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import { env } from "$env/dynamic/private"; import { buildPrompt } from "$lib/buildPrompt"; import { textGenerationStream } from "@huggingface/inference"; import type { Endpoint, EndpointMessage } from "../endpoints"; import { z } from "zod"; import { createImageProcessorOptionsValidator, makeImageProcessor, type Im...
chat-ui/src/lib/server/endpoints/tgi/endpointTgi.ts/0
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import pino from "pino"; import { dev } from "$app/environment"; import { env } from "$env/dynamic/private"; let options: pino.LoggerOptions = {}; if (dev) { options = { transport: { target: "pino-pretty", options: { colorize: true, }, }, }; } export const logger = pino({ ...options, level: env.LO...
chat-ui/src/lib/server/logger.ts/0
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import { env } from "$env/dynamic/private"; import { Client } from "@gradio/client"; import { SignJWT } from "jose"; import JSON5 from "json5"; import { MessageToolUpdateType, MessageUpdateType, type MessageToolUpdate, } from "$lib/types/MessageUpdate"; import { logger } from "$lib/server/logger"; export async funct...
chat-ui/src/lib/server/tools/utils.ts/0
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import type { WebSearchScrapedSource, WebSearchSource } from "$lib/types/WebSearch"; import type { MessageWebSearchUpdate } from "$lib/types/MessageUpdate"; import { withPage } from "./playwright"; import { spatialParser } from "./parser"; import { htmlToMarkdownTree } from "../markdown/tree"; import { timeout } from ...
chat-ui/src/lib/server/websearch/scrape/scrape.ts/0
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import { browser } from "$app/environment"; export function cookiesAreEnabled(): boolean { if (!browser) return false; if (navigator.cookieEnabled) return navigator.cookieEnabled; // Create cookie document.cookie = "cookietest=1"; const ret = document.cookie.indexOf("cookietest=") != -1; // Delete cookie docum...
chat-ui/src/lib/utils/cookiesAreEnabled.ts/0
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import type { Conversation } from "$lib/types/Conversation"; import type { Message } from "$lib/types/Message"; import { v4 } from "uuid"; export function addSibling( conv: Pick<Conversation, "messages" | "rootMessageId">, message: Omit<Message, "id">, siblingId: Message["id"] ): Message["id"] { if (conv.messages....
chat-ui/src/lib/utils/tree/addSibling.ts/0
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import { collections } from "$lib/server/database"; import { authCondition } from "$lib/server/auth"; import { z } from "zod"; import { models } from "$lib/server/models"; import { ObjectId } from "mongodb"; export async function GET({ locals, params }) { const id = z.string().parse(params.id); const convId = new Ob...
chat-ui/src/routes/api/conversation/[id]/+server.ts/0
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<script lang="ts"> import { run } from "svelte/legacy"; import ChatWindow from "$lib/components/chat/ChatWindow.svelte"; import { pendingMessage } from "$lib/stores/pendingMessage"; import { isAborted } from "$lib/stores/isAborted"; import { onMount } from "svelte"; import { page } from "$app/state"; import { g...
chat-ui/src/routes/conversation/[id]/+page.svelte/0
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<script lang="ts"> import type { PageData, ActionData } from "./$types"; import { page } from "$app/state"; import AssistantSettings from "$lib/components/AssistantSettings.svelte"; interface Props { data: PageData; form: ActionData; } let { data, form = $bindable() }: Props = $props(); let assistant = da...
chat-ui/src/routes/settings/(nav)/assistants/[assistantId]/edit/+page@settings.svelte/0
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import { env } from "$env/dynamic/private"; import { authCondition, requiresUser } from "$lib/server/auth.js"; import { collections } from "$lib/server/database.js"; import { editableToolSchema } from "$lib/server/tools/index.js"; import { usageLimits } from "$lib/server/usageLimits.js"; import { ReviewStatus } from "$...
chat-ui/src/routes/tools/new/+page.server.ts/0
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const defaultTheme = require("tailwindcss/defaultTheme"); const colors = require("tailwindcss/colors"); /** @type {import('tailwindcss').Config} */ export default { darkMode: "class", mode: "jit", content: ["./src/**/*.{html,js,svelte,ts}"], theme: { extend: { colors: { primary: colors[process.env.PUBLIC_...
