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import init, { Model } from "./build/m.js"; async function fetchArrayBuffer(url) { const cacheName = "phi-mixformer-candle-cache"; const cache = await caches.open(cacheName); const cachedResponse = await cache.match(url); if (cachedResponse) { const data = await cachedResponse.arrayBuffer(); return new...
candle/candle-wasm-examples/phi/phiWorker.js/0
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use candle::{Device, Tensor}; use candle_transformers::generation::LogitsProcessor; pub use candle_transformers::models::quantized_t5::{ Config, T5EncoderModel, T5ForConditionalGeneration, VarBuilder, }; use candle_wasm_example_t5::console_log; use tokenizers::Tokenizer; use wasm_bindgen::prelude::*; const DEVICE:...
candle/candle-wasm-examples/t5/src/bin/m-quantized.rs/0
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pub const WITH_TIMER: bool = true; mod app; mod audio; pub mod languages; pub mod worker; pub use app::App; pub use worker::Worker;
candle/candle-wasm-examples/whisper/src/lib.rs/0
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//load the candle yolo wasm module import init, { Model, ModelPose } from "./build/m.js"; async function fetchArrayBuffer(url) { const cacheName = "yolo-candle-cache"; const cache = await caches.open(cacheName); const cachedResponse = await cache.match(url); if (cachedResponse) { const data = await cachedR...
candle/candle-wasm-examples/yolo/yoloWorker.js/0
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54
apiVersion: v2 name: chat-ui version: 0.0.0-latest type: application icon: https://huggingface.co/front/assets/huggingface_logo-noborder.svg
chat-ui/chart/Chart.yaml/0
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# Multimodal We currently support [IDEFICS](https://huggingface.co/blog/idefics) (hosted on [TGI](./providers/tgi)), OpenAI and Anthropic Claude 3 as multimodal models. You can enable it by setting `multimodal: true` in your `MODELS` configuration. For IDEFICS, you must have a [PRO HF Api token](https://huggingface.co...
chat-ui/docs/source/configuration/models/multimodal.md/0
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# Web Search Chat UI features a powerful Web Search feature. A high level overview of how it works: 1. Generate an appropriate search query from the user prompt using the `TASK_MODEL` 2. Perform web search via an external provider (i.e. Serper) or via locally scrape Google results 3. Load each search result into play...
chat-ui/docs/source/configuration/web-search.md/0
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57
/// <reference types="@sveltejs/kit" /> /// <reference types="unplugin-icons/types/svelte" /> import type { User } from "$lib/types/User"; // See https://kit.svelte.dev/docs/types#app // for information about these interfaces declare global { namespace App { // interface Error {} interface Locals { sessionId:...
chat-ui/src/app.d.ts/0
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<script lang="ts"> import { navigating } from "$app/stores"; import { createEventDispatcher } from "svelte"; import { browser } from "$app/environment"; import { base } from "$app/paths"; import { page } from "$app/stores"; import CarbonClose from "~icons/carbon/close"; import CarbonTextAlignJustify from "~icon...
chat-ui/src/lib/components/MobileNav.svelte/0
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59
<script lang="ts"> import ToolLogo from "./ToolLogo.svelte"; import { base } from "$app/paths"; import { browser } from "$app/environment"; export let toolId: string; </script> <div class="relative flex items-center justify-center space-x-2 rounded border border-gray-300 bg-gray-200 px-2 py-1" > {#if browser} ...
chat-ui/src/lib/components/ToolBadge.svelte/0
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<script lang="ts"> export let classNames = ""; </script> <svg xmlns="http://www.w3.org/2000/svg" width="1em" height="1em" class={classNames} fill="none" viewBox="0 0 26 23" > <path fill="url(#a)" d="M.93 10.65A10.17 10.17 0 0 1 11.11.48h4.67a9.45 9.45 0 0 1 0 18.89H4.53L1.62 22.2a.38.38 0 0 1-.69-.28V10.65...
