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// 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 super::{conv, quantization, seanet, transformer};
use candle::{DType, Device, Module, Result, StreamTensor, StreamingModule, Tensor};
use candle_nn::VarBuilder;
#[derive(Debug, Copy, Clone, PartialEq, Eq)]
pub enum ResampleMethod {
Conv,
Interpolate,
}
#[derive(Debug, Clone)]
pub struct Config {
pub channels: usize,
pub sample_rate: f64,
pub frame_rate: f64,
pub renormalize: bool,
pub resample_method: ResampleMethod,
pub seanet: seanet::Config,
pub transformer: transformer::Config,
pub quantizer_n_q: usize,
pub quantizer_bins: usize,
pub quantizer_dim: usize,
}
impl Config {
// /lustre/scwpod02/client/kyutai/alex/mimi_exp/xps/b7d2bd5a/.hydra/config.yaml
pub fn v0_1(num_codebooks: Option<usize>) -> Self {
let seanet_cfg = seanet::Config {
dimension: 512,
channels: 1,
causal: true,
n_filters: 64,
n_residual_layers: 1,
activation: candle_nn::Activation::Elu(1.),
compress: 2,
dilation_base: 2,
disable_norm_outer_blocks: 0,
final_activation: None,
kernel_size: 7,
residual_kernel_size: 3,
last_kernel_size: 3,
lstm: 0,
norm: conv::Norm::WeightNorm,
pad_mode: conv::PadMode::Constant,
ratios: vec![8, 6, 5, 4],
true_skip: true,
};
let transformer_cfg = transformer::Config {
d_model: seanet_cfg.dimension,
num_heads: 8,
num_layers: 8,
causal: true,
norm_first: true,
bias_ff: false,
bias_attn: false,
layer_scale: Some(0.01),
context: 250,
conv_kernel_size: 5,
use_conv_bias: true,
use_conv_block: false,
cross_attention: false,
max_period: 10000,
gating: None,
norm: super::NormType::LayerNorm,
positional_embedding: transformer::PositionalEmbedding::Rope,
dim_feedforward: 2048,
kv_repeat: 1,
conv_layout: true, // see builders.py
max_seq_len: 8192, // the transformer works at 25hz so this is ~5 mins.
};
Config {
channels: 1,
sample_rate: 24_000.,
frame_rate: 12.5,
renormalize: true,
resample_method: ResampleMethod::Conv,
seanet: seanet_cfg,
transformer: transformer_cfg,
quantizer_n_q: num_codebooks.unwrap_or(16),
quantizer_bins: 2048,
quantizer_dim: 256,
}
}
}
#[derive(Debug, Clone)]
pub struct Encodec {
encoder: seanet::SeaNetEncoder,
decoder: seanet::SeaNetDecoder,
encoder_transformer: transformer::ProjectedTransformer,
decoder_transformer: transformer::ProjectedTransformer,
downsample: conv::ConvDownsample1d,
upsample: conv::ConvTrUpsample1d,
quantizer: quantization::SplitResidualVectorQuantizer,
config: Config,
}
impl Encodec {
pub fn new(cfg: Config, vb: VarBuilder) -> Result<Self> {
let dim = cfg.seanet.dimension;
let encoder = seanet::SeaNetEncoder::new(&cfg.seanet, vb.pp("encoder"))?;
let decoder = seanet::SeaNetDecoder::new(&cfg.seanet, vb.pp("decoder"))?;
let encoder_transformer = transformer::ProjectedTransformer::new(
dim,
&[dim],
&cfg.transformer,
vb.pp("encoder_transformer"),
)?;
let decoder_transformer = transformer::ProjectedTransformer::new(
dim,
&[dim],
&cfg.transformer,
vb.pp("decoder_transformer"),
)?;
let quantizer = quantization::SplitResidualVectorQuantizer::new(
/* dim */ cfg.quantizer_dim,
/* input_dim */ Some(dim),
/* output_dim */ Some(dim),
/* n_q */ cfg.quantizer_n_q,
/* bins */ cfg.quantizer_bins,
vb.pp("quantizer"),
)?;
let encoder_frame_rate =
cfg.sample_rate / cfg.seanet.ratios.iter().product::<usize>() as f64;
let downsample_stride = (encoder_frame_rate / cfg.frame_rate) as usize;
// `upsample` and `downsample` only apply if frame_rate is different from encoder_frame_rate.
let downsample = conv::ConvDownsample1d::new(
/* stride */ downsample_stride,
/* dim */ dim,
/* causal */ true,
/* learnt */ true,
vb.pp("downsample"),
)?;
let upsample = conv::ConvTrUpsample1d::new(
/* stride */ downsample_stride,
/* dim */ dim,
/* causal */ true,
/* learnt */ true,
vb.pp("upsample"),
)?;
Ok(Self {
encoder,
decoder,
encoder_transformer,
decoder_transformer,
quantizer,
downsample,
upsample,
config: cfg,
})
}
pub fn config(&self) -> &Config {
&self.config
}
pub fn encode_pre_quantize(&mut self, xs: &Tensor) -> Result<Tensor> {
let xs = self.encoder.forward(xs)?;
self.encoder_transformer.reset_state();
let xs = self.encoder_transformer.forward(&xs)?;
let xs = &xs[0];
xs.apply(&self.downsample)
}
pub fn encode(&mut self, xs: &Tensor) -> Result<Tensor> {
let xs = self.encoder.forward(xs)?;
self.encoder_transformer.reset_state();
let xs = self.encoder_transformer.forward(&xs)?;
let xs = &xs[0];
let xs = xs.apply(&self.downsample)?;
let codes = self.quantizer.encode(&xs)?;
Ok(codes)
}
pub fn encode_step(&mut self, xs: &StreamTensor) -> Result<StreamTensor> {
let xs = self.encoder.step(xs)?;
let xs = self.encoder_transformer.step(&xs)?;
let xs = self.downsample.step(&xs)?;
match xs.as_option() {
None => Ok(().into()),
Some(xs) => {
let codes = self.quantizer.encode(xs)?;
Ok(codes.into())
}
}
}
pub fn decode(&mut self, codes: &Tensor) -> Result<Tensor> {
let emb = self.quantizer.decode(codes)?;
let emb = emb.apply(&self.upsample)?;
self.decoder_transformer.reset_state();
let outs = self.decoder_transformer.forward(&emb)?;
let out = &outs[0];
self.decoder.forward(out)
}
pub fn decode_step(&mut self, codes: &StreamTensor) -> Result<StreamTensor> {
let emb = match codes.as_option() {
Some(codes) => StreamTensor::from_tensor(self.quantizer.decode(codes)?),
None => StreamTensor::empty(),
};
let emb = self.upsample.step(&emb)?;
let out = self.decoder_transformer.step(&emb)?;
self.decoder.step(&out)
}
pub fn reset_state(&mut self) {
self.encoder.reset_state();
self.encoder_transformer.reset_state();
self.decoder.reset_state();
self.decoder_transformer.reset_state();
self.upsample.reset_state();
}
}
pub fn load(model_file: &str, num_codebooks: Option<usize>, dev: &Device) -> Result<Encodec> {
let vb =
unsafe { candle_nn::VarBuilder::from_mmaped_safetensors(&[model_file], DType::F32, dev)? };
let cfg = Config::v0_1(num_codebooks);
let encodec = Encodec::new(cfg, vb)?;
Ok(encodec)
}
| candle/candle-transformers/src/models/mimi/encodec.rs/0 | {
"file_path": "candle/candle-transformers/src/models/mimi/encodec.rs",
"repo_id": "candle",
"token_count": 3773
} | 60 |
//! Candle implementations for various deep learning models
//!
//! This crate provides implementations of popular machine learning models and architectures for different modalities.
//!
//! - Large language models: [`llama`], [`phi3`], [`mamba`], [`mixtral`], [`bert`], ...
//! - Text to text models: [`t5`], ...
//! - Image to text models: [`blip`], ...
//! - Text to image models: [`stable_diffusion`] and [`wuerstchen`], ...
//! - Audio models: [`whisper`], [`encodec`], [`metavoice`], [`parler_tts`], ...
//! - Computer vision models: [`dinov2`], [`convmixer`], [`efficientnet`], ...
//!
//! Some of the models also have quantized variants, e.g. [`quantized_blip`], [`quantized_llama`] and [`quantized_qwen2`].
//!
//! The implementations aim to be readable while maintaining good performance. For more information
//! on each model see the model's module docs in the links below.
pub mod based;
pub mod beit;
pub mod bert;
pub mod bigcode;
pub mod blip;
pub mod blip_text;
pub mod chatglm;
pub mod chinese_clip;
pub mod clip;
pub mod codegeex4_9b;
pub mod colpali;
pub mod convmixer;
pub mod convnext;
pub mod csm;
pub mod dac;
pub mod debertav2;
pub mod deepseek2;
pub mod depth_anything_v2;
pub mod dinov2;
pub mod dinov2reg4;
pub mod distilbert;
pub mod efficientnet;
pub mod efficientvit;
pub mod encodec;
pub mod eva2;
pub mod falcon;
pub mod fastvit;
pub mod flux;
pub mod gemma;
pub mod gemma2;
pub mod gemma3;
pub mod glm4;
pub mod glm4_new;
pub mod granite;
pub mod helium;
pub mod hiera;
pub mod jina_bert;
pub mod llama;
pub mod llama2_c;
pub mod llama2_c_weights;
pub mod llava;
pub mod mamba;
pub mod marian;
pub mod metavoice;
pub mod mimi;
pub mod mistral;
pub mod mixformer;
pub mod mixtral;
pub mod mmdit;
pub mod mobileclip;
pub mod mobilenetv4;
pub mod mobileone;
pub mod modernbert;
pub mod moondream;
pub mod mpt;
pub mod nvembed_v2;
pub mod olmo;
pub mod olmo2;
pub mod openclip;
pub mod paligemma;
pub mod parler_tts;
pub mod persimmon;
pub mod phi;
pub mod phi3;
pub mod pixtral;
pub mod quantized_blip;
pub mod quantized_blip_text;
pub mod quantized_gemma3;
pub mod quantized_llama;
pub mod quantized_llama2_c;
pub mod quantized_metavoice;
pub mod quantized_mistral;
pub mod quantized_mixformer;
pub mod quantized_moondream;
pub mod quantized_mpt;
pub mod quantized_phi;
pub mod quantized_phi3;
pub mod quantized_qwen2;
pub mod quantized_qwen3;
pub mod quantized_recurrent_gemma;
pub mod quantized_rwkv_v5;
pub mod quantized_rwkv_v6;
pub mod quantized_stable_lm;
pub mod quantized_t5;
pub mod qwen2;
pub mod qwen2_moe;
pub mod qwen3;
pub mod qwen3_moe;
pub mod recurrent_gemma;
pub mod repvgg;
pub mod resnet;
pub mod rwkv_v5;
pub mod rwkv_v6;
pub mod segformer;
pub mod segment_anything;
pub mod siglip;
pub mod snac;
pub mod stable_diffusion;
pub mod stable_lm;
pub mod starcoder2;
pub mod stella_en_v5;
pub mod t5;
pub mod trocr;
pub mod vgg;
pub mod vit;
pub mod voxtral;
pub mod whisper;
pub mod with_tracing;
pub mod wuerstchen;
pub mod xlm_roberta;
pub mod yi;
| candle/candle-transformers/src/models/mod.rs/0 | {
"file_path": "candle/candle-transformers/src/models/mod.rs",
"repo_id": "candle",
"token_count": 1145
} | 61 |
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: vision_model::Config,
pub image_token_index: usize,
pub image_seq_length: usize,
}
#[derive(Debug, Clone)]
pub struct MultiModalProjector {
linear_1: Linear,
act: candle_nn::Activation,
linear_2: Linear,
}
impl MultiModalProjector {
pub fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
let (hidden_v, hidden_t) = (cfg.vision_config.hidden_size, cfg.text_config.hidden_size);
let linear_1 = linear(hidden_v, hidden_t, vb.pp("linear_1"))?;
let linear_2 = linear(hidden_t, hidden_t, vb.pp("linear_2"))?;
Ok(Self {
linear_1,
act: cfg.projector_hidden_act,
linear_2,
})
}
}
impl Module for MultiModalProjector {
fn forward(&self, xs: &Tensor) -> Result<Tensor> {
xs.apply(&self.linear_1)?
.apply(&self.act)?
.apply(&self.linear_2)
}
}
#[derive(Debug, Clone)]
pub struct Model {
pub multi_modal_projector: MultiModalProjector,
pub language_model: mistral::Model,
pub vision_tower: vision_model::Model,
pub patch_size: usize,
pub dtype: candle::DType,
pub pos: usize,
}
impl Model {
pub fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
let language_model = mistral::Model::new(&cfg.text_config, vb.pp("language_model"))?;
let vision_tower = vision_model::Model::new(
&cfg.vision_config,
vb.pp("vision_tower").to_dtype(candle::DType::F32),
)?;
let multi_modal_projector = MultiModalProjector::new(
cfg,
vb.pp("multi_modal_projector").to_dtype(candle::DType::F32),
)?;
Ok(Self {
multi_modal_projector,
language_model,
vision_tower,
patch_size: cfg.vision_config.patch_size,
dtype: vb.dtype(),
pos: 0,
})
}
pub fn clear_kv_cache(&mut self) {
self.language_model.clear_kv_cache();
self.pos = 0;
}
pub fn encode_image(&self, image: &Tensor) -> Result<Tensor> {
let image_embeds = self.vision_tower.forward(image)?;
self.multi_modal_projector.forward(&image_embeds)
}
pub fn lm_forward(&mut self, input_ids: &Tensor) -> Result<Tensor> {
let (_, seq_len) = input_ids.dims2()?;
let logits = self.language_model.forward(input_ids, self.pos)?;
self.pos += seq_len;
Ok(logits)
}
pub fn lm_forward_embeds(&mut self, xs: &Tensor) -> Result<Tensor> {
let (_, seq_len, _) = xs.dims3()?;
let logits = self.language_model.forward_embeds(xs, None, self.pos)?;
self.pos += seq_len;
Ok(logits)
}
}
| 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
} | 62 |
//! Qwen3 implementation with quantization support.
//!
//! Based on the Qwen3 architecture and implemented with quantized weights
//! for reduced memory usage and faster inference on compatible hardware.
//!
//! References:
//! - [Qwen3 Models](https://huggingface.co/Qwen/Qwen3-0.6B) (architecture based on official implementations)
//!
use super::with_tracing::QMatMul;
use crate::{quantized_nn::RmsNorm, utils::repeat_kv};
use candle::quantized::{gguf_file, QTensor};
use candle::{DType, Device, Result, Tensor};
use candle_nn::{kv_cache::KvCache, Activation, Embedding, Module};
use std::io::{Read, Seek};
use std::sync::Arc;
struct Gguf<R: Read + Seek> {
ct: gguf_file::Content,
reader: R,
device: Device,
}
impl<R: Read + Seek> Gguf<R> {
fn new(ct: gguf_file::Content, reader: R, device: Device) -> Self {
Self { ct, reader, device }
}
fn qmatmul(&mut self, name: &str) -> Result<QMatMul> {
let ws = self.ct.tensor(&mut self.reader, name, &self.device)?;
QMatMul::from_weights(ws.into())
}
fn rms_norm(&mut self, name: &str, eps: f64) -> Result<RmsNorm> {
let ws = self.ct.tensor(&mut self.reader, name, &self.device)?;
RmsNorm::from_qtensor(ws, eps)
}
fn metadata(&self) -> &std::collections::HashMap<String, gguf_file::Value> {
&self.ct.metadata
}
fn tensor(&mut self, name: &str) -> Result<QTensor> {
self.ct.tensor(&mut self.reader, name, &self.device)
}
}
#[derive(Debug, Clone)]
struct MlpWeights {
gate_proj: QMatMul,
up_proj: QMatMul,
down_proj: QMatMul,
act_fn: Activation,
span: tracing::Span,
}
impl MlpWeights {
fn new<R: Read + Seek>(gg: &mut Gguf<R>, prefix: &str) -> Result<Self> {
let gate_proj = gg.qmatmul(&format!("{prefix}.ffn_gate.weight"))?;
let up_proj = gg.qmatmul(&format!("{prefix}.ffn_up.weight"))?;
let down_proj = gg.qmatmul(&format!("{prefix}.ffn_down.weight"))?;
let act_fn = Activation::Silu;
let span = tracing::span!(tracing::Level::TRACE, "mlp");
Ok(Self {
gate_proj,
up_proj,
down_proj,
act_fn,
span,
})
}
}
impl Module for MlpWeights {
fn forward(&self, x: &Tensor) -> Result<Tensor> {
let _enter = self.span.enter();
let gate = self.gate_proj.forward(x)?.apply(&self.act_fn)?;
let up = self.up_proj.forward(x)?;
let gated = (gate * up)?;
self.down_proj.forward(&gated)
}
}
#[derive(Debug, Clone)]
struct RotaryEmbedding {
sin: Tensor,
cos: Tensor,
}
impl RotaryEmbedding {
fn new(
dtype: DType,
head_dim: usize,
max_position_embeddings: usize,
rope_theta: f64,
dev: &Device,
) -> Result<Self> {
let dim = head_dim;
let max_seq_len = max_position_embeddings;
let inv_freq: Vec<_> = (0..dim)
.step_by(2)
.map(|i| 1f32 / rope_theta.powf(i as f64 / dim as f64) as f32)
.collect();
let inv_freq_len = inv_freq.len();
let inv_freq = Tensor::from_vec(inv_freq, (1, inv_freq_len), dev)?.to_dtype(dtype)?;
let t = Tensor::arange(0u32, max_seq_len as u32, dev)?
.to_dtype(dtype)?
.reshape((max_seq_len, 1))?;
let freqs = t.matmul(&inv_freq)?;
Ok(Self {
sin: freqs.sin()?,
cos: freqs.cos()?,
})
}
/// Apply RoPE (q, k shape: B x H x L x D)
fn apply(&self, q: &Tensor, k: &Tensor, offset: usize) -> Result<(Tensor, Tensor)> {
let (_, _, seq_len, _) = q.dims4()?;
let cos = self.cos.narrow(0, offset, seq_len)?.to_dtype(q.dtype())?;
let sin = self.sin.narrow(0, offset, seq_len)?.to_dtype(q.dtype())?;
let q_embed = candle_nn::rotary_emb::rope(&q.contiguous()?, &cos, &sin)?;
let k_embed = candle_nn::rotary_emb::rope(&k.contiguous()?, &cos, &sin)?;
Ok((q_embed, k_embed))
}
}
#[derive(Debug, Clone)]
struct AttentionWeights {
q_proj: QMatMul,
k_proj: QMatMul,
v_proj: QMatMul,
o_proj: QMatMul,
q_norm: RmsNorm,
k_norm: RmsNorm,
num_heads: usize,
num_kv_heads: usize,
num_kv_groups: usize,
head_dim: usize,
rotary_emb: Arc<RotaryEmbedding>,
kv_cache: KvCache,
span_attn: tracing::Span,
}
impl AttentionWeights {
fn new<R: Read + Seek>(
gg: &mut Gguf<R>,
num_heads: usize,
num_kv_heads: usize,
head_dim: usize,
rms_norm_eps: f64,
rotary_emb: Arc<RotaryEmbedding>,
prefix: &str,
) -> Result<Self> {
let num_kv_groups = num_heads / num_kv_heads;
let q_proj = gg.qmatmul(&format!("{prefix}.attn_q.weight"))?;
let k_proj = gg.qmatmul(&format!("{prefix}.attn_k.weight"))?;
let v_proj = gg.qmatmul(&format!("{prefix}.attn_v.weight"))?;
let o_proj = gg.qmatmul(&format!("{prefix}.attn_output.weight"))?;
let q_norm = gg.rms_norm(&format!("{prefix}.attn_q_norm.weight"), rms_norm_eps)?;
let k_norm = gg.rms_norm(&format!("{prefix}.attn_k_norm.weight"), rms_norm_eps)?;
// Initialize KV cache with 512 tokens capacity to reduce initial memory allocation.
// The cache will grow in chunks of 512 tokens when needed.
let kv_cache = KvCache::new(2, 512);
let span_attn = tracing::span!(tracing::Level::TRACE, "attn");
Ok(Self {
q_proj,
k_proj,
v_proj,
o_proj,
q_norm,
k_norm,
num_heads,
num_kv_heads,
num_kv_groups,
head_dim,
rotary_emb,
kv_cache,
span_attn,
})
}
fn forward(&mut self, x: &Tensor, attn_mask: Option<&Tensor>, offset: usize) -> Result<Tensor> {
let _enter = self.span_attn.enter();
let (b, l, _) = x.dims3()?;
let q = self.q_proj.forward(x)?;
let k = self.k_proj.forward(x)?;
let v = self.v_proj.forward(x)?;
let q = q
.reshape((b, l, self.num_heads, self.head_dim))?
.transpose(1, 2)?;
let k = k
.reshape((b, l, self.num_kv_heads, self.head_dim))?
.transpose(1, 2)?;
let v = v
.reshape((b, l, self.num_kv_heads, self.head_dim))?
.transpose(1, 2)?;
let q_flat = q.flatten(0, 2)?;
let k_flat = k.flatten(0, 2)?;
let q_flat = self.q_norm.forward(&q_flat)?;
let k_flat = self.k_norm.forward(&k_flat)?;
let q = q_flat.reshape((b, self.num_heads, l, self.head_dim))?;
let k = k_flat.reshape((b, self.num_kv_heads, l, self.head_dim))?;
let (q, k) = self.rotary_emb.apply(&q, &k, offset)?;
// Reset KV cache if we're at the first position
if offset == 0 {
self.kv_cache.reset();
}
let (k, v) = self.kv_cache.append(&k.contiguous()?, &v.contiguous()?)?;
// Make tensor contiguous to avoid some strided copies
let k = k.contiguous()?;
let v = v.contiguous()?;
let k = repeat_kv(k, self.num_kv_groups)?.contiguous()?;
let v = repeat_kv(v, self.num_kv_groups)?.contiguous()?;
let scale = 1.0 / (self.head_dim as f64).sqrt();
let mut scores = (q.matmul(&k.transpose(2, 3)?)? * scale)?;
if let Some(m) = attn_mask {
let m_dtype = m.dtype();
let scores_dtype = scores.dtype();
let mask = if m_dtype != scores_dtype {
m.to_dtype(scores_dtype)?
} else {
m.clone()
};
scores = scores.broadcast_add(&mask)?;
}
let probs = candle_nn::ops::softmax_last_dim(&scores)?;
let ctx = probs.matmul(&v)?; // (B, H, L, D)
let reshaped_ctx = ctx
.transpose(1, 2)?
.reshape((b, l, self.num_heads * self.head_dim))?;
self.o_proj.forward(&reshaped_ctx)
}
}
#[derive(Debug, Clone)]
struct LayerWeights {
self_attn: AttentionWeights,
mlp: MlpWeights,
ln1: RmsNorm,
ln2: RmsNorm,
}
impl LayerWeights {
fn new<R: Read + Seek>(
gg: &mut Gguf<R>,
num_attention_heads: usize,
num_key_value_heads: usize,
head_dim: usize,
rms_norm_eps: f64,
rotary: Arc<RotaryEmbedding>,
layer_idx: usize,
) -> Result<Self> {
let prefix = format!("blk.{layer_idx}");
let ln1 = gg.rms_norm(&format!("{prefix}.attn_norm.weight"), rms_norm_eps)?;
let ln2 = gg.rms_norm(&format!("{prefix}.ffn_norm.weight"), rms_norm_eps)?;
let self_attn = AttentionWeights::new(
gg,
num_attention_heads,
num_key_value_heads,
head_dim,
rms_norm_eps,
rotary,
&prefix,
)?;
let mlp = MlpWeights::new(gg, &prefix)?;
Ok(Self {
self_attn,
mlp,
ln1,
ln2,
})
}
fn forward(&mut self, x: &Tensor, mask: Option<&Tensor>, offset: usize) -> Result<Tensor> {
let h = self.ln1.forward(x)?;
let h = self.self_attn.forward(&h, mask, offset)?;
let x = (x + h)?;
let h2 = self.ln2.forward(&x)?;
let h2 = h2.apply(&self.mlp)?;
x + h2
}
}
#[derive(Debug, Clone)]
pub struct ModelWeights {
embed_tokens: Embedding,
layers: Vec<LayerWeights>,
norm: RmsNorm,
lm_head: QMatMul,
device: Device,
dtype: DType,
span: tracing::Span,
span_output: tracing::Span,
}
impl ModelWeights {
pub fn from_gguf<R: Read + Seek>(
ct: gguf_file::Content,
reader: &mut R,
device: &Device,
) -> Result<Self> {
let mut gg = Gguf::new(ct, reader, device.clone());
let md_get = |s: &str| match gg.metadata().get(s) {
None => candle::bail!("cannot find {s} in metadata"),
Some(v) => Ok(v),
};
let num_attention_heads = md_get("qwen3.attention.head_count")?.to_u32()? as usize;
let num_kv_heads = md_get("qwen3.attention.head_count_kv")?.to_u32()? as usize;
let head_dim = md_get("qwen3.attention.key_length")?.to_u32()? as usize;
let num_layers = md_get("qwen3.block_count")?.to_u32()? as usize;
let hidden_size = md_get("qwen3.embedding_length")?.to_u32()? as usize;
let max_position_embeddings = md_get("qwen3.context_length")?.to_u32()? as usize;
let rms_norm_eps = md_get("qwen3.attention.layer_norm_rms_epsilon")?.to_f32()? as f64;
let rope_freq_base = md_get("qwen3.rope.freq_base")?.to_f32()? as f64;
let dtype = match gg.metadata().get("general.dtype") {
Some(v) => match v.to_u32() {
Ok(0) => DType::F32,
Ok(1) => DType::F16,
_ => DType::F16,
},
None => DType::F16,
};
let embed_tensor = gg.tensor("token_embd.weight")?;
let embed_tokens = Embedding::new(embed_tensor.dequantize(device)?, hidden_size);
let rotary = Arc::new(RotaryEmbedding::new(
dtype,
head_dim,
max_position_embeddings,
rope_freq_base,
device,
)?);
let mut layers = Vec::with_capacity(num_layers);
for i in 0..num_layers {
layers.push(LayerWeights::new(
&mut gg,
num_attention_heads,
num_kv_heads,
head_dim,
rms_norm_eps,
rotary.clone(),
i,
)?);
}
let norm = gg.rms_norm("output_norm.weight", rms_norm_eps)?;
// Load output projection tensor, falling back to tied embeddings like gemma3
let lm_head_tensor = match gg.tensor("output.weight") {
Ok(tensor) => tensor,
Err(_) => gg.tensor("token_embd.weight")?,
};
let lm_head = QMatMul::from_weights(lm_head_tensor.into())?;
let span = tracing::span!(tracing::Level::TRACE, "model");
let span_output = tracing::span!(tracing::Level::TRACE, "output");
Ok(Self {
embed_tokens,
layers,
norm,
lm_head,
device: device.clone(),
dtype,
span,
span_output,
})
}
fn causal_mask(
&self,
b: usize,
tgt: usize,
offset: usize,
sw: Option<usize>,
) -> Result<Tensor> {
let minf = f32::NEG_INFINITY;
let mask: Vec<_> = (0..tgt)
.flat_map(|i| {
(0..(tgt + offset)).map(move |j| {
let past_ok = j <= i + offset;
let sw_ok = match sw {
Some(w) => (i + offset) as i64 - j as i64 <= w as i64,
None => true,
};
if past_ok && sw_ok {
0.
} else {
minf
}
})
})
.collect();
Tensor::from_slice(&mask, (b, 1, tgt, tgt + offset), &self.device)?.to_dtype(self.dtype)
}
pub fn forward(&mut self, input: &Tensor, offset: usize) -> Result<Tensor> {
let _enter = self.span.enter();
let (b, l) = input.dims2()?;
let mut h = self.embed_tokens.forward(input)?;
let causal_mask = if l == 1 {
None
} else {
Some(self.causal_mask(b, l, offset, None)?)
};
for layer in &mut self.layers {
h = layer.forward(&h, causal_mask.as_ref(), offset)?;
}
let h = self.norm.forward(&h)?;
let _enter = self.span_output.enter();
let last_hidden = h.narrow(1, l - 1, 1)?;
self.lm_head.forward(&last_hidden)?.squeeze(1)
}
}
| candle/candle-transformers/src/models/quantized_qwen3.rs/0 | {
"file_path": "candle/candle-transformers/src/models/quantized_qwen3.rs",
"repo_id": "candle",
"token_count": 7391
} | 63 |
use candle::{DType, IndexOp, Result, Tensor};
use candle_nn::{layer_norm, LayerNorm, Module, VarBuilder};
#[derive(Debug)]
struct PatchEmbed {
proj: candle_nn::Conv2d,
span: tracing::Span,
}
impl PatchEmbed {
fn new(
in_chans: usize,
embed_dim: usize,
k_size: usize,
stride: usize,
padding: usize,
vb: VarBuilder,
) -> Result<Self> {
let cfg = candle_nn::Conv2dConfig {
stride,
padding,
..Default::default()
};
let proj = candle_nn::conv2d(in_chans, embed_dim, k_size, cfg, vb.pp("proj"))?;
let span = tracing::span!(tracing::Level::TRACE, "patch-embed");
Ok(Self { proj, span })
}
}
impl Module for PatchEmbed {
fn forward(&self, xs: &Tensor) -> Result<Tensor> {
let _enter = self.span.enter();
xs.apply(&self.proj)?.permute((0, 2, 3, 1))
}
}
// A custom op to make add_decomposed_rel_pos faster. Most of the time is spent on the final
// addition in the case where b = 12, q_h = q_w = 4096, k_h = k_w = 4096
// (attn.reshape((b, q_h, q_w, k_h, k_w))?
// + rel_h.unsqueeze(4)?.broadcast_add(&rel_w.unsqueeze(3)?)?)?
// .reshape((b, q_h * q_w, k_h * k_w))
// Ideally we would perform this operation in place but this is not supported in candle at the
// moment. We should also investigate using f16 rather than f32.
struct Add3(usize, usize, usize, usize, usize);
impl candle::CustomOp3 for Add3 {
fn name(&self) -> &'static str {
"add3"
}
fn cpu_fwd(
&self,
s1: &candle::CpuStorage,
l1: &candle::Layout,
s2: &candle::CpuStorage,
l2: &candle::Layout,
s3: &candle::CpuStorage,
l3: &candle::Layout,
) -> Result<(candle::CpuStorage, candle::Shape)> {
use rayon::prelude::*;
let Add3(b, q_h, q_w, k_h, k_w) = *self;
let s1 = s1.as_slice::<f32>()?;
let s1 = match l1.contiguous_offsets() {
None => candle::bail!("input1 has to be contiguous"),
Some((o1, o2)) => &s1[o1..o2],
};
let s2 = s2.as_slice::<f32>()?;
let s2 = match l2.contiguous_offsets() {
None => candle::bail!("input2 has to be contiguous"),
Some((o1, o2)) => &s2[o1..o2],
};
let s3 = s3.as_slice::<f32>()?;
let s3 = match l3.contiguous_offsets() {
None => candle::bail!("input3 has to be contiguous"),
Some((o1, o2)) => &s3[o1..o2],
};
let mut dst = vec![0f32; b * q_h * q_w * k_h * k_w];
dst.par_chunks_exact_mut(k_h * k_w)
.enumerate()
.for_each(|(b_idx, dst)| {
let s1_idx = b_idx * k_h * k_w;
let s2_idx = b_idx * k_h;
let s3_idx = b_idx * k_w;
for h_idx in 0..k_h {
let s1_idx = s1_idx + h_idx * k_w;
let s2_idx = s2_idx + h_idx;
let dst_idx = h_idx * k_w;
for w_idx in 0..k_w {
let s1_idx = s1_idx + w_idx;
let s3_idx = s3_idx + w_idx;
let dst_idx = dst_idx + w_idx;
dst[dst_idx] = s1[s1_idx] + s2[s2_idx] + s3[s3_idx]
}
}
});
let dst = candle::WithDType::to_cpu_storage_owned(dst);
Ok((dst, (b, q_h * q_w, k_h * k_w).into()))
}
}
#[derive(Debug)]
struct Attention {
qkv: super::Linear,
proj: super::Linear,
num_heads: usize,
scale: f64,
rel_pos_hw: Option<(Tensor, Tensor)>,
span: tracing::Span,
span_matmul: tracing::Span,
span_rel_pos: tracing::Span,
span_softmax: tracing::Span,
}
impl Attention {
fn new(
dim: usize,
num_heads: usize,
qkv_bias: bool,
use_rel_pos: bool,
input_size: (usize, usize),
vb: VarBuilder,
) -> Result<Self> {
let span = tracing::span!(tracing::Level::TRACE, "attention");
let span_matmul = tracing::span!(tracing::Level::TRACE, "attn-matmul");
let span_rel_pos = tracing::span!(tracing::Level::TRACE, "attn-rel-pos");
let span_softmax = tracing::span!(tracing::Level::TRACE, "attn-sm");
let qkv = super::linear(vb.pp("qkv"), dim, dim * 3, qkv_bias)?;
let proj = super::linear(vb.pp("proj"), dim, dim, true)?;
let head_dim = dim / num_heads;
let scale = 1. / (head_dim as f64).sqrt();
let rel_pos_hw = if use_rel_pos {
let h = vb.get((2 * input_size.0 - 1, head_dim), "rel_pos_h")?;
let w = vb.get((2 * input_size.1 - 1, head_dim), "rel_pos_w")?;
Some((h, w))
} else {
None
};
Ok(Self {
qkv,
proj,
num_heads,
scale,
rel_pos_hw,
span,
span_matmul,
span_rel_pos,
span_softmax,
})
}
fn add_decomposed_rel_pos(
&self,
attn: Tensor,
q: &Tensor,
(q_h, q_w): (usize, usize),
(k_h, k_w): (usize, usize),
) -> Result<Tensor> {
match &self.rel_pos_hw {
Some((rel_pos_h, rel_pos_w)) => {
let r_h = get_rel_pos(q_h, k_h, rel_pos_h)?;
let r_w = get_rel_pos(q_w, k_w, rel_pos_w)?;
let (b, _, dim) = q.dims3()?;
let r_q = q.reshape((b, q_h, q_w, dim))?;
// rel_h = torch.einsum("bhwc,hkc->bhwk", r_q, Rh)
let rel_h = r_q.matmul(&r_h.broadcast_left(b)?.t()?.contiguous()?)?;
// rel_w = torch.einsum("bhwc,wkc->bhwk", r_q, Rw)
let rel_w = r_q
.transpose(1, 2)? // -> bwhc
.contiguous()?
.matmul(&r_w.broadcast_left(b)?.t()?.contiguous()?)? // bwhc,bwck -> bwhk
.transpose(1, 2)?
.contiguous()?;
if attn.device().is_cpu() {
let op = Add3(b, q_h, q_w, k_h, k_w);
attn.apply_op3_no_bwd(&rel_h, &rel_w, &op)
} else {
(attn.reshape((b, q_h, q_w, k_h, k_w))?
+ rel_h.unsqueeze(4)?.broadcast_add(&rel_w.unsqueeze(3)?)?)?
.reshape((b, q_h * q_w, k_h * k_w))
}
}
None => Ok(attn),
}
}
}
fn get_rel_pos(q_size: usize, k_size: usize, rel_pos: &Tensor) -> Result<Tensor> {
let max_rel_dist = 2 * usize::max(q_size, k_size) - 1;
let dev = rel_pos.device();
let rel_pos_resized = if rel_pos.dim(0)? != max_rel_dist {
todo!("interpolation")
} else {
rel_pos
};
let q_coords = Tensor::arange(0u32, q_size as u32, dev)?
.reshape((q_size, 1))?
.to_dtype(DType::F32)?;
let k_coords = Tensor::arange(0u32, k_size as u32, dev)?
.reshape((1, k_size))?
.to_dtype(DType::F32)?;
let q_coords = (q_coords * f64::max(1f64, k_size as f64 / q_size as f64))?;
let k_coords = (k_coords * f64::max(1f64, q_size as f64 / k_size as f64))?;
let relative_coords = (q_coords.broadcast_sub(&k_coords)?
+ (k_size as f64 - 1.) * f64::max(1f64, q_size as f64 / k_size as f64))?;
let (d1, d2) = relative_coords.dims2()?;
let relative_coords = relative_coords.to_dtype(DType::U32)?;
rel_pos_resized
.index_select(&relative_coords.reshape(d1 * d2)?, 0)?
.reshape((d1, d2, ()))
}
impl Module for Attention {
fn forward(&self, xs: &Tensor) -> Result<Tensor> {
let _enter = self.span.enter();
let (b, h, w, c) = xs.dims4()?;
let qkv = self
.qkv
.forward(&xs.flatten_to(1)?)?
.reshape((b, h * w, 3, self.num_heads, c / self.num_heads))?
.permute((2, 0, 3, 1, 4))?
.reshape((3, b * self.num_heads, h * w, c / self.num_heads))?;
let q = qkv.i(0)?;
let k = qkv.i(1)?;
let v = qkv.i(2)?;
let attn = {
let _enter = self.span_matmul.enter();
(&q * self.scale)?.matmul(&k.t()?)?
};
let attn = {
let _enter = self.span_rel_pos.enter();
self.add_decomposed_rel_pos(attn, &q, (h, w), (h, w))?
};
let attn = {
let _enter = self.span_softmax.enter();
candle_nn::ops::softmax_last_dim(&attn)?
};
let attn = {
let _enter = self.span_matmul.enter();
attn.matmul(&v)?
};
let attn = attn
.reshape((b, self.num_heads, h, w, c / self.num_heads))?
.permute((0, 2, 3, 1, 4))?
.reshape((b, h * w, c))?;
self.proj.forward(&attn)?.reshape((b, h, w, c))
}
}
#[derive(Debug)]
struct Block {
norm1: LayerNorm,
attn: Attention,
norm2: LayerNorm,
mlp: super::MlpBlock,
window_size: usize,
span: tracing::Span,
}
impl Block {
fn new(
dim: usize,
num_heads: usize,
qkv_bias: bool,
use_rel_pos: bool,
window_size: usize,
input_size: (usize, usize),
vb: VarBuilder,
) -> Result<Self> {
let norm1 = layer_norm(dim, 1e-6, vb.pp("norm1"))?;
let norm2 = layer_norm(dim, 1e-6, vb.pp("norm2"))?;
let input_size_attn = if window_size == 0 {
input_size
} else {
(window_size, window_size)
};
let attn = Attention::new(
dim,
num_heads,
qkv_bias,
use_rel_pos,
input_size_attn,
vb.pp("attn"),
)?;
let mlp = super::MlpBlock::new(dim, dim * 4, candle_nn::Activation::Gelu, vb.pp("mlp"))?;
let span = tracing::span!(tracing::Level::TRACE, "ie-block");
Ok(Self {
norm1,
attn,
norm2,
mlp,
window_size,
span,
})
}
}
fn window_partition(xs: Tensor, window_size: usize) -> Result<(Tensor, (usize, usize))> {
let (b, h, w, c) = xs.dims4()?;
let pad_h = (window_size - h % window_size) % window_size;
let pad_w = (window_size - w % window_size) % window_size;
let xs = if pad_h > 0 {
xs.pad_with_zeros(1, 0, pad_h)?
} else {
xs
};
let xs = if pad_w > 0 {
xs.pad_with_zeros(2, 0, pad_w)?
} else {
xs
};
let (h_p, w_p) = (h + pad_h, w + pad_w);
let windows = xs
.reshape((
b,
h_p / window_size,
window_size,
w_p / window_size,
window_size,
c,
))?
.transpose(2, 3)?
.contiguous()?
.flatten_to(2)?;
Ok((windows, (h_p, w_p)))
}
fn window_unpartition(
windows: Tensor,
window_size: usize,
(h_p, w_p): (usize, usize),
(h, w): (usize, usize),
) -> Result<Tensor> {
let b = windows.dim(0)? / (h_p * w_p / window_size / window_size);
let xs = windows
.reshape((
b,
h_p / window_size,
w_p / window_size,
window_size,
window_size,
windows.elem_count() / b / h_p / w_p,
))?
.transpose(2, 3)?
.contiguous()?
.reshape((b, h_p, w_p, ()))?;
let xs = if h_p > h { xs.narrow(1, 0, h)? } else { xs };
let xs = if w_p > w { xs.narrow(2, 0, w)? } else { xs };
Ok(xs)
}
impl Module for Block {
fn forward(&self, xs: &Tensor) -> Result<Tensor> {
let _enter = self.span.enter();
let shortcut = xs;
let xs = self.norm1.forward(xs)?;
let hw = (xs.dim(1)?, xs.dim(2)?);
let (xs, pad_hw) = if self.window_size > 0 {
window_partition(xs, self.window_size)?
} else {
(xs, (0, 0))
};
let xs = self.attn.forward(&xs)?;
let xs = if self.window_size > 0 {
window_unpartition(xs, self.window_size, pad_hw, hw)?
} else {
xs
};
let xs = (xs + shortcut)?;
&xs + xs.apply(&self.norm2)?.apply(&self.mlp)?
}
}
#[derive(Debug)]
pub struct ImageEncoderViT {
patch_embed: PatchEmbed,
blocks: Vec<Block>,
neck_conv1: candle_nn::Conv2d,
neck_ln1: super::LayerNorm2d,
neck_conv2: candle_nn::Conv2d,
neck_ln2: super::LayerNorm2d,
pos_embed: Option<Tensor>,
span: tracing::Span,
}
impl ImageEncoderViT {
#[allow(clippy::too_many_arguments)]
pub fn new(
img_size: usize,
patch_size: usize,
in_chans: usize,
embed_dim: usize,
depth: usize,
num_heads: usize,
out_chans: usize,
qkv_bias: bool,
use_rel_pos: bool,
use_abs_pos: bool,
window_size: usize,
global_attn_indexes: &[usize],
vb: VarBuilder,
) -> Result<Self> {
let patch_embed = PatchEmbed::new(
in_chans,
embed_dim,
patch_size,
patch_size,
0,
vb.pp("patch_embed"),
)?;
let mut blocks = Vec::with_capacity(depth);
let vb_b = vb.pp("blocks");
for i in 0..depth {
let window_size = if global_attn_indexes.contains(&i) {
0
} else {
window_size
};
let block = Block::new(
embed_dim,
num_heads,
qkv_bias,
use_rel_pos,
window_size,
(img_size / patch_size, img_size / patch_size),
vb_b.pp(i),
)?;
blocks.push(block)
}
let neck_conv1 = candle_nn::conv2d_no_bias(
embed_dim,
out_chans,
1,
Default::default(),
vb.pp("neck.0"),
)?;
let neck_ln1 = super::LayerNorm2d::new(out_chans, 1e-6, vb.pp("neck.1"))?;
let cfg = candle_nn::Conv2dConfig {
padding: 1,
..Default::default()
};
let neck_conv2 = candle_nn::conv2d_no_bias(out_chans, out_chans, 3, cfg, vb.pp("neck.2"))?;
let neck_ln2 = super::LayerNorm2d::new(out_chans, 1e-6, vb.pp("neck.3"))?;
let pos_embed = if use_abs_pos {
let p = vb.get(
(1, img_size / patch_size, img_size / patch_size, embed_dim),
"pos_embed",
)?;
Some(p)
} else {
None
};
let span = tracing::span!(tracing::Level::TRACE, "image-encoder-vit");
Ok(Self {
patch_embed,
blocks,
neck_conv1,
neck_ln1,
neck_conv2,
neck_ln2,
pos_embed,
span,
})
}
}
impl Module for ImageEncoderViT {
fn forward(&self, xs: &Tensor) -> Result<Tensor> {
let _enter = self.span.enter();
let xs = self.patch_embed.forward(xs)?;
let mut xs = match &self.pos_embed {
Some(pos_embed) => (xs + pos_embed)?,
None => xs,
};
for block in self.blocks.iter() {
xs = block.forward(&xs)?
}
xs.permute((0, 3, 1, 2))?
.apply(&self.neck_conv1)?
.apply(&self.neck_ln1)?
.apply(&self.neck_conv2)?
.apply(&self.neck_ln2)
}
}
| candle/candle-transformers/src/models/segment_anything/image_encoder.rs/0 | {
"file_path": "candle/candle-transformers/src/models/segment_anything/image_encoder.rs",
"repo_id": "candle",
"token_count": 8848
} | 64 |
//! ResNet Building Blocks
//!
//! Some Residual Network blocks used in UNet models.
//!
//! Denoising Diffusion Implicit Models, K. He and al, 2015.
//! - [Paper](https://arxiv.org/abs/1512.03385)
//!
use crate::models::with_tracing::{conv2d, Conv2d};
use candle::{Result, Tensor, D};
use candle_nn as nn;
use candle_nn::Module;
/// Configuration for a ResNet block.
#[derive(Debug, Clone, Copy)]
pub struct ResnetBlock2DConfig {
/// The number of output channels, defaults to the number of input channels.
pub out_channels: Option<usize>,
pub temb_channels: Option<usize>,
/// The number of groups to use in group normalization.
pub groups: usize,
pub groups_out: Option<usize>,
/// The epsilon to be used in the group normalization operations.
pub eps: f64,
/// Whether to use a 2D convolution in the skip connection. When using None,
/// such a convolution is used if the number of input channels is different from
/// the number of output channels.
pub use_in_shortcut: Option<bool>,
// non_linearity: silu
/// The final output is scaled by dividing by this value.
pub output_scale_factor: f64,
}
impl Default for ResnetBlock2DConfig {
fn default() -> Self {
Self {
out_channels: None,
temb_channels: Some(512),
groups: 32,
groups_out: None,
eps: 1e-6,
use_in_shortcut: None,
output_scale_factor: 1.,
}
}
}
#[derive(Debug)]
pub struct ResnetBlock2D {
norm1: nn::GroupNorm,
conv1: Conv2d,
norm2: nn::GroupNorm,
conv2: Conv2d,
time_emb_proj: Option<nn::Linear>,
conv_shortcut: Option<Conv2d>,
span: tracing::Span,
config: ResnetBlock2DConfig,
}
impl ResnetBlock2D {
pub fn new(
vs: nn::VarBuilder,
in_channels: usize,
config: ResnetBlock2DConfig,
) -> Result<Self> {
let out_channels = config.out_channels.unwrap_or(in_channels);
let conv_cfg = nn::Conv2dConfig {
stride: 1,
padding: 1,
groups: 1,
dilation: 1,
cudnn_fwd_algo: None,
};
let norm1 = nn::group_norm(config.groups, in_channels, config.eps, vs.pp("norm1"))?;
let conv1 = conv2d(in_channels, out_channels, 3, conv_cfg, vs.pp("conv1"))?;
let groups_out = config.groups_out.unwrap_or(config.groups);
let norm2 = nn::group_norm(groups_out, out_channels, config.eps, vs.pp("norm2"))?;
let conv2 = conv2d(out_channels, out_channels, 3, conv_cfg, vs.pp("conv2"))?;
let use_in_shortcut = config
.use_in_shortcut
.unwrap_or(in_channels != out_channels);
let conv_shortcut = if use_in_shortcut {
let conv_cfg = nn::Conv2dConfig {
stride: 1,
padding: 0,
groups: 1,
dilation: 1,
cudnn_fwd_algo: None,
};
Some(conv2d(
in_channels,
out_channels,
1,
conv_cfg,
vs.pp("conv_shortcut"),
)?)
} else {
None
};
let time_emb_proj = match config.temb_channels {
None => None,
Some(temb_channels) => Some(nn::linear(
temb_channels,
out_channels,
vs.pp("time_emb_proj"),
)?),
};
let span = tracing::span!(tracing::Level::TRACE, "resnet2d");
Ok(Self {
norm1,
conv1,
norm2,
conv2,
time_emb_proj,
span,
config,
conv_shortcut,
})
}
pub fn forward(&self, xs: &Tensor, temb: Option<&Tensor>) -> Result<Tensor> {
let _enter = self.span.enter();
let shortcut_xs = match &self.conv_shortcut {
Some(conv_shortcut) => conv_shortcut.forward(xs)?,
None => xs.clone(),
};
let xs = self.norm1.forward(xs)?;
let xs = self.conv1.forward(&nn::ops::silu(&xs)?)?;
let xs = match (temb, &self.time_emb_proj) {
(Some(temb), Some(time_emb_proj)) => time_emb_proj
.forward(&nn::ops::silu(temb)?)?
.unsqueeze(D::Minus1)?
.unsqueeze(D::Minus1)?
