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cargo build --target wasm32-unknown-unknown --release wasm-bindgen ../../target/wasm32-unknown-unknown/release/m.wasm --out-dir build --target web wasm-bindgen ../../target/wasm32-unknown-unknown/release/m-quantized.wasm --out-dir build --target web
candle/candle-wasm-examples/t5/build-lib.sh/0
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use yew_agent::PublicWorker; fn main() { candle_wasm_example_whisper::Worker::register(); }
candle/candle-wasm-examples/whisper/src/bin/worker.rs/0
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use candle::{DType, IndexOp, Result, Tensor, D}; use candle_nn::{ batch_norm, conv2d, conv2d_no_bias, BatchNorm, Conv2d, Conv2dConfig, Module, VarBuilder, }; use image::DynamicImage; // Model architecture from https://github.com/ultralytics/ultralytics/issues/189 // https://github.com/tinygrad/tinygrad/blob/master...
candle/candle-wasm-examples/yolo/src/model.rs/0
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{ "useTabs": true, "trailingComma": "es5", "printWidth": 100, "plugins": ["prettier-plugin-svelte", "prettier-plugin-tailwindcss"], "pluginSearchDirs": ["."], "overrides": [{ "files": "*.svelte", "options": { "parser": "svelte" } }] }
chat-ui/.prettierrc/0
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/// <reference types="@sveltejs/kit" /> /// <reference types="unplugin-icons/types/svelte" /> import type { User } from "$lib/types/User"; // See https://kit.svelte.dev/docs/types#app // for information about these interfaces declare global { namespace App { // interface Error {} interface Locals { sessionId:...
chat-ui/src/app.d.ts/0
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<script lang="ts"> import CarbonEarth from "~icons/carbon/earth"; import CarbonArrowUpRight from "~icons/carbon/arrow-up-right"; import type { Model } from "$lib/types/Model"; export let model: Pick<Model, "name" | "datasetName" | "websiteUrl" | "modelUrl" | "datasetUrl">; export let variant: "light" | "dark" = ...
chat-ui/src/lib/components/ModelCardMetadata.svelte/0
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<script lang="ts"> import { createEventDispatcher } from "svelte"; import IconGear from "~icons/bi/gear-fill"; import { base } from "$app/paths"; import type { Assistant } from "$lib/types/Assistant"; import IconInternet from "../icons/IconInternet.svelte"; export let assistant: Pick< Assistant, | "avatar" ...
chat-ui/src/lib/components/chat/AssistantIntroduction.svelte/0
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import { afterEach, describe, expect, it } from "vitest"; import { migrations } from "./routines"; import { acquireLock, isDBLocked, refreshLock, releaseLock } from "./lock"; import { collections } from "$lib/server/database"; describe("migrations", () => { it("should not have duplicates guid", async () => { const ...
chat-ui/src/lib/migrations/migrations.spec.ts/0
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import { buildPrompt } from "$lib/buildPrompt"; import type { TextGenerationStreamOutput } from "@huggingface/inference"; import type { Endpoint } from "../endpoints"; import { z } from "zod"; export const endpointOllamaParametersSchema = z.object({ weight: z.number().int().positive().default(1), model: z.any(), ty...
chat-ui/src/lib/server/endpoints/ollama/endpointOllama.ts/0
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import { JSDOM, VirtualConsole } from "jsdom"; export async function parseWeb(url: string) { const abortController = new AbortController(); setTimeout(() => abortController.abort(), 10000); const r = await fetch(url, { signal: abortController.signal, credentials: "omit" }).catch(); if (r.headers.get("content-type...
chat-ui/src/lib/server/websearch/parseWeb.ts/0
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export interface ConvSidebar { id: string; title: string; updatedAt: Date; model?: string; assistantId?: string; avatarHash?: string; }
chat-ui/src/lib/types/ConvSidebar.ts/0
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import type { ObjectId } from "mongodb"; import type { Timestamps } from "./Timestamps"; export interface User extends Timestamps { _id: ObjectId; username?: string; name: string; email?: string; avatarUrl: string | undefined; hfUserId: string; }
chat-ui/src/lib/types/User.ts/0
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import type { Model } from "$lib/types/Model"; export const findCurrentModel = (models: Model[], id?: string): Model => models.find((m) => m.id === id) ?? models[0];
chat-ui/src/lib/utils/models.ts/0
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import { collections } from "$lib/server/database"; import { ObjectId } from "mongodb"; import { describe, expect, it } from "vitest"; import { convertLegacyConversation } from "./convertLegacyConversation"; import { insertLegacyConversation } from "./treeHelpers.spec"; describe("convertLegacyConversation", () => { ...