chat-ui/src/lib/components/icons/IconDazzled.svelte/0
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import type { Migration } from "."; import { collections } from "$lib/server/database"; import { ObjectId } from "mongodb"; import { logger } from "$lib/server/logger"; const addToolsToSettings: Migration = { _id: new ObjectId("5c9c4c4c4c4c4c4c4c4c4c4c"), name: "Add empty 'tools' record in settings", up: async () =...
chat-ui/src/lib/migrations/routines/03-add-tools-in-settings.ts/0
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import { z } from "zod"; import type { Endpoint } from "../endpoints"; import type { TextGenerationStreamOutput } from "@huggingface/inference"; import { createImageProcessorOptionsValidator } from "../images"; import { endpointMessagesToAnthropicMessages } from "./utils"; export const endpointAnthropicVertexParameter...
chat-ui/src/lib/server/endpoints/anthropic/endpointAnthropicVertex.ts/0
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import type { Message } from "$lib/types/Message"; import { format } from "date-fns"; import type { EndpointMessage } from "./endpoints"; import { downloadFile } from "../files/downloadFile"; import type { ObjectId } from "mongodb"; export async function preprocessMessages( messages: Message[], webSearch: Message["w...
chat-ui/src/lib/server/endpoints/preprocessMessages.ts/0
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import { ToolResultStatus, type ToolCall, type Tool, type ToolResult } from "$lib/types/Tool"; import { v4 as uuidV4 } from "uuid"; import { getCallMethod, toolFromConfigs, type BackendToolContext } from "../tools"; import { MessageToolUpdateType, MessageUpdateStatus, MessageUpdateType, type MessageUpdate, } from "...
chat-ui/src/lib/server/textGeneration/tools.ts/0
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import { sentences as splitBySentences } from "sbd"; import { MarkdownElementType, type MarkdownElement } from "../types"; export function chunkElements(elements: MarkdownElement[], maxLength: number): MarkdownElement[] { return elements.flatMap((elem) => { // Can't split headers because it would break the tree, an...
chat-ui/src/lib/server/websearch/markdown/utils/chunk.ts/0
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import { env } from "$env/dynamic/private"; import { isURL } from "$lib/utils/isUrl"; import type { WebSearchSource } from "$lib/types/WebSearch"; interface YouWebSearch { hits: YouSearchHit[]; latency: number; } interface YouSearchHit { url: string; title: string; description: string; snippets: string[]; } ex...
chat-ui/src/lib/server/websearch/search/endpoints/youApi.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 { User } from "./User"; import type { Timestamps } from "./Timestamps"; import type { BackendToolContext } from "$lib/server/tools"; import type { MessageUpdate } from "./MessageUpdate"; import { z } from "zod"; export const ToolColor = z.union([ z.literal("purple"...
chat-ui/src/lib/types/Tool.ts/0
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69
import { env as envPublic } from "$env/dynamic/public"; export const isHuggingChat = envPublic.PUBLIC_APP_ASSETS === "huggingchat";
chat-ui/src/lib/utils/isHuggingChat.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 addChildren( conv: Pick<Conversation, "messages" | "rootMessageId">, message: Omit<Message, "id">, parentId?: Message["id"] ): Message["id"] { // if this is the...
chat-ui/src/lib/utils/tree/addChildren.ts/0
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import { collections } from "$lib/server/database"; import { ObjectId } from "mongodb"; export async function GET({ params }) { const id = params.id; const assistantId = new ObjectId(id); const assistant = await collections.assistants.findOne({ _id: assistantId, }); if (assistant) { return Response.json(ass...
chat-ui/src/routes/api/assistant/[id]/+server.ts/0
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import type { RequestHandler } from "./$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 "$lib/types/Message"; import { models, validat...
