text stringlengths 96 319k | id stringlengths 14 178 | metadata dict |
|---|---|---|
pub const AFFINE: &str = include_str!(concat!(env!("OUT_DIR"), "/affine.ptx"));
pub const BINARY: &str = include_str!(concat!(env!("OUT_DIR"), "/binary.ptx"));
pub const CAST: &str = include_str!(concat!(env!("OUT_DIR"), "/cast.ptx"));
pub const CONV: &str = include_str!(concat!(env!("OUT_DIR"), "/conv.ptx"));
pub cons... | candle/candle-kernels/src/lib.rs/0 | {
"file_path": "candle/candle-kernels/src/lib.rs",
"repo_id": "candle",
"token_count": 365
} |
// MLX Kernel extracted from:
// https://github.com/ml-explore/mlx/blob/main/mlx/backend/metal/kernels/steel/gemm
// Copyright © 2024 Apple Inc.
#include <metal_simdgroup>
#include <metal_simdgroup_matrix>
#include <metal_stdlib>
#define STEEL_CONST static constant constexpr const
#define STEEL_PRAGMA_UNROLL _Pragma(... | candle/candle-metal-kernels/src/mlx_gemm.metal/0 | {
"file_path": "candle/candle-metal-kernels/src/mlx_gemm.metal",
"repo_id": "candle",
"token_count": 20231
} |
use candle_metal_kernels::{call_cast_contiguous, Kernels};
use metal::objc::rc::autoreleasepool;
use metal::{Device, MTLResourceOptions};
use rand;
use std::any::type_name;
use std::time::Instant;
fn main() {
let device = Device::system_default().unwrap();
let kernels = Kernels::new();
let f32_1k = (0..10... | candle/candle-metal-kernels/tmp/cast.rs/0 | {
"file_path": "candle/candle-metal-kernels/tmp/cast.rs",
"repo_id": "candle",
"token_count": 1299
} |
//! Layers defined by closures.
use candle::{Result, Tensor};
use std::sync::Arc;
/// A layer defined by a simple closure.
#[derive(Clone)]
pub struct Func<'a> {
#[allow(clippy::type_complexity)]
f: Arc<dyn 'a + Fn(&Tensor) -> Result<Tensor> + Send + Sync>,
}
impl std::fmt::Debug for Func<'_> {
fn fmt(&se... | candle/candle-nn/src/func.rs/0 | {
"file_path": "candle/candle-nn/src/func.rs",
"repo_id": "candle",
"token_count": 784
} |
/* 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 | {
"file_path": "candle/candle-nn/tests/group_norm.rs",
"repo_id": "candle",
"token_count": 2154
} |
import math
from typing import Any
import candle
from candle import Tensor
from .module import Module
# See https://github.com/pytorch/pytorch/blob/main/torch/nn/modules/linear.py
class Identity(Module):
r"""A placeholder identity operator that is argument-insensitive.
Args:
args: any argument (unu... | candle/candle-pyo3/py_src/candle/nn/linear.py/0 | {
"file_path": "candle/candle-pyo3/py_src/candle/nn/linear.py",
"repo_id": "candle",
"token_count": 1947
} |
# See: https://raw.githubusercontent.com/huggingface/tokenizers/main/bindings/python/stub.py
import argparse
import inspect
import os
from typing import Optional
import black
from pathlib import Path
import re
INDENT = " " * 4
GENERATED_COMMENT = "# Generated content DO NOT EDIT\n"
TYPING = """from typing import Any,... | candle/candle-pyo3/stub.py/0 | {
"file_path": "candle/candle-pyo3/stub.py",
"repo_id": "candle",
"token_count": 3931
} |
//! BERT (Bidirectional Encoder Representations from Transformers)
//!
//! Bert is a general large language model that can be used for various language tasks:
//! - Compute sentence embeddings for a prompt.
//! - Compute similarities between a set of sentences.
//! - [Arxiv](https://arxiv.org/abs/1810.04805) "BERT: Pre... | candle/candle-transformers/src/models/bert.rs/0 | {
"file_path": "candle/candle-transformers/src/models/bert.rs",
"repo_id": "candle",
"token_count": 10056
} |
use std::collections::HashMap;
use candle::{bail, Context, DType, Device, Module, Result, Tensor, D};
use candle_nn::{
conv1d, embedding, layer_norm, Conv1d, Conv1dConfig, Embedding, LayerNorm, VarBuilder,
};
use serde::{Deserialize, Deserializer};
pub const DTYPE: DType = DType::F32;
// NOTE: HiddenAct and Hidd... | candle/candle-transformers/src/models/debertav2.rs/0 | {
"file_path": "candle/candle-transformers/src/models/debertav2.rs",
"repo_id": "candle",
"token_count": 24495
} |
//! Gemma inference implementation.
//!
//! See ["Gemma: Open Models Based on Gemini Technology"](https://blog.google/technology/developers/gemma-open-ai-model/)
//!
