face Transformers, Sentence Transformers and Amazon SageMaker - Custom Inference for creating document embeddings with Hugging Face's Transformers Welcome to this getting started guide. We will use the Hugging Face Inference DLCs and Amazon SageMaker Python ... | notebooks/sagemaker/17_custom_inference_script/sagemaker-notebook.ipynb/0 | {
"file_path": "notebooks/sagemaker/17_custom_inference_script/sagemaker-notebook.ipynb",
"repo_id": "notebooks",
"token_count": 3804
} | 159 |
accelerate launch --config_file accelerate_config.yaml train_using_s3_data.py \
--mixed_precision "fp16" | notebooks/sagemaker/22_accelerate_sagemaker_examples/src/text-classification/launch.sh/0 | {
"file_path": "notebooks/sagemaker/22_accelerate_sagemaker_examples/src/text-classification/launch.sh",
"repo_id": "notebooks",
"token_count": 40
} | 160 |
import nbformat
import os
import re
import shutil
# Paths are set to work by invoking this scrip from the notebooks repo, presuming the transformers repo is in the
# same parent folder as the notebooks repo.
PATH_TO_DOCS = '../transformers/docs/source'
PATH_TO_DEST = 'transformers_doc'
DOC_BASE_URL = "https://huggingf... | notebooks/utils/convert_doc_to_notebooks.py/0 | {
"file_path": "notebooks/utils/convert_doc_to_notebooks.py",
"repo_id": "notebooks",
"token_count": 7880
} | 161 |
# docstyle-ignore
INSTALL_CONTENT = """
# PEFT installation
! pip install peft accelerate transformers
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/peft.git
"""
| peft/docs/source/_config.py/0 | {
"file_path": "peft/docs/source/_config.py",
"repo_id": "peft",
"token_count": 75
} | 162 |
<jupyter_start><jupyter_code>from transformers import AutoModelForCausalLM
from peft import get_peft_config, get_peft_model, PrefixTuningConfig, TaskType, PeftType
import torch
from datasets import load_dataset
import os
from transformers import AutoTokenizer
from torch.utils.data import DataLoader
from transformers im... | peft/examples/causal_language_modeling/peft_prefix_tuning_clm.ipynb/0 | {
"file_path": "peft/examples/causal_language_modeling/peft_prefix_tuning_clm.ipynb",
"repo_id": "peft",
"token_count": 4714
} | 163 |
<jupyter_start><jupyter_code>import argparse
import json
import logging
import math
import os
import random
from pathlib import Path
from tqdm import tqdm
import datasets
from datasets import load_dataset, DatasetDict
import evaluate
import torch
from torch import nn
from torch.utils.data import DataLoader
import tr... | peft/examples/feature_extraction/peft_lora_embedding_semantic_similarity_inference.ipynb/0 | {
"file_path": "peft/examples/feature_extraction/peft_lora_embedding_semantic_similarity_inference.ipynb",
"repo_id": "peft",
"token_count": 2663
} | 164 |
import argparse
import os
from collections import Counter
from dataclasses import dataclass
from typing import Dict, Optional
import safetensors
import torch
from diffusers import UNet2DConditionModel
from transformers import CLIPTextModel
from peft import LoraConfig, get_peft_model, get_peft_model_state_dict, set_pe... | peft/examples/lora_dreambooth/convert_kohya_ss_sd_lora_to_peft.py/0 | {
"file_path": "peft/examples/lora_dreambooth/convert_kohya_ss_sd_lora_to_peft.py",
"repo_id": "peft",
"token_count": 2947
} | 165 |
<jupyter_start><jupyter_code>import argparse
import os
import torch
from torch.optim import AdamW
from torch.utils.data import DataLoader
from peft import (
get_peft_config,
get_peft_model,
get_peft_model_state_dict,
set_peft_model_state_dict,
PeftType,
PrefixTuningConfig,
PromptEncoderConf... | peft/examples/sequence_classification/Prompt_Tuning.ipynb/0 | {
"file_path": "peft/examples/sequence_classification/Prompt_Tuning.ipynb",
"repo_id": "peft",
"token_count": 2018
} | 166 |
# coding=utf-8
# 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 ap... | peft/src/peft/config.py/0 | {
"file_path": "peft/src/peft/config.py",
"repo_id": "peft",
"token_count": 4408
} | 167 |
# coding=utf-8
# 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 ap... | peft/src/peft/tuners/adaption_prompt/layer.py/0 | {
"file_path": "peft/src/peft/tuners/adaption_prompt/layer.py",
"repo_id": "peft",
"token_count": 2247
} | 168 |
# coding=utf-8
# 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 ap... | peft/src/peft/tuners/lora/__init__.py/0 | {
"file_path": "peft/src/peft/tuners/lora/__init__.py",
"repo_id": "peft",
"token_count": 420
} | 169 |
# coding=utf-8
# 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 ap... | peft/src/peft/tuners/oft/model.py/0 | {
"file_path": "peft/src/peft/tuners/oft/model.py",
"repo_id": "peft",
"token_count": 1607
} | 170 |
# 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": 703
} | 171 |
# coding=utf-8
# 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 ap... | peft/tests/test_feature_extraction_models.py/0 | {
"file_path": "peft/tests/test_feature_extraction_models.py",
"repo_id": "peft",
"token_count": 3363
} | 172 |
*This guideline is very much a work-in-progress.*
Contributions to `timm` for code, documentation, tests are more than welcome!