chat-ui/src/routes/conversation/+server.ts/0
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<script lang="ts"> import type { PageData } from "./$types"; import { env as envPublic } from "$env/dynamic/public"; import { isHuggingChat } from "$lib/utils/isHuggingChat"; import { base } from "$app/paths"; import { page } from "$app/stores"; import CarbonHelpFilled from "~icons/carbon/help-filled"; import...
chat-ui/src/routes/models/+page.svelte/0
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import { collections } from "$lib/server/database"; import { error, type RequestHandler } from "@sveltejs/kit"; import { ObjectId } from "mongodb"; export const GET: RequestHandler = async ({ params }) => { const assistant = await collections.assistants.findOne({ _id: new ObjectId(params.assistantId), }); if (!a...
chat-ui/src/routes/settings/(nav)/assistants/[assistantId]/avatar.jpg/+server.ts/0
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import { base } from "$app/paths"; import { requiresUser } from "$lib/server/auth.js"; import { collections } from "$lib/server/database.js"; import { editableToolSchema } from "$lib/server/tools/index.js"; import { generateSearchTokens } from "$lib/utils/searchTokens.js"; import { error, fail, redirect } from "@svelte...
chat-ui/src/routes/tools/[toolId]/edit/+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": { "type": "line" }, "encoding": { "x": { "field": "<DVC_METRIC_X>", "type": "quantitative", ...
datasets/.dvc/plots/default.json/0
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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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# Create an audio dataset You can share a dataset with your team or with anyone in the community by creating a dataset repository on the Hugging Face Hub: ```py from datasets import load_dataset dataset = load_dataset("<username>/my_dataset") ``` There are several methods for creating and sharing an audio dataset: ...
datasets/docs/source/audio_dataset.mdx/0
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# Structure your repository To host and share your dataset, create a dataset repository on the Hugging Face Hub and upload your data files. This guide will show you how to structure your dataset repository when you upload it. A dataset with a supported structure and file format (`.txt`, `.csv`, `.parquet`, `.jsonl`, ...
datasets/docs/source/repository_structure.mdx/0
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# Lint as: python3 """HuggingFace/Datasets is an open library of datasets. Note: VERSION needs to be formatted following the MAJOR.MINOR.PATCH convention (we need to follow this convention to be able to retrieve versioned scripts) Simple check list for release from AllenNLP repo: https://github.com/allenai/all...
datasets/setup.py/0
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import contextlib import copy import fnmatch import json import math import posixpath import re from io import BytesIO from pathlib import Path from typing import Callable, Dict, List, Optional, Sequence, Tuple, Union import fsspec import numpy as np from fsspec.core import url_to_fs from huggingface_hub import ( ...
datasets/src/datasets/dataset_dict.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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import multiprocessing from typing import TYPE_CHECKING, Optional, Union from .. import Dataset, Features, config from ..formatting import query_table from ..packaged_modules.sql.sql import Sql from ..utils import tqdm as hf_tqdm from .abc import AbstractDatasetInputStream if TYPE_CHECKING: import sqlite3 i...
datasets/src/datasets/io/sql.py/0
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84
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 logger = datasets.utils.logging.get_logger(__name__) ...
datasets/src/datasets/packaged_modules/folder_based_builder/folder_based_builder.py/0
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import itertools from dataclasses import 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__) @dataclass class TextCo...
datasets/src/datasets/packaged_modules/text/text.py/0
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"""Contains utilities to flag a feature as "experimental" in datasets.""" import warnings from functools import wraps from typing import Callable def experimental(fn: Callable) -> Callable: """Decorator to flag a feature as experimental. An experimental feature trigger a warning when used as it might be sub...
datasets/src/datasets/utils/experimental.py/0
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from typing import List import numpy as np def _number_of_shards_in_gen_kwargs(gen_kwargs: dict) -> int: """Return the number of possible shards according to the input gen_kwargs""" # Having lists of different sizes makes sharding ambigious, raise an error in this case # until we decide how to define sha...
