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# An introduction to Multi-Agents Reinforcement Learning (MARL)
## From single agent to multiple agents
In the first unit, we learned to train agents in a single-agent system. When our agent was alone in its environment: **it was not cooperating or collaborating with other agents**.
<figure>
<img src="https://huggin... | deep-rl-class/units/en/unit7/introduction-to-marl.mdx/0 | {
"file_path": "deep-rl-class/units/en/unit7/introduction-to-marl.mdx",
"repo_id": "deep-rl-class",
"token_count": 982
} | 87 |
# How Huggy works [[how-huggy-works]]
Huggy is a Deep Reinforcement Learning environment made by Hugging Face and based on [Puppo the Corgi, a project by the Unity MLAgents team](https://blog.unity.com/technology/puppo-the-corgi-cuteness-overload-with-the-unity-ml-agents-toolkit).
This environment was created using th... | deep-rl-class/units/en/unitbonus1/how-huggy-works.mdx/0 | {
"file_path": "deep-rl-class/units/en/unitbonus1/how-huggy-works.mdx",
"repo_id": "deep-rl-class",
"token_count": 1245
} | 88 |
# Offline vs. Online Reinforcement Learning
Deep Reinforcement Learning (RL) is a framework **to build decision-making agents**. These agents aim to learn optimal behavior (policy) by interacting with the environment through **trial and error and receiving rewards as unique feedback**.
The agent’s goal **is to maximi... | deep-rl-class/units/en/unitbonus3/offline-online.mdx/0 | {
"file_path": "deep-rl-class/units/en/unitbonus3/offline-online.mdx",
"repo_id": "deep-rl-class",
"token_count": 708
} | 89 |
FROM ubuntu:20.04
LABEL maintainer="Hugging Face"
LABEL repository="diffusers"
ENV DEBIAN_FRONTEND=noninteractive
RUN apt update && \
apt install -y bash \
build-essential \
git \
git-lfs \
curl \
ca-certificates \
... | diffusers/docker/diffusers-onnxruntime-cpu/Dockerfile/0 | {
"file_path": "diffusers/docker/diffusers-onnxruntime-cpu/Dockerfile",
"repo_id": "diffusers",
"token_count": 642
} | 90 |
<!--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/loaders/lora.md/0 | {
"file_path": "diffusers/docs/source/en/api/loaders/lora.md",
"repo_id": "diffusers",
"token_count": 463
} | 91 |
<!--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/controlnet.md/0 | {
"file_path": "diffusers/docs/source/en/api/pipelines/controlnet.md",
"repo_id": "diffusers",
"token_count": 1146
} | 92 |
<!--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/overview.md/0 | {
"file_path": "diffusers/docs/source/en/api/pipelines/overview.md",
"repo_id": "diffusers",
"token_count": 1909
} | 93 |
<!--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/k_diffusion.md/0 | {
"file_path": "diffusers/docs/source/en/api/pipelines/stable_diffusion/k_diffusion.md",
"repo_id": "diffusers",
"token_count": 379
} | 94 |
<!--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/opt_overview.md/0 | {
"file_path": "diffusers/docs/source/en/optimization/opt_overview.md",
"repo_id": "diffusers",
"token_count": 353
} | 95 |
<!--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/lora.md/0 | {
"file_path": "diffusers/docs/source/en/training/lora.md",
"repo_id": "diffusers",
"token_count": 3336
} | 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/using-diffusers/control_brightness.md/0 | {
"file_path": "diffusers/docs/source/en/using-diffusers/control_brightness.md",
"repo_id": "diffusers",
"token_count": 874
} | 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/using-diffusers/loading.md/0 | {
"file_path": "diffusers/docs/source/en/using-diffusers/loading.md",
"repo_id": "diffusers",
"token_count": 7191
} | 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/using-diffusers/text-img2vid.md/0 | {
"file_path": "diffusers/docs/source/en/using-diffusers/text-img2vid.md",
"repo_id": "diffusers",
"token_count": 7527
} | 99 |
# 학습을 위한 데이터셋 만들기
[Hub](https://huggingface.co/datasets?task_categories=task_categories:text-to-image&sort=downloads) 에는 모델 교육을 위한 많은 데이터셋이 있지만,
관심이 있거나 사용하고 싶은 데이터셋을 찾을 수 없는 경우 🤗 [Datasets](hf.co/docs/datasets) 라이브러리를 사용하여 데이터셋을 만들 수 있습니다.
데이터셋 구조는 모델을 학습하려는 작업에 따라 달라집니다.