.broadcast_add(&xs)?,
_ => xs,
};
let xs = self
.conv2
.forward(&nn::ops::silu(&self.norm2.forward(&xs)?)?)?;
(shortcut_xs + xs)? / self.config.output_scale_factor
}
}
| candle/candle-transformers/src/models/stable_diffusion/resnet.rs/0 | {
"file_path": "candle/candle-transformers/src/models/stable_diffusion/resnet.rs",
"repo_id": "candle",
"token_count": 2344
} | 65 |
use super::voxtral_llama::{VoxtralLlama, VoxtralLlamaCache, VoxtralLlamaConfig};
use candle::{DType, Device, IndexOp, Module, Result, Tensor, D};
use candle_nn::{
layer_norm, linear, linear_no_bias, Conv1d, Dropout, LayerNorm, Linear, VarBuilder,
};
use rand::Rng;
#[derive(Debug, Clone)]
pub struct VoxtralEncoderConfig {
pub vocab_size: usize,
pub hidden_size: usize,
pub intermediate_size: usize,
pub num_hidden_layers: usize,
pub num_attention_heads: usize,
pub num_key_value_heads: usize,
pub head_dim: usize,
pub scale_embedding: bool,
pub activation_function: String,
pub num_mel_bins: usize,
pub max_source_positions: usize,
pub initializer_range: f64,
pub attention_dropout: f64,
// These are set to 0.0 for compatibility with Whisper modular architecture
pub dropout: f64,
pub layerdrop: f64,
pub activation_dropout: f64,
}
#[derive(Debug, Clone)]
pub struct VoxtralConfig {
pub audio_config: VoxtralEncoderConfig,
pub text_config: VoxtralLlamaConfig,
pub audio_token_id: usize,
pub projector_hidden_act: String,
}
impl Default for VoxtralConfig {
fn default() -> Self {
Self {
audio_config: VoxtralEncoderConfig::default(),
text_config: VoxtralLlamaConfig::voxtral_3b(),
audio_token_id: 24,
projector_hidden_act: "gelu".to_string(),
}
}
}
impl Default for VoxtralEncoderConfig {
fn default() -> Self {
Self {
vocab_size: 51866,
hidden_size: 1280,
intermediate_size: 5120,
num_hidden_layers: 32,
num_attention_heads: 20,
num_key_value_heads: 20,
head_dim: 64,
scale_embedding: false,
activation_function: "gelu".to_string(),
num_mel_bins: 128,
max_source_positions: 1500,
initializer_range: 0.02,
attention_dropout: 0.0,
// Set for Whisper compatibility
dropout: 0.0,
layerdrop: 0.0,
activation_dropout: 0.0,
}
}
}
impl VoxtralEncoderConfig {
/// Ensures dropout values are properly set for Whisper compatibility
pub fn with_whisper_compatibility(mut self) -> Self {
self.dropout = 0.0;
self.layerdrop = 0.0;
self.activation_dropout = 0.0;
self
}
}
/// Custom cache for multimodal inputs
#[derive(Debug, Clone)]
pub struct VoxtralCache {
cache: VoxtralLlamaCache,
audio_processed: bool,
cached_audio_embeds: Option<Tensor>,
cached_audio_positions: Option<Vec<(usize, usize)>>,
}
#[derive(Debug, Clone)]
pub struct VoxtralGenerationConfig {
pub max_new_tokens: usize,
pub temperature: f64,
pub top_p: Option<f64>,
pub device: Device,
/// If cache is None, the model will create a new cache.
pub cache: Option<VoxtralCache>,
}
impl VoxtralGenerationConfig {
pub fn new(device: Device) -> Self {
Self {
max_new_tokens: 500,
temperature: 0.0,
top_p: None,
device,
cache: None,
}
}
}
impl VoxtralCache {
pub fn new(
use_kv_cache: bool,
dtype: DType,
config: &VoxtralLlamaConfig,
device: &Device,
) -> Result<Self> {
Ok(Self {
cache: VoxtralLlamaCache::new(use_kv_cache, dtype, config, device)?,
audio_processed: false,
cached_audio_embeds: None,
cached_audio_positions: None,
})
}
pub fn reset(&mut self) {
// Reset the audio cache state
self.audio_processed = false;
self.cached_audio_embeds = None;
self.cached_audio_positions = None;
// Note: LlamaCache reset needs to be handled at a higher level
// as it requires device access
}
}
/// Safely clamp tensor values for different dtypes
fn safe_clamp(x: &Tensor) -> Result<Tensor> {
match x.dtype() {
DType::F16 => {
// Match PyTorch exactly: torch.finfo(torch.float16).max - 1000 = 64504.0
let max_val = 64504.0;
x.clamp(-max_val, max_val)
}
DType::BF16 => {
// BF16 has larger range, typically doesn't need clamping
Ok(x.clone())
}
_ => Ok(x.clone()),
}
}
/// Replace audio tokens in embeddings with projected audio features
pub fn replace_audio_tokens(
inputs_embeds: &Tensor,
audio_embeds: &Tensor,
audio_positions: &[(usize, usize)],
device: &Device,
) -> Result<Tensor> {
if audio_positions.is_empty() {
return Ok(inputs_embeds.clone());
}
let (batch_size, seq_len, hidden_size) = inputs_embeds.dims3()?;
let num_audio_tokens = audio_positions.len();
// HF-style: audio_embeds shape is (total_audio_seq_len, hidden_size)
let audio_embeds_dims = audio_embeds.dims2()?;
let total_audio_embeds = audio_embeds_dims.0;
// HF-style: Use audio embeddings one-to-one with audio tokens
// We should now have the right number of audio tokens in the input sequence
let audio_embeds = if total_audio_embeds >= num_audio_tokens {
// Take the first num_audio_tokens embeddings to match the audio tokens
if num_audio_tokens == total_audio_embeds {
audio_embeds.clone()
} else {
audio_embeds.i(0..num_audio_tokens)?
}
} else {
candle::bail!(
"Not enough audio embeddings: need {}, got {}. Input sequence should have {} audio tokens.",
num_audio_tokens,
total_audio_embeds,
total_audio_embeds
);
};
// Create result tensor starting with text embeddings
let mut result = inputs_embeds.clone();
// Replace audio tokens with audio embeddings
// Since we don't have scatter operations, we'll do this manually
for (idx, &(batch_idx, seq_idx)) in audio_positions.iter().enumerate() {
if batch_idx >= batch_size || seq_idx >= seq_len {
candle::bail!(
"Invalid audio position: ({}, {}) for tensor shape ({}, {}, {})",
batch_idx,
seq_idx,
batch_size,
seq_len,
hidden_size
);
}
// Get the audio embedding for this position
let audio_embed = audio_embeds.i(idx)?;
// Create a mask for this specific position
let mut position_mask = vec![0f32; batch_size * seq_len];
position_mask[batch_idx * seq_len + seq_idx] = 1.0;
let position_mask = Tensor::new(position_mask.as_slice(), device)?
.reshape((batch_size, seq_len, 1))?
.to_dtype(inputs_embeds.dtype())?;
// Broadcast audio embedding to full tensor shape
let audio_embed_broadcast = audio_embed.unsqueeze(0)?.unsqueeze(0)?.broadcast_as((
batch_size,
seq_len,
hidden_size,
))?;
// Update result: keep original where mask is 0, use audio where mask is 1
let inverse_mask = (1.0 - &position_mask)?;
result = (result.broadcast_mul(&inverse_mask)?
+ audio_embed_broadcast.broadcast_mul(&position_mask)?)?;
}
Ok(result)
}
/// Find positions of audio tokens in input sequences
pub fn find_audio_token_positions(
input_ids: &Tensor,
audio_token_id: usize,
) -> Result<Vec<(usize, usize)>> {
// Handle both i64 and u32 token types by converting to i64 first if needed
let input_ids = if input_ids.dtype() == candle::DType::U32 {
input_ids.to_dtype(candle::DType::I64)?
} else {
input_ids.clone()
};
let input_ids = input_ids.to_vec2::<i64>()?;
let mut positions = Vec::new();
for (batch_idx, sequence) in input_ids.iter().enumerate() {
for (seq_idx, &token_id) in sequence.iter().enumerate() {
if token_id as usize == audio_token_id {
positions.push((batch_idx, seq_idx));
}
}
}
Ok(positions)
}
#[derive(Debug, Clone)]
struct VoxtralAttention {
q_proj: Linear,
k_proj: Linear,
v_proj: Linear,
out_proj: Linear,
num_heads: usize,
head_dim: usize,
scaling: f64,
attention_dropout: Dropout,
}
impl VoxtralAttention {
fn new(cfg: &VoxtralEncoderConfig, vb: VarBuilder) -> Result<Self> {
let embed_dim = cfg.hidden_size;
let num_heads = cfg.num_attention_heads;
let head_dim = embed_dim / num_heads;
if head_dim * num_heads != embed_dim {
candle::bail!(
"embed_dim must be divisible by num_heads ({} % {} != 0)",
embed_dim,
num_heads
);
}
let scaling = (head_dim as f64).powf(-0.5);
let q_proj = linear(embed_dim, embed_dim, vb.pp("q_proj"))?;
let k_proj = linear_no_bias(embed_dim, embed_dim, vb.pp("k_proj"))?;
let v_proj = linear(embed_dim, embed_dim, vb.pp("v_proj"))?;
let out_proj = linear(embed_dim, embed_dim, vb.pp("out_proj"))?;
let attention_dropout = Dropout::new(cfg.attention_dropout as f32);
Ok(Self {
q_proj,
k_proj,
v_proj,
out_proj,
num_heads,
head_dim,
scaling,
attention_dropout,
})
}
fn reshape_for_scores(&self, x: &Tensor, seq_len: usize, bsz: usize) -> Result<Tensor> {
x.reshape((bsz, seq_len, self.num_heads, self.head_dim))?
.transpose(1, 2)?
.contiguous()
}
}
impl Module for VoxtralAttention {
fn forward(&self, x: &Tensor) -> Result<Tensor> {
let (bsz, seq_len, _) = x.dims3()?;
// Project queries, keys, and values - apply scaling to queries to match PyTorch SDPA
let q = (self.q_proj.forward(x)? * self.scaling)?;
let k = self.k_proj.forward(x)?;
let v = self.v_proj.forward(x)?;
// Reshape for multi-head attention: (batch, seq_len, num_heads, head_dim) -> (batch, num_heads, seq_len, head_dim)
let q = self.reshape_for_scores(&q, seq_len, bsz)?;
let k = self.reshape_for_scores(&k, seq_len, bsz)?;
let v = self.reshape_for_scores(&v, seq_len, bsz)?;
// Manual SDPA-like implementation to match Python's numerical behavior exactly
// Use F16 precision throughout to match PyTorch's F16 model
let scores = q.matmul(&k.transpose(D::Minus2, D::Minus1)?)?;
// Apply softmax in same precision as input (F16) to match Python
let attn_weights = candle_nn::ops::softmax_last_dim(&scores)?;
// Apply attention dropout (disabled during inference)
let attn_weights = self.attention_dropout.forward(&attn_weights, false)?;
// Apply attention to values
let attn_output = attn_weights.matmul(&v)?;
// Reshape back to (batch, seq_len, embed_dim)
let attn_output = attn_output.transpose(1, 2)?.contiguous()?.reshape((
bsz,
seq_len,
self.num_heads * self.head_dim,
))?;
self.out_proj.forward(&attn_output)
}
}
#[derive(Debug, Clone)]
struct VoxtralEncoderLayer {
self_attn: VoxtralAttention,
self_attn_layer_norm: LayerNorm,
fc1: Linear,
fc2: Linear,
final_layer_norm: LayerNorm,
activation: candle_nn::Activation,
dropout: Dropout,
activation_dropout: Dropout,
}
impl VoxtralEncoderLayer {
fn new(cfg: &VoxtralEncoderConfig, vb: VarBuilder) -> Result<Self> {
let embed_dim = cfg.hidden_size;
let self_attn = VoxtralAttention::new(cfg, vb.pp("self_attn"))?;
let self_attn_layer_norm = layer_norm(embed_dim, 1e-5, vb.pp("self_attn_layer_norm"))?;
let fc1 = linear(embed_dim, cfg.intermediate_size, vb.pp("fc1"))?;
let fc2 = linear(cfg.intermediate_size, embed_dim, vb.pp("fc2"))?;
let final_layer_norm = layer_norm(embed_dim, 1e-5, vb.pp("final_layer_norm"))?;
let activation = match cfg.activation_function.as_str() {
"gelu" => candle_nn::Activation::Gelu,
"relu" => candle_nn::Activation::Relu,
_ => candle::bail!(
"Unsupported activation function: {}",
cfg.activation_function
),
};
let dropout = Dropout::new(cfg.dropout as f32);
let activation_dropout = Dropout::new(cfg.activation_dropout as f32);
Ok(Self {
self_attn,
self_attn_layer_norm,
fc1,
fc2,
final_layer_norm,
activation,
dropout,
activation_dropout,
})
}
pub fn get_fc1_out_dim(&self) -> usize {
// Return the intermediate size from the config
// Since Linear doesn't expose out_dim
self.fc1.weight().dims()[0]
}
fn forward(&self, x: &Tensor, training: bool) -> Result<Tensor> {
// Self-attention with residual connection
let residual = x;
let x = self.self_attn_layer_norm.forward(x)?;
let x = self.self_attn.forward(&x)?;
let x = self.dropout.forward(&x, training)?;
let x = (x + residual)?;
// Feed-forward network with residual connection
let residual = &x;
let x = self.final_layer_norm.forward(&x)?;
let x = self.fc1.forward(&x)?;
let x = x.apply(&self.activation)?;
let x = self.activation_dropout.forward(&x, training)?;
let x = self.fc2.forward(&x)?;
let x = self.dropout.forward(&x, training)?;
let x = (x + residual)?;
// Safe clamping for numerical stability
safe_clamp(&x)
}
}
#[derive(Debug, Clone)]
pub struct VoxtralEncoder {
conv1: Conv1d,
conv2: Conv1d,
embed_positions: Tensor,
layers: Vec<VoxtralEncoderLayer>,
layer_norm: LayerNorm,
dropout: Dropout,
layerdrop: f64,
}
impl VoxtralEncoder {
pub fn new(cfg: &VoxtralEncoderConfig, vb: VarBuilder) -> Result<Self> {
// Ensure Whisper compatibility
let cfg = cfg.clone().with_whisper_compatibility();
let embed_dim = cfg.hidden_size;
// Convolutional layers for processing mel features
let conv1 = candle_nn::conv1d(
cfg.num_mel_bins,
embed_dim,
3,
candle_nn::Conv1dConfig {
padding: 1,
..Default::default()
},
vb.pp("conv1"),
)?;
let conv2 = candle_nn::conv1d(
embed_dim,
embed_dim,
3,
candle_nn::Conv1dConfig {
stride: 2,
padding: 1,
..Default::default()
},
vb.pp("conv2"),
)?;
// Position embeddings
let embed_positions = vb.get(
(cfg.max_source_positions, embed_dim),
"embed_positions.weight",
)?;
// Transformer layers
let mut layers = Vec::with_capacity(cfg.num_hidden_layers);
for i in 0..cfg.num_hidden_layers {
layers.push(VoxtralEncoderLayer::new(
&cfg,
vb.pp(format!("layers.{i}")),
)?);
}
let layer_norm = layer_norm(embed_dim, 1e-5, vb.pp("layer_norm"))?;
let dropout = Dropout::new(cfg.dropout as f32);
Ok(Self {
conv1,
conv2,
embed_positions,
layers,
layer_norm,
dropout,
layerdrop: cfg.layerdrop,
})
}
pub fn forward(&self, input_features: &Tensor) -> Result<Tensor> {
self.forward_with_training(input_features, false)
}
pub fn forward_with_training(&self, input_features: &Tensor, training: bool) -> Result<Tensor> {
// Keep conv layers in F16 to avoid shape issues
let expected_dtype = self.conv1.weight().dtype();
let input_features = if input_features.dtype() != expected_dtype {
input_features.to_dtype(expected_dtype)?
} else {
input_features.clone()
};
// Apply convolutional layers with GELU activation
let x = if false {
// Keep conv layers in F16
// Convert conv1 weights to F32 for computation
let conv1_weight_f32 = self.conv1.weight().to_dtype(DType::F32)?;
let conv1_bias_f32 = if let Some(bias) = self.conv1.bias() {
Some(bias.to_dtype(DType::F32)?)
} else {
None
};
// Manual conv1d operation with F32 precision - conv1 has stride=1, padding=1
let mut conv_result = input_features.conv1d(&conv1_weight_f32, 1, 1, 1, 1)?;
if let Some(bias) = conv1_bias_f32 {
conv_result = conv_result.broadcast_add(&bias.unsqueeze(0)?.unsqueeze(2)?)?;
}
conv_result
} else {
self.conv1.forward(&input_features)?
};
// Apply GELU activation after conv1 (matches Python: conv1 -> GELU)
let x = x.gelu()?;
// Apply conv2 (matches Python: conv2)
let x = if false {
// Keep conv layers in F16
// Convert conv2 weights to F32 for computation
let conv2_weight_f32 = self.conv2.weight().to_dtype(DType::F32)?;
let conv2_bias_f32 = if let Some(bias) = self.conv2.bias() {
Some(bias.to_dtype(DType::F32)?)
} else {
None
};
// Manual conv1d operation with F32 precision - conv2 has stride=2, padding=1
let mut conv_result = x.conv1d(&conv2_weight_f32, 2, 1, 1, 1)?;
if let Some(bias) = conv2_bias_f32 {
conv_result = conv_result.broadcast_add(&bias.unsqueeze(0)?.unsqueeze(2)?)?;
}
conv_result
} else {
self.conv2.forward(&x)?
};
// Apply GELU activation after conv2 (FIX: matches Python: conv2 -> GELU)
let x = x.gelu()?;
// Reshape: (batch, embed_dim, seq_len) -> (batch, seq_len, embed_dim)
let x = x.transpose(1, 2)?;
// Add position embeddings - handle F32 position embeddings + F16 hidden states like PyTorch
let seq_len = x.dim(1)?;
let positions = self.embed_positions.i(..seq_len)?;
// PyTorch automatically promotes F16 + F32 -> F32, then converts back to original dtype
// We need to match this behavior exactly
let x = if false {
// Keep position embeddings in mixed precision
// Force F32 computation for position embeddings
let x_f32 = x.to_dtype(candle::DType::F32)?;
let positions_f32 = positions.to_dtype(candle::DType::F32)?;
x_f32.broadcast_add(&positions_f32)? // Keep result in F32
} else if x.dtype() != positions.dtype() {
// Convert hidden states to F32 for addition (positions are already F32)
let x_f32 = x.to_dtype(candle::DType::F32)?;
let result_f32 = x_f32.broadcast_add(&positions)?;
// Convert back to original hidden states dtype (F16)
result_f32.to_dtype(x.dtype())?
} else {
x.broadcast_add(&positions)?
};
// Apply dropout
let mut x = self.dropout.forward(&x, training)?;
for (idx, layer) in self.layers.iter().enumerate() {
// Keep all computation in F16
x = self.forward_layer_with_dropout(&x, layer, idx, training)?;
}
// Apply final layer normalization (critical for proper output values!)
let x = self.layer_norm.forward(&x)?;
Ok(x)
}
/// Forward a single layer with stochastic depth (layer dropout)
fn forward_layer_with_dropout(
&self,
x: &Tensor,
layer: &VoxtralEncoderLayer,
_layer_idx: usize,
training: bool,
) -> Result<Tensor> {
if training && self.layerdrop > 0.0 {
// Apply stochastic depth with proper randomization
let mut rng = rand::rng();
let keep_prob = 1.0 - self.layerdrop;
let keep: bool = rng.random::<f64>() < keep_prob;
if !keep {
// Skip layer entirely (identity mapping)
return Ok(x.clone());
}
}
layer.forward(x, training)
}
/// Get the output dimension of the first FC layer (needed for projector)
pub fn get_intermediate_size(&self) -> usize {
if !self.layers.is_empty() {
self.layers[0].get_fc1_out_dim()
} else {
// Fallback to config value
5120 // Default intermediate size
}
}
/// Process long audio sequences in chunks to save memory
pub fn process_long_audio(
&self,
input_features: &Tensor,
chunk_size: usize,
overlap: usize,
) -> Result<Tensor> {
let (_batch_size, _num_mel, seq_len) = input_features.dims3()?;
if seq_len <= chunk_size {
return self.forward(input_features);
}
let mut outputs = Vec::new();
let step = chunk_size - overlap;
for start in (0..seq_len).step_by(step) {
let end = (start + chunk_size).min(seq_len);
let chunk = input_features.i((.., .., start..end))?;
// Process chunk
let output = self.forward(&chunk)?;
// Handle overlap by averaging
if !outputs.is_empty() && overlap > 0 {
let overlap_frames = overlap / 2; // Account for conv2 stride
let last_output: &mut Tensor = outputs.last_mut().unwrap();
let last_len = last_output.dim(1)?;
// Average overlapping regions
let overlap_start = last_len.saturating_sub(overlap_frames);
let overlap_new = output.i((.., ..overlap_frames, ..))?;
let overlap_old = last_output.i((.., overlap_start.., ..))?;
let averaged = ((overlap_old + overlap_new)? * 0.5)?;
// Update last output
*last_output =
Tensor::cat(&[&last_output.i((.., ..overlap_start, ..))?, &averaged], 1)?;
// Add non-overlapping part of current chunk
outputs.push(output.i((.., overlap_frames.., ..))?);
} else {
outputs.push(output);
}
}
// Concatenate all outputs
let outputs_ref: Vec<&Tensor> = outputs.iter().collect();
Tensor::cat(&outputs_ref, 1)
}
}
#[derive(Debug, Clone)]
pub struct VoxtralMultiModalProjector {
linear_1: Linear,
linear_2: Linear,
activation: candle_nn::Activation,
}
impl VoxtralMultiModalProjector {
pub fn new(cfg: &VoxtralConfig, vb: VarBuilder) -> Result<Self> {
let linear_1 = linear_no_bias(
cfg.audio_config.intermediate_size,
cfg.text_config.hidden_size,
vb.pp("linear_1"),
)?;
let linear_2 = linear_no_bias(
cfg.text_config.hidden_size,
cfg.text_config.hidden_size,
vb.pp("linear_2"),
)?;
let activation = match cfg.projector_hidden_act.as_str() {
"gelu" => candle_nn::Activation::Gelu,
"relu" => candle_nn::Activation::Relu,
_ => candle::bail!(
"Unsupported projector activation: {}",
cfg.projector_hidden_act
),
};
Ok(Self {
linear_1,
linear_2,
activation,
})
}
pub fn forward(&self, audio_features: &Tensor) -> Result<Tensor> {
let x = self.linear_1.forward(audio_features)?;
let x = x.apply(&self.activation)?;
self.linear_2.forward(&x)
}
}
#[derive(Debug, Clone)]
pub struct VoxtralForConditionalGeneration {
audio_tower: VoxtralEncoder,
language_model: VoxtralLlama,
multi_modal_projector: VoxtralMultiModalProjector,
audio_token_id: usize,
audio_config: VoxtralEncoderConfig,
text_config: VoxtralLlamaConfig,
}
impl VoxtralForConditionalGeneration {
pub fn new(cfg: &VoxtralConfig, vb: VarBuilder) -> Result<Self> {
let audio_tower = VoxtralEncoder::new(&cfg.audio_config, vb.pp("audio_tower"))?;
let language_model = VoxtralLlama::load(vb.pp("language_model"), &cfg.text_config)?;
let multi_modal_projector =
VoxtralMultiModalProjector::new(cfg, vb.pp("multi_modal_projector"))?;
Ok(Self {
audio_tower,
language_model,
multi_modal_projector,
audio_token_id: cfg.audio_token_id,
audio_config: cfg.audio_config.clone(),
text_config: cfg.text_config.clone(),
})
}
/// Get the audio token ID used for this model
pub fn audio_token_id(&self) -> usize {
self.audio_token_id
}
/// Get the text model configuration
pub fn text_config(&self) -> &VoxtralLlamaConfig {
&self.text_config
}
/// Get the audio encoder configuration
pub fn audio_config(&self) -> &VoxtralEncoderConfig {
&self.audio_config
}
/// Process audio features through encoder and projector
pub fn get_audio_embeds(&self, input_features: &Tensor) -> Result<Tensor> {
let audio_outputs = self.audio_tower.forward(input_features)?;
// Following HF implementation: reshape to (-1, config.intermediate_size) before projection
// Python: audio_hidden_states.reshape(-1, self.config.audio_config.intermediate_size)
// This transforms [1, 1500, 1280] -> [375, 5120] using intermediate_size from config
let (batch_size, seq_len, hidden_size) = audio_outputs.dims3()?;
// The key insight: Python reshapes from [1, 1500, 1280] to [375, 5120]
// This means 1500 * 1280 = 375 * 5120 (1920000 elements)
// So we need: new_batch_size = (batch_size * seq_len * hidden_size) / intermediate_size
let total_elements = batch_size * seq_len * hidden_size;
let new_batch_size = total_elements / self.audio_config.intermediate_size;
// Verify the division is exact
if total_elements % self.audio_config.intermediate_size != 0 {
return Err(candle::Error::DimOutOfRange {
shape: candle::Shape::from_dims(&[batch_size, seq_len, hidden_size]),
dim: 0,
op: "reshape",
});
}
let audio_hidden =
audio_outputs.reshape((new_batch_size, self.audio_config.intermediate_size))?;
// Project to text space - this gives us embeddings for each audio position
let projected = self.multi_modal_projector.forward(&audio_hidden)?;
// Return shape: (batch_size * seq_len, text_hidden_size)
// This matches HF implementation - no pooling, keep all audio token embeddings
Ok(projected)
}
/// Process long audio sequences efficiently
pub fn get_audio_embeds_chunked(
&self,
input_features: &Tensor,
chunk_size: usize,
overlap: usize,
) -> Result<Tensor> {
let audio_outputs =
self.audio_tower
.process_long_audio(input_features, chunk_size, overlap)?;
// Reshape and project (now outputs hidden_size, needs reshape to intermediate_size)
let (batch_size, seq_len, hidden_size) = audio_outputs.dims3()?;
// Apply same reshape logic as get_audio_embeds
let total_elements = batch_size * seq_len * hidden_size;
let new_batch_size = total_elements / self.audio_config.intermediate_size;
let audio_hidden =
audio_outputs.reshape((new_batch_size, self.audio_config.intermediate_size))?;
let projected = self.multi_modal_projector.forward(&audio_hidden)?;
// Reshape back to (batch_size, seq_len, text_hidden_size) for pooling
let text_hidden_size = self.text_config.hidden_size;
let projected = projected.reshape((batch_size, seq_len, text_hidden_size))?;
// Apply mean pooling to reduce to single audio embedding per batch
let pooled = projected.mean(1)?; // Mean across sequence dimension
// Return shape: (batch_size, text_hidden_size)
Ok(pooled)
}
/// Forward pass with audio features and text input
pub fn forward(
&self,
input_ids: &Tensor,
input_features: Option<&Tensor>,
cache: &mut VoxtralCache,
index_pos: usize,
) -> Result<Tensor> {
// Get text embeddings
let mut inputs_embeds = self.language_model.embed(input_ids)?;
// If audio features are provided and not yet processed
if let Some(features) = input_features {
if !cache.audio_processed {
let audio_embeds = self.get_audio_embeds(features)?;
let audio_positions = find_audio_token_positions(input_ids, self.audio_token_id)?;
// Cache for future use
cache.cached_audio_embeds = Some(audio_embeds.clone());
cache.cached_audio_positions = Some(audio_positions.clone());
cache.audio_processed = true;
inputs_embeds = replace_audio_tokens(
&inputs_embeds,
&audio_embeds,
&audio_positions,
input_ids.device(),
)?;
}
}
// Forward through language model using forward_input_embed
self.language_model
.forward_input_embed(&inputs_embeds, index_pos, &mut cache.cache)
}
/// Generate text given audio input
pub fn generate(
&self,
input_ids: &Tensor,
input_features: Option<&Tensor>,
config: VoxtralGenerationConfig,
) -> Result<Vec<u32>> {
// Validate inputs
if config.max_new_tokens == 0 {
return input_ids.i(0)?.to_vec1::<u32>(); // Get first batch
}
if config.temperature < 0.0 {
candle::bail!(
"Temperature must be non-negative, got {}",
config.temperature
);
}
if let Some(p) = config.top_p {
if !(0.0..=1.0).contains(&p) {
candle::bail!("top_p must be between 0 and 1, got {}", p);
}
}
let mut final_cache = if let Some(cache) = config.cache {
cache
} else {
// Get the dtype from the language model by creating a small embedding
let dummy_token = Tensor::new(&[1u32], &config.device)?;
let dummy_embed = self.language_model.embed(&dummy_token)?;
let model_dtype = dummy_embed.dtype();
VoxtralCache::new(true, model_dtype, &self.text_config, &config.device)?
};
let mut tokens = input_ids.i(0)?.to_vec1::<u32>()?; // Get first batch
let initial_len = tokens.len();
for idx in 0..config.max_new_tokens {
let (input, index_pos) = if idx == 0 {
(input_ids.clone(), 0)
} else {
// For subsequent generation steps, use only the last token
let last_token = tokens[tokens.len() - 1];
let calculated_pos = initial_len + idx - 1;
(
Tensor::new(&[last_token], &config.device)?.unsqueeze(0)?,
calculated_pos,
)
};
let logits = if idx == 0 {
// First pass - include audio features
match self.forward(&input, input_features, &mut final_cache, index_pos) {
Ok(logits) => logits,
Err(e) => {
return Err(candle::Error::Msg(format!(
"Failed to generate tokens: {e}"
)));
}
}
} else {
// Subsequent passes - text only
match self.forward(&input, None, &mut final_cache, index_pos) {
Ok(logits) => logits,
Err(e) => {
return Err(candle::Error::Msg(format!(
"Failed to generate tokens: {e}"
)));
}
}
};
// Handle both 2D [batch, vocab] and 3D [batch, seq_len, vocab] logits
let logits = if logits.dims().len() == 3 {
// 3D case: [batch, seq_len, vocab] -> get last token
logits.i((.., logits.dim(1)? - 1, ..))?
} else {
// 2D case: [batch, vocab] -> already the right shape
logits
};
let next_token = if config.temperature > 0.0 {
// Sample with temperature
let prs = (logits / config.temperature)?;
let prs = candle_nn::ops::softmax_last_dim(&prs)?;
if let Some(top_p_val) = config.top_p {
// Apply top-p sampling
sample_top_p(&prs.squeeze(0)?, top_p_val, &config.device)?
} else {
// Sample from full distribution
let probs_vec = prs.squeeze(0)?.to_vec1::<f32>()?;
let mut rng = rand::rng();
let mut cumsum = 0.0;
let rand_val: f32 = rng.random();
let mut sampled = 0u32;
for (idx, &prob) in probs_vec.iter().enumerate() {
cumsum += prob;
if cumsum > rand_val {
sampled = idx as u32;
break;
}
}
sampled
}
} else {
// Greedy decoding - find the token with highest probability
let argmax_result = match logits.argmax(D::Minus1) {
Ok(result) => result,
Err(e) => {
return Err(candle::Error::Msg(format!("Argmax failed: {e}")));
}
};
// Handle the case where argmax returns [1] instead of scalar
if argmax_result.dims().is_empty() {
// Already a scalar
match argmax_result.to_scalar::<u32>() {
Ok(token) => token,
Err(e) => {
return Err(candle::Error::Msg(format!("to_scalar failed: {e}")));
}
}
} else if argmax_result.dims() == [1] {
// Shape [1] - extract the single element
match argmax_result.i(0) {
Ok(scalar_tensor) => match scalar_tensor.to_scalar::<u32>() {
Ok(token) => token,
Err(e) => {
return Err(candle::Error::Msg(format!(
"to_scalar on extracted element failed: {e}"
)));
}
},
Err(e) => {
return Err(candle::Error::Msg(format!(
"indexing argmax result failed: {e}"
)));
}
}
} else {
return Err(candle::Error::Msg(format!(
"Unexpected argmax result shape: {:?}",
argmax_result.shape()
)));
}
};
tokens.push(next_token);
// Check for EOS tokens - Voxtral uses different EOS tokens than hardcoded 2
// Based on the Mistral/Voxtral tokenizer, common EOS tokens are:
// 2 = </s>, 0 = <pad>, 128001, 128009 from various chat formats
let eos_tokens = [2u32, 128001, 128009, 128256]; // Don't include 0 as it might be valid generation
// Check for EOS tokens only if not ignoring them
if eos_tokens.contains(&next_token) {
break;
}
// Also break if we get repeated pad tokens (might indicate the model is stuck)
if next_token == 0 && tokens.len() > 5 {
let last_5_tokens = &tokens[tokens.len() - 5..];
if last_5_tokens.iter().all(|&t| t == 0) {
break;
}
}
}
Ok(tokens)
}
}
/// Sample from top-p probability distribution
fn sample_top_p(probs: &Tensor, top_p: f64, _device: &Device) -> Result<u32> {
let (sorted_probs, sorted_indices) = probs.sort_last_dim(false)?;
let cumsum = sorted_probs.cumsum(D::Minus1)?;
let mask = cumsum.le(top_p)?;
// Apply mask and renormalize
let filtered_probs = sorted_probs.where_cond(&mask, &Tensor::zeros_like(&sorted_probs)?)?;
let filtered_probs = (&filtered_probs / filtered_probs.sum_keepdim(D::Minus1)?)?;
// Sample from filtered distribution
// Since multinomial is not available, we'll use a simple sampling approach
let probs_vec = filtered_probs.to_vec1::<f32>()?;
let mut cumsum = 0.0;
let mut rng = rand::rng();
let rand_val: f32 = rng.random();
let mut sample_idx = 0;
for (idx, &prob) in probs_vec.iter().enumerate() {
cumsum += prob;
if cumsum > rand_val {
sample_idx = idx;
break;
}
}
sorted_indices.i(sample_idx)?.to_scalar::<u32>()
}
| candle/candle-transformers/src/models/voxtral/model.rs/0 | {
"file_path": "candle/candle-transformers/src/models/voxtral/model.rs",
"repo_id": "candle",
"token_count": 18523
} | 66 |
//! Bounding Boxes and Intersection
//!
//! This module provides functionality for handling bounding boxes and their manipulation,
//! particularly in the context of object detection. It includes tools for calculating
//! intersection over union (IoU) and non-maximum suppression (NMS).
/// A bounding box around an object.
#[derive(Debug, Clone)]
pub struct Bbox<D> {
pub xmin: f32,
pub ymin: f32,
pub xmax: f32,
pub ymax: f32,
pub confidence: f32,
pub data: D,
}
#[derive(Debug, Clone, Copy, PartialEq)]
pub struct KeyPoint {
pub x: f32,
pub y: f32,
pub mask: f32,
}
/// Intersection over union of two bounding boxes.
pub fn iou<D>(b1: &Bbox<D>, b2: &Bbox<D>) -> f32 {
let b1_area = (b1.xmax - b1.xmin + 1.) * (b1.ymax - b1.ymin + 1.);
let b2_area = (b2.xmax - b2.xmin + 1.) * (b2.ymax - b2.ymin + 1.);
let i_xmin = b1.xmin.max(b2.xmin);
let i_xmax = b1.xmax.min(b2.xmax);
let i_ymin = b1.ymin.max(b2.ymin);
let i_ymax = b1.ymax.min(b2.ymax);
let i_area = (i_xmax - i_xmin + 1.).max(0.) * (i_ymax - i_ymin + 1.).max(0.);
i_area / (b1_area + b2_area - i_area)
}
pub fn non_maximum_suppression<D>(bboxes: &mut [Vec<Bbox<D>>], threshold: f32) {
// Perform non-maximum suppression.
for bboxes_for_class in bboxes.iter_mut() {
bboxes_for_class.sort_by(|b1, b2| b2.confidence.partial_cmp(&b1.confidence).unwrap());
let mut current_index = 0;
for index in 0..bboxes_for_class.len() {
let mut drop = false;
for prev_index in 0..current_index {
let iou = iou(&bboxes_for_class[prev_index], &bboxes_for_class[index]);
if iou > threshold {
drop = true;
break;
}
}
if !drop {
bboxes_for_class.swap(current_index, index);
current_index += 1;
}
}
bboxes_for_class.truncate(current_index);
}
}
// Updates confidences starting at highest and comparing subsequent boxes.
fn update_confidences<D>(
bboxes_for_class: &[Bbox<D>],
updated_confidences: &mut [f32],
iou_threshold: f32,
sigma: f32,
) {
let len = bboxes_for_class.len();
for current_index in 0..len {
let current_bbox = &bboxes_for_class[current_index];
for index in (current_index + 1)..len {
let iou_val = iou(current_bbox, &bboxes_for_class[index]);
if iou_val > iou_threshold {
// Decay calculation from page 4 of: https://arxiv.org/pdf/1704.04503
let decay = (-iou_val * iou_val / sigma).exp();
let updated_confidence = bboxes_for_class[index].confidence * decay;
updated_confidences[index] = updated_confidence;
}
}
}
}
// Sorts the bounding boxes by confidence and applies soft non-maximum suppression.
// This function is based on the algorithm described in https://arxiv.org/pdf/1704.04503
pub fn soft_non_maximum_suppression<D>(
bboxes: &mut [Vec<Bbox<D>>],
iou_threshold: Option<f32>,
confidence_threshold: Option<f32>,
sigma: Option<f32>,
) {
let iou_threshold = iou_threshold.unwrap_or(0.5);
let confidence_threshold = confidence_threshold.unwrap_or(0.1);
let sigma = sigma.unwrap_or(0.5);
for bboxes_for_class in bboxes.iter_mut() {
// Sort boxes by confidence in descending order
bboxes_for_class.sort_by(|b1, b2| b2.confidence.partial_cmp(&b1.confidence).unwrap());
let mut updated_confidences = bboxes_for_class
.iter()
.map(|bbox| bbox.confidence)
.collect::<Vec<_>>();
update_confidences(
bboxes_for_class,
&mut updated_confidences,
iou_threshold,
sigma,
);
// Update confidences, set to 0.0 if below threshold
for (i, &confidence) in updated_confidences.iter().enumerate() {
bboxes_for_class[i].confidence = if confidence < confidence_threshold {
0.0
} else {
confidence
};
}
}
}
| candle/candle-transformers/src/object_detection.rs/0 | {
"file_path": "candle/candle-transformers/src/object_detection.rs",
"repo_id": "candle",
"token_count": 1950
} | 67 |
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,
}
impl Model {
fn process(&mut self, tokens: &[u32]) -> candle::Result<String> {
const REPEAT_LAST_N: usize = 64;
let dev = Device::Cpu;
let input = Tensor::new(tokens, &dev)?.unsqueeze(0)?;
let logits = self.inner.llama.forward(&input, tokens.len())?;
let logits = logits.squeeze(0)?;
let logits = if self.repeat_penalty == 1. || tokens.is_empty() {
logits
} else {
let start_at = self.tokens.len().saturating_sub(REPEAT_LAST_N);
candle_transformers::utils::apply_repeat_penalty(
&logits,
self.repeat_penalty,
&self.tokens[start_at..],
)?
};
let next_token = self.logits_processor.sample(&logits)?;
self.tokens.push(next_token);
let text = match self.inner.tokenizer.id_to_token(next_token) {
Some(text) => text.replace('▁', " ").replace("<0x0A>", "\n"),
None => "".to_string(),
};
Ok(text)
}
}
#[wasm_bindgen]
impl Model {
#[wasm_bindgen(constructor)]
pub fn new(weights: Vec<u8>, tokenizer: Vec<u8>) -> Result<Model, JsError> {
let model = M::load(ModelData {
tokenizer,
model: weights,
});
let logits_processor = LogitsProcessor::new(299792458, None, None);
match model {
Ok(inner) => Ok(Self {
inner,
logits_processor,
tokens: vec![],
repeat_penalty: 1.,
}),
Err(e) => Err(JsError::new(&e.to_string())),
}
}
#[wasm_bindgen]
pub fn get_seq_len(&mut self) -> usize {
self.inner.config.seq_len
}
#[wasm_bindgen]
pub fn init_with_prompt(
&mut self,
prompt: String,
temp: f64,
top_p: f64,
repeat_penalty: f32,
seed: u64,
) -> Result<String, JsError> {
// First reset the cache.
{
let mut cache = self.inner.cache.kvs.lock().unwrap();
for elem in cache.iter_mut() {
*elem = None
}
}
let temp = if temp <= 0. { None } else { Some(temp) };
let top_p = if top_p <= 0. || top_p >= 1. {
None
} else {
Some(top_p)
};
self.logits_processor = LogitsProcessor::new(seed, temp, top_p);
self.repeat_penalty = repeat_penalty;
self.tokens.clear();
let tokens = self
.inner
.tokenizer
.encode(prompt, true)
.map_err(|m| JsError::new(&m.to_string()))?
.get_ids()
.to_vec();
let text = self
.process(&tokens)
.map_err(|m| JsError::new(&m.to_string()))?;
Ok(text)
}
#[wasm_bindgen]
pub fn next_token(&mut self) -> Result<String, JsError> {
let last_token = *self.tokens.last().unwrap();
let text = self
.process(&[last_token])
.map_err(|m| JsError::new(&m.to_string()))?;
Ok(text)
}
}
fn main() {}
| candle/candle-wasm-examples/llama2-c/src/bin/m.rs/0 | {
"file_path": "candle/candle-wasm-examples/llama2-c/src/bin/m.rs",
"repo_id": "candle",
"token_count": 1807
} | 68 |
<html>
<head>
<meta content="text/html;charset=utf-8" http-equiv="Content-Type" />
<title>Candle Phi 1.5 / Phi 2.0 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
rel="stylesheet"
href="https://cdn.jsdelivr.net/gh/highlightjs/cdn-release@11.8.0/build/styles/default.min.css"
/>
<style>
@import url("https://fonts.googleapis.com/css2?family=Source+Code+Pro:wght@200;300;400&family=Source+Sans+3:wght@100;200;300;400;500;600;700;800;900&display=swap");
html,
body {
font-family: "Source Sans 3", sans-serif;
}
code,
output,
select,
pre {
font-family: "Source Code Pro", monospace;
}
</style>
<style type="text/tailwindcss">
.link {
@apply underline hover:text-blue-500 hover:no-underline;
}
</style>
<script src="https://cdn.tailwindcss.com"></script>
<script type="module">
import snarkdown from "https://cdn.skypack.dev/snarkdown";
import hljs from "https://cdn.skypack.dev/highlight.js";
// models base url
const MODELS = {
phi_1_5_q4k: {
base_url:
"https://huggingface.co/lmz/candle-quantized-phi/resolve/main/",
model: "model-q4k.gguf",
tokenizer: "tokenizer.json",
config: "phi-1_5.json",
quantized: true,
seq_len: 2048,
size: "800 MB",
},
phi_1_5_q80: {
base_url:
"https://huggingface.co/lmz/candle-quantized-phi/resolve/main/",
model: "model-q80.gguf",
tokenizer: "tokenizer.json",
config: "phi-1_5.json",
quantized: true,
seq_len: 2048,
size: "1.51 GB",
},
phi_2_0_q4k: {
base_url:
"https://huggingface.co/radames/phi-2-quantized/resolve/main/",
model: [
"model-v2-q4k.gguf_aa.part",
"model-v2-q4k.gguf_ab.part",
"model-v2-q4k.gguf_ac.part",
],
tokenizer: "tokenizer.json",
config: "config.json",
quantized: true,
seq_len: 2048,
size: "1.57GB",
},
puffin_phi_v2_q4k: {
base_url:
"https://huggingface.co/lmz/candle-quantized-phi/resolve/main/",
model: "model-puffin-phi-v2-q4k.gguf",
tokenizer: "tokenizer-puffin-phi-v2.json",
config: "puffin-phi-v2.json",
quantized: true,
seq_len: 2048,
size: "798 MB",
},
puffin_phi_v2_q80: {
base_url:
"https://huggingface.co/lmz/candle-quantized-phi/resolve/main/",
model: "model-puffin-phi-v2-q80.gguf",
tokenizer: "tokenizer-puffin-phi-v2.json",
config: "puffin-phi-v2.json",
quantized: true,
seq_len: 2048,
size: "1.50 GB",
},
};
const TEMPLATES = [
{
title: "Simple prompt",
prompt: `Sebastien is in London today, it’s the middle of July yet it’s raining, so Sebastien is feeling gloomy. He`,
},
{
title: "Think step by step",
prompt: `Suppose Alice originally had 3 apples, then Bob gave Alice 7 apples, then Alice gave Cook 5 apples, and then Tim gave Alice 3x the amount of apples Alice had. How many apples does Alice have now?
Let’s think step by step.`,
},
{
title: "Explaing a code snippet",
prompt: `What does this script do?
\`\`\`python
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.bind(('', 0))
s.listen(1)
conn, addr = s.accept()
print('Connected by', addr)
return conn.getsockname()[1]
\`\`\`
Let’s think step by step.`,
},
{
title: "Question answering",
prompt: `Instruct: What is the capital of France?
Output:`,
},
{
title: "Chat mode",
prompt: `Alice: Can you tell me how to create a python application to go through all the files
in one directory where the file’s name DOES NOT end with '.json'?
Bob:`,
},
{
title: "Python code completion",
prompt: `"""write a python function called batch(function, list) which call function(x) for x in
list in parallel"""
Solution:`,
},
{
title: "Python Sample",
prompt: `"""Can you make sure those histograms appear side by side on the same plot:
\`\`\`python
plt.hist(intreps_retrained[0][1].view(64,-1).norm(dim=1).detach().cpu().numpy(), bins = 20)
plt.hist(intreps_pretrained[0][1].view(64,-1).norm(dim=1).detach().cpu().numpy(), bins = 20)
\`\`\`
"""`,
},
{
title: "Write a Twitter post",
prompt: `Write a twitter post for the discovery of gravitational wave.
Twitter Post:`,
},
{
title: "Write a review",
prompt: `Write a polite review complaining that the video game 'Random Game' was too badly optimized and it burned my laptop.
Very polite review:`,
},
];
const phiWorker = new Worker("./phiWorker.js", {
type: "module",
});
async function generateSequence(controller) {
const getValue = (id) => document.querySelector(`#${id}`).value;
const modelID = getValue("model");
const model = MODELS[modelID];
const weightsURL =
model.model instanceof Array
? model.model.map((m) => model.base_url + m)
: model.base_url + model.model;
const tokenizerURL = model.base_url + model.tokenizer;
const configURL = model.base_url + model.config;
const prompt = getValue("prompt").trim();
const temperature = getValue("temperature");
const topP = getValue("top-p");
const repeatPenalty = getValue("repeat_penalty");
const seed = getValue("seed");
const maxSeqLen = getValue("max-seq");
function updateStatus(data) {
const outStatus = document.querySelector("#output-status");
const outGen = document.querySelector("#output-generation");
const outCounter = document.querySelector("#output-counter");
switch (data.status) {
case "loading":
outStatus.hidden = false;
outStatus.textContent = data.message;
outGen.hidden = true;
outCounter.hidden = true;
break;
case "generating":
const { message, prompt, sentence, tokensSec, totalTime } = data;
outStatus.hidden = true;
outCounter.hidden = false;
outGen.hidden = false;
outGen.innerHTML = snarkdown(prompt + sentence);
outCounter.innerHTML = `${(totalTime / 1000).toFixed(
2
)}s (${tokensSec.toFixed(2)} tok/s)`;
hljs.highlightAll();
break;
case "complete":
outStatus.hidden = true;
outGen.hidden = false;
break;
}
}
return new Promise((resolve, reject) => {
phiWorker.postMessage({
weightsURL,
modelID,
tokenizerURL,
configURL,
quantized: model.quantized,
prompt,
temp: temperature,
top_p: topP,
repeatPenalty,
seed: seed,
maxSeqLen,
command: "start",
});
const handleAbort = () => {
phiWorker.postMessage({ command: "abort" });
};
const handleMessage = (event) => {
const { status, error, message, prompt, sentence } = event.data;
if (status) updateStatus(event.data);
if (error) {
phiWorker.removeEventListener("message", handleMessage);
reject(new Error(error));
}
if (status === "aborted") {
phiWorker.removeEventListener("message", handleMessage);
resolve(event.data);
}
if (status === "complete") {
phiWorker.removeEventListener("message", handleMessage);
resolve(event.data);
}
};
controller.signal.addEventListener("abort", handleAbort);
phiWorker.addEventListener("message", handleMessage);
});
}
const form = document.querySelector("#form");
const prompt = document.querySelector("#prompt");
const clearBtn = document.querySelector("#clear-btn");
const runBtn = document.querySelector("#run");
const modelSelect = document.querySelector("#model");
const promptTemplates = document.querySelector("#prompt-templates");
let runController = new AbortController();
let isRunning = false;
document.addEventListener("DOMContentLoaded", () => {
for (const [id, model] of Object.entries(MODELS)) {
const option = document.createElement("option");
option.value = id;
option.innerText = `${id} (${model.size})`;
modelSelect.appendChild(option);
}
const query = new URLSearchParams(window.location.search);
const modelID = query.get("model");
if (modelID) {
modelSelect.value = modelID;
} else {
modelSelect.value = "phi_1_5_q4k";
}
for (const [i, { title, prompt }] of TEMPLATES.entries()) {
const div = document.createElement("div");
const input = document.createElement("input");
input.type = "radio";
input.name = "task";
input.id = `templates-${i}`;
input.classList.add("font-light", "cursor-pointer");
input.value = prompt;
const label = document.createElement("label");
label.htmlFor = `templates-${i}`;
label.classList.add("cursor-pointer");
label.innerText = title;
div.appendChild(input);
div.appendChild(label);
promptTemplates.appendChild(div);
}
});
promptTemplates.addEventListener("change", (e) => {
const template = e.target.value;
prompt.value = template;
prompt.style.height = "auto";
prompt.style.height = prompt.scrollHeight + "px";
runBtn.disabled = false;
clearBtn.classList.remove("invisible");
});
modelSelect.addEventListener("change", (e) => {
const query = new URLSearchParams(window.location.search);
query.set("model", e.target.value);
window.history.replaceState(
{},
"",
`${window.location.pathname}?${query}`
);
window.parent.postMessage({ queryString: "?" + query }, "*");
const model = MODELS[e.target.value];
document.querySelector("#max-seq").max = model.seq_len;
document.querySelector("#max-seq").nextElementSibling.value = 200;
});
form.addEventListener("submit", async (e) => {
e.preventDefault();
if (isRunning) {
stopRunning();
} else {
startRunning();
await generateSequence(runController);
stopRunning();
}
});
function startRunning() {
isRunning = true;
runBtn.textContent = "Stop";
}
function stopRunning() {
runController.abort();
runController = new AbortController();
runBtn.textContent = "Run";
isRunning = false;
}
clearBtn.addEventListener("click", (e) => {
e.preventDefault();
prompt.value = "";
clearBtn.classList.add("invisible");
runBtn.disabled = true;
stopRunning();
});
prompt.addEventListener("input", (e) => {
runBtn.disabled = false;
if (e.target.value.length > 0) {
clearBtn.classList.remove("invisible");
} else {
clearBtn.classList.add("invisible");
}
});
</script>
</head>
<body class="container max-w-4xl mx-auto p-4 text-gray-800">
<main class="grid grid-cols-1 gap-8 relative">
<span class="absolute text-5xl -ml-[1em]"> 🕯️ </span>
<div>
<h1 class="text-5xl font-bold">Candle Phi 1.5 / Phi 2.0</h1>
<h2 class="text-2xl font-bold">Rust/WASM Demo</h2>
<p class="max-w-lg">
The
<a
href="https://huggingface.co/microsoft/phi-1_5"
class="link"
target="_blank"
>Phi-1.5</a
>
and
<a
href="https://huggingface.co/microsoft/phi-2"
class="link"
target="_blank"
>Phi-2</a
>
models achieve state-of-the-art performance with only 1.3 billion and
2.7 billion parameters, compared to larger models with up to 13
billion parameters. Here you can try the quantized versions.