datasets/src/datasets/utils/sharding.py/0
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import pytest import datasets import datasets.config # Import fixture modules as plugins pytest_plugins = ["tests.fixtures.files", "tests.fixtures.hub", "tests.fixtures.fsspec"] def pytest_collection_modifyitems(config, items): # Mark tests as "unit" by default if not marked as "integration" (or already marked...
datasets/tests/conftest.py/0
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import posixpath from pathlib import Path from unittest.mock import patch import pytest from fsspec.implementations.local import AbstractFileSystem, LocalFileSystem, stringify_path from fsspec.registry import _registry as _fsspec_registry class MockFileSystem(AbstractFileSystem): protocol = "mock" def __ini...
datasets/tests/fixtures/fsspec.py/0
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90
import importlib import shutil import textwrap import pytest from datasets import ClassLabel, DownloadManager, Features, Value from datasets.builder import InvalidConfigName from datasets.data_files import DataFilesDict, DataFilesList, get_data_patterns from datasets.download.streaming_download_manager import Streami...
datasets/tests/packaged_modules/test_folder_based_builder.py/0
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import os import sys from pathlib import Path import pytest from datasets import Dataset, IterableDataset from datasets.distributed import split_dataset_by_node from .utils import execute_subprocess_async, get_torch_dist_unique_port, require_torch def test_split_dataset_by_node_map_style(): full_ds = Dataset.f...
datasets/tests/test_distributed.py/0
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import re import sys import tempfile import unittest from pathlib import Path import pytest import yaml from huggingface_hub import DatasetCard, DatasetCardData from datasets.config import METADATA_CONFIGS_FIELD from datasets.info import DatasetInfo from datasets.utils.metadata import MetadataConfigs def _dedent(st...
datasets/tests/test_metadata_util.py/0
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# [The Hugging Face Deep Reinforcement Learning Course 🤗 (v2.0)](https://huggingface.co/deep-rl-course/unit0/introduction) <img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit0/thumbnail.jpg" alt="Thumbnail"/> If you like the course, don't hesitate to **⭐ star this ...
deep-rl-class/README.md/0
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# The certification process The certification process is **completely free**: - To get a *certificate of completion*: you need **to pass 80% of the assignments**. - To get a *certificate of excellence*: you need **to pass 100% of the assignments**. There's **no deadlines, the course is self-paced**. <img src="http...
deep-rl-class/units/en/communication/certification.mdx/0
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# Type of tasks [[tasks]] A task is an **instance** of a Reinforcement Learning problem. We can have two types of tasks: **episodic** and **continuing**. ## Episodic task [[episodic-task]] In this case, we have a starting point and an ending point **(a terminal state). This creates an episode**: a list of States, Ac...
deep-rl-class/units/en/unit1/tasks.mdx/0
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# Two types of value-based methods [[two-types-value-based-methods]] In value-based methods, **we learn a value function** that **maps a state to the expected value of being at that state.** <img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit3/vbm-1.jpg" alt="Value ...
deep-rl-class/units/en/unit2/two-types-value-based-methods.mdx/0
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# Introduction [[introduction]] <img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit6/thumbnail.png" alt="thumbnail"/> In the last unit, we learned about Deep Q-Learning. In this value-based deep reinforcement learning algorithm, we **used a deep neural network to ...
deep-rl-class/units/en/unit4/introduction.mdx/0
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# Conclusion [[conclusion]] Congrats on finishing this unit and the tutorial. You've just trained your first virtual robots 🥳. **Take time to grasp the material before continuing**. You can also look at the additional reading materials we provided in the *additional reading* section. Finally, we would love **to hea...
deep-rl-class/units/en/unit6/conclusion.mdx/0
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# Conclusion [[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 improve the implementation to handle frames as input?**. See you on second part of this Unit 🔥 ## Keep Learning, Stay awesome 🤗
deep-rl-class/units/en/unit8/conclusion.mdx/0
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# Decision Transformers The Decision Transformer model was introduced by ["Decision Transformer: Reinforcement Learning via Sequence Modeling” by Chen L. et al](https://arxiv.org/abs/2106.01345). It abstracts Reinforcement Learning as a conditional-sequence modeling problem. The main idea is that instead of training ...
