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use candle::{DType, Device, Error as E, IndexOp, Module, Result, Tensor, D}; use candle_nn::{embedding, linear_b, rms_norm, Embedding, Linear, RmsNorm, VarBuilder}; // Equivalent to torch.repeat_interleave pub(crate) fn repeat_interleave(img: &Tensor, repeats: usize, dim: usize) -> Result<Tensor> { let img = img.u...
candle/candle-transformers/src/models/metavoice.rs/0
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39
use crate::quantized_nn::{layer_norm_no_bias, linear_no_bias, Embedding, Linear}; pub use crate::quantized_var_builder::VarBuilder; /// MPT model used by replit-code-v1_5-3b /// https://huggingface.co/replit/replit-code-v1_5-3b/blob/main/modeling_mpt.py use candle::{IndexOp, Module, Result, Tensor, D}; use candle_nn::L...
candle/candle-transformers/src/models/quantized_mpt.rs/0
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40
// Adapted from: // https://github.com/ChaoningZhang/MobileSAM/blob/master/mobile_sam/modeling/tiny_vit_sam.py use candle::{IndexOp, Result, Tensor, D}; use candle_nn::{Conv2dConfig, Module, VarBuilder}; const MBCONV_EXPAND_RATIO: usize = 4; const MLP_RATIO: usize = 4; const LOCAL_CONV_SIZE: usize = 3; const IMG_SIZE:...
candle/candle-transformers/src/models/segment_anything/tiny_vit.rs/0
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41
#![allow(unused)] use candle::{DType, Device, Module, Result, Tensor, D}; use candle_nn::{layer_norm, linear_b, LayerNorm, Linear, VarBuilder}; use std::sync::Arc; #[derive(Debug, Clone, serde::Deserialize)] pub struct Config { vocab_size: usize, hidden_size: usize, intermediate_size: usize, num_hidden...
candle/candle-transformers/src/models/starcoder2.rs/0
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use super::common::{AttnBlock, ResBlock, TimestepBlock}; use candle::{DType, Result, Tensor, D}; use candle_nn::VarBuilder; #[derive(Debug)] struct Block { res_block: ResBlock, ts_block: TimestepBlock, attn_block: AttnBlock, } #[derive(Debug)] pub struct WPrior { projection: candle_nn::Conv2d, con...
candle/candle-transformers/src/models/wuerstchen/prior.rs/0
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43
export async function getEmbeddings( worker, weightsURL, tokenizerURL, configURL, modelID, sentences, updateStatus = null ) { return new Promise((resolve, reject) => { worker.postMessage({ weightsURL, tokenizerURL, configURL, modelID, sentences, }); function mes...
candle/candle-wasm-examples/bert/utils.js/0
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44
fn main() { wasm_logger::init(wasm_logger::Config::new(log::Level::Trace)); console_error_panic_hook::set_once(); yew::Renderer::<candle_wasm_example_llama2::App>::new().render(); }
candle/candle-wasm-examples/llama2-c/src/bin/app.rs/0
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45
<html> <head> <meta content="text/html;charset=utf-8" http-equiv="Content-Type" /> <title>Candle Segment Anything Model (SAM) Rust/WASM</title> </head> <body></body> </html> <!DOCTYPE html> <html> <head> <meta charset="UTF-8" /> <meta name="viewport" content="width=device-width, initial-scale=1...
candle/candle-wasm-examples/segment-anything/lib-example.html/0
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<!DOCTYPE html> <html lang="en"> <head> <meta charset="utf-8" /> <title>Welcome to Candle!</title> <link data-trunk rel="copy-file" href="yolov8s.safetensors" /> <link data-trunk rel="copy-file" href="bike.jpeg" /> <link data-trunk rel="rust" href="Cargo.toml" data-bin="app" data-type="main" /> ...