Additional prompt examples are available in the
<a
href="https://arxiv.org/pdf/2309.05463.pdf#page=8"
class="link"
target="_blank"
>
technical report </a
>.
</p>
<p class="max-w-lg">
You can also try
<a
href="https://huggingface.co/teknium/Puffin-Phi-v2"
class="link"
target="_blank"
>Puffin-Phi V2
</a>
quantized version, a fine-tuned version of Phi-1.5 on the
<a
href="https://huggingface.co/datasets/LDJnr/Puffin"
class="link"
target="_blank"
>Puffin dataset
</a>
</p>
</div>
<div>
<p class="text-xs italic max-w-lg">
<b>Note:</b>
When first run, the app will download and cache the model, which could
take a few minutes. The models are <b>~800MB</b> or <b>~1.57GB</b> in
size.
</p>
</div>
<div>
<label for="model" class="font-medium">Models Options: </label>
<select
id="model"
class="border-2 border-gray-500 rounded-md font-light"
></select>
</div>
<div>
<details>
<summary class="font-medium cursor-pointer">Prompt Templates</summary>
<form
id="prompt-templates"
class="grid grid-cols-1 sm:grid-cols-2 gap-1 my-2"
></form>
</details>
</div>
<form
id="form"
class="flex text-normal px-1 py-1 border border-gray-700 rounded-md items-center"
>
<input type="submit" hidden />
<textarea
type="text"
id="prompt"
class="font-light text-lg w-full px-3 py-2 mx-1 resize-none outline-none"
oninput="this.style.height = 0;this.style.height = this.scrollHeight + 'px'"
placeholder="Add your prompt here..."
>
Instruct: Write a detailed analogy between mathematics and a lighthouse.
Output:</textarea
>
<button id="clear-btn">
<svg
fill="none"
xmlns="http://www.w3.org/2000/svg"
width="40"
viewBox="0 0 70 40"
>
<path opacity=".5" d="M39 .2v40.2" stroke="#1F2937" />
<path
d="M1.5 11.5 19 29.1m0-17.6L1.5 29.1"
opacity=".5"
stroke="#1F2937"
stroke-width="2"
/>
</svg>
</button>
<button
id="run"
class="bg-gray-700 hover:bg-gray-800 text-white font-normal py-2 w-16 rounded disabled:bg-gray-300 disabled:cursor-not-allowed"
>
Run
</button>
</form>
<details>
<summary class="font-medium cursor-pointer">Advanced Options</summary>
<div class="grid grid-cols-3 max-w-md items-center gap-3 py-3">
<label class="text-sm font-medium" for="max-seq"
>Maximum length
</label>
<input
type="range"
id="max-seq"
name="max-seq"
min="1"
max="2048"
step="1"
value="200"
oninput="this.nextElementSibling.value = Number(this.value)"
/>
<output
class="text-xs w-[50px] text-center font-light px-1 py-1 border border-gray-700 rounded-md"
>
200</output
>
<label class="text-sm font-medium" for="temperature"
>Temperature</label
>
<input
type="range"
id="temperature"
name="temperature"
min="0"
max="2"
step="0.01"
value="0.00"
oninput="this.nextElementSibling.value = Number(this.value).toFixed(2)"
/>
<output
class="text-xs w-[50px] text-center font-light px-1 py-1 border border-gray-700 rounded-md"
>
0.00</output
>
<label class="text-sm font-medium" for="top-p">Top-p</label>
<input
type="range"
id="top-p"
name="top-p"
min="0"
max="1"
step="0.01"
value="1.00"
oninput="this.nextElementSibling.value = Number(this.value).toFixed(2)"
/>
<output
class="text-xs w-[50px] text-center font-light px-1 py-1 border border-gray-700 rounded-md"
>
1.00</output
>
<label class="text-sm font-medium" for="repeat_penalty"
>Repeat Penalty</label
>
<input
type="range"
id="repeat_penalty"
name="repeat_penalty"
min="1"
max="2"
step="0.01"
value="1.10"
oninput="this.nextElementSibling.value = Number(this.value).toFixed(2)"
/>
<output
class="text-xs w-[50px] text-center font-light px-1 py-1 border border-gray-700 rounded-md"
>1.10</output
>
<label class="text-sm font-medium" for="seed">Seed</label>
<input
type="number"
id="seed"
name="seed"
value="299792458"
class="font-light border border-gray-700 text-right rounded-md p-2"
/>
<button
id="run"
onclick="document.querySelector('#seed').value = Math.floor(Math.random() * Number.MAX_SAFE_INTEGER)"
class="bg-gray-700 hover:bg-gray-800 text-white font-normal py-1 w-[50px] rounded disabled:bg-gray-300 disabled:cursor-not-allowed text-sm"
>
Rand
</button>
</div>
</details>
<div>
<h3 class="font-medium">Generation:</h3>
<div
class="min-h-[250px] bg-slate-100 text-gray-500 p-4 rounded-md flex flex-col gap-2"
>
<div
id="output-counter"
hidden
class="ml-auto font-semibold grid-rows-1"
></div>
<p hidden id="output-generation" class="grid-rows-2 text-lg"></p>
<span id="output-status" class="m-auto font-light"
>No output yet</span
>
</div>
</div>
</main>
</body>
</html>
| candle/candle-wasm-examples/phi/index.html/0 | {
"file_path": "candle/candle-wasm-examples/phi/index.html",
"repo_id": "candle",
"token_count": 9818
} | 69 |
<html>
<head>
<meta content="text/html;charset=utf-8" http-equiv="Content-Type" />
<title>Candle T5</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>
@import url("https://fonts.googleapis.com/css2?family=Source+Code+Pro:wght@200;300;400&family=Source+Sans+3:wght@100;200;300;400;500;600;700;800;900&display=swap");
html,
body {
font-family: "Source Sans 3", sans-serif;
}
</style>
<style type="text/tailwindcss">
.link {
@apply underline hover:text-blue-500 hover:no-underline;
}
</style>
<script src="https://cdn.tailwindcss.com"></script>
<script type="module">
import {
getModelInfo,
MODELS,
extractEmbeddings,
generateText,
} from "./utils.js";
const t5ModelEncoderWorker = new Worker("./T5ModelEncoderWorker.js", {
type: "module",
});
const t5ModelConditionalGeneration = new Worker(
"./T5ModelConditionalGeneration.js",
{ type: "module" }
);
const formEl = document.querySelector("#form");
const modelEl = document.querySelector("#model");
const promptEl = document.querySelector("#prompt");
const temperatureEl = document.querySelector("#temperature");
const toppEL = document.querySelector("#top-p");
const repeatPenaltyEl = document.querySelector("#repeat_penalty");
const seedEl = document.querySelector("#seed");
const outputEl = document.querySelector("#output-generation");
const tasksEl = document.querySelector("#tasks");
let selectedTaskID = "";
document.addEventListener("DOMContentLoaded", () => {
for (const [id, model] of Object.entries(MODELS)) {
const option = document.createElement("option");
option.value = id;
option.innerText = `${id} (${model.size})`;
modelEl.appendChild(option);
}
populateTasks(modelEl.value);
modelEl.addEventListener("change", (e) => {
populateTasks(e.target.value);
});
tasksEl.addEventListener("change", (e) => {
const task = e.target.value;
const modelID = modelEl.value;
promptEl.value = MODELS[modelID].tasks[task].prefix;
selectedTaskID = task;
});
});
function populateTasks(modelID) {
const tasks = MODELS[modelID].tasks;
tasksEl.innerHTML = "";
for (const [task, params] of Object.entries(tasks)) {
const div = document.createElement("div");
div.innerHTML = `
<input
type="radio"
name="task"
id="${task}"
class="font-light cursor-pointer"
value="${task}" />
<label for="${task}" class="cursor-pointer">
${params.prefix}
</label>
`;
tasksEl.appendChild(div);
}
selectedTaskID = Object.keys(tasks)[0];
tasksEl.querySelector(`#${selectedTaskID}`).checked = true;
}
form.addEventListener("submit", (e) => {
e.preventDefault();
const promptText = promptEl.value;
const modelID = modelEl.value;
const { modelURL, configURL, tokenizerURL, maxLength } = getModelInfo(
modelID,
selectedTaskID
);
const params = {
temperature: Number(temperatureEl.value),
top_p: Number(toppEL.value),
repetition_penalty: Number(repeatPenaltyEl.value),
seed: BigInt(seedEl.value),
max_length: maxLength,
};
generateText(
t5ModelConditionalGeneration,
modelURL,
tokenizerURL,
configURL,
modelID,
promptText,
params,
(status) => {
if (status.status === "loading") {
outputEl.innerText = "Loading model...";
}
if (status.status === "decoding") {
outputEl.innerText = "Generating...";
}
}
).then(({ output }) => {
outputEl.innerText = output.generation;
});
});
</script>
</head>
<body class="container max-w-4xl mx-auto p-4">
<main class="grid grid-cols-1 gap-8 relative">
<span class="absolute text-5xl -ml-[1em]"> 🕯️ </span>
<div>
<h1 class="text-5xl font-bold">Candle T5 Transformer</h1>
<h2 class="text-2xl font-bold">Rust/WASM Demo</h2>
<p class="max-w-lg">
This demo showcase Text-To-Text Transfer Transformer (<a
href="https://blog.research.google/2020/02/exploring-transfer-learning-with-t5.html"
target="_blank"
class="link"
>T5</a
>) models right in your browser, thanks to
<a
href="https://github.com/huggingface/candle/"
target="_blank"
class="link">
Candle
</a>
ML framework and rust/wasm. You can choose from a range of available
models, including
<a
href="https://huggingface.co/t5-small"
target="_blank"
class="link">
t5-small</a
>,
<a href="https://huggingface.co/t5-base" target="_blank" class="link"
>t5-base</a
>,
<a
href="https://huggingface.co/google/flan-t5-small"
target="_blank"
class="link"
>flan-t5-small</a
>,
several
<a
href="https://huggingface.co/lmz/candle-quantized-t5/tree/main"
target="_blank"
class="link">
t5 quantized gguf models</a
>, and also a quantized
<a
href="https://huggingface.co/jbochi/candle-coedit-quantized/tree/main"
target="_blank"
class="link">
CoEdIT model for text rewrite</a
>.
</p>
</div>
<div>
<label for="model" class="font-medium">Models Options: </label>
<select
id="model"
class="border-2 border-gray-500 rounded-md font-light"></select>
</div>
<div>
<h3 class="font-medium">Task Prefix:</h3>
<form id="tasks" class="flex flex-col gap-1 my-2"></form>
</div>
<form
id="form"
class="flex text-normal px-1 py-1 border border-gray-700 rounded-md items-center">
<input type="submit" hidden />
<input
type="text"
id="prompt"
class="font-light w-full px-3 py-2 mx-1 resize-none outline-none"
placeholder="Add prompt here, e.g. 'translate English to German: Today I'm going to eat Ice Cream'"
value="translate English to German: Today I'm going to eat Ice Cream" />
<button
class="bg-gray-700 hover:bg-gray-800 text-white font-normal py-2 w-16 rounded disabled:bg-gray-300 disabled:cursor-not-allowed">
Run
</button>
</form>
<div class="grid grid-cols-3 max-w-md items-center gap-3">
<label class="text-sm font-medium" for="temperature">Temperature</label>
<input
type="range"
id="temperature"
name="temperature"
min="0"
max="2"
step="0.01"
value="0.00"
oninput="this.nextElementSibling.value = Number(this.value).toFixed(2)" />
<output
class="text-xs w-[50px] text-center font-light px-1 py-1 border border-gray-700 rounded-md">
0.00</output
>
<label class="text-sm font-medium" for="top-p">Top-p</label>
<input
type="range"
id="top-p"
name="top-p"
min="0"
max="1"
step="0.01"
value="1.00"
oninput="this.nextElementSibling.value = Number(this.value).toFixed(2)" />
<output
class="text-xs w-[50px] text-center font-light px-1 py-1 border border-gray-700 rounded-md">
1.00</output
>
<label class="text-sm font-medium" for="repeat_penalty"
>Repeat Penalty</label
>
<input
type="range"
id="repeat_penalty"
name="repeat_penalty"
min="1"
max="2"
step="0.01"
value="1.10"
oninput="this.nextElementSibling.value = Number(this.value).toFixed(2)" />
<output
class="text-xs w-[50px] text-center font-light px-1 py-1 border border-gray-700 rounded-md"
>1.10</output
>
<label class="text-sm font-medium" for="seed">Seed</label>
<input
type="number"
id="seed"
name="seed"
value="299792458"
class="font-light border border-gray-700 text-right rounded-md p-2" />
<button
id="run"
onclick="document.querySelector('#seed').value = BigInt(Math.floor(Math.random() * 2**64-1))"
class="bg-gray-700 hover:bg-gray-800 text-white font-normal py-1 w-[50px] rounded disabled:bg-gray-300 disabled:cursor-not-allowed text-sm">
Rand
</button>
</div>
<div>
<h3 class="font-medium">Generation:</h3>
<div
class="min-h-[250px] bg-slate-100 text-gray-500 p-4 rounded-md flex flex-col gap-2 text-lg">
<p id="output-generation" class="grid-rows-2">No output yet</p>
</div>
</div>
</main>
</body>
</html>
| candle/candle-wasm-examples/t5/index.html/0 | {
"file_path": "candle/candle-wasm-examples/t5/index.html",
"repo_id": "candle",
"token_count": 4724
} | 70 |
pub const LANGUAGES: [(&str, &str); 99] = [
("en", "english"),
("zh", "chinese"),
("de", "german"),
("es", "spanish"),
("ru", "russian"),
("ko", "korean"),
("fr", "french"),
("ja", "japanese"),
("pt", "portuguese"),
("tr", "turkish"),
("pl", "polish"),
("ca", "catalan"),
("nl", "dutch"),
("ar", "arabic"),
("sv", "swedish"),
("it", "italian"),
("id", "indonesian"),
("hi", "hindi"),
("fi", "finnish"),
("vi", "vietnamese"),
("he", "hebrew"),
("uk", "ukrainian"),
("el", "greek"),
("ms", "malay"),
("cs", "czech"),
("ro", "romanian"),
("da", "danish"),
("hu", "hungarian"),
("ta", "tamil"),
("no", "norwegian"),
("th", "thai"),
("ur", "urdu"),
("hr", "croatian"),
("bg", "bulgarian"),
("lt", "lithuanian"),
("la", "latin"),
("mi", "maori"),
("ml", "malayalam"),
("cy", "welsh"),
("sk", "slovak"),
("te", "telugu"),
("fa", "persian"),
("lv", "latvian"),
("bn", "bengali"),
("sr", "serbian"),
("az", "azerbaijani"),
("sl", "slovenian"),
("kn", "kannada"),
("et", "estonian"),
("mk", "macedonian"),
("br", "breton"),
("eu", "basque"),
("is", "icelandic"),
("hy", "armenian"),
("ne", "nepali"),
("mn", "mongolian"),
("bs", "bosnian"),
("kk", "kazakh"),
("sq", "albanian"),
("sw", "swahili"),
("gl", "galician"),
("mr", "marathi"),
("pa", "punjabi"),
("si", "sinhala"),
("km", "khmer"),
("sn", "shona"),
("yo", "yoruba"),
("so", "somali"),
("af", "afrikaans"),
("oc", "occitan"),
("ka", "georgian"),
("be", "belarusian"),
("tg", "tajik"),
("sd", "sindhi"),
("gu", "gujarati"),
("am", "amharic"),
("yi", "yiddish"),
("lo", "lao"),
("uz", "uzbek"),
("fo", "faroese"),
("ht", "haitian creole"),
("ps", "pashto"),
("tk", "turkmen"),
("nn", "nynorsk"),
("mt", "maltese"),
("sa", "sanskrit"),
("lb", "luxembourgish"),
("my", "myanmar"),
("bo", "tibetan"),
("tl", "tagalog"),
("mg", "malagasy"),
("as", "assamese"),
("tt", "tatar"),
("haw", "hawaiian"),
("ln", "lingala"),
("ha", "hausa"),
("ba", "bashkir"),
("jw", "javanese"),
("su", "sundanese"),
];
| candle/candle-wasm-examples/whisper/src/languages.rs/0 | {
"file_path": "candle/candle-wasm-examples/whisper/src/languages.rs",
"repo_id": "candle",
"token_count": 1175
} | 71 |
use crate::model::{report_detect, report_pose, Bbox, Multiples, YoloV8, YoloV8Pose};
use candle::{DType, Device, Result, Tensor};
use candle_nn::{Module, VarBuilder};
use serde::{Deserialize, Serialize};
use wasm_bindgen::prelude::*;
use yew_agent::{HandlerId, Public, WorkerLink};
#[wasm_bindgen]
extern "C" {
// Use `js_namespace` here to bind `console.log(..)` instead of just
// `log(..)`
#[wasm_bindgen(js_namespace = console)]
pub fn log(s: &str);
}
#[macro_export]
macro_rules! console_log {
// Note that this is using the `log` function imported above during
// `bare_bones`
($($t:tt)*) => ($crate::worker::log(&format_args!($($t)*).to_string()))
}
// Communication to the worker happens through bincode, the model weights and configs are fetched
// on the main thread and transferred via the following structure.
#[derive(Serialize, Deserialize)]
pub struct ModelData {
pub weights: Vec<u8>,
pub model_size: String,
}
#[derive(Serialize, Deserialize)]
pub struct RunData {
pub image_data: Vec<u8>,
pub conf_threshold: f32,
pub iou_threshold: f32,
}
pub struct Model {
model: YoloV8,
}
impl Model {
pub fn run(
&self,
image_data: Vec<u8>,
conf_threshold: f32,
iou_threshold: f32,
) -> Result<Vec<Vec<Bbox>>> {
console_log!("image data: {}", image_data.len());
let image_data = std::io::Cursor::new(image_data);
let original_image = image::ImageReader::new(image_data)
.with_guessed_format()?
.decode()
.map_err(candle::Error::wrap)?;
let (width, height) = {
let w = original_image.width() as usize;
let h = original_image.height() as usize;
if w < h {
let w = w * 640 / h;
// Sizes have to be divisible by 32.
(w / 32 * 32, 640)
} else {
let h = h * 640 / w;
(640, h / 32 * 32)
}
};
let image_t = {
let img = original_image.resize_exact(
width as u32,
height as u32,
image::imageops::FilterType::CatmullRom,
);
let data = img.to_rgb8().into_raw();
Tensor::from_vec(
data,
(img.height() as usize, img.width() as usize, 3),
&Device::Cpu,
)?
.permute((2, 0, 1))?
};
let image_t = (image_t.unsqueeze(0)?.to_dtype(DType::F32)? * (1. / 255.))?;
let predictions = self.model.forward(&image_t)?.squeeze(0)?;
console_log!("generated predictions {predictions:?}");
let bboxes = report_detect(
&predictions,
original_image,
width,
height,
conf_threshold,
iou_threshold,
)?;
Ok(bboxes)
}
pub fn load_(weights: Vec<u8>, model_size: &str) -> Result<Self> {
let multiples = match model_size {
"n" => Multiples::n(),
"s" => Multiples::s(),
"m" => Multiples::m(),
"l" => Multiples::l(),
"x" => Multiples::x(),
_ => Err(candle::Error::Msg(
"invalid model size: must be n, s, m, l or x".to_string(),
))?,
};
let dev = &Device::Cpu;
let vb = VarBuilder::from_buffered_safetensors(weights, DType::F32, dev)?;
let model = YoloV8::load(vb, multiples, 80)?;
Ok(Self { model })
}
pub fn load(md: ModelData) -> Result<Self> {
Self::load_(md.weights, &md.model_size.to_string())
}
}
pub struct ModelPose {
model: YoloV8Pose,
}
impl ModelPose {
pub fn run(
&self,
image_data: Vec<u8>,
conf_threshold: f32,
iou_threshold: f32,
) -> Result<Vec<Bbox>> {
console_log!("image data: {}", image_data.len());
let image_data = std::io::Cursor::new(image_data);
let original_image = image::ImageReader::new(image_data)
.with_guessed_format()?
.decode()
.map_err(candle::Error::wrap)?;
let (width, height) = {
let w = original_image.width() as usize;
let h = original_image.height() as usize;
if w < h {
let w = w * 640 / h;
// Sizes have to be divisible by 32.
(w / 32 * 32, 640)
} else {
let h = h * 640 / w;
(640, h / 32 * 32)
}
};
let image_t = {
let img = original_image.resize_exact(
width as u32,
height as u32,
image::imageops::FilterType::CatmullRom,
);
let data = img.to_rgb8().into_raw();
Tensor::from_vec(
data,
(img.height() as usize, img.width() as usize, 3),
&Device::Cpu,
)?
.permute((2, 0, 1))?
};
let image_t = (image_t.unsqueeze(0)?.to_dtype(DType::F32)? * (1. / 255.))?;
let predictions = self.model.forward(&image_t)?.squeeze(0)?;
console_log!("generated predictions {predictions:?}");
let bboxes = report_pose(
&predictions,
original_image,
width,
height,
conf_threshold,
iou_threshold,
)?;
Ok(bboxes)
}
pub fn load_(weights: Vec<u8>, model_size: &str) -> Result<Self> {
let multiples = match model_size {
"n" => Multiples::n(),
"s" => Multiples::s(),
"m" => Multiples::m(),
"l" => Multiples::l(),
"x" => Multiples::x(),
_ => Err(candle::Error::Msg(
"invalid model size: must be n, s, m, l or x".to_string(),
))?,
};
let dev = &Device::Cpu;
let vb = VarBuilder::from_buffered_safetensors(weights, DType::F32, dev)?;
let model = YoloV8Pose::load(vb, multiples, 1, (17, 3))?;
Ok(Self { model })
}
pub fn load(md: ModelData) -> Result<Self> {
Self::load_(md.weights, &md.model_size.to_string())
}
}
pub struct Worker {
link: WorkerLink<Self>,
model: Option<Model>,
}
#[derive(Serialize, Deserialize)]
pub enum WorkerInput {
ModelData(ModelData),
RunData(RunData),
}
#[derive(Serialize, Deserialize)]
pub enum WorkerOutput {
ProcessingDone(std::result::Result<Vec<Vec<Bbox>>, String>),
WeightsLoaded,
}
impl yew_agent::Worker for Worker {
type Input = WorkerInput;
type Message = ();
type Output = std::result::Result<WorkerOutput, String>;
type Reach = Public<Self>;
fn create(link: WorkerLink<Self>) -> Self {
Self { link, model: None }
}
fn update(&mut self, _msg: Self::Message) {
// no messaging
}
fn handle_input(&mut self, msg: Self::Input, id: HandlerId) {
let output = match msg {
WorkerInput::ModelData(md) => match Model::load(md) {
Ok(model) => {
self.model = Some(model);
Ok(WorkerOutput::WeightsLoaded)
}
Err(err) => Err(format!("model creation error {err:?}")),
},
WorkerInput::RunData(rd) => match &mut self.model {
None => Err("model has not been set yet".to_string()),
Some(model) => {
let result = model
.run(rd.image_data, rd.conf_threshold, rd.iou_threshold)
.map_err(|e| e.to_string());
Ok(WorkerOutput::ProcessingDone(result))
}
},
};
self.link.respond(id, output);
}
fn name_of_resource() -> &'static str {
"worker.js"
}
fn resource_path_is_relative() -> bool {
true
}
}
| candle/candle-wasm-examples/yolo/src/worker.rs/0 | {
"file_path": "candle/candle-wasm-examples/yolo/src/worker.rs",
"repo_id": "candle",
"token_count": 4075
} | 72 |
apiVersion: v1
kind: Service
metadata:
name: "{{ include "name" . }}"
annotations: {{ toYaml .Values.service.annotations | nindent 4 }}
namespace: {{ .Release.Namespace }}
labels: {{ include "labels.standard" . | nindent 4 }}
spec:
ports:
- name: http
port: 80
protocol: TCP
targetPort: http
{{- if $.Values.monitoring.enabled }}
- name: metrics
port: 5565
protocol: TCP
targetPort: metrics
{{- end }}
selector: {{ include "labels.standard" . | nindent 4 }}
type: {{.Values.service.type}}
| chat-ui/chart/templates/service.yaml/0 | {
"file_path": "chat-ui/chart/templates/service.yaml",
"repo_id": "chat-ui",
"token_count": 192
} | 73 |
# OpenAI
| Feature | Available |
| --------------------------- | --------- |
| [Tools](../tools) | No |
| [Multimodal](../multimodal) | Yes |
Chat UI can be used with any API server that supports OpenAI API compatibility, for example [text-generation-webui](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/openai), [LocalAI](https://github.com/go-skynet/LocalAI), [FastChat](https://github.com/lm-sys/FastChat/blob/main/docs/openai_api.md), [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), and [ialacol](https://github.com/chenhunghan/ialacol) and [vllm](https://docs.vllm.ai/en/latest/serving/openai_compatible_server.html).
The following example config makes Chat UI works with [text-generation-webui](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/openai), the `endpoint.baseUrl` is the url of the OpenAI API compatible server, this overrides the baseUrl to be used by OpenAI instance. The `endpoint.completion` determine which endpoint to be used, default is `chat_completions` which uses `/chat/completions`, change to `endpoint.completion` to `completions` to use the `/completions` endpoint.
```ini
MODELS=`[
{
"name": "text-generation-webui",
"id": "text-generation-webui",
"parameters": {
"temperature": 0.9,
"top_p": 0.95,
"repetition_penalty": 1.2,
"top_k": 50,
"truncate": 1000,
"max_new_tokens": 1024,
"stop": []
},
"endpoints": [{
"type" : "openai",
"baseURL": "http://localhost:8000/v1"
}]
}
]`
```
The `openai` type includes official OpenAI models. You can add, for example, GPT4/GPT3.5 as a "openai" model:
```ini
OPENAI_API_KEY=#your openai api key here
MODELS=`[{
"name": "gpt-4",
"displayName": "GPT 4",
"endpoints" : [{
"type": "openai",
"apiKey": "or your openai api key here"
}]
},{
"name": "gpt-3.5-turbo",
"displayName": "GPT 3.5 Turbo",
"endpoints" : [{
"type": "openai",
"apiKey": "or your openai api key here"
}]
}]`
```
We also support models in the `o1` family. You need to add a few more options ot the config: Here is an example for `o1-mini`:
```ini
MODELS=`[
{
"name": "o1-mini",
"description": "ChatGPT o1-mini",
"systemRoleSupported": false,
"parameters": {
"max_new_tokens": 2048,
},
"endpoints" : [{
"type": "openai",
"useCompletionTokens": true,
}]
}
]
```
You may also consume any model provider that provides compatible OpenAI API endpoint. For example, you may self-host [Portkey](https://github.com/Portkey-AI/gateway) gateway and experiment with Claude or GPTs offered by Azure OpenAI. Example for Claude from Anthropic:
```ini
MODELS=`[{
"name": "claude-2.1",
"displayName": "Claude 2.1",
"description": "Anthropic has been founded by former OpenAI researchers...",
"parameters": {
"temperature": 0.5,
"max_new_tokens": 4096,
},
"endpoints": [
{
"type": "openai",
"baseURL": "https://gateway.example.com/v1",
"defaultHeaders": {
"x-portkey-config": '{"provider":"anthropic","api_key":"sk-ant-abc...xyz"}'
}
}
]
}]`
```
Example for GPT 4 deployed on Azure OpenAI:
```ini
MODELS=`[{
"id": "gpt-4-1106-preview",
"name": "gpt-4-1106-preview",
"displayName": "gpt-4-1106-preview",
"parameters": {
"temperature": 0.5,
"max_new_tokens": 4096,
},
"endpoints": [
{
"type": "openai",
"baseURL": "https://{resource-name}.openai.azure.com/openai/deployments/{deployment-id}",
"defaultHeaders": {
"api-key": "{api-key}"
},
"defaultQuery": {
"api-version": "2023-05-15"
}
}
]
}]`
```
## DeepInfra
Or try Mistral from [Deepinfra](https://deepinfra.com/mistralai/Mistral-7B-Instruct-v0.1/api?example=openai-http):
> Note, apiKey can either be set custom per endpoint, or globally using `OPENAI_API_KEY` variable.
```ini
MODELS=`[{
"name": "mistral-7b",
"displayName": "Mistral 7B",
"description": "A 7B dense Transformer, fast-deployed and easily customisable. Small, yet powerful for a variety of use cases. Supports English and code, and a 8k context window.",
"parameters": {
"temperature": 0.5,
"max_new_tokens": 4096,
},
"endpoints": [
{
"type": "openai",
"baseURL": "https://api.deepinfra.com/v1/openai",
"apiKey": "abc...xyz"
}
]
}]`
```
_Non-streaming endpoints_
For endpoints that don´t support streaming like o1 on Azure, you can pass `streamingSupported: false` in your endpoint config:
```
MODELS=`[{
"id": "o1-preview",
"name": "o1-preview",
"displayName": "o1-preview",
"systemRoleSupported": false,
"endpoints": [
{
"type": "openai",
"baseURL": "https://my-deployment.openai.azure.com/openai/deployments/o1-preview",
"defaultHeaders": {
"api-key": "$SECRET"
},
"streamingSupported": false,
}
]
}]`
```
## Other
Some other providers and their `baseURL` for reference.
[Groq](https://groq.com/): https://api.groq.com/openai/v1
[Fireworks](https://fireworks.ai/): https://api.fireworks.ai/inference/v1
```
```
| chat-ui/docs/source/configuration/models/providers/openai.md/0 | {
"file_path": "chat-ui/docs/source/configuration/models/providers/openai.md",
"repo_id": "chat-ui",
"token_count": 2177
} | 74 |
{
"name": "chat-ui",
"version": "0.10.0",
"lockfileVersion": 3,
"requires": true,
"packages": {
"": {
"name": "chat-ui",
"version": "0.10.0",
"dependencies": {
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"express": "^4.21.2",
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"satori": "^0.10.11",
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"sbd": "^1.0.19",
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"sharp": "^0.33.4",
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"tailwindcss": "^3.4.0",
"uuid": "^10.0.0",
"vitest-browser-svelte": "^0.1.0",
"zod": "^3.22.3"
},
"devDependencies": {
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},
"optionalDependencies": {
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"aws4fetch": "^1.0.17",
"cohere-ai": "^7.9.0",
"openai": "^4.44.0"
}
},
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"license": "MIT",
"engines": {
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},
"funding": {
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}
},
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}
| chat-ui/package-lock.json/0 | {
"file_path": "chat-ui/package-lock.json",
"repo_id": "chat-ui",
"token_count": 369480
} | 75 |
import { navigating } from "$app/stores";
import { tick } from "svelte";
import { get } from "svelte/store";
const detachedOffset = 10;
/**
* @param node element to snap scroll to bottom
* @param dependency pass in a dependency to update scroll on changes.
*/
export const snapScrollToBottom = (node: HTMLElement, dependency: unknown) => {
let prevScrollValue = node.scrollTop;
let isDetached = false;
const handleScroll = () => {
// if user scrolled up, we detach
if (node.scrollTop < prevScrollValue) {
isDetached = true;
}
// if user scrolled back to within 10px of bottom, we reattach
if (node.scrollTop - (node.scrollHeight - node.clientHeight) >= -detachedOffset) {
isDetached = false;
}
prevScrollValue = node.scrollTop;
};
const updateScroll = async (_options: { force?: boolean } = {}) => {
const defaultOptions = { force: false };
const options = { ...defaultOptions, ..._options };
const { force } = options;
if (!force && isDetached && !get(navigating)) return;
// wait for next tick to ensure that the DOM is updated
await tick();
node.scrollTo({ top: node.scrollHeight });
};
node.addEventListener("scroll", handleScroll);
if (dependency) {
updateScroll({ force: true });
}
return {
update: updateScroll,
destroy: () => {
node.removeEventListener("scroll", handleScroll);
},
};
};
| chat-ui/src/lib/actions/snapScrollToBottom.ts/0 | {
"file_path": "chat-ui/src/lib/actions/snapScrollToBottom.ts",
"repo_id": "chat-ui",
"token_count": 437
} | 76 |
<script lang="ts">
import { base } from "$app/paths";
import { page } from "$app/state";
import { createEventDispatcher } from "svelte";
import CarbonCheckmark from "~icons/carbon/checkmark";
import CarbonTrashCan from "~icons/carbon/trash-can";
import CarbonClose from "~icons/carbon/close";
import CarbonEdit from "~icons/carbon/edit";
import type { ConvSidebar } from "$lib/types/ConvSidebar";
interface Props {
conv: ConvSidebar;
readOnly?: true;
showDescription?: boolean;
description?: string;
searchInput?: string;
}
let { conv, readOnly, showDescription, description, searchInput }: Props = $props();
let confirmDelete = $state(false);
const dispatch = createEventDispatcher<{
deleteConversation: string;
editConversationTitle: { id: string; title: string };
}>();
</script>
<a
data-sveltekit-noscroll
onmouseleave={() => {
confirmDelete = false;
}}
href="{base}/conversation/{conv.id}"
class="group flex h-10 flex-none items-center gap-1.5 rounded-lg pl-2.5 pr-2 text-gray-600 hover:bg-gray-100 dark:text-gray-300 dark:hover:bg-gray-700
{conv.id === page.params.id ? 'bg-gray-100 dark:bg-gray-700' : ''}
{showDescription ? 'sm:h-[3.5rem]' : 'sm:h-[2.35rem]'}
"
>
<div class="my-2 flex flex-1 flex-col items-start truncate">
<span>
{#if confirmDelete}
<span class="mr-1 font-semibold"> Delete </span>
{/if}
{#if conv.avatarUrl}
{#await conv.avatarUrl then avatarUrl}
{#if avatarUrl}
<img
src="{base}{avatarUrl}"
alt="Assistant avatar"
class="mr-1.5 inline size-4 flex-none rounded-full object-cover"
/>
{/if}
{/await}
{conv.title.replace(/\p{Emoji}/gu, "")}
{:else if conv.assistantId}
<div
class="mr-1.5 flex size-4 flex-none items-center justify-center rounded-full bg-gray-300 text-xs font-bold uppercase text-gray-500"
></div>
{conv.title.replace(/\p{Emoji}/gu, "")}
{:else}
{conv.title}
{/if}
</span>
{#if showDescription && description && searchInput}
<p class="ml-7 text-sm text-gray-500">
{#each description.split(searchInput) as segment, i}{segment}{#if i < description.split(searchInput).length - 1}<strong
>{searchInput}</strong
>{/if}
{/each}
</p>
{/if}
</div>
{#if !readOnly}
{#if confirmDelete}
<button
type="button"
class="flex h-5 w-5 items-center justify-center rounded md:hidden md:group-hover:flex"
title="Cancel delete action"
onclick={(e) => {
e.preventDefault();
confirmDelete = false;
}}
>
<CarbonClose class="text-xs text-gray-400 hover:text-gray-500 dark:hover:text-gray-300" />
</button>
<button
type="button"
class="flex h-5 w-5 items-center justify-center rounded md:hidden md:group-hover:flex"
title="Confirm delete action"
onclick={(e) => {
e.preventDefault();
confirmDelete = false;
dispatch("deleteConversation", conv.id.toString());
}}
>
<CarbonCheckmark
class="text-xs text-gray-400 hover:text-gray-500 dark:hover:text-gray-300"
/>
</button>
{:else}
<button
type="button"
class="flex h-5 w-5 items-center justify-center rounded md:hidden md:group-hover:flex"
title="Edit conversation title"
onclick={(e) => {
e.preventDefault();
const newTitle = prompt("Edit this conversation title:", conv.title);
if (!newTitle) return;
dispatch("editConversationTitle", { id: conv.id.toString(), title: newTitle });
}}
>
<CarbonEdit class="text-xs text-gray-400 hover:text-gray-500 dark:hover:text-gray-300" />
</button>
<button
type="button"
class="flex h-5 w-5 items-center justify-center rounded md:hidden md:group-hover:flex"
title="Delete conversation"
onclick={(event) => {
event.preventDefault();
if (event.shiftKey) {
dispatch("deleteConversation", conv.id.toString());
} else {
confirmDelete = true;
}
}}
>
<CarbonTrashCan
class="text-xs text-gray-400 hover:text-gray-500 dark:hover:text-gray-300"
/>
</button>
{/if}
{/if}
</a>
| chat-ui/src/lib/components/NavConversationItem.svelte/0 | {
"file_path": "chat-ui/src/lib/components/NavConversationItem.svelte",
"repo_id": "chat-ui",
"token_count": 1740
} | 77 |
<script lang="ts">
import CarbonWikis from "~icons/carbon/wikis";
import CarbonTools from "~icons/carbon/tools";
import CarbonCamera from "~icons/carbon/camera";
import CarbonCode from "~icons/carbon/code";
import CarbonEmail from "~icons/carbon/email";
import CarbonCloud from "~icons/carbon/cloud-upload";
import CarbonTerminal from "~icons/carbon/terminal";
import CarbonGame from "~icons/carbon/game-console";
import CarbonChat from "~icons/carbon/chat-bot";
import CarbonSpeaker from "~icons/carbon/volume-up";
import CarbonVideo from "~icons/carbon/video";
interface Props {
color: string;
icon: string;
size?: "xs" | "sm" | "md" | "lg";
}
let { color, icon, size = "md" }: Props = $props();
let gradientColor = $derived(
(() => {
switch (color) {
case "purple":
return "#653789";
case "blue":
return "#375889";
case "green":
return "#37894E";
case "yellow":
return "#897C37";
case "red":
return "#893737";
default:
return "#FFF";
}
})()
);
let iconEl = $state(CarbonWikis);
switch (icon) {
case "wikis":
iconEl = CarbonWikis;
break;
case "tools":
iconEl = CarbonTools;
break;
case "camera":
iconEl = CarbonCamera;
break;
case "code":
iconEl = CarbonCode;
break;
case "email":
iconEl = CarbonEmail;
break;
case "cloud":
iconEl = CarbonCloud;
break;
case "terminal":
iconEl = CarbonTerminal;
break;
case "game":
iconEl = CarbonGame;
break;
case "chat":
iconEl = CarbonChat;
break;
case "speaker":
iconEl = CarbonSpeaker;
break;
case "video":
iconEl = CarbonVideo;
break;
}
let sizeClass = $derived(
(() => {
switch (size) {
case "xs":
return "size-4";
case "sm":
return "size-8";
case "md":
return "size-14";
case "lg":
return "size-24";
}
})()
);
const SvelteComponent = $derived(iconEl);
</script>
<div class="flex {sizeClass} relative items-center justify-center">
<svg xmlns="http://www.w3.org/2000/svg" class="absolute {sizeClass} h-full" viewBox="0 0 52 58">
<defs>
<linearGradient id="gradient-{gradientColor}" gradientTransform="rotate(90)">
<stop offset="0%" stop-color="#0E1523" />
<stop offset="100%" stop-color={gradientColor} />
</linearGradient>
<mask id="mask">
<path
d="M22.3043 1.2486C23.4279 0.603043 24.7025 0.263184 26 0.263184C27.2975 0.263184 28.5721 0.603043 29.6957 1.2486L48.3043 11.9373C49.4279 12.5828 50.361 13.5113 51.0097 14.6294C51.6584 15.7475 52 17.0158 52 18.3069V39.6902C52 40.9813 51.6584 42.2496 51.0097 43.3677C50.361 44.4858 49.4279 45.4143 48.3043 46.0598L29.6957 56.7514C28.5721 57.397 27.2975 57.7369 26 57.7369C24.7025 57.7369 23.4279 57.397 22.3043 56.7514L3.6957 46.0598C2.57209 45.4143 1.63904 44.4858 0.990308 43.3677C0.341578 42.2496 3.34785e-05 40.9813 5.18628e-07 39.6902V18.3099C-0.000485629 17.0183 0.340813 15.7494 0.989568 14.6307C1.63832 13.512 2.57166 12.5831 3.6957 11.9373L22.3043 1.2486Z"
fill="white"
/>
</mask>
</defs>
<rect width="100%" height="100%" fill="url(#gradient-{gradientColor})" mask="url(#mask)" />
</svg>
<SvelteComponent class="relative {sizeClass} scale-50 text-clip text-gray-200" />
</div>
| chat-ui/src/lib/components/ToolLogo.svelte/0 | {
"file_path": "chat-ui/src/lib/components/ToolLogo.svelte",
"repo_id": "chat-ui",
"token_count": 1446
} | 78 |
<script lang="ts" module>
export function toggleSearch() {
searchOpen = !searchOpen;
}
let searchOpen: boolean = $state(false);
</script>
<script lang="ts">
import { debounce } from "$lib/utils/debounce";
import NavConversationItem from "../NavConversationItem.svelte";
import { titles } from "../NavMenu.svelte";
import { beforeNavigate } from "$app/navigation";
import CarbonClose from "~icons/carbon/close";
import { fly } from "svelte/transition";
import InfiniteScroll from "../InfiniteScroll.svelte";
import { handleResponse, useAPIClient, type Success } from "$lib/APIClient";
const client = useAPIClient();
let searchContainer: HTMLDivElement | undefined = $state(undefined);
let inputElement: HTMLInputElement | undefined = $state(undefined);
let searchInput: string = $state("");
let debouncedInput: string = $state("");
let hasMore = $state(true);
let pending: boolean = $state(false);
let conversations: NonNullable<Success<typeof client.conversations.search.get>> = $state([]);
let page: number = $state(0);
const dateRanges = [
new Date().setDate(new Date().getDate() - 1),
new Date().setDate(new Date().getDate() - 7),
new Date().setMonth(new Date().getMonth() - 1),
];
let groupedConversations = $derived({
today: conversations.filter(({ updatedAt }) => updatedAt.getTime() > dateRanges[0]),
week: conversations.filter(
({ updatedAt }) => updatedAt.getTime() > dateRanges[1] && updatedAt.getTime() < dateRanges[0]
),
month: conversations.filter(
({ updatedAt }) => updatedAt.getTime() > dateRanges[2] && updatedAt.getTime() < dateRanges[1]
),
older: conversations.filter(({ updatedAt }) => updatedAt.getTime() < dateRanges[2]),
});
const update = debounce(async (v: string) => {
if (debouncedInput !== v) {
conversations = [];
page = 0;
hasMore = true;
}
debouncedInput = v;
pending = true;
try {
await handleVisible(v);
} finally {
pending = false;
}
}, 300);
const handleBackdropClick = (event: MouseEvent) => {
if (!searchOpen || !searchContainer) return;
const target = event.target;
if (!(target instanceof Node) || !searchContainer.contains(target)) {
searchOpen = false;
}
};
async function handleVisible(v: string) {
const newConvs = await client.conversations.search
.get({
query: {
q: v,
p: page++,
},
})
.then(handleResponse)
.catch(() => []);
if (newConvs.length === 0) {
hasMore = false;
}
conversations = [...conversations, ...newConvs];
}
$effect(() => update(searchInput));
function handleKeydown(event: KeyboardEvent) {
if ((event.ctrlKey || event.metaKey) && event.key.toLowerCase() === "k") {
if (!searchOpen) {
searchOpen = true;
}
event.preventDefault();
event.stopPropagation();
}
if (searchOpen && event.key === "Escape") {
if (searchOpen) {
searchOpen = false;
}
event.preventDefault();
}
}
beforeNavigate(() => {
searchOpen = false;
searchInput = "";
});
$effect(() => {
if (searchOpen) {
inputElement?.focus();
}
});
$effect(() => {
if (!searchOpen) {
searchInput = "";
debouncedInput = ""; // reset debouncedInput on search bar close
}
});
</script>
<svelte:window onkeydown={handleKeydown} onmousedown={handleBackdropClick} />
{#if searchOpen}
<div
bind:this={searchContainer}
class="fixed bottom-0 left-[5%] right-[5%] top-[10%] z-50
m-4 mx-auto h-fit max-w-2xl
overflow-hidden rounded-xl
border border-gray-500/50 bg-gray-200 text-gray-800
shadow-[0_10px_40px_rgba(100,100,100,0.2)]
dark:bg-gray-800
dark:text-gray-200 dark:shadow-[0_10px_40px_rgba(255,255,255,0.1)] lg:top-[20%]"
in:fly={{ y: 100 }}
>
<button
class="absolute right-1 top-2.5 rounded-full p-1 hover:bg-gray-500/50"
onclick={toggleSearch}
>
<CarbonClose class="text-lg text-gray-400/80" />
</button>
<input
bind:value={searchInput}
bind:this={inputElement}
type="text"
name="searchbar"
placeholder="Search for chats..."
