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#![allow(unused)] use candle::{DType, IndexOp, Layout, Module, Result, Shape, Tensor, D}; use candle_nn::{conv1d, Conv1d, Conv1dConfig, ConvTranspose1d, VarBuilder}; // Encodec Model // https://github.com/huggingface/transformers/blob/main/src/transformers/models/encodec/modeling_encodec.py #[derive(Debug, Copy, Clon...
candle/candle-transformers/src/models/encodec.rs/0
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use candle::DType; use serde::Deserialize; pub const DTYPE: DType = DType::F32; #[derive(Debug, Clone, Copy, PartialEq, Eq, Deserialize)] #[serde(rename_all = "lowercase")] pub enum PositionEmbeddingType { Absolute, Alibi, } // https://github.com/huggingface/transformers/blob/main/src/transformers/models/per...
candle/candle-transformers/src/models/persimmon.rs/0
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//! ResNet implementation. //! //! See "Deep Residual Learning for Image Recognition" He et al. 2015 //! <https://arxiv.org/abs/1512.03385> use candle::{Result, D}; use candle_nn::{batch_norm, Conv2d, Func, VarBuilder}; fn conv2d( c_in: usize, c_out: usize, ksize: usize, padding: usize, stride: usi...
candle/candle-transformers/src/models/resnet.rs/0
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//! Ancestral sampling with Euler method steps. //! //! Reference implementation in Rust: //! //! https://github.com/pykeio/diffusers/blob/250b9ad1898af41e76a74c0d8d4292652823338a/src/schedulers/euler_ancestral_discrete.rs //! //! Based on the original [`k-diffusion` implementation by Katherine Crowson][kd]. /// /// [k...
candle/candle-transformers/src/models/stable_diffusion/euler_ancestral_discrete.rs/0
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use super::Config; use crate::models::with_tracing::{linear, linear_no_bias, Linear}; use candle::{Device, IndexOp, Result, Tensor, D}; use candle_nn::{embedding, Conv1d, Conv1dConfig, Embedding, LayerNorm, Module, VarBuilder}; fn conv1d( in_channels: usize, out_channels: usize, kernel_size: usize, con...
candle/candle-transformers/src/models/whisper/model.rs/0
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use candle::{Result, Tensor}; pub fn apply_repeat_penalty(logits: &Tensor, penalty: f32, context: &[u32]) -> Result<Tensor> { let device = logits.device(); let mut logits = logits.to_vec1::<f32>()?; let context: std::collections::HashSet<_> = context.iter().collect(); for (token_id, logit) in logits.it...
candle/candle-transformers/src/utils.rs/0
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45
//load Candle Bert Module wasm module let init, ModelEncoder; async function fetchArrayBuffer(url) { const cacheName = "t5-candle-cache"; const cache = await caches.open(cacheName); const cachedResponse = await cache.match(url); if (cachedResponse) { const data = await cachedResponse.arrayBuffer(); ret...
candle/candle-wasm-examples/t5/T5ModelEncoderWorker.js/0
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use candle_wasm_example_whisper::worker::{Decoder as D, ModelData}; use wasm_bindgen::prelude::*; #[wasm_bindgen] pub struct Decoder { decoder: D, } #[wasm_bindgen] impl Decoder { #[wasm_bindgen(constructor)] #[allow(clippy::too_many_arguments)] pub fn new( weights: Vec<u8>, tokenizer:...
candle/candle-wasm-examples/whisper/src/bin/m.rs/0
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mod app; pub mod coco_classes; pub mod model; pub mod worker; pub use app::App; pub use worker::Worker;
candle/candle-wasm-examples/yolo/src/lib.rs/0
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48
declare module "*.ttf" { const value: ArrayBuffer; export default value; }
chat-ui/src/ambient.d.ts/0
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<script lang="ts"> import { createEventDispatcher, onDestroy, onMount } from "svelte"; import { cubicOut } from "svelte/easing"; import { fade } from "svelte/transition"; import Portal from "./Portal.svelte"; import { browser } from "$app/environment"; export let width = "max-w-sm"; let backdropEl: HTMLDivElem...