//! Based on implementation from Google and PyTorch
use std::sync::Arc;
use candle::{DType, Device, Module, Result, Tensor, D};
use candle_nn::{linear_b... | candle/candle-transformers/src/models/gemma.rs/0 | {
"file_path": "candle/candle-transformers/src/models/gemma.rs",
"repo_id": "candle",
"token_count": 7496
} |
// Copyright (c) Kyutai, all rights reserved.
// This source code is licensed under the license found in the
// LICENSE file in the root directory of this source tree.
use candle::{Module, Result, StreamTensor, StreamingModule, Tensor, D};
use candle_nn::{Conv1d, VarBuilder};
#[allow(clippy::enum_variant_names)]
#[de... | candle/candle-transformers/src/models/mimi/conv.rs/0 | {
"file_path": "candle/candle-transformers/src/models/mimi/conv.rs",
"repo_id": "candle",
"token_count": 11113
} |
//! # MobileOne
//!
//! MobileOne inference implementation based on timm and candle-repvgg
//!
//! See ["MobileOne: An Improved One millisecond Mobile Backbone"](https://arxiv.org/abs/2206.04040)
use candle::{DType, Result, Tensor, D};
use candle_nn::{
batch_norm, conv2d, conv2d_no_bias, linear, ops::sigmoid, Batc... | candle/candle-transformers/src/models/mobileone.rs/0 | {
"file_path": "candle/candle-transformers/src/models/mobileone.rs",
"repo_id": "candle",
"token_count": 4729
} |
use candle::{Module, Result, Tensor};
use candle_nn::{linear, Linear, VarBuilder};
use super::vision_model;
use crate::models::mistral;
#[derive(serde::Deserialize, Debug, Clone)]
pub struct Config {
pub projector_hidden_act: candle_nn::Activation,
pub text_config: mistral::Config,
pub vision_config: visi... | candle/candle-transformers/src/models/pixtral/llava.rs/0 | {
"file_path": "candle/candle-transformers/src/models/pixtral/llava.rs",
"repo_id": "candle",
"token_count": 1393
} |
//! RWKV v5 model implementation with quantization support.
//!
//! RWKV v5 is an attention-free language model optimized for efficiency.
//! This implementation provides quantization for reduced memory and compute.
//!
//! Key characteristics:
//! - Linear attention mechanism
//! - GroupNorm layer normalization
//! - ... | candle/candle-transformers/src/models/quantized_rwkv_v5.rs/0 | {
"file_path": "candle/candle-transformers/src/models/quantized_rwkv_v5.rs",
"repo_id": "candle",
"token_count": 5686
} |
use candle::{DType, IndexOp, Result, Tensor};
use candle_nn::{Module, VarBuilder};
use super::image_encoder::ImageEncoderViT;
use super::mask_decoder::MaskDecoder;
use super::prompt_encoder::PromptEncoder;
use super::tiny_vit::{tiny_vit_5m, TinyViT};
const PROMPT_EMBED_DIM: usize = 256;
pub const IMAGE_SIZE: usize = ... | candle/candle-transformers/src/models/segment_anything/sam.rs/0 | {
"file_path": "candle/candle-transformers/src/models/segment_anything/sam.rs",
"repo_id": "candle",
"token_count": 8444
} |
use candle::{Device, Result, Tensor};
pub fn linspace(start: f64, stop: f64, steps: usize) -> Result<Tensor> {
if steps == 0 {
Tensor::from_vec(Vec::<f64>::new(), steps, &Device::Cpu)
} else if steps == 1 {
Tensor::from_vec(vec![start], steps, &Device::Cpu)
} else {
let delta = (sto... | candle/candle-transformers/src/models/stable_diffusion/utils.rs/0 | {
"file_path": "candle/candle-transformers/src/models/stable_diffusion/utils.rs",
"repo_id": "candle",
"token_count": 971
} |
use candle::{Result, Tensor};
#[derive(Debug, Clone)]
pub struct DDPMWSchedulerConfig {
scaler: f64,
s: f64,
}
impl Default for DDPMWSchedulerConfig {
fn default() -> Self {
Self {
scaler: 1f64,
s: 0.008f64,
}
}
}
pub struct DDPMWScheduler {
init_alpha_cump... | candle/candle-transformers/src/models/wuerstchen/ddpm.rs/0 | {
"file_path": "candle/candle-transformers/src/models/wuerstchen/ddpm.rs",
"repo_id": "candle",
"token_count": 1537
} |
## Running [llama2.c](https://github.com/karpathy/llama2.c) Examples
Here, we provide two examples of how to run [llama2.c](https://github.com/karpathy/llama2.c) written in Rust using a Candle-compiled WASM binary and runtimes.