chat-ui/src/lib/utils/tree/convertLegacyConversation.spec.ts/0
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<script lang="ts"> import { base } from "$app/paths"; import { clickOutside } from "$lib/actions/clickOutside"; import { afterNavigate, goto } from "$app/navigation"; import { useSettingsStore } from "$lib/stores/settings"; import type { PageData } from "./$types"; import { applyAction, enhance } from "$app/form...
chat-ui/src/routes/assistant/[assistantId]/+page.svelte/0
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import { redirect, error } from "@sveltejs/kit"; import { getOIDCUserData, validateAndParseCsrfToken } from "$lib/server/auth"; import { z } from "zod"; import { base } from "$app/paths"; import { updateUser } from "./updateUser"; import { ALLOWED_USER_EMAILS } from "$env/static/private"; import JSON5 from "json5"; co...
chat-ui/src/routes/login/callback/+page.server.ts/0
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import { collections } from "$lib/server/database"; import { type Actions, fail, redirect } from "@sveltejs/kit"; import { ObjectId } from "mongodb"; import { authCondition } from "$lib/server/auth"; import { base } from "$app/paths"; import { PUBLIC_ORIGIN, PUBLIC_SHARE_PREFIX } from "$env/static/public"; import { WEB...
chat-ui/src/routes/settings/(nav)/assistants/[assistantId]/+page.server.ts/0
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{ "$schema": "https://vega.github.io/schema/vega-lite/v4.json", "data": { "values": "<DVC_METRIC_DATA>" }, "title": "<DVC_METRIC_TITLE>", "mark": "rect", "encoding": { "x": { "field": "<DVC_METRIC_X>", "type": "nominal", "sort": "ascending", ...
datasets/.dvc/plots/confusion.json/0
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55
cff-version: 1.2.0 message: "If you use this software, please cite it as below." title: "huggingface/datasets" authors: - family-names: Lhoest given-names: Quentin - family-names: Villanova del Moral given-names: Albert orcid: "https://orcid.org/0000-0003-1727-1045" - family-names: von Platen given-names: Patri...
datasets/CITATION.cff/0
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# Know your dataset There are two types of dataset objects, a regular [`Dataset`] and then an ✨ [`IterableDataset`] ✨. A [`Dataset`] provides fast random access to the rows, and memory-mapping so that loading even large datasets only uses a relatively small amount of device memory. But for really, really big datasets ...
datasets/docs/source/access.mdx/0
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# Load image data Image datasets have [`Image`] type columns, which contain PIL objects. <Tip> To work with image datasets, you need to have the `vision` dependency installed. Check out the [installation](./installation#vision) guide to learn how to install it. </Tip> When you load an image dataset and call the i...
datasets/docs/source/image_load.mdx/0
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# Task templates <Tip warning={true}> The Task API is deprecated in favor of [`train-eval-index`](https://github.com/huggingface/hub-docs/blob/9ab2555e1c146122056aba6f89af404a8bc9a6f1/datasetcard.md?plain=1#L90-L106) and will be removed in the next major release. </Tip> The tasks supported by [`Dataset.prepare_for_...
datasets/docs/source/package_reference/task_templates.mdx/0
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# Using Datasets with TensorFlow This document is a quick introduction to using `datasets` with TensorFlow, with a particular focus on how to get `tf.Tensor` objects out of our datasets, and how to stream data from Hugging Face `Dataset` objects to Keras methods like `model.fit()`. ## Dataset format By default, data...
datasets/docs/source/use_with_tensorflow.mdx/0
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# Copyright 2020 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/code_eval/execute.py/0
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# Metric Card for GLUE ## Metric description This metric is used to compute the GLUE evaluation metric associated to each [GLUE dataset](https://huggingface.co/datasets/glue). GLUE, the General Language Understanding Evaluation benchmark is a collection of resources for training, evaluating, and analyzing natural la...
datasets/metrics/glue/README.md/0
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# Metric Card for METEOR ## Metric description METEOR (Metric for Evaluation of Translation with Explicit ORdering) is a machine translation evaluation metric, which is calculated based on the harmonic mean of precision and recall, with recall weighted more than precision. METEOR is based on a generalized concept o...