candle/candle-wasm-examples/yolo/index.html/0
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47
 backend-test:J  xytest"Relu SingleReluZ x   b y   B
candle/test.onnx/0
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48
ENV_LOCAL_PATH=/app/.env.local if test -z "${DOTENV_LOCAL}" ; then if ! test -f "${ENV_LOCAL_PATH}" ; then echo "DOTENV_LOCAL was not found in the ENV variables and .env.local is not set using a bind volume. We are using the default .env config." fi; else echo "DOTENV_LOCAL was found in the ENV var...
chat-ui/entrypoint.sh/0
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49
<script lang="ts"> import { afterUpdate } from "svelte"; import CopyToClipBoardBtn from "./CopyToClipBoardBtn.svelte"; export let code = ""; export let lang = ""; $: highlightedCode = ""; afterUpdate(async () => { const { default: hljs } = await import("highlight.js"); const language = hljs.getLanguage(lan...
chat-ui/src/lib/components/CodeBlock.svelte/0
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50
<script lang="ts"> import { fade } from "svelte/transition"; import { onDestroy } from "svelte"; import IconChevron from "./icons/IconChevron.svelte"; export let scrollNode: HTMLElement; export { className as class }; let visible = false; let className = ""; let observer: ResizeObserver | null = null; $: if...
chat-ui/src/lib/components/ScrollToBottomBtn.svelte/0
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<script lang="ts"> export let classNames = ""; </script> <svg xmlns="http://www.w3.org/2000/svg" width="1em" height="1em" class={classNames} fill="none" viewBox="0 0 26 23" > <path fill="url(#a)" d="M.93 10.65A10.17 10.17 0 0 1 11.11.48h4.67a9.45 9.45 0 0 1 0 18.89H4.53L1.62 22.2a.38.38 0 0 1-.69-.28V10.65...
chat-ui/src/lib/components/icons/IconDazzled.svelte/0
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import { z } from "zod"; import type { EmbeddingEndpoint, Embedding } from "../embeddingEndpoints"; import { chunk } from "$lib/utils/chunk"; import { OPENAI_API_KEY } from "$env/static/private"; export const embeddingEndpointOpenAIParametersSchema = z.object({ weight: z.number().int().positive().default(1), model: ...
chat-ui/src/lib/server/embeddingEndpoints/openai/embeddingEndpoints.ts/0
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import { isURLLocal } from "./isURLLocal"; import { describe, expect, it } from "vitest"; describe("isURLLocal", async () => { it("should return true for localhost", async () => { expect(await isURLLocal(new URL("http://localhost"))).toBe(true); }); it("should return true for 127.0.0.1", async () => { expect(aw...
chat-ui/src/lib/server/isURLLocal.spec.ts/0
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import { writable } from "svelte/store"; export const isAborted = writable<boolean>(false);
chat-ui/src/lib/stores/isAborted.ts/0
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import type { ObjectId } from "mongodb"; import type { User } from "./User"; import type { Assistant } from "./Assistant"; import type { Timestamps } from "./Timestamps"; export interface Report extends Timestamps { _id: ObjectId; createdBy: User["_id"] | string; assistantId: Assistant["_id"]; reason?: string; }
chat-ui/src/lib/types/Report.ts/0
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56
const file2base64 = (file: File): Promise<string> => { return new Promise<string>((resolve, reject) => { const reader = new FileReader(); reader.readAsDataURL(file); reader.onload = () => { const dataUrl = reader.result as string; const base64 = dataUrl.split(",")[1]; resolve(base64); }; reader.oner...
chat-ui/src/lib/utils/file2base64.ts/0
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import type { Message } from "$lib/types/Message"; import type { LegacyParamatersTemplateInput } from "$lib/types/Template"; import Handlebars from "handlebars"; Handlebars.registerHelper("ifUser", function (this: Pick<Message, "from" | "content">, options) { if (this.from == "user") return options.fn(this); }); Han...
chat-ui/src/lib/utils/template.ts/0
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<script lang="ts"> import { goto } from "$app/navigation"; import { base } from "$app/paths"; import { PUBLIC_APP_NAME } from "$env/static/public"; import ChatWindow from "$lib/components/chat/ChatWindow.svelte"; import { ERROR_MESSAGES, error } from "$lib/stores/errors"; import { pendingMessage } from "$lib/stor...