### Pure Rust UI
To build and test the UI made in Rust you will need [Trunk](https://trun... | candle/candle-wasm-examples/llama2-c/README.md/0 | {
"file_path": "candle/candle-wasm-examples/llama2-c/README.md",
"repo_id": "candle",
"token_count": 449
} |
<html>
<head>
<meta content="text/html;charset=utf-8" http-equiv="Content-Type" />
<title>Candle Moondream 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" />
<link
... | candle/candle-wasm-examples/moondream/index.html/0 | {
"file_path": "candle/candle-wasm-examples/moondream/index.html",
"repo_id": "candle",
"token_count": 6120
} |
use candle::{DType, Device, Tensor};
use candle_nn::VarBuilder;
use candle_wasm_example_sam as sam;
use wasm_bindgen::prelude::*;
struct Embeddings {
original_width: u32,
original_height: u32,
width: u32,
height: u32,
data: Tensor,
}
#[wasm_bindgen]
pub struct Model {
sam: sam::Sam,
embedd... | candle/candle-wasm-examples/segment-anything/src/bin/m.rs/0 | {
"file_path": "candle/candle-wasm-examples/segment-anything/src/bin/m.rs",
"repo_id": "candle",
"token_count": 2399
} |
<html>
<head>
<meta content="text/html;charset=utf-8" http-equiv="Content-Type" />
<title>Candle Whisper 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/whisper/lib-example.html/0 | {
"file_path": "candle/candle-wasm-examples/whisper/lib-example.html",
"repo_id": "candle",
"token_count": 6488
} |
use crate::console_log;
use crate::worker::{ModelData, RunData, Worker, WorkerInput, WorkerOutput};
use wasm_bindgen::prelude::*;
use wasm_bindgen_futures::JsFuture;
use yew::{html, Component, Context, Html};
use yew_agent::{Bridge, Bridged};
async fn fetch_url(url: &str) -> Result<Vec<u8>, JsValue> {
use web_sys:... | candle/candle-wasm-examples/yolo/src/app.rs/0 | {
"file_path": "candle/candle-wasm-examples/yolo/src/app.rs",
"repo_id": "candle",
"token_count": 5961
} |
backend-test:J
xytest"Relu
SingleReluZ
x
b
y
B | candle/test.onnx/0 | {
"file_path": "candle/test.onnx",
"repo_id": "candle",
"token_count": 76
} |
{{- if .Values.infisical.enabled }}
apiVersion: secrets.infisical.com/v1alpha1
kind: InfisicalSecret
metadata:
name: {{ include "name" $ }}-infisical-secret
namespace: {{ $.Release.Namespace }}
spec:
authentication:
universalAuth:
credentialsRef:
secretName: {{ .Values.infisical.operatorSecretNa... | chat-ui/chart/templates/infisical.yaml/0 | {
"file_path": "chat-ui/chart/templates/infisical.yaml",
"repo_id": "chat-ui",
"token_count": 311
} |
# Cohere
| Feature | Available |
| --------------------------- | --------- |
| [Tools](../tools) | Yes |
| [Multimodal](../multimodal) | No |
You may use Cohere to run their models directly from Chat UI. You will need to have a Cohere account, then get your [API token](https... | chat-ui/docs/source/configuration/models/providers/cohere.md/0 | {
"file_path": "chat-ui/docs/source/configuration/models/providers/cohere.md",
"repo_id": "chat-ui",
"token_count": 342
} |
# Helm
<Tip warning={true}>
**We highly discourage using the chart**. The Helm chart is a work in progress and should be considered unstable. Breaking changes to the chart may be pushed without migration guides or notice. Contributions welcome!
</Tip>
For installation on Kubernetes, you may use the helm chart in `/... | chat-ui/docs/source/installation/helm.md/0 | {
"file_path": "chat-ui/docs/source/installation/helm.md",
"repo_id": "chat-ui",
"token_count": 292
} |
import type { EndpointParameters } from "./server/endpoints/endpoints";
import type { BackendModel } from "./server/models";
import type { Tool, ToolResult } from "./types/Tool";
type buildPromptOptions = Pick<EndpointParameters, "messages" | "preprompt" | "continueMessage"> & {
model: BackendModel;
tools?: Tool[];
... | chat-ui/src/lib/buildPrompt.ts/0 | {
"file_path": "chat-ui/src/lib/buildPrompt.ts",
"repo_id": "chat-ui",
"token_count": 514
} |
<script lang="ts">
import { base } from "$app/paths";
import Logo from "$lib/components/icons/Logo.svelte";
import { switchTheme } from "$lib/switchTheme";
import { isAborted } from "$lib/stores/isAborted";
import { env as envPublic } from "$env/dynamic/public";
import NavConversationItem from "./NavConversation... | chat-ui/src/lib/components/NavMenu.svelte/0 | {
"file_path": "chat-ui/src/lib/components/NavMenu.svelte",
"repo_id": "chat-ui",
"token_count": 3075
} |
<script lang="ts">
interface Props {
classNames?: string;
label?: string;
position?: string;
}
let {
classNames = "",
label = "Copied",
position = "left-1/2 top-full transform -translate-x-1/2 translate-y-2",