chat-ui/tailwind.config.cjs/0
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import json import os import tempfile import datasets from utils import generate_example_dataset, get_duration SPEED_TEST_N_EXAMPLES = 50_000 SMALL_TEST = 5_000 RESULTS_BASEPATH, RESULTS_FILENAME = os.path.split(__file__) RESULTS_FILE_PATH = os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ...
datasets/benchmarks/benchmark_iterating.py/0
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# Dataset features [`Features`] defines the internal structure of a dataset. It is used to specify the underlying serialization format. What's more interesting to you though is that [`Features`] contains high-level information about everything from the column names and types, to the [`ClassLabel`]. You can think of [`...
datasets/docs/source/about_dataset_features.mdx/0
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# Main classes ## DatasetInfo [[autodoc]] datasets.DatasetInfo ## Dataset The base class [`Dataset`] implements a Dataset backed by an Apache Arrow table. [[autodoc]] datasets.Dataset - add_column - add_item - from_file - from_buffer - from_pandas - from_dict - from_generator - dat...
datasets/docs/source/package_reference/main_classes.mdx/0
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# Use with Pandas This document is a quick introduction to using `datasets` with Pandas, with a particular focus on how to process datasets using Pandas functions, and how to convert a dataset to Pandas or from Pandas. This is particularly useful as it allows fast operations, since `datasets` uses PyArrow under the h...
datasets/docs/source/use_with_pandas.mdx/0
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from typing import List, Optional, TypeVar from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets from .dataset_dict import DatasetDict, IterableDatasetDict from .info import DatasetInfo from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interle...
datasets/src/datasets/combine.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 TrackedIterableFromGenerator, tracked_list, tracked_str class Datase...
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 io import json import re from itertools import islice from typing import Any, Callable, Dict, List import fsspec import numpy as np import pyarrow as pa import datasets from datasets.features.features import cast_to_python_objects from datasets.utils.file_utils import SINGLE_FILE_COMPRESSION_EXTENSION_TO_PROTO...
datasets/src/datasets/packaged_modules/webdataset/webdataset.py/0
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import bz2 import gzip import lzma import os import shutil import struct import tarfile import warnings import zipfile from abc import ABC, abstractmethod from pathlib import Path from typing import Dict, List, Optional, Type, Union from .. import config from ._filelock import FileLock from .logging import get_logger ...
datasets/src/datasets/utils/extract.py/0
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import numpy as np def approximate_mode(class_counts, n_draws, rng): """Computes approximate mode of multivariate hypergeometric. This is an approximation to the mode of the multivariate hypergeometric given by class_counts and n_draws. It shouldn't be off by more than one. It is the mostly likely...
datasets/src/datasets/utils/stratify.py/0
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import os from argparse import ArgumentParser from typing import List import torch.utils.data from datasets import Dataset, IterableDataset from datasets.distributed import split_dataset_by_node NUM_SHARDS = 4 NUM_ITEMS_PER_SHARD = 3 class FailedTestError(RuntimeError): pass def gen(shards: List[str]): ...
datasets/tests/distributed_scripts/run_torch_distributed.py/0
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import contextlib import csv import json import os import sqlite3 import tarfile import textwrap import zipfile import pandas as pd import pyarrow as pa import pyarrow.parquet as pq import pytest import datasets import datasets.config # dataset + arrow_file @pytest.fixture(scope="session") def dataset(): n = ...
datasets/tests/fixtures/files.py/0
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import os import textwrap import pyarrow as pa import pytest from datasets import ClassLabel, Features, Image from datasets.builder import InvalidConfigName from datasets.data_files import DataFilesList from datasets.packaged_modules.csv.csv import Csv, CsvConfig from ..utils import require_pil @pytest.fixture def...
datasets/tests/packaged_modules/test_csv.py/0
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from unittest import TestCase from datasets import Sequence, Value from datasets.arrow_dataset import Dataset class DatasetListTest(TestCase): def _create_example_records(self): return [ {"col_1": 3, "col_2": "a"}, {"col_1": 2, "col_2": "b"}, {"col_1": 1, "col_2": "c"}...