chat-ui/src/routes/+page.svelte/0
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import { MESSAGES_BEFORE_LOGIN, ENABLE_ASSISTANTS_RAG } from "$env/static/private"; import { authCondition, requiresUser } from "$lib/server/auth"; import { collections } from "$lib/server/database"; import { models } from "$lib/server/models"; import { ERROR_MESSAGES } from "$lib/stores/errors"; import type { Message ...
chat-ui/src/routes/conversation/[id]/+server.ts/0
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<script lang="ts"> import { PUBLIC_APP_COLOR } from "$env/static/public"; import { isHuggingChat } from "$lib/utils/isHuggingChat"; export let name: string; export let logoUrl: string | undefined; import logo from "../../../../../static/huggingchat/logo.svg?raw"; </script> <div class=" flex h-[648px] w-full fle...
chat-ui/src/routes/models/[...model]/thumbnail.png/ModelThumbnail.svelte/0
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<script lang="ts"> import type { ActionData, PageData } from "./$types"; import AssistantSettings from "$lib/components/AssistantSettings.svelte"; export let data: PageData; export let form: ActionData; </script> <AssistantSettings bind:form models={data.models} />
chat-ui/src/routes/settings/(nav)/assistants/new/+page@settings.svelte/0
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{ "background_color": "#ffffff", "name": "HuggingChat", "short_name": "HuggingChat", "display": "standalone", "start_url": "/chat", "icons": [ { "src": "/chat/huggingchat/icon-128x128.png", "sizes": "128x128", "type": "image/png" }, { "src": "/chat/huggingchat/icon-256x256.png", "sizes": "256...
chat-ui/static/huggingchat/manifest.json/0
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import json import os import tempfile import datasets from datasets.arrow_writer import ArrowWriter from datasets.features import Array2D from utils import generate_examples, get_duration SHAPE_TEST_1 = (30, 487) SHAPE_TEST_2 = (36, 1024) SPEED_TEST_SHAPE = (100, 100) SPEED_TEST_N_EXAMPLES = 100 DEFAULT_FEATURES = ...
datasets/benchmarks/benchmark_array_xd.py/0
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64
- sections: - local: index title: 🤗 Datasets - local: quickstart title: Quickstart - local: installation title: Installation title: Get started - sections: - local: tutorial title: Overview - local: load_hub title: Load a dataset from the Hub - local: access title: Know your data...
datasets/docs/source/_toctree.yml/0
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65
# Create a dataset loading script <Tip> The dataset loading script is likely not needed if your dataset is in one of the following formats: CSV, JSON, JSON lines, text, images, audio or Parquet. With those formats, you should be able to load your dataset automatically with [`~datasets.load_dataset`], as long as your...
datasets/docs/source/dataset_script.mdx/0
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66
# Evaluate predictions <Tip warning={true}> Metrics is deprecated in 🤗 Datasets. To learn more about how to use metrics, take a look at the library 🤗 [Evaluate](https://huggingface.co/docs/evaluate/index)! In addition to metrics, you can find more tools for evaluating models and datasets. </Tip> 🤗 Datasets provi...
datasets/docs/source/metrics.mdx/0
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# Load tabular data A tabular dataset is a generic dataset used to describe any data stored in rows and columns, where the rows represent an example and the columns represent a feature (can be continuous or categorical). These datasets are commonly stored in CSV files, Pandas DataFrames, and in database tables. This g...
datasets/docs/source/tabular_load.mdx/0
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68
# 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/bleurt/bleurt.py/0
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69
# 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/cuad/cuad.py/0
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70
# Metric Card for Mahalanobis Distance ## Metric Description Mahalonobis distance is the distance between a point and a distribution (as opposed to the distance between two points), making it the multivariate equivalent of the Euclidean distance. It is often used in multivariate anomaly detection, classification on h...
