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import type { RequestHandler } from "./$types"; import { collections } from "$lib/server/database"; import { ObjectId } from "mongodb"; import { error, redirect } from "@sveltejs/kit"; import { base } from "$app/paths"; import { z } from "zod"; import type { Message } from "$lib/types/Message"; import { models, validat...
chat-ui/src/routes/conversation/+server.ts/0
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import { base } from "$app/paths"; import { authCondition } from "$lib/server/auth.js"; import { collections } from "$lib/server/database.js"; import { models } from "$lib/server/models"; import { redirect } from "@sveltejs/kit"; export async function load({ params, locals, parent }) { const model = models.find(({ id...
chat-ui/src/routes/models/[...model]/+page.server.ts/0
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import { base } from "$app/paths"; import { requiresUser } from "$lib/server/auth"; import { collections } from "$lib/server/database"; import { fail, type Actions, redirect } from "@sveltejs/kit"; import { ObjectId } from "mongodb"; import { z } from "zod"; import { sha256 } from "$lib/utils/sha256"; import sharp fr...
chat-ui/src/routes/settings/(nav)/assistants/[assistantId]/edit/+page.server.ts/0
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{ "background_color": "#ffffff", "name": "Chat UI", "short_name": "Chat UI", "display": "standalone", "start_url": "/", "icons": [ { "src": "/chatui/icon-128x128.png", "sizes": "128x128", "type": "image/png" }, { "src": "/chatui/icon-256x256.png", "sizes": "256x256", "type": "image/png" ...
chat-ui/static/chatui/manifest.json/0
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<p align="center"> <picture> <source media="(prefers-color-scheme: dark)" srcset="https://huggingface.co/datasets/huggingface/documentation-images/raw/main/datasets-logo-dark.svg"> <source media="(prefers-color-scheme: light)" srcset="https://huggingface.co/datasets/huggingface/documentation-images/raw/main/d...
datasets/README.md/0
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<!--- Copyright 2020 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 ...
datasets/docs/README.md/0
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# Cache management When you download a dataset, the processing scripts and data are stored locally on your computer. The cache allows 🤗 Datasets to avoid re-downloading or processing the entire dataset every time you use it. This guide will show you how to: - Change the cache directory. - Control how a dataset is ...
datasets/docs/source/cache.mdx/0
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# Installation Before you start, you'll need to setup your environment and install the appropriate packages. 🤗 Datasets is tested on **Python 3.7+**. <Tip> If you want to use 🤗 Datasets with TensorFlow or PyTorch, you'll need to install them separately. Refer to the [TensorFlow installation page](https://www.tenso...
datasets/docs/source/installation.md/0
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# Semantic segmentation Semantic segmentation datasets are used to train a model to classify every pixel in an image. There are a wide variety of applications enabled by these datasets such as background removal from images, stylizing images, or scene understanding for autonomous driving. This guide will show you how ...
datasets/docs/source/semantic_segmentation.mdx/0
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# Copyright 2020 The HuggingFace Datasets Authors. # # 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 ...
datasets/metrics/bertscore/bertscore.py/0
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# Metric Card for COVAL ## Metric description CoVal is a coreference evaluation tool for the [CoNLL](https://huggingface.co/datasets/conll2003) and [ARRAU](https://catalog.ldc.upenn.edu/LDC2013T22) datasets which implements of the common evaluation metrics including MUC [Vilain et al, 1995](https://aclanthology.org/M...
datasets/metrics/coval/README.md/0
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# Copyright 2020 The HuggingFace Datasets Authors. # # 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 ...
datasets/metrics/indic_glue/indic_glue.py/0
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# Copyright 2021 The HuggingFace Datasets Authors and the current dataset script contributor. # # 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....
datasets/metrics/pearsonr/pearsonr.py/0
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# Copyright 2020 The HuggingFace Datasets Authors. # # 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 ...
datasets/metrics/seqeval/seqeval.py/0
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# Metric Card for WikiSplit ## Metric description WikiSplit is the combination of three metrics: [SARI](https://huggingface.co/metrics/sari), [exact match](https://huggingface.co/metrics/exact_match) and [SacreBLEU](https://huggingface.co/metrics/sacrebleu). It can be used to evaluate the quality of sentence splitt...
datasets/metrics/wiki_split/README.md/0
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from abc import ABC, abstractmethod from argparse import ArgumentParser class BaseDatasetsCLICommand(ABC): @staticmethod @abstractmethod def register_subcommand(parser: ArgumentParser): raise NotImplementedError() @abstractmethod def run(self): raise NotImplementedError()
datasets/src/datasets/commands/__init__.py/0
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# SPDX-License-Identifier: Apache-2.0 # Copyright 2023 The HuggingFace Authors. from typing import Any, Dict, List, Optional, Union from huggingface_hub import HfFileSystem from . import config from .table import CastError from .utils.track import TrackedIterable, tracked_list, tracked_str class DatasetsError(Excep...
