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/* Equivalent PyTorch code. import torch from torch.nn.functional import group_norm t = torch.tensor( [[[-0.3034, 0.2726, -0.9659], [-1.1845, -1.3236, 0.0172], [ 1.9507, 1.2554, -0.8625], [ 1.0682, 0.3604, 0.3985], [-0.4957, -0.4461, -0.9721], [ 1.5157, -0....
candle/candle-nn/tests/group_norm.rs/0
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## Installation From the `candle-pyo3` directory, enable a virtual env where you will want the candle package to be installed then run. ```bash maturin develop -r python test.py ``` ## Generating Stub Files for Type Hinting For type hinting support, the `candle-pyo3` package requires `*.pyi` files. You can automa...
candle/candle-pyo3/README.md/0
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from .module import Module from typing import Optional, Tuple, Any from candle import Tensor import candle class Embedding(Module): """A simple lookup table that stores embeddings of a fixed dictionary and size. This module is often used to store word embeddings and retrieve them using indices. The input...
candle/candle-pyo3/py_src/candle/nn/sparse.py/0
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use candle::Result; use candle_nn::{batch_norm, Conv2dConfig, Module, VarBuilder}; #[allow(clippy::many_single_char_names)] fn conv2d_same( i: usize, o: usize, k: usize, c: Conv2dConfig, vb: VarBuilder, ) -> Result<impl Module> { let conv2d = candle_nn::conv2d(i, o, k, c, vb)?; let s = c.st...
candle/candle-transformers/src/models/convmixer.rs/0
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use crate::{ quantized_nn::{layer_norm, linear_no_bias as linear, Embedding, Linear}, quantized_var_builder::VarBuilder, }; use candle::{IndexOp, Result, Tensor}; use candle_nn::{GroupNorm, LayerNorm, Module}; pub use crate::models::rwkv_v5::{Config, State, Tokenizer}; #[derive(Debug, Clone)] struct SelfAtten...
candle/candle-transformers/src/models/quantized_rwkv_v5.rs/0
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use candle::{Result, Tensor}; use candle_nn::{layer_norm, LayerNorm, Linear, Module, VarBuilder}; #[derive(Debug)] struct Attention { q_proj: Linear, k_proj: Linear, v_proj: Linear, out_proj: Linear, num_heads: usize, } impl Attention { fn new( embedding_dim: usize, num_heads: ...
candle/candle-transformers/src/models/segment_anything/transformer.rs/0
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// T5 Text Model // https://github.com/huggingface/transformers/blob/main/src/transformers/models/t5/modeling_t5.py use crate::models::with_tracing::{linear_no_bias, Embedding, Linear}; use candle::{DType, Device, Module, Result, Tensor, D}; use candle_nn::{Activation, VarBuilder}; use serde::Deserialize; use std::syn...
candle/candle-transformers/src/models/t5.rs/0
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use candle::{Device, Tensor}; use candle_transformers::generation::LogitsProcessor; use candle_wasm_example_llama2::worker::{Model as M, ModelData}; use wasm_bindgen::prelude::*; #[wasm_bindgen] pub struct Model { inner: M, logits_processor: LogitsProcessor, tokens: Vec<u32>, repeat_penalty: f32, } im...
candle/candle-wasm-examples/llama2-c/src/bin/m.rs/0
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//load the candle SAM Model wasm module import init, { Model } from "./build/m.js"; async function fetchArrayBuffer(url, cacheModel = true) { if (!cacheModel) return new Uint8Array(await (await fetch(url)).arrayBuffer()); const cacheName = "sam-candle-cache"; const cache = await caches.open(cacheName); con...
candle/candle-wasm-examples/segment-anything/samWorker.js/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="mel_filters.safetensors" /> <!-- samples --> <link data-trunk rel="copy-dir" href="audios" /> <!-- tiny.en --> <link data-trunk rel="copy-dir" href="whi...
candle/candle-wasm-examples/whisper/index.html/0
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<html> <head> <meta content="text/html;charset=utf-8" http-equiv="Content-Type" /> <title>Candle YOLOv8 Rust/WASM</title> </head> <body></body> </html> <!DOCTYPE html> <html> <head> <meta charset="UTF-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0" /> <style> ...