datasets/metrics/mahalanobis/README.md/0
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# Metric Card for Precision ## Metric Description Precision is the fraction of correctly labeled positive examples out of all of the examples that were labeled as positive. It is computed via the equation: Precision = TP / (TP + FP) where TP is the True positives (i.e. the examples correctly labeled as positive) and...
datasets/metrics/precision/README.md/0
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# Metric Card for SQuAD ## Metric description This metric wraps the official scoring script for version 1 of the [Stanford Question Answering Dataset (SQuAD)](https://huggingface.co/datasets/squad). SQuAD is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles...
datasets/metrics/squad/README.md/0
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73
# 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/xnli/xnli.py/0
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import fnmatch import json import os import shutil import tempfile import xml.etree.ElementTree as ET from argparse import ArgumentParser from pathlib import Path from typing import Optional from datasets import config from datasets.commands import BaseDatasetsCLICommand from datasets.download.download_config import D...
datasets/src/datasets/commands/dummy_data.py/0
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75
# 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/features/features.py/0
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76
import itertools from dataclasses import dataclass from typing import Optional import pyarrow as pa import datasets from datasets.table import table_cast logger = datasets.utils.logging.get_logger(__name__) @dataclass class ArrowConfig(datasets.BuilderConfig): """BuilderConfig for Arrow.""" features: Opt...
datasets/src/datasets/packaged_modules/arrow/arrow.py/0
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import itertools import warnings from dataclasses import dataclass from typing import Optional import pandas as pd import pyarrow as pa import datasets from datasets.table import table_cast @dataclass class PandasConfig(datasets.BuilderConfig): """BuilderConfig for Pandas.""" features: Optional[datasets.Fe...
datasets/src/datasets/packaged_modules/pandas/pandas.py/0
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import importlib import inspect from functools import wraps from typing import TYPE_CHECKING, Optional from .download.download_config import DownloadConfig from .download.streaming_download_manager import ( xbasename, xdirname, xet_parse, xexists, xgetsize, xglob, xgzip_open, xisdir, ...
datasets/src/datasets/streaming.py/0
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import enum import inspect import warnings from functools import wraps from typing import Callable, Optional from .logging import get_logger _emitted_deprecation_warnings = set() logger = get_logger(__name__) def deprecated(help_message: Optional[str] = None): """Decorator to mark a class or a function as depr...
datasets/src/datasets/utils/deprecation_utils.py/0
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{ "code": "Programming language (C++, Java, Javascript, Python, etc.)", "aa": "Afar", "aaa": "Ghotuo", "aab": "Alumu-Tesu", "aac": "Ari", "aad": "Amal", "aae": "Arbëreshë Albanian", "aaf": "Aranadan", "aag": "Ambrak", "aah": "Abu' Arapesh", "aai": "Arifama-Miniafia", "aak...
datasets/src/datasets/utils/resources/languages.json/0
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81
import os import tarfile import pyarrow as pa import pytest from datasets import Dataset, concatenate_datasets, load_dataset from datasets.features import Audio, Features, Sequence, Value from ..utils import ( require_sndfile, ) @pytest.fixture() def tar_wav_path(shared_datadir, tmp_path_factory): audio_pa...
datasets/tests/features/test_audio.py/0
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82
import os import tempfile from unittest import TestCase import numpy as np import pandas as pd import pytest from datasets import load_from_disk from datasets.arrow_dataset import Dataset from datasets.dataset_dict import DatasetDict, IterableDatasetDict from datasets.features import ClassLabel, Features, Sequence, V...
datasets/tests/test_dataset_dict.py/0
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import pickle from copy import deepcopy from itertools import chain, islice import numpy as np import pandas as pd import pyarrow as pa import pyarrow.compute as pc import pytest from datasets import Dataset, load_dataset from datasets.combine import concatenate_datasets, interleave_datasets from datasets.features im...