}: Props = $props();
</script>
<div
class="
pointer-events-none absolute rounded bg-black ... | chat-ui/src/lib/components/Tooltip.svelte/0 | {
"file_path": "chat-ui/src/lib/components/Tooltip.svelte",
"repo_id": "chat-ui",
"token_count": 260
} |
<script lang="ts">
interface Props {
classNames?: string;
}
let { classNames = "" }: Props = $props();
</script>
<svg
width="1em"
height="1em"
viewBox="0 0 15 6"
class={classNames}
fill="none"
xmlns="http://www.w3.org/2000/svg"
>
<path
d="M1.67236 1L7.67236 7L13.6724 1"
stroke="currentColor"
stroke-... | chat-ui/src/lib/components/icons/IconChevron.svelte/0 | {
"file_path": "chat-ui/src/lib/components/icons/IconChevron.svelte",
"repo_id": "chat-ui",
"token_count": 181
} |
import type { ConversationStats } from "$lib/types/ConversationStats";
import { CONVERSATION_STATS_COLLECTION, collections } from "$lib/server/database";
import { logger } from "$lib/server/logger";
import type { ObjectId } from "mongodb";
import { acquireLock, refreshLock } from "$lib/migrations/lock";
export async f... | chat-ui/src/lib/jobs/refresh-conversation-stats.ts/0 | {
"file_path": "chat-ui/src/lib/jobs/refresh-conversation-stats.ts",
"repo_id": "chat-ui",
"token_count": 2646
} |
import {
Issuer,
type BaseClient,
type UserinfoResponse,
type TokenSet,
custom,
} from "openid-client";
import { addHours, addWeeks } from "date-fns";
import { env } from "$env/dynamic/private";
import { sha256 } from "$lib/utils/sha256";
import { z } from "zod";
import { dev } from "$app/environment";
import type... | chat-ui/src/lib/server/auth.ts/0 | {
"file_path": "chat-ui/src/lib/server/auth.ts",
"repo_id": "chat-ui",
"token_count": 1854
} |
import type { Conversation } from "$lib/types/Conversation";
import type { Message } from "$lib/types/Message";
import type { TextGenerationStreamOutput, TextGenerationStreamToken } from "@huggingface/inference";
import { endpointTgi, endpointTgiParametersSchema } from "./tgi/endpointTgi";
import { z } from "zod";
impo... | chat-ui/src/lib/server/endpoints/endpoints.ts/0 | {
"file_path": "chat-ui/src/lib/server/endpoints/endpoints.ts",
"repo_id": "chat-ui",
"token_count": 1103
} |
import { isURLLocal } from "../isURLLocal";
import { env } from "$env/dynamic/private";
import { collections } from "$lib/server/database";
import type { Assistant } from "$lib/types/Assistant";
import type { ObjectId } from "mongodb";
export async function processPreprompt(preprompt: string, user_message: string | un... | chat-ui/src/lib/server/textGeneration/assistant.ts/0 | {
"file_path": "chat-ui/src/lib/server/textGeneration/assistant.ts",
"repo_id": "chat-ui",
"token_count": 886
} |
import { MetricsServer } from "$lib/server/metrics";
import type { WebSearchScrapedSource, WebSearchUsedSource } from "$lib/types/WebSearch";
import type { EmbeddingBackendModel } from "../../embeddingModels";
import { getSentenceSimilarity, innerProduct } from "../../sentenceSimilarity";
import { MarkdownElementType, ... | chat-ui/src/lib/server/websearch/embed/embed.ts/0 | {
"file_path": "chat-ui/src/lib/server/websearch/embed/embed.ts",
"repo_id": "chat-ui",
"token_count": 1027
} |
import { env } from "$env/dynamic/private";
import { logger } from "$lib/server/logger";
import type { WebSearchSource } from "$lib/types/WebSearch";
import { isURL } from "$lib/utils/isUrl";
export default async function searchSearxng(query: string): Promise<WebSearchSource[]> {
const abortController = new AbortCont... | chat-ui/src/lib/server/websearch/search/endpoints/searxng.ts/0 | {
"file_path": "chat-ui/src/lib/server/websearch/search/endpoints/searxng.ts",
"repo_id": "chat-ui",
"token_count": 417
} |
import type { ObjectId } from "bson";
import type { Timestamps } from "./Timestamps";
import type { User } from "./User";
export interface Session extends Timestamps {
_id: ObjectId;
sessionId: string;
userId: User["_id"];
userAgent?: string;
ip?: string;
expiresAt: Date;
}
| chat-ui/src/lib/types/Session.ts/0 | {
"file_path": "chat-ui/src/lib/types/Session.ts",
"repo_id": "chat-ui",
"token_count": 97
} |
import { base } from "$app/paths";
import type { Client } from "@gradio/client";
export type ApiReturnType = Awaited<ReturnType<typeof Client.prototype.view_api>>;
export async function getGradioApi(space: string) {
const api: ApiReturnType = await fetch(`${base}/api/spaces-config?space=${space}`).then(
async (res... | chat-ui/src/lib/utils/getGradioApi.ts/0 | {
"file_path": "chat-ui/src/lib/utils/getGradioApi.ts",
"repo_id": "chat-ui",
"token_count": 166
} |