가장 기본적인 데이터셋 구조는 unconditional 이미지 생성과 같은 작업... | diffusers/docs/source/ko/training/create_dataset.md/0 | {
"file_path": "diffusers/docs/source/ko/training/create_dataset.md",
"repo_id": "diffusers",
"token_count": 3214
} | 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/ko/using-diffusers/custom_pipeline_examples.md/0 | {
"file_path": "diffusers/docs/source/ko/using-diffusers/custom_pipeline_examples.md",
"repo_id": "diffusers",
"token_count": 10864
} | 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/ko/using-diffusers/weighted_prompts.md/0 | {
"file_path": "diffusers/docs/source/ko/using-diffusers/weighted_prompts.md",
"repo_id": "diffusers",
"token_count": 3375
} | 102 |
## Amused training
Amused can be finetuned on simple datasets relatively cheaply and quickly. Using 8bit optimizers, lora, and gradient accumulation, amused can be finetuned with as little as 5.5 GB. Here are a set of examples for finetuning amused on some relatively simple datasets. These training recipies are aggres... | diffusers/examples/amused/README.md/0 | {
"file_path": "diffusers/examples/amused/README.md",
"repo_id": "diffusers",
"token_count": 5921
} | 103 |
#!/usr/bin/env python3
import torch
from diffusers import DiffusionPipeline
class UnetSchedulerOneForwardPipeline(DiffusionPipeline):
def __init__(self, unet, scheduler):
super().__init__()
self.register_modules(unet=unet, scheduler=scheduler)
def __call__(self):
image = torch.randn... | diffusers/examples/community/one_step_unet.py/0 | {
"file_path": "diffusers/examples/community/one_step_unet.py",
"repo_id": "diffusers",
"token_count": 299
} | 104 |
import argparse
import inspect
import os
import time
import warnings
from typing import Any, Callable, Dict, List, Optional, Union
import numpy as np
import PIL.Image
import torch
from PIL import Image
from transformers import CLIPTokenizer
from diffusers import OnnxRuntimeModel, StableDiffusionImg2ImgPipeline, UniPC... | diffusers/examples/community/run_onnx_controlnet.py/0 | {
"file_path": "diffusers/examples/community/run_onnx_controlnet.py",
"repo_id": "diffusers",
"token_count": 19745
} | 105 |
#
# Copyright 2024 The HuggingFace Inc. team.
# SPDX-FileCopyrightText: Copyright (c) 1993-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the Licens... | diffusers/examples/community/stable_diffusion_tensorrt_img2img.py/0 | {
"file_path": "diffusers/examples/community/stable_diffusion_tensorrt_img2img.py",
"repo_id": "diffusers",
"token_count": 19790
} | 106 |
#!/usr/bin/env python
# coding=utf-8
# Copyright 2024 The LCM team and 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.apach... | diffusers/examples/consistency_distillation/train_lcm_distill_lora_sdxl.py/0 | {
"file_path": "diffusers/examples/consistency_distillation/train_lcm_distill_lora_sdxl.py",
"repo_id": "diffusers",
"token_count": 27390
} | 107 |
# 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/examples/custom_diffusion/test_custom_diffusion.py/0 | {
"file_path": "diffusers/examples/custom_diffusion/test_custom_diffusion.py",
"repo_id": "diffusers",
"token_count": 2234
} | 108 |
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
"The `inpainting.py` script is outdated. Please use directly `from diffusers import"
" StableDiffusionInpaintPipeline` instead."
)
| diffusers/examples/inference/inpainting.py/0 | {
"file_path": "diffusers/examples/inference/inpainting.py",
"repo_id": "diffusers",
"token_count": 89
} | 109 |
# [DreamBooth](https://github.com/huggingface/diffusers/tree/main/examples/dreambooth) by [colossalai](https://github.com/hpcaitech/ColossalAI.git)
[DreamBooth](https://arxiv.org/abs/2208.12242) is a method to personalize text2image models like stable diffusion given just a few(3~5) images of a subject.