There haven't been any formal guidelines to date so please bear with me, and feel free to add to this guide.
# Coding style
Code linting and auto-format (black) are not currently in place ... | pytorch-image-models/CONTRIBUTING.md/0 | {
"file_path": "pytorch-image-models/CONTRIBUTING.md",
"repo_id": "pytorch-image-models",
"token_count": 1224
} | 173 |
# Model Summaries
The model architectures included come from a wide variety of sources. Sources, including papers, original impl ("reference code") that I rewrote / adapted, and PyTorch impl that I leveraged directly ("code") are listed below.
Most included models have pretrained weights. The weights are either:
1. ... | pytorch-image-models/docs/models.md/0 | {
"file_path": "pytorch-image-models/docs/models.md",
"repo_id": "pytorch-image-models",
"token_count": 4347
} | 174 |
# # 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/docs/models/.templates/models/ensemble-adversarial.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/ensemble-adversarial.md",
"repo_id": "pytorch-image-models",
"token_count": 1379
} | 175 |
# (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/docs/models/.templates/models/legacy-senet.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/legacy-senet.md",
"repo_id": "pytorch-image-models",
"token_count": 793
} | 176 |
# RexNet
**Rank Expansion Networks** (ReXNets) follow a set of new design principles for designing bottlenecks in image classification models. Authors refine each layer by 1) expanding the input channel size of the convolution layer and 2) replacing the [ReLU6s](https://www.paperswithcode.com/method/relu6).
{% includ... | pytorch-image-models/docs/models/.templates/models/rexnet.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/rexnet.md",
"repo_id": "pytorch-image-models",
"token_count": 2278
} | 177 |
# (Tensorflow) MobileNet v3
**MobileNetV3** is a convolutional neural network that is designed for mobile phone CPUs. The network design includes the use of a [hard swish activation](https://paperswithcode.com/method/hard-swish) and [squeeze-and-excitation](https://paperswithcode.com/method/squeeze-and-excitation-bloc... | pytorch-image-models/docs/models/.templates/models/tf-mobilenet-v3.md/0 | {
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"token_count": 3951
} | 178 |
# EfficientNet
**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 scales network wi... | pytorch-image-models/docs/models/efficientnet.md/0 | {
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# ResNeXt
A **ResNeXt** repeats a [building block](https://paperswithcode.com/method/resnext-block) that aggregates a set of transformations with the same topology. Compared to a [ResNet](https://paperswithcode.com/method/resnet), it exposes a new dimension, *cardinality* (the size of the set of transformations) $C$,... | pytorch-image-models/docs/models/resnext.md/0 | {
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"token_count": 3053
} | 180 |
# (Tensorflow) MixNet
**MixNet** is a type of convolutional neural network discovered via AutoML that utilises [MixConvs](https://paperswithcode.com/method/mixconv) instead of regular [depthwise convolutions](https://paperswithcode.com/method/depthwise-convolution).
The weights from this model were ported from [Tenso... | pytorch-image-models/docs/models/tf-mixnet.md/0 | {
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# Adversarial 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://paper... | pytorch-image-models/hfdocs/source/models/adversarial-inception-v3.mdx/0 | {
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"token_count": 2247
} | 182 |
# (Gluon) ResNet
**Residual Networks**, or **ResNets**, learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. Instead of hoping each few stacked layers directly fit a desired underlying mapping, residual nets let these layers fit a residual mapping. They stack [residu... | pytorch-image-models/hfdocs/source/models/gloun-resnet.mdx/0 | {
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"repo_id": "pytorch-image-models",
"token_count": 7210
} | 183 |
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