import { describe, expect, it } from "vitest";
import { isMessageId } from "./isMessageId";
import { v4 } from "uuid";
describe("isMessageId", () => {
it("should return true for a valid message id", () => {
expect(isMessageId(v4())).toBe(true);
});
it("should return false for an invalid message id", () => {
exp... | chat-ui/src/lib/utils/tree/isMessageId.spec.ts/0 | {
"file_path": "chat-ui/src/lib/utils/tree/isMessageId.spec.ts",
"repo_id": "chat-ui",
"token_count": 170
} |
import { env } from "$env/dynamic/private";
import { collections } from "$lib/server/database.js";
import { toolFromConfigs } from "$lib/server/tools/index.js";
import type { BaseTool, CommunityToolDB } from "$lib/types/Tool.js";
import { generateQueryTokens, generateSearchTokens } from "$lib/utils/searchTokens.js";
im... | chat-ui/src/routes/api/tools/search/+server.ts/0 | {
"file_path": "chat-ui/src/routes/api/tools/search/+server.ts",
"repo_id": "chat-ui",
"token_count": 683
} |
import { authCondition } from "$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 | {
"file_path": "chat-ui/src/routes/conversation/[id]/share/+server.ts",
"repo_id": "chat-ui",
"token_count": 760
} |
<script lang="ts">
import { onMount } from "svelte";
import { base } from "$app/paths";
import { afterNavigate, goto } from "$app/navigation";
import { page } from "$app/state";
import { useSettingsStore } from "$lib/stores/settings";
import CarbonClose from "~icons/carbon/close";
import CarbonArrowUpRight from ... | chat-ui/src/routes/settings/(nav)/+layout.svelte/0 | {
"file_path": "chat-ui/src/routes/settings/(nav)/+layout.svelte",
"repo_id": "chat-ui",
"token_count": 3297
} |
<script lang="ts">
import { env as envPublic } from "$env/dynamic/public";
import { isHuggingChat } from "$lib/utils/isHuggingChat";
import { base } from "$app/paths";
import { page } from "$app/state";
interface Props {
children?: import("svelte").Snippet;
}
let { children }: Props = $props();
</script>
<sv... | chat-ui/src/routes/tools/+layout.svelte/0 | {
"file_path": "chat-ui/src/routes/tools/+layout.svelte",
"repo_id": "chat-ui",
"token_count": 320
} |
{
"$schema": "https://vega.github.io/schema/vega-lite/v4.json",
"data": {
"values": "<DVC_METRIC_DATA>"
},
"title": "<DVC_METRIC_TITLE>",
"mark": "rect",
"encoding": {
"x": {
"field": "<DVC_METRIC_X>",
"type": "nominal",
"sort": "ascending",
... | datasets/.dvc/plots/confusion.json/0 | {
"file_path": "datasets/.dvc/plots/confusion.json",
"repo_id": "datasets",
"token_count": 450
} |
# Create an audio dataset
You can share a dataset with your team or with anyone in the community by creating a dataset repository on the Hugging Face Hub:
```py
from datasets import load_dataset
dataset = load_dataset("<username>/my_dataset")
```
There are several methods for creating and sharing an audio dataset:
... | datasets/docs/source/audio_dataset.mdx/0 | {
"file_path": "datasets/docs/source/audio_dataset.mdx",
"repo_id": "datasets",
"token_count": 9772
} |
# Structure your repository
To host and share your dataset, create a dataset repository on the Hugging Face Hub and upload your data files.
This guide will show you how to structure your dataset repository when you upload it.
A dataset with a supported structure and file format (`.txt`, `.csv`, `.parquet`, `.jsonl`, ... | datasets/docs/source/repository_structure.mdx/0 | {
"file_path": "datasets/docs/source/repository_structure.mdx",
"repo_id": "datasets",
"token_count": 2555
} |
# Using Datasets with TensorFlow
This document is a quick introduction to using `datasets` with TensorFlow, with a particular focus on how to get
`tf.Tensor` objects out of our datasets, and how to stream data from Hugging Face `Dataset` objects to Keras methods
like `model.fit()`.
## Dataset format
By default, data... | datasets/docs/source/use_with_tensorflow.mdx/0 | {
"file_path": "datasets/docs/source/use_with_tensorflow.mdx",
"repo_id": "datasets",
"token_count": 3825
} |
from argparse import ArgumentParser
from typing import Optional
from datasets.commands import BaseDatasetsCLICommand
from datasets.hub import delete_from_hub
def _command_factory(args):
return DeleteFromHubCommand(
args.dataset_id,
args.config_name,
args.token,
args.revision,
... | datasets/src/datasets/commands/delete_from_hub.py/0 | {
"file_path": "datasets/src/datasets/commands/delete_from_hub.py",
"repo_id": "datasets",
"token_count": 562
} |
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class Translation:
"""`Feature` for translations with fixed languages per example.