chat-ui/src/lib/components/Modal.svelte/0
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50
<script lang="ts"> import { webSearchParameters } from "$lib/stores/webSearchParameters"; import CarbonInformation from "~icons/carbon/information"; import Switch from "./Switch.svelte"; const toggle = () => ($webSearchParameters.useSearch = !$webSearchParameters.useSearch); </script> <div class="flex h-8 cursor...
chat-ui/src/lib/components/WebSearchToggle.svelte/0
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import { collections } from "$lib/server/database"; export async function acquireLock(key = "migrations") { try { const insert = await collections.semaphores.insertOne({ key, createdAt: new Date(), updatedAt: new Date(), }); return !!insert.acknowledged; // true if the document was inserted } catch (...
chat-ui/src/lib/migrations/lock.ts/0
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52
import { HF_ACCESS_TOKEN, HF_TOKEN } from "$env/static/private"; import { buildPrompt } from "$lib/buildPrompt"; import type { TextGenerationStreamOutput } from "@huggingface/inference"; import type { Endpoint } from "../endpoints"; import { z } from "zod"; export const endpointLlamacppParametersSchema = z.object({ w...
chat-ui/src/lib/server/endpoints/llamacpp/endpointLlamacpp.ts/0
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import type { Message } from "$lib/types/Message"; import { format } from "date-fns"; import { generateFromDefaultEndpoint } from "../generateFromDefaultEndpoint"; export async function generateQuery(messages: Message[]) { const currentDate = format(new Date(), "MMMM d, yyyy"); const userMessages = messages.filter((...
chat-ui/src/lib/server/websearch/generateQuery.ts/0
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import type { ObjectId } from "mongodb"; import type { User } from "./User"; import type { Timestamps } from "./Timestamps"; export interface Assistant extends Timestamps { _id: ObjectId; createdById: User["_id"] | string; // user id or session createdByName?: User["username"]; avatar?: string; name: string; des...
chat-ui/src/lib/types/Assistant.ts/0
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/* eslint-disable no-shadow */ export enum UrlDependency { ConversationList = "conversation:list", Conversation = "conversation", }
chat-ui/src/lib/types/UrlDependency.ts/0
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import * as fs from "fs"; import { setGlobalDispatcher, Agent } from "undici"; /** * Load client certificates for mutual TLS authentication. This function must be called before any HTTP requests are made. * This is a global setting that affects all HTTP requests made by the application using the native fetch API. *...
chat-ui/src/lib/utils/loadClientCerts.ts/0
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import type { Conversation } from "$lib/types/Conversation"; import type { Message } from "$lib/types/Message"; export function buildSubtree( conv: Pick<Conversation, "messages" | "rootMessageId">, id: Message["id"] ): Message[] { if (!conv.rootMessageId) { if (conv.messages.length === 0) return []; // legacy c...
chat-ui/src/lib/utils/tree/buildSubtree.ts/0
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import { base } from "$app/paths"; import { collections } from "$lib/server/database.js"; import { redirect } from "@sveltejs/kit"; import { ObjectId } from "mongodb"; export const load = async ({ params }) => { try { const assistant = await collections.assistants.findOne({ _id: new ObjectId(params.assistantId),...
chat-ui/src/routes/assistant/[assistantId]/+page.server.ts/0
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import { redirect } from "@sveltejs/kit"; import { getOIDCAuthorizationUrl } from "$lib/server/auth"; import { base } from "$app/paths"; import { ALTERNATIVE_REDIRECT_URLS } from "$env/static/private"; export const actions = { async default({ url, locals, request }) { const referer = request.headers.get("referer");...
chat-ui/src/routes/login/+page.server.ts/0
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import { base } from "$app/paths"; import { redirect } from "@sveltejs/kit"; export async function load({ parent, params }) { const data = await parent(); const model = data.models.find((m: { id: string }) => m.id === params.model); if (!model || model.unlisted) { throw redirect(302, `${base}/settings`); } r...
chat-ui/src/routes/settings/(nav)/[...model]/+page.ts/0
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# This is the list of HuggingFace Datasets authors for copyright purposes. # # This does not necessarily list everyone who has contributed code, since in # some cases, their employer may be the copyright holder. To see the full list # of contributors, see the revision history in source control. Google Inc. HuggingFac...