autocomplete="off"
class={{
"h-12 w-full p-4 text-lg dark:bg-gray-800 dark:text-gray-200": true,
"border-b border-b-gray-500/50": searchInput && searchInput.length >= 3,
}}
/>
<div class="scrollbar-custom max-h-[40dvh] overflow-y-scroll">
{#if debouncedInput && debouncedInput.length >= 3}
{#if pending}
{#each Array(5) as _}
<div
class="m-2 h-6 w-full animate-pulse gap-5 rounded bg-gray-300 first:mt-4 dark:bg-gray-700"
></div>
{/each}
{:else if conversations.length === 0}
<p class="bg-gray-200 p-2 text-gray-700 dark:bg-gray-800 dark:text-gray-300">
No conversations found matching that query
</p>
{:else}
{#each Object.entries(groupedConversations) as [group, convs]}
{#if convs.length}
<h4 class="mb-1.5 mt-4 pl-1.5 text-sm text-gray-700 dark:text-gray-300">
{titles[group]}
</h4>
{#each convs as conv}
<NavConversationItem
{conv}
readOnly={true}
showDescription={true}
description={conv.content}
searchInput={conv.matchedText}
/>
{/each}
{/if}
{/each}
{#if hasMore}
<InfiniteScroll on:visible={() => handleVisible(searchInput)} />
{/if}
{/if}
{/if}
</div>
</div>
{/if}
| chat-ui/src/lib/components/chat/Search.svelte/0 | {
"file_path": "chat-ui/src/lib/components/chat/Search.svelte",
"repo_id": "chat-ui",
"token_count": 2200
} | 79 |
<script lang="ts">
import CarbonPause from "~icons/carbon/pause";
import CarbonPlay from "~icons/carbon/play";
interface Props {
src: string;
name: string;
}
let { src, name }: Props = $props();
let time = $state(0);
let duration = $state(0);
let paused = $state(true);
function format(time: number) {
if (isNaN(time)) return "...";
const minutes = Math.floor(time / 60);
const seconds = Math.floor(time % 60);
return `${minutes}:${seconds < 10 ? `0${seconds}` : seconds}`;
}
function seek(e: PointerEvent) {
if (!e.currentTarget) return;
const { left, width } = (e.currentTarget as HTMLElement).getBoundingClientRect();
let p = (e.clientX - left) / width;
if (p < 0) p = 0;
if (p > 1) p = 1;
time = p * duration;
}
</script>
<div
class="flex h-14 w-72 items-center gap-4 rounded-2xl border border-gray-200 bg-white p-2.5 text-gray-600 shadow-sm transition-all dark:border-gray-800 dark:bg-gray-900 dark:text-gray-300"
>
<audio
{src}
bind:currentTime={time}
bind:duration
bind:paused
preload="metadata"
onended={() => {
time = 0;
}}
></audio>
<button
class="mx-auto my-auto aspect-square size-8 rounded-full border border-gray-400 bg-gray-100 dark:border-gray-800 dark:bg-gray-700"
aria-label={paused ? "play" : "pause"}
onclick={() => (paused = !paused)}
>
{#if paused}
<CarbonPlay class="mx-auto my-auto text-gray-600 dark:text-gray-300" />
{:else}
<CarbonPause class="mx-auto my-auto text-gray-600 dark:text-gray-300" />
{/if}
</button>
<div class="overflow-hidden">
<div class="truncate font-medium">{name}</div>
{#if duration !== Infinity}
<div class="flex items-center gap-2">
<span class="text-xs">{format(time)}</span>
<div
class="relative h-2 flex-1 rounded-full bg-gray-200 dark:bg-gray-700"
onpointerdown={() => {
paused = true;
}}
onpointerup={seek}
>
<div
class="absolute inset-0 h-full bg-gray-400 dark:bg-gray-600"
style="width: {(time / duration) * 100}%"
></div>
</div>
<span class="text-xs">{duration ? format(duration) : "--:--"}</span>
</div>
{/if}
</div>
</div>
| chat-ui/src/lib/components/players/AudioPlayer.svelte/0 | {
"file_path": "chat-ui/src/lib/components/players/AudioPlayer.svelte",
"repo_id": "chat-ui",
"token_count": 909
} | 80 |
import type { Session } from "$lib/types/Session";
import type { User } from "$lib/types/User";
import type { Conversation } from "$lib/types/Conversation";
import { ObjectId } from "mongodb";
import { deleteConversations } from "./09-delete-empty-conversations";
import { afterAll, afterEach, beforeAll, describe, expect, test } from "vitest";
import { collections } from "$lib/server/database";
type Message = Conversation["messages"][number];
const userData = {
_id: new ObjectId(),
createdAt: new Date(),
updatedAt: new Date(),
username: "new-username",
name: "name",
avatarUrl: "https://example.com/avatar.png",
hfUserId: "9999999999",
} satisfies User;
Object.freeze(userData);
const sessionForUser = {
_id: new ObjectId(),
createdAt: new Date(),
updatedAt: new Date(),
userId: userData._id,
sessionId: "session-id-9999999999",
expiresAt: new Date(Date.now() + 1000 * 60 * 60 * 24),
} satisfies Session;
Object.freeze(sessionForUser);
const userMessage = {
from: "user",
id: "user-message-id",
content: "Hello, how are you?",
} satisfies Message;
const assistantMessage = {
from: "assistant",
id: "assistant-message-id",
content: "I'm fine, thank you!",
} satisfies Message;
const systemMessage = {
from: "system",
id: "system-message-id",
content: "This is a system message",
} satisfies Message;
const conversationBase = {
_id: new ObjectId(),
createdAt: new Date(Date.now() - 7 * 24 * 60 * 60 * 1000),
updatedAt: new Date(Date.now() - 7 * 24 * 60 * 60 * 1000),
model: "model-id",
embeddingModel: "embedding-model-id",
title: "title",
messages: [],
} satisfies Conversation;
describe.sequential("Deleting discarded conversations", async () => {
test("a conversation with no messages should get deleted", async () => {
await collections.conversations.insertOne({
...conversationBase,
sessionId: sessionForUser.sessionId,
});
const result = await deleteConversations(collections);
expect(result).toBe(1);
});
test("a conversation with no messages that is less than 1 hour old should not get deleted", async () => {
await collections.conversations.insertOne({
...conversationBase,
sessionId: sessionForUser.sessionId,
createdAt: new Date(Date.now() - 30 * 60 * 1000),
});
const result = await deleteConversations(collections);
expect(result).toBe(0);
});
test("a conversation with only system messages should get deleted", async () => {
await collections.conversations.insertOne({
...conversationBase,
sessionId: sessionForUser.sessionId,
messages: [systemMessage],
});
const result = await deleteConversations(collections);
expect(result).toBe(1);
});
test("a conversation with a user message should not get deleted", async () => {
await collections.conversations.insertOne({
...conversationBase,
sessionId: sessionForUser.sessionId,
messages: [userMessage],
});
const result = await deleteConversations(collections);
expect(result).toBe(0);
});
test("a conversation with an assistant message should not get deleted", async () => {
await collections.conversations.insertOne({
...conversationBase,
sessionId: sessionForUser.sessionId,
messages: [assistantMessage],
});
const result = await deleteConversations(collections);
expect(result).toBe(0);
});
test("a conversation with a mix of messages should not get deleted", async () => {
await collections.conversations.insertOne({
...conversationBase,
sessionId: sessionForUser.sessionId,
messages: [systemMessage, userMessage, assistantMessage, userMessage, assistantMessage],
});
const result = await deleteConversations(collections);
expect(result).toBe(0);
});
test("a conversation with a userId and no sessionId should not get deleted", async () => {
await collections.conversations.insertOne({
...conversationBase,
messages: [userMessage, assistantMessage],
userId: userData._id,
});
const result = await deleteConversations(collections);
expect(result).toBe(0);
});
test("a conversation with no userId or sessionId should get deleted", async () => {
await collections.conversations.insertOne({
...conversationBase,
messages: [userMessage, assistantMessage],
});
const result = await deleteConversations(collections);
expect(result).toBe(1);
});
test("a conversation with a sessionId that exists should not get deleted", async () => {
await collections.conversations.insertOne({
...conversationBase,
messages: [userMessage, assistantMessage],
sessionId: sessionForUser.sessionId,
});
const result = await deleteConversations(collections);
expect(result).toBe(0);
});
test("a conversation with a userId and a sessionId that doesn't exist should NOT get deleted", async () => {
await collections.conversations.insertOne({
...conversationBase,
userId: userData._id,
messages: [userMessage, assistantMessage],
sessionId: new ObjectId().toString(),
});
const result = await deleteConversations(collections);
expect(result).toBe(0);
});
test("a conversation with only a sessionId that doesn't exist, should get deleted", async () => {
await collections.conversations.insertOne({
...conversationBase,
messages: [userMessage, assistantMessage],
sessionId: new ObjectId().toString(),
});
const result = await deleteConversations(collections);
expect(result).toBe(1);
});
test("many conversations should get deleted", async () => {
const conversations = Array.from({ length: 10010 }, () => ({
...conversationBase,
_id: new ObjectId(),
}));
await collections.conversations.insertMany(conversations);
const result = await deleteConversations(collections);
expect(result).toBe(10010);
});
});
beforeAll(async () => {
await collections.users.insertOne(userData);
await collections.sessions.insertOne(sessionForUser);
});
afterAll(async () => {
await collections.users.deleteOne({
_id: userData._id,
});
await collections.sessions.deleteOne({
_id: sessionForUser._id,
});
await collections.conversations.deleteMany({});
});
afterEach(async () => {
await collections.conversations.deleteMany({
_id: { $in: [conversationBase._id] },
});
});
| chat-ui/src/lib/migrations/routines/09-delete-empty-conversations.spec.ts/0 | {
"file_path": "chat-ui/src/lib/migrations/routines/09-delete-empty-conversations.spec.ts",
"repo_id": "chat-ui",
"token_count": 2019
} | 81 |
import { GridFSBucket, MongoClient } from "mongodb";
import type { Conversation } from "$lib/types/Conversation";
import type { SharedConversation } from "$lib/types/SharedConversation";
import type { AbortedGeneration } from "$lib/types/AbortedGeneration";
import type { Settings } from "$lib/types/Settings";
import type { User } from "$lib/types/User";
import type { MessageEvent } from "$lib/types/MessageEvent";
import type { Session } from "$lib/types/Session";
import type { Assistant } from "$lib/types/Assistant";
import type { Report } from "$lib/types/Report";
import type { ConversationStats } from "$lib/types/ConversationStats";
import type { MigrationResult } from "$lib/types/MigrationResult";
import type { Semaphore } from "$lib/types/Semaphore";
import type { AssistantStats } from "$lib/types/AssistantStats";
import type { CommunityToolDB } from "$lib/types/Tool";
import { MongoMemoryServer } from "mongodb-memory-server";
import { logger } from "$lib/server/logger";
import { building } from "$app/environment";
import type { TokenCache } from "$lib/types/TokenCache";
import { onExit } from "./exitHandler";
import { fileURLToPath } from "url";
import { dirname, join } from "path";
import { existsSync, mkdirSync } from "fs";
import { findRepoRoot } from "./findRepoRoot";
import type { ConfigKey } from "$lib/types/ConfigKey";
import { config } from "$lib/server/config";
export const CONVERSATION_STATS_COLLECTION = "conversations.stats";
export class Database {
private client?: MongoClient;
private mongoServer?: MongoMemoryServer;
private static instance: Database;
private async init() {
const DB_FOLDER =
config.MONGO_STORAGE_PATH ||
join(findRepoRoot(dirname(fileURLToPath(import.meta.url))), "db");
if (!config.MONGODB_URL) {
logger.warn("No MongoDB URL found, using in-memory server");
logger.info(`Using database path: ${DB_FOLDER}`);
// Create db directory if it doesn't exist
if (!existsSync(DB_FOLDER)) {
logger.info(`Creating database directory at ${DB_FOLDER}`);
mkdirSync(DB_FOLDER, { recursive: true });
}
this.mongoServer = await MongoMemoryServer.create({
instance: {
dbName: config.MONGODB_DB_NAME + (import.meta.env.MODE === "test" ? "-test" : ""),
dbPath: DB_FOLDER,
},
binary: {
version: "7.0.18",
},
});
this.client = new MongoClient(this.mongoServer.getUri(), {
directConnection: config.MONGODB_DIRECT_CONNECTION === "true",
});
} else {
this.client = new MongoClient(config.MONGODB_URL, {
directConnection: config.MONGODB_DIRECT_CONNECTION === "true",
});
}
try {
await this.client.connect();
this.client.db(config.MONGODB_DB_NAME + (import.meta.env.MODE === "test" ? "-test" : ""));
this.client.on("open", () => this.initDatabase());
} catch (err) {
logger.error(err, "Connection error");
process.exit(1);
}
// Disconnect DB on exit
onExit(async () => {
logger.info("Closing database connection");
await this.client?.close(true);
await this.mongoServer?.stop();
});
}
public static async getInstance(): Promise<Database> {
if (!Database.instance) {
Database.instance = new Database();
await Database.instance.init();
}
return Database.instance;
}
/**
* Return mongoClient
*/
public getClient(): MongoClient {
if (!this.client) {
throw new Error("Database not initialized");
}
return this.client;
}
/**
* Return map of database's collections
*/
public getCollections() {
if (!this.client) {
throw new Error("Database not initialized");
}
const db = this.client.db(
config.MONGODB_DB_NAME + (import.meta.env.MODE === "test" ? "-test" : "")
);
const conversations = db.collection<Conversation>("conversations");
const conversationStats = db.collection<ConversationStats>(CONVERSATION_STATS_COLLECTION);
const assistants = db.collection<Assistant>("assistants");
const assistantStats = db.collection<AssistantStats>("assistants.stats");
const reports = db.collection<Report>("reports");
const sharedConversations = db.collection<SharedConversation>("sharedConversations");
const abortedGenerations = db.collection<AbortedGeneration>("abortedGenerations");
const settings = db.collection<Settings>("settings");
const users = db.collection<User>("users");
const sessions = db.collection<Session>("sessions");
const messageEvents = db.collection<MessageEvent>("messageEvents");
const bucket = new GridFSBucket(db, { bucketName: "files" });
const migrationResults = db.collection<MigrationResult>("migrationResults");
const semaphores = db.collection<Semaphore>("semaphores");
const tokenCaches = db.collection<TokenCache>("tokens");
const tools = db.collection<CommunityToolDB>("tools");
const configCollection = db.collection<ConfigKey>("config");
return {
conversations,
conversationStats,
assistants,
assistantStats,
reports,
sharedConversations,
abortedGenerations,
settings,
users,
sessions,
messageEvents,
bucket,
migrationResults,
semaphores,
tokenCaches,
tools,
config: configCollection,
};
}
/**
* Init database once connected: Index creation
* @private
*/
private initDatabase() {
const {
conversations,
conversationStats,
assistants,
assistantStats,
reports,
sharedConversations,
abortedGenerations,
settings,
users,
sessions,
messageEvents,
semaphores,
tokenCaches,
tools,
config,
} = this.getCollections();
conversations
.createIndex(
{ sessionId: 1, updatedAt: -1 },
{ partialFilterExpression: { sessionId: { $exists: true } } }
)
.catch((e) => logger.error(e));
conversations
.createIndex(
{ userId: 1, updatedAt: -1 },
{ partialFilterExpression: { userId: { $exists: true } } }
)
.catch((e) => logger.error(e));
conversations
.createIndex(
{ "message.id": 1, "message.ancestors": 1 },
{ partialFilterExpression: { userId: { $exists: true } } }
)
.catch((e) => logger.error(e));
// Not strictly necessary, could use _id, but more convenient. Also for stats
// To do stats on conversation messages
conversations
.createIndex({ "messages.createdAt": 1 }, { sparse: true })
.catch((e) => logger.error(e));
// Unique index for stats
conversationStats
.createIndex(
{
type: 1,
"date.field": 1,
"date.span": 1,
"date.at": 1,
distinct: 1,
},
{ unique: true }
)
.catch((e) => logger.error(e));
// Allow easy check of last computed stat for given type/dateField
conversationStats
.createIndex({
type: 1,
"date.field": 1,
"date.at": 1,
})
.catch((e) => logger.error(e));
abortedGenerations
.createIndex({ updatedAt: 1 }, { expireAfterSeconds: 30 })
.catch((e) => logger.error(e));
abortedGenerations
.createIndex({ conversationId: 1 }, { unique: true })
.catch((e) => logger.error(e));
sharedConversations.createIndex({ hash: 1 }, { unique: true }).catch((e) => logger.error(e));
settings
.createIndex({ sessionId: 1 }, { unique: true, sparse: true })
.catch((e) => logger.error(e));
settings
.createIndex({ userId: 1 }, { unique: true, sparse: true })
.catch((e) => logger.error(e));
settings.createIndex({ assistants: 1 }).catch((e) => logger.error(e));
users.createIndex({ hfUserId: 1 }, { unique: true }).catch((e) => logger.error(e));
users
.createIndex({ sessionId: 1 }, { unique: true, sparse: true })
.catch((e) => logger.error(e));
// No unicity because due to renames & outdated info from oauth provider, there may be the same username on different users
users.createIndex({ username: 1 }).catch((e) => logger.error(e));
messageEvents
.createIndex({ expiresAt: 1 }, { expireAfterSeconds: 1 })
.catch((e) => logger.error(e));
sessions.createIndex({ expiresAt: 1 }, { expireAfterSeconds: 0 }).catch((e) => logger.error(e));
sessions.createIndex({ sessionId: 1 }, { unique: true }).catch((e) => logger.error(e));
assistants.createIndex({ createdById: 1, userCount: -1 }).catch((e) => logger.error(e));
assistants.createIndex({ userCount: 1 }).catch((e) => logger.error(e));
assistants.createIndex({ review: 1, userCount: -1 }).catch((e) => logger.error(e));
assistants.createIndex({ modelId: 1, userCount: -1 }).catch((e) => logger.error(e));
assistants.createIndex({ searchTokens: 1 }).catch((e) => logger.error(e));
assistants.createIndex({ last24HoursCount: 1 }).catch((e) => logger.error(e));
assistants
.createIndex({ last24HoursUseCount: -1, useCount: -1, _id: 1 })
.catch((e) => logger.error(e));
assistantStats
// Order of keys is important for the queries
.createIndex({ "date.span": 1, "date.at": 1, assistantId: 1 }, { unique: true })
.catch((e) => logger.error(e));
reports.createIndex({ assistantId: 1 }).catch((e) => logger.error(e));
reports.createIndex({ createdBy: 1, assistantId: 1 }).catch((e) => logger.error(e));
// Unique index for semaphore and migration results
semaphores.createIndex({ key: 1 }, { unique: true }).catch((e) => logger.error(e));
semaphores
.createIndex({ deleteAt: 1 }, { expireAfterSeconds: 1 })
.catch((e) => logger.error(e));
tokenCaches
.createIndex({ createdAt: 1 }, { expireAfterSeconds: 5 * 60 })
.catch((e) => logger.error(e));
tokenCaches.createIndex({ tokenHash: 1 }).catch((e) => logger.error(e));
tools.createIndex({ createdById: 1, userCount: -1 }).catch((e) => logger.error(e));
tools.createIndex({ userCount: 1 }).catch((e) => logger.error(e));
tools.createIndex({ last24HoursCount: 1 }).catch((e) => logger.error(e));
conversations
.createIndex({
"messages.from": 1,
createdAt: 1,
})
.catch((e) => logger.error(e));
conversations
.createIndex({
userId: 1,
sessionId: 1,
})
.catch((e) => logger.error(e));
config.createIndex({ key: 1 }, { unique: true }).catch((e) => logger.error(e));
}
}
export let collections: ReturnType<typeof Database.prototype.getCollections>;
export const ready = (async () => {
if (!building) {
const db = await Database.getInstance();
collections = db.getCollections();
} else {
collections = {} as unknown as ReturnType<typeof Database.prototype.getCollections>;
}
})();
export async function getCollectionsEarly(): Promise<
ReturnType<typeof Database.prototype.getCollections>
> {
await ready;
if (!collections) {
throw new Error("Database not initialized");
}
return collections;
}
| chat-ui/src/lib/server/database.ts/0 | {
"file_path": "chat-ui/src/lib/server/database.ts",
"repo_id": "chat-ui",
"token_count": 3799
} | 82 |
import { GoogleGenerativeAI, HarmBlockThreshold, HarmCategory } from "@google/generative-ai";
import type { Content, Part, SafetySetting, TextPart } from "@google/generative-ai";
import { z } from "zod";
import type { Message, MessageFile } from "$lib/types/Message";
import type { TextGenerationStreamOutput } from "@huggingface/inference";
import type { Endpoint } from "../endpoints";
import { createImageProcessorOptionsValidator, makeImageProcessor } from "../images";
import type { ImageProcessorOptions } from "../images";
import { config } from "$lib/server/config";
export const endpointGenAIParametersSchema = z.object({
weight: z.number().int().positive().default(1),
model: z.any(),
type: z.literal("genai"),
apiKey: z.string().default(config.GOOGLE_GENAI_API_KEY),
safetyThreshold: z
.enum([
HarmBlockThreshold.HARM_BLOCK_THRESHOLD_UNSPECIFIED,
HarmBlockThreshold.BLOCK_LOW_AND_ABOVE,
HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
HarmBlockThreshold.BLOCK_NONE,
HarmBlockThreshold.BLOCK_ONLY_HIGH,
])
.optional(),
multimodal: z
.object({
image: createImageProcessorOptionsValidator({
supportedMimeTypes: ["image/png", "image/jpeg", "image/webp"],
preferredMimeType: "image/webp",
// The 4 / 3 compensates for the 33% increase in size when converting to base64
maxSizeInMB: (5 / 4) * 3,
maxWidth: 4096,
maxHeight: 4096,
}),
})
.default({}),
});
export function endpointGenAI(input: z.input<typeof endpointGenAIParametersSchema>): Endpoint {
const { model, apiKey, safetyThreshold, multimodal } = endpointGenAIParametersSchema.parse(input);
const genAI = new GoogleGenerativeAI(apiKey);
const safetySettings = safetyThreshold
? Object.keys(HarmCategory)
.filter((cat) => cat !== HarmCategory.HARM_CATEGORY_UNSPECIFIED)
.reduce((acc, val) => {
acc.push({
category: val as HarmCategory,
threshold: safetyThreshold,
});
return acc;
}, [] as SafetySetting[])
: undefined;
return async ({ messages, preprompt, generateSettings }) => {
const parameters = { ...model.parameters, ...generateSettings };
const generativeModel = genAI.getGenerativeModel({
model: model.id ?? model.name,
safetySettings,
generationConfig: {
maxOutputTokens: parameters?.max_new_tokens ?? 4096,
stopSequences: parameters?.stop,
temperature: parameters?.temperature ?? 1,
},
});
let systemMessage = preprompt;
if (messages[0].from === "system") {
systemMessage = messages[0].content;
messages.shift();
}
const genAIMessages = await Promise.all(
messages.map(async ({ from, content, files }: Omit<Message, "id">): Promise<Content> => {
return {
role: from === "user" ? "user" : "model",
parts: [
...(await Promise.all(
(files ?? []).map((file) => fileToImageBlock(file, multimodal.image))
)),
{ text: content },
],
};
})
);
const result = await generativeModel.generateContentStream({
contents: genAIMessages,
systemInstruction:
systemMessage && systemMessage.trim() !== ""
? {
role: "system",
parts: [{ text: systemMessage }],
}
: undefined,
});
let tokenId = 0;
return (async function* () {
let generatedText = "";
for await (const data of result.stream) {
if (!data?.candidates?.length) break; // Handle case where no candidates are present
const candidate = data.candidates[0];
if (!candidate.content?.parts?.length) continue; // Skip if no parts are present
const firstPart = candidate.content.parts.find((part) => "text" in part) as
| TextPart
| undefined;
if (!firstPart) continue; // Skip if no text part is found
const content = firstPart.text;
generatedText += content;
const output: TextGenerationStreamOutput = {
token: {
id: tokenId++,
text: content,
logprob: 0,
special: false,
},
generated_text: null,
details: null,
};
yield output;
}
const output: TextGenerationStreamOutput = {
token: {
id: tokenId++,
text: "",
logprob: 0,
special: true,
},
generated_text: generatedText,
details: null,
};
yield output;
})();
};
}
async function fileToImageBlock(
file: MessageFile,
opts: ImageProcessorOptions<"image/png" | "image/jpeg" | "image/webp">
): Promise<Part> {
const processor = makeImageProcessor(opts);
const { image, mime } = await processor(file);
return {
inlineData: {
mimeType: mime,
data: image.toString("base64"),
},
};
}
export default endpointGenAI;
| chat-ui/src/lib/server/endpoints/google/endpointGenAI.ts/0 | {
"file_path": "chat-ui/src/lib/server/endpoints/google/endpointGenAI.ts",
"repo_id": "chat-ui",
"token_count": 1779
} | 83 |
import { existsSync } from "fs";
import { join, dirname } from "path";
export function findRepoRoot(startPath: string): string {
let currentPath = startPath;
while (currentPath !== "/") {
if (existsSync(join(currentPath, "package.json"))) {
return currentPath;
}
currentPath = dirname(currentPath);
}
throw new Error("Could not find repository root (no package.json found)");
}
| chat-ui/src/lib/server/findRepoRoot.ts/0 | {
"file_path": "chat-ui/src/lib/server/findRepoRoot.ts",
"repo_id": "chat-ui",
"token_count": 127
} | 84 |
import { config } from "$lib/server/config";
import { logger } from "$lib/server/logger";
export async function sendSlack(text: string) {
if (!config.WEBHOOK_URL_REPORT_ASSISTANT) {
logger.warn("WEBHOOK_URL_REPORT_ASSISTANT is not set, tried to send a slack message.");
return;
}
const res = await fetch(config.WEBHOOK_URL_REPORT_ASSISTANT, {
method: "POST",
headers: {
"Content-type": "application/json",
},
body: JSON.stringify({
text,
}),
});
if (!res.ok) {
logger.error(`Webhook message failed. ${res.statusText} ${res.text}`);
}
}
| chat-ui/src/lib/server/sendSlack.ts/0 | {
"file_path": "chat-ui/src/lib/server/sendSlack.ts",
"repo_id": "chat-ui",
"token_count": 225
} | 85 |
import { stringifyMarkdownElementTree } from "$lib/server/websearch/markdown/utils/stringify";
import { scrapeUrl } from "$lib/server/websearch/scrape/scrape";
import type { ConfigTool } from "$lib/types/Tool";
import { ObjectId } from "mongodb";
const fetchUrl: ConfigTool = {
_id: new ObjectId("00000000000000000000000B"),
type: "config",
description: "Fetch the contents of a URL",
color: "blue",
icon: "cloud",
displayName: "Fetch URL",
name: "fetchUrl",
endpoint: null,
inputs: [
{
name: "url",
type: "str",
description: "The URL of the webpage to fetch",
paramType: "required",
},
],
outputComponent: null,
outputComponentIdx: null,
showOutput: false,
async *call({ url }) {
const blocks = String(url).split("\n");
const urlStr = blocks[blocks.length - 1];
const { title, markdownTree } = await scrapeUrl(urlStr, Infinity);
return {
outputs: [{ title, text: stringifyMarkdownElementTree(markdownTree) }],
display: false,
};
},
};
export default fetchUrl;
| chat-ui/src/lib/server/tools/web/url.ts/0 | {
"file_path": "chat-ui/src/lib/server/tools/web/url.ts",
"repo_id": "chat-ui",
"token_count": 357
} | 86 |
import { WebSearchProvider, type WebSearchSource } from "$lib/types/WebSearch";
import { config } from "$lib/server/config";
import searchSerper from "./endpoints/serper";
import searchSerpApi from "./endpoints/serpApi";
import searchSerpStack from "./endpoints/serpStack";
import searchYouApi from "./endpoints/youApi";
import searchWebLocal from "./endpoints/webLocal";
import searchSearxng from "./endpoints/searxng";
import searchSearchApi from "./endpoints/searchApi";
import searchBing from "./endpoints/bing";
export function getWebSearchProvider() {
if (config.YDC_API_KEY) return WebSearchProvider.YOU;
if (config.SEARXNG_QUERY_URL) return WebSearchProvider.SEARXNG;
if (config.BING_SUBSCRIPTION_KEY) return WebSearchProvider.BING;
return WebSearchProvider.GOOGLE;
}
/** Searches the web using the first available provider, based on the env */
export async function searchWeb(query: string): Promise<WebSearchSource[]> {
if (config.USE_LOCAL_WEBSEARCH) return searchWebLocal(query);
if (config.SEARXNG_QUERY_URL) return searchSearxng(query);
if (config.SERPER_API_KEY) return searchSerper(query);
if (config.YDC_API_KEY) return searchYouApi(query);
if (config.SERPAPI_KEY) return searchSerpApi(query);
if (config.SERPSTACK_API_KEY) return searchSerpStack(query);
if (config.SEARCHAPI_KEY) return searchSearchApi(query);
if (config.BING_SUBSCRIPTION_KEY) return searchBing(query);
throw new Error(
"No configuration found for web search. Please set USE_LOCAL_WEBSEARCH, SEARXNG_QUERY_URL, SERPER_API_KEY, YDC_API_KEY, SERPSTACK_API_KEY, or SEARCHAPI_KEY in your environment variables."
);
}
| chat-ui/src/lib/server/websearch/search/endpoints.ts/0 | {
"file_path": "chat-ui/src/lib/server/websearch/search/endpoints.ts",
"repo_id": "chat-ui",
"token_count": 542
} | 87 |
import { writable } from "svelte/store";
export const pendingMessage = writable<
| {
content: string;
files: File[];
}
| undefined
>();
| chat-ui/src/lib/stores/pendingMessage.ts/0 | {
"file_path": "chat-ui/src/lib/stores/pendingMessage.ts",
"repo_id": "chat-ui",
"token_count": 56
} | 88 |
import type { BackendModel } from "$lib/server/models";
export type Model = Pick<
BackendModel,
| "id"
| "name"
| "displayName"
| "websiteUrl"
| "datasetName"
| "promptExamples"
| "parameters"
| "description"
| "logoUrl"
| "modelUrl"
| "tokenizer"
| "datasetUrl"
| "preprompt"
| "multimodal"
| "multimodalAcceptedMimetypes"
| "unlisted"
| "tools"
| "hasInferenceAPI"
>;
| chat-ui/src/lib/types/Model.ts/0 | {
"file_path": "chat-ui/src/lib/types/Model.ts",
"repo_id": "chat-ui",
"token_count": 175
} | 89 |
import { browser } from "$app/environment";
export function cookiesAreEnabled(): boolean {
if (!browser) return false;
if (navigator.cookieEnabled) return navigator.cookieEnabled;
// Create cookie
document.cookie = "cookietest=1";
const ret = document.cookie.indexOf("cookietest=") != -1;
// Delete cookie
document.cookie = "cookietest=1; expires=Thu, 01-Jan-1970 00:00:01 GMT";
return ret;
}
| chat-ui/src/lib/utils/cookiesAreEnabled.ts/0 | {
"file_path": "chat-ui/src/lib/utils/cookiesAreEnabled.ts",
"repo_id": "chat-ui",
"token_count": 127
} | 90 |
import type { Model } from "$lib/types/Model";
export const findCurrentModel = (models: Model[], id?: string): Model =>
models.find((m) => m.id === id) ?? models[0];
| chat-ui/src/lib/utils/models.ts/0 | {
"file_path": "chat-ui/src/lib/utils/models.ts",
"repo_id": "chat-ui",
"token_count": 54
} | 91 |
import { collections } from "$lib/server/database";
import { ObjectId } from "mongodb";
import { describe, expect, it } from "vitest";
import { insertLegacyConversation, insertSideBranchesConversation } from "./treeHelpers.spec";
import type { Message } from "$lib/types/Message";
import { addSibling } from "./addSibling";
import type { Conversation } from "$lib/types/Conversation";
const newMessage = {
content: "new message",
from: "user" as const,
};
Object.freeze(newMessage);
describe("addSibling", async () => {
it("should fail on empty conversations", () => {
const conv = {
_id: new ObjectId(),
rootMessageId: undefined,
messages: [] as Message[],
} satisfies Pick<Conversation, "_id" | "rootMessageId" | "messages">;
expect(() => addSibling(conv, newMessage, "not-a-real-id-test")).toThrow(
"Cannot add a sibling to an empty conversation"
);
});
it("should fail on legacy conversations", async () => {
const convId = await insertLegacyConversation();
const conv = await collections.conversations.findOne({ _id: new ObjectId(convId) });
if (!conv) throw new Error("Conversation not found");
expect(() => addSibling(conv, newMessage, conv.messages[0].id)).toThrow(
"Cannot add a sibling to a legacy conversation"
);
});
it("should fail if the sibling message doesn't exist", async () => {
const convId = await insertSideBranchesConversation();
const conv = await collections.conversations.findOne({ _id: new ObjectId(convId) });
if (!conv) throw new Error("Conversation not found");
expect(() => addSibling(conv, newMessage, "not-a-real-id-test")).toThrow(
"The sibling message doesn't exist"
);
});
// TODO: This behaviour should be fixed, we do not need to fail on the root message.
it("should fail if the sibling message is the root message", async () => {
const convId = await insertSideBranchesConversation();
const conv = await collections.conversations.findOne({ _id: new ObjectId(convId) });
if (!conv) throw new Error("Conversation not found");
if (!conv.rootMessageId) throw new Error("Root message not found");
expect(() => addSibling(conv, newMessage, conv.rootMessageId as Message["id"])).toThrow(
"The sibling message is the root message, therefore we can't add a sibling"
);
});
it("should add a sibling to a message", async () => {
const convId = await insertSideBranchesConversation();
const conv = await collections.conversations.findOne({ _id: new ObjectId(convId) });
if (!conv) throw new Error("Conversation not found");
// add sibling and check children count for parnets
const nChildren = conv.messages[1].children?.length;
const siblingId = addSibling(conv, newMessage, conv.messages[2].id);
const nChildrenNew = conv.messages[1].children?.length;
if (!nChildren) throw new Error("No children found");
expect(nChildrenNew).toBe(nChildren + 1);
// make sure siblings have the same ancestors
const sibling = conv.messages.find((m) => m.id === siblingId);
expect(sibling?.ancestors).toEqual(conv.messages[2].ancestors);
});
});
| chat-ui/src/lib/utils/tree/addSibling.spec.ts/0 | {
"file_path": "chat-ui/src/lib/utils/tree/addSibling.spec.ts",
"repo_id": "chat-ui",
"token_count": 985
} | 92 |
import { config } from "$lib/server/config";
import { collections } from "$lib/server/database";
import type { Message } from "$lib/types/Message";
import { error } from "@sveltejs/kit";
import { pathToFileURL } from "node:url";
import { unlink } from "node:fs/promises";
import { uploadFile } from "@huggingface/hub";
import parquet from "parquetjs";
import { z } from "zod";
import { logger } from "$lib/server/logger.js";
// Triger like this:
// curl -X POST "http://localhost:5173/chat/admin/export" -H "Authorization: Bearer <ADMIN_API_SECRET>" -H "Content-Type: application/json" -d '{"model": "OpenAssistant/oasst-sft-6-llama-30b-xor"}'
export async function POST({ request }) {
if (!config.PARQUET_EXPORT_DATASET || !config.PARQUET_EXPORT_HF_TOKEN) {
error(500, "Parquet export is not configured.");
}
const { model } = z
.object({
model: z.string(),
})
.parse(await request.json());
const schema = new parquet.ParquetSchema({
title: { type: "UTF8" },
created_at: { type: "TIMESTAMP_MILLIS" },
updated_at: { type: "TIMESTAMP_MILLIS" },
messages: {
repeated: true,
fields: {
from: { type: "UTF8" },
content: { type: "UTF8" },
score: { type: "INT_8", optional: true },
},
},
});
const fileName = `/tmp/conversations-${new Date().toJSON().slice(0, 10)}-${Date.now()}.parquet`;
const writer = await parquet.ParquetWriter.openFile(schema, fileName);
let count = 0;
logger.info("Exporting conversations for model", model);
for await (const conversation of collections.settings.aggregate<{
title: string;
created_at: Date;
updated_at: Date;
messages: Message[];
}>([
{
$match: {
shareConversationsWithModelAuthors: true,
sessionId: { $exists: true },
userId: { $exists: false },
},
},
{
$lookup: {
from: "conversations",
localField: "sessionId",
foreignField: "sessionId",
as: "conversations",
pipeline: [{ $match: { model, userId: { $exists: false } } }],
},
},
{ $unwind: "$conversations" },
{
$project: {
title: "$conversations.title",
created_at: "$conversations.createdAt",
updated_at: "$conversations.updatedAt",
messages: "$conversations.messages",
},
},
])) {
await writer.appendRow({
title: conversation.title,
created_at: conversation.created_at,
updated_at: conversation.updated_at,
messages: conversation.messages.map((message: Message) => ({
from: message.from,
content: message.content,
...(message.score ? { score: message.score } : undefined),
})),
});
++count;
if (count % 1_000 === 0) {
logger.info("Exported", count, "conversations");
}
}
logger.info("exporting convos with userId");
for await (const conversation of collections.settings.aggregate<{
title: string;
created_at: Date;
updated_at: Date;
messages: Message[];
}>([
{ $match: { shareConversationsWithModelAuthors: true, userId: { $exists: true } } },
{
$lookup: {
from: "conversations",
localField: "userId",
foreignField: "userId",
as: "conversations",
pipeline: [{ $match: { model } }],
},
},
{ $unwind: "$conversations" },
{
$project: {
title: "$conversations.title",
created_at: "$conversations.createdAt",
updated_at: "$conversations.updatedAt",
messages: "$conversations.messages",
},
},
])) {
await writer.appendRow({
title: conversation.title,
created_at: conversation.created_at,
updated_at: conversation.updated_at,
messages: conversation.messages.map((message: Message) => ({
from: message.from,
content: message.content,
...(message.score ? { score: message.score } : undefined),
})),
});
++count;
if (count % 1_000 === 0) {
logger.info("Exported", count, "conversations");
}
}
await writer.close();
logger.info("Uploading", fileName, "to Hugging Face Hub");
await uploadFile({
file: pathToFileURL(fileName) as URL,
credentials: { accessToken: config.PARQUET_EXPORT_HF_TOKEN },
repo: {
type: "dataset",
name: config.PARQUET_EXPORT_DATASET,
},
});
logger.info("Upload done");
await unlink(fileName);
return new Response();
}
| chat-ui/src/routes/admin/export/+server.ts/0 | {
"file_path": "chat-ui/src/routes/admin/export/+server.ts",
"repo_id": "chat-ui",
"token_count": 1660
} | 93 |
import { collections } from "$lib/server/database";
import { error } from "@sveltejs/kit";
import { ObjectId } from "mongodb";
import { base } from "$app/paths";
import { config } from "$lib/server/config";
import { ReviewStatus } from "$lib/types/Review";
import { sendSlack } from "$lib/server/sendSlack";
import { z } from "zod";
const schema = z.object({
status: z.nativeEnum(ReviewStatus),
});
export async function PATCH({ params, request, locals, url }) {
const toolId = params.toolId;
const { status } = schema.parse(await request.json());
if (!toolId) {
return error(400, "Tool ID is required");
}
const tool = await collections.tools.findOne({
_id: new ObjectId(toolId),
});
if (!tool) {
return error(404, "Tool not found");
}
if (
!locals.user ||
(!locals.isAdmin && tool.createdById.toString() !== locals.user._id.toString())
) {
return error(403, "Permission denied");
}
// only admins can set the status to APPROVED or DENIED
// if the status is already APPROVED or DENIED, only admins can change it
if (
(status === ReviewStatus.APPROVED ||
status === ReviewStatus.DENIED ||
tool.review === ReviewStatus.APPROVED ||
tool.review === ReviewStatus.DENIED) &&
!locals.isAdmin
) {
return error(403, "Permission denied");
}
const result = await collections.tools.updateOne({ _id: tool._id }, { $set: { review: status } });
if (result.modifiedCount === 0) {
return error(500, "Failed to update review status");
}
if (status === ReviewStatus.PENDING) {
const prefixUrl = config.PUBLIC_SHARE_PREFIX || `${config.PUBLIC_ORIGIN || url.origin}${base}`;
const toolUrl = `${prefixUrl}/tools/${toolId}`;
const username = locals.user?.username;
await sendSlack(
`🟢🛠️ Tool <${toolUrl}|${tool?.displayName}> requested to be featured by ${
username ? `<http://hf.co/${username}|${username}>` : "non-logged in user"
}.`
);
}
return new Response("Review status updated", { status: 200 });
}
| chat-ui/src/routes/api/tools/[toolId]/review/+server.ts/0 | {
"file_path": "chat-ui/src/routes/api/tools/[toolId]/review/+server.ts",
"repo_id": "chat-ui",
"token_count": 691
} | 94 |
import { buildPrompt } from "$lib/buildPrompt";
import { authCondition } from "$lib/server/auth";
import { collections } from "$lib/server/database";
import { models } from "$lib/server/models";
import { buildSubtree } from "$lib/utils/tree/buildSubtree";
import { isMessageId } from "$lib/utils/tree/isMessageId";
import { error } from "@sveltejs/kit";
import { ObjectId } from "mongodb";
export async function GET({ params, locals }) {
const conv =
params.id.length === 7
? await collections.sharedConversations.findOne({
_id: params.id,
})
: await collections.conversations.findOne({
_id: new ObjectId(params.id),
...authCondition(locals),
});
if (conv === null) {
error(404, "Conversation not found");
}
const messageId = params.messageId;
const messageIndex = conv.messages.findIndex((msg) => msg.id === messageId);
if (!isMessageId(messageId) || messageIndex === -1) {
error(404, "Message not found");
}
const model = models.find((m) => m.id === conv.model);
if (!model) {
error(404, "Conversation model not found");
}
let assistant;
if (conv.assistantId) {
assistant = await collections.assistants.findOne({
_id: new ObjectId(conv.assistantId),
});
}
const messagesUpTo = buildSubtree(conv, messageId);
const prompt = await buildPrompt({
preprompt: conv.preprompt,
messages: messagesUpTo,
model,
}).catch((err) => {
console.error(err);
return "Prompt generation failed";
});
return Response.json({
prompt,
model: model.name,
assistant: assistant?.name,
parameters: {
...model.parameters,
...(assistant?.generateSettings || {}),
return_full_text: false,
},
messages: messagesUpTo.map((msg) => ({
role: msg.from,
content: msg.content,
createdAt: msg.createdAt,
updatedAt: msg.updatedAt,
reasoning: msg.reasoning,
updates: msg.updates?.filter(
(u) => (u.type === "webSearch" && u.subtype === "sources") || u.type === "title"
),
files: msg.files,
})),
});
}
| chat-ui/src/routes/conversation/[id]/message/[messageId]/prompt/+server.ts/0 | {
"file_path": "chat-ui/src/routes/conversation/[id]/message/[messageId]/prompt/+server.ts",
"repo_id": "chat-ui",
"token_count": 744
} | 95 |
<script lang="ts">
import { marked } from "marked";
import privacy from "../../../PRIVACY.md?raw";
</script>
<div class="overflow-auto p-6">
<div class="prose mx-auto px-4 pb-24 pt-6 dark:prose-invert md:pt-12">
<!-- eslint-disable-next-line svelte/no-at-html-tags -->
{@html marked(privacy, { gfm: true })}
</div>
</div>
| chat-ui/src/routes/privacy/+page.svelte/0 | {
"file_path": "chat-ui/src/routes/privacy/+page.svelte",
"repo_id": "chat-ui",
"token_count": 141
} | 96 |
<script lang="ts">
import { page } from "$app/state";
import { usePublicConfig } from "$lib/utils/PublicConfig.svelte";
const publicConfig = usePublicConfig();
interface Props {
children?: import("svelte").Snippet;
}
let { children }: Props = $props();
</script>
<svelte:head>
{#if publicConfig.isHuggingChat}
<title>HuggingChat - Tools</title>
<meta property="og:title" content="HuggingChat - Tools" />
<meta property="og:type" content="link" />
<meta property="og:description" content="Browse HuggingChat tools made by the community." />
<meta property="og:image" content="{publicConfig.assetPath}/tools-thumbnail.png" />
<meta property="og:url" content={page.url.href} />
{/if}
</svelte:head>
{@render children?.()}
| chat-ui/src/routes/tools/+layout.svelte/0 | {
"file_path": "chat-ui/src/routes/tools/+layout.svelte",
"repo_id": "chat-ui",
"token_count": 263
} | 97 |
{
"$schema": "https://vega.github.io/schema/vega-lite/v4.json",
"data": {
"values": "<DVC_METRIC_DATA>"
},
"title": "<DVC_METRIC_TITLE>",
"mark": {
"type": "line"
},
"encoding": {
"x": {
"field": "<DVC_METRIC_X>",
"type": "quantitative",
"title": "<DVC_METRIC_X_LABEL>"
},
"y": {
"field": "<DVC_METRIC_Y>",
"type": "quantitative",
"title": "<DVC_METRIC_Y_LABEL>",
"scale": {
"zero": false
}
},
"color": {
"field": "rev",
"type": "nominal"
}
}
}
| datasets/.dvc/plots/default.json/0 | {
"file_path": "datasets/.dvc/plots/default.json",
"repo_id": "datasets",
"token_count": 419
} | 98 |
# This is the list of HuggingFace Datasets authors for copyright purposes.
#
# This does not necessarily list everyone who has contributed code, since in
# some cases, their employer may be the copyright holder. To see the full list
# of contributors, see the revision history in source control.
Google Inc.
HuggingFace Inc.
| datasets/AUTHORS/0 | {
"file_path": "datasets/AUTHORS",
"repo_id": "datasets",
"token_count": 78
} | 99 |
# 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:
- Create an audio dataset from local files in python with [`Dataset.push_to_hub`]. This is an easy way that requires only a few steps in python.
- Create an audio dataset repository with the `AudioFolder` builder. This is a no-code solution for quickly creating an audio dataset with several thousand audio files.
<Tip>
You can control access to your dataset by requiring users to share their contact information first. Check out the [Gated datasets](https://huggingface.co/docs/hub/datasets-gated) guide for more information about how to enable this feature on the Hub.
</Tip>
## Local files
You can load your own dataset using the paths to your audio files. Use the [`~Dataset.cast_column`] function to take a column of audio file paths, and cast it to the [`Audio`] feature:
```py
>>> audio_dataset = Dataset.from_dict({"audio": ["path/to/audio_1", "path/to/audio_2", ..., "path/to/audio_n"]}).cast_column("audio", Audio())
>>> audio_dataset[0]["audio"]
<datasets.features._torchcodec.AudioDecoder object at 0x11642b6a0>
```
Then upload the dataset to the Hugging Face Hub using [`Dataset.push_to_hub`]:
```py
audio_dataset.push_to_hub("<username>/my_dataset")
```
This will create a dataset repository containing your audio dataset:
```
my_dataset/
├── README.md
└── data/
└── train-00000-of-00001.parquet
```
## AudioFolder
The `AudioFolder` is a dataset builder designed to quickly load an audio dataset with several thousand audio files without requiring you to write any code.
<Tip>
💡 Take a look at the [Split pattern hierarchy](repository_structure#split-pattern-hierarchy) to learn more about how `AudioFolder` creates dataset splits based on your dataset repository structure.
</Tip>
`AudioFolder` automatically infers the class labels of your dataset based on the directory name. Store your dataset in a directory structure like:
```
folder/train/dog/golden_retriever.mp3
folder/train/dog/german_shepherd.mp3
folder/train/dog/chihuahua.mp3
folder/train/cat/maine_coon.mp3
folder/train/cat/bengal.mp3
folder/train/cat/birman.mp3
```
If the dataset follows the `AudioFolder` structure, then you can load it directly with [`load_dataset`]:
```py
>>> from datasets import load_dataset
>>> dataset = load_dataset("username/dataset_name")
```
This is equivalent to passing `audiofolder` manually in [`load_dataset`] and the directory in `data_dir`:
```py
>>> dataset = load_dataset("audiofolder", data_dir="/path/to/folder")
```
You can also use `audiofolder` to load datasets involving multiple splits. To do so, your dataset directory should have the following structure:
```
folder/train/dog/golden_retriever.mp3
folder/train/cat/maine_coon.mp3
folder/test/dog/german_shepherd.mp3
folder/test/cat/bengal.mp3
```
<Tip warning={true}>
If all audio files are contained in a single directory or if they are not on the same level of directory structure, `label` column won't be added automatically. If you need it, set `drop_labels=False` explicitly.
</Tip>
If there is additional information you'd like to include about your dataset, like text captions or bounding boxes, add it as a `metadata.csv` file in your folder. This lets you quickly create datasets for different computer vision tasks like text captioning or object detection. You can also use a JSONL file `metadata.jsonl` or a Parquet file `metadata.parquet`.
```
folder/train/metadata.csv
folder/train/0001.mp3
folder/train/0002.mp3
folder/train/0003.mp3
```
You can also zip your audio files, and in this case each zip should contain both the audio files and the metadata
```
folder/train.zip
folder/test.zip
folder/validation.zip
```
Your `metadata.csv` file must have a `file_name` or `*_file_name` field which links audio files with their metadata:
```csv
file_name,additional_feature
0001.mp3,This is a first value of a text feature you added to your audio files
0002.mp3,This is a second value of a text feature you added to your audio files
0003.mp3,This is a third value of a text feature you added to your audio files
```
or using `metadata.jsonl`:
```jsonl
{"file_name": "0001.mp3", "additional_feature": "This is a first value of a text feature you added to your audio files"}
{"file_name": "0002.mp3", "additional_feature": "This is a second value of a text feature you added to your audio files"}
{"file_name": "0003.mp3", "additional_feature": "This is a third value of a text feature you added to your audio files"}
```
Here the `file_name` must be the name of the audio file next to the metadata file. More generally, it must be the relative path from the directory containing the metadata to the audio file.
It's possible to point to more than one audio in each row in your dataset, for example if both your input and output are audio files:
```jsonl
{"input_file_name": "0001.mp3", "output_file_name": "0001_output.mp3"}
{"input_file_name": "0002.mp3", "output_file_name": "0002_output.mp3"}
{"input_file_name": "0003.mp3", "output_file_name": "0003_output.mp3"}
```
You can also define lists of audio files. In that case you need to name the field `file_names` or `*_file_names`. Here is an example:
```jsonl
{"recordings_file_names": ["0001_r0.mp3", "0001_r1.mp3"], label: "same_person"}
{"recordings_file_names": ["0002_r0.mp3", "0002_r1.mp3"], label: "same_person"}
{"recordings_file_names": ["0003_r0.mp3", "0003_r1.mp3"], label: "different_person"}
```
## WebDataset
The [WebDataset](https://github.com/webdataset/webdataset) format is based on TAR archives and is suitable for big audio datasets.
Indeed you can group your audio files in TAR archives (e.g. 1GB of audio files per TAR archive) and have thousands of TAR archives:
```
folder/train/00000.tar
folder/train/00001.tar
folder/train/00002.tar
...
```
In the archives, each example is made of files sharing the same prefix:
```
e39871fd9fd74f55.mp3
e39871fd9fd74f55.json
f18b91585c4d3f3e.mp3
f18b91585c4d3f3e.json
ede6e66b2fb59aab.mp3
ede6e66b2fb59aab.json
ed600d57fcee4f94.mp3
ed600d57fcee4f94.json
...
```
You can put your audio files labels/captions/bounding boxes using JSON or text files for example.
Load your WebDataset and it will create on column per file suffix (here "mp3" and "json"):
```python
>>> from datasets import load_dataset
>>> dataset = load_dataset("webdataset", data_dir="/path/to/folder", split="train")
>>> dataset[0]["json"]
{"transcript": "Hello there !", "speaker": "Obi-Wan Kenobi"}
```
It's also possible to have several audio files per example like this:
```
e39871fd9fd74f55.input.mp3
e39871fd9fd74f55.output.mp3
e39871fd9fd74f55.json
f18b91585c4d3f3e.input.mp3
f18b91585c4d3f3e.output.mp3
f18b91585c4d3f3e.json
...
```
For more details on the WebDataset format and the python library, please check the [WebDataset documentation](https://webdataset.github.io/webdataset).
| datasets/docs/source/audio_dataset.mdx/0 | {
"file_path": "datasets/docs/source/audio_dataset.mdx",
"repo_id": "datasets",
"token_count": 2304
} | 100 |
# Process image data
This guide shows specific methods for processing image datasets. Learn how to:
- Use [`~Dataset.map`] with image dataset.
- Apply data augmentations to a dataset with [`~Dataset.set_transform`].
For a guide on how to process any type of dataset, take a look at the <a class="underline decoration-sky-400 decoration-2 font-semibold" href="./process">general process guide</a>.
## Map
The [`~Dataset.map`] function can apply transforms over an entire dataset.
For example, create a basic [`Resize`](https://pytorch.org/vision/stable/generated/torchvision.transforms.Resize.html) function:
```py
>>> def transforms(examples):
... examples["pixel_values"] = [image.convert("RGB").resize((100,100)) for image in examples["image"]]
... return examples
```
Now use the [`~Dataset.map`] function to resize the entire dataset, and set `batched=True` to speed up the process by accepting batches of examples. The transform returns `pixel_values` as a cacheable `PIL.Image` object:
```py
>>> dataset = dataset.map(transforms, remove_columns=["image"], batched=True)
>>> dataset[0]
{'label': 6,
'pixel_values': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=100x100 at 0x7F058237BB10>}
```
The cache file saves time because you don't have to execute the same transform twice. The [`~Dataset.map`] function is best for operations you only run once per training - like resizing an image - instead of using it for operations executed for each epoch, like data augmentations.
[`~Dataset.map`] takes up some memory, but you can reduce its memory requirements with the following parameters:
- [`batch_size`](./package_reference/main_classes#datasets.DatasetDict.map.batch_size) determines the number of examples that are processed in one call to the transform function.
- [`writer_batch_size`](./package_reference/main_classes#datasets.DatasetDict.map.writer_batch_size) determines the number of processed examples that are kept in memory before they are stored away.
Both parameter values default to 1000, which can be expensive if you are storing images. Lower these values to use less memory when you use [`~Dataset.map`].
## Apply transforms
🤗 Datasets applies data augmentations from any library or package to your dataset. Transforms can be applied on-the-fly on batches of data with [`~Dataset.set_transform`], which consumes less disk space.
<Tip>
The following example uses [torchvision](https://pytorch.org/vision/stable/index.html), but feel free to use other data augmentation libraries like [Albumentations](https://albumentations.ai/docs/), [Kornia](https://kornia.readthedocs.io/en/latest/), and [imgaug](https://imgaug.readthedocs.io/en/latest/).
</Tip>
For example, if you'd like to change the color properties of an image randomly:
```py
>>> from torchvision.transforms import Compose, ColorJitter, ToTensor
>>> jitter = Compose(
... [
... ColorJitter(brightness=0.25, contrast=0.25, saturation=0.25, hue=0.7),
... ToTensor(),
... ]
... )
```
Create a function to apply the `ColorJitter` transform:
```py
>>> def transforms(examples):
... examples["pixel_values"] = [jitter(image.convert("RGB")) for image in examples["image"]]
... return examples
```
Apply the transform with the [`~Dataset.set_transform`] function:
```py
>>> dataset.set_transform(transforms)
``` | datasets/docs/source/image_process.mdx/0 | {
"file_path": "datasets/docs/source/image_process.mdx",
"repo_id": "datasets",
"token_count": 1031
} | 101 |
<!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# Quickstart
[[open-in-colab]]
This quickstart is intended for developers who are ready to dive into the code and see an example of how to integrate 🤗 Datasets into their model training workflow. If you're a beginner, we recommend starting with our [tutorials](./tutorial), where you'll get a more thorough introduction.