Here for compatibl... | datasets/src/datasets/features/translation.py/0 | {
"file_path": "datasets/src/datasets/features/translation.py",
"repo_id": "datasets",
"token_count": 1677
} |
from abc import ABC, abstractmethod
from typing import Optional, Union
from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class AbstractDatasetReader(ABC):
def __init__(
self,
path_or_paths: ... | datasets/src/datasets/io/abc.py/0 | {
"file_path": "datasets/src/datasets/io/abc.py",
"repo_id": "datasets",
"token_count": 721
} |
from typing import List
import datasets
from ..folder_based_builder import folder_based_builder
logger = datasets.utils.logging.get_logger(__name__)
class AudioFolderConfig(folder_based_builder.FolderBasedBuilderConfig):
"""Builder Config for AudioFolder."""
drop_labels: bool = None
drop_metadata: bo... | datasets/src/datasets/packaged_modules/audiofolder/audiofolder.py/0 | {
"file_path": "datasets/src/datasets/packaged_modules/audiofolder/audiofolder.py",
"repo_id": "datasets",
"token_count": 588
} |
import itertools
from dataclasses import dataclass
from typing import List, Optional, Union
import pyarrow as pa
import pyarrow.dataset as ds
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
logger = datasets.utils.logging.get_logger(__name__)
@dataclass
class ParquetConfig(datas... | datasets/src/datasets/packaged_modules/parquet/parquet.py/0 | {
"file_path": "datasets/src/datasets/packaged_modules/parquet/parquet.py",
"repo_id": "datasets",
"token_count": 2415
} |
import importlib.util
import os
import tempfile
from pathlib import PurePath
from typing import TYPE_CHECKING, Dict, List, NamedTuple, Optional, Union
import fsspec
import numpy as np
from .features import Sequence
from .utils import logging
from .utils import tqdm as hf_tqdm
if TYPE_CHECKING:
from .arrow_datas... | datasets/src/datasets/search.py/0 | {
"file_path": "datasets/src/datasets/search.py",
"repo_id": "datasets",
"token_count": 15341
} |
# Copyright 2020 Optuna, Hugging Face
#
# 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 in ... | datasets/src/datasets/utils/logging.py/0 | {
"file_path": "datasets/src/datasets/utils/logging.py",
"repo_id": "datasets",
"token_count": 1914
} |
# 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/utils/version.py/0 | {
"file_path": "datasets/src/datasets/utils/version.py",
"repo_id": "datasets",
"token_count": 1291
} |
import pytest
from datasets.builder import InvalidConfigName
from datasets.data_files import DataFilesList
from datasets.packaged_modules.parquet.parquet import ParquetConfig
def test_config_raises_when_invalid_name() -> None:
with pytest.raises(InvalidConfigName, match="Bad characters"):
_ = ParquetConf... | datasets/tests/packaged_modules/test_parquet.py/0 | {
"file_path": "datasets/tests/packaged_modules/test_parquet.py",
"repo_id": "datasets",
"token_count": 227
} |
import os
import zipfile
import pytest
from datasets.utils.extract import (
Bzip2Extractor,
Extractor,
GzipExtractor,
Lz4Extractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lz4, require_py7zr, require_zstandard
@pyte... | datasets/tests/test_extract.py/0 | {
"file_path": "datasets/tests/test_extract.py",
"repo_id": "datasets",
"token_count": 2984
} |
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered,
map_nested,
temp_... | datasets/tests/test_py_utils.py/0 | {
"file_path": "datasets/tests/test_py_utils.py",
"repo_id": "datasets",
"token_count": 4313
} |
# Files for typos
# Instruction: https://github.com/marketplace/actions/typos-action#getting-started
[default.extend-identifiers]
[default.extend-words]
NIN="NIN" # NIN is used in scripts/convert_ncsnpp_original_checkpoint_to_diffusers.py
nd="np" # nd may be np (numpy)
parms="parms" # parms is used in scripts/conver... | diffusers/_typos.toml/0 | {
"file_path": "diffusers/_typos.toml",
"repo_id": "diffusers",
"token_count": 151
} |
<!--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 | {
"file_path": "diffusers/docs/source/en/api/internal_classes_overview.md",
"repo_id": "diffusers",
"token_count": 211
} |
<!--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 | {
"file_path": "diffusers/docs/source/en/api/models/autoencoderkl.md",
"repo_id": "diffusers",
"token_count": 783
} |
<!--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/auto_pipeline.md/0 | {
"file_path": "diffusers/docs/source/en/api/pipelines/auto_pipeline.md",
"repo_id": "diffusers",
"token_count": 378
} |
<!--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/ddim.md/0 | {
"file_path": "diffusers/docs/source/en/api/pipelines/ddim.md",
"repo_id": "diffusers",
"token_count": 477
} |
<!--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/cosine_dpm.md/0 | {
"file_path": "diffusers/docs/source/en/api/schedulers/cosine_dpm.md",
"repo_id": "diffusers",
"token_count": 357
} |
<!--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 | {
"file_path": "diffusers/docs/source/en/api/schedulers/lcm.md",
"repo_id": "diffusers",
"token_count": 291
} |
<!--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/community_projects.md/0 | {
"file_path": "diffusers/docs/source/en/community_projects.md",
"repo_id": "diffusers",
"token_count": 1417
} |
<!--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": 1206
} |
<!--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/controlnet.md/0 | {
"file_path": "diffusers/docs/source/en/training/controlnet.md",
"repo_id": "diffusers",
"token_count": 4995
} |