The `train_dre... | diffusers/examples/research_projects/colossalai/README.md/0 | {
"file_path": "diffusers/examples/research_projects/colossalai/README.md",
"repo_id": "diffusers",
"token_count": 1659
} | 110 |
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNet2DConditionModel
def parse_args():
parser = argparse.ArgumentParser()
... | diffusers/examples/research_projects/intel_opts/textual_inversion_dfq/text2images.py/0 | {
"file_path": "diffusers/examples/research_projects/intel_opts/textual_inversion_dfq/text2images.py",
"repo_id": "diffusers",
"token_count": 1518
} | 111 |
import argparse
import logging
import math
import os
import random
from pathlib import Path
import jax
import jax.numpy as jnp
import numpy as np
import optax
import PIL
import torch
import torch.utils.checkpoint
import transformers
from flax import jax_utils
from flax.training import train_state
from flax.training.co... | diffusers/examples/research_projects/multi_token_textual_inversion/textual_inversion_flax.py/0 | {
"file_path": "diffusers/examples/research_projects/multi_token_textual_inversion/textual_inversion_flax.py",
"repo_id": "diffusers",
"token_count": 10599
} | 112 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from PIL import Image
from retriever import Retriever, normalize_images, preprocess_images
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionP... | diffusers/examples/research_projects/rdm/pipeline_rdm.py/0 | {
"file_path": "diffusers/examples/research_projects/rdm/pipeline_rdm.py",
"repo_id": "diffusers",
"token_count": 7145
} | 113 |
# Stable Diffusion XL text-to-image fine-tuning
The `train_text_to_image_sdxl.py` script shows how to fine-tune Stable Diffusion XL (SDXL) on your own dataset.
🚨 This script is experimental. The script fine-tunes the whole model and often times the model overfits and runs into issues like catastrophic forgetting. It... | diffusers/examples/text_to_image/README_sdxl.md/0 | {
"file_path": "diffusers/examples/text_to_image/README_sdxl.md",
"repo_id": "diffusers",
"token_count": 4083
} | 114 |
# coding=utf-8
# Copyright 2024 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/scripts/change_naming_configs_and_checkpoints.py/0 | {
"file_path": "diffusers/scripts/change_naming_configs_and_checkpoints.py",
"repo_id": "diffusers",
"token_count": 1631
} | 115 |
# coding=utf-8
# Copyright 2024 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/scripts/convert_i2vgen_to_diffusers.py/0 | {
"file_path": "diffusers/scripts/convert_i2vgen_to_diffusers.py",
"repo_id": "diffusers",
"token_count": 9968
} | 116 |
# coding=utf-8
# Copyright 2024 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/scripts/convert_original_stable_diffusion_to_diffusers.py/0 | {
"file_path": "diffusers/scripts/convert_original_stable_diffusion_to_diffusers.py",
"repo_id": "diffusers",
"token_count": 2890
} | 117 |
"""
This script ports models from VQ-diffusion (https://github.com/microsoft/VQ-Diffusion) to diffusers.
It currently only supports porting the ITHQ dataset.
ITHQ dataset:
```sh
# From the root directory of diffusers.
# Download the VQVAE checkpoint
$ wget https://facevcstandard.blob.core.windows.net/v-zhictang/Impr... | diffusers/scripts/convert_vq_diffusion_to_diffusers.py/0 | {
"file_path": "diffusers/scripts/convert_vq_diffusion_to_diffusers.py",
"repo_id": "diffusers",
"token_count": 14916
} | 118 |
from .value_guided_sampling import ValueGuidedRLPipeline
| diffusers/src/diffusers/experimental/rl/__init__.py/0 | {
"file_path": "diffusers/src/diffusers/experimental/rl/__init__.py",
"repo_id": "diffusers",
"token_count": 17
} | 119 |
# 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/__init__.py/0 | {
"file_path": "diffusers/src/diffusers/models/__init__.py",
"repo_id": "diffusers",
"token_count": 1772
} | 120 |
from ...utils import is_torch_available
if is_torch_available():
from .dual_transformer_2d import DualTransformer2DModel
from .prior_transformer import PriorTransformer
from .t5_film_transformer import T5FilmDecoder
from .transformer_2d import Transformer2DModel
from .transformer_temporal import T... | diffusers/src/diffusers/models/transformers/__init__.py/0 | {
"file_path": "diffusers/src/diffusers/models/transformers/__init__.py",
"repo_id": "diffusers",
"token_count": 110