Each dataset is unique, and depending on the task, some datasets may require additional steps to prepare it for training. But you can always use 🤗 Datasets tools to load and process a dataset. The fastest and easiest way to get started is by loading an existing dataset from the [Hugging Face Hub](https://huggingface.co/datasets). There are thousands of datasets to choose from, spanning many tasks. Choose the type of dataset you want to work with, and let's get started!
<div class="mt-4">
<div class="w-full flex flex-col space-y-4 md:space-y-0 md:grid md:grid-cols-3 md:gap-y-4 md:gap-x-5">
<a
class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg"
href="#audio"
>
<div class="w-full text-center bg-gradient-to-r from-violet-300 via-sky-400 to-green-500 rounded-lg py-1.5 font-semibold mb-5 text-white text-lg leading-relaxed">
Audio
</div>
<p class="text-gray-700">
Resample an audio dataset and get it ready for a model to classify what
type of banking issue a speaker is calling about.
</p>
</a>
<a
class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg"
href="#vision"
>
<div class="w-full text-center bg-gradient-to-r from-pink-400 via-purple-400 to-blue-500 rounded-lg py-1.5 font-semibold mb-5 text-white text-lg leading-relaxed">
Vision
</div>
<p class="text-gray-700">
Apply data augmentation to an image dataset and get it ready for a model
to diagnose disease in bean plants.
</p>
</a>
<a
class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg"
href="#nlp"
>
<div class="w-full text-center bg-gradient-to-r from-orange-300 via-red-400 to-violet-500 rounded-lg py-1.5 font-semibold mb-5 text-white text-lg leading-relaxed">
NLP
</div>
<p class="text-gray-700">
Tokenize a dataset and get it ready for a model to determine whether a
pair of sentences have the same meaning.
</p>
</a>
</div>
</div>
<Tip>
Check out [Chapter 5](https://huggingface.co/course/chapter5/1?fw=pt) of the Hugging Face course to learn more about other important topics such as loading remote or local datasets, tools for cleaning up a dataset, and creating your own dataset.
</Tip>
Start by installing 🤗 Datasets:
```bash
pip install datasets
```
🤗 Datasets also support audio and image data formats:
- To work with audio datasets, install the [`Audio`] feature:
```bash
pip install datasets[audio]
```
- To work with image datasets, install the [`Image`] feature:
```bash
pip install datasets[vision]
```
Besides 🤗 Datasets, make sure your preferred machine learning framework is installed:
<frameworkcontent>
<pt>```bash pip install torch ```</pt>
<tf>```bash pip install tensorflow ```</tf>
</frameworkcontent>
## Audio
Audio datasets are loaded just like text datasets. However, an audio dataset is preprocessed a bit differently. Instead of a tokenizer, you'll need a [feature extractor](https://huggingface.co/docs/transformers/main_classes/feature_extractor#feature-extractor). An audio input may also require resampling its sampling rate to match the sampling rate of the pretrained model you're using. In this quickstart, you'll prepare the [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14) dataset for a model train on and classify the banking issue a customer is having.
**1**. Load the MInDS-14 dataset by providing the [`load_dataset`] function with the dataset name, dataset configuration (not all datasets will have a configuration), and a dataset split:
```py
>>> from datasets import load_dataset, Audio
>>> dataset = load_dataset("PolyAI/minds14", "en-US", split="train")
```
**2**. Next, load a pretrained [Wav2Vec2](https://huggingface.co/facebook/wav2vec2-base) model and its corresponding feature extractor from the [🤗 Transformers](https://huggingface.co/transformers/) library. It is totally normal to see a warning after you load the model about some weights not being initialized. This is expected because you are loading this model checkpoint for training with another task.
```py
>>> from transformers import AutoModelForAudioClassification, AutoFeatureExtractor
>>> model = AutoModelForAudioClassification.from_pretrained("facebook/wav2vec2-base")
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base")
```
**3**. The [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14) dataset card indicates the sampling rate is 8kHz, but the Wav2Vec2 model was pretrained on a sampling rate of 16kHZ. You'll need to upsample the `audio` column with the [`~Dataset.cast_column`] function and [`Audio`] feature to match the model's sampling rate.
```py
>>> dataset = dataset.cast_column("audio", Audio(sampling_rate=16000))
>>> dataset[0]["audio"]
<datasets.features._torchcodec.AudioDecoder object at 0x11642b6a0>
```
**4**. Create a function to preprocess the audio `array` with the feature extractor, and truncate and pad the sequences into tidy rectangular tensors. The most important thing to remember is to call the audio `array` in the feature extractor since the `array` - the actual speech signal - is the model input.
Once you have a preprocessing function, use the [`~Dataset.map`] function to speed up processing by applying the function to batches of examples in the dataset.
```py
>>> def preprocess_function(examples):
... audio_arrays = [x.get_all_samples().data for x in examples["audio"]]
... inputs = feature_extractor(
... audio_arrays,
... sampling_rate=16000,
... padding=True,
... max_length=100000,
... truncation=True,
... )
... return inputs
>>> dataset = dataset.map(preprocess_function, batched=True)
```
**5**. Use the [`~Dataset.rename_column`] function to rename the `intent_class` column to `labels`, which is the expected input name in [Wav2Vec2ForSequenceClassification](https://huggingface.co/docs/transformers/main/en/model_doc/wav2vec2#transformers.Wav2Vec2ForSequenceClassification):
```py
>>> dataset = dataset.rename_column("intent_class", "labels")
```
**6**. Set the dataset format according to the machine learning framework you're using.
<frameworkcontent>
<pt>
Use the [`~Dataset.set_format`] function to set the dataset format to `torch` and specify the columns you want to format. This function applies formatting on-the-fly. After converting to PyTorch tensors, wrap the dataset in [`torch.utils.data.DataLoader`](https://alband.github.io/doc_view/data.html?highlight=torch%20utils%20data%20dataloader#torch.utils.data.DataLoader):
```py
>>> from torch.utils.data import DataLoader
>>> dataset.set_format(type="torch", columns=["input_values", "labels"])
>>> dataloader = DataLoader(dataset, batch_size=4)
```
</pt>
<tf>
Use the [`~transformers.TFPreTrainedModel.prepare_tf_dataset`] method from 🤗 Transformers to prepare the dataset to be compatible with
TensorFlow, and ready to train/fine-tune a model, as it wraps a HuggingFace [`~datasets.Dataset`] as a `tf.data.Dataset`
with collation and batching, so one can pass it directly to Keras methods like `fit()` without further modification.
```py
>>> import tensorflow as tf
>>> tf_dataset = model.prepare_tf_dataset(
... dataset,
... batch_size=4,
... shuffle=True,
... )
```
</tf>
</frameworkcontent>
**7**. Start training with your machine learning framework! Check out the 🤗 Transformers [audio classification guide](https://huggingface.co/docs/transformers/tasks/audio_classification) for an end-to-end example of how to train a model on an audio dataset.
## Vision
Image datasets are loaded just like text datasets. However, instead of a tokenizer, you'll need a [feature extractor](https://huggingface.co/docs/transformers/main_classes/feature_extractor#feature-extractor) to preprocess the dataset. Applying data augmentation to an image is common in computer vision to make the model more robust against overfitting. You're free to use any data augmentation library you want, and then you can apply the augmentations with 🤗 Datasets. In this quickstart, you'll load the [Beans](https://huggingface.co/datasets/beans) dataset and get it ready for the model to train on and identify disease from the leaf images.
**1**. Load the Beans dataset by providing the [`load_dataset`] function with the dataset name and a dataset split:
```py
>>> from datasets import load_dataset, Image
>>> dataset = load_dataset("AI-Lab-Makerere/beans", split="train")
```
Most image models work with RBG images. If your dataset contains images in a different mode, you can use the [`~Dataset.cast_column`] function to set the mode to RGB:
```py
>>> dataset = dataset.cast_column("image", Image(mode="RGB"))
```
The Beans dataset contains only RGB images, so this step is unnecessary here.
**2**. Now you can add some data augmentations with any library ([Albumentations](https://albumentations.ai/), [imgaug](https://imgaug.readthedocs.io/en/latest/), [Kornia](https://kornia.readthedocs.io/en/latest/)) you like. Here, you'll use [torchvision](https://pytorch.org/vision/stable/transforms.html) to randomly change the color properties of an image:
```py
>>> from torchvision.transforms import Compose, ColorJitter, ToTensor
>>> jitter = Compose(
... [ColorJitter(brightness=0.5, hue=0.5), ToTensor()]
... )
```
**3**. Create a function to apply your transform to the dataset and generate the model input: `pixel_values`.
```python
>>> def transforms(examples):
... examples["pixel_values"] = [jitter(image.convert("RGB")) for image in examples["image"]]
... return examples
```
**4**. Use the [`~Dataset.with_transform`] function to apply the data augmentations on-the-fly:
```py
>>> dataset = dataset.with_transform(transforms)
```
**5**. Set the dataset format according to the machine learning framework you're using.
<frameworkcontent>
<pt>
Wrap the dataset in [`torch.utils.data.DataLoader`](https://alband.github.io/doc_view/data.html?highlight=torch%20utils%20data%20dataloader#torch.utils.data.DataLoader). You'll also need to create a collate function to collate the samples into batches:
```py
>>> from torch.utils.data import DataLoader
>>> def collate_fn(examples):
... images = []
... labels = []
... for example in examples:
... images.append((example["pixel_values"]))
... labels.append(example["labels"])
...
... pixel_values = torch.stack(images)
... labels = torch.tensor(labels)
... return {"pixel_values": pixel_values, "labels": labels}
>>> dataloader = DataLoader(dataset, collate_fn=collate_fn, batch_size=4)
```
</pt>
<tf>
Use the [`~transformers.TFPreTrainedModel.prepare_tf_dataset`] method from 🤗 Transformers to prepare the dataset to be compatible with
TensorFlow, and ready to train/fine-tune a model, as it wraps a HuggingFace [`~datasets.Dataset`] as a `tf.data.Dataset`
with collation and batching, so one can pass it directly to Keras methods like `fit()` without further modification.
Before you start, make sure you have up-to-date versions of `albumentations` and `cv2` installed:
```bash
pip install -U albumentations opencv-python
```
```py
>>> import albumentations
>>> import numpy as np
>>> transform = albumentations.Compose([
... albumentations.RandomCrop(width=256, height=256),
... albumentations.HorizontalFlip(p=0.5),
... albumentations.RandomBrightnessContrast(p=0.2),
... ])
>>> def transforms(examples):
... examples["pixel_values"] = [
... transform(image=np.array(image))["image"] for image in examples["image"]
... ]
... return examples
>>> dataset.set_transform(transforms)
>>> tf_dataset = model.prepare_tf_dataset(
... dataset,
... batch_size=4,
... shuffle=True,
... )
```
</tf>
</frameworkcontent>
**6**. Start training with your machine learning framework! Check out the 🤗 Transformers [image classification guide](https://huggingface.co/docs/transformers/tasks/image_classification) for an end-to-end example of how to train a model on an image dataset.
## NLP
Text needs to be tokenized into individual tokens by a [tokenizer](https://huggingface.co/docs/transformers/main_classes/tokenizer). For the quickstart, you'll load the [Microsoft Research Paraphrase Corpus (MRPC)](https://huggingface.co/datasets/glue/viewer/mrpc) training dataset to train a model to determine whether a pair of sentences mean the same thing.
**1**. Load the MRPC dataset by providing the [`load_dataset`] function with the dataset name, dataset configuration (not all datasets will have a configuration), and dataset split:
```py
>>> from datasets import load_dataset
>>> dataset = load_dataset("nyu-mll/glue", "mrpc", split="train")
```
**2**. Next, load a pretrained [BERT](https://huggingface.co/bert-base-uncased) model and its corresponding tokenizer from the [🤗 Transformers](https://huggingface.co/transformers/) library. It is totally normal to see a warning after you load the model about some weights not being initialized. This is expected because you are loading this model checkpoint for training with another task.
```py
>>> from transformers import AutoModelForSequenceClassification, AutoTokenizer
>>> model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased")
>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
===PT-TF-SPLIT===
>>> from transformers import TFAutoModelForSequenceClassification, AutoTokenizer
>>> model = TFAutoModelForSequenceClassification.from_pretrained("bert-base-uncased")
>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
```
**3**. Create a function to tokenize the dataset, and you should also truncate and pad the text into tidy rectangular tensors. The tokenizer generates three new columns in the dataset: `input_ids`, `token_type_ids`, and an `attention_mask`. These are the model inputs.
Use the [`~Dataset.map`] function to speed up processing by applying your tokenization function to batches of examples in the dataset:
```py
>>> def encode(examples):
... return tokenizer(examples["sentence1"], examples["sentence2"], truncation=True, padding="max_length")
>>> dataset = dataset.map(encode, batched=True)
>>> dataset[0]
{'sentence1': 'Amrozi accused his brother , whom he called " the witness " , of deliberately distorting his evidence .',
'sentence2': 'Referring to him as only " the witness " , Amrozi accused his brother of deliberately distorting his evidence .',
'label': 1,
'idx': 0,
'input_ids': [ 101, 7277, 2180, 5303, 4806, 1117, 1711, 117, 2292, 1119, 1270, 107, 1103, 7737, 107, 117, 1104, 9938, 4267, 12223, 21811, 1117, 2554, 119, 102, 11336, 6732, 3384, 1106, 1140, 1112, 1178, 107, 1103, 7737, 107, 117, 7277, 2180, 5303, 4806, 1117, 1711, 1104, 9938, 4267, 12223, 21811, 1117, 2554, 119, 102, 0, 0, ...],
'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, ...],
'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, ...]}
```
**4**. Rename the `label` column to `labels`, which is the expected input name in [BertForSequenceClassification](https://huggingface.co/docs/transformers/main/en/model_doc/bert#transformers.BertForSequenceClassification):
```py
>>> dataset = dataset.map(lambda examples: {"labels": examples["label"]}, batched=True)
```
**5**. Set the dataset format according to the machine learning framework you're using.
<frameworkcontent>
<pt>
Use the [`~Dataset.with_format`] function to set the dataset format to `torch` and specify the columns you want to format. This function applies formatting on-the-fly. After converting to PyTorch tensors, wrap the dataset in [`torch.utils.data.DataLoader`](https://alband.github.io/doc_view/data.html?highlight=torch%20utils%20data%20dataloader#torch.utils.data.DataLoader):
```py
>>> import torch
>>> dataset = dataset.select_columns(["input_ids", "token_type_ids", "attention_mask", "labels"])
>>> dataset = dataset.with_format(type="torch")
>>> dataloader = torch.utils.data.DataLoader(dataset, batch_size=32)
```
</pt>
<tf>
Use the [`~transformers.TFPreTrainedModel.prepare_tf_dataset`] method from 🤗 Transformers to prepare the dataset to be compatible with
TensorFlow, and ready to train/fine-tune a model, as it wraps a HuggingFace [`~datasets.Dataset`] as a `tf.data.Dataset`
with collation and batching, so one can pass it directly to Keras methods like `fit()` without further modification.
```py
>>> import tensorflow as tf
>>> tf_dataset = model.prepare_tf_dataset(
... dataset,
... batch_size=4,
... shuffle=True,
... )
```
</tf>
</frameworkcontent>
**6**. Start training with your machine learning framework! Check out the 🤗 Transformers [text classification guide](https://huggingface.co/docs/transformers/tasks/sequence_classification) for an end-to-end example of how to train a model on a text dataset.
## What's next?
This completes the 🤗 Datasets quickstart! You can load any text, audio, or image dataset with a single function and get it ready for your model to train on.
For your next steps, take a look at our [How-to guides](./how_to) and learn how to do more specific things like loading different dataset formats, aligning labels, and streaming large datasets. If you're interested in learning more about 🤗 Datasets core concepts, grab a cup of coffee and read our [Conceptual Guides](./about_arrow)!
| datasets/docs/source/quickstart.mdx/0 | {
"file_path": "datasets/docs/source/quickstart.mdx",
"repo_id": "datasets",
"token_count": 6103
} | 102 |
# Use with Spark
This document is a quick introduction to using 🤗 Datasets with Spark, with a particular focus on how to load a Spark DataFrame into a [`Dataset`] object.
From there, you have fast access to any element and you can use it as a data loader to train models.
## Load from Spark
A [`Dataset`] object is a wrapper of an Arrow table, which allows fast reads from arrays in the dataset to PyTorch, TensorFlow and JAX tensors.
The Arrow table is memory mapped from disk, which can load datasets bigger than your available RAM.
You can get a [`Dataset`] from a Spark DataFrame using [`Dataset.from_spark`]:
```py
>>> from datasets import Dataset
>>> df = spark.createDataFrame(
... data=[[1, "Elia"], [2, "Teo"], [3, "Fang"]],
... columns=["id", "name"],
... )
>>> ds = Dataset.from_spark(df)
```
The Spark workers write the dataset on disk in a cache directory as Arrow files, and the [`Dataset`] is loaded from there.
Alternatively, you can skip materialization by using [`IterableDataset.from_spark`], which returns an [`IterableDataset`]:
```py
>>> from datasets import IterableDataset
>>> df = spark.createDataFrame(
... data=[[1, "Elia"], [2, "Teo"], [3, "Fang"]],
... columns=["id", "name"],
... )
>>> ds = IterableDataset.from_spark(df)
>>> print(next(iter(ds)))
{"id": 1, "name": "Elia"}
```
### Caching
When using [`Dataset.from_spark`], the resulting [`Dataset`] is cached; if you call [`Dataset.from_spark`] multiple
times on the same DataFrame it won't re-run the Spark job that writes the dataset as Arrow files on disk.
You can set the cache location by passing `cache_dir=` to [`Dataset.from_spark`].
Make sure to use a disk that is available to both your workers and your current machine (the driver).
<Tip warning={true}>
In a different session, a Spark DataFrame doesn't have the same [semantic hash](https://spark.apache.org/docs/3.2.0/api/python/reference/api/pyspark.sql.DataFrame.semanticHash.html), and it will rerun a Spark job and store it in a new cache.
</Tip>
### Feature types
If your dataset is made of images, audio data or N-dimensional arrays, you can specify the `features=` argument in
[`Dataset.from_spark`] (or [`IterableDataset.from_spark`]):
```py
>>> from datasets import Dataset, Features, Image, Value
>>> data = [(0, open("image.png", "rb").read())]
>>> df = spark.createDataFrame(data, "idx: int, image: binary")
>>> # Also works if you have arrays
>>> # data = [(0, np.zeros(shape=(32, 32, 3), dtype=np.int32).tolist())]
>>> # df = spark.createDataFrame(data, "idx: int, image: array<array<array<int>>>")
>>> features = Features({"idx": Value("int64"), "image": Image()})
>>> dataset = Dataset.from_spark(df, features=features)
>>> dataset[0]
{'idx': 0, 'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=32x32>}
```
You can check the [`Features`] documentation to know about all the feature types available.
| datasets/docs/source/use_with_spark.mdx/0 | {
"file_path": "datasets/docs/source/use_with_spark.mdx",
"repo_id": "datasets",
"token_count": 962
} | 103 |
import platform
from argparse import ArgumentParser
import fsspec
import huggingface_hub
import pandas
import pyarrow
from datasets import __version__ as version
from datasets.commands import BaseDatasetsCLICommand
def info_command_factory(_):
return EnvironmentCommand()
class EnvironmentCommand(BaseDatasetsCLICommand):
@staticmethod
def register_subcommand(parser: ArgumentParser):
download_parser = parser.add_parser("env", help="Print relevant system environment info.")
download_parser.set_defaults(func=info_command_factory)
def run(self):
info = {
"`datasets` version": version,
"Platform": platform.platform(),
"Python version": platform.python_version(),
"`huggingface_hub` version": huggingface_hub.__version__,
"PyArrow version": pyarrow.__version__,
"Pandas version": pandas.__version__,
"`fsspec` version": fsspec.__version__,
}
print("\nCopy-and-paste the text below in your GitHub issue.\n")
print(self.format_dict(info))
return info
@staticmethod
def format_dict(d):
return "\n".join([f"- {prop}: {val}" for prop, val in d.items()]) + "\n"
| datasets/src/datasets/commands/env.py/0 | {
"file_path": "datasets/src/datasets/commands/env.py",
"repo_id": "datasets",
"token_count": 476
} | 104 |
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import pyarrow as pa
from .. import config
from ..download.download_config import DownloadConfig
from ..table import array_cast
from ..utils.file_utils import is_local_path, xopen
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict
if TYPE_CHECKING:
import pdfplumber
from .features import FeatureType
def pdf_to_bytes(pdf: "pdfplumber.pdf.PDF") -> bytes:
"""Convert a pdfplumber.pdf.PDF object to bytes."""
with BytesIO() as buffer:
for page in pdf.pages:
buffer.write(page.pdf.stream)
return buffer.getvalue()
@dataclass
class Pdf:
"""
**Experimental.**
Pdf [`Feature`] to read pdf documents from a pdf file.
Input: The Pdf feature accepts as input:
- A `str`: Absolute path to the pdf file (i.e. random access is allowed).
- A `dict` with the keys:
- `path`: String with relative path of the pdf file in a dataset repository.
- `bytes`: Bytes of the pdf file.
This is useful for archived files with sequential access.
- A `pdfplumber.pdf.PDF`: pdfplumber pdf object.
Args:
mode (`str`, *optional*):
The mode to convert the pdf to. If `None`, the native mode of the pdf is used.
decode (`bool`, defaults to `True`):
Whether to decode the pdf data. If `False`,
returns the underlying dictionary in the format `{"path": pdf_path, "bytes": pdf_bytes}`.
Examples:
```py
>>> from datasets import Dataset, Pdf
>>> ds = Dataset.from_dict({"pdf": ["path/to/pdf/file.pdf"]}).cast_column("pdf", Pdf())
>>> ds.features["pdf"]
Pdf(decode=True, id=None)
>>> ds[0]["pdf"]
<pdfplumber.pdf.PDF object at 0x7f8a1c2d8f40>
>>> ds = ds.cast_column("pdf", Pdf(decode=False))
>>> ds[0]["pdf"]
{'bytes': None,
'path': 'path/to/pdf/file.pdf'}
```
"""
decode: bool = True
id: Optional[str] = field(default=None, repr=False)
# Automatically constructed
dtype: ClassVar[str] = "pdfplumber.pdf.PDF"
pa_type: ClassVar[Any] = pa.struct({"bytes": pa.binary(), "path": pa.string()})
_type: str = field(default="Pdf", init=False, repr=False)
def __call__(self):
return self.pa_type
def encode_example(self, value: Union[str, bytes, bytearray, dict, "pdfplumber.pdf.PDF"]) -> dict:
"""Encode example into a format for Arrow.
Args:
value (`str`, `bytes`, `pdfplumber.pdf.PDF` or `dict`):
Data passed as input to Pdf feature.
Returns:
`dict` with "path" and "bytes" fields
"""
if config.PDFPLUMBER_AVAILABLE:
import pdfplumber
else:
pdfplumber = None
if isinstance(value, str):
return {"path": value, "bytes": None}
elif isinstance(value, (bytes, bytearray)):
return {"path": None, "bytes": value}
elif pdfplumber is not None and isinstance(value, pdfplumber.pdf.PDF):
# convert the pdfplumber.pdf.PDF to bytes
return encode_pdfplumber_pdf(value)
elif value.get("path") is not None and os.path.isfile(value["path"]):
# we set "bytes": None to not duplicate the data if they're already available locally
return {"bytes": None, "path": value.get("path")}
elif value.get("bytes") is not None or value.get("path") is not None:
# store the pdf bytes, and path is used to infer the pdf format using the file extension
return {"bytes": value.get("bytes"), "path": value.get("path")}
else:
raise ValueError(
f"A pdf sample should have one of 'path' or 'bytes' but they are missing or None in {value}."
)
def decode_example(self, value: dict, token_per_repo_id=None) -> "pdfplumber.pdf.PDF":
"""Decode example pdf file into pdf data.
Args:
value (`str` or `dict`):
A string with the absolute pdf file path, a dictionary with
keys:
- `path`: String with absolute or relative pdf file path.
- `bytes`: The bytes of the pdf file.
token_per_repo_id (`dict`, *optional*):
To access and decode pdf files from private repositories on
the Hub, you can pass a dictionary
repo_id (`str`) -> token (`bool` or `str`).
Returns:
`pdfplumber.pdf.PDF`
"""
if not self.decode:
raise RuntimeError("Decoding is disabled for this feature. Please use Pdf(decode=True) instead.")
if config.PDFPLUMBER_AVAILABLE:
import pdfplumber
else:
raise ImportError("To support decoding pdfs, please install 'pdfplumber'.")
if token_per_repo_id is None:
token_per_repo_id = {}
path, bytes_ = value["path"], value["bytes"]
if bytes_ is None:
if path is None:
raise ValueError(f"A pdf should have one of 'path' or 'bytes' but both are None in {value}.")
else:
if is_local_path(path):
pdf = pdfplumber.open(path)
else:
source_url = path.split("::")[-1]
pattern = (
config.HUB_DATASETS_URL
if source_url.startswith(config.HF_ENDPOINT)
else config.HUB_DATASETS_HFFS_URL
)
try:
repo_id = string_to_dict(source_url, pattern)["repo_id"]
token = token_per_repo_id.get(repo_id)
except ValueError:
token = None
download_config = DownloadConfig(token=token)
f = xopen(path, "rb", download_config=download_config)
return pdfplumber.open(f)
else:
with pdfplumber.open(BytesIO(bytes_)) as p:
pdf = p
return pdf
def flatten(self) -> Union["FeatureType", Dict[str, "FeatureType"]]:
"""If in the decodable state, return the feature itself, otherwise flatten the feature into a dictionary."""
from .features import Value
return (
self
if self.decode
else {
"bytes": Value("binary"),
"path": Value("string"),
}
)
def cast_storage(self, storage: Union[pa.StringArray, pa.StructArray, pa.ListArray]) -> pa.StructArray:
"""Cast an Arrow array to the Pdf arrow storage type.
The Arrow types that can be converted to the Pdf pyarrow storage type are:
- `pa.string()` - it must contain the "path" data
- `pa.binary()` - it must contain the image bytes
- `pa.struct({"bytes": pa.binary()})`
- `pa.struct({"path": pa.string()})`
- `pa.struct({"bytes": pa.binary(), "path": pa.string()})` - order doesn't matter
- `pa.list(*)` - it must contain the pdf array data
Args:
storage (`Union[pa.StringArray, pa.StructArray, pa.ListArray]`):
PyArrow array to cast.
Returns:
`pa.StructArray`: Array in the Pdf arrow storage type, that is
`pa.struct({"bytes": pa.binary(), "path": pa.string()})`.
"""
if pa.types.is_string(storage.type):
bytes_array = pa.array([None] * len(storage), type=pa.binary())
storage = pa.StructArray.from_arrays([bytes_array, storage], ["bytes", "path"], mask=storage.is_null())
elif pa.types.is_binary(storage.type):
path_array = pa.array([None] * len(storage), type=pa.string())
storage = pa.StructArray.from_arrays([storage, path_array], ["bytes", "path"], mask=storage.is_null())
elif pa.types.is_struct(storage.type):
if storage.type.get_field_index("bytes") >= 0:
bytes_array = storage.field("bytes")
else:
bytes_array = pa.array([None] * len(storage), type=pa.binary())
if storage.type.get_field_index("path") >= 0:
path_array = storage.field("path")
else:
path_array = pa.array([None] * len(storage), type=pa.string())
storage = pa.StructArray.from_arrays([bytes_array, path_array], ["bytes", "path"], mask=storage.is_null())
return array_cast(storage, self.pa_type)
def embed_storage(self, storage: pa.StructArray, token_per_repo_id=None) -> pa.StructArray:
"""Embed PDF files into the Arrow array.
Args:
storage (`pa.StructArray`):
PyArrow array to embed.
Returns:
`pa.StructArray`: Array in the PDF arrow storage type, that is
`pa.struct({"bytes": pa.binary(), "path": pa.string()})`.
"""
if token_per_repo_id is None:
token_per_repo_id = {}
@no_op_if_value_is_null
def path_to_bytes(path):
source_url = path.split("::")[-1]
pattern = (
config.HUB_DATASETS_URL if source_url.startswith(config.HF_ENDPOINT) else config.HUB_DATASETS_HFFS_URL
)
source_url_fields = string_to_dict(source_url, pattern)
token = token_per_repo_id.get(source_url_fields["repo_id"]) if source_url_fields is not None else None
download_config = DownloadConfig(token=token)
with xopen(path, "rb", download_config=download_config) as f:
return f.read()
bytes_array = pa.array(
[
(path_to_bytes(x["path"]) if x["bytes"] is None else x["bytes"]) if x is not None else None
for x in storage.to_pylist()
],
type=pa.binary(),
)
path_array = pa.array(
[os.path.basename(path) if path is not None else None for path in storage.field("path").to_pylist()],
type=pa.string(),
)
storage = pa.StructArray.from_arrays([bytes_array, path_array], ["bytes", "path"], mask=bytes_array.is_null())
return array_cast(storage, self.pa_type)
def encode_pdfplumber_pdf(pdf: "pdfplumber.pdf.PDF") -> dict:
"""
Encode a pdfplumber.pdf.PDF object into a dictionary.
If the PDF has an associated file path, returns the path. Otherwise, serializes
the PDF content into bytes.
Args:
pdf (pdfplumber.pdf.PDF): A pdfplumber PDF object.
Returns:
dict: A dictionary with "path" or "bytes" field.
"""
if hasattr(pdf, "stream") and hasattr(pdf.stream, "name") and pdf.stream.name:
# Return the path if the PDF has an associated file path
return {"path": pdf.stream.name, "bytes": None}
else:
# Convert the PDF to bytes if no path is available
return {"path": None, "bytes": pdf_to_bytes(pdf)}
| datasets/src/datasets/features/pdf.py/0 | {
"file_path": "datasets/src/datasets/features/pdf.py",
"repo_id": "datasets",
"token_count": 4989
} | 105 |
import io
import json
import re
from itertools import islice
from typing import Any, Callable
import fsspec
import numpy as np
import pyarrow as pa
import datasets
from datasets.features.features import cast_to_python_objects
from datasets.utils.file_utils import SINGLE_FILE_COMPRESSION_EXTENSION_TO_PROTOCOL, xbasename
logger = datasets.utils.logging.get_logger(__name__)
class WebDataset(datasets.GeneratorBasedBuilder):
DEFAULT_WRITER_BATCH_SIZE = 100
IMAGE_EXTENSIONS: list[str] # definition at the bottom of the script
AUDIO_EXTENSIONS: list[str] # definition at the bottom of the script
VIDEO_EXTENSIONS: list[str] # definition at the bottom of the script
DECODERS: dict[str, Callable[[Any], Any]] # definition at the bottom of the script
NUM_EXAMPLES_FOR_FEATURES_INFERENCE = 5
@classmethod
def _get_pipeline_from_tar(cls, tar_path, tar_iterator):
current_example = {}
fs: fsspec.AbstractFileSystem = fsspec.filesystem("memory")
streaming_download_manager = datasets.StreamingDownloadManager()
for filename, f in tar_iterator:
example_key, field_name = base_plus_ext(filename)
if example_key is None:
continue
if current_example and current_example["__key__"] != example_key:
# reposition some keys in last position
current_example["__key__"] = current_example.pop("__key__")
current_example["__url__"] = current_example.pop("__url__")
yield current_example
current_example = {}
current_example["__key__"] = example_key
current_example["__url__"] = tar_path
current_example[field_name] = f.read()
if field_name.split(".")[-1].lower() in SINGLE_FILE_COMPRESSION_EXTENSION_TO_PROTOCOL:
fs.write_bytes(filename, current_example[field_name])
extracted_file_path = streaming_download_manager.extract(f"memory://{filename}")
with fsspec.open(extracted_file_path) as f:
current_example[field_name] = f.read()
fs.delete(filename)
data_extension = xbasename(extracted_file_path).split(".")[-1].lower()
else:
data_extension = field_name.split(".")[-1].lower()
if data_extension in cls.DECODERS:
current_example[field_name] = cls.DECODERS[data_extension](current_example[field_name])
if current_example:
yield current_example
def _info(self) -> datasets.DatasetInfo:
return datasets.DatasetInfo()
def _split_generators(self, dl_manager):
"""We handle string, list and dicts in datafiles"""
# Download the data files
if not self.config.data_files:
raise ValueError(f"At least one data file must be specified, but got data_files={self.config.data_files}")
data_files = dl_manager.download(self.config.data_files)
splits = []
for split_name, tar_paths in data_files.items():
if isinstance(tar_paths, str):
tar_paths = [tar_paths]
tar_iterators = [dl_manager.iter_archive(tar_path) for tar_path in tar_paths]
splits.append(
datasets.SplitGenerator(
name=split_name, gen_kwargs={"tar_paths": tar_paths, "tar_iterators": tar_iterators}
)
)
if not self.info.features:
# Get one example to get the feature types
pipeline = self._get_pipeline_from_tar(tar_paths[0], tar_iterators[0])
first_examples = list(islice(pipeline, self.NUM_EXAMPLES_FOR_FEATURES_INFERENCE))
if any(example.keys() != first_examples[0].keys() for example in first_examples):
raise ValueError(
"The TAR archives of the dataset should be in WebDataset format, "
"but the files in the archive don't share the same prefix or the same types."
)
pa_tables = [
pa.Table.from_pylist(cast_to_python_objects([example], only_1d_for_numpy=True))
for example in first_examples
]
inferred_arrow_schema = pa.concat_tables(pa_tables, promote_options="default").schema
features = datasets.Features.from_arrow_schema(inferred_arrow_schema)
for field_name in first_examples[0]:
extension = field_name.rsplit(".", 1)[-1].lower()
# Set Image types
if extension in self.IMAGE_EXTENSIONS:
features[field_name] = datasets.Image()
# Set Audio types
if extension in self.AUDIO_EXTENSIONS:
features[field_name] = datasets.Audio()
# Set Video types
if extension in self.VIDEO_EXTENSIONS:
features[field_name] = datasets.Video()
self.info.features = features
return splits
def _generate_examples(self, tar_paths, tar_iterators):
image_field_names = [
field_name for field_name, feature in self.info.features.items() if isinstance(feature, datasets.Image)
]
audio_field_names = [
field_name for field_name, feature in self.info.features.items() if isinstance(feature, datasets.Audio)
]
all_field_names = list(self.info.features.keys())
for tar_idx, (tar_path, tar_iterator) in enumerate(zip(tar_paths, tar_iterators)):
for example_idx, example in enumerate(self._get_pipeline_from_tar(tar_path, tar_iterator)):
for field_name in all_field_names:
if field_name not in example:
example[field_name] = None
for field_name in image_field_names + audio_field_names:
if example[field_name] is not None:
example[field_name] = {
"path": example["__key__"] + "." + field_name,
"bytes": example[field_name],
}
yield f"{tar_idx}_{example_idx}", example
# Source: https://github.com/webdataset/webdataset/blob/87bd5aa41602d57f070f65a670893ee625702f2f/webdataset/tariterators.py#L25
def base_plus_ext(path):
"""Split off all file extensions.
Returns base, allext.
"""
match = re.match(r"^((?:.*/|)[^.]+)[.]([^/]*)$", path)
if not match:
return None, None
return match.group(1), match.group(2)
# Obtained with:
# ```
# import PIL.Image
# IMAGE_EXTENSIONS = []
# PIL.Image.init()
# for ext, format in PIL.Image.EXTENSION.items():
# if format in PIL.Image.OPEN:
# IMAGE_EXTENSIONS.append(ext[1:])
# ```
# We intentionally do not run this code on launch because:
# (1) Pillow is an optional dependency, so importing Pillow in global namespace is not allowed
# (2) To ensure the list of supported extensions is deterministic
IMAGE_EXTENSIONS = [
"blp",
"bmp",
"dib",
"bufr",
"cur",
"pcx",
"dcx",
"dds",
"ps",
"eps",
"fit",
"fits",
"fli",
"flc",
"ftc",
"ftu",
"gbr",
"gif",
"grib",
"h5",
"hdf",
"png",
"apng",
"jp2",
"j2k",
"jpc",
"jpf",
"jpx",
"j2c",
"icns",
"ico",
"im",
"iim",
"tif",
"tiff",
"jfif",
"jpe",
"jpg",
"jpeg",
"mpg",
"mpeg",
"msp",
"pcd",
"pxr",
"pbm",
"pgm",
"ppm",
"pnm",
"psd",
"bw",
"rgb",
"rgba",
"sgi",
"ras",
"tga",
"icb",
"vda",
"vst",
"webp",
"wmf",
"emf",
"xbm",
"xpm",
]
WebDataset.IMAGE_EXTENSIONS = IMAGE_EXTENSIONS
# Obtained with:
# ```
# import soundfile as sf
#
# AUDIO_EXTENSIONS = [f".{format.lower()}" for format in sf.available_formats().keys()]
#
# # .opus decoding is supported if libsndfile >= 1.0.31:
# AUDIO_EXTENSIONS.extend([".mp3", ".opus"])
# ```
# We intentionally do not run this code on launch because:
# (1) Soundfile is an optional dependency, so importing it in global namespace is not allowed
# (2) To ensure the list of supported extensions is deterministic
AUDIO_EXTENSIONS = [
"aiff",
"au",
"avr",
"caf",
"flac",
"htk",
"svx",
"mat4",
"mat5",
"mpc2k",
"ogg",
"paf",
"pvf",
"raw",
"rf64",
"sd2",
"sds",
"ircam",
"voc",
"w64",
"wav",
"nist",
"wavex",
"wve",
"xi",
"mp3",
"opus",
]
WebDataset.AUDIO_EXTENSIONS = AUDIO_EXTENSIONS
# TODO: initial list, we should check the compatibility of other formats
VIDEO_EXTENSIONS = [
".mkv",
".mp4",
".avi",
".mpeg",
".mov",
]
WebDataset.VIDEO_EXTENSIONS = VIDEO_EXTENSIONS
def text_loads(data: bytes):
return data.decode("utf-8")
def tenbin_loads(data: bytes):
from . import _tenbin
return _tenbin.decode_buffer(data)
def msgpack_loads(data: bytes):
import msgpack
return msgpack.unpackb(data)
def npy_loads(data: bytes):
import numpy.lib.format
stream = io.BytesIO(data)
return numpy.lib.format.read_array(stream, allow_pickle=False)
def npz_loads(data: bytes):
return np.load(io.BytesIO(data), allow_pickle=False)
def cbor_loads(data: bytes):
import cbor
return cbor.loads(data)
def torch_loads(data: bytes):
import torch
return torch.load(io.BytesIO(data), weights_only=True)
# Obtained by checking `decoders` in `webdataset.autodecode`
# and removing unsafe extension decoders.
# Removed Pickle decoders:
# - "pyd": lambda data: pickle.loads(data)
# - "pickle": lambda data: pickle.loads(data)
# Modified NumPy decoders to fix CVE-2019-6446 (add allow_pickle=False and weights_only=True):
# - "npy": npy_loads,
# - "npz": lambda data: np.load(io.BytesIO(data)),
# - "pth": lambda data: torch_loads(data)
DECODERS = {
"txt": text_loads,
"text": text_loads,
"transcript": text_loads,
"cls": int,
"cls2": int,
"index": int,
"inx": int,
"id": int,
"json": json.loads,
"jsn": json.loads,
"ten": tenbin_loads,
"tb": tenbin_loads,
"mp": msgpack_loads,
"msg": msgpack_loads,
"npy": npy_loads,
"npz": npz_loads,
"cbor": cbor_loads,
"pth": torch_loads,
}
WebDataset.DECODERS = DECODERS
| datasets/src/datasets/packaged_modules/webdataset/webdataset.py/0 | {
"file_path": "datasets/src/datasets/packaged_modules/webdataset/webdataset.py",
"repo_id": "datasets",
"token_count": 4806
} | 106 |
import bz2
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Optional, Union
from .. import config
from ._filelock import FileLock
from .logging import get_logger
logger = get_logger(__name__)
class ExtractManager:
def __init__(self, cache_dir: Optional[str] = None):
self.extract_dir = (
os.path.join(cache_dir, config.EXTRACTED_DATASETS_DIR) if cache_dir else config.EXTRACTED_DATASETS_PATH
)
self.extractor = Extractor
def _get_output_path(self, path: str) -> str:
from .file_utils import hash_url_to_filename
# Path where we extract compressed archives
# We extract in the cache dir, and get the extracted path name by hashing the original path"
abs_path = os.path.abspath(path)
return os.path.join(self.extract_dir, hash_url_to_filename(abs_path))
def _do_extract(self, output_path: str, force_extract: bool) -> bool:
return force_extract or (
not os.path.isfile(output_path) and not (os.path.isdir(output_path) and os.listdir(output_path))
)
def extract(self, input_path: str, force_extract: bool = False) -> str:
extractor_format = self.extractor.infer_extractor_format(input_path)
if not extractor_format:
return input_path
output_path = self._get_output_path(input_path)
if self._do_extract(output_path, force_extract):
self.extractor.extract(input_path, output_path, extractor_format)
return output_path
class BaseExtractor(ABC):
@classmethod
@abstractmethod
def is_extractable(cls, path: Union[Path, str], **kwargs) -> bool: ...
@staticmethod
@abstractmethod
def extract(input_path: Union[Path, str], output_path: Union[Path, str]) -> None: ...
class MagicNumberBaseExtractor(BaseExtractor, ABC):
magic_numbers: list[bytes] = []
@staticmethod
def read_magic_number(path: Union[Path, str], magic_number_length: int):
with open(path, "rb") as f:
return f.read(magic_number_length)
@classmethod
def is_extractable(cls, path: Union[Path, str], magic_number: bytes = b"") -> bool:
if not magic_number:
magic_number_length = max(len(cls_magic_number) for cls_magic_number in cls.magic_numbers)
try:
magic_number = cls.read_magic_number(path, magic_number_length)
except OSError:
return False
return any(magic_number.startswith(cls_magic_number) for cls_magic_number in cls.magic_numbers)
class TarExtractor(BaseExtractor):
@classmethod
def is_extractable(cls, path: Union[Path, str], **kwargs) -> bool:
return tarfile.is_tarfile(path)
@staticmethod
def safemembers(members, output_path):
"""
Fix for CVE-2007-4559
Desc:
Directory traversal vulnerability in the (1) extract and (2) extractall functions in the tarfile
module in Python allows user-assisted remote attackers to overwrite arbitrary files via a .. (dot dot)
sequence in filenames in a TAR archive, a related issue to CVE-2001-1267.
See: https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2007-4559
From: https://stackoverflow.com/a/10077309
"""
def resolved(path: str) -> str:
return os.path.realpath(os.path.abspath(path))
def badpath(path: str, base: str) -> bool:
# joinpath will ignore base if path is absolute
return not resolved(os.path.join(base, path)).startswith(base)
def badlink(info, base: str) -> bool:
# Links are interpreted relative to the directory containing the link
tip = resolved(os.path.join(base, os.path.dirname(info.name)))
return badpath(info.linkname, base=tip)
base = resolved(output_path)
for finfo in members:
if badpath(finfo.name, base):
logger.error(f"Extraction of {finfo.name} is blocked (illegal path)")
elif finfo.issym() and badlink(finfo, base):
logger.error(f"Extraction of {finfo.name} is blocked: Symlink to {finfo.linkname}")
elif finfo.islnk() and badlink(finfo, base):
logger.error(f"Extraction of {finfo.name} is blocked: Hard link to {finfo.linkname}")
else:
yield finfo
@staticmethod
def extract(input_path: Union[Path, str], output_path: Union[Path, str]) -> None:
os.makedirs(output_path, exist_ok=True)
tar_file = tarfile.open(input_path)
tar_file.extractall(output_path, members=TarExtractor.safemembers(tar_file, output_path))
tar_file.close()
class GzipExtractor(MagicNumberBaseExtractor):
magic_numbers = [b"\x1f\x8b"]
@staticmethod
def extract(input_path: Union[Path, str], output_path: Union[Path, str]) -> None:
with gzip.open(input_path, "rb") as gzip_file:
with open(output_path, "wb") as extracted_file:
shutil.copyfileobj(gzip_file, extracted_file)
class ZipExtractor(MagicNumberBaseExtractor):
magic_numbers = [
b"PK\x03\x04",
b"PK\x05\x06", # empty archive
b"PK\x07\x08", # spanned archive
]
@classmethod
def is_extractable(cls, path: Union[Path, str], magic_number: bytes = b"") -> bool:
if super().is_extractable(path, magic_number=magic_number):
return True
try:
# Alternative version of zipfile.is_zipfile that has less false positives, but misses executable zip archives.
# From: https://github.com/python/cpython/pull/5053
from zipfile import (
_CD_SIGNATURE,
_ECD_DISK_NUMBER,
_ECD_DISK_START,
_ECD_ENTRIES_TOTAL,
_ECD_OFFSET,
_ECD_SIZE,
_EndRecData,
sizeCentralDir,
stringCentralDir,
structCentralDir,
)
with open(path, "rb") as fp:
endrec = _EndRecData(fp)
if endrec:
if endrec[_ECD_ENTRIES_TOTAL] == 0 and endrec[_ECD_SIZE] == 0 and endrec[_ECD_OFFSET] == 0:
return True # Empty zipfiles are still zipfiles
elif endrec[_ECD_DISK_NUMBER] == endrec[_ECD_DISK_START]:
fp.seek(endrec[_ECD_OFFSET]) # Central directory is on the same disk
if fp.tell() == endrec[_ECD_OFFSET] and endrec[_ECD_SIZE] >= sizeCentralDir:
data = fp.read(sizeCentralDir) # CD is where we expect it to be
if len(data) == sizeCentralDir:
centdir = struct.unpack(structCentralDir, data) # CD is the right size
if centdir[_CD_SIGNATURE] == stringCentralDir:
return True # First central directory entry has correct magic number
return False
except Exception: # catch all errors in case future python versions change the zipfile internals
return False
@staticmethod
def extract(input_path: Union[Path, str], output_path: Union[Path, str]) -> None:
os.makedirs(output_path, exist_ok=True)
with zipfile.ZipFile(input_path, "r") as zip_file:
zip_file.extractall(output_path)
zip_file.close()
class XzExtractor(MagicNumberBaseExtractor):
magic_numbers = [b"\xfd\x37\x7a\x58\x5a\x00"]
@staticmethod
def extract(input_path: Union[Path, str], output_path: Union[Path, str]) -> None:
with lzma.open(input_path) as compressed_file:
with open(output_path, "wb") as extracted_file:
shutil.copyfileobj(compressed_file, extracted_file)
class RarExtractor(MagicNumberBaseExtractor):
magic_numbers = [b"Rar!\x1a\x07\x00", b"Rar!\x1a\x07\x01\x00"] # RAR_ID # RAR5_ID
@staticmethod
def extract(input_path: Union[Path, str], output_path: Union[Path, str]) -> None:
if not config.RARFILE_AVAILABLE:
raise ImportError("Please pip install rarfile")
import rarfile
os.makedirs(output_path, exist_ok=True)
rf = rarfile.RarFile(input_path)
rf.extractall(output_path)
rf.close()
class ZstdExtractor(MagicNumberBaseExtractor):
magic_numbers = [b"\x28\xb5\x2f\xfd"]
@staticmethod
def extract(input_path: Union[Path, str], output_path: Union[Path, str]) -> None:
if not config.ZSTANDARD_AVAILABLE:
raise ImportError("Please pip install zstandard")
import zstandard as zstd
dctx = zstd.ZstdDecompressor()
with open(input_path, "rb") as ifh, open(output_path, "wb") as ofh:
dctx.copy_stream(ifh, ofh)
class Bzip2Extractor(MagicNumberBaseExtractor):
magic_numbers = [b"\x42\x5a\x68"]
@staticmethod
def extract(input_path: Union[Path, str], output_path: Union[Path, str]) -> None:
with bz2.open(input_path, "rb") as compressed_file:
with open(output_path, "wb") as extracted_file:
shutil.copyfileobj(compressed_file, extracted_file)
class SevenZipExtractor(MagicNumberBaseExtractor):
magic_numbers = [b"\x37\x7a\xbc\xaf\x27\x1c"]
@staticmethod
def extract(input_path: Union[Path, str], output_path: Union[Path, str]) -> None:
if not config.PY7ZR_AVAILABLE:
raise ImportError("Please pip install py7zr")
import py7zr
os.makedirs(output_path, exist_ok=True)
with py7zr.SevenZipFile(input_path, "r") as archive:
archive.extractall(output_path)
class Lz4Extractor(MagicNumberBaseExtractor):
magic_numbers = [b"\x04\x22\x4d\x18"]
@staticmethod
def extract(input_path: Union[Path, str], output_path: Union[Path, str]) -> None:
if not config.LZ4_AVAILABLE:
raise ImportError("Please pip install lz4")
import lz4.frame
with lz4.frame.open(input_path, "rb") as compressed_file:
with open(output_path, "wb") as extracted_file:
shutil.copyfileobj(compressed_file, extracted_file)
class Extractor:
# Put zip file to the last, b/c it is possible wrongly detected as zip (I guess it means: as tar or gzip)
extractors: dict[str, type[BaseExtractor]] = {
"tar": TarExtractor,
"gzip": GzipExtractor,
"zip": ZipExtractor,
"xz": XzExtractor,
"rar": RarExtractor,
"zstd": ZstdExtractor,
"bz2": Bzip2Extractor,
"7z": SevenZipExtractor, # <Added version="2.4.0"/>
"lz4": Lz4Extractor, # <Added version="2.4.0"/>
}
@classmethod
def _get_magic_number_max_length(cls):
return max(
len(extractor_magic_number)
for extractor in cls.extractors.values()
if issubclass(extractor, MagicNumberBaseExtractor)
for extractor_magic_number in extractor.magic_numbers
)
@staticmethod
def _read_magic_number(path: Union[Path, str], magic_number_length: int):
try:
return MagicNumberBaseExtractor.read_magic_number(path, magic_number_length=magic_number_length)
except OSError:
return b""
@classmethod
def is_extractable(cls, path: Union[Path, str], return_extractor: bool = False) -> bool:
warnings.warn(
"Method 'is_extractable' was deprecated in version 2.4.0 and will be removed in 3.0.0. "
"Use 'infer_extractor_format' instead.",
category=FutureWarning,
)
extractor_format = cls.infer_extractor_format(path)
if extractor_format:
return True if not return_extractor else (True, cls.extractors[extractor_format])
return False if not return_extractor else (False, None)
@classmethod
def infer_extractor_format(cls, path: Union[Path, str]) -> Optional[str]: # <Added version="2.4.0"/>
magic_number_max_length = cls._get_magic_number_max_length()
magic_number = cls._read_magic_number(path, magic_number_max_length)
for extractor_format, extractor in cls.extractors.items():
if extractor.is_extractable(path, magic_number=magic_number):
return extractor_format
@classmethod
def extract(
cls,
input_path: Union[Path, str],
output_path: Union[Path, str],
extractor_format: str,
) -> None:
os.makedirs(os.path.dirname(output_path), exist_ok=True)
# Prevent parallel extractions
lock_path = str(Path(output_path).with_suffix(".lock"))
with FileLock(lock_path):
shutil.rmtree(output_path, ignore_errors=True)
extractor = cls.extractors[extractor_format]
return extractor.extract(input_path, output_path)
| datasets/src/datasets/utils/extract.py/0 | {
"file_path": "datasets/src/datasets/utils/extract.py",
"repo_id": "datasets",
"token_count": 5801
} | 107 |
import numpy as np
def approximate_mode(class_counts, n_draws, rng):
"""Computes approximate mode of multivariate hypergeometric.