<!--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/wuerstchen.md/0 | {
"file_path": "diffusers/docs/source/en/training/wuerstchen.md",
"repo_id": "diffusers",
"token_count": 2906
} |
<!--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/diffedit.md/0 | {
"file_path": "diffusers/docs/source/en/using-diffusers/diffedit.md",
"repo_id": "diffusers",
"token_count": 3847
} |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | diffusers/docs/source/en/using-diffusers/reusing_seeds.md/0 | {
"file_path": "diffusers/docs/source/en/using-diffusers/reusing_seeds.md",
"repo_id": "diffusers",
"token_count": 3005
} |
<!--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 | {
"file_path": "diffusers/docs/source/ja/installation.md",
"repo_id": "diffusers",
"token_count": 2493
} |
<!--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/habana.md/0 | {
"file_path": "diffusers/docs/source/ko/optimization/habana.md",
"repo_id": "diffusers",
"token_count": 1911
} |
<!--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/lora.md/0 | {
"file_path": "diffusers/docs/source/ko/training/lora.md",
"repo_id": "diffusers",
"token_count": 4756
} |
<!--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/loading_adapters.md/0 | {
"file_path": "diffusers/docs/source/ko/using-diffusers/loading_adapters.md",
"repo_id": "diffusers",
"token_count": 12272
} |
- sections:
- local: index
title: 🧨 Diffusers
- local: quicktour
title: 快速入门
- local: stable_diffusion
title: 有效和高效的扩散
- local: consisid
title: 身份保持的文本到视频生成
- local: installation
title: 安装
title: 开始
| diffusers/docs/source/zh/_toctree.yml/0 | {
"file_path": "diffusers/docs/source/zh/_toctree.yml",
"repo_id": "diffusers",
"token_count": 141
} |
# coding=utf-8
# Copyright 2025 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 | {
"file_path": "diffusers/examples/amused/train_amused.py",
"repo_id": "diffusers",
"token_count": 17513
} |
from typing import Optional
import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNet2DConditionModel
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils impor... | diffusers/examples/community/edict_pipeline.py/0 | {
"file_path": "diffusers/examples/community/edict_pipeline.py",
"repo_id": "diffusers",
"token_count": 4682
} |
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 | {
"file_path": "diffusers/examples/community/lpw_stable_diffusion_onnx.py",
"repo_id": "diffusers",
"token_count": 24240
} |
# Inspired by: https://github.com/haofanwang/ControlNet-for-Diffusers/
import inspect
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import numpy as np
import PIL.Image
import torch
import torch.nn.functional as F
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from di... | diffusers/examples/community/stable_diffusion_controlnet_inpaint.py/0 | {
"file_path": "diffusers/examples/community/stable_diffusion_controlnet_inpaint.py",
"repo_id": "diffusers",
"token_count": 23770
} |
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 | {
"file_path": "diffusers/examples/community/unclip_text_interpolation.py",
"repo_id": "diffusers",
"token_count": 10699
} |
# DreamBooth training example
[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.
The `train_dreambooth.py` script shows how to implement the training procedure and adapt it for stable diffusion.
## Running local... | diffusers/examples/dreambooth/README.md/0 | {
"file_path": "diffusers/examples/dreambooth/README.md",
"repo_id": "diffusers",
"token_count": 10149
} |
# 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_flux.py/0 | {
"file_path": "diffusers/examples/dreambooth/test_dreambooth_lora_flux.py",
"repo_id": "diffusers",
"token_count": 4875
} |
# Search models on Civitai and Hugging Face
The [auto_diffusers](https://github.com/suzukimain/auto_diffusers) library provides additional functionalities to Diffusers such as searching for models on Civitai and the Hugging Face Hub.
Please refer to the original library [here](https://pypi.org/project/auto-diffusers/)... | diffusers/examples/model_search/README.md/0 | {
"file_path": "diffusers/examples/model_search/README.md",
"repo_id": "diffusers",
"token_count": 3696
} |
#!/usr/bin/env python
# coding=utf-8
# Copyright 2025 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/research_projects/consistency_training/train_cm_ct_unconditional.py/0 | {
"file_path": "diffusers/examples/research_projects/consistency_training/train_cm_ct_unconditional.py",
"repo_id": "diffusers",
"token_count": 26288
} |
#!/usr/bin/env python
# coding=utf-8
# Copyright 2025 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/research_projects/flux_lora_quantization/compute_embeddings.py/0 | {
"file_path": "diffusers/examples/research_projects/flux_lora_quantization/compute_embeddings.py",
"repo_id": "diffusers",
"token_count": 1459
} |
## Textual Inversion fine-tuning example
[Textual inversion](https://arxiv.org/abs/2208.01618) is a method to personalize text2image models like stable diffusion on your own images using just 3-5 examples.
The `textual_inversion.py` script shows how to implement the training procedure and adapt it for stable diffusion... | diffusers/examples/research_projects/intel_opts/textual_inversion/README.md/0 | {
"file_path": "diffusers/examples/research_projects/intel_opts/textual_inversion/README.md",
"repo_id": "diffusers",
"token_count": 1013
} |
# Multi Subject DreamBooth training
[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.