datasets/tests/test_iterable_dataset.py/0
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import unittest from unittest.mock import patch import pytest from pytest import CaptureFixture from datasets.utils import ( are_progress_bars_disabled, disable_progress_bars, enable_progress_bars, tqdm, ) class TestTqdmUtils(unittest.TestCase): @pytest.fixture(autouse=True) def capsys(self,...
datasets/tests/test_tqdm.py/0
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<jupyter_start><jupyter_text>Unit 4: Code your first Deep Reinforcement Learning Algorithm with PyTorch: Reinforce. And test its robustness 💪In this notebook, you'll code your first Deep Reinforcement Learning algorithm from scratch: Reinforce (also called Monte Carlo Policy Gradient).Reinforce is a *Policy-based meth...
deep-rl-class/notebooks/unit4/unit4.ipynb/0
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# The Exploration/Exploitation trade-off [[exp-exp-tradeoff]] Finally, before looking at the different methods to solve Reinforcement Learning problems, we must cover one more very important topic: *the exploration/exploitation trade-off.* - *Exploration* is exploring the environment by trying random actions in order...
deep-rl-class/units/en/unit1/exp-exp-tradeoff.mdx/0
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# Monte Carlo vs Temporal Difference Learning [[mc-vs-td]] The last thing we need to discuss before diving into Q-Learning is the two learning strategies. Remember that an RL agent **learns by interacting with its environment.** The idea is that **given the experience and the received reward, the agent will update it...
deep-rl-class/units/en/unit2/mc-vs-td.mdx/0
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# Deep Q-Learning [[deep-q-learning]] <img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit4/thumbnail.jpg" alt="Unit 3 thumbnail" width="100%"> In the last unit, we learned our first reinforcement learning algorithm: Q-Learning, **implemented it from scratch**, and...
deep-rl-class/units/en/unit3/introduction.mdx/0
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# How do Unity ML-Agents work? [[how-mlagents-works]] Before training our agent, we need to understand **what ML-Agents is and how it works**. ## What is Unity ML-Agents? [[what-is-mlagents]] [Unity ML-Agents](https://github.com/Unity-Technologies/ml-agents) is a toolkit for the game engine Unity that **allows us to...
deep-rl-class/units/en/unit5/how-mlagents-works.mdx/0
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# Introduction [[introduction]] <img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit0/thumbnail.png" alt="Thumbnail"/> Since the beginning of this course, we learned to train agents in a *single-agent system* where our agent was alone in its environment: it was **no...
deep-rl-class/units/en/unit7/introduction.mdx/0
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# Introduction [[introduction]] In this bonus unit, we'll reinforce what we learned in the first unit by teaching Huggy the Dog to fetch the stick and then [play with him directly in your browser](https://huggingface.co/spaces/ThomasSimonini/Huggy) 🐶 <img src="https://huggingface.co/datasets/huggingface-deep-rl-cour...
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# Brief introduction to RL documentation In this advanced topic, we address the question: **how should we monitor and keep track of powerful reinforcement learning agents that we are training in the real world and interfacing with humans?** As machine learning systems have increasingly impacted modern life, the **cal...
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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 applicable law o...
diffusers/README.md/0
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<!--Copyright 2024 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed...
diffusers/docs/source/en/api/loaders/peft.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/paint_by_example.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/schedulers/lcm.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/ethical_guidelines.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/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/en/training/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/controlling_generation.md/0
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<!--Copyright 2024 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed...
diffusers/docs/source/en/using-diffusers/loading_adapters.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/textual_inversion_inference.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/installation.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/ko/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/ko/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/ko/using-diffusers/write_own_pipeline.md/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/examples/amused/train_amused.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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# 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/pipeline_animatediff_controlnet.py/0
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#!/usr/bin/env python # coding=utf-8 # Copyright 2024 Custom Diffusion authors and the HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http...
diffusers/examples/custom_diffusion/train_custom_diffusion.py/0
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# InstructPix2Pix training example [InstructPix2Pix](https://arxiv.org/abs/2211.09800) is a method to fine-tune text-conditioned diffusion models such that they can follow an edit instruction for an input image. Models fine-tuned using this method take the following as inputs: <p align="center"> <img src="https:/...