} | 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/unets/unet_2d_blocks_flax.py/0 | {
"file_path": "diffusers/src/diffusers/models/unets/unet_2d_blocks_flax.py",
"repo_id": "diffusers",
"token_count": 6961
} | 122 |
from typing import TYPE_CHECKING
from ..utils import (
DIFFUSERS_SLOW_IMPORT,
OptionalDependencyNotAvailable,
_LazyModule,
get_objects_from_module,
is_flax_available,
is_k_diffusion_available,
is_librosa_available,
is_note_seq_available,
is_onnx_available,
is_torch_available,
... | diffusers/src/diffusers/pipelines/__init__.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/__init__.py",
"repo_id": "diffusers",
"token_count": 10349
} | 123 |
# 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/LICENSE-2.0
#
# Unless r... | diffusers/src/diffusers/pipelines/blip_diffusion/blip_image_processing.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/blip_diffusion/blip_image_processing.py",
"repo_id": "diffusers",
"token_count": 6939
} | 124 |
from typing import TYPE_CHECKING
from ...utils import DIFFUSERS_SLOW_IMPORT, _LazyModule
_import_structure = {"pipeline_dance_diffusion": ["DanceDiffusionPipeline"]}
if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
from .pipeline_dance_diffusion import DanceDiffusionPipeline
else:
import sys
sys.modules[__na... | diffusers/src/diffusers/pipelines/dance_diffusion/__init__.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/dance_diffusion/__init__.py",
"repo_id": "diffusers",
"token_count": 189
} | 125 |
from typing import List
import PIL.Image
import torch
from PIL import Image
from ...configuration_utils import ConfigMixin
from ...models.modeling_utils import ModelMixin
from ...utils import PIL_INTERPOLATION
class IFWatermarker(ModelMixin, ConfigMixin):
def __init__(self):
super().__init__()
... | diffusers/src/diffusers/pipelines/deepfloyd_if/watermark.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/deepfloyd_if/watermark.py",
"repo_id": "diffusers",
"token_count": 736
} | 126 |
# Copyright 2024 ETH Zurich Computer Vision Lab 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... | diffusers/src/diffusers/pipelines/deprecated/repaint/pipeline_repaint.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/deprecated/repaint/pipeline_repaint.py",
"repo_id": "diffusers",
"token_count": 4202
} | 127 |
# 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/deprecated/stochastic_karras_ve/pipeline_stochastic_karras_ve.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/deprecated/stochastic_karras_ve/pipeline_stochastic_karras_ve.py",
"repo_id": "diffusers",
"token_count": 2264
} | 128 |
# 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/kandinsky/pipeline_kandinsky_combined.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py",
"repo_id": "diffusers",
"token_count": 16903
} | 129 |
from typing import Callable, Dict, List, Optional, Union
import torch
from transformers import T5EncoderModel, T5Tokenizer
from ...loaders import LoraLoaderMixin
from ...models import Kandinsky3UNet, VQModel
from ...schedulers import DDPMScheduler
from ...utils import (
deprecate,
is_accelerate_available,
... | diffusers/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3.py",
"repo_id": "diffusers",
"token_count": 12850
} | 130 |
# 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/paint_by_example/image_encoder.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/paint_by_example/image_encoder.py",
"repo_id": "diffusers",
"token_count": 942
} | 131 |
# 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/renderer.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/shap_e/renderer.py",
"repo_id": "diffusers",
"token_count": 18166
} | 132 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...utils import BaseOutput, is_flax_available
@dataclass
class StableDiffusionPipelineOutput(BaseOutput):
"""
Output class for Stable Diffusion pipelines.
Args:
images (`List[PIL.... | diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_output.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_output.py",
"repo_id": "diffusers",
"token_count": 598
} | 133 |
from dataclasses import dataclass
from typing import List, Union
import numpy as np
import PIL
import torch
from ...utils import (
BaseOutput,
)
@dataclass
class TextToVideoSDPipelineOutput(BaseOutput):
"""
Output class for text-to-video pipelines.