This `train_multi_subject_dreambooth.py` script shows how to implement the training procedure for one or more subjects and ada... | diffusers/examples/research_projects/multi_subject_dreambooth/README.md/0 | {
"file_path": "diffusers/examples/research_projects/multi_subject_dreambooth/README.md",
"repo_id": "diffusers",
"token_count": 4800
} |
## Textual Inversion fine-tuning example
[Textual inversion](https://arxiv.org/abs/2208.01618) is a method to personalize text2image models like stable diffusion on your own images using just 3-5 examples.
The `textual_inversion.py` script shows how to implement the training procedure and adapt it for stable diffusion... | diffusers/examples/research_projects/onnxruntime/textual_inversion/README.md/0 | {
"file_path": "diffusers/examples/research_projects/onnxruntime/textual_inversion/README.md",
"repo_id": "diffusers",
"token_count": 1129
} |
# 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 | {
"file_path": "diffusers/examples/research_projects/promptdiffusion/promptdiffusioncontrolnet.py",
"repo_id": "diffusers",
"token_count": 8426
} |
#!/usr/bin/env python
# coding=utf-8
# Copyright 2025 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/research_projects/scheduled_huber_loss_training/dreambooth/train_dreambooth_lora_sdxl.py/0 | {
"file_path": "diffusers/examples/research_projects/scheduled_huber_loss_training/dreambooth/train_dreambooth_lora_sdxl.py",
"repo_id": "diffusers",
"token_count": 40446
} |
import torch.nn as nn
from torchvision.models import efficientnet_v2_l, efficientnet_v2_s
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.models.modeling_utils import ModelMixin
class EfficientNetEncoder(ModelMixin, ConfigMixin):
@register_to_config
def __init__(self,... | diffusers/examples/research_projects/wuerstchen/text_to_image/modeling_efficient_net_encoder.py/0 | {
"file_path": "diffusers/examples/research_projects/wuerstchen/text_to_image/modeling_efficient_net_encoder.py",
"repo_id": "diffusers",
"token_count": 374
} |
#!/usr/bin/env python
# coding=utf-8
# Copyright 2025 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 | {
"file_path": "diffusers/examples/textual_inversion/textual_inversion.py",
"repo_id": "diffusers",
"token_count": 17436
} |
import inspect
import os
from argparse import ArgumentParser
import numpy as np
import torch
from muse import MaskGiTUViT, VQGANModel
from muse import PipelineMuse as OldPipelineMuse
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import VQModel
from diffusers.models.attention_proce... | diffusers/scripts/convert_amused.py/0 | {
"file_path": "diffusers/scripts/convert_amused.py",
"repo_id": "diffusers",
"token_count": 12883
} |
import argparse
from pathlib import Path
from typing import Any, Dict
import torch
from accelerate import init_empty_weights
from safetensors.torch import load_file
from transformers import T5EncoderModel, T5Tokenizer
from diffusers import AutoencoderKLLTXVideo, FlowMatchEulerDiscreteScheduler, LTXPipeline, LTXVideoT... | diffusers/scripts/convert_ltx_to_diffusers.py/0 | {
"file_path": "diffusers/scripts/convert_ltx_to_diffusers.py",
"repo_id": "diffusers",
"token_count": 4898
} |
"""
A script to convert Stable Diffusion 3.5 ControlNet checkpoints to the Diffusers format.
Example:
Convert a SD3.5 ControlNet checkpoint to Diffusers format using local file:
```bash
python scripts/convert_sd3_controlnet_to_diffusers.py \
--checkpoint_path "path/to/local/sd3.5_large_controlnet_c... | diffusers/scripts/convert_sd3_controlnet_to_diffusers.py/0 | {
"file_path": "diffusers/scripts/convert_sd3_controlnet_to_diffusers.py",
"repo_id": "diffusers",
"token_count": 3453
} |
"""
This script ports models from VQ-diffusion (https://github.com/microsoft/VQ-Diffusion) to diffusers.
It currently only supports porting the ITHQ dataset.
ITHQ dataset:
```sh
# From the root directory of diffusers.
# Download the VQVAE checkpoint
$ wget https://facevcstandard.blob.core.windows.net/v-zhictang/Impr... | diffusers/scripts/convert_vq_diffusion_to_diffusers.py/0 | {
"file_path": "diffusers/scripts/convert_vq_diffusion_to_diffusers.py",
"repo_id": "diffusers",
"token_count": 14916
} |
# coding=utf-8
# Copyright 2025 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable... | diffusers/src/diffusers/loaders/single_file_utils.py/0 | {
"file_path": "diffusers/src/diffusers/loaders/single_file_utils.py",
"repo_id": "diffusers",
"token_count": 56617
} |
# Copyright 2024 MIT, Tsinghua University, NVIDIA CORPORATION 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/li... | diffusers/src/diffusers/models/autoencoders/autoencoder_dc.py/0 | {
"file_path": "diffusers/src/diffusers/models/autoencoders/autoencoder_dc.py",
"repo_id": "diffusers",
"token_count": 13842
} |
# Copyright 2024 Stability AI, The HuggingFace Team and The InstantX 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-... | diffusers/src/diffusers/models/controlnet_sd3.py/0 | {
"file_path": "diffusers/src/diffusers/models/controlnet_sd3.py",
"repo_id": "diffusers",
"token_count": 1263
} |
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