datasets/src/datasets/exceptions.py/0
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# Copyright 2020 The HuggingFace Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to...
datasets/src/datasets/formatting/torch_formatter.py/0
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# Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors. # # 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 # # U...
datasets/src/datasets/naming.py/0
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import contextlib from multiprocessing import Pool, RLock from tqdm.auto import tqdm from ..utils import experimental, logging logger = logging.get_logger(__name__) class ParallelBackendConfig: backend_name = None @experimental def parallel_map(function, iterable, num_proc, types, disable_tqdm, desc, single...
datasets/src/datasets/parallel/parallel.py/0
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# 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...
datasets/src/datasets/utils/_dill.py/0
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# loading package files: https://stackoverflow.com/a/20885799 import importlib.resources as pkg_resources import logging from pathlib import Path from typing import Any, List, Tuple import yaml from . import resources from .deprecation_utils import deprecated BASE_REF_URL = "https://github.com/huggingface/datasets/...
datasets/src/datasets/utils/readme.py/0
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# Metric Card for *Current Metric* ***Metric Card Instructions:*** *Copy this file into the relevant metric folder, then fill it out and save it as README.md. Feel free to take a look at existing metric cards if you'd like examples.* ## Metric Description *Give a brief overview of this metric.* ## How to Use *Give g...
datasets/templates/metric_card_template.md/0
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import fsspec import pyarrow.parquet as pq import pytest from datasets import Audio, Dataset, DatasetDict, Features, IterableDatasetDict, NamedSplit, Sequence, Value, config from datasets.features.image import Image from datasets.info import DatasetInfo from datasets.io.parquet import ParquetDatasetReader, ParquetData...
datasets/tests/io/test_parquet.py/0
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import os import tempfile from functools import partial from unittest import TestCase from unittest.mock import patch import datasets import datasets.config from .utils import require_beam class DummyBeamDataset(datasets.BeamBasedBuilder): """Dummy beam dataset.""" def _info(self): return datasets....
datasets/tests/test_beam.py/0
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import os import pytest import yaml from datasets.features.features import Features, Value from datasets.info import DatasetInfo, DatasetInfosDict @pytest.mark.parametrize( "files", [ ["full:README.md", "dataset_infos.json"], ["empty:README.md", "dataset_infos.json"], ["dataset_infos...
datasets/tests/test_info.py/0
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import json import os import re from pathlib import Path import pytest from fsspec.registry import _registry as _fsspec_registry from fsspec.spec import AbstractBufferedFile, AbstractFileSystem from datasets.download.download_config import DownloadConfig from datasets.download.streaming_download_manager import ( ...
datasets/tests/test_streaming_download_manager.py/0
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<jupyter_start><jupyter_text>Unit 2: Q-Learning with FrozenLake-v1 ⛄ and Taxi-v3 🚕In this notebook, **you'll code your first Reinforcement Learning agent from scratch** to play FrozenLake ❄️ using Q-Learning, share it with the community, and experiment with different configurations.⬇️ Here is an example of what **you ...
deep-rl-class/notebooks/unit2/unit2.ipynb/0
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# Additional Readings [[additional-readings]] These are **optional readings** if you want to go deeper. ## Deep Reinforcement Learning [[deep-rl]] - [Reinforcement Learning: An Introduction, Richard Sutton and Andrew G. Barto Chapter 1, 2 and 3](http://incompleteideas.net/book/RLbook2020.pdf) - [Foundations of Deep ...
deep-rl-class/units/en/unit1/additional-readings.mdx/0
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# Glossary [[glossary]] This is a community-created glossary. Contributions are welcomed! ### Strategies to find the optimal policy - **Policy-based methods.** The policy is usually trained with a neural network to select what action to take given a state. In this case it is the neural network which outputs the act...
deep-rl-class/units/en/unit2/glossary.mdx/0
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# From Q-Learning to Deep Q-Learning [[from-q-to-dqn]] We learned that **Q-Learning is an algorithm we use to train our Q-Function**, an **action-value function** that determines the value of being at a particular state and taking a specific action at that state. <figure> <img src="https://huggingface.co/datasets/h...