Args:
frames (`torch.Tensor`, `np.ndarr... | diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_output.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_output.py",
"repo_id": "diffusers",
"token_count": 260
} | 134 |
# Copyright (c) 2023 Dominic Rampas MIT License
# 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/licen... | diffusers/src/diffusers/pipelines/wuerstchen/modeling_wuerstchen_diffnext.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/wuerstchen/modeling_wuerstchen_diffnext.py",
"repo_id": "diffusers",
"token_count": 5544
} | 135 |
# 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_ddim_inverse.py/0 | {
"file_path": "diffusers/src/diffusers/schedulers/scheduling_ddim_inverse.py",
"repo_id": "diffusers",
"token_count": 7323
} | 136 |
# 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_euler_discrete_flax.py/0 | {
"file_path": "diffusers/src/diffusers/schedulers/scheduling_euler_discrete_flax.py",
"repo_id": "diffusers",
"token_count": 4573
} | 137 |
# Copyright 2024 Kakao 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 requi... | diffusers/src/diffusers/schedulers/scheduling_unclip.py/0 | {
"file_path": "diffusers/src/diffusers/schedulers/scheduling_unclip.py",
"repo_id": "diffusers",
"token_count": 6252
} | 138 |
# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends
class AudioDiffusionPipeline(metaclass=DummyObject):
_backends = ["torch", "librosa"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "librosa"])
... | diffusers/src/diffusers/utils/dummy_torch_and_librosa_objects.py/0 | {
"file_path": "diffusers/src/diffusers/utils/dummy_torch_and_librosa_objects.py",
"repo_id": "diffusers",
"token_count": 397
} | 139 |
from typing import List
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
PIL_INTERPOLATION = {
"linear": PIL.Image.Resampling.BILINEAR,
"bilinear": PIL.Image.Resampling... | diffusers/src/diffusers/utils/pil_utils.py/0 | {
"file_path": "diffusers/src/diffusers/utils/pil_utils.py",
"repo_id": "diffusers",
"token_count": 849
} | 140 |
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from ..test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class FlaxAutoencoderKLTests(FlaxModelTeste... | diffusers/tests/models/autoencoders/test_models_vae_flax.py/0 | {
"file_path": "diffusers/tests/models/autoencoders/test_models_vae_flax.py",
"repo_id": "diffusers",
"token_count": 513
} | 141 |
# 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/models/unets/test_models_unet_spatiotemporal.py/0 | {
"file_path": "diffusers/tests/models/unets/test_models_unet_spatiotemporal.py",
"repo_id": "diffusers",
"token_count": 4294
} | 142 |
# 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/amused/test_amused.py/0 | {
"file_path": "diffusers/tests/pipelines/amused/test_amused.py",
"repo_id": "diffusers",
"token_count": 3130
} | 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/pipelines/deepfloyd_if/test_if_img2img_superresolution.py/0 | {
"file_path": "diffusers/tests/pipelines/deepfloyd_if/test_if_img2img_superresolution.py",
"repo_id": "diffusers",
"token_count": 2051
} | 144 |
# 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/kandinsky2_2/test_kandinsky.py/0 | {
"file_path": "diffusers/tests/pipelines/kandinsky2_2/test_kandinsky.py",
"repo_id": "diffusers",
"token_count": 3973
} | 145 |
# 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/latent_diffusion/test_latent_diffusion_superresolution.py/0 | {
"file_path": "diffusers/tests/pipelines/latent_diffusion/test_latent_diffusion_superresolution.py",
"repo_id": "diffusers",
"token_count": 2042
} | 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/semantic_stable_diffusion/test_semantic_diffusion.py/0 | {
"file_path": "diffusers/tests/pipelines/semantic_stable_diffusion/test_semantic_diffusion.py",
"repo_id": "diffusers",
"token_count": 9604
} | 147 |
# 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_instruction_pix2pix.py/0 | {
"file_path": "diffusers/tests/pipelines/stable_diffusion/test_stable_diffusion_instruction_pix2pix.py",
"repo_id": "diffusers",
"token_count": 7749
} | 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_xl/test_stable_diffusion_xl_adapter.py/0 | {
"file_path": "diffusers/tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_adapter.py",
"repo_id": "diffusers",
"token_count": 13533
} | 149 |
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class OnnxPipelineTesterMixin:
"""
This mixin is designed to be used with unittest.TestCase classes.
It provides a set of common tests for each ONNXRuntime pipeline, e.g. saving and loading the pipeline,