This is an approximation to the mode of the multivariate
hypergeometric given by class_counts and n_draws.
It shouldn't be off by more than one.
It is the mostly likely outcome of drawing n_draws many
samples from the population given by class_counts.
Args
----------
class_counts : ndarray of int
Population per class.
n_draws : int
Number of draws (samples to draw) from the overall population.
rng : random state
Used to break ties.
Returns
-------
sampled_classes : ndarray of int
Number of samples drawn from each class.
np.sum(sampled_classes) == n_draws
"""
# this computes a bad approximation to the mode of the
# multivariate hypergeometric given by class_counts and n_draws
continuous = n_draws * class_counts / class_counts.sum()
# floored means we don't overshoot n_samples, but probably undershoot
floored = np.floor(continuous)
# we add samples according to how much "left over" probability
# they had, until we arrive at n_samples
need_to_add = int(n_draws - floored.sum())
if need_to_add > 0:
remainder = continuous - floored
values = np.sort(np.unique(remainder))[::-1]
# add according to remainder, but break ties
# randomly to avoid biases
for value in values:
(inds,) = np.where(remainder == value)
# if we need_to_add less than what's in inds
# we draw randomly from them.
# if we need to add more, we add them all and
# go to the next value
add_now = min(len(inds), need_to_add)
inds = rng.choice(inds, size=add_now, replace=False)
floored[inds] += 1
need_to_add -= add_now
if need_to_add == 0:
break
return floored.astype(np.int64)
def stratified_shuffle_split_generate_indices(y, n_train, n_test, rng, n_splits=10):
"""
Provides train/test indices to split data in train/test sets.
It's reference is taken from StratifiedShuffleSplit implementation
of scikit-learn library.
Args
----------
n_train : int,
represents the absolute number of train samples.
n_test : int,
represents the absolute number of test samples.
random_state : int or RandomState instance, default=None
Controls the randomness of the training and testing indices produced.
Pass an int for reproducible output across multiple function calls.
n_splits : int, default=10
Number of re-shuffling & splitting iterations.
"""
classes, y_indices = np.unique(y, return_inverse=True)
n_classes = classes.shape[0]
class_counts = np.bincount(y_indices)
if np.min(class_counts) < 2:
raise ValueError("Minimum class count error")
if n_train < n_classes:
raise ValueError(
"The train_size = %d should be greater or equal to the number of classes = %d" % (n_train, n_classes)
)
if n_test < n_classes:
raise ValueError(
"The test_size = %d should be greater or equal to the number of classes = %d" % (n_test, n_classes)
)
class_indices = np.split(np.argsort(y_indices, kind="mergesort"), np.cumsum(class_counts)[:-1])
for _ in range(n_splits):
n_i = approximate_mode(class_counts, n_train, rng)
class_counts_remaining = class_counts - n_i
t_i = approximate_mode(class_counts_remaining, n_test, rng)
train = []
test = []
for i in range(n_classes):
permutation = rng.permutation(class_counts[i])
perm_indices_class_i = class_indices[i].take(permutation, mode="clip")
train.extend(perm_indices_class_i[: n_i[i]])
test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]])
train = rng.permutation(train)
test = rng.permutation(test)
yield train, test
| datasets/src/datasets/utils/stratify.py/0 | {
"file_path": "datasets/src/datasets/utils/stratify.py",
"repo_id": "datasets",
"token_count": 1670
} | 108 |
import contextlib
import csv
import json
import os
import sqlite3
import tarfile
import textwrap
import zipfile
import pandas as pd
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
# dataset + arrow_file
@pytest.fixture(scope="session")
def dataset():
n = 10
features = datasets.Features(
{
"tokens": datasets.List(datasets.Value("string")),
"labels": datasets.List(datasets.ClassLabel(names=["negative", "positive"])),
"answers": {
"text": datasets.List(datasets.Value("string")),
"answer_start": datasets.List(datasets.Value("int32")),
},
"id": datasets.Value("int64"),
}
)
dataset = datasets.Dataset.from_dict(
{
"tokens": [["foo"] * 5] * n,
"labels": [[1] * 5] * n,
"answers": [{"answer_start": [97], "text": ["1976"]}] * 10,
"id": list(range(n)),
},
features=features,
)
return dataset
@pytest.fixture(scope="session")
def arrow_file(tmp_path_factory, dataset):
filename = str(tmp_path_factory.mktemp("data") / "file.arrow")
dataset.map(cache_file_name=filename)
return filename
# FILE_CONTENT + files
FILE_CONTENT = """\
Text data.
Second line of data."""
@pytest.fixture(scope="session")
def text_file_content():
return FILE_CONTENT
@pytest.fixture(scope="session")
def text_file(tmp_path_factory):
filename = tmp_path_factory.mktemp("data") / "file.txt"
data = FILE_CONTENT
with open(filename, "w") as f:
f.write(data)
return filename
@pytest.fixture(scope="session")
def bz2_file(tmp_path_factory):
import bz2
path = tmp_path_factory.mktemp("data") / "file.txt.bz2"
data = bytes(FILE_CONTENT, "utf-8")
with bz2.open(path, "wb") as f:
f.write(data)
return path
@pytest.fixture(scope="session")
def gz_file(tmp_path_factory):
import gzip
path = str(tmp_path_factory.mktemp("data") / "file.txt.gz")
data = bytes(FILE_CONTENT, "utf-8")
with gzip.open(path, "wb") as f:
f.write(data)
return path
@pytest.fixture(scope="session")
def lz4_file(tmp_path_factory):
if datasets.config.LZ4_AVAILABLE:
import lz4.frame
path = tmp_path_factory.mktemp("data") / "file.txt.lz4"
data = bytes(FILE_CONTENT, "utf-8")
with lz4.frame.open(path, "wb") as f:
f.write(data)
return path
@pytest.fixture(scope="session")
def seven_zip_file(tmp_path_factory, text_file):
if datasets.config.PY7ZR_AVAILABLE:
import py7zr
path = tmp_path_factory.mktemp("data") / "file.txt.7z"
with py7zr.SevenZipFile(path, "w") as archive:
archive.write(text_file, arcname=os.path.basename(text_file))
return path
@pytest.fixture(scope="session")
def tar_file(tmp_path_factory, text_file):
import tarfile
path = tmp_path_factory.mktemp("data") / "file.txt.tar"
with tarfile.TarFile(path, "w") as f:
f.add(text_file, arcname=os.path.basename(text_file))
return path
@pytest.fixture(scope="session")
def xz_file(tmp_path_factory):
import lzma
path = tmp_path_factory.mktemp("data") / "file.txt.xz"
data = bytes(FILE_CONTENT, "utf-8")
with lzma.open(path, "wb") as f:
f.write(data)
return path
@pytest.fixture(scope="session")
def zip_file(tmp_path_factory, text_file):
import zipfile
path = tmp_path_factory.mktemp("data") / "file.txt.zip"
with zipfile.ZipFile(path, "w") as f:
f.write(text_file, arcname=os.path.basename(text_file))
return path
@pytest.fixture(scope="session")
def zstd_file(tmp_path_factory):
if datasets.config.ZSTANDARD_AVAILABLE:
import zstandard as zstd
path = tmp_path_factory.mktemp("data") / "file.txt.zst"
data = bytes(FILE_CONTENT, "utf-8")
with zstd.open(path, "wb") as f:
f.write(data)
return path
# xml_file
@pytest.fixture(scope="session")
def xml_file(tmp_path_factory):
filename = tmp_path_factory.mktemp("data") / "file.xml"
data = textwrap.dedent(
"""\
<?xml version="1.0" encoding="UTF-8" ?>
<tmx version="1.4">
<header segtype="sentence" srclang="ca" />
<body>
<tu>
<tuv xml:lang="ca"><seg>Contingut 1</seg></tuv>
<tuv xml:lang="en"><seg>Content 1</seg></tuv>
</tu>
<tu>
<tuv xml:lang="ca"><seg>Contingut 2</seg></tuv>
<tuv xml:lang="en"><seg>Content 2</seg></tuv>
</tu>
<tu>
<tuv xml:lang="ca"><seg>Contingut 3</seg></tuv>
<tuv xml:lang="en"><seg>Content 3</seg></tuv>
</tu>
<tu>
<tuv xml:lang="ca"><seg>Contingut 4</seg></tuv>
<tuv xml:lang="en"><seg>Content 4</seg></tuv>
</tu>
<tu>
<tuv xml:lang="ca"><seg>Contingut 5</seg></tuv>
<tuv xml:lang="en"><seg>Content 5</seg></tuv>
</tu>
</body>
</tmx>"""
)
with open(filename, "w") as f:
f.write(data)
return filename
DATA = [
{"col_1": "0", "col_2": 0, "col_3": 0.0},
{"col_1": "1", "col_2": 1, "col_3": 1.0},
{"col_1": "2", "col_2": 2, "col_3": 2.0},
{"col_1": "3", "col_2": 3, "col_3": 3.0},
]
DATA2 = [
{"col_1": "4", "col_2": 4, "col_3": 4.0},
{"col_1": "5", "col_2": 5, "col_3": 5.0},
]
DATA_DICT_OF_LISTS = {
"col_1": ["0", "1", "2", "3"],
"col_2": [0, 1, 2, 3],
"col_3": [0.0, 1.0, 2.0, 3.0],
}
DATA_312 = [
{"col_3": 0.0, "col_1": "0", "col_2": 0},
{"col_3": 1.0, "col_1": "1", "col_2": 1},
]
DATA_STR = [
{"col_1": "s0", "col_2": 0, "col_3": 0.0},
{"col_1": "s1", "col_2": 1, "col_3": 1.0},
{"col_1": "s2", "col_2": 2, "col_3": 2.0},
{"col_1": "s3", "col_2": 3, "col_3": 3.0},
]
@pytest.fixture(scope="session")
def dataset_dict():
return DATA_DICT_OF_LISTS
@pytest.fixture(scope="session")
def arrow_path(tmp_path_factory):
dataset = datasets.Dataset.from_dict(DATA_DICT_OF_LISTS)
path = str(tmp_path_factory.mktemp("data") / "dataset.arrow")
dataset.map(cache_file_name=path)
return path
@pytest.fixture(scope="session")
def sqlite_path(tmp_path_factory):
path = str(tmp_path_factory.mktemp("data") / "dataset.sqlite")
with contextlib.closing(sqlite3.connect(path)) as con:
cur = con.cursor()
cur.execute("CREATE TABLE dataset(col_1 text, col_2 int, col_3 real)")
for item in DATA:
cur.execute("INSERT INTO dataset(col_1, col_2, col_3) VALUES (?, ?, ?)", tuple(item.values()))
con.commit()
return path
@pytest.fixture(scope="session")
def csv_path(tmp_path_factory):
path = str(tmp_path_factory.mktemp("data") / "dataset.csv")
with open(path, "w", newline="") as f:
writer = csv.DictWriter(f, fieldnames=["col_1", "col_2", "col_3"])
writer.writeheader()
for item in DATA:
writer.writerow(item)
return path
@pytest.fixture(scope="session")
def csv2_path(tmp_path_factory):
path = str(tmp_path_factory.mktemp("data") / "dataset2.csv")
with open(path, "w", newline="") as f:
writer = csv.DictWriter(f, fieldnames=["col_1", "col_2", "col_3"])
writer.writeheader()
for item in DATA:
writer.writerow(item)
return path
@pytest.fixture(scope="session")
def bz2_csv_path(csv_path, tmp_path_factory):
import bz2
path = tmp_path_factory.mktemp("data") / "dataset.csv.bz2"
with open(csv_path, "rb") as f:
data = f.read()
# data = bytes(FILE_CONTENT, "utf-8")
with bz2.open(path, "wb") as f:
f.write(data)
return path
@pytest.fixture(scope="session")
def zip_csv_path(csv_path, csv2_path, tmp_path_factory):
path = tmp_path_factory.mktemp("zip_csv_path") / "csv-dataset.zip"
with zipfile.ZipFile(path, "w") as f:
f.write(csv_path, arcname=os.path.basename(csv_path))
f.write(csv2_path, arcname=os.path.basename(csv2_path))
return path
@pytest.fixture(scope="session")
def zip_uppercase_csv_path(csv_path, csv2_path, tmp_path_factory):
path = tmp_path_factory.mktemp("data") / "dataset.csv.zip"
with zipfile.ZipFile(path, "w") as f:
f.write(csv_path, arcname=os.path.basename(csv_path.replace(".csv", ".CSV")))
f.write(csv2_path, arcname=os.path.basename(csv2_path.replace(".csv", ".CSV")))
return path
@pytest.fixture(scope="session")
def zip_csv_with_dir_path(csv_path, csv2_path, tmp_path_factory):
path = tmp_path_factory.mktemp("data") / "dataset_with_dir.csv.zip"
with zipfile.ZipFile(path, "w") as f:
f.write(csv_path, arcname=os.path.join("main_dir", os.path.basename(csv_path)))
f.write(csv2_path, arcname=os.path.join("main_dir", os.path.basename(csv2_path)))
return path
@pytest.fixture(scope="session")
def parquet_path(tmp_path_factory):
path = str(tmp_path_factory.mktemp("data") / "dataset.parquet")
schema = pa.schema(
{
"col_1": pa.string(),
"col_2": pa.int64(),
"col_3": pa.float64(),
}
)
with open(path, "wb") as f:
writer = pq.ParquetWriter(f, schema=schema)
pa_table = pa.Table.from_pydict({k: [DATA[i][k] for i in range(len(DATA))] for k in DATA[0]}, schema=schema)
writer.write_table(pa_table)
writer.close()
return path
@pytest.fixture(scope="session")
def geoparquet_path(tmp_path_factory):
df = pd.read_parquet(path="https://github.com/opengeospatial/geoparquet/raw/v1.0.0/examples/example.parquet")
path = str(tmp_path_factory.mktemp("data") / "dataset.geoparquet")
df.to_parquet(path=path)
return path
@pytest.fixture(scope="session")
def json_list_of_dicts_path(tmp_path_factory):
path = str(tmp_path_factory.mktemp("data") / "dataset.json")
data = {"data": DATA}
with open(path, "w") as f:
json.dump(data, f)
return path
@pytest.fixture(scope="session")
def json_dict_of_lists_path(tmp_path_factory):
path = str(tmp_path_factory.mktemp("data") / "dataset.json")
data = {"data": DATA_DICT_OF_LISTS}
with open(path, "w") as f:
json.dump(data, f)
return path
@pytest.fixture(scope="session")
def jsonl_path(tmp_path_factory):
path = str(tmp_path_factory.mktemp("data") / "dataset.jsonl")
with open(path, "w") as f:
for item in DATA:
f.write(json.dumps(item) + "\n")
return path
@pytest.fixture(scope="session")
def jsonl2_path(tmp_path_factory):
path = str(tmp_path_factory.mktemp("data") / "dataset2.jsonl")
with open(path, "w") as f:
for item in DATA:
f.write(json.dumps(item) + "\n")
return path
@pytest.fixture(scope="session")
def jsonl_312_path(tmp_path_factory):
path = str(tmp_path_factory.mktemp("data") / "dataset_312.jsonl")
with open(path, "w") as f:
for item in DATA_312:
f.write(json.dumps(item) + "\n")
return path
@pytest.fixture(scope="session")
def jsonl_str_path(tmp_path_factory):
path = str(tmp_path_factory.mktemp("data") / "dataset-str.jsonl")
with open(path, "w") as f:
for item in DATA_STR:
f.write(json.dumps(item) + "\n")
return path
@pytest.fixture(scope="session")
def text_gz_path(tmp_path_factory, text_path):
import gzip
path = str(tmp_path_factory.mktemp("data") / "dataset.txt.gz")
with open(text_path, "rb") as orig_file:
with gzip.open(path, "wb") as zipped_file:
zipped_file.writelines(orig_file)
return path
@pytest.fixture(scope="session")
def jsonl_gz_path(tmp_path_factory, jsonl_path):
import gzip
path = str(tmp_path_factory.mktemp("data") / "dataset.jsonl.gz")
with open(jsonl_path, "rb") as orig_file:
with gzip.open(path, "wb") as zipped_file:
zipped_file.writelines(orig_file)
return path
@pytest.fixture(scope="session")
def zip_jsonl_path(jsonl_path, jsonl2_path, tmp_path_factory):
path = tmp_path_factory.mktemp("data") / "dataset.jsonl.zip"
with zipfile.ZipFile(path, "w") as f:
f.write(jsonl_path, arcname=os.path.basename(jsonl_path))
f.write(jsonl2_path, arcname=os.path.basename(jsonl2_path))
return path
@pytest.fixture(scope="session")
def zip_nested_jsonl_path(zip_jsonl_path, jsonl_path, jsonl2_path, tmp_path_factory):
path = tmp_path_factory.mktemp("data") / "dataset_nested.jsonl.zip"
with zipfile.ZipFile(path, "w") as f:
f.write(zip_jsonl_path, arcname=os.path.join("nested", os.path.basename(zip_jsonl_path)))
return path
@pytest.fixture(scope="session")
def zip_jsonl_with_dir_path(jsonl_path, jsonl2_path, tmp_path_factory):
path = tmp_path_factory.mktemp("data") / "dataset_with_dir.jsonl.zip"
with zipfile.ZipFile(path, "w") as f:
f.write(jsonl_path, arcname=os.path.join("main_dir", os.path.basename(jsonl_path)))
f.write(jsonl2_path, arcname=os.path.join("main_dir", os.path.basename(jsonl2_path)))
return path
@pytest.fixture(scope="session")
def tar_jsonl_path(jsonl_path, jsonl2_path, tmp_path_factory):
path = tmp_path_factory.mktemp("data") / "dataset.jsonl.tar"
with tarfile.TarFile(path, "w") as f:
f.add(jsonl_path, arcname=os.path.basename(jsonl_path))
f.add(jsonl2_path, arcname=os.path.basename(jsonl2_path))
return path
@pytest.fixture(scope="session")
def tar_nested_jsonl_path(tar_jsonl_path, jsonl_path, jsonl2_path, tmp_path_factory):
path = tmp_path_factory.mktemp("data") / "dataset_nested.jsonl.tar"
with tarfile.TarFile(path, "w") as f:
f.add(tar_jsonl_path, arcname=os.path.join("nested", os.path.basename(tar_jsonl_path)))
return path
@pytest.fixture(scope="session")
def text_path(tmp_path_factory):
data = ["0", "1", "2", "3"]
path = str(tmp_path_factory.mktemp("data") / "dataset.txt")
with open(path, "w") as f:
for item in data:
f.write(item + "\n")
return path
@pytest.fixture(scope="session")
def text2_path(tmp_path_factory):
data = ["0", "1", "2", "3"]
path = str(tmp_path_factory.mktemp("data") / "dataset2.txt")
with open(path, "w") as f:
for item in data:
f.write(item + "\n")
return path
@pytest.fixture(scope="session")
def text_dir(tmp_path_factory):
data = ["0", "1", "2", "3"]
path = tmp_path_factory.mktemp("data_text_dir") / "dataset.txt"
with open(path, "w") as f:
for item in data:
f.write(item + "\n")
return path.parent
@pytest.fixture(scope="session")
def text_dir_with_unsupported_extension(tmp_path_factory):
data = ["0", "1", "2", "3"]
path = tmp_path_factory.mktemp("data") / "dataset.abc"
with open(path, "w") as f:
for item in data:
f.write(item + "\n")
return path
@pytest.fixture(scope="session")
def zip_text_path(text_path, text2_path, tmp_path_factory):
path = tmp_path_factory.mktemp("data") / "dataset.text.zip"
with zipfile.ZipFile(path, "w") as f:
f.write(text_path, arcname=os.path.basename(text_path))
f.write(text2_path, arcname=os.path.basename(text2_path))
return path
@pytest.fixture(scope="session")
def zip_text_with_dir_path(text_path, text2_path, tmp_path_factory):
path = tmp_path_factory.mktemp("data") / "dataset_with_dir.text.zip"
with zipfile.ZipFile(path, "w") as f:
f.write(text_path, arcname=os.path.join("main_dir", os.path.basename(text_path)))
f.write(text2_path, arcname=os.path.join("main_dir", os.path.basename(text2_path)))
return path
@pytest.fixture(scope="session")
def zip_unsupported_ext_path(text_path, text2_path, tmp_path_factory):
path = tmp_path_factory.mktemp("data") / "dataset.ext.zip"
with zipfile.ZipFile(path, "w") as f:
f.write(text_path, arcname=os.path.basename("unsupported.ext"))
f.write(text2_path, arcname=os.path.basename("unsupported_2.ext"))
return path
@pytest.fixture(scope="session")
def text_path_with_unicode_new_lines(tmp_path_factory):
text = "\n".join(["First", "Second\u2029with Unicode new line", "Third"])
path = str(tmp_path_factory.mktemp("data") / "dataset_with_unicode_new_lines.txt")
with open(path, "w", encoding="utf-8") as f:
f.write(text)
return path
@pytest.fixture(scope="session")
def image_file():
return os.path.join("tests", "features", "data", "test_image_rgb.jpg")
@pytest.fixture(scope="session")
def audio_file():
return os.path.join("tests", "features", "data", "test_audio_44100.wav")
@pytest.fixture(scope="session")
def audio_file_44100():
return os.path.join("tests", "features", "data", "test_audio_44100.mp3")
@pytest.fixture(scope="session")
def audio_file_16000():
return os.path.join("tests", "features", "data", "test_audio_16000.mp3")
@pytest.fixture(scope="session")
def tensor_file(tmp_path_factory):
import torch
path = tmp_path_factory.mktemp("data") / "tensor.pth"
with open(path, "wb") as f:
torch.save(torch.ones(128), f)
return path
@pytest.fixture(scope="session")
def zip_image_path(image_file, tmp_path_factory):
path = tmp_path_factory.mktemp("data") / "dataset.img.zip"
with zipfile.ZipFile(path, "w") as f:
f.write(image_file, arcname=os.path.basename(image_file))
f.write(image_file, arcname=os.path.basename(image_file).replace(".jpg", "2.jpg"))
return path
@pytest.fixture(scope="session")
def data_dir_with_hidden_files(tmp_path_factory):
data_dir = tmp_path_factory.mktemp("data_dir")
(data_dir / "subdir").mkdir()
with open(data_dir / "subdir" / "train.txt", "w") as f:
f.write("foo\n" * 10)
with open(data_dir / "subdir" / "test.txt", "w") as f:
f.write("bar\n" * 10)
# hidden file
with open(data_dir / "subdir" / ".test.txt", "w") as f:
f.write("bar\n" * 10)
# hidden directory
(data_dir / ".subdir").mkdir()
with open(data_dir / ".subdir" / "train.txt", "w") as f:
f.write("foo\n" * 10)
with open(data_dir / ".subdir" / "test.txt", "w") as f:
f.write("bar\n" * 10)
return data_dir
| datasets/tests/fixtures/files.py/0 | {
"file_path": "datasets/tests/fixtures/files.py",
"repo_id": "datasets",
"token_count": 8409
} | 109 |
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.builder import InvalidConfigName
from datasets.data_files import DataFilesList
from datasets.packaged_modules.csv.csv import Csv, CsvConfig
from ..utils import require_pil
@pytest.fixture
def csv_file(tmp_path):
filename = tmp_path / "file.csv"
data = textwrap.dedent(
"""\
header1,header2
1,2
10,20
"""
)
with open(filename, "w") as f:
f.write(data)
return str(filename)
@pytest.fixture
def malformed_csv_file(tmp_path):
filename = tmp_path / "malformed_file.csv"
data = textwrap.dedent(
"""\
header1,header2
1,2
10,20,
"""
)
with open(filename, "w") as f:
f.write(data)
return str(filename)
@pytest.fixture
def csv_file_with_image(tmp_path, image_file):
filename = tmp_path / "csv_with_image.csv"
data = textwrap.dedent(
f"""\
image
{image_file}
"""
)
with open(filename, "w") as f:
f.write(data)
return str(filename)
@pytest.fixture
def csv_file_with_label(tmp_path):
filename = tmp_path / "csv_with_label.csv"
data = textwrap.dedent(
"""\
label
good
bad
good
"""
)
with open(filename, "w") as f:
f.write(data)
return str(filename)
@pytest.fixture
def csv_file_with_int_list(tmp_path):
filename = tmp_path / "csv_with_int_list.csv"
data = textwrap.dedent(
"""\
int_list
1 2 3
4 5 6
7 8 9
"""
)
with open(filename, "w") as f:
f.write(data)
return str(filename)
def test_config_raises_when_invalid_name() -> None:
with pytest.raises(InvalidConfigName, match="Bad characters"):
_ = CsvConfig(name="name-with-*-invalid-character")
@pytest.mark.parametrize("data_files", ["str_path", ["str_path"], DataFilesList(["str_path"], [()])])
def test_config_raises_when_invalid_data_files(data_files) -> None:
with pytest.raises(ValueError, match="Expected a DataFilesDict"):
_ = CsvConfig(name="name", data_files=data_files)
def test_csv_generate_tables_raises_error_with_malformed_csv(csv_file, malformed_csv_file, caplog):
csv = Csv()
generator = csv._generate_tables([[csv_file, malformed_csv_file]])
with pytest.raises(ValueError, match="Error tokenizing data"):
for _ in generator:
pass
assert any(
record.levelname == "ERROR"
and "Failed to read file" in record.message
and os.path.basename(malformed_csv_file) in record.message
for record in caplog.records
)
@require_pil
def test_csv_cast_image(csv_file_with_image):
with open(csv_file_with_image, encoding="utf-8") as f:
image_file = f.read().splitlines()[1]
csv = Csv(encoding="utf-8", features=Features({"image": Image()}))
generator = csv._generate_tables([[csv_file_with_image]])
pa_table = pa.concat_tables([table for _, table in generator])
assert pa_table.schema.field("image").type == Image()()
generated_content = pa_table.to_pydict()["image"]
assert generated_content == [{"path": image_file, "bytes": None}]
def test_csv_cast_label(csv_file_with_label):
with open(csv_file_with_label, encoding="utf-8") as f:
labels = f.read().splitlines()[1:]
csv = Csv(encoding="utf-8", features=Features({"label": ClassLabel(names=["good", "bad"])}))
generator = csv._generate_tables([[csv_file_with_label]])
pa_table = pa.concat_tables([table for _, table in generator])
assert pa_table.schema.field("label").type == ClassLabel(names=["good", "bad"])()
generated_content = pa_table.to_pydict()["label"]
assert generated_content == [ClassLabel(names=["good", "bad"]).str2int(label) for label in labels]
def test_csv_convert_int_list(csv_file_with_int_list):
csv = Csv(encoding="utf-8", sep=",", converters={"int_list": lambda x: [int(i) for i in x.split()]})
generator = csv._generate_tables([[csv_file_with_int_list]])
pa_table = pa.concat_tables([table for _, table in generator])
assert pa.types.is_list(pa_table.schema.field("int_list").type)
generated_content = pa_table.to_pydict()["int_list"]
assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
| datasets/tests/packaged_modules/test_csv.py/0 | {
"file_path": "datasets/tests/packaged_modules/test_csv.py",
"repo_id": "datasets",
"token_count": 1880
} | 110 |
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, List, Value
from datasets.iterable_dataset import IterableDataset
from datasets.splits import NamedSplit
from .utils import (
assert_arrow_memory_doesnt_increase,
assert_arrow_memory_increases,
require_numpy1_on_windows,
require_polars,
require_tf,
require_torch,
)
class DatasetDictTest(TestCase):
def _create_dummy_dataset(self, multiple_columns=False, int_to_float=False):
if multiple_columns:
data = {"col_1": [3, 2, 1, 0], "col_2": ["a", "b", "c", "d"]}
dset = Dataset.from_dict(data)
elif int_to_float:
data = {
"text": ["text1", "text2", "text3", "text4"],
"labels": [[1, 1, 1, 0, 0], [0, 0, 0, 1, 0], [0, 0, 0, 1, 1], [0, 0, 0, 1, 0]],
}
dset = Dataset.from_dict(data)
else:
dset = Dataset.from_dict(
{"filename": ["my_name-train" + "_" + f"{x:03d}" for x in np.arange(30).tolist()]}
)
return dset
def _create_dummy_dataset_dict(self, multiple_columns=False, int_to_float=False) -> DatasetDict:
return DatasetDict(
{
"train": self._create_dummy_dataset(multiple_columns=multiple_columns, int_to_float=int_to_float),
"test": self._create_dummy_dataset(multiple_columns=multiple_columns, int_to_float=int_to_float),
}
)
def _create_dummy_iterable_dataset(self, multiple_columns=False) -> IterableDataset:
def gen():
if multiple_columns:
data = {"col_1": [3, 2, 1, 0], "col_2": ["a", "b", "c", "d"]}
for v1, v2 in zip(data["col_1"], data["col_2"]):
yield {"col_1": v1, "col_2": v2}
else:
for x in range(30):
yield {"filename": "my_name-train" + "_" + f"{x:03d}"}
return IterableDataset.from_generator(gen)
def _create_dummy_iterable_dataset_dict(self, multiple_columns=False) -> IterableDatasetDict:
return IterableDatasetDict(
{
"train": self._create_dummy_iterable_dataset(multiple_columns=multiple_columns),
"test": self._create_dummy_iterable_dataset(multiple_columns=multiple_columns),
}
)
def test_flatten(self):
dset_split = Dataset.from_dict(
{"a": [{"b": {"c": ["text"]}}] * 10, "foo": [1] * 10},
features=Features({"a": {"b": {"c": List(Value("string"))}}, "foo": Value("int64")}),
)
dset = DatasetDict({"train": dset_split, "test": dset_split})
dset = dset.flatten()
self.assertDictEqual(dset.column_names, {"train": ["a.b.c", "foo"], "test": ["a.b.c", "foo"]})
self.assertListEqual(sorted(dset["train"].features.keys()), ["a.b.c", "foo"])
self.assertDictEqual(dset["train"].features, Features({"a.b.c": List(Value("string")), "foo": Value("int64")}))
del dset
def test_set_format_numpy(self):
dset = self._create_dummy_dataset_dict(multiple_columns=True)
dset.set_format(type="numpy", columns=["col_1"])
for dset_split in dset.values():
self.assertEqual(len(dset_split[0]), 1)
self.assertIsInstance(dset_split[0]["col_1"], np.int64)
self.assertEqual(dset_split[0]["col_1"].item(), 3)
dset.reset_format()
with dset.formatted_as(type="numpy", columns=["col_1"]):
for dset_split in dset.values():
self.assertEqual(len(dset_split[0]), 1)
self.assertIsInstance(dset_split[0]["col_1"], np.int64)
self.assertEqual(dset_split[0]["col_1"].item(), 3)
for dset_split in dset.values():
self.assertEqual(dset_split.format["type"], None)
self.assertEqual(dset_split.format["format_kwargs"], {})
self.assertEqual(dset_split.format["columns"], dset_split.column_names)
self.assertEqual(dset_split.format["output_all_columns"], False)
dset.set_format(type="numpy", columns=["col_1"], output_all_columns=True)
for dset_split in dset.values():
self.assertEqual(len(dset_split[0]), 2)
self.assertIsInstance(dset_split[0]["col_2"], str)
self.assertEqual(dset_split[0]["col_2"], "a")
dset.set_format(type="numpy", columns=["col_1", "col_2"])
for dset_split in dset.values():
self.assertEqual(len(dset_split[0]), 2)
self.assertIsInstance(dset_split[0]["col_2"], np.str_)
self.assertEqual(dset_split[0]["col_2"].item(), "a")
del dset
@require_numpy1_on_windows
@require_torch
def test_set_format_torch(self):
import torch
dset = self._create_dummy_dataset_dict(multiple_columns=True)
dset.set_format(type="torch", columns=["col_1"])
for dset_split in dset.values():
self.assertEqual(len(dset_split[0]), 1)
self.assertIsInstance(dset_split[0]["col_1"], torch.Tensor)
self.assertListEqual(list(dset_split[0]["col_1"].shape), [])
self.assertEqual(dset_split[0]["col_1"].item(), 3)
dset.set_format(type="torch", columns=["col_1"], output_all_columns=True)
for dset_split in dset.values():
self.assertEqual(len(dset_split[0]), 2)
self.assertIsInstance(dset_split[0]["col_2"], str)
self.assertEqual(dset_split[0]["col_2"], "a")
dset.set_format(type="torch")
for dset_split in dset.values():
self.assertEqual(len(dset_split[0]), 2)
self.assertIsInstance(dset_split[0]["col_1"], torch.Tensor)
self.assertListEqual(list(dset_split[0]["col_1"].shape), [])
self.assertEqual(dset_split[0]["col_1"].item(), 3)
self.assertIsInstance(dset_split[0]["col_2"], str)
self.assertEqual(dset_split[0]["col_2"], "a")
del dset
@require_tf
def test_set_format_tf(self):
import tensorflow as tf
dset = self._create_dummy_dataset_dict(multiple_columns=True)
dset.set_format(type="tensorflow", columns=["col_1"])
for dset_split in dset.values():
self.assertEqual(len(dset_split[0]), 1)
self.assertIsInstance(dset_split[0]["col_1"], tf.Tensor)
self.assertListEqual(list(dset_split[0]["col_1"].shape), [])
self.assertEqual(dset_split[0]["col_1"].numpy().item(), 3)
dset.set_format(type="tensorflow", columns=["col_1"], output_all_columns=True)
for dset_split in dset.values():
self.assertEqual(len(dset_split[0]), 2)
self.assertIsInstance(dset_split[0]["col_2"], str)
self.assertEqual(dset_split[0]["col_2"], "a")
dset.set_format(type="tensorflow", columns=["col_1", "col_2"])
for dset_split in dset.values():
self.assertEqual(len(dset_split[0]), 2)
self.assertEqual(dset_split[0]["col_2"].numpy().decode("utf-8"), "a")
del dset
def test_set_format_pandas(self):
dset = self._create_dummy_dataset_dict(multiple_columns=True)
dset.set_format(type="pandas", columns=["col_1"])
for dset_split in dset.values():
self.assertEqual(len(dset_split[0].columns), 1)
self.assertIsInstance(dset_split[0], pd.DataFrame)
self.assertListEqual(list(dset_split[0].shape), [1, 1])
self.assertEqual(dset_split[0]["col_1"].item(), 3)
dset.set_format(type="pandas", columns=["col_1", "col_2"])
for dset_split in dset.values():
self.assertEqual(len(dset_split[0].columns), 2)
self.assertEqual(dset_split[0]["col_2"].item(), "a")
del dset
@require_polars
def test_set_format_polars(self):
import polars as pl
dset = self._create_dummy_dataset_dict(multiple_columns=True)
dset.set_format(type="polars", columns=["col_1"])
for dset_split in dset.values():
self.assertEqual(len(dset_split[0].columns), 1)
self.assertIsInstance(dset_split[0], pl.DataFrame)
self.assertEqual(dset_split[0].shape, (1, 1))
self.assertEqual(dset_split[0]["col_1"].item(), 3)
dset.set_format(type="polars", columns=["col_1", "col_2"])
for dset_split in dset.values():
self.assertEqual(len(dset_split[0].columns), 2)
self.assertEqual(dset_split[0]["col_2"].item(), "a")
del dset
def test_set_transform(self):
def transform(batch):
return {k: [str(i).upper() for i in v] for k, v in batch.items()}
dset = self._create_dummy_dataset_dict(multiple_columns=True)
dset.set_transform(transform=transform, columns=["col_1"])
for dset_split in dset.values():
self.assertEqual(dset_split.format["type"], "custom")
self.assertEqual(len(dset_split[0].keys()), 1)
self.assertEqual(dset_split[0]["col_1"], "3")
self.assertEqual(dset_split[:2]["col_1"], ["3", "2"])
self.assertEqual(dset_split["col_1"][:2], ["3", "2"])
prev_format = dset[list(dset.keys())[0]].format
for dset_split in dset.values():
dset_split.set_format(**dset_split.format)
self.assertEqual(prev_format, dset_split.format)
dset.set_transform(transform=transform, columns=["col_1", "col_2"])
for dset_split in dset.values():
self.assertEqual(len(dset_split[0].keys()), 2)
self.assertEqual(dset_split[0]["col_2"], "A")
del dset
def test_with_format(self):
dset = self._create_dummy_dataset_dict(multiple_columns=True)
dset2 = dset.with_format("numpy", columns=["col_1"])
dset.set_format("numpy", columns=["col_1"])
for dset_split, dset_split2 in zip(dset.values(), dset2.values()):
self.assertDictEqual(dset_split.format, dset_split2.format)
del dset, dset2
def test_with_transform(self):
def transform(batch):
return {k: [str(i).upper() for i in v] for k, v in batch.items()}
dset = self._create_dummy_dataset_dict(multiple_columns=True)
dset2 = dset.with_transform(transform, columns=["col_1"])
dset.set_transform(transform, columns=["col_1"])
for dset_split, dset_split2 in zip(dset.values(), dset2.values()):
self.assertDictEqual(dset_split.format, dset_split2.format)
del dset, dset2
def test_cast(self):
dset = self._create_dummy_dataset_dict(multiple_columns=True)
features = dset["train"].features
features["col_1"] = Value("float64")
dset = dset.cast(features)
for dset_split in dset.values():
self.assertEqual(dset_split.num_columns, 2)
self.assertEqual(dset_split.features["col_1"], Value("float64"))
self.assertIsInstance(dset_split[0]["col_1"], float)
del dset
def test_remove_columns(self):
dset = self._create_dummy_dataset_dict(multiple_columns=True)
dset = dset.remove_columns(column_names="col_1")
for dset_split in dset.values():
self.assertEqual(dset_split.num_columns, 1)
self.assertListEqual(list(dset_split.column_names), ["col_2"])
dset = self._create_dummy_dataset_dict(multiple_columns=True)
dset = dset.remove_columns(column_names=["col_1", "col_2"])
for dset_split in dset.values():
self.assertEqual(dset_split.num_columns, 0)
dset = self._create_dummy_dataset_dict(multiple_columns=True)
for dset_split in dset.values():
dset_split._format_columns = ["col_1", "col_2"]
dset = dset.remove_columns(column_names=["col_1"])
for dset_split in dset.values():
self.assertListEqual(dset_split._format_columns, ["col_2"])
self.assertEqual(dset_split.num_columns, 1)
self.assertListEqual(list(dset_split.column_names), ["col_2"])
del dset
def test_rename_column(self):
dset = self._create_dummy_dataset_dict(multiple_columns=True)
dset = dset.rename_column(original_column_name="col_1", new_column_name="new_name")
for dset_split in dset.values():
self.assertEqual(dset_split.num_columns, 2)
self.assertListEqual(list(dset_split.column_names), ["new_name", "col_2"])
del dset
def test_select_columns(self):
dset = self._create_dummy_dataset_dict(multiple_columns=True)
dset = dset.select_columns(column_names=[])
for dset_split in dset.values():
self.assertEqual(dset_split.num_columns, 0)
dset = self._create_dummy_dataset_dict(multiple_columns=True)
dset = dset.select_columns(column_names="col_1")
for dset_split in dset.values():
self.assertEqual(dset_split.num_columns, 1)
self.assertListEqual(list(dset_split.column_names), ["col_1"])
dset = self._create_dummy_dataset_dict(multiple_columns=True)
dset = dset.select_columns(column_names=["col_1", "col_2"])
for dset_split in dset.values():
self.assertEqual(dset_split.num_columns, 2)
dset = self._create_dummy_dataset_dict(multiple_columns=True)
for dset_split in dset.values():
dset_split._format_columns = ["col_1", "col_2"]
dset = dset.select_columns(column_names=["col_1"])
for dset_split in dset.values():
self.assertEqual(dset_split.num_columns, 1)
self.assertListEqual(list(dset_split.column_names), ["col_1"])
self.assertListEqual(dset_split._format_columns, ["col_1"])
def test_map(self):
with tempfile.TemporaryDirectory() as tmp_dir:
dsets = self._create_dummy_dataset_dict()
mapped_dsets_1: DatasetDict = dsets.map(lambda ex: {"foo": ["bar"] * len(ex["filename"])}, batched=True)
self.assertListEqual(list(dsets.keys()), list(mapped_dsets_1.keys()))
self.assertListEqual(mapped_dsets_1["train"].column_names, ["filename", "foo"])
cache_file_names = {
"train": os.path.join(tmp_dir, "train.arrow"),
"test": os.path.join(tmp_dir, "test.arrow"),
}
mapped_dsets_2: DatasetDict = mapped_dsets_1.map(
lambda ex: {"bar": ["foo"] * len(ex["filename"])}, batched=True, cache_file_names=cache_file_names
)
self.assertListEqual(list(dsets.keys()), list(mapped_dsets_2.keys()))
self.assertListEqual(sorted(mapped_dsets_2["train"].column_names), sorted(["filename", "foo", "bar"]))
del dsets, mapped_dsets_1, mapped_dsets_2
# casting int labels to float labels
with tempfile.TemporaryDirectory() as tmp_dir:
dset_dict = self._create_dummy_dataset_dict(int_to_float=True)
def _preprocess(examples):
result = {"labels": [list(map(float, labels)) for labels in examples["labels"]]}
return result
with dset_dict.map(
_preprocess, remove_columns=["labels", "text"], batched=True, try_original_type=True
) as dset_test:
for labels in dset_test["test"]["labels"]:
for label in labels:
self.assertIsInstance(label, int)
with dset_dict.map(
_preprocess, remove_columns=["labels", "text"], batched=True, try_original_type=False
) as dset_test:
for labels in dset_test["test"]["labels"]:
for label in labels:
self.assertIsInstance(label, float)
def test_iterable_map(self):
dsets = self._create_dummy_iterable_dataset_dict()
fn_kwargs = {"n": 3}
mapped_dsets: IterableDatasetDict = dsets.map(
lambda x, n: {"foo": [n] * len(x["filename"])},
batched=True,
fn_kwargs=fn_kwargs,
)
mapped_example = next(iter(mapped_dsets["train"]))
self.assertListEqual(sorted(mapped_example.keys()), sorted(["filename", "foo"]))
self.assertLessEqual(mapped_example["foo"], 3)
del dsets, mapped_dsets
def test_filter(self):
with tempfile.TemporaryDirectory() as tmp_dir:
dsets = self._create_dummy_dataset_dict()
filtered_dsets_1: DatasetDict = dsets.filter(lambda ex: int(ex["filename"].split("_")[-1]) < 10)
self.assertListEqual(list(dsets.keys()), list(filtered_dsets_1.keys()))
self.assertEqual(len(filtered_dsets_1["train"]), 10)
cache_file_names = {
"train": os.path.join(tmp_dir, "train.arrow"),
"test": os.path.join(tmp_dir, "test.arrow"),
}
filtered_dsets_2: DatasetDict = filtered_dsets_1.filter(
lambda ex: int(ex["filename"].split("_")[-1]) < 5, cache_file_names=cache_file_names
)
self.assertListEqual(list(dsets.keys()), list(filtered_dsets_2.keys()))
self.assertEqual(len(filtered_dsets_2["train"]), 5)
filtered_dsets_3: DatasetDict = dsets.filter(
lambda examples: [int(ex.split("_")[-1]) < 10 for ex in examples["filename"]], batched=True
)
self.assertListEqual(list(dsets.keys()), list(filtered_dsets_3.keys()))
self.assertEqual(len(filtered_dsets_3["train"]), 10)
del dsets, filtered_dsets_1, filtered_dsets_2, filtered_dsets_3
def test_iterable_filter(self):
dsets = self._create_dummy_iterable_dataset_dict()
example = next(iter(dsets["train"]))
fn_kwargs = {"n": 3}
filtered_dsets: IterableDatasetDict = dsets.filter(
lambda ex, n: n < int(ex["filename"].split("_")[-1]), fn_kwargs=fn_kwargs
)
filtered_example = next(iter(filtered_dsets["train"]))
self.assertListEqual(list(example.keys()), list(filtered_example.keys()))
self.assertEqual(int(filtered_example["filename"].split("_")[-1]), 4) # id starts from 3
del dsets, filtered_dsets
def test_sort(self):
with tempfile.TemporaryDirectory() as tmp_dir:
dsets = self._create_dummy_dataset_dict()
sorted_dsets_1: DatasetDict = dsets.sort("filename")
self.assertListEqual(list(dsets.keys()), list(sorted_dsets_1.keys()))
self.assertListEqual(
[f.split("_")[-1] for f in sorted_dsets_1["train"]["filename"]],
sorted(f"{x:03d}" for x in range(30)),
)
indices_cache_file_names = {
"train": os.path.join(tmp_dir, "train.arrow"),
"test": os.path.join(tmp_dir, "test.arrow"),
}
sorted_dsets_2: DatasetDict = sorted_dsets_1.sort(
"filename", indices_cache_file_names=indices_cache_file_names, reverse=True
)
self.assertListEqual(list(dsets.keys()), list(sorted_dsets_2.keys()))
self.assertListEqual(
[f.split("_")[-1] for f in sorted_dsets_2["train"]["filename"]],
sorted((f"{x:03d}" for x in range(30)), reverse=True),
)
del dsets, sorted_dsets_1, sorted_dsets_2
def test_shuffle(self):
with tempfile.TemporaryDirectory() as tmp_dir:
dsets = self._create_dummy_dataset_dict()
indices_cache_file_names = {
"train": os.path.join(tmp_dir, "train.arrow"),
"test": os.path.join(tmp_dir, "test.arrow"),
}
seeds = {
"train": 1234,
"test": 1234,
}
dsets_shuffled = dsets.shuffle(
seeds=seeds, indices_cache_file_names=indices_cache_file_names, load_from_cache_file=False
)
self.assertSequenceEqual(dsets_shuffled["train"]["filename"], dsets_shuffled["test"]["filename"])
self.assertEqual(len(dsets_shuffled["train"]), 30)
self.assertEqual(dsets_shuffled["train"][0]["filename"], "my_name-train_028")
self.assertEqual(dsets_shuffled["train"][2]["filename"], "my_name-train_010")
self.assertDictEqual(dsets["train"].features, Features({"filename": Value("string")}))
self.assertDictEqual(dsets_shuffled["train"].features, Features({"filename": Value("string")}))
# Reproducibility
indices_cache_file_names_2 = {
"train": os.path.join(tmp_dir, "train_2.arrow"),
"test": os.path.join(tmp_dir, "test_2.arrow"),
}
dsets_shuffled_2 = dsets.shuffle(
seeds=seeds, indices_cache_file_names=indices_cache_file_names_2, load_from_cache_file=False
)
self.assertSequenceEqual(dsets_shuffled["train"]["filename"], dsets_shuffled_2["train"]["filename"])
seeds = {
"train": 1234,
"test": 1,
}
indices_cache_file_names_3 = {
"train": os.path.join(tmp_dir, "train_3.arrow"),
"test": os.path.join(tmp_dir, "test_3.arrow"),
}
dsets_shuffled_3 = dsets.shuffle(
seeds=seeds, indices_cache_file_names=indices_cache_file_names_3, load_from_cache_file=False
)
self.assertNotEqual(dsets_shuffled_3["train"]["filename"], dsets_shuffled_3["test"]["filename"])
# other input types
dsets_shuffled_int = dsets.shuffle(42)
dsets_shuffled_alias = dsets.shuffle(seed=42)
dsets_shuffled_none = dsets.shuffle()
self.assertEqual(len(dsets_shuffled_int["train"]), 30)
self.assertEqual(len(dsets_shuffled_alias["train"]), 30)
self.assertEqual(len(dsets_shuffled_none["train"]), 30)
del dsets, dsets_shuffled, dsets_shuffled_2, dsets_shuffled_3
del dsets_shuffled_int, dsets_shuffled_alias, dsets_shuffled_none
def test_flatten_indices(self):
with tempfile.TemporaryDirectory() as tmp_dir:
dsets = self._create_dummy_dataset_dict()
indices_cache_file_names = {
"train": os.path.join(tmp_dir, "train.arrow"),
"test": os.path.join(tmp_dir, "test.arrow"),
}
dsets_shuffled = dsets.shuffle(
seed=42, indices_cache_file_names=indices_cache_file_names, load_from_cache_file=False
)
self.assertIsNotNone(dsets_shuffled["train"]._indices)
self.assertIsNotNone(dsets_shuffled["test"]._indices)
dsets_flat = dsets_shuffled.flatten_indices()
self.assertIsNone(dsets_flat["train"]._indices)
self.assertIsNone(dsets_flat["test"]._indices)
del dsets, dsets_shuffled, dsets_flat
def test_check_values_type(self):
dsets = self._create_dummy_dataset_dict()
dsets["bad_split"] = None
self.assertRaises(TypeError, dsets.map, lambda x: x)
self.assertRaises(TypeError, dsets.filter, lambda x: True)
self.assertRaises(TypeError, dsets.shuffle)
self.assertRaises(TypeError, dsets.sort, "filename")
del dsets
def test_serialization(self):
with tempfile.TemporaryDirectory() as tmp_dir:
dsets = self._create_dummy_dataset_dict()
dsets.save_to_disk(tmp_dir)
reloaded_dsets = DatasetDict.load_from_disk(tmp_dir)
self.assertListEqual(sorted(reloaded_dsets), ["test", "train"])
self.assertEqual(len(reloaded_dsets["train"]), 30)
self.assertListEqual(reloaded_dsets["train"].column_names, ["filename"])
self.assertEqual(len(reloaded_dsets["test"]), 30)
self.assertListEqual(reloaded_dsets["test"].column_names, ["filename"])
del reloaded_dsets
del dsets["test"]
dsets.save_to_disk(tmp_dir)
reloaded_dsets = DatasetDict.load_from_disk(tmp_dir)
self.assertListEqual(sorted(reloaded_dsets), ["train"])
self.assertEqual(len(reloaded_dsets["train"]), 30)
self.assertListEqual(reloaded_dsets["train"].column_names, ["filename"])
del dsets, reloaded_dsets
dsets = self._create_dummy_dataset_dict()
dsets.save_to_disk(tmp_dir, num_shards={"train": 3, "test": 2})
reloaded_dsets = DatasetDict.load_from_disk(tmp_dir)
self.assertListEqual(sorted(reloaded_dsets), ["test", "train"])
self.assertEqual(len(reloaded_dsets["train"]), 30)
self.assertListEqual(reloaded_dsets["train"].column_names, ["filename"])
self.assertEqual(len(reloaded_dsets["train"].cache_files), 3)
self.assertEqual(len(reloaded_dsets["test"]), 30)
self.assertListEqual(reloaded_dsets["test"].column_names, ["filename"])
self.assertEqual(len(reloaded_dsets["test"].cache_files), 2)
del reloaded_dsets
dsets = self._create_dummy_dataset_dict()
dsets.save_to_disk(tmp_dir, num_proc=2)
reloaded_dsets = DatasetDict.load_from_disk(tmp_dir)
self.assertListEqual(sorted(reloaded_dsets), ["test", "train"])
self.assertEqual(len(reloaded_dsets["train"]), 30)
self.assertListEqual(reloaded_dsets["train"].column_names, ["filename"])
self.assertEqual(len(reloaded_dsets["train"].cache_files), 2)
self.assertEqual(len(reloaded_dsets["test"]), 30)
self.assertListEqual(reloaded_dsets["test"].column_names, ["filename"])
self.assertEqual(len(reloaded_dsets["test"].cache_files), 2)
del reloaded_dsets