deep-rl-class/units/en/unitbonus3/decision-transformers.mdx/0
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# The environment <img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit13/il_tutorial_env.png" alt="IL tutorial environment"/> The tutorial environment features a robot that needs to: - Pull a lever to raise the stairs leading to the second room, - Navigate to the ke...
deep-rl-class/units/en/unitbonus5/the-environment.mdx/0
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import glob import sys import pandas as pd from huggingface_hub import hf_hub_download, upload_file from huggingface_hub.utils._errors import EntryNotFoundError sys.path.append(".") from utils import BASE_PATH, FINAL_CSV_FILE, GITHUB_SHA, REPO_ID, collate_csv # noqa: E402 def has_previous_benchmark() -> str: ...
diffusers/benchmarks/push_results.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...
diffusers/docs/source/en/advanced_inference/outpaint.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/models/autoencoder_tiny.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/amused.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_audio.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/stable_diffusion_xl.md/0
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diffusers/docs/source/en/api/schedulers/multistep_dpm_solver_inverse.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/conceptual/evaluation.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/torch2.0.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/custom_pipeline_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/using-diffusers/pag.md/0
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- sections: - local: index title: 🧨 Diffusers - local: quicktour title: クイックツアー - local: stable_diffusion title: 有効で効率の良い拡散モデル - local: installation title: インストール title: はじめに - sections: - local: tutorials/tutorial_overview title: 概要 - local: tutorials/autopipeline title: AutoPipe...
diffusers/docs/source/ja/_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/ko/optimization/coreml.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/dreambooth.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/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/pt/installation.md/0
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from typing import Optional, Tuple, Union import torch from einops import rearrange, reduce from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNet2DConditionModel from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput from diffusers.schedulers.scheduling_ddpm im...
diffusers/examples/community/bit_diffusion.py/0
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import inspect import time from pathlib import Path from typing import Callable, List, Optional, Union import numpy as np import torch from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer from diffusers.configuration_utils import FrozenDict from diffusers.models import AutoencoderKL, UNet2DCondit...
diffusers/examples/community/interpolate_stable_diffusion.py/0
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#!/usr/bin/env python3 import torch from diffusers import DiffusionPipeline class UnetSchedulerOneForwardPipeline(DiffusionPipeline): def __init__(self, unet, scheduler): super().__init__() self.register_modules(unet=unet, scheduler=scheduler) def __call__(self): image = torch.randn...
diffusers/examples/community/one_step_unet.py/0
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# Inspired by: https://github.com/haofanwang/ControlNet-for-Diffusers/ import inspect from typing import Any, Callable, Dict, List, Optional, Tuple, Union import numpy as np import PIL.Image import torch import torch.nn.functional as F from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer from di...
diffusers/examples/community/stable_diffusion_controlnet_inpaint.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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# Overview These examples show how to run [Diffuser](https://arxiv.org/abs/2205.09991) in Diffusers. There are two ways to use the script, `run_diffuser_locomotion.py`. The key option is a change of the variable `n_guide_steps`. When `n_guide_steps=0`, the trajectories are sampled from the diffusion model, but not fi...
diffusers/examples/reinforcement_learning/README.md/0
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# InstructPix2Pix text-to-edit-image fine-tuning This extended LoRA training script was authored by [Aiden-Frost](https://github.com/Aiden-Frost). This is an experimental LoRA extension of [this example](https://github.com/huggingface/diffusers/blob/main/examples/instruct_pix2pix/train_instruct_pix2pix.py). This script...
diffusers/examples/research_projects/instructpix2pix_lora/README.md/0
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import argparse import itertools import json import logging import math import uuid import warnings from os import environ, listdir, makedirs from os.path import basename, join from pathlib import Path from typing import List import datasets import numpy as np import torch import torch.nn.functional as F import torch....