datasets/metrics/meteor/README.md/0
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# Metric Card for SacreBLEU ## Metric Description SacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores. Inspired by Rico Sennrich's `multi-bleu-detok.perl`, it produces the official Workshop on Machine Translation (WMT) scores but works with plain text. It also knows all t...
datasets/metrics/sacrebleu/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/super_glue/super_glue.py/0
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__all__ = [ "DownloadConfig", "DownloadManager", "DownloadMode", "StreamingDownloadManager", ] from .download_config import DownloadConfig from .download_manager import DownloadManager, DownloadMode from .streaming_download_manager import StreamingDownloadManager
datasets/src/datasets/download/__init__.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/formatting.py/0
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from typing import Optional from .. import Features, NamedSplit from ..packaged_modules.text.text import Text from ..utils.typing import NestedDataStructureLike, PathLike from .abc import AbstractDatasetReader class TextDatasetReader(AbstractDatasetReader): def __init__( self, path_or_paths: Nest...
datasets/src/datasets/io/text.py/0
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import collections import itertools import os from dataclasses import dataclass from typing import List, Optional, Tuple, Type import pandas as pd import pyarrow as pa import pyarrow.json as paj import datasets from datasets.features.features import FeatureType from datasets.tasks.base import TaskTemplate logger = ...
datasets/src/datasets/packaged_modules/folder_based_builder/folder_based_builder.py/0
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import itertools import warnings from dataclasses import InitVar, dataclass from io import StringIO from typing import Optional import pyarrow as pa import datasets from datasets.features.features import require_storage_cast from datasets.table import table_cast logger = datasets.utils.logging.get_logger(__name__) ...
datasets/src/datasets/packaged_modules/text/text.py/0
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from dataclasses import dataclass, field from typing import ClassVar, Dict from ..features import Features, Sequence, Value from .base import TaskTemplate @dataclass(frozen=True) class QuestionAnsweringExtractive(TaskTemplate): # `task` is not a ClassVar since we want it to be part of the `asdict` output for JSO...
datasets/src/datasets/tasks/question_answering.py/0
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import enum import os from typing import Optional from huggingface_hub.utils import insecure_hashlib from .. import config from .logging import get_logger logger = get_logger(__name__) class VerificationMode(enum.Enum): """`Enum` that specifies which verification checks to run. The default mode is `BASIC...
datasets/src/datasets/utils/info_utils.py/0
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from collections.abc import Iterator from typing import Iterable class tracked_str(str): origins = {} def set_origin(self, origin: str): if super().__repr__() not in self.origins: self.origins[super().__repr__()] = origin def get_origin(self): return self.origins.get(super()....
datasets/src/datasets/utils/track.py/0
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import textwrap import pyarrow as pa import pytest from datasets import Features, Image from datasets.packaged_modules.text.text import Text from ..utils import require_pil @pytest.fixture def text_file(tmp_path): filename = tmp_path / "text.txt" data = textwrap.dedent( """\ Lorem ipsum dol...
datasets/tests/packaged_modules/test_text.py/0
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import importlib import os import fsspec import pytest from fsspec import register_implementation from fsspec.core import url_to_fs from fsspec.registry import _registry as _fsspec_registry from datasets.filesystems import COMPRESSION_FILESYSTEMS, extract_path_from_uri, is_remote_filesystem from .utils import requir...
datasets/tests/test_filesystem.py/0
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import time from dataclasses import dataclass from multiprocessing import Pool from unittest import TestCase from unittest.mock import patch import multiprocess import numpy as np import pytest from datasets.utils.py_utils import ( NestedDataStructure, asdict, iflatmap_unordered, map_nested, temp_...
datasets/tests/test_py_utils.py/0
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# Congratulations <img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/communication/thumbnail.png" alt="Thumbnail"/> **Congratulations on finishing this course!** With perseverance, hard work, and determination, **you've acquired a solid background in Deep Reinforcement...
deep-rl-class/units/en/communication/conclusion.mdx/0
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# Two main approaches for solving RL problems [[two-methods]] <Tip> Now that we learned the RL framework, how do we solve the RL problem? </Tip> In other words, how do we build an RL agent that can **select the actions that maximize its expected cumulative reward?** ## The Policy π: the agent’s brain [[policy]] The...
deep-rl-class/units/en/unit1/two-methods.mdx/0
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# What is RL? A short recap [[what-is-rl]] In RL, we build an agent that can **make smart decisions**. For instance, an agent that **learns to play a video game.** Or a trading agent that **learns to maximize its benefits** by deciding on **what stocks to buy and when to sell.** <img src="https://huggingface.co/datas...