datasets/AUTHORS/0
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# Create an image dataset There are two methods for creating and sharing an image dataset. This guide will show you how to: * Create an image dataset with `ImageFolder` and some metadata. This is a no-code solution for quickly creating an image dataset with several thousand images. * Create an image dataset by writin...
datasets/docs/source/image_dataset.mdx/0
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# Table Classes Each `Dataset` object is backed by a PyArrow Table. A Table can be loaded from either the disk (memory mapped) or in memory. Several Table types are available, and they all inherit from [`table.Table`]. ## Table [[autodoc]] datasets.table.Table - validate - equals - to_batches - to_py...
datasets/docs/source/package_reference/table_classes.mdx/0
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# Use with Spark This document is a quick introduction to using 🤗 Datasets with Spark, with a particular focus on how to load a Spark DataFrame into a [`Dataset`] object. From there, you have fast access to any element and you can use it as a data loader to train models. ## Load from Spark A [`Dataset`] object is ...
datasets/docs/source/use_with_spark.mdx/0
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65
# 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/code_eval.py/0
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66
# Copyright 2022 The HuggingFace Datasets Authors and the current metric 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-2.0...
datasets/metrics/frugalscore/frugalscore.py/0
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67
# Copyright 2022 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/mean_iou/mean_iou.py/0
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# Copyright 2020 The HuggingFace Datasets Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or ...
datasets/metrics/rouge/rouge.py/0
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69
""" Official evaluation script for ReCoRD v1.0. (Some functions are adopted from the SQuAD evaluation script.) """ import argparse import json import re import string import sys from collections import Counter def normalize_answer(s): """Lower text and remove punctuation, articles and extra whitespace.""" d...
datasets/metrics/super_glue/record_evaluation.py/0
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# ruff: noqa # 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/LICE...
datasets/src/datasets/__init__.py/0
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71
from typing import TypeVar from .arrow_dataset import Dataset, _split_by_node_map_style_dataset from .iterable_dataset import IterableDataset, _split_by_node_iterable_dataset DatasetType = TypeVar("DatasetType", Dataset, IterableDataset) def split_dataset_by_node(dataset: DatasetType, rank: int, world_size: int) -...
datasets/src/datasets/distributed.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/formatting/__init__.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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from dataclasses import dataclass, field from typing import ClassVar, Dict from ..features import Features, Value from .base import TaskTemplate @dataclass(frozen=True) class LanguageModeling(TaskTemplate): task: str = field(default="language-modeling", metadata={"include_in_asdict_even_if_is_default": True}) ...
datasets/src/datasets/tasks/language_modeling.py/0
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import time from functools import partial from huggingface_hub import HfApi, hf_hub_url from huggingface_hub.hf_api import RepoFile from packaging import version from requests import ConnectionError, HTTPError from .. import config from . import logging logger = logging.get_logger(__name__) # Retry `preupload_lfs_...
datasets/src/datasets/utils/hub.py/0
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"""Utility helpers to handle progress bars in `datasets`. Example: 1. Use `datasets.utils.tqdm` as you would use `tqdm.tqdm` or `tqdm.auto.tqdm`. 2. To disable progress bars, either use `disable_progress_bars()` helper or set the environment variable `HF_DATASETS_DISABLE_PROGRESS_BARS` to 1. 3. To r...
datasets/src/datasets/utils/tqdm.py/0
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from unittest.mock import patch import pyspark from datasets.packaged_modules.spark.spark import ( Spark, SparkExamplesIterable, _generate_iterable_examples, ) from ..utils import ( require_dill_gt_0_3_2, require_not_windows, ) def _get_expected_row_ids_and_row_dicts_for_partition_order(df, par...