candle/candle-wasm-examples/yolo/lib-example.html/0
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Dockerfile .vscode/ .idea .gitignore LICENSE README.md node_modules/ .svelte-kit/ .env* !.env !.env.local
chat-ui/.dockerignore/0
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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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<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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<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 "$env/static/private"; export const embeddingEndpointOpenAIParametersSchema = z.object({ weight: z.number().int().positive().default(1), model: ...
chat-ui/src/lib/server/embeddingEndpoints/openai/embeddingEndpoints.ts/0
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import type { Conversation } from "$lib/types/Conversation"; import type { Message } from "$lib/types/Message"; import { format } from "date-fns"; import { downloadFile } from "./files/downloadFile"; export async function preprocessMessages( messages: Message[], webSearch: Message["webSearch"], multimodal: boolean,...
chat-ui/src/lib/server/preprocessMessages.ts/0
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import { writable } from "svelte/store"; export interface TitleUpdate { convId: string; title: string; } export default writable<TitleUpdate | null>(null);
chat-ui/src/lib/stores/titleUpdate.ts/0
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import { defaultModel } from "$lib/server/models"; import type { Assistant } from "./Assistant"; import type { Timestamps } from "./Timestamps"; import type { User } from "./User"; export interface Settings extends Timestamps { userId?: User["_id"]; sessionId?: string; /** * Note: Only conversations with this se...
chat-ui/src/lib/types/Settings.ts/0
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import { base } from "$app/paths"; import { PUBLIC_ORIGIN, PUBLIC_SHARE_PREFIX } from "$env/static/public"; export function getShareUrl(url: URL, shareId: string): string { return `${PUBLIC_SHARE_PREFIX || `${PUBLIC_ORIGIN || url.origin}${base}`}/r/${shareId}`; }
chat-ui/src/lib/utils/getShareUrl.ts/0
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import { collections } from "$lib/server/database"; import { ObjectId } from "mongodb"; import { describe, expect, it } from "vitest"; import { insertLegacyConversation, insertSideBranchesConversation } from "./treeHelpers.spec"; import type { Message } from "$lib/types/Message"; import { addSibling } from "./addSibli...
chat-ui/src/lib/utils/tree/addSibling.spec.ts/0
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import { collections } from "$lib/server/database"; import { authCondition } from "$lib/server/auth"; import type { Conversation } from "$lib/types/Conversation"; export async function GET({ locals }) { if (locals.user?._id || locals.sessionId) { const convs = await collections.conversations .find({ ...authC...
chat-ui/src/routes/api/conversations/+server.ts/0
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import { authCondition } from "$lib/server/auth"; import { collections } from "$lib/server/database"; import type { SharedConversation } from "$lib/types/SharedConversation"; import { getShareUrl } from "$lib/utils/getShareUrl"; import { hashConv } from "$lib/utils/hashConv"; import { error } from "@sveltejs/kit"; impo...
chat-ui/src/routes/conversation/[id]/share/+server.ts/0
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<script lang="ts"> import { createEventDispatcher } from "svelte"; import Modal from "$lib/components/Modal.svelte"; import CarbonClose from "~icons/carbon/close"; import CarbonTrashCan from "~icons/carbon/trash-can"; import CarbonArrowUpRight from "~icons/carbon/arrow-up-right"; import { enhance } from "$app/f...
chat-ui/src/routes/settings/(nav)/+page.svelte/0
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@import "./highlight-js.css"; @tailwind base; @tailwind components; @tailwind utilities; @layer components { .btn { @apply inline-flex flex-shrink-0 cursor-pointer select-none items-center justify-center whitespace-nowrap outline-none transition-all focus:ring disabled:cursor-default; } } @layer utilities { .sc...
chat-ui/src/styles/main.css/0
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{ "extends": "./.svelte-kit/tsconfig.json", "compilerOptions": { "allowJs": true, "checkJs": true, "esModuleInterop": true, "forceConsistentCasingInFileNames": true, "resolveJsonModule": true, "skipLibCheck": true, "sourceMap": true, "strict": true, "target": "ES2018" } // Path aliases are handled...
chat-ui/tsconfig.json/0
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import json import os import tempfile import datasets from utils import generate_example_dataset, get_duration SPEED_TEST_N_EXAMPLES = 50_000 SMALL_TEST = 5_000 RESULTS_BASEPATH, RESULTS_FILENAME = os.path.split(__file__) RESULTS_FILE_PATH = os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ...
datasets/benchmarks/benchmark_iterating.py/0
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# Dataset features [`Features`] defines the internal structure of a dataset. It is used to specify the underlying serialization format. What's more interesting to you though is that [`Features`] contains high-level information about everything from the column names and types, to the [`ClassLabel`]. You can think of [`...