datasets/tests/test_dataset_list.py/0
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import importlib import os import pickle import shutil import tempfile import time from hashlib import sha256 from multiprocessing import Pool from pathlib import Path from unittest import TestCase from unittest.mock import patch import dill import pyarrow as pa import pytest import requests import datasets from data...
datasets/tests/test_load.py/0
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import argparse import csv import gc import os from dataclasses import dataclass from typing import Dict, List, Union import torch import torch.utils.benchmark as benchmark GITHUB_SHA = os.getenv("GITHUB_SHA", None) BENCHMARK_FIELDS = [ "pipeline_cls", "ckpt_id", "batch_size", "num_inference_steps", ...
diffusers/benchmarks/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 applicable law or agreed to...
diffusers/docs/source/en/api/pipelines/kandinsky_v22.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/api/pipelines/pix2pix.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/api/pipelines/stable_diffusion/latent_upscale.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/fp16.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 agree...
diffusers/docs/source/en/quantization/torchao.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/sdxl.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/merge_loras.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/unconditional_image_generation.md/0
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# 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](https://huggingface.co/docs/datasets) 라이브러리를 사용하여 데이터셋을 만들 수 있습니다. 데이터셋 구조는 모델을 학습하려는 작업에 따라 달라집니다. 가장 기본적인 데이터셋 구조는 unconditio...
diffusers/docs/source/ko/training/create_dataset.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 applicable law or agreed...
diffusers/docs/source/ko/using-diffusers/diffedit.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/write_own_pipeline.md/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/advanced_diffusion_training/test_dreambooth_lora_flux_advanced.py/0
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import inspect from typing import List, Optional, Union import torch from torch import nn from torch.nn import functional as F from torchvision import transforms from transformers import CLIPImageProcessor, CLIPModel, CLIPTextModel, CLIPTokenizer from diffusers import ( AutoencoderKL, DDIMScheduler, DPMSo...
diffusers/examples/community/clip_guided_stable_diffusion.py/0
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import math import tempfile from typing import List, Optional import numpy as np import PIL.Image import torch from accelerate import Accelerator from torchvision import transforms from tqdm.auto import tqdm from transformers import CLIPTextModel, CLIPTokenizer from diffusers import AutoencoderKL, DiffusionPipeline, ...
diffusers/examples/community/sde_drag.py/0
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# Custom Diffusion training example [Custom Diffusion](https://arxiv.org/abs/2212.04488) is a method to customize text-to-image models like Stable Diffusion given just a few (4~5) images of a subject. The `train_custom_diffusion.py` script shows how to implement the training procedure and adapt it for stable diffusion...
diffusers/examples/custom_diffusion/README.md/0
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import torch from diffusers import StableDiffusionPipeline model_id = "path-to-your-trained-model" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda") prompt = "A photo of sks dog in a bucket" image = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0] imag...
diffusers/examples/research_projects/colossalai/inference.py/0
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# from accelerate.utils import write_basic_config # # write_basic_config() import argparse import logging import math import os import shutil from pathlib import Path import accelerate import torch import torch.nn.functional as F import torch.utils.checkpoint import transformers from accelerate import Accelerator from...
diffusers/examples/research_projects/gligen/train_gligen_text.py/0
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#!/bin/bash # run # accelerate config # check with # accelerate env export MODEL_DIR="PixArt-alpha/PixArt-XL-2-512x512" export OUTPUT_DIR="output/pixart-controlnet-hf-diffusers-test" accelerate launch ./train_pixart_controlnet_hf.py --mixed_precision="fp16" \ --pretrained_model_name_or_path=$MODEL_DIR \ --output_...
diffusers/examples/research_projects/pixart/train_controlnet_hf_diffusers.sh/0
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import time import jax import jax.numpy as jnp import numpy as np from flax.jax_utils import replicate from jax import pmap # Let's cache the model compilation, so that it doesn't take as long the next time around. from jax.experimental.compilation_cache import compilation_cache as cc from diffusers import FlaxStabl...