diffusers/examples/research_projects/multi_subject_dreambooth/train_multi_subject_dreambooth.py/0
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""" Ported from Paella """ import torch from torch import nn from diffusers.configuration_utils import ConfigMixin, register_to_config from diffusers.models.modeling_utils import ModelMixin # Discriminator model ported from Paella https://github.com/dome272/Paella/blob/main/src_distributed/vqgan.py class Discrimina...
diffusers/examples/vqgan/discriminator.py/0
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import argparse import torch from safetensors.torch import load_file from diffusers import MotionAdapter def convert_motion_module(original_state_dict): converted_state_dict = {} for k, v in original_state_dict.items(): if "pos_encoder" in k: continue else: converted...
diffusers/scripts/convert_animatediff_motion_module_to_diffusers.py/0
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import argparse import torch from diffusers import HunyuanDiT2DControlNetModel def main(args): state_dict = torch.load(args.pt_checkpoint_path, map_location="cpu") if args.load_key != "none": try: state_dict = state_dict[args.load_key] except KeyError: raise KeyError...
diffusers/scripts/convert_hunyuandit_controlnet_to_diffusers.py/0
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import argparse import safetensors.torch from diffusers import AutoencoderTiny """ Example - From the diffusers root directory: Download the weights: ```sh $ wget -q https://huggingface.co/madebyollin/taesd/resolve/main/taesd_encoder.safetensors $ wget -q https://huggingface.co/madebyollin/taesd/resolve/main/taesd...
diffusers/scripts/convert_tiny_autoencoder_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/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/single_file_model.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/autoencoders/autoencoder_kl_cogvideox.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/embeddings_flax.py/0
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# Copyright 2024 HunyuanDiT Authors, Qixun Wang 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/models/transformers/hunyuan_transformer_2d.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_blocks_flax.py/0
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from typing import TYPE_CHECKING from ..utils import ( DIFFUSERS_SLOW_IMPORT, OptionalDependencyNotAvailable, _LazyModule, get_objects_from_module, is_flax_available, is_k_diffusion_available, is_librosa_available, is_note_seq_available, is_onnx_available, is_sentencepiece_avail...
diffusers/src/diffusers/pipelines/__init__.py/0
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# Copyright 2024 CVSSP, ByteDance 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/audioldm2/pipeline_audioldm2.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/dance_diffusion/pipeline_dance_diffusion.py/0
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# Deprecated Pipelines This folder contains pipelines that have very low usage as measured by model downloads, issues and PRs. While you can still use the pipelines just as before, we will stop testing the pipelines and will not accept any changes to existing files.
diffusers/src/diffusers/pipelines/deprecated/README.md/0
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from typing import TYPE_CHECKING from ....utils import DIFFUSERS_SLOW_IMPORT, _LazyModule _import_structure = {"pipeline_score_sde_ve": ["ScoreSdeVePipeline"]} if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT: from .pipeline_score_sde_ve import ScoreSdeVePipeline else: import sys sys.modules[__name__] = _Laz...
diffusers/src/diffusers/pipelines/deprecated/score_sde_ve/__init__.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 i...
diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/__init__.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/kandinsky2_2/pipeline_kandinsky2_2_img2img.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/camera.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_flax_stable_diffusion_img2img.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_img2img.py/0
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from typing import TYPE_CHECKING from ...utils import ( DIFFUSERS_SLOW_IMPORT, OptionalDependencyNotAvailable, _LazyModule, get_objects_from_module, is_k_diffusion_available, is_k_diffusion_version, is_torch_available, is_transformers_available, ) _dummy_objects = {} _import_structure...
diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/__init__.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/unclip/__init__.py/0
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149
# Copyright 2024 UC Berkeley 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/schedulers/scheduling_ddpm_flax.py/0
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150