datasets/tests/packaged_modules/test_spark.py/0
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import os from datasets.utils._filelock import FileLock def test_long_path(tmpdir): filename = "a" * 1000 + ".lock" lock1 = FileLock(str(tmpdir / filename)) assert lock1.lock_file.endswith(".lock") assert not lock1.lock_file.endswith(filename) assert len(os.path.basename(lock1.lock_file)) <= 255
datasets/tests/test_filelock.py/0
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from datasets.utils.patching import _PatchedModuleObj, patch_submodule from . import _test_patching def test_patch_submodule(): import os as original_os from os import path as original_path from os import rename as original_rename from os.path import dirname as original_dirname from os.path impor...
datasets/tests/test_patching.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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import argparse import sys sys.path.append(".") from base_classes import T2IAdapterBenchmark, T2IAdapterSDXLBenchmark # noqa: E402 if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( "--ckpt", type=str, default="TencentARC/t2iadapter_canny_sd14v1", ...
diffusers/benchmarks/benchmark_t2i_adapter.py/0
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# docstyle-ignore INSTALL_CONTENT = """ # Diffusers installation ! pip install diffusers transformers datasets accelerate # To install from source instead of the last release, comment the command above and uncomment the following one. # ! pip install git+https://github.com/huggingface/diffusers.git """ notebook_first_...
diffusers/docs/source/_config.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/api/models/autoencoderkl.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 to...
diffusers/docs/source/en/api/pipelines/shap_e.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/coreml.md/0
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# Create a dataset for training There are many datasets on the [Hub](https://huggingface.co/datasets?task_categories=task_categories:text-to-image&sort=downloads) to train a model on, but if you can't find one you're interested in or want to use your own, you can create a dataset with the 🤗 [Datasets](hf.co/docs/data...
diffusers/docs/source/en/training/create_dataset.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/autopipeline.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/freeu.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/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/ja/installation.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/opt_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 agree...
diffusers/docs/source/ko/training/text_inversion.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/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/zh/installation.md/0
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# Copyright 2024 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicabl...
diffusers/examples/community/composable_stable_diffusion.py/0
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import inspect import re from typing import Callable, List, Optional, Union import numpy as np import PIL.Image import torch from packaging import version from transformers import CLIPImageProcessor, CLIPTokenizer import diffusers from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, SchedulerMixin fro...
diffusers/examples/community/lpw_stable_diffusion_onnx.py/0
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# Copyright 2024 The Intel Labs Team 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 ...
diffusers/examples/community/pipeline_stable_diffusion_upscale_ldm3d.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 from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer from diffusers import AutoencoderKL, Co...
diffusers/examples/community/stable_diffusion_controlnet_img2img.py/0
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import inspect from typing import List, Optional, Tuple, Union import torch from torch.nn import functional as F from transformers import CLIPTextModelWithProjection, CLIPTokenizer from transformers.models.clip.modeling_clip import CLIPTextModelOutput from diffusers import ( DiffusionPipeline, ImagePipelineOu...
diffusers/examples/community/unclip_text_interpolation.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.py/0
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# Multi Subject Dreambooth for Inpainting Models Please note that this project is not actively maintained. However, you can open an issue and tag @gzguevara. [DreamBooth](https://arxiv.org/abs/2208.12242) is a method to personalize text2image models like stable diffusion given just a few(3~5) images of a subject. Thi...
diffusers/examples/research_projects/multi_subject_dreambooth_inpainting/README.md/0
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## Training examples Creating a training image set is [described in a different document](https://huggingface.co/docs/datasets/image_process#image-datasets). ### Installing the dependencies Before running the scripts, make sure to install the library's training dependencies: **Important** To make sure you can succ...
diffusers/examples/research_projects/onnxruntime/unconditional_image_generation/README.md/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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#!/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/text_to_image/train_text_to_image_lora.py/0
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# Würstchen text-to-image fine-tuning ## Running locally with PyTorch Before running the scripts, make sure to install the library's training dependencies: **Important** To make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the i...
diffusers/examples/wuerstchen/text_to_image/README.md/0
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import argparse import os import torch from diffusers import ( CMStochasticIterativeScheduler, ConsistencyModelPipeline, UNet2DModel, ) TEST_UNET_CONFIG = { "sample_size": 32, "in_channels": 3, "out_channels": 3, "layers_per_block": 2, "num_class_embeds": 1000, "block_out_channel...