deep-rl-class/units/en/unit3/from-q-to-dqn.mdx/0
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# Conclusion Congrats on finishing this unit! You’ve just trained your first ML-Agents and shared it to the Hub 🥳. The best way to learn is to **practice and try stuff**. Why not try another environment? [ML-Agents has 18 different environments](https://github.com/Unity-Technologies/ml-agents/blob/develop/docs/Learn...
deep-rl-class/units/en/unit5/conclusion.mdx/0
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# 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
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# 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
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# 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
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include LICENSE include src/diffusers/utils/model_card_template.md
diffusers/MANIFEST.in/0
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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-flax-cpu/Dockerfile/0
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<!--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/internal_classes_overview.md/0
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<!--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/onnx.md/0
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<!--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/kandinsky.md/0
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<!--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/conditional_image_generation.md/0
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<!--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/ip_adapter.md/0
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<!--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/stable_diffusion_jax_how_to.md/0
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<!--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/api/pipelines/stable_diffusion/stable_diffusion_xl.md/0
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<!--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/training/adapt_a_model.md/0
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# 이미지 밝기 조절하기 Stable Diffusion 파이프라인은 [일반적인 디퓨전 노이즈 스케줄과 샘플 단계에 결함이 있음](https://huggingface.co/papers/2305.08891) 논문에서 설명한 것처럼 매우 밝거나 어두운 이미지를 생성하는 데는 성능이 평범합니다. 이 논문에서 제안한 솔루션은 현재 [`DDIMScheduler`]에 구현되어 있으며 이미지의 밝기를 개선하는 데 사용할 수 있습니다. <Tip> 💡 제안된 솔루션에 대한 자세한 내용은 위에 링크된 논문을 참고하세요! </Tip> 해결책 중 하나는 *v 예측값*과 *v 로스...
diffusers/docs/source/ko/using-diffusers/control_brightness.md/0
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<!--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/unconditional_image_generation.md/0
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#!/usr/bin/env python # coding=utf-8 # Copyright 2024 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LI...
diffusers/examples/advanced_diffusion_training/train_dreambooth_lora_sd15_advanced.py/0
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""" modeled after the textual_inversion.py / train_dreambooth.py and the work of justinpinkney here: https://github.com/justinpinkney/stable-diffusion/blob/main/notebooks/imagic.ipynb """ import inspect import warnings from typing import List, Optional, Union import numpy as np import PIL.Image import torch import to...
diffusers/examples/community/imagic_stable_diffusion.py/0
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import inspect from copy import deepcopy from enum import Enum from typing import List, Optional, Tuple, Union import torch from tqdm.auto import tqdm from diffusers.models import AutoencoderKL, UNet2DConditionModel from diffusers.pipelines.pipeline_utils import DiffusionPipeline from diffusers.pipelines.stable_diffu...
diffusers/examples/community/mixture_tiling.py/0
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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
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# Inspired by: https://github.com/Mikubill/sd-webui-controlnet/discussions/1236 and https://github.com/Mikubill/sd-webui-controlnet/discussions/1280 import inspect from typing import Any, Callable, Dict, List, Optional, Tuple, Union import numpy as np import PIL.Image import torch from packaging import version from tr...
diffusers/examples/community/stable_diffusion_reference.py/0
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# 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/consistency_distillation/test_lcm_lora.py/0
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# Inference Examples **The inference examples folder is deprecated and will be removed in a future version**. **Officially supported inference examples can be found in the [Pipelines folder](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines)**. - For `Image-to-Image text-guided generation wit...
diffusers/examples/inference/README.md/0
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import d4rl # noqa import gym import tqdm from diffusers.experimental import ValueGuidedRLPipeline config = { "n_samples": 64, "horizon": 32, "num_inference_steps": 20, "n_guide_steps": 2, # can set to 0 for faster sampling, does not use value network "scale_grad_by_std": True, "scale": 0.1,...
diffusers/examples/reinforcement_learning/run_diffuser_locomotion.py/0
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# 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/examples/research_projects/controlnetxs/pipeline_controlnet_xs_sd_xl.py/0
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# Distillation for quantization on Textual Inversion models to personalize text2image [Textual inversion](https://arxiv.org/abs/2208.01618) is a method to personalize text2image models like stable diffusion on your own images._By using just 3-5 images new concepts can be taught to Stable Diffusion and the model person...
diffusers/examples/research_projects/intel_opts/textual_inversion_dfq/README.md/0
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# 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/examples/research_projects/promptdiffusion/promptdiffusioncontrolnet.py/0
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# 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/test_examples_utils.py/0
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[tool.ruff] # Never enforce `E501` (line length violations). ignore = ["C901", "E501", "E741", "F402", "F823"] select = ["C", "E", "F", "I", "W"] line-length = 119 # Ignore import violations in all `__init__.py` files. [tool.ruff.per-file-ignores] "__init__.py" = ["E402", "F401", "F403", "F811"] "src/diffusers/utils/d...