equivalence of ... | diffusers/tests/pipelines/test_pipelines_onnx_common.py/0 | {
"file_path": "diffusers/tests/pipelines/test_pipelines_onnx_common.py",
"repo_id": "diffusers",
"token_count": 118
} | 150 |
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class CMStochasticIterativeSchedulerTest(SchedulerCommonTest):
scheduler_classes = (CMStochasticIterativeScheduler,)
num_inference_steps = 10
def get_scheduler_config(self, **kwargs):
... | diffusers/tests/schedulers/test_scheduler_consistency_model.py/0 | {
"file_path": "diffusers/tests/schedulers/test_scheduler_consistency_model.py",
"repo_id": "diffusers",
"token_count": 3029
} | 151 |
import torch
from diffusers import HeunDiscreteScheduler
from diffusers.utils.testing_utils import torch_device
from .test_schedulers import SchedulerCommonTest
class HeunDiscreteSchedulerTest(SchedulerCommonTest):
scheduler_classes = (HeunDiscreteScheduler,)
num_inference_steps = 10
def get_scheduler_... | diffusers/tests/schedulers/test_scheduler_heun.py/0 | {
"file_path": "diffusers/tests/schedulers/test_scheduler_heun.py",
"repo_id": "diffusers",
"token_count": 3279
} | 152 |
# coding=utf-8
# Copyright 2024 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": 2177
} | 153 |
# DDIM Inversion
<CourseFloatingBanner unit={4}
classNames="absolute z-10 right-0 top-0"
notebooks={[
{label: "DDIM Inversion", value: "https://colab.research.google.com/github/huggingface/diffusion-models-class/blob/main/units/en/unit4/ddim_inversion.ipynb"},
{label: "DDIM Inversion", value: "https://stud... | diffusion-models-class/units/en/unit4/2.mdx/0 | {
"file_path": "diffusion-models-class/units/en/unit4/2.mdx",
"repo_id": "diffusion-models-class",
"token_count": 5413
} | 154 |
<jupyter_start><jupyter_text>Modèles (PyTorch) Installez la bibliothèque 🤗 *Transformers* pour exécuter ce *notebook*.<jupyter_code>!pip install transformers[sentencepiece]
from transformers import CamembertConfig, CamembertModel
# Construire la configuration
config = CamembertConfig()
# Construire le modèle à parti... | notebooks/course/fr/chapter2/section3_pt.ipynb/0 | {
"file_path": "notebooks/course/fr/chapter2/section3_pt.ipynb",
"repo_id": "notebooks",
"token_count": 341
} | 155 |
<jupyter_start><jupyter_text>Utilisation de modèles pré-entraînés (TensorFlow) Installez la bibliothèque 🤗 *Transformers* pour exécuter ce *notebook*.<jupyter_code>!pip install datasets transformers[sentencepiece]
from transformers import pipeline
camembert_fill_mask = pipeline("fill-mask", model="camembert-base")
re... | notebooks/course/fr/chapter4/section2_tf.ipynb/0 | {
"file_path": "notebooks/course/fr/chapter4/section2_tf.ipynb",
"repo_id": "notebooks",
"token_count": 270
} | 156 |
<jupyter_start><jupyter_text>Tokenisation *Byte-Pair Encoding* Installez les bibliothèques 🤗 *Transformers* et 🤗 *Datasets* pour exécuter ce *notebook*.<jupyter_code>!pip install datasets transformers[sentencepiece]
corpus = [
"C'est le cours d'Hugging Face.",
"Ce chapitre traite de la tokenisation.",
"Ce... | notebooks/course/fr/chapter6/section5.ipynb/0 | {
"file_path": "notebooks/course/fr/chapter6/section5.ipynb",
"repo_id": "notebooks",
"token_count": 1572
} | 157 |
<jupyter_start><jupyter_text>Que faire quand vous obtenez une erreurCe chapitre portant sur le débogage, la langue nous importe peu ici. Nous nous intéressons surtout à la logique du code pour comprendre d'où provient l'erreur. Installez les bibliothèques 🤗 Transformers et 🤗 Datasets pour exécuter ce *notebook*.<jupy... | notebooks/course/fr/chapter8/section2.ipynb/0 | {
"file_path": "notebooks/course/fr/chapter8/section2.ipynb",
"repo_id": "notebooks",
"token_count": 1365
} | 158 |
<jupyter_start><jupyter_text>Running IF with 🧨 diffusers on a Free Tier Google Colab_**TL;DR**: We show how to run one of the most powerful open-source text to image models **IF** on a free-tier Google Colab with 🧨 diffusers._*by DeepFloyd &* 🤗 *HuggingFace* *Image taken from official IF GitHub repo [here](https://... | notebooks/diffusers/deepfloyd_if_free_tier_google_colab.ipynb/0 | {
"file_path": "notebooks/diffusers/deepfloyd_if_free_tier_google_colab.ipynb",
"repo_id": "notebooks",
"token_count": 9958
} | 159 |
<jupyter_start><jupyter_text>🧨 Stable Diffusion in JAX / Flax ! 🤗 Hugging Face [Diffusers](https://github.com/huggingface/diffusers) supports Flax since version `0.5.1`! This allows for super fast inference on Google TPUs, such as those available in Colab, Kaggle or Google Cloud Platform.This notebook shows how to ru... | notebooks/diffusers/stable_diffusion_jax_how_to.ipynb/0 | {