diffusers/examples/research_projects/sdxl_flax/sdxl_single_aot.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/t2i_adapter/test_t2i_adapter.py/0
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## Textual Inversion fine-tuning example for SDXL ```sh export MODEL_NAME="stabilityai/stable-diffusion-xl-base-1.0" export DATA_DIR="./cat" accelerate launch textual_inversion_sdxl.py \ --pretrained_model_name_or_path=$MODEL_NAME \ --train_data_dir=$DATA_DIR \ --learnable_property="object" \ --placeholder_to...
diffusers/examples/textual_inversion/README_sdxl.md/0
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# coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable...
diffusers/examples/vqgan/train_vqgan.py/0
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#!/usr/bin/env python3 import argparse import math import os from copy import deepcopy import requests import torch from audio_diffusion.models import DiffusionAttnUnet1D from diffusion import sampling from torch import nn from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNet1DModel MODELS_MAP = { ...
diffusers/scripts/convert_dance_diffusion_to_diffusers.py/0
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import argparse import tempfile import torch from accelerate import load_checkpoint_and_dispatch from transformers import CLIPTextModelWithProjection, CLIPTokenizer from diffusers import UnCLIPPipeline, UNet2DConditionModel, UNet2DModel from diffusers.models.transformers.prior_transformer import PriorTransformer from...
diffusers/scripts/convert_kakao_brain_unclip_to_diffusers.py/0
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# coding=utf-8 # Copyright 2025 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
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import argparse from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to...
diffusers/scripts/convert_unclip_txt2img_to_image_variation.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/commands/fp16_safetensors.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/loaders/lora_conversion_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/attention.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/autoencoders/vae.py/0
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import os from typing import Any, Callable, Dict, List, Optional, Tuple, Union import torch from torch import nn from ...models.controlnets.controlnet import ControlNetModel, ControlNetOutput from ...models.modeling_utils import ModelMixin from ...utils import logging logger = logging.get_logger(__name__) class M...
diffusers/src/diffusers/models/controlnets/multicontrolnet.py/0
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# Copyright 2024 The CogVideoX team, Tsinghua University & ZhipuAI 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.o...
diffusers/src/diffusers/models/transformers/cogvideox_transformer_3d.py/0
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# Copyright 2024 The Hunyuan 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 # # Unless ...
diffusers/src/diffusers/models/transformers/transformer_hunyuan_video.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_kandinsky3.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/amused/pipeline_amused_img2img.py/0
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# Copyright 2024 AuraFlow Authors and The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless ...
diffusers/src/diffusers/pipelines/aura_flow/pipeline_aura_flow.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/ddpm/pipeline_ddpm.py/0
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# Copyright 2022 The Music Spectrogram Diffusion Authors. # 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...
diffusers/src/diffusers/pipelines/deprecated/spectrogram_diffusion/midi_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/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_image_variation.py/0
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import inspect import math from itertools import repeat from typing import Any, Callable, Dict, List, Optional, Tuple, Union import torch import torch.nn.functional as F from packaging import version from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer from ...configuration_utils import FrozenDic...
diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.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/stable_cascade/pipeline_stable_cascade.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/stable_diffusion/pipeline_stable_diffusion_depth2img.py/0
{ "file_path": "diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_depth2img.py", "repo_id": "diffusers", "token_count": 19605 }
# Copyright 2024 Stability 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 requ...
diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_inpaint.py/0
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from dataclasses import dataclass from typing import List, Optional, Union import numpy as np import PIL.Image from ...utils import ( BaseOutput, ) @dataclass class StableDiffusionSafePipelineOutput(BaseOutput): """ Output class for Safe Stable Diffusion pipelines. Args: images (`List[PIL.I...
diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_output.py/0
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import inspect from dataclasses import dataclass from typing import Callable, List, Optional, Union import numpy as np import PIL.Image import torch from transformers import ( CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection, GPT2Tokenizer, ) from ...image_processor ...
diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py/0
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# Copyright 2024 Katherine Crowson and The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless...
diffusers/src/diffusers/schedulers/scheduling_edm_euler.py/0
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# 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/schedulers/scheduling_repaint.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 FlaxStableDiffusionControlNetPipeline(metaclass=DummyObject): _backends = ["flax", "transformers"] def __init__(self, *args, **kwargs): requires_backends(self, ["flax",...