deep-rl-class/units/en/unit2/what-is-rl.mdx/0
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# (Optional) the Policy Gradient Theorem In this optional section where we're **going to study how we differentiate the objective function that we will use to approximate the policy gradient**. Let's first recap our different formulas: 1. The Objective function <img src="https://huggingface.co/datasets/huggingface-...
deep-rl-class/units/en/unit4/pg-theorem.mdx/0
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# Advantage Actor Critic (A2C) using Robotics Simulations with Panda-Gym 🤖 [[hands-on]] <CourseFloatingBanner classNames="absolute z-10 right-0 top-0" notebooks={[ {label: "Google Colab", value: "https://colab.research.google.com/github/huggingface/deep-rl-class/blob/main/notebooks/unit6/unit6.ip...
deep-rl-class/units/en/unit6/hands-on.mdx/0
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# Interesting Environments to try Here we provide a list of interesting environments you can try to train your agents on: ## DIAMBRA Arena <img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit12/diambraarena.png" alt="diambraArena"/> DIAMBRA Arena is a software pac...
deep-rl-class/units/en/unitbonus3/envs-to-try.mdx/0
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import argparse import sys sys.path.append(".") from base_classes import LCMLoRATextToImageBenchmark # noqa: E402 if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( "--ckpt", type=str, default="stabilityai/stable-diffusion-xl-base-1.0", ) pars...
diffusers/benchmarks/benchmark_t2i_lcm_lora.py/0
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- sections: - local: index title: 🧨 Diffusers - local: quicktour title: Quicktour - local: stable_diffusion title: Effective and efficient diffusion - local: installation title: Installation title: Get started - sections: - local: tutorials/tutorial_overview title: Overview - local: u...
diffusers/docs/source/en/_toctree.yml/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/models/consistency_decoder_vae.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 to...
diffusers/docs/source/en/api/pipelines/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/api/pipelines/stable_cascade.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/deepcache.md/0
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<!--Copyright 2024 Custom Diffusion authors 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...
diffusers/docs/source/en/training/custom_diffusion.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/tutorials/basic_training.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/img2img.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/reusing_seeds.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/ja/quicktour.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/optimization/tome.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/unconditional_training.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/reproducibility.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/zh/quicktour.md/0
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# Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicabl...
diffusers/examples/community/ddim_noise_comparative_analysis.py/0
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## ---------------------------------------------------------- # A SDXL pipeline can take unlimited weighted prompt # # Author: Andrew Zhu # Github: https://github.com/xhinker # Medium: https://medium.com/@xhinker ## ----------------------------------------------------------- import inspect import os from typing import...
diffusers/examples/community/lpw_stable_diffusion_xl.py/0
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# Copyright 2024 TencentARC and The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless requir...
diffusers/examples/community/pipeline_stable_diffusion_xl_controlnet_adapter.py/0
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import inspect import os import random import re from dataclasses import dataclass from typing import Callable, Dict, List, Optional, Union import torch from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer from diffusers.configuration_utils import FrozenDict from diffusers.models import Autoencod...
diffusers/examples/community/wildcard_stable_diffusion.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/controlnet/test_controlnet.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/dreambooth/test_dreambooth_lora_edm.py/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/kandinsky2_2/text_to_image/train_text_to_image_decoder.py/0
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## Diffusers examples with Intel optimizations **This research project is not actively maintained by the diffusers team. For any questions or comments, please make sure to tag @hshen14 .** This aims to provide diffusers examples with Intel optimizations such as Bfloat16 for training/fine-tuning acceleration and 8-bit...
diffusers/examples/research_projects/intel_opts/README.md/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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from diffusers.utils import is_accelerate_available, logging if is_accelerate_available(): pass logger = logging.get_logger(__name__) # pylint: disable=invalid-name def create_unet_diffusers_config(original_config, image_size: int, controlnet=False): """ Creates a config for the diffusers based on the...
diffusers/scripts/convert_svd_to_diffusers.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/env.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/loaders/peft.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/autoencoder_kl_temporal_decoder.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/src/diffusers/models/normalization.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/unet_2d_blocks.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_stable_cascade.py/0
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from typing import TYPE_CHECKING from ...utils import ( DIFFUSERS_SLOW_IMPORT, OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_transformers_available, is_transformers_version, ) _dummy_objects = {} _import_structure = {} try: if not (is_transformers_available() and is...