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import torch from diffusers import StableDiffusionPipeline model_id = "path-to-your-trained-model" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda") prompt = "A photo of sks dog in a bucket" image = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0] imag...
diffusers/examples/research_projects/colossalai/inference.py/0
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#!/usr/bin/env python # coding=utf-8 # Copyright 2024 bram-w, 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/lic...
diffusers/examples/research_projects/diffusion_dpo/train_diffusion_dpo.py/0
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import argparse import itertools import math import os import random from pathlib import Path from typing import Iterable import numpy as np import PIL import torch import torch.nn.functional as F import torch.utils.checkpoint from accelerate import Accelerator from accelerate.utils import ProjectConfiguration, set_se...
diffusers/examples/research_projects/intel_opts/textual_inversion_dfq/textual_inversion.py/0
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## Diffusers examples with ONNXRuntime optimizations **This research project is not actively maintained by the diffusers team. For any questions or comments, please contact Prathik Rao (prathikr), Sunghoon Choi (hanbitmyths), Ashwini Khade (askhade), or Peng Wang (pengwa) on github with any questions.** This aims to ...
diffusers/examples/research_projects/onnxruntime/README.md/0
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import os from typing import List import faiss import numpy as np import torch from datasets import Dataset, load_dataset from PIL import Image from transformers import CLIPFeatureExtractor, CLIPModel, PretrainedConfig from diffusers import logging logger = logging.get_logger(__name__) # pylint: disable=invalid-na...
diffusers/examples/research_projects/rdm/retriever.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/textual_inversion/textual_inversion.py/0
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import argparse import torch import yaml from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel def convert_ldm_original(checkpoint_path, config_path, output_path): config = yaml.safe_load(config_path) state_dict = torch.load(checkpoint_path, map_location="cpu")["model"] keys = list(sta...
diffusers/scripts/conversion_ldm_uncond.py/0
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import argparse import inspect import os import numpy as np import torch import yaml from torch.nn import functional as F from transformers import CLIPConfig, CLIPImageProcessor, CLIPVisionModelWithProjection, T5EncoderModel, T5Tokenizer from diffusers import DDPMScheduler, IFPipeline, IFSuperResolutionPipeline, UNet...
diffusers/scripts/convert_if.py/0
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# coding=utf-8 # Copyright 2024 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable...
diffusers/scripts/convert_original_t2i_adapter.py/0
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# Run inside root directory of official source code: https://github.com/dome272/wuerstchen/ import os import torch from transformers import AutoTokenizer, CLIPTextModel from vqgan import VQModel from diffusers import ( DDPMWuerstchenScheduler, WuerstchenCombinedPipeline, WuerstchenDecoderPipeline, Wue...
diffusers/scripts/convert_wuerstchen.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/experimental/rl/value_guided_sampling.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/activations.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.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/transformers/dual_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_condition.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/amused/__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/blip_diffusion/modeling_blip2.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/kandinsky/pipeline_kandinsky_img2img.py/0
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import inspect from typing import Callable, Dict, List, Optional, Union import numpy as np import PIL import PIL.Image import torch from transformers import T5EncoderModel, T5Tokenizer from ...loaders import LoraLoaderMixin from ...models import Kandinsky3UNet, VQModel from ...schedulers import DDPMScheduler from ......
diffusers/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3_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/paint_by_example/pipeline_paint_by_example.py/0
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from typing import TYPE_CHECKING from ...utils import ( DIFFUSERS_SLOW_IMPORT, OptionalDependencyNotAvailable, _LazyModule, get_objects_from_module, is_torch_available, is_transformers_available, ) _dummy_objects = {} _import_structure = {} try: if not (is_transformers_available() and is...
diffusers/src/diffusers/pipelines/stable_cascade/__init__.py/0
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# Copyright 2024 DiffEdit Authors and Pix2Pix Zero Authors and The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/license...
diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py/0
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