datasets/docs/source/about_dataset_features.mdx/0
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# Cloud storage 🤗 Datasets supports access to cloud storage providers through a `fsspec` FileSystem implementations. You can save and load datasets from any cloud storage in a Pythonic way. Take a look at the following table for some example of supported cloud storage providers: | Storage provider | Filesystem i...
datasets/docs/source/filesystems.mdx/0
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# Object detection Object detection models identify something in an image, and object detection datasets are used for applications such as autonomous driving and detecting natural hazards like wildfire. This guide will show you how to apply transformations to an object detection dataset following the [tutorial](https:...
datasets/docs/source/object_detection.mdx/0
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# Share a dataset to the Hub The [Hub](https://huggingface.co/datasets) is home to an extensive collection of community-curated and popular research datasets. We encourage you to share your dataset to the Hub to help grow the ML community and accelerate progress for everyone. All contributions are welcome; adding a da...
datasets/docs/source/upload_dataset.mdx/0
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# Copyright 2021 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/cer/test_cer.py/0
{ "file_path": "datasets/metrics/cer/test_cer.py", "repo_id": "datasets", "token_count": 2407 }
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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2....
datasets/metrics/exact_match/exact_match.py/0
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# Copyright 2021 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/matthews_correlation/matthews_correlation.py/0
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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2....
datasets/metrics/recall/recall.py/0
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# Metric Card for SQuAD v2 ## Metric description This metric wraps the official scoring script for version 2 of the [Stanford Question Answering Dataset (SQuAD)](https://huggingface.co/datasets/squad_v2). SQuAD is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia art...
datasets/metrics/squad_v2/README.md/0
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<jupyter_start><jupyter_text>**⚠️ This notebook is deprecated in favor of the [Quickstart notebook](https://github.com/huggingface/notebooks/blob/main/datasets_doc/en/quickstart.ipynb)** HuggingFace 🤗 Datasets library - Quick overviewModels come and go (linear models, LSTM, Transformers, ...) but two core elements ha...
datasets/notebooks/Overview.ipynb/0
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import logging import os from argparse import ArgumentParser from pathlib import Path from shutil import copyfile, rmtree from typing import Generator import datasets.config from datasets.builder import DatasetBuilder from datasets.commands import BaseDatasetsCLICommand from datasets.download.download_manager import D...
datasets/src/datasets/commands/test.py/0
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import importlib import shutil import threading import warnings from typing import List import fsspec import fsspec.asyn from fsspec.implementations.local import LocalFileSystem from ..utils.deprecation_utils import deprecated from . import compression _has_s3fs = importlib.util.find_spec("s3fs") is not None if _h...
datasets/src/datasets/filesystems/__init__.py/0
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from typing import Callable, Optional from .. import Features from ..packaged_modules.generator.generator import Generator from .abc import AbstractDatasetInputStream class GeneratorDatasetInputStream(AbstractDatasetInputStream): def __init__( self, generator: Callable, features: Optional...
datasets/src/datasets/io/generator.py/0
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import copy from dataclasses import dataclass, field from typing import ClassVar, Dict from ..features import Audio, ClassLabel, Features from .base import TaskTemplate @dataclass(frozen=True) class AudioClassification(TaskTemplate): task: str = field(default="audio-classification", metadata={"include_in_asdict_...
datasets/src/datasets/tasks/audio_classification.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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[ "unknown", "n<1K", "1K<n<10K", "10K<n<100K", "100K<n<1M", "1M<n<10M", "10M<n<100M", "100M<n<1B", "1B<n<10B", "10B<n<100B", "100B<n<1T", "n>1T" ]
datasets/src/datasets/utils/resources/size_categories.json/0
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import pytest DATASET_LOADING_SCRIPT_NAME = "__dummy_dataset1__" DATASET_LOADING_SCRIPT_CODE = """ import json import os import datasets REPO_URL = "https://huggingface.co/datasets/hf-internal-testing/raw_jsonl/resolve/main/" URLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", "validation": REPO_URL + "wikiann-...
datasets/tests/commands/conftest.py/0
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import os import textwrap import pyarrow as pa import pytest from datasets import ClassLabel, Features, Image from datasets.packaged_modules.csv.csv import Csv from ..utils import require_pil @pytest.fixture def csv_file(tmp_path): filename = tmp_path / "file.csv" data = textwrap.dedent( """\ ...