diffusers/src/diffusers/pipelines/audioldm/__init__.py/0
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# Copyright 2024 Salesforce.com, inc. # 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/LICENS...
diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_blip_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/deprecated/audio_diffusion/mel.py/0
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from typing import TYPE_CHECKING from ...utils import DIFFUSERS_SLOW_IMPORT, _LazyModule _import_structure = {"pipeline_dit": ["DiTPipeline"]} if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT: from .pipeline_dit import DiTPipeline else: import sys sys.modules[__name__] = _LazyModule( __name__, ...
diffusers/src/diffusers/pipelines/dit/__init__.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_diffusion/pipeline_stable_unclip.py/0
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import numpy as np import torch from ...utils import is_invisible_watermark_available if is_invisible_watermark_available(): from imwatermark import WatermarkEncoder # Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L...
diffusers/src/diffusers/pipelines/stable_diffusion_xl/watermark.py/0
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from typing import TYPE_CHECKING from ...utils import ( DIFFUSERS_SLOW_IMPORT, OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_transformers_available, ) _dummy_objects = {} _import_structure = {} try: if not (is_transformers_available() and is_torch_available()): ...
diffusers/src/diffusers/pipelines/unidiffuser/__init__.py/0
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# Copyright 2024 NVIDIA 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 b...
diffusers/src/diffusers/schedulers/deprecated/scheduling_karras_ve.py/0
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# Copyright 2024 Zhejiang 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_pndm.py/0
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import inspect import warnings from typing import Any, Dict, Optional, Union from packaging import version def deprecate(*args, take_from: Optional[Union[Dict, Any]] = None, standard_warn=True, stacklevel=2): from .. import __version__ deprecated_kwargs = take_from values = () if not isinstance(args...
diffusers/src/diffusers/utils/deprecation_utils.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/utils/hub_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/pipelines/audioldm2/test_audioldm2.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/kandinsky3/test_kandinsky3.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_onnx_stable_diffusion.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_latent_upscale.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_panorama/test_stable_diffusion_panorama.py/0
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import gc import random import tempfile import unittest import numpy as np import torch from transformers import ( CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModelWithProjection, ) import diffusers from diffusers import ( AutoencoderKLTemporalDecoder, EulerDiscreteScheduler, StableVideoDi...
diffusers/tests/pipelines/stable_video_diffusion/test_stable_video_diffusion.py/0
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import torch from diffusers import DPMSolverSDEScheduler from diffusers.utils.testing_utils import require_torchsde, torch_device from .test_schedulers import SchedulerCommonTest @require_torchsde class DPMSolverSDESchedulerTest(SchedulerCommonTest): scheduler_classes = (DPMSolverSDEScheduler,) num_inferenc...
diffusers/tests/schedulers/test_scheduler_dpm_sde.py/0
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import torch from diffusers import TCDScheduler from .test_schedulers import SchedulerCommonTest class TCDSchedulerTest(SchedulerCommonTest): scheduler_classes = (TCDScheduler,) forward_default_kwargs = (("num_inference_steps", 10),) def get_scheduler_config(self, **kwargs): config = { ...
diffusers/tests/schedulers/test_scheduler_tcd.py/0
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# coding=utf-8 # 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 requir...
diffusers/utils/notify_slack_about_release.py/0
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# Introduction to 🤗 Diffusers <CourseFloatingBanner unit={1} classNames="absolute z-10 right-0 top-0" notebooks={[ {label: "Introduction to Diffusers", value: "https://colab.research.google.com/github/huggingface/diffusion-models-class/blob/main/units/en/unit1/introduction_to_diffusers.ipynb"}, {label: "I...
diffusion-models-class/units/en/unit1/2.mdx/0
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<jupyter_start><jupyter_text>Hackathon DreamBooth 🏆 Bienvenue au Hackathon DreamBooth ! Dans cette compétition, vous allez **personnaliser un modèle de Stable Diffusion en le *finetunant* sur une poignée de vos propres images**. Pour cela, nous allons utiliser une technique appelée [_DreamBooth_](https://arxiv.org/abs...
diffusion-models-class/units/fr/events/dreambooth.ipynb/0
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<jupyter_start><jupyter_text>Traduction (TensorFlow) Installez les bibliothèques 🤗 *Datasets* et 🤗 *Transformers* pour exécuter ce *notebook*.<jupyter_code>!pip install datasets transformers[sentencepiece] !apt install git-lfs<jupyter_output>Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab...
notebooks/course/fr/chapter7/section4_tf.ipynb/0
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