diffusers/pyproject.toml/0
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import argparse import os import torch from torchvision.datasets.utils import download_url from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, Transformer2DModel pretrained_models = {512: "DiT-XL-2-512x512.pt", 256: "DiT-XL-2-256x256.pt"} def download_model(model_name): """ Downloads a pre-tr...
diffusers/scripts/convert_dit_to_diffusers.py/0
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# 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_controlnet_to_diffusers.py/0
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import argparse import io import requests import torch import yaml from diffusers import AutoencoderKL from diffusers.pipelines.stable_diffusion.convert_from_ckpt import ( assign_to_checkpoint, conv_attn_to_linear, create_vae_diffusers_config, renew_vae_attention_paths, renew_vae_resnet_paths, ) ...
diffusers/scripts/convert_vae_pt_to_diffusers.py/0
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# 🧨 Diffusers Experimental We are adding experimental code to support novel applications and usages of the Diffusers library. Currently, the following experiments are supported: * Reinforcement learning via an implementation of the [Diffuser](https://arxiv.org/abs/2205.09991) model.
diffusers/src/diffusers/experimental/README.md/0
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# Copyright 2024 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicabl...
diffusers/src/diffusers/loaders/utils.py/0
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# Copyright 2024 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicabl...
diffusers/src/diffusers/models/controlnet_flax.py/0
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# 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.py/0
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# 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/src/diffusers/optimization.py/0
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# 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/src/diffusers/pipelines/auto_pipeline.py/0
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import numpy as np import torch import torch.nn as nn from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel from ...utils import logging logger = logging.get_logger(__name__) class IFSafetyChecker(PreTrainedModel): config_class = CLIPConfig _no_split_modules = ["CLIPEncoderLa...
diffusers/src/diffusers/pipelines/deepfloyd_if/safety_checker.py/0
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# 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/pndm/pipeline_pndm.py/0
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# Copyright 2024 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/licenses/LICENSE-2.0 # # Unl...
diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py/0
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from typing import TYPE_CHECKING from ...utils import ( DIFFUSERS_SLOW_IMPORT, OptionalDependencyNotAvailable, _LazyModule, get_objects_from_module, is_torch_available, is_transformers_available, ) _dummy_objects = {} _import_structure = {} try: if not (is_transformers_available() and is...
diffusers/src/diffusers/pipelines/kandinsky/__init__.py/0
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# coding=utf-8 # Copyright 2024 The HuggingFace Inc. team. # Copyright (c) 2022, NVIDIA CORPORATION. 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.a...
diffusers/src/diffusers/pipelines/onnx_utils.py/0
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# 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/pipeline_shap_e.py/0
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import inspect import warnings from typing import Callable, List, Optional, Union import numpy as np import torch from packaging import version from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection from ...configuration_utils import FrozenDict from ...image_processor...
diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py/0
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# Copyright (c) 2022 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_paella_vq_model.py/0
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# Copyright 2024 Stanford University Team 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 # #...
diffusers/src/diffusers/schedulers/scheduling_ddim.py/0
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# Copyright 2024 Google Brain and The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless requ...
diffusers/src/diffusers/schedulers/scheduling_sde_ve_flax.py/0
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# This file is autogenerated by the command `make fix-copies`, do not edit. from ..utils import DummyObject, requires_backends class OnnxRuntimeModel(metaclass=DummyObject): _backends = ["onnx"] def __init__(self, *args, **kwargs): requires_backends(self, ["onnx"]) @classmethod def from_conf...
diffusers/src/diffusers/utils/dummy_onnx_objects.py/0
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# 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/utils/outputs.py/0
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# 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_3d_condition.py/0
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# 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.py/0
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# coding=utf-8 # Copyright 2024 HuggingFace Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or ag...
diffusers/tests/pipelines/kandinsky/test_kandinsky_prior.py/0
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# 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/pndm/test_pndm.py/0
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# 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_img2img.py/0
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import contextlib import gc import inspect import io import json import os import re import tempfile import unittest import uuid from typing import Any, Callable, Dict, Union import numpy as np import PIL.Image import torch from huggingface_hub import ModelCard, delete_repo from huggingface_hub.utils import is_jinja_a...
diffusers/tests/pipelines/test_pipelines_common.py/0
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# coding=utf-8 # Copyright 2024 HuggingFace Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or ag...