def test_load_from_disk(self):
with tempfile.TemporaryDirectory() as tmp_dir:
dsets = self._create_dummy_dataset_dict()
dsets.save_to_disk(tmp_dir)
del dsets
dsets = load_from_disk(tmp_dir)
self.assertListEqual(sorted(dsets), ["test", "train"])
self.assertEqual(len(dsets["train"]), 30)
self.assertListEqual(dsets["train"].column_names, ["filename"])
self.assertEqual(len(dsets["test"]), 30)
self.assertListEqual(dsets["test"].column_names, ["filename"])
del dsets
def test_align_labels_with_mapping(self):
train_features = Features(
{
"input_text": Value("string"),
"input_labels": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]),
}
)
test_features = Features(
{
"input_text": Value("string"),
"input_labels": ClassLabel(num_classes=3, names=["entailment", "contradiction", "neutral"]),
}
)
train_data = {"input_text": ["a", "a", "b", "b", "c", "c"], "input_labels": [0, 0, 1, 1, 2, 2]}
test_data = {"input_text": ["a", "a", "c", "c", "b", "b"], "input_labels": [0, 0, 1, 1, 2, 2]}
label2id = {"CONTRADICTION": 0, "ENTAILMENT": 2, "NEUTRAL": 1}
id2label = {v: k for k, v in label2id.items()}
train_expected_labels = [2, 2, 1, 1, 0, 0]
test_expected_labels = [2, 2, 0, 0, 1, 1]
train_expected_label_names = [id2label[idx] for idx in train_expected_labels]
test_expected_label_names = [id2label[idx] for idx in test_expected_labels]
dsets = DatasetDict(
{
"train": Dataset.from_dict(train_data, features=train_features),
"test": Dataset.from_dict(test_data, features=test_features),
}
)
dsets = dsets.align_labels_with_mapping(label2id, "input_labels")
self.assertListEqual(train_expected_labels, dsets["train"]["input_labels"][:])
self.assertListEqual(test_expected_labels, dsets["test"]["input_labels"][:])
train_aligned_label_names = [
dsets["train"].features["input_labels"].int2str(idx) for idx in dsets["train"]["input_labels"]
]
test_aligned_label_names = [
dsets["test"].features["input_labels"].int2str(idx) for idx in dsets["test"]["input_labels"]
]
self.assertListEqual(train_expected_label_names, train_aligned_label_names)
self.assertListEqual(test_expected_label_names, test_aligned_label_names)
def test_dummy_datasetdict_serialize_fs(mockfs):
dataset_dict = DatasetDict(
{
"train": Dataset.from_dict({"a": range(30)}),
"test": Dataset.from_dict({"a": range(10)}),
}
)
dataset_path = "mock://my_dataset"
dataset_dict.save_to_disk(dataset_path, storage_options=mockfs.storage_options)
assert mockfs.isdir(dataset_path)
assert mockfs.glob(dataset_path + "/*")
reloaded = DatasetDict.load_from_disk(dataset_path, storage_options=mockfs.storage_options)
assert list(reloaded) == list(dataset_dict)
for k in dataset_dict:
assert reloaded[k].features == dataset_dict[k].features
assert reloaded[k].to_dict() == dataset_dict[k].to_dict()
def _check_csv_datasetdict(dataset_dict, expected_features, splits=("train",)):
assert isinstance(dataset_dict, DatasetDict)
for split in splits:
dataset = dataset_dict[split]
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("keep_in_memory", [False, True])
def test_datasetdict_from_csv_keep_in_memory(keep_in_memory, csv_path, tmp_path):
cache_dir = tmp_path / "cache"
expected_features = {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
dataset = DatasetDict.from_csv({"train": csv_path}, cache_dir=cache_dir, keep_in_memory=keep_in_memory)
_check_csv_datasetdict(dataset, expected_features)
@pytest.mark.parametrize(
"features",
[
None,
{"col_1": "string", "col_2": "int64", "col_3": "float64"},
{"col_1": "string", "col_2": "string", "col_3": "string"},
{"col_1": "int32", "col_2": "int32", "col_3": "int32"},
{"col_1": "float32", "col_2": "float32", "col_3": "float32"},
],
)
def test_datasetdict_from_csv_features(features, csv_path, tmp_path):
cache_dir = tmp_path / "cache"
# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
default_expected_features = {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
expected_features = features.copy() if features else default_expected_features
features = (
Features({feature: Value(dtype) for feature, dtype in features.items()}) if features is not None else None
)
dataset = DatasetDict.from_csv({"train": csv_path}, features=features, cache_dir=cache_dir)
_check_csv_datasetdict(dataset, expected_features)
@pytest.mark.parametrize("split", [None, NamedSplit("train"), "train", "test"])
def test_datasetdict_from_csv_split(split, csv_path, tmp_path):
if split:
path = {split: csv_path}
else:
split = "train"
path = {"train": csv_path, "test": csv_path}
cache_dir = tmp_path / "cache"
expected_features = {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
dataset = DatasetDict.from_csv(path, cache_dir=cache_dir)
_check_csv_datasetdict(dataset, expected_features, splits=list(path.keys()))
assert all(dataset[split].split == split for split in path.keys())
def _check_json_datasetdict(dataset_dict, expected_features, splits=("train",)):
assert isinstance(dataset_dict, DatasetDict)
for split in splits:
dataset = dataset_dict[split]
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("keep_in_memory", [False, True])
def test_datasetdict_from_json_keep_in_memory(keep_in_memory, jsonl_path, tmp_path):
cache_dir = tmp_path / "cache"
expected_features = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
dataset = DatasetDict.from_json({"train": jsonl_path}, cache_dir=cache_dir, keep_in_memory=keep_in_memory)
_check_json_datasetdict(dataset, expected_features)
@pytest.mark.parametrize(
"features",
[
None,
{"col_1": "string", "col_2": "int64", "col_3": "float64"},
{"col_1": "string", "col_2": "string", "col_3": "string"},
{"col_1": "int32", "col_2": "int32", "col_3": "int32"},
{"col_1": "float32", "col_2": "float32", "col_3": "float32"},
],
)
def test_datasetdict_from_json_features(features, jsonl_path, tmp_path):
cache_dir = tmp_path / "cache"
default_expected_features = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
expected_features = features.copy() if features else default_expected_features
features = (
Features({feature: Value(dtype) for feature, dtype in features.items()}) if features is not None else None
)
dataset = DatasetDict.from_json({"train": jsonl_path}, features=features, cache_dir=cache_dir)
_check_json_datasetdict(dataset, expected_features)
@pytest.mark.parametrize("split", [None, NamedSplit("train"), "train", "test"])
def test_datasetdict_from_json_splits(split, jsonl_path, tmp_path):
if split:
path = {split: jsonl_path}
else:
split = "train"
path = {"train": jsonl_path, "test": jsonl_path}
cache_dir = tmp_path / "cache"
expected_features = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
dataset = DatasetDict.from_json(path, cache_dir=cache_dir)
_check_json_datasetdict(dataset, expected_features, splits=list(path.keys()))
assert all(dataset[split].split == split for split in path.keys())
def _check_parquet_datasetdict(dataset_dict, expected_features, splits=("train",)):
assert isinstance(dataset_dict, DatasetDict)
for split in splits:
dataset = dataset_dict[split]
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("keep_in_memory", [False, True])
def test_datasetdict_from_parquet_keep_in_memory(keep_in_memory, parquet_path, tmp_path):
cache_dir = tmp_path / "cache"
expected_features = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
dataset = DatasetDict.from_parquet({"train": parquet_path}, cache_dir=cache_dir, keep_in_memory=keep_in_memory)
_check_parquet_datasetdict(dataset, expected_features)
@pytest.mark.parametrize(
"features",
[
None,
{"col_1": "string", "col_2": "int64", "col_3": "float64"},
{"col_1": "string", "col_2": "string", "col_3": "string"},
{"col_1": "int32", "col_2": "int32", "col_3": "int32"},
{"col_1": "float32", "col_2": "float32", "col_3": "float32"},
],
)
def test_datasetdict_from_parquet_features(features, parquet_path, tmp_path):
cache_dir = tmp_path / "cache"
default_expected_features = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
expected_features = features.copy() if features else default_expected_features
features = (
Features({feature: Value(dtype) for feature, dtype in features.items()}) if features is not None else None
)
dataset = DatasetDict.from_parquet({"train": parquet_path}, features=features, cache_dir=cache_dir)
_check_parquet_datasetdict(dataset, expected_features)
@pytest.mark.parametrize("split", [None, NamedSplit("train"), "train", "test"])
def test_datasetdict_from_parquet_split(split, parquet_path, tmp_path):
if split:
path = {split: parquet_path}
else:
split = "train"
path = {"train": parquet_path, "test": parquet_path}
cache_dir = tmp_path / "cache"
expected_features = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
dataset = DatasetDict.from_parquet(path, cache_dir=cache_dir)
_check_parquet_datasetdict(dataset, expected_features, splits=list(path.keys()))
assert all(dataset[split].split == split for split in path.keys())
def _check_text_datasetdict(dataset_dict, expected_features, splits=("train",)):
assert isinstance(dataset_dict, DatasetDict)
for split in splits:
dataset = dataset_dict[split]
assert dataset.num_rows == 4
assert dataset.num_columns == 1
assert dataset.column_names == ["text"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("keep_in_memory", [False, True])
def test_datasetdict_from_text_keep_in_memory(keep_in_memory, text_path, tmp_path):
cache_dir = tmp_path / "cache"
expected_features = {"text": "string"}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
dataset = DatasetDict.from_text({"train": text_path}, cache_dir=cache_dir, keep_in_memory=keep_in_memory)
_check_text_datasetdict(dataset, expected_features)
@pytest.mark.parametrize(
"features",
[
None,
{"text": "string"},
{"text": "int32"},
{"text": "float32"},
],
)
def test_datasetdict_from_text_features(features, text_path, tmp_path):
cache_dir = tmp_path / "cache"
default_expected_features = {"text": "string"}
expected_features = features.copy() if features else default_expected_features
features = (
Features({feature: Value(dtype) for feature, dtype in features.items()}) if features is not None else None
)
dataset = DatasetDict.from_text({"train": text_path}, features=features, cache_dir=cache_dir)
_check_text_datasetdict(dataset, expected_features)
@pytest.mark.parametrize("split", [None, NamedSplit("train"), "train", "test"])
def test_datasetdict_from_text_split(split, text_path, tmp_path):
if split:
path = {split: text_path}
else:
split = "train"
path = {"train": text_path, "test": text_path}
cache_dir = tmp_path / "cache"
expected_features = {"text": "string"}
dataset = DatasetDict.from_text(path, cache_dir=cache_dir)
_check_text_datasetdict(dataset, expected_features, splits=list(path.keys()))
assert all(dataset[split].split == split for split in path.keys())
| datasets/tests/test_dataset_dict.py/0 | {
"file_path": "datasets/tests/test_dataset_dict.py",
"repo_id": "datasets",
"token_count": 18532
} | 111 |
import asyncio
import pickle
import time
from copy import deepcopy
from dataclasses import dataclass
from itertools import chain, cycle, islice
from unittest.mock import patch
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.distributed import split_dataset_by_node
from datasets.features import (
ClassLabel,
Features,
Image,
List,
Value,
)
from datasets.formatting import Formatter, get_format_type_from_alias
from datasets.info import DatasetInfo
from datasets.iterable_dataset import (
ArrowExamplesIterable,
BufferShuffledExamplesIterable,
CyclingMultiSourcesExamplesIterable,
ExamplesIterable,
FilteredExamplesIterable,
FormattedExamplesIterable,
FormattingConfig,
HorizontallyConcatenatedMultiSourcesExamplesIterable,
IterableColumn,
IterableDataset,
MappedExamplesIterable,
RandomlyCyclingMultiSourcesExamplesIterable,
RebatchedArrowExamplesIterable,
RepeatExamplesIterable,
SelectColumnsIterable,
ShuffledDataSourcesArrowExamplesIterable,
ShuffledDataSourcesExamplesIterable,
ShufflingConfig,
SkipExamplesIterable,
StepExamplesIterable,
TakeExamplesIterable,
VerticallyConcatenatedMultiSourcesExamplesIterable,
_BaseExamplesIterable,
_batch_to_examples,
_convert_to_arrow,
_examples_to_batch,
)
from .utils import (
assert_arrow_memory_doesnt_increase,
is_rng_equal,
require_dill_gt_0_3_2,
require_jax,
require_not_windows,
require_numpy1_on_windows,
require_polars,
require_pyspark,
require_tf,
require_torch,
require_torchdata_stateful_dataloader,
)
SAMPLE_DATASET_IDENTIFIER = "hf-internal-testing/dataset_with_data_files"
DEFAULT_N_EXAMPLES = 20
DEFAULT_BATCH_SIZE = 4
DEFAULT_FILEPATH = "file.txt"
def generate_examples_fn(**kwargs):
kwargs = kwargs.copy()
n = kwargs.pop("n", DEFAULT_N_EXAMPLES)
filepaths = kwargs.pop("filepaths", None)
for filepath in filepaths or [DEFAULT_FILEPATH]:
if filepaths is not None:
kwargs["filepath"] = filepath
for i in range(n):
yield f"{filepath}_{i}", {"id": i, **kwargs}
def generate_tables_fn(**kwargs):
kwargs = kwargs.copy()
n = kwargs.pop("n", DEFAULT_N_EXAMPLES)
batch_size = kwargs.pop("batch_size", DEFAULT_BATCH_SIZE)
filepaths = kwargs.pop("filepaths", None)
for filepath in filepaths or [DEFAULT_FILEPATH]:
buffer = []
batch_idx = 0
if filepaths is not None:
kwargs["filepath"] = filepath
for i in range(n):
buffer.append({"id": i, **kwargs})
if len(buffer) == batch_size:
yield f"{filepath}_{batch_idx}", pa.Table.from_pylist(buffer)
buffer = []
batch_idx += 1
yield batch_idx, pa.Table.from_pylist(buffer)
@pytest.fixture
def dataset():
ex_iterable = ExamplesIterable(generate_examples_fn, {})
return IterableDataset(ex_iterable, info=DatasetInfo(description="dummy"), split="train")
@pytest.fixture
def dataset_with_several_columns():
ex_iterable = ExamplesIterable(
generate_examples_fn,
{"filepath": ["data0.txt", "data1.txt", "data2.txt"], "metadata": {"sources": ["https://foo.bar"]}},
)
return IterableDataset(ex_iterable, info=DatasetInfo(description="dummy"), split="train")
@pytest.fixture
def arrow_file(tmp_path_factory, dataset: IterableDataset):
filename = str(tmp_path_factory.mktemp("data") / "file.arrow")
Dataset.from_generator(dataset.__iter__).map(cache_file_name=filename)
return filename
def assert_load_state_dict_resumes_iteration(ex_iterable: _BaseExamplesIterable):
ex_iterable._init_state_dict()
state_dicts = [ex_iterable.state_dict()]
examples = []
for _, example in ex_iterable:
state_dicts.append(ex_iterable.state_dict())
examples.append(example)
for i, state_dict in enumerate(state_dicts):
ex_iterable.load_state_dict(state_dict)
examples_after_resuming = [example for _, example in ex_iterable]
assert examples_after_resuming == examples[i:], f"resuming from idx {i} with {state_dict=}"
def assert_load_state_dict_resumes_arrow_iteration(ex_iterable: _BaseExamplesIterable):
assert ex_iterable.iter_arrow is not None
ex_iterable._init_state_dict()
state_dicts = [ex_iterable.state_dict()]
examples = []
indices = [0]
for _, pa_table in ex_iterable.iter_arrow():
state_dicts.append(ex_iterable.state_dict())
examples.extend(pa_table.to_pylist())
indices.append(indices[-1] + len(pa_table))
for i, state_dict in zip(indices, state_dicts):
ex_iterable.load_state_dict(state_dict)
examples_after_resuming = [
example for _, pa_table in ex_iterable.iter_arrow() for example in pa_table.to_pylist()
]
assert examples_after_resuming == examples[i:], f"resuming from idx {i} with {state_dict=}"
################################
#
# Utilities tests
#
################################
@pytest.mark.parametrize("batch_size", [1, 2, 3, 9, 10, 11, 20])
@pytest.mark.parametrize("drop_last_batch", [False, True])
def test_convert_to_arrow(batch_size, drop_last_batch):
examples = [{"foo": i} for i in range(10)]
full_table = pa.Table.from_pylist(examples)
num_rows = len(full_table) if not drop_last_batch else len(full_table) // batch_size * batch_size
num_batches = (num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size
subtables = list(
_convert_to_arrow(
list(enumerate(examples)),
batch_size=batch_size,
drop_last_batch=drop_last_batch,
)
)
assert len(subtables) == num_batches
if drop_last_batch:
assert all(len(subtable) == batch_size for _, subtable in subtables)
else:
assert all(len(subtable) == batch_size for _, subtable in subtables[:-1])
assert len(subtables[-1][1]) <= batch_size
if num_rows > 0:
reloaded = pa.concat_tables([subtable for _, subtable in subtables])
assert full_table.slice(0, num_rows).to_pydict() == reloaded.to_pydict()
################################
#
# _BaseExampleIterable tests
#
################################
def test_examples_iterable():
ex_iterable = ExamplesIterable(generate_examples_fn, {})
expected = list(generate_examples_fn())
assert next(iter(ex_iterable)) == expected[0]
assert list(ex_iterable) == expected
assert ex_iterable.iter_arrow is None
assert_load_state_dict_resumes_iteration(ex_iterable)
def test_examples_iterable_with_kwargs():
ex_iterable = ExamplesIterable(generate_examples_fn, {"filepaths": ["0.txt", "1.txt"], "split": "train"})
expected = list(generate_examples_fn(filepaths=["0.txt", "1.txt"], split="train"))
assert list(ex_iterable) == expected
assert all("split" in ex for _, ex in ex_iterable)
assert sorted({ex["filepath"] for _, ex in ex_iterable}) == ["0.txt", "1.txt"]
assert_load_state_dict_resumes_iteration(ex_iterable)
def test_examples_iterable_shuffle_data_sources():
ex_iterable = ExamplesIterable(generate_examples_fn, {"filepaths": ["0.txt", "1.txt"]})
ex_iterable = ex_iterable.shuffle_data_sources(np.random.default_rng(40))
expected = list(generate_examples_fn(filepaths=["1.txt", "0.txt"])) # shuffle the filepaths
assert list(ex_iterable) == expected
assert_load_state_dict_resumes_iteration(ex_iterable)
def test_examples_iterable_shuffle_shards_and_metadata():
def gen(filepaths, all_metadata):
for i, (filepath, metadata) in enumerate(zip(filepaths, all_metadata)):
yield i, {"filepath": filepath, "metadata": metadata}
ex_iterable = ExamplesIterable(
gen,
{
"filepaths": [f"{i}.txt" for i in range(100)],
"all_metadata": [{"id": str(i)} for i in range(100)],
},
)
ex_iterable = ex_iterable.shuffle_data_sources(np.random.default_rng(42))
out = list(ex_iterable)
filepaths_ids = [x["filepath"].split(".")[0] for _, x in out]
metadata_ids = [x["metadata"]["id"] for _, x in out]
assert filepaths_ids == metadata_ids, "entangled lists of shards/metadata should be shuffled the same way"
assert_load_state_dict_resumes_iteration(ex_iterable)
def test_arrow_examples_iterable():
ex_iterable = ArrowExamplesIterable(generate_tables_fn, {})
expected = sum([pa_table.to_pylist() for _, pa_table in generate_tables_fn()], [])
assert next(iter(ex_iterable))[1] == expected[0]
assert [example for _, example in ex_iterable] == expected
expected = list(generate_tables_fn())
assert list(ex_iterable.iter_arrow()) == expected
assert_load_state_dict_resumes_iteration(ex_iterable)
def test_arrow_examples_iterable_with_kwargs():
ex_iterable = ArrowExamplesIterable(generate_tables_fn, {"filepaths": ["0.txt", "1.txt"], "split": "train"})
expected = sum(
[pa_table.to_pylist() for _, pa_table in generate_tables_fn(filepaths=["0.txt", "1.txt"], split="train")], []
)
assert [example for _, example in ex_iterable] == expected
assert all("split" in ex for _, ex in ex_iterable)
assert sorted({ex["filepath"] for _, ex in ex_iterable}) == ["0.txt", "1.txt"]
expected = list(generate_tables_fn(filepaths=["0.txt", "1.txt"], split="train"))
assert list(ex_iterable.iter_arrow()) == expected
assert_load_state_dict_resumes_iteration(ex_iterable)
def test_arrow_examples_iterable_shuffle_data_sources():
ex_iterable = ArrowExamplesIterable(generate_tables_fn, {"filepaths": ["0.txt", "1.txt"]})
ex_iterable = ex_iterable.shuffle_data_sources(np.random.default_rng(40))
expected = sum(
[pa_table.to_pylist() for _, pa_table in generate_tables_fn(filepaths=["1.txt", "0.txt"])], []
) # shuffle the filepaths
assert [example for _, example in ex_iterable] == expected
expected = list(generate_tables_fn(filepaths=["1.txt", "0.txt"]))
assert list(ex_iterable.iter_arrow()) == expected
assert_load_state_dict_resumes_iteration(ex_iterable)
@pytest.mark.parametrize(
"tables",
[
[pa.table({"foo": range(10)})],
[pa.table({"foo": range(5 * i, 5 * (i + 1))}) for i in range(2)],
[pa.table({"foo": range(5 * i, 5 * (i + 1))}) for i in range(7)],
[pa.table({"foo": [i]}) for i in range(10)],
],
)
@pytest.mark.parametrize("batch_size", [1, 2, 3, 7, 9, 10, 11, 13, 20])
@pytest.mark.parametrize("drop_last_batch", [False, True])
def test_rebatched_arrow_examples_iterable(tables, batch_size, drop_last_batch):
full_table = pa.concat_tables(tables)
num_rows = len(full_table) if not drop_last_batch else len(full_table) // batch_size * batch_size
num_batches = (num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size
def gen(tables):
for i, table in enumerate(tables):
yield str(i), table
ex_iterable = ArrowExamplesIterable(gen, {"tables": tables})
ex_iterable = RebatchedArrowExamplesIterable(ex_iterable, batch_size=batch_size, drop_last_batch=drop_last_batch)
subtables = list(ex_iterable.iter_arrow())
assert len(subtables) == num_batches
if drop_last_batch:
assert all(len(subtable) == batch_size for _, subtable in subtables)
else:
assert all(len(subtable) == batch_size for _, subtable in subtables[:-1])
assert len(subtables[-1][1]) <= batch_size
if num_rows > 0:
reloaded = pa.concat_tables([subtable for _, subtable in subtables])
assert full_table.slice(0, num_rows).to_pydict() == reloaded.to_pydict()
assert_load_state_dict_resumes_iteration(ex_iterable)
assert_load_state_dict_resumes_arrow_iteration(ex_iterable)
@pytest.mark.parametrize("seed", [42, 1337, 101010, 123456])
def test_buffer_shuffled_examples_iterable(seed):
n, buffer_size = 100, 30
generator = np.random.default_rng(seed)
base_ex_iterable = ExamplesIterable(generate_examples_fn, {"n": n})
ex_iterable = BufferShuffledExamplesIterable(base_ex_iterable, buffer_size=buffer_size, generator=generator)
rng = deepcopy(generator)
expected_indices_used_for_shuffling = list(
islice(BufferShuffledExamplesIterable._iter_random_indices(rng, buffer_size=buffer_size), n - buffer_size)
)
# indices to pick in the shuffle buffer should all be in the right range
assert all(0 <= index_to_pick < buffer_size for index_to_pick in expected_indices_used_for_shuffling)
# it should be random indices
assert expected_indices_used_for_shuffling != list(range(buffer_size))
# The final order of examples is the result of a shuffle buffer.
all_examples = list(generate_examples_fn(n=n))
# We create a buffer and we pick random examples from it.
buffer, rest = all_examples[:buffer_size], all_examples[buffer_size:]
expected = []
for i, index_to_pick in enumerate(expected_indices_used_for_shuffling):
expected.append(buffer[index_to_pick])
# The picked examples are directly replaced by the next examples from the iterable.
buffer[index_to_pick] = rest.pop(0)
# Once we have reached the end of the iterable, we shuffle the buffer and return the remaining examples.
rng.shuffle(buffer)
expected += buffer
assert next(iter(ex_iterable)) == expected[0]
assert list(ex_iterable) == expected
assert sorted(ex_iterable) == sorted(all_examples)
def test_cycling_multi_sources_examples_iterable():
ex_iterable1 = ExamplesIterable(generate_examples_fn, {"text": "foo"})
ex_iterable2 = ExamplesIterable(generate_examples_fn, {"text": "bar"})
ex_iterable = CyclingMultiSourcesExamplesIterable([ex_iterable1, ex_iterable2])
expected = list(chain(*zip(generate_examples_fn(text="foo"), generate_examples_fn(text="bar"))))
# The cycling stops as soon as one iterable is out of examples (here ex_iterable1), so the last sample from ex_iterable2 is unecessary
expected = expected[:-1]
assert next(iter(ex_iterable)) == expected[0]
assert list(ex_iterable) == expected
assert all((x["id"], x["text"]) == (i // 2, "bar" if i % 2 else "foo") for i, (_, x) in enumerate(ex_iterable))
assert_load_state_dict_resumes_iteration(ex_iterable)
@pytest.mark.parametrize("probabilities", [None, (0.5, 0.5), (0.9, 0.1)])
def test_randomly_cycling_multi_sources_examples_iterable(probabilities):
seed = 42
generator = np.random.default_rng(seed)
ex_iterable1 = ExamplesIterable(generate_examples_fn, {"text": "foo"})
ex_iterable2 = ExamplesIterable(generate_examples_fn, {"text": "bar"})
ex_iterable = RandomlyCyclingMultiSourcesExamplesIterable(
[ex_iterable1, ex_iterable2], generator=generator, probabilities=probabilities
)
# The source used randomly changes at each example. It stops when one of the iterators is empty.
rng = deepcopy(generator)
iterators = (generate_examples_fn(text="foo"), generate_examples_fn(text="bar"))
indices_iterator = cycle(rng.choice(len(iterators), size=1000, p=probabilities))
expected = []
lengths = [len(list(ex_iterable1)), len(list(ex_iterable2))]
for i in indices_iterator:
if lengths[0] == 0 or lengths[1] == 0:
break
for key, example in iterators[i]:
expected.append((key, example))
lengths[i] -= 1
break
else:
break
assert next(iter(ex_iterable)) == expected[0]
assert list(ex_iterable) == expected
assert_load_state_dict_resumes_iteration(ex_iterable)
@pytest.mark.parametrize("probabilities", [None, (0.5, 0.5), (0.9, 0.1)])
@pytest.mark.parametrize("stopping_strategy", ["first_exhausted", "all_exhausted"])
@pytest.mark.parametrize("step", [-1, 0, 5, 20, 30, 300])
def test_randomly_cycling_multi_sources_examples_iterable_state(probabilities, stopping_strategy, step):
seed = 42
generator = np.random.default_rng(seed)
ex_iterable1 = ExamplesIterable(generate_examples_fn, {"text": "foo"})
ex_iterable2 = ExamplesIterable(generate_examples_fn, {"text": "bar"})
ex_iterable = RandomlyCyclingMultiSourcesExamplesIterable(
[ex_iterable1, ex_iterable2],
generator=generator,
probabilities=probabilities,
stopping_strategy=stopping_strategy,
)
step = min(step, len(list(ex_iterable)) - 1)
ex_iterable._init_state_dict()
state_dict = ex_iterable.state_dict()
examples = []
for i, x in enumerate(ex_iterable):
examples.append(x)
if i == step:
state_dict = ex_iterable.state_dict()
ex_iterable.load_state_dict(state_dict)
assert examples[step + 1 :] == list(ex_iterable)
@pytest.mark.parametrize(
"n, func, batched, batch_size",
[
(3, lambda x: {"id+1": x["id"] + 1}, False, None), # just add 1 to the id
(3, lambda x: {"id+1": [x["id"][0] + 1]}, True, 1), # same with bs=1
(5, lambda x: {"id+1": [i + 1 for i in x["id"]]}, True, 10), # same with bs=10
(25, lambda x: {"id+1": [i + 1 for i in x["id"]]}, True, 10), # same with bs=10
(5, lambda x: {"id+1": [i + 1 for i in x["id"]]}, True, None), # same with bs=None
(5, lambda x: {"id+1": [i + 1 for i in x["id"]]}, True, -1), # same with bs<=0
(3, lambda x: {k: v * 2 for k, v in x.items()}, True, 1), # make a duplicate of each example
],
)
def test_mapped_examples_iterable(n, func, batched, batch_size):
base_ex_iterable = ExamplesIterable(generate_examples_fn, {"n": n})
ex_iterable = MappedExamplesIterable(base_ex_iterable, func, batched=batched, batch_size=batch_size)
all_examples = [x for _, x in generate_examples_fn(n=n)]
if batched is False:
expected = [{**x, **func(x)} for x in all_examples]
else:
# For batched map we have to format the examples as a batch (i.e. in one single dictionary) to pass the batch to the function
all_transformed_examples = []
# If batch_size is None or <=0, we use the whole dataset as a single batch
if batch_size is None or batch_size <= 0:
batch_size = len(all_examples)
for batch_offset in range(0, len(all_examples), batch_size):
examples = all_examples[batch_offset : batch_offset + batch_size]
batch = _examples_to_batch(examples)
transformed_batch = func(batch)
all_transformed_examples.extend(_batch_to_examples(transformed_batch))
expected = _examples_to_batch(all_examples)
expected.update(_examples_to_batch(all_transformed_examples))
expected = list(_batch_to_examples(expected))
assert next(iter(ex_iterable))[1] == expected[0]
assert [x for _, x in ex_iterable] == expected
assert_load_state_dict_resumes_iteration(ex_iterable)
@pytest.mark.parametrize(
"n, func, batched, batch_size",
[
(3, lambda x: {"id+1": x["id"] + 1}, False, None), # just add 1 to the id
(3, lambda x: {"id+1": [x["id"][0] + 1]}, True, 1), # same with bs=1
(5, lambda x: {"id+1": [i + 1 for i in x["id"]]}, True, 10), # same with bs=10
(25, lambda x: {"id+1": [i + 1 for i in x["id"]]}, True, 10), # same with bs=10
(5, lambda x: {"id+1": [i + 1 for i in x["id"]]}, True, None), # same with bs=None
(5, lambda x: {"id+1": [i + 1 for i in x["id"]]}, True, -1), # same with bs<=0
(3, lambda x: {k: v * 2 for k, v in x.items()}, True, 1), # make a duplicate of each example
],
)
def test_mapped_examples_iterable_drop_last_batch(n, func, batched, batch_size):
base_ex_iterable = ExamplesIterable(generate_examples_fn, {"n": n})
ex_iterable = MappedExamplesIterable(
base_ex_iterable, func, batched=batched, batch_size=batch_size, drop_last_batch=True
)
all_examples = [x for _, x in generate_examples_fn(n=n)]
is_empty = False
if batched is False:
# `drop_last_batch` has no effect here
expected = [{**x, **func(x)} for x in all_examples]
else:
# For batched map we have to format the examples as a batch (i.e. in one single dictionary) to pass the batch to the function
all_transformed_examples = []
# If batch_size is None or <=0, we use the whole dataset as a single batch
if batch_size is None or batch_size <= 0:
batch_size = len(all_examples)
for batch_offset in range(0, len(all_examples), batch_size):
examples = all_examples[batch_offset : batch_offset + batch_size]
if len(examples) < batch_size: # ignore last batch
break
batch = _examples_to_batch(examples)
transformed_batch = func(batch)
all_transformed_examples.extend(_batch_to_examples(transformed_batch))
all_examples = all_examples if n % batch_size == 0 else all_examples[: n // batch_size * batch_size]
if all_examples:
expected = _examples_to_batch(all_examples)
expected.update(_examples_to_batch(all_transformed_examples))
expected = list(_batch_to_examples(expected))
else:
is_empty = True
if not is_empty:
assert next(iter(ex_iterable))[1] == expected[0]
assert [x for _, x in ex_iterable] == expected
else:
with pytest.raises(StopIteration):
next(iter(ex_iterable))
def _wrap_async(func, *args, **kwargs):
async def wrapped_func(*args, **kwargs):
return func(*args, **kwargs)
return wrapped_func
@pytest.mark.parametrize(
"n, func, batched, batch_size",
[
(3, lambda x, index: {"id+idx": x["id"] + index}, False, None), # add the index to the id
(
25,
lambda x, indices: {"id+idx": [i + j for i, j in zip(x["id"], indices)]},
True,
10,
), # add the index to the id
(5, lambda x, indices: {"id+idx": [i + j for i, j in zip(x["id"], indices)]}, True, None), # same with bs=None
(5, lambda x, indices: {"id+idx": [i + j for i, j in zip(x["id"], indices)]}, True, -1), # same with bs<=0
],
)
@pytest.mark.parametrize("wrapper", [lambda x: x, _wrap_async])
def test_mapped_examples_iterable_with_indices(n, func, batched, batch_size, wrapper):
base_ex_iterable = ExamplesIterable(generate_examples_fn, {"n": n})
ex_iterable = MappedExamplesIterable(
base_ex_iterable, wrapper(func), batched=batched, batch_size=batch_size, with_indices=True
)
all_examples = [x for _, x in generate_examples_fn(n=n)]
if batched is False:
expected = [{**x, **func(x, idx)} for idx, x in enumerate(all_examples)]
else:
# For batched map we have to format the examples as a batch (i.e. in one single dictionary) to pass the batch to the function
all_transformed_examples = []
# If batch_size is None or <=0, we use the whole dataset as a single batch
if batch_size is None or batch_size <= 0:
batch_size = len(all_examples)
for batch_offset in range(0, len(all_examples), batch_size):
examples = all_examples[batch_offset : batch_offset + batch_size]
batch = _examples_to_batch(examples)
indices = list(range(batch_offset, batch_offset + len(examples)))
transformed_batch = func(batch, indices)
all_transformed_examples.extend(_batch_to_examples(transformed_batch))
expected = _examples_to_batch(all_examples)
expected.update(_examples_to_batch(all_transformed_examples))
expected = list(_batch_to_examples(expected))
assert next(iter(ex_iterable))[1] == expected[0]
assert [x for _, x in ex_iterable] == expected
assert_load_state_dict_resumes_iteration(ex_iterable)
@pytest.mark.parametrize(
"n, func, batched, batch_size, remove_columns",
[
(3, lambda x: {"id+1": x["id"] + 1}, False, None, ["extra_column"]), # just add 1 to the id
(25, lambda x: {"id+1": [i + 1 for i in x["id"]]}, True, 10, ["extra_column"]), # same with bs=10
(
50,
lambda x: {"foo": ["bar"] * np.random.default_rng(x["id"][0]).integers(0, 10)},
True,
8,
["extra_column", "id"],
), # make a duplicate of each example
(5, lambda x: {"id+1": [i + 1 for i in x["id"]]}, True, None, ["extra_column"]), # same with bs=None
(5, lambda x: {"id+1": [i + 1 for i in x["id"]]}, True, -1, ["extra_column"]), # same with bs<=0
],
)
def test_mapped_examples_iterable_remove_columns(n, func, batched, batch_size, remove_columns):
base_ex_iterable = ExamplesIterable(generate_examples_fn, {"n": n, "extra_column": "foo"})
ex_iterable = MappedExamplesIterable(
base_ex_iterable, func, batched=batched, batch_size=batch_size, remove_columns=remove_columns
)
all_examples = [x for _, x in generate_examples_fn(n=n)]
columns_to_remove = remove_columns if isinstance(remove_columns, list) else [remove_columns]
if batched is False:
expected = [{**{k: v for k, v in x.items() if k not in columns_to_remove}, **func(x)} for x in all_examples]
else:
# For batched map we have to format the examples as a batch (i.e. in one single dictionary) to pass the batch to the function
all_transformed_examples = []
# If batch_size is None or <=0, we use the whole dataset as a single batch
if batch_size is None or batch_size <= 0:
batch_size = len(all_examples)
for batch_offset in range(0, len(all_examples), batch_size):
examples = all_examples[batch_offset : batch_offset + batch_size]
batch = _examples_to_batch(examples)
transformed_batch = func(batch)
all_transformed_examples.extend(_batch_to_examples(transformed_batch))
expected = {k: v for k, v in _examples_to_batch(all_examples).items() if k not in columns_to_remove}
expected.update(_examples_to_batch(all_transformed_examples))
expected = list(_batch_to_examples(expected))
assert next(iter(ex_iterable))[1] == expected[0]
assert [x for _, x in ex_iterable] == expected
assert_load_state_dict_resumes_iteration(ex_iterable)
# issue #7345 and PR #7353
@pytest.mark.parametrize("batched", [False, True])
@pytest.mark.parametrize("batch_size", [None, 2])
@pytest.mark.parametrize("input_columns", [None, ["i"]])
@pytest.mark.parametrize("remove_columns", [None, ["i"]])
@pytest.mark.parametrize("new_output", [False, True])
def test_iterable_dataset_vs_dataset_map(batched, batch_size, input_columns, remove_columns, new_output):
if input_columns is not None and not new_output:
return
ds1 = Dataset.from_list([{"i": i} for i in range(4)])
if batched:
def f1(i):
return {"i": [j + 1 for j in i]}
else:
def f1(i):
return {"i": i + 1}
if input_columns is None:
def f2(x):
return f1(x["i"])
else:
f2 = f1
if new_output:
f = f2
else:
def f(x):
x["i"] = f2(x)["i"]
return x
r = [
list(
ds2.map(
f,
batch_size=batch_size,
batched=batched,
remove_columns=remove_columns,
input_columns=input_columns,
)
)
for ds2 in [ds1, ds1.to_iterable_dataset()]
]
r[1] = [x for x in r[1] if len(x) > 0]
assert len(r[0]) == len(r[1])
assert all(x == y for x, y in zip(*r))
@pytest.mark.parametrize(
"n, func, batched, batch_size, fn_kwargs",
[
(3, lambda x, y=0: {"id+y": x["id"] + y}, False, None, None),
(3, lambda x, y=0: {"id+y": x["id"] + y}, False, None, {"y": 3}),
(25, lambda x, y=0: {"id+y": [i + y for i in x["id"]]}, True, 10, {"y": 3}),
(5, lambda x, y=0: {"id+y": [i + y for i in x["id"]]}, True, None, {"y": 3}), # same with bs=None
(5, lambda x, y=0: {"id+y": [i + y for i in x["id"]]}, True, -1, {"y": 3}), # same with bs<=0
],
)
def test_mapped_examples_iterable_fn_kwargs(n, func, batched, batch_size, fn_kwargs):
base_ex_iterable = ExamplesIterable(generate_examples_fn, {"n": n})
ex_iterable = MappedExamplesIterable(
base_ex_iterable, func, batched=batched, batch_size=batch_size, fn_kwargs=fn_kwargs
)
all_examples = [x for _, x in generate_examples_fn(n=n)]
if fn_kwargs is None:
fn_kwargs = {}
if batched is False:
expected = [{**x, **func(x, **fn_kwargs)} for x in all_examples]
else:
# For batched map we have to format the examples as a batch (i.e. in one single dictionary) to pass the batch to the function
all_transformed_examples = []
# If batch_size is None or <=0, we use the whole dataset as a single batch
if batch_size is None or batch_size <= 0:
batch_size = len(all_examples)
for batch_offset in range(0, len(all_examples), batch_size):
examples = all_examples[batch_offset : batch_offset + batch_size]
batch = _examples_to_batch(examples)
transformed_batch = func(batch, **fn_kwargs)
all_transformed_examples.extend(_batch_to_examples(transformed_batch))
expected = _examples_to_batch(all_examples)
expected.update(_examples_to_batch(all_transformed_examples))
expected = list(_batch_to_examples(expected))
assert next(iter(ex_iterable))[1] == expected[0]
assert [x for _, x in ex_iterable] == expected
assert_load_state_dict_resumes_iteration(ex_iterable)
@pytest.mark.parametrize(
"n, func, batched, batch_size, input_columns",
[
(3, lambda id_: {"id+1": id_ + 1}, False, None, ["id"]), # just add 1 to the id
(25, lambda ids_: {"id+1": [i + 1 for i in ids_]}, True, 10, ["id"]), # same with bs=10
(5, lambda ids_: {"id+1": [i + 1 for i in ids_]}, True, None, ["id"]), # same with bs=None
(5, lambda ids_: {"id+1": [i + 1 for i in ids_]}, True, -1, ["id"]), # same with bs<=0
],
)
def test_mapped_examples_iterable_input_columns(n, func, batched, batch_size, input_columns):
base_ex_iterable = ExamplesIterable(generate_examples_fn, {"n": n})
ex_iterable = MappedExamplesIterable(
base_ex_iterable, func, batched=batched, batch_size=batch_size, input_columns=input_columns
)
all_examples = [x for _, x in generate_examples_fn(n=n)]
columns_to_input = input_columns if isinstance(input_columns, list) else [input_columns]
if batched is False:
expected = [{**x, **func(*[x[col] for col in columns_to_input])} for x in all_examples]
else:
# For batched map we have to format the examples as a batch (i.e. in one single dictionary) to pass the batch to the function
all_transformed_examples = []
# If batch_size is None or <=0, we use the whole dataset as a single batch
if batch_size is None or batch_size <= 0:
batch_size = len(all_examples)
for batch_offset in range(0, len(all_examples), batch_size):
examples = all_examples[batch_offset : batch_offset + batch_size]
batch = _examples_to_batch(examples)
transformed_batch = func(*[batch[col] for col in columns_to_input])
all_transformed_examples.extend(_batch_to_examples(transformed_batch))
expected = _examples_to_batch(all_examples)
expected.update(_examples_to_batch(all_transformed_examples))
expected = list(_batch_to_examples(expected))
assert next(iter(ex_iterable))[1] == expected[0]
assert [x for _, x in ex_iterable] == expected
assert_load_state_dict_resumes_iteration(ex_iterable)
@pytest.mark.parametrize(
"n, func, batched, batch_size",
[
(3, lambda t: t.append_column("id+1", pc.add(t["id"], 1)), False, None), # just add 1 to the id
(3, lambda t: t.append_column("id+1", pc.add(t["id"], 1)), True, 1), # same with bs=1
(5, lambda t: t.append_column("id+1", pc.add(t["id"], 1)), True, 10), # same with bs=10
(25, lambda t: t.append_column("id+1", pc.add(t["id"], 1)), True, 10), # same with bs=10
(5, lambda t: t.append_column("id+1", pc.add(t["id"], 1)), True, None), # same with bs=None
(5, lambda t: t.append_column("id+1", pc.add(t["id"], 1)), True, -1), # same with bs<=0
(3, lambda t: pa.concat_tables([t] * 2), True, 1), # make a duplicate of each example
],
)
def test_mapped_examples_iterable_arrow_format(n, func, batched, batch_size):
base_ex_iterable = ExamplesIterable(generate_examples_fn, {"n": n})
base_ex_iterable = RebatchedArrowExamplesIterable(base_ex_iterable, batch_size=batch_size if batched else 1)
ex_iterable = MappedExamplesIterable(
base_ex_iterable,
func,
batched=batched,
batch_size=batch_size,
formatting=FormattingConfig(format_type="arrow"),
)
all_examples = [x for _, x in generate_examples_fn(n=n)]
if batched is False:
expected = [func(pa.Table.from_pylist([x])).to_pylist()[0] for x in all_examples]
else:
expected = []
# If batch_size is None or <=0, we use the whole dataset as a single batch
if batch_size is None or batch_size <= 0:
batch_size = len(all_examples)
for batch_offset in range(0, len(all_examples), batch_size):
examples = all_examples[batch_offset : batch_offset + batch_size]
batch = pa.Table.from_pylist(examples)
expected.extend(func(batch).to_pylist())
assert next(iter(ex_iterable))[1] == expected[0]
assert [x for _, x in ex_iterable] == expected
assert_load_state_dict_resumes_iteration(ex_iterable)
assert_load_state_dict_resumes_arrow_iteration(ex_iterable)
@pytest.mark.parametrize(
"n, func, batched, batch_size",
[
(3, lambda t: t.append_column("id+1", pc.add(t["id"], 1)), False, None), # just add 1 to the id
(3, lambda t: t.append_column("id+1", pc.add(t["id"], 1)), True, 1), # same with bs=1
(5, lambda t: t.append_column("id+1", pc.add(t["id"], 1)), True, 10), # same with bs=10
(25, lambda t: t.append_column("id+1", pc.add(t["id"], 1)), True, 10), # same with bs=10
(5, lambda t: t.append_column("id+1", pc.add(t["id"], 1)), True, None), # same with bs=None
(5, lambda t: t.append_column("id+1", pc.add(t["id"], 1)), True, -1), # same with bs<=0
(3, lambda t: pa.concat_tables([t] * 2), True, 1), # make a duplicate of each example
],
)
def test_mapped_examples_iterable_arrow_format_from_arrow_examples_iterable(n, func, batched, batch_size):
base_ex_iterable = ArrowExamplesIterable(generate_tables_fn, {"n": n})
base_ex_iterable = RebatchedArrowExamplesIterable(base_ex_iterable, batch_size=batch_size if batched else 1)
ex_iterable = MappedExamplesIterable(
base_ex_iterable,
func,
batched=batched,
batch_size=batch_size,
formatting=FormattingConfig(format_type="arrow"),
)
all_examples = [x for _, x in generate_examples_fn(n=n)]
if batched is False:
expected = [func(pa.Table.from_pylist([x])).to_pylist()[0] for x in all_examples]
else:
expected = []
# If batch_size is None or <=0, we use the whole dataset as a single batch
if batch_size is None or batch_size <= 0:
batch_size = len(all_examples)
for batch_offset in range(0, len(all_examples), batch_size):
examples = all_examples[batch_offset : batch_offset + batch_size]
batch = pa.Table.from_pylist(examples)
expected.extend(func(batch).to_pylist())
assert next(iter(ex_iterable))[1] == expected[0]
assert [x for _, x in ex_iterable] == expected
assert_load_state_dict_resumes_iteration(ex_iterable)
assert_load_state_dict_resumes_arrow_iteration(ex_iterable)
@pytest.mark.parametrize(
"n, func, batched, batch_size",
[
(3, lambda t: t.append_column("id+1", pc.add(t["id"], 1)), False, None), # just add 1 to the id
(3, lambda t: t.append_column("id+1", pc.add(t["id"], 1)), True, 1), # same with bs=1
(5, lambda t: t.append_column("id+1", pc.add(t["id"], 1)), True, 10), # same with bs=10
(25, lambda t: t.append_column("id+1", pc.add(t["id"], 1)), True, 10), # same with bs=10
(5, lambda t: t.append_column("id+1", pc.add(t["id"], 1)), True, None), # same with bs=None
(5, lambda t: t.append_column("id+1", pc.add(t["id"], 1)), True, -1), # same with bs<=0
(3, lambda t: pa.concat_tables([t] * 2), True, 1), # make a duplicate of each example
],
)
def test_mapped_examples_iterable_drop_last_batch_and_arrow_format(n, func, batched, batch_size):
base_ex_iterable = ExamplesIterable(generate_examples_fn, {"n": n})
base_ex_iterable = RebatchedArrowExamplesIterable(base_ex_iterable, batch_size=batch_size if batched else 1)
ex_iterable = MappedExamplesIterable(
base_ex_iterable,
func,
batched=batched,
batch_size=batch_size,
drop_last_batch=True,
formatting=FormattingConfig(format_type="arrow"),
)
all_examples = [x for _, x in generate_examples_fn(n=n)]
is_empty = False
if batched is False:
# `drop_last_batch` has no effect here
expected = [func(pa.Table.from_pylist([x])).to_pylist()[0] for x in all_examples]
else:
all_transformed_examples = []
# If batch_size is None or <=0, we use the whole dataset as a single batch
if batch_size is None or batch_size <= 0:
batch_size = len(all_examples)
for batch_offset in range(0, len(all_examples), batch_size):
examples = all_examples[batch_offset : batch_offset + batch_size]