datasets/tests/packaged_modules/test_csv.py/0
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import json import os from pathlib import Path import pytest from datasets.download.download_config import DownloadConfig from datasets.download.download_manager import DownloadManager from datasets.utils.file_utils import hash_url_to_filename URL = "http://www.mocksite.com/file1.txt" CONTENT = '"text": ["foo", "fo...
datasets/tests/test_download_manager.py/0
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import os import pickle import tempfile import time from multiprocessing import Pool from unittest import TestCase import pytest from datasets.features import Features, Sequence, Value from datasets.metric import Metric, MetricInfo from .utils import require_tf, require_torch class DummyMetric(Metric): def _in...
datasets/tests/test_metric.py/0
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import pytest from datasets import inspect_metric, list_metrics, load_metric @pytest.fixture def mock_emitted_deprecation_warnings(monkeypatch): monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings", set()) # Used by list_metrics @pytest.fixture def mock_hfh(monkeypatch): cla...
datasets/tests/test_warnings.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...
deep-rl-class/units/en/unitbonus1/introduction.mdx/0
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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...
deep-rl-class/units/en/unitbonus3/rl-documentation.mdx/0
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include LICENSE include src/diffusers/utils/model_card_template.md
diffusers/MANIFEST.in/0
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FROM ubuntu:20.04 LABEL maintainer="Hugging Face" LABEL repository="diffusers" ENV DEBIAN_FRONTEND=noninteractive RUN apt update && \ apt install -y bash \ build-essential \ git \ git-lfs \ curl \ ca-certificates \ ...
diffusers/docker/diffusers-flax-cpu/Dockerfile/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/internal_classes_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/optimization/onnx.md/0
{ "file_path": "diffusers/docs/source/en/optimization/onnx.md", "repo_id": "diffusers", "token_count": 1193 }
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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/kandinsky.md/0
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<!--Copyright 2024 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed...
diffusers/docs/source/en/using-diffusers/conditional_image_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/ip_adapter.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/stable_diffusion_jax_how_to.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/api/pipelines/stable_diffusion/stable_diffusion_xl.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/adapt_a_model.md/0
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# 이미지 밝기 조절하기 Stable Diffusion 파이프라인은 [일반적인 디퓨전 노이즈 스케줄과 샘플 단계에 결함이 있음](https://huggingface.co/papers/2305.08891) 논문에서 설명한 것처럼 매우 밝거나 어두운 이미지를 생성하는 데는 성능이 평범합니다. 이 논문에서 제안한 솔루션은 현재 [`DDIMScheduler`]에 구현되어 있으며 이미지의 밝기를 개선하는 데 사용할 수 있습니다. <Tip> 💡 제안된 솔루션에 대한 자세한 내용은 위에 링크된 논문을 참고하세요! </Tip> 해결책 중 하나는 *v 예측값*과 *v 로스...
diffusers/docs/source/ko/using-diffusers/control_brightness.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/unconditional_image_generation.md/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/advanced_diffusion_training/train_dreambooth_lora_sd15_advanced.py/0
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""" modeled after the textual_inversion.py / train_dreambooth.py and the work of justinpinkney here: https://github.com/justinpinkney/stable-diffusion/blob/main/notebooks/imagic.ipynb """ import inspect import warnings from typing import List, Optional, Union import numpy as np import PIL.Image import torch im...
diffusers/examples/community/imagic_stable_diffusion.py/0
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import inspect from copy import deepcopy from enum import Enum from typing import List, Optional, Tuple, Union import torch from tqdm.auto import tqdm from diffusers.models import AutoencoderKL, UNet2DConditionModel from diffusers.pipelines.pipeline_utils import DiffusionPipeline from diffusers.pipelines.stable_diffu...
diffusers/examples/community/mixture_tiling.py/0
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import math from typing import Dict, Optional import torch import torchvision.transforms.functional as FF from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer from diffusers import StableDiffusionPipeline from diffusers.models import AutoencoderKL, UNet2DConditionModel from diffusers.pipelines....
diffusers/examples/community/regional_prompting_stable_diffusion.py/0
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# Inspired by: https://github.com/Mikubill/sd-webui-controlnet/discussions/1236 and https://github.com/Mikubill/sd-webui-controlnet/discussions/1280 import inspect from typing import Any, Callable, Dict, List, Optional, Tuple, Union import numpy as np import PIL.Image import torch from packaging import version from tr...