diffusers/src/diffusers/utils/dummy_flax_and_transformers_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/import_utils.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/autoencoders/test_models_autoencoder_dc.py/0
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import inspect from diffusers.utils import is_flax_available from diffusers.utils.testing_utils import require_flax if is_flax_available(): import jax @require_flax class FlaxModelTesterMixin: def test_output(self): init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common() mo...
diffusers/tests/models/test_modeling_common_flax.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/transformers/test_models_transformer_mochi.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/tests/others/test_check_dummies.py/0
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import gc import unittest import numpy as np import pytest import torch from huggingface_hub import hf_hub_download from transformers import AutoTokenizer, CLIPTextConfig, CLIPTextModel, CLIPTokenizer, T5EncoderModel from diffusers import AutoencoderKL, FlowMatchEulerDiscreteScheduler, FluxPipeline, FluxTransformer2D...
diffusers/tests/pipelines/flux/test_pipeline_flux.py/0
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# Copyright 2024 The HuggingFace 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 agreed to in...
diffusers/tests/pipelines/ltx/test_ltx.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/pag/test_pag_controlnet_sdxl_img2img.py/0
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import random import unittest import numpy as np import torch from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer import diffusers from diffusers import ( AutoencoderKL, DDIMScheduler, DPMSolverMultistepScheduler, LCMScheduler, MotionAdapter, PIAPipeline, StableDiffusionP...
diffusers/tests/pipelines/pia/test_pia.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_audio/test_stable_audio.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_2/test_stable_diffusion_attend_and_excite.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_gligen/test_stable_diffusion_gligen.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_xl/test_stable_diffusion_xl.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/test_pipelines_flax.py/0
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The tests here are adapted from [`transformers` tests](https://github.com/huggingface/transformers/tree/409fcfdfccde77a14b7cc36972b774cabc371ae1/tests/quantization/bnb). They were conducted on the `audace` machine, using a single RTX 4090. Below is `nvidia-smi`: ```bash +----------------------------------------------...
diffusers/tests/quantization/bnb/README.md/0
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import tempfile import torch from diffusers import DPMSolverMultistepInverseScheduler, DPMSolverMultistepScheduler from .test_schedulers import SchedulerCommonTest class DPMSolverMultistepSchedulerTest(SchedulerCommonTest): scheduler_classes = (DPMSolverMultistepInverseScheduler,) forward_default_kwargs = ...
diffusers/tests/schedulers/test_scheduler_dpm_multi_inverse.py/0
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import tempfile import unittest import numpy as np import torch from diffusers import ScoreSdeVeScheduler class ScoreSdeVeSchedulerTest(unittest.TestCase): # TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration) scheduler_classes = (ScoreSdeVeScheduler,) forward_default_kwargs = ...
diffusers/tests/schedulers/test_scheduler_score_sde_ve.py/0
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# coding=utf-8 # Copyright 2025 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_table.py/0
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#!/usr/bin/env python # 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 # # ...
lerobot/benchmarks/video/capture_camera_feed.py/0
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import shutil from pathlib import Path import numpy as np import torch from lerobot.common.datasets.lerobot_dataset import LEROBOT_HOME, LeRobotDataset from lerobot.common.datasets.push_dataset_to_hub._download_raw import download_raw PUSHT_TASK = "Push the T-shaped blue block onto the T-shaped green target surface....
lerobot/examples/port_datasets/pusht_zarr.py/0
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#!/usr/bin/env python # 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 # # ...
lerobot/lerobot/common/datasets/sampler.py/0
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#!/usr/bin/env python # Copyright 2024 Tony Z. Zhao 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.apache.org/licenses...
lerobot/lerobot/common/policies/act/configuration_act.py/0
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#!/usr/bin/env python # Copyright 2024 Nicklas Hansen, Xiaolong Wang, Hao Su, # 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 # # ht...
lerobot/lerobot/common/policies/tdmpc/modeling_tdmpc.py/0
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"""Logic to calibrate a robot arm built with dynamixel motors""" # TODO(rcadene, aliberts): move this logic into the robot code when refactoring import numpy as np from lerobot.common.robot_devices.motors.dynamixel import ( CalibrationMode, TorqueMode, convert_degrees_to_steps, ) from lerobot.common.robot...
lerobot/lerobot/common/robot_devices/robots/dynamixel_calibration.py/0
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