"file_path": "notebooks/diffusers/stable_diffusion_jax_how_to.ipynb",
"repo_id": "notebooks",
"token_count": 3380
} | 160 |
<jupyter_start><jupyter_text>The Annotated Diffusion Model nielsr Niels Rogge kashif Kashif Rasul In this blog post, we'll take a deeper look into **Denoising Diffusion Probabilistic Models** (also known as D... | notebooks/examples/annotated_diffusion.ipynb/0 | {
"file_path": "notebooks/examples/annotated_diffusion.ipynb",
"repo_id": "notebooks",
"token_count": 16579
} | 161 |
<jupyter_start><jupyter_text>**Fine-tuning for Image Classification with 🤗 Transformers**This notebook shows how to fine-tune any pretrained Vision model for Image Classification on a custom dataset. The idea is to add a randomly initialized classification head on top of a pre-trained encoder, and fine-tune the model ... | notebooks/examples/image_classification-tf.ipynb/0 | {
"file_path": "notebooks/examples/image_classification-tf.ipynb",
"repo_id": "notebooks",
"token_count": 9526
} | 162 |
<jupyter_start><jupyter_text>Fine-tunining DeBERTa model on a question answering task with ORTTrainer In this notebook, we will see how to fine-tune the [DeBERTa base](https://huggingface.co/microsoft/deberta-base/tree/main) model to a question answering task, which is the task of extracting the answer to a question fr... | notebooks/examples/question_answering_ort.ipynb/0 | {
"file_path": "notebooks/examples/question_answering_ort.ipynb",
"repo_id": "notebooks",
"token_count": 10005
} | 163 |
<jupyter_start><jupyter_text>If you're opening this Notebook on colab, you will probably need to install 🤗 Transformers and 🤗 Datasets. We will also use the `seqeval` library to compute some evaluation metrics. Uncomment the following cell and run it.<jupyter_code>#! pip install transformers
#! pip install datasets
#... | notebooks/examples/token_classification-tf.ipynb/0 | {
"file_path": "notebooks/examples/token_classification-tf.ipynb",
"repo_id": "notebooks",
"token_count": 9592
} | 164 |
import argparse
import logging
import os
import sys
import tensorflow as tf
from datasets import load_dataset
from transformers import AutoTokenizer, TFAutoModelForSequenceClassification, DataCollatorWithPadding, create_optimizer
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Hyperparamete... | notebooks/sagemaker/02_getting_started_tensorflow/scripts/train.py/0 | {
"file_path": "notebooks/sagemaker/02_getting_started_tensorflow/scripts/train.py",
"repo_id": "notebooks",
"token_count": 1677
} | 165 |
base_job_name: accelerate-sagemaker-1
compute_environment: AMAZON_SAGEMAKER
distributed_type: DATA_PARALLEL
ec2_instance_type: ml.p3.16xlarge
iam_role_name: xxxxx
image_uri: null
mixed_precision: fp16
num_machines: 1
profile: xxxxx
py_version: py38
pytorch_version: 1.10.2
region: us-east-1
transformers_version: 4.17.0
... | notebooks/sagemaker/22_accelerate_sagemaker_examples/src/seq2seq/accelerate_config.yaml/0 | {
"file_path": "notebooks/sagemaker/22_accelerate_sagemaker_examples/src/seq2seq/accelerate_config.yaml",
"repo_id": "notebooks",
"token_count": 138
} | 166 |
<jupyter_start><jupyter_text>Efficient Large Language Model training with LoRA and Hugging FaceIn this sagemaker example, we are going to learn how to apply [Low-Rank Adaptation of Large Language Models (LoRA)](https://arxiv.org/abs/2106.09685) to fine-tune BLOOMZ (7 billion parameter version instruction tuned version ... | notebooks/sagemaker/24_train_bloom_peft_lora/sagemaker-notebook.ipynb/0 | {
"file_path": "notebooks/sagemaker/24_train_bloom_peft_lora/sagemaker-notebook.ipynb",
"repo_id": "notebooks",
"token_count": 4824
} | 167 |
<jupyter_start><jupyter_text>Deploy Zephyr 7B on AWS Inferentia2 using Amazon SageMakerThis tutorial will show how easy it is to deploy Zephyr 7B on AWS Infernetia2 using Amazon SageMaker. Zephyr is a 7B parameter LLM fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) th... | notebooks/sagemaker/29_deploy_llms_on_inferentia2/sagemaker-notebook.ipynb/0 | {
"file_path": "notebooks/sagemaker/29_deploy_llms_on_inferentia2/sagemaker-notebook.ipynb",
"repo_id": "notebooks",
"token_count": 3797
} | 168 |
repos:
- repo: https://github.com/astral-sh/ruff-pre-commit
rev: v0.2.1
hooks:
- id: ruff
args:
- --fix
- id: ruff-format
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v4.5.0
hooks:
- id: check-merge-conflict
- id: check-yaml
| peft/.pre-commit-config.yaml/0 | {
"file_path": "peft/.pre-commit-config.yaml",
"repo_id": "peft",
"token_count": 158
} | 169 |
<!--⚠️ 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.