diffusers/tests/pipelines/wuerstchen/test_wuerstchen_prior.py/0
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import torch from diffusers import EulerAncestralDiscreteScheduler from diffusers.utils.testing_utils import torch_device from .test_schedulers import SchedulerCommonTest class EulerAncestralDiscreteSchedulerTest(SchedulerCommonTest): scheduler_classes = (EulerAncestralDiscreteScheduler,) num_inference_step...
diffusers/tests/schedulers/test_scheduler_euler_ancestral.py/0
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# 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_config_docstrings.py/0
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<jupyter_start><jupyter_text>DDIM InversionIn this notebook we will explore **inversion**, see how it relates to sampling, and apply it to the task of editing images with Stable Diffusion. What You Will Learn- How DDIM sampling works- Deterministic vs Stochastic samplers- The theory behind DDIM inversion- Editing imag...
diffusion-models-class/unit4/01_ddim_inversion.ipynb/0
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<jupyter_start><jupyter_text>Implémentation à partir de 0Il est parfois utile de considérer la version la plus simple possible d'une chose pour mieux en comprendre le fonctionnement. C'est ce que nous allons essayer de faire dans ce *notebook*, en commençant par un modèle de diffusion "jouet" pour voir comment les diff...
diffusion-models-class/units/fr/unit1/diffusion_models_from_scratch.ipynb/0
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<jupyter_start><jupyter_text>Stable Diffusion : plongée en profondeurStable Diffusion est un puissant modèle de texte à image. Il existe plusieurs sites web et outils pour rendre son utilisation aussi simple que possible. Il est également intégré à la bibliothèque de Diffusers d'Huggingface, ce qui permet de générer de...
diffusion-models-class/units/fr/unit3/stable_diffusion_deep_dive.ipynb/0
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<jupyter_start><jupyter_text>Derrière le pipeline (PyTorch) Installez la bibliothèque 🤗 *Transformers* pour exécuter ce *notebook*.<jupyter_code>!pip install transformers[sentencepiece] from transformers import pipeline classifier = pipeline("sentiment-analysis", model="tblard/tf-allocine") classifier( ["J'ai att...
notebooks/course/fr/chapter2/section2_pt.ipynb/0
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<jupyter_start><jupyter_text>Un entraînement complet Installez les bibliothèques 🤗 Transformers et 🤗 Datasets pour exécuter ce notebook.<jupyter_code>!pip install datasets transformers[sentencepiece] !pip install accelerate # Pour exécuter l'entraînement sur TPU, vous devez décommenter la ligne suivante : # !pip inst...
notebooks/course/fr/chapter3/section4.ipynb/0
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<jupyter_start><jupyter_text>Réponses aux questions (PyTorch) Installez les bibliothèques 🤗 *Datasets* et 🤗 *Transformers* pour exécuter ce *notebook*.<jupyter_code>!pip install datasets transformers[sentencepiece] !pip install accelerate # Pour exécuter l'entraînement sur TPU, vous devez décommenter la ligne suivant...
notebooks/course/fr/chapter7/section7_pt.ipynb/0
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<jupyter_start><jupyter_text>LoRAs of the World Unite - Training SOTA DreamBooth LoRA with Pivotal Tuning 🧨In this notebook, we show how to fine-tune [Stable Diffusion XL (SDXL)](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/stable_diffusion_xl) with [DreamBooth](https://huggingface.co/d...
notebooks/diffusers/SDXL_Dreambooth_LoRA_advanced_example.ipynb/0
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<jupyter_start><jupyter_text>**Stable Diffusion** 🎨 *...using `🧨diffusers`*Stable Diffusion is a text-to-image latent diffusion model created by the researchers and engineers from [CompVis](https://github.com/CompVis), [Stability AI](https://stability.ai/) and [LAION](https://laion.ai/). It's trained on 512x512 image...
notebooks/diffusers/stable_diffusion.ipynb/0
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<jupyter_start><jupyter_text>Launching Multi-Node Training from a Jupyter Environment> Using the `notebook_launcher` to use Accelerate from inside a Jupyter Notebook General OverviewThis notebook covers how to run the `cv_example.py` script as a Jupyter Notebook and train it on a distributed system. It will also cover...
notebooks/examples/accelerate_examples/simple_cv_example.ipynb/0
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<jupyter_start><jupyter_text>Fine-tune BLIP using Hugging Face `transformers` and `datasets` 🤗This tutorial is largely based from the [GiT tutorial](https://colab.research.google.com/drive/1HLxgrG7xZJ9FvXckNG61J72FkyrbqKAA?usp=sharing) on how to fine-tune GiT on a custom image captioning dataset. Here we will use a du...
notebooks/examples/image_captioning_blip.ipynb/0
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