diffusers/examples/community/stable_diffusion_reference.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/consistency_distillation/test_lcm_lora.py/0
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# Inference Examples **The inference examples folder is deprecated and will be removed in a future version**. **Officially supported inference examples can be found in the [Pipelines folder](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines)**. - For `Image-to-Image text-guided generation wit...
diffusers/examples/inference/README.md/0
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import d4rl # noqa import gym import tqdm from diffusers.experimental import ValueGuidedRLPipeline config = { "n_samples": 64, "horizon": 32, "num_inference_steps": 20, "n_guide_steps": 2, # can set to 0 for faster sampling, does not use value network "scale_grad_by_std": True, "scale": 0.1,...
diffusers/examples/reinforcement_learning/run_diffuser_locomotion.py/0
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# Distillation for quantization on Textual Inversion models to personalize text2image [Textual inversion](https://arxiv.org/abs/2208.01618) is a method to personalize text2image models like stable diffusion on your own images._By using just 3-5 images new concepts can be taught to Stable Diffusion and the model person...
diffusers/examples/research_projects/intel_opts/textual_inversion_dfq/README.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 applicabl...
diffusers/examples/research_projects/promptdiffusion/promptdiffusioncontrolnet.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/test_examples_utils.py/0
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[tool.ruff] # Never enforce `E501` (line length violations). ignore = ["C901", "E501", "E741", "F402", "F823"] select = ["C", "E", "F", "I", "W"] line-length = 119 # Ignore import violations in all `__init__.py` files. [tool.ruff.per-file-ignores] "__init__.py" = ["E402", "F401", "F403", "F811"] "src/diffusers/utils/d...
diffusers/pyproject.toml/0
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import argparse import os import torch from torchvision.datasets.utils import download_url from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, Transformer2DModel pretrained_models = {512: "DiT-XL-2-512x512.pt", 256: "DiT-XL-2-256x256.pt"} def download_model(model_name): """ Downloads a pre-tr...
diffusers/scripts/convert_dit_to_diffusers.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_controlnet_to_diffusers.py/0
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import argparse import io import requests import torch import yaml from diffusers import AutoencoderKL from diffusers.pipelines.stable_diffusion.convert_from_ckpt import ( assign_to_checkpoint, conv_attn_to_linear, create_vae_diffusers_config, renew_vae_attention_paths, renew_vae_resnet_paths, ) ...
diffusers/scripts/convert_vae_pt_to_diffusers.py/0
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# 🧨 Diffusers Experimental We are adding experimental code to support novel applications and usages of the Diffusers library. Currently, the following experiments are supported: * Reinforcement learning via an implementation of the [Diffuser](https://arxiv.org/abs/2205.09991) model.
diffusers/src/diffusers/experimental/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 applicabl...
diffusers/src/diffusers/loaders/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/controlnet_flax.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.py/0
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# coding=utf-8 # Copyright 2024 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable...
diffusers/src/diffusers/optimization.py/0
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# coding=utf-8 # Copyright 2024 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable...
diffusers/src/diffusers/pipelines/auto_pipeline.py/0
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import numpy as np import torch import torch.nn as nn from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel from ...utils import logging logger = logging.get_logger(__name__) class IFSafetyChecker(PreTrainedModel): config_class = CLIPConfig _no_split_modules = ["CLIPEncoderLa...
diffusers/src/diffusers/pipelines/deepfloyd_if/safety_checker.py/0
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# Copyright 2024 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicabl...
diffusers/src/diffusers/pipelines/deprecated/pndm/pipeline_pndm.py/0
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# Copyright 2024 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/licenses/LICENSE-2.0 # # Unl...
diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.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/kandinsky/__init__.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/pipelines/onnx_utils.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/pipeline_shap_e.py/0
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import inspect import warnings from typing import Callable, List, Optional, Union import numpy as np import torch from packaging import version from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection from ...configuration_utils import FrozenDict from ...image_processor...
diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py/0
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# Copyright (c) 2022 Dominic Rampas MIT License # 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/licen...
diffusers/src/diffusers/pipelines/wuerstchen/modeling_paella_vq_model.py/0
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# Copyright 2024 Stanford University Team and The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # #...
diffusers/src/diffusers/schedulers/scheduling_ddim.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_euler_ancestral_discrete.py/0
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