-->
# Soft prompts
Training large pretrained language models is very time-consuming and compute-intensive. As they continue to grow in size, there is in... | peft/docs/source/conceptual_guides/prompting.md/0 | {
"file_path": "peft/docs/source/conceptual_guides/prompting.md",
"repo_id": "peft",
"token_count": 1830
} | 170 |
<!--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... | peft/docs/source/task_guides/lora_based_methods.md/0 | {
"file_path": "peft/docs/source/task_guides/lora_based_methods.md",
"repo_id": "peft",
"token_count": 4847
} | 171 |
<jupyter_start><jupyter_code>from transformers import AutoModelForSeq2SeqLM
from peft import PeftModel, PeftConfig
import torch
from datasets import load_dataset
import os
from transformers import AutoTokenizer
from torch.utils.data import DataLoader
from transformers import default_data_collator, get_linear_schedule_w... | peft/examples/conditional_generation/peft_lora_seq2seq_accelerate_big_model_inference.ipynb/0 | {
"file_path": "peft/examples/conditional_generation/peft_lora_seq2seq_accelerate_big_model_inference.ipynb",
"repo_id": "peft",
"token_count": 1695
} | 172 |
# 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/examples/int8_training/fine_tune_blip2_int8.py/0 | {
"file_path": "peft/examples/int8_training/fine_tune_blip2_int8.py",
"repo_id": "peft",
"token_count": 1309
} | 173 |
<jupyter_start><jupyter_text>This notebook shows how to use the adapter merging methods from `peft` and apply them image generation models using `diffusers`. Turn `diffusers` LoRA checkpoints into `PeftModel`<jupyter_code>!pip install diffusers accelerate transformers -U -q
!pip install git+https://github.com/huggingf... | peft/examples/multi_adapter_examples/multi_adapter_weighted_inference_diffusers.ipynb/0 | {
"file_path": "peft/examples/multi_adapter_examples/multi_adapter_weighted_inference_diffusers.ipynb",
"repo_id": "peft",
"token_count": 1802
} | 174 |
# 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/peft_model.py/0 | {
"file_path": "peft/src/peft/peft_model.py",
"repo_id": "peft",
"token_count": 42321
} | 175 |
# 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/ia3/config.py/0 | {
"file_path": "peft/src/peft/tuners/ia3/config.py",
"repo_id": "peft",
"token_count": 1842
} | 176 |
# 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/lora/gptq.py/0 | {
"file_path": "peft/src/peft/tuners/lora/gptq.py",
"repo_id": "peft",
"token_count": 1708
} | 177 |
# 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/p_tuning/model.py/0 | {
"file_path": "peft/src/peft/tuners/p_tuning/model.py",
"repo_id": "peft",
"token_count": 2476
} | 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/utils/loftq_utils.py/0 | {
"file_path": "peft/src/peft/utils/loftq_utils.py",
"repo_id": "peft",
"token_count": 7192
} | 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/tests/test_gpu_examples.py/0 | {
"file_path": "peft/tests/test_gpu_examples.py",
"repo_id": "peft",
"token_count": 39080
} | 180 |
app.location$.subscribe(function() {
var tables = document.querySelectorAll("article table")
tables.forEach(function(table) {
new Tablesort(table)
})
}) | pytorch-image-models/docs/javascripts/tables.js/0 | {
"file_path": "pytorch-image-models/docs/javascripts/tables.js",
"repo_id": "pytorch-image-models",
"token_count": 52
} | 181 |
# 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/.templates/models/efficientnet.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/efficientnet.md",
"repo_id": "pytorch-image-models",
"token_count": 4091
} | 182 |
# 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/.templates/models/resnext.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/resnext.md",
"repo_id": "pytorch-image-models",
"token_count": 2238
} | 183 |
# (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/.templates/models/tf-mixnet.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/tf-mixnet.md",
"repo_id": "pytorch-image-models",
"token_count": 1547
} | 184 |
# 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/hfdocs/source/models.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models.mdx",
"repo_id": "pytorch-image-models",
"token_count": 4494
} | 185 |
# MobileNet v2
**MobileNetV2** is a convolutional neural network architecture that seeks to perform well on mobile devices. It is based on an [inverted residual structure](https://paperswithcode.com/method/inverted-residual-block) where the residual connections are between the bottleneck layers. The intermediate expa... | pytorch-image-models/hfdocs/source/models/mobilenet-v2.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/mobilenet-v2.mdx",
"repo_id": "pytorch-image-models",
"token_count": 3401
} | 186 |
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