diffusers/scripts/convert_consistency_to_diffusers.py/0
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import json import os import torch from diffusers import UNet1DModel os.makedirs("hub/hopper-medium-v2/unet/hor32", exist_ok=True) os.makedirs("hub/hopper-medium-v2/unet/hor128", exist_ok=True) os.makedirs("hub/hopper-medium-v2/value_function", exist_ok=True) def unet(hor): if hor == 128: down_block_...
diffusers/scripts/convert_models_diffuser_to_diffusers.py/0
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import argparse import sys import tensorrt as trt def convert_models(onnx_path: str, num_controlnet: int, output_path: str, fp16: bool = False, sd_xl: bool = False): """ Function to convert models in stable diffusion controlnet pipeline into TensorRT format Example: python convert_stable_diffusion_c...
diffusers/scripts/convert_stable_diffusion_controlnet_to_tensorrt.py/0
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#!/usr/bin/env python # 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...
diffusers/src/diffusers/commands/diffusers_cli.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/autoencoders/autoencoder_kl.py/0
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# coding=utf-8 # Copyright 2024 The HuggingFace Inc. team. # Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.a...
diffusers/src/diffusers/models/modeling_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/unet_2d.py/0
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from dataclasses import dataclass from typing import Dict, Optional, Tuple, Union import torch import torch.nn as nn from ...configuration_utils import ConfigMixin, register_to_config from ...loaders import UNet2DConditionLoadersMixin from ...utils import BaseOutput, logging from ..attention_processor import CROSS_AT...
diffusers/src/diffusers/models/unets/unet_spatio_temporal_condition.py/0
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from typing import TYPE_CHECKING from ....utils import DIFFUSERS_SLOW_IMPORT, _LazyModule _import_structure = { "mel": ["Mel"], "pipeline_audio_diffusion": ["AudioDiffusionPipeline"], } if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT: from .mel import Mel from .pipeline_audio_diffusion import AudioDiffusi...
diffusers/src/diffusers/pipelines/deprecated/audio_diffusion/__init__.py/0
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from typing import TYPE_CHECKING from ....utils import ( DIFFUSERS_SLOW_IMPORT, OptionalDependencyNotAvailable, _LazyModule, get_objects_from_module, is_torch_available, is_transformers_available, ) _dummy_objects = {} _import_structure = {} try: if not (is_transformers_available() and i...
diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/__init__.py/0
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# Copyright 2024 Microsoft 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 require...
diffusers/src/diffusers/pipelines/deprecated/vq_diffusion/pipeline_vq_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/kandinsky2_2/pipeline_kandinsky2_2_controlnet.py/0
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# Copyright 2024 PixArt-Alpha Authors and The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unl...
diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.py/0
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# Copyright 2024 Harutatsu Akiyama 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/stable_diffusion_xl/pipeline_stable_diffusion_xl_instruct_pix2pix.py/0
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# Copyright 2024 Kakao Brain and The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless requi...
diffusers/src/diffusers/pipelines/unclip/text_proj.py/0
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from typing import TYPE_CHECKING from ...utils import ( DIFFUSERS_SLOW_IMPORT, OptionalDependencyNotAvailable, _LazyModule, get_objects_from_module, is_torch_available, is_transformers_available, ) _dummy_objects = {} _import_structure = {} try: if not (is_transformers_available() and is...
diffusers/src/diffusers/schedulers/deprecated/__init__.py/0
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# Copyright 2024 TSAIL 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 requir...
diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_flax.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_lms_discrete_flax.py/0
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# Copyright 2024 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by appl...
diffusers/src/diffusers/utils/constants.py/0
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import io import random import struct import tempfile from contextlib import contextmanager from typing import List, Union import numpy as np import PIL.Image import PIL.ImageOps from .import_utils import ( BACKENDS_MAPPING, is_opencv_available, ) from .logging import get_logger global_rng = random.Random()...
diffusers/src/diffusers/utils/export_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/transformers/test_models_prior.py/0
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# coding=utf-8 # Copyright 2024 HuggingFace Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or ag...
diffusers/tests/others/test_ema.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/dance_diffusion/test_dance_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/ip_adapters/test_ip_adapter_stable_diffusion.py/0
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