if len(examples) < batch_size: # ignore last batch
break
batch = pa.Table.from_pylist(examples)
out = func(batch)
all_transformed_examples.extend(
out.to_pylist()
) # we don't merge with input since they're arrow tables and not dictionaries
all_examples = all_examples if n % batch_size == 0 else all_examples[: n // batch_size * batch_size]
if all_examples:
expected = all_transformed_examples
else:
is_empty = True
if not is_empty:
assert next(iter(ex_iterable))[1] == expected[0]
assert [x for _, x in ex_iterable] == expected
else:
with pytest.raises(StopIteration):
next(iter(ex_iterable))
@pytest.mark.parametrize(
"n, func, batched, batch_size",
[
(
3,
lambda t, index: t.append_column("id+idx", pc.add(t["id"], index)),
False,
None,
), # add the index to the id
(
25,
lambda t, indices: t.append_column("id+idx", pc.add(t["id"], indices)),
True,
10,
), # add the index to the id
(5, lambda t, indices: t.append_column("id+idx", pc.add(t["id"], indices)), True, None), # same with bs=None
(5, lambda t, indices: t.append_column("id+idx", pc.add(t["id"], indices)), True, -1), # same with bs<=0
],
)
def test_mapped_examples_iterable_with_indices_and_arrow_format(n, func, batched, batch_size):
base_ex_iterable = ExamplesIterable(generate_examples_fn, {"n": n})
base_ex_iterable = RebatchedArrowExamplesIterable(base_ex_iterable, batch_size=batch_size if batched else 1)
ex_iterable = MappedExamplesIterable(
base_ex_iterable,
func,
batched=batched,
batch_size=batch_size,
with_indices=True,
formatting=FormattingConfig(format_type="arrow"),
)
all_examples = [x for _, x in generate_examples_fn(n=n)]
if batched is False:
expected = [func(pa.Table.from_pylist([x]), i).to_pylist()[0] for i, x in enumerate(all_examples)]
else:
expected = []
# If batch_size is None or <=0, we use the whole dataset as a single batch
if batch_size is None or batch_size <= 0:
batch_size = len(all_examples)
for batch_offset in range(0, len(all_examples), batch_size):
examples = all_examples[batch_offset : batch_offset + batch_size]
batch = pa.Table.from_pylist(examples)
expected.extend(func(batch, list(range(batch_offset, batch_offset + len(batch)))).to_pylist())
assert next(iter(ex_iterable))[1] == expected[0]
assert [x for _, x in ex_iterable] == expected
assert_load_state_dict_resumes_iteration(ex_iterable)
assert_load_state_dict_resumes_arrow_iteration(ex_iterable)
@pytest.mark.parametrize(
"n, func, batched, batch_size, remove_columns",
[
(
3,
lambda t: t.append_column("id+1", pc.add(t["id"], 1)),
False,
None,
["extra_column"],
), # just add 1 to the id
(25, lambda t: t.append_column("id+1", pc.add(t["id"], 1)), True, 10, ["extra_column"]), # same with bs=10
(
50,
lambda t: pa.table({"foo": ["bar"] * np.random.default_rng(t["id"][0].as_py()).integers(0, 10)}),
True,
8,
["extra_column", "id"],
), # make a duplicate of each example
(5, lambda t: t.append_column("id+1", pc.add(t["id"], 1)), True, None, ["extra_column"]), # same with bs=None
(5, lambda t: t.append_column("id+1", pc.add(t["id"], 1)), True, -1, ["extra_column"]), # same with bs<=0
],
)
def test_mapped_examples_iterable_remove_columns_arrow_format(n, func, batched, batch_size, remove_columns):
base_ex_iterable = ExamplesIterable(generate_examples_fn, {"n": n, "extra_column": "foo"})
base_ex_iterable = RebatchedArrowExamplesIterable(base_ex_iterable, batch_size=batch_size if batched else 1)
ex_iterable = MappedExamplesIterable(
base_ex_iterable,
func,
batched=batched,
batch_size=batch_size,
remove_columns=remove_columns,
formatting=FormattingConfig(format_type="arrow"),
)
all_examples = [x for _, x in generate_examples_fn(n=n)]
columns_to_remove = remove_columns if isinstance(remove_columns, list) else [remove_columns]
if batched is False:
expected = [
{**{k: v for k, v in func(pa.Table.from_pylist([x])).to_pylist()[0].items() if k not in columns_to_remove}}
for x in all_examples
]
else:
expected = []
# If batch_size is None or <=0, we use the whole dataset as a single batch
if batch_size is None or batch_size <= 0:
batch_size = len(all_examples)
for batch_offset in range(0, len(all_examples), batch_size):
examples = all_examples[batch_offset : batch_offset + batch_size]
batch = pa.Table.from_pylist(examples)
expected.extend(
[{k: v for k, v in x.items() if k not in columns_to_remove} for x in func(batch).to_pylist()]
)
assert next(iter(ex_iterable))[1] == expected[0]
assert [x for _, x in ex_iterable] == expected
assert_load_state_dict_resumes_iteration(ex_iterable)
assert_load_state_dict_resumes_arrow_iteration(ex_iterable)
@pytest.mark.parametrize(
"n, func, batched, batch_size, fn_kwargs",
[
(3, lambda t, y=0: t.append_column("id+idx", pc.add(t["id"], y)), False, None, None),
(3, lambda t, y=0: t.append_column("id+idx", pc.add(t["id"], y)), False, None, {"y": 3}),
(25, lambda t, y=0: t.append_column("id+idx", pc.add(t["id"], y)), True, 10, {"y": 3}),
(5, lambda t, y=0: t.append_column("id+idx", pc.add(t["id"], y)), True, None, {"y": 3}), # same with bs=None
(5, lambda t, y=0: t.append_column("id+idx", pc.add(t["id"], y)), True, -1, {"y": 3}), # same with bs<=0
],
)
def test_mapped_examples_iterable_fn_kwargs_and_arrow_format(n, func, batched, batch_size, fn_kwargs):
base_ex_iterable = ExamplesIterable(generate_examples_fn, {"n": n})
base_ex_iterable = RebatchedArrowExamplesIterable(base_ex_iterable, batch_size=batch_size if batched else 1)
ex_iterable = MappedExamplesIterable(
base_ex_iterable,
func,
batched=batched,
batch_size=batch_size,
fn_kwargs=fn_kwargs,
formatting=FormattingConfig(format_type="arrow"),
)
all_examples = [x for _, x in generate_examples_fn(n=n)]
if fn_kwargs is None:
fn_kwargs = {}
if batched is False:
expected = [func(pa.Table.from_pylist([x]), **fn_kwargs).to_pylist()[0] for x in all_examples]
else:
expected = []
# If batch_size is None or <=0, we use the whole dataset as a single batch
if batch_size is None or batch_size <= 0:
batch_size = len(all_examples)
for batch_offset in range(0, len(all_examples), batch_size):
examples = all_examples[batch_offset : batch_offset + batch_size]
batch = pa.Table.from_pylist(examples)
expected.extend(func(batch, **fn_kwargs).to_pylist())
assert next(iter(ex_iterable))[1] == expected[0]
assert [x for _, x in ex_iterable] == expected
assert_load_state_dict_resumes_iteration(ex_iterable)
assert_load_state_dict_resumes_arrow_iteration(ex_iterable)
@pytest.mark.parametrize(
"n, func, batched, batch_size, input_columns",
[
(3, lambda id_: pa.table({"id+1": pc.add(id_, 1)}), False, None, ["id"]), # just add 1 to the id
(25, lambda ids_: pa.table({"id+1": pc.add(ids_, 1)}), True, 10, ["id"]), # same with bs=10
(5, lambda ids_: pa.table({"id+1": pc.add(ids_, 1)}), True, None, ["id"]), # same with bs=None
(5, lambda ids_: pa.table({"id+1": pc.add(ids_, 1)}), True, -1, ["id"]), # same with bs<=0
],
)
def test_mapped_examples_iterable_input_columns_and_arrow_format(n, func, batched, batch_size, input_columns):
base_ex_iterable = ExamplesIterable(generate_examples_fn, {"n": n})
base_ex_iterable = RebatchedArrowExamplesIterable(base_ex_iterable, batch_size=batch_size if batched else 1)
ex_iterable = MappedExamplesIterable(
base_ex_iterable,
func,
batched=batched,
batch_size=batch_size,
input_columns=input_columns,
formatting=FormattingConfig(format_type="arrow"),
)
all_examples = [x for _, x in generate_examples_fn(n=n)]
columns_to_input = input_columns if isinstance(input_columns, list) else [input_columns]
if batched is False:
expected = [
func(*[pa.Table.from_pylist([x])[col] for col in columns_to_input]).to_pylist()[0] for x in all_examples
]
else:
expected = []
# If batch_size is None or <=0, we use the whole dataset as a single batch
if batch_size is None or batch_size <= 0:
batch_size = len(all_examples)
for batch_offset in range(0, len(all_examples), batch_size):
examples = all_examples[batch_offset : batch_offset + batch_size]
batch = pa.Table.from_pylist(examples)
expected.extend(func(*[batch[col] for col in columns_to_input]).to_pylist())
assert next(iter(ex_iterable))[1] == expected[0]
assert [x for _, x in ex_iterable] == expected
assert_load_state_dict_resumes_iteration(ex_iterable)
assert_load_state_dict_resumes_arrow_iteration(ex_iterable)
@pytest.mark.parametrize(
"n, func, batched, batch_size",
[
(3, lambda x: x["id"] % 2 == 0, False, None), # keep even number
(3, lambda x: [x["id"][0] % 2 == 0], True, 1), # same with bs=1
(25, lambda x: [i % 2 == 0 for i in x["id"]], True, 10), # same with bs=10
(5, lambda x: [i % 2 == 0 for i in x["id"]], True, None), # same with bs=None
(5, lambda x: [i % 2 == 0 for i in x["id"]], True, -1), # same with bs<=0
(3, lambda x: False, False, None), # return 0 examples
(3, lambda x: [False] * len(x["id"]), True, 10), # same with bs=10
],
)
def test_filtered_examples_iterable(n, func, batched, batch_size):
base_ex_iterable = ExamplesIterable(generate_examples_fn, {"n": n})
ex_iterable = FilteredExamplesIterable(base_ex_iterable, func, batched=batched, batch_size=batch_size)
all_examples = [x for _, x in generate_examples_fn(n=n)]
if batched is False:
expected = [x for x in all_examples if func(x)]
else:
# For batched filter we have to format the examples as a batch (i.e. in one single dictionary) to pass the batch to the function
expected = []
# If batch_size is None or <=0, we use the whole dataset as a single batch
if batch_size is None or batch_size <= 0:
batch_size = len(all_examples)
for batch_offset in range(0, len(all_examples), batch_size):
examples = all_examples[batch_offset : batch_offset + batch_size]
batch = _examples_to_batch(examples)
mask = func(batch)
expected.extend([x for x, to_keep in zip(examples, mask) if to_keep])
if expected:
assert next(iter(ex_iterable))[1] == expected[0]
assert [x for _, x in ex_iterable] == expected
assert_load_state_dict_resumes_iteration(ex_iterable)
@pytest.mark.parametrize(
"n, func, batched, batch_size",
[
(3, lambda x, index: index % 2 == 0, False, None), # keep even number
(25, lambda x, indices: [idx % 2 == 0 for idx in indices], True, 10), # same with bs=10
(5, lambda x, indices: [idx % 2 == 0 for idx in indices], True, None), # same with bs=None
(5, lambda x, indices: [idx % 2 == 0 for idx in indices], True, -1), # same with bs<=0
],
)
def test_filtered_examples_iterable_with_indices(n, func, batched, batch_size):
base_ex_iterable = ExamplesIterable(generate_examples_fn, {"n": n})
ex_iterable = FilteredExamplesIterable(
base_ex_iterable, func, batched=batched, batch_size=batch_size, with_indices=True
)
all_examples = [x for _, x in generate_examples_fn(n=n)]
if batched is False:
expected = [x for idx, x in enumerate(all_examples) if func(x, idx)]
else:
# For batched filter we have to format the examples as a batch (i.e. in one single dictionary) to pass the batch to the function
expected = []
# If batch_size is None or <=0, we use the whole dataset as a single batch
if batch_size is None or batch_size <= 0:
batch_size = len(all_examples)
for batch_offset in range(0, len(all_examples), batch_size):
examples = all_examples[batch_offset : batch_offset + batch_size]
batch = _examples_to_batch(examples)
indices = list(range(batch_offset, batch_offset + len(examples)))
mask = func(batch, indices)
expected.extend([x for x, to_keep in zip(examples, mask) if to_keep])
assert next(iter(ex_iterable))[1] == expected[0]
assert [x for _, x in ex_iterable] == expected
assert_load_state_dict_resumes_iteration(ex_iterable)
@pytest.mark.parametrize(
"n, func, batched, batch_size, input_columns",
[
(3, lambda id_: id_ % 2 == 0, False, None, ["id"]), # keep even number
(25, lambda ids_: [i % 2 == 0 for i in ids_], True, 10, ["id"]), # same with bs=10
(3, lambda ids_: [i % 2 == 0 for i in ids_], True, None, ["id"]), # same with bs=None
(3, lambda ids_: [i % 2 == 0 for i in ids_], True, None, ["id"]), # same with bs=None
],
)
def test_filtered_examples_iterable_input_columns(n, func, batched, batch_size, input_columns):
base_ex_iterable = ExamplesIterable(generate_examples_fn, {"n": n})
ex_iterable = FilteredExamplesIterable(
base_ex_iterable, func, batched=batched, batch_size=batch_size, input_columns=input_columns
)
all_examples = [x for _, x in generate_examples_fn(n=n)]
columns_to_input = input_columns if isinstance(input_columns, list) else [input_columns]
if batched is False:
expected = [x for x in all_examples if func(*[x[col] for col in columns_to_input])]
else:
# For batched filter we have to format the examples as a batch (i.e. in one single dictionary) to pass the batch to the function
expected = []
# If batch_size is None or <=0, we use the whole dataset as a single batch
if batch_size is None or batch_size <= 0:
batch_size = len(all_examples)
for batch_offset in range(0, len(all_examples), batch_size):
examples = all_examples[batch_offset : batch_offset + batch_size]
batch = _examples_to_batch(examples)
mask = func(*[batch[col] for col in columns_to_input])
expected.extend([x for x, to_keep in zip(examples, mask) if to_keep])
assert next(iter(ex_iterable))[1] == expected[0]
assert [x for _, x in ex_iterable] == expected
assert_load_state_dict_resumes_iteration(ex_iterable)
def test_map_async():
dset = Dataset.from_dict({"x": range(100)}).to_iterable_dataset()
async def f(example):
await asyncio.sleep(0.1)
return {"y": 1}
_start = time.time()
out = dset.map(f)
assert time.time() - _start < 2.0
assert next(iter(out))["y"] == 1
async def f(batch):
await asyncio.sleep(0.1)
return {"y": [1] * len(batch["x"])}
_start = time.time()
out = dset.map(f, batched=True)
assert time.time() - _start < 2.0
assert next(iter(out))["y"] == 1
def test_filter_async():
dset = Dataset.from_dict({"x": range(100)}).to_iterable_dataset()
async def f(example):
await asyncio.sleep(0.1)
return example["x"] == 42
_start = time.time()
out = dset.filter(f)
assert time.time() - _start < 2.0
assert len(list(out)) == 1
async def f(batch):
await asyncio.sleep(0.1)
return [x == 42 for x in batch["x"]]
_start = time.time()
out = dset.filter(f, batched=True)
assert time.time() - _start < 2.0
assert len(list(out)) == 1
def test_skip_examples_iterable():
total, count = 10, 2
base_ex_iterable = ExamplesIterable(generate_examples_fn, {"n": total})
skip_ex_iterable = SkipExamplesIterable(base_ex_iterable, n=count)
expected = list(generate_examples_fn(n=total))[count:]
assert list(skip_ex_iterable) == expected
assert skip_ex_iterable.shuffle_data_sources(np.random.default_rng(42)) is skip_ex_iterable, (
"skip examples makes the shards order fixed"
)
assert_load_state_dict_resumes_iteration(skip_ex_iterable)
def test_take_examples_iterable():
total, count = 10, 2
base_ex_iterable = ExamplesIterable(generate_examples_fn, {"n": total})
take_ex_iterable = TakeExamplesIterable(base_ex_iterable, n=count)
expected = list(generate_examples_fn(n=total))[:count]
assert list(take_ex_iterable) == expected
assert take_ex_iterable.shuffle_data_sources(np.random.default_rng(42)) is take_ex_iterable, (
"skip examples makes the shards order fixed"
)
assert_load_state_dict_resumes_iteration(take_ex_iterable)
@pytest.mark.parametrize(
"n, num_times",
[
(3, None),
(3, 3),
(3, 0),
],
)
def test_repeat_examples_iterable(n, num_times):
base_ex_iterable = ExamplesIterable(generate_examples_fn, {"n": n})
ex_iterable = RepeatExamplesIterable(base_ex_iterable, num_times=num_times)
all_examples = [x for _, x in generate_examples_fn(n=n)]
if num_times is not None:
expected = all_examples * max(num_times, 0)
assert [x for _, x in ex_iterable] == expected
else:
max_iters = 135
iterator = iter(ex_iterable)
for i in range(max_iters):
assert next(iterator)[1] == all_examples[i % len(all_examples)], f"iteration {i} failed,"
def test_vertically_concatenated_examples_iterable():
ex_iterable1 = ExamplesIterable(generate_examples_fn, {"label": 10})
ex_iterable2 = ExamplesIterable(generate_examples_fn, {"label": 5})
concatenated_ex_iterable = VerticallyConcatenatedMultiSourcesExamplesIterable([ex_iterable1, ex_iterable2])
expected = [x for _, x in ex_iterable1] + [x for _, x in ex_iterable2]
assert [x for _, x in concatenated_ex_iterable] == expected
assert_load_state_dict_resumes_iteration(concatenated_ex_iterable)
def test_vertically_concatenated_examples_iterable_with_different_columns():
# having different columns is supported
# Though iterable datasets fill the missing data with nulls
ex_iterable1 = ExamplesIterable(generate_examples_fn, {"label": 10})
ex_iterable2 = ExamplesIterable(generate_examples_fn, {})
concatenated_ex_iterable = VerticallyConcatenatedMultiSourcesExamplesIterable([ex_iterable1, ex_iterable2])
expected = [x for _, x in ex_iterable1] + [x for _, x in ex_iterable2]
assert [x for _, x in concatenated_ex_iterable] == expected
assert_load_state_dict_resumes_iteration(concatenated_ex_iterable)
def test_vertically_concatenated_examples_iterable_shuffle_data_sources():
ex_iterable1 = ExamplesIterable(generate_examples_fn, {"label": 10})
ex_iterable2 = ExamplesIterable(generate_examples_fn, {"label": 5})
concatenated_ex_iterable = VerticallyConcatenatedMultiSourcesExamplesIterable([ex_iterable1, ex_iterable2])
rng = np.random.default_rng(42)
shuffled_ex_iterable = concatenated_ex_iterable.shuffle_data_sources(rng)
# make sure the list of examples iterables is shuffled, and each examples iterable is shuffled
expected = [x for _, x in ex_iterable2.shuffle_data_sources(rng)] + [
x for _, x in ex_iterable1.shuffle_data_sources(rng)
]
assert [x for _, x in shuffled_ex_iterable] == expected
assert_load_state_dict_resumes_iteration(shuffled_ex_iterable)
def test_horizontally_concatenated_examples_iterable():
ex_iterable1 = ExamplesIterable(generate_examples_fn, {"label1": 10})
ex_iterable2 = ExamplesIterable(generate_examples_fn, {"label2": 5})
concatenated_ex_iterable = HorizontallyConcatenatedMultiSourcesExamplesIterable([ex_iterable1, ex_iterable2])
with pytest.raises(ValueError): # column "id" is duplicated -> raise an error
list(concatenated_ex_iterable)
ex_iterable2 = MappedExamplesIterable(ex_iterable2, lambda x: x, remove_columns=["id"])
concatenated_ex_iterable = HorizontallyConcatenatedMultiSourcesExamplesIterable([ex_iterable1, ex_iterable2])
expected = [{**x, **y} for (_, x), (_, y) in zip(ex_iterable1, ex_iterable2)]
assert [x for _, x in concatenated_ex_iterable] == expected
assert concatenated_ex_iterable.shuffle_data_sources(np.random.default_rng(42)) is concatenated_ex_iterable, (
"horizontally concatenated examples makes the shards order fixed"
)
assert_load_state_dict_resumes_iteration(concatenated_ex_iterable)
@pytest.mark.parametrize(
"ex_iterable",
[
ExamplesIterable(generate_examples_fn, {}),
ShuffledDataSourcesExamplesIterable(generate_examples_fn, {}, np.random.default_rng(42)),
SelectColumnsIterable(ExamplesIterable(generate_examples_fn, {}), ["id"]),
StepExamplesIterable(ExamplesIterable(generate_examples_fn, {}), 2, 0),
CyclingMultiSourcesExamplesIterable([ExamplesIterable(generate_examples_fn, {})]),
VerticallyConcatenatedMultiSourcesExamplesIterable([ExamplesIterable(generate_examples_fn, {})]),
HorizontallyConcatenatedMultiSourcesExamplesIterable([ExamplesIterable(generate_examples_fn, {})]),
RandomlyCyclingMultiSourcesExamplesIterable(
[ExamplesIterable(generate_examples_fn, {})], np.random.default_rng(42)
),
MappedExamplesIterable(ExamplesIterable(generate_examples_fn, {}), lambda x: x),
MappedExamplesIterable(ArrowExamplesIterable(generate_tables_fn, {}), lambda x: x),
FilteredExamplesIterable(ExamplesIterable(generate_examples_fn, {}), lambda x: True),
FilteredExamplesIterable(ArrowExamplesIterable(generate_tables_fn, {}), lambda x: True),
BufferShuffledExamplesIterable(ExamplesIterable(generate_examples_fn, {}), 10, np.random.default_rng(42)),
SkipExamplesIterable(ExamplesIterable(generate_examples_fn, {}), 10),
TakeExamplesIterable(ExamplesIterable(generate_examples_fn, {}), 10),
FormattedExamplesIterable(
ExamplesIterable(generate_examples_fn, {}), None, Features({"id": Value("int32")}), token_per_repo_id={}
),
],
)
def test_no_iter_arrow(ex_iterable: _BaseExamplesIterable):
assert ex_iterable.iter_arrow is None
if not isinstance(ex_iterable, BufferShuffledExamplesIterable):
assert_load_state_dict_resumes_iteration(ex_iterable)
@pytest.mark.parametrize(
"ex_iterable",
[
ArrowExamplesIterable(generate_tables_fn, {}),
ShuffledDataSourcesArrowExamplesIterable(generate_tables_fn, {}, np.random.default_rng(42)),
SelectColumnsIterable(ArrowExamplesIterable(generate_tables_fn, {}), ["id"]),
# StepExamplesIterable(ArrowExamplesIterable(generate_tables_fn, {}), 2, 0), # not implemented
# CyclingMultiSourcesExamplesIterable([ArrowExamplesIterable(generate_tables_fn, {})]), # not implemented
VerticallyConcatenatedMultiSourcesExamplesIterable([ArrowExamplesIterable(generate_tables_fn, {})]),
# HorizontallyConcatenatedMultiSourcesExamplesIterable([ArrowExamplesIterable(generate_tables_fn, {})]), # not implemented
# RandomlyCyclingMultiSourcesExamplesIterable([ArrowExamplesIterable(generate_tables_fn, {})], np.random.default_rng(42)), # not implemented
MappedExamplesIterable(
RebatchedArrowExamplesIterable(ExamplesIterable(generate_examples_fn, {}), batch_size=1),
lambda t: t,
formatting=FormattingConfig(format_type="arrow"),
),
MappedExamplesIterable(
RebatchedArrowExamplesIterable(ArrowExamplesIterable(generate_tables_fn, {}), batch_size=1),
lambda t: t,
formatting=FormattingConfig(format_type="arrow"),
),
FilteredExamplesIterable(
RebatchedArrowExamplesIterable(ExamplesIterable(generate_examples_fn, {}), batch_size=1),
lambda t: True,
formatting=FormattingConfig(format_type="arrow"),
),
FilteredExamplesIterable(
RebatchedArrowExamplesIterable(ArrowExamplesIterable(generate_tables_fn, {}), batch_size=1),
lambda t: True,
formatting=FormattingConfig(format_type="arrow"),
),
# BufferShuffledExamplesIterable(ArrowExamplesIterable(generate_tables_fn, {}), 10, np.random.default_rng(42)), # not implemented
# SkipExamplesIterable(ArrowExamplesIterable(generate_tables_fn, {}), 10), # not implemented
# TakeExamplesIterable(ArrowExamplesIterable(generate_tables_fn, {}), 10), # not implemented
FormattedExamplesIterable(
ArrowExamplesIterable(generate_tables_fn, {}), None, Features({"id": Value("int32")}), token_per_repo_id={}
),
],
)
def test_iter_arrow(ex_iterable: _BaseExamplesIterable):
assert ex_iterable.iter_arrow is not None
key, pa_table = next(ex_iterable.iter_arrow())
assert isinstance(pa_table, pa.Table)
assert_load_state_dict_resumes_arrow_iteration(ex_iterable)
############################
#
# IterableDataset tests
#
############################
def test_iterable_dataset():
dataset = IterableDataset(ExamplesIterable(generate_examples_fn, {}))
expected = [x for _, x in generate_examples_fn()]
assert next(iter(dataset)) == expected[0]
assert list(dataset) == expected
def test_iterable_dataset_from_generator():
data = [
{"col_1": "0", "col_2": 0, "col_3": 0.0},
{"col_1": "1", "col_2": 1, "col_3": 1.0},
{"col_1": "2", "col_2": 2, "col_3": 2.0},
{"col_1": "3", "col_2": 3, "col_3": 3.0},
]
def gen():
yield from data
dataset = IterableDataset.from_generator(gen)
assert isinstance(dataset, IterableDataset)
assert list(dataset) == data
def test_iterable_dataset_from_generator_with_shards():
def gen(shard_names):
for shard_name in shard_names:
for i in range(10):
yield {"shard_name": shard_name, "i": i}
shard_names = [f"data{shard_idx}.txt" for shard_idx in range(4)]
dataset = IterableDataset.from_generator(gen, gen_kwargs={"shard_names": shard_names})
assert isinstance(dataset, IterableDataset)
assert dataset.num_shards == len(shard_names)
@require_numpy1_on_windows
def test_iterable_dataset_from_file(dataset: IterableDataset, arrow_file: str):
with assert_arrow_memory_doesnt_increase():
dataset_from_file = IterableDataset.from_file(arrow_file)
expected_features = dataset._resolve_features().features
assert dataset_from_file.features.type == expected_features.type
assert dataset_from_file.features == expected_features
assert isinstance(dataset_from_file, IterableDataset)
assert list(dataset_from_file) == list(dataset)
@require_not_windows
@require_dill_gt_0_3_2
@require_pyspark
def test_from_spark_streaming():
import pyspark
spark = pyspark.sql.SparkSession.builder.master("local[*]").appName("pyspark").getOrCreate()
data = [
("0", 0, 0.0),
("1", 1, 1.0),
("2", 2, 2.0),
("3", 3, 3.0),
]
df = spark.createDataFrame(data, "col_1: string, col_2: int, col_3: float")
dataset = IterableDataset.from_spark(df)
assert isinstance(dataset, IterableDataset)
results = []
for ex in dataset:
results.append(ex)
assert results == [
{"col_1": "0", "col_2": 0, "col_3": 0.0},
{"col_1": "1", "col_2": 1, "col_3": 1.0},
{"col_1": "2", "col_2": 2, "col_3": 2.0},
{"col_1": "3", "col_2": 3, "col_3": 3.0},
]
@require_not_windows
@require_dill_gt_0_3_2
@require_pyspark
def test_from_spark_streaming_features():
import PIL.Image
import pyspark
spark = pyspark.sql.SparkSession.builder.master("local[*]").appName("pyspark").getOrCreate()
data = [(0, np.arange(4 * 4 * 3).reshape(4, 4, 3).tolist())]
df = spark.createDataFrame(data, "idx: int, image: array<array<array<int>>>")
features = Features({"idx": Value("int64"), "image": Image()})
dataset = IterableDataset.from_spark(
df,
features=features,
)
assert isinstance(dataset, IterableDataset)
results = []
for ex in dataset:
results.append(ex)
assert len(results) == 1
isinstance(results[0]["image"], PIL.Image.Image)
@require_torch
def test_iterable_dataset_torch_integration():
ex_iterable = ExamplesIterable(generate_examples_fn, {})
dataset = IterableDataset(ex_iterable)
import torch.utils.data
assert isinstance(dataset, torch.utils.data.IterableDataset)
assert isinstance(dataset, IterableDataset)
@require_torch
def test_iterable_dataset_torch_picklable():
import pickle
ex_iterable = ExamplesIterable(generate_examples_fn, {})
dataset = IterableDataset(ex_iterable, formatting=FormattingConfig(format_type="torch"))
reloaded_dataset = pickle.loads(pickle.dumps(dataset))
import torch.utils.data
assert isinstance(reloaded_dataset, IterableDataset)
assert isinstance(reloaded_dataset, torch.utils.data.IterableDataset)
assert reloaded_dataset._formatting.format_type == "torch"
assert len(list(dataset)) == len(list(reloaded_dataset))
@require_torch
def test_iterable_dataset_with_format_torch():
ex_iterable = ExamplesIterable(generate_examples_fn, {})
dataset = IterableDataset(ex_iterable)
from torch.utils.data import DataLoader
dataloader = DataLoader(dataset)
assert len(list(dataloader)) == len(list(ex_iterable))
@require_torch
def test_iterable_dataset_torch_dataloader_parallel():
from torch.utils.data import DataLoader
ex_iterable = ExamplesIterable(generate_examples_fn, {})
dataset = IterableDataset(ex_iterable)
dataloader = DataLoader(dataset, num_workers=2, batch_size=None)
result = list(dataloader)
expected = [example for _, example in ex_iterable]
assert len(result) == len(expected)
assert {str(x) for x in result} == {str(x) for x in expected}
@require_torch
@pytest.mark.filterwarnings("ignore:This DataLoader will create:UserWarning")
@pytest.mark.parametrize("num_shards, num_workers", [(2, 1), (2, 2), (3, 2), (2, 3)])
def test_sharded_iterable_dataset_torch_dataloader_parallel(num_shards, num_workers):
from torch.utils.data import DataLoader
ex_iterable = ExamplesIterable(generate_examples_fn, {"filepaths": [f"{i}.txt" for i in range(num_shards)]})
dataset = IterableDataset(ex_iterable)
dataloader = DataLoader(dataset, batch_size=None, num_workers=num_workers)
result = list(dataloader)
expected = [example for _, example in ex_iterable]
assert len(result) == len(expected)
assert {str(x) for x in result} == {str(x) for x in expected}
@require_torch
@pytest.mark.integration
@pytest.mark.parametrize("num_workers", [1, 2])
def test_iterable_dataset_from_hub_torch_dataloader_parallel(num_workers, tmp_path):
from torch.utils.data import DataLoader
dataset = load_dataset(SAMPLE_DATASET_IDENTIFIER, cache_dir=str(tmp_path), streaming=True, split="train")
dataloader = DataLoader(dataset, batch_size=None, num_workers=num_workers)
result = list(dataloader)
assert len(result) == 10
@pytest.mark.parametrize("batch_size", [4, 5])
@pytest.mark.parametrize("drop_last_batch", [False, True])
def test_iterable_dataset_iter_batch(batch_size, drop_last_batch):
n = 25
dataset = IterableDataset(ExamplesIterable(generate_examples_fn, {"n": n}))
all_examples = [ex for _, ex in generate_examples_fn(n=n)]
expected = []
for i in range(0, len(all_examples), batch_size):
if len(all_examples[i : i + batch_size]) < batch_size and drop_last_batch:
continue
expected.append(_examples_to_batch(all_examples[i : i + batch_size]))
assert next(iter(dataset.iter(batch_size, drop_last_batch=drop_last_batch))) == expected[0]
assert list(dataset.iter(batch_size, drop_last_batch=drop_last_batch)) == expected
def test_iterable_dataset_info():
info = DatasetInfo(description="desc", citation="@article{}", size_in_bytes=42)
ex_iterable = ExamplesIterable(generate_examples_fn, {})
dataset = IterableDataset(ex_iterable, info=info)
assert dataset.info == info
assert dataset.description == info.description
assert dataset.citation == info.citation
assert dataset.size_in_bytes == info.size_in_bytes
def test_iterable_dataset_set_epoch(dataset: IterableDataset):
assert dataset._epoch == 0
dataset.set_epoch(42)
assert dataset._epoch == 42
def test_iterable_dataset_set_epoch_resuming(dataset: IterableDataset):
dataset_length = len(list(dataset))
assert len(list(dataset)) == dataset_length > 0
dataset.load_state_dict(dataset.state_dict())
assert len(list(dataset)) == 0
dataset.set_epoch(1)
assert len(list(dataset)) == dataset_length > 0
dataset.load_state_dict(dataset.state_dict())
assert len(list(dataset)) == 0
@pytest.mark.parametrize("seed", [None, 42, 1337])
@pytest.mark.parametrize("epoch", [None, 0, 1, 10])
def test_iterable_dataset_set_epoch_of_shuffled_dataset(dataset: IterableDataset, seed, epoch):
buffer_size = 10
shuffled_dataset = dataset.shuffle(seed, buffer_size=buffer_size)
base_generator = shuffled_dataset._shuffling.generator
if epoch is not None:
shuffled_dataset.set_epoch(epoch)
effective_generator = shuffled_dataset._effective_generator()
assert effective_generator is not None
if epoch is None or epoch == 0:
assert is_rng_equal(base_generator, shuffled_dataset._effective_generator())
else:
assert not is_rng_equal(base_generator, shuffled_dataset._effective_generator())
effective_seed = deepcopy(base_generator).integers(0, 1 << 63) - epoch
assert is_rng_equal(np.random.default_rng(effective_seed), shuffled_dataset._effective_generator())
def test_iterable_dataset_map(
dataset: IterableDataset,
):
func = lambda x: {"id+1": x["id"] + 1} # noqa: E731
mapped_dataset = dataset.map(func)
assert isinstance(mapped_dataset._ex_iterable, MappedExamplesIterable)
assert mapped_dataset._ex_iterable.function is func
assert mapped_dataset._ex_iterable.batched is False
assert next(iter(mapped_dataset)) == {**next(iter(dataset)), **func(next(iter(generate_examples_fn()))[1])}
def test_iterable_dataset_map_batched(
dataset: IterableDataset,
):
func = lambda x: {"id+1": [i + 1 for i in x["id"]]} # noqa: E731
batch_size = 3
dataset = dataset.map(func, batched=True, batch_size=batch_size)
assert isinstance(dataset._ex_iterable, MappedExamplesIterable)
assert dataset._ex_iterable.function is func
assert dataset._ex_iterable.batch_size == batch_size
assert next(iter(dataset)) == {"id": 0, "id+1": 1}
def test_iterable_dataset_map_complex_features(
dataset: IterableDataset,
):
# https://github.com/huggingface/datasets/issues/3505
ex_iterable = ExamplesIterable(generate_examples_fn, {"label": "positive"})
features = Features(
{
"id": Value("int64"),
"label": Value("string"),
}
)
dataset = IterableDataset(ex_iterable, info=DatasetInfo(features=features))
dataset = dataset.cast_column("label", ClassLabel(names=["negative", "positive"]))
dataset = dataset.map(lambda x: {"id+1": x["id"] + 1, **x})
assert isinstance(dataset._ex_iterable, MappedExamplesIterable)
features["label"] = ClassLabel(names=["negative", "positive"])
assert [{k: v for k, v in ex.items() if k != "id+1"} for ex in dataset] == [
features.encode_example(ex) for _, ex in ex_iterable
]
def test_iterable_dataset_map_with_features(dataset: IterableDataset) -> None:
# https://github.com/huggingface/datasets/issues/3888
ex_iterable = ExamplesIterable(generate_examples_fn, {"label": "positive"})
features_before_map = Features(
{
"id": Value("int64"),
"label": Value("string"),
}
)
dataset = IterableDataset(ex_iterable, info=DatasetInfo(features=features_before_map))
assert dataset.info.features is not None
assert dataset.info.features == features_before_map
features_after_map = Features(
{
"id": Value("int64"),
"label": Value("string"),
"target": Value("string"),
}
)
dataset = dataset.map(lambda x: {"target": x["label"]}, features=features_after_map)
assert dataset.info.features is not None
assert dataset.info.features == features_after_map
def test_iterable_dataset_map_with_fn_kwargs(dataset: IterableDataset) -> None:
fn_kwargs = {"y": 1}
mapped_dataset = dataset.map(lambda x, y: {"id+y": x["id"] + y}, fn_kwargs=fn_kwargs)
assert mapped_dataset._ex_iterable.batched is False
assert next(iter(mapped_dataset)) == {"id": 0, "id+y": 1}
batch_size = 3
mapped_dataset = dataset.map(
lambda x, y: {"id+y": [i + y for i in x["id"]]}, batched=True, batch_size=batch_size, fn_kwargs=fn_kwargs
)
assert isinstance(mapped_dataset._ex_iterable, MappedExamplesIterable)
assert mapped_dataset._ex_iterable.batch_size == batch_size
assert next(iter(mapped_dataset)) == {"id": 0, "id+y": 1}
def test_iterable_dataset_filter(dataset: IterableDataset) -> None:
fn_kwargs = {"y": 1}
filtered_dataset = dataset.filter(lambda x, y: x["id"] == y, fn_kwargs=fn_kwargs)
assert filtered_dataset._ex_iterable.batched is False
assert next(iter(filtered_dataset)) == {"id": 1}
@pytest.mark.parametrize("seed", [42, 1337, 101010, 123456])
@pytest.mark.parametrize("epoch", [None, 0, 1])
def test_iterable_dataset_shuffle(dataset: IterableDataset, seed, epoch):
buffer_size = 3
dataset = deepcopy(dataset)
dataset._ex_iterable.kwargs["filepaths"] = ["0.txt", "1.txt"]
dataset = dataset.shuffle(seed, buffer_size=buffer_size)
assert isinstance(dataset._shuffling, ShufflingConfig)
assert isinstance(dataset._shuffling.generator, np.random.Generator)
assert is_rng_equal(dataset._shuffling.generator, np.random.default_rng(seed))
# Effective seed is sum of seed and epoch
if epoch is None or epoch == 0:
effective_seed = seed
else:
dataset.set_epoch(epoch)
effective_seed = np.random.default_rng(seed).integers(0, 1 << 63) - epoch
# Shuffling adds a shuffle buffer
expected_first_example_index = next(
iter(BufferShuffledExamplesIterable._iter_random_indices(np.random.default_rng(effective_seed), buffer_size))
)
assert isinstance(dataset._ex_iterable, BufferShuffledExamplesIterable)
# It also shuffles the underlying examples iterable
expected_ex_iterable = ExamplesIterable(
generate_examples_fn, {"filepaths": ["0.txt", "1.txt"]}
).shuffle_data_sources(np.random.default_rng(effective_seed))
assert isinstance(dataset._ex_iterable.ex_iterable, ExamplesIterable)
assert next(iter(dataset)) == list(islice(expected_ex_iterable, expected_first_example_index + 1))[-1][1]
@pytest.mark.parametrize(
"features",
[
None,
Features(
{
"id": Value("int64"),
"label": Value("int64"),
}
),
Features(
{
"id": Value("int64"),
"label": ClassLabel(names=["negative", "positive"]),
}
),
],
)
def test_iterable_dataset_features(features):
ex_iterable = ExamplesIterable(generate_examples_fn, {"label": 0})
dataset = IterableDataset(ex_iterable, info=DatasetInfo(features=features))
if features:
expected = [features.encode_example(x) for _, x in ex_iterable]
else:
expected = [x for _, x in ex_iterable]
assert list(dataset) == expected
def test_iterable_dataset_features_cast_to_python():
ex_iterable = ExamplesIterable(
generate_examples_fn, {"timestamp": pd.Timestamp(2020, 1, 1), "array": np.ones(5), "n": 1}
)
features = Features(
{
"id": Value("int64"),
"timestamp": Value("timestamp[us]"),
"array": List(Value("int64")),
}
)
dataset = IterableDataset(ex_iterable, info=DatasetInfo(features=features))
assert list(dataset) == [{"timestamp": pd.Timestamp(2020, 1, 1).to_pydatetime(), "array": [1] * 5, "id": 0}]
@require_torch
@require_tf
@require_jax
@pytest.mark.parametrize(
"format_type", [None, "torch", "python", "tf", "tensorflow", "np", "numpy", "jax", "arrow", "pd", "pandas"]
)
def test_iterable_dataset_with_format(dataset: IterableDataset, format_type):
formatted_dataset = dataset.with_format(format_type)
assert formatted_dataset._formatting.format_type == get_format_type_from_alias(format_type)
@require_torch
def test_iterable_dataset_is_torch_iterable_dataset(dataset: IterableDataset):
from torch.utils.data import DataLoader, _DatasetKind
dataloader = DataLoader(dataset)
assert dataloader._dataset_kind == _DatasetKind.Iterable
out = list(dataloader)
assert len(out) == DEFAULT_N_EXAMPLES
@require_torch
def test_iterable_dataset_persists_epoch_in_torch_workers(dataset: IterableDataset):
from torch.utils.data import DataLoader
dataset = dataset.shuffle(seed=42)
dataloader = DataLoader(dataset, num_workers=1, persistent_workers=True)
epoch0 = list(dataloader)
assert list(dataloader) == epoch0
dataset.set_epoch(1)
assert list(dataloader) != epoch0
# Make sure pickle works even with torch objects in shared memory
dataset_copy: IterableDataset = pickle.loads(pickle.dumps(dataset))
dataloader = DataLoader(dataset_copy, num_workers=1, persistent_workers=True)
epoch1 = list(dataloader)
assert list(dataloader) == epoch1
dataset.set_epoch(2) # this should not affect the copy
assert list(dataloader) == epoch1
dataset_copy.set_epoch(2)
assert list(dataloader) != epoch1
@pytest.mark.parametrize("n", [0, 2, int(1e10)])
def test_iterable_dataset_skip(dataset: IterableDataset, n):
skip_dataset = dataset.skip(n)
assert isinstance(skip_dataset._ex_iterable, SkipExamplesIterable)
assert skip_dataset._ex_iterable.n == n
assert list(skip_dataset) == list(dataset)[n:]
@pytest.mark.parametrize("n", [0, 2, int(1e10)])
def test_iterable_dataset_take(dataset: IterableDataset, n):
take_dataset = dataset.take(n)
assert isinstance(take_dataset._ex_iterable, TakeExamplesIterable)
assert take_dataset._ex_iterable.n == n
assert list(take_dataset) == list(dataset)[:n]
@pytest.mark.parametrize("n", [0, 2])
def test_iterable_dataset_repeat(dataset: IterableDataset, n):
repeat_dataset = dataset.repeat(n)
assert isinstance(repeat_dataset._ex_iterable, RepeatExamplesIterable)
assert repeat_dataset._ex_iterable.num_times == n
assert list(repeat_dataset) == list(dataset) * n
def test_iterable_dataset_shard():
num_examples = 20
num_shards = 5
dataset = Dataset.from_dict({"a": range(num_examples)}).to_iterable_dataset(num_shards=num_shards)
assert sum(dataset.shard(num_shards, i).num_shards for i in range(num_shards)) == dataset.num_shards
assert list(concatenate_datasets([dataset.shard(num_shards, i) for i in range(num_shards)])) == list(dataset)
num_shards = 2
assert sum(dataset.shard(num_shards, i).num_shards for i in range(num_shards)) == dataset.num_shards
assert list(concatenate_datasets([dataset.shard(num_shards, i) for i in range(num_shards)])) == list(dataset)
assert (
sum(dataset.shard(num_shards, i, contiguous=False).num_shards for i in range(num_shards)) == dataset.num_shards
)
assert list(
concatenate_datasets([dataset.shard(num_shards, i, contiguous=False) for i in range(num_shards)])
) != list(dataset)
assert sorted(
concatenate_datasets([dataset.shard(num_shards, i, contiguous=False) for i in range(num_shards)]),
key=lambda x: x["a"],
) == list(dataset)
@pytest.mark.parametrize("method", ["skip", "take"])
@pytest.mark.parametrize("after_shuffle", [False, True])
@pytest.mark.parametrize("count", [2, 5, 11])
def test_iterable_dataset_skip_or_take_after_shuffle(method, after_shuffle, count):
seed = 42
n, num_shards = 3, 10
ex_iterable = ExamplesIterable(
generate_examples_fn, {"n": n, "filepaths": [f"{i}.txt" for i in range(num_shards)]}
)
dataset = IterableDataset(ex_iterable)
shuffled_dataset = dataset
if after_shuffle:
shuffled_dataset = shuffled_dataset.shuffle(seed, buffer_size=DEFAULT_N_EXAMPLES)
shuffled_dataset = shuffled_dataset.skip(count) if method == "skip" else shuffled_dataset.take(count)
# skip/take a shuffled dataset should not keep the same examples and shuffle the shards
key = lambda x: f"{x['filepath']}_{x['id']}" # noqa: E731
assert (len(list(dataset)) - count if method == "skip" else count) == len(list(shuffled_dataset))
assert sorted(list(dataset)[count:] if method == "skip" else list(dataset)[:count], key=key) != sorted(
shuffled_dataset, key=key
)
else:
shuffled_dataset = shuffled_dataset.skip(count) if method == "skip" else shuffled_dataset.take(count)
shuffled_dataset = shuffled_dataset.shuffle(seed, buffer_size=DEFAULT_N_EXAMPLES)
# shuffling a skip/take dataset should keep the same examples and don't shuffle the shards
key = lambda x: f"{x['filepath']}_{x['id']}" # noqa: E731
assert (len(list(dataset)) - count if method == "skip" else count) == len(list(shuffled_dataset))
assert sorted(list(dataset)[count:] if method == "skip" else list(dataset)[:count], key=key) == sorted(
shuffled_dataset, key=key
)
@pytest.mark.parametrize("method", ["skip", "take"])
@pytest.mark.parametrize("after_split_by_node", [False, True])
@pytest.mark.parametrize("count", [2, 5, 11])
def test_iterable_dataset_skip_or_take_after_split_by_node(method, after_split_by_node, count):
n, num_shards = 3, 10
rank, world_size = 1, 2
ex_iterable = ExamplesIterable(
generate_examples_fn, {"n": n, "filepaths": [f"{i}.txt" for i in range(num_shards)]}
)
dataset = IterableDataset(ex_iterable)
distributed_dataset = dataset
true_distributed_dataset = split_dataset_by_node(dataset, rank=rank, world_size=world_size)
if after_split_by_node:
distributed_dataset = split_dataset_by_node(distributed_dataset, rank=rank, world_size=world_size)
distributed_dataset = distributed_dataset.skip(count) if method == "skip" else distributed_dataset.take(count)
assert (
list(true_distributed_dataset)[count:]
if method == "skip"
else list(true_distributed_dataset)[:count] == list(distributed_dataset)
)
else:
distributed_dataset = distributed_dataset.skip(count) if method == "skip" else distributed_dataset.take(count)
distributed_dataset = split_dataset_by_node(distributed_dataset, rank=rank, world_size=world_size)
assert len(
list(true_distributed_dataset)[count // world_size :]
if method == "skip"
else list(true_distributed_dataset)[: count // world_size]
) == len(list(distributed_dataset))
def test_iterable_dataset_add_column(dataset_with_several_columns: IterableDataset):
new_column = list(range(3 * DEFAULT_N_EXAMPLES))
new_dataset = dataset_with_several_columns.add_column("new_column", new_column)
assert list(new_dataset) == [
{**example, "new_column": idx} for idx, example in enumerate(dataset_with_several_columns)
]
new_dataset = new_dataset._resolve_features()
assert "new_column" in new_dataset.column_names
def test_iterable_dataset_rename_column(dataset_with_several_columns: IterableDataset):
new_dataset = dataset_with_several_columns.rename_column("id", "new_id")
assert list(new_dataset) == [
{("new_id" if k == "id" else k): v for k, v in example.items()} for example in dataset_with_several_columns
]
assert new_dataset.features is None
assert new_dataset.column_names is None
# rename the column if ds.features was not None
new_dataset = dataset_with_several_columns._resolve_features().rename_column("id", "new_id")
assert new_dataset.features is not None
assert new_dataset.column_names is not None
assert "id" not in new_dataset.column_names
assert "new_id" in new_dataset.column_names
def test_iterable_dataset_rename_columns(dataset_with_several_columns: IterableDataset):
column_mapping = {"id": "new_id", "filepath": "filename"}
new_dataset = dataset_with_several_columns.rename_columns(column_mapping)
assert list(new_dataset) == [
{column_mapping.get(k, k): v for k, v in example.items()} for example in dataset_with_several_columns
]
assert new_dataset.features is None
assert new_dataset.column_names is None
# rename the columns if ds.features was not None
new_dataset = dataset_with_several_columns._resolve_features().rename_columns(column_mapping)
assert new_dataset.features is not None
assert new_dataset.column_names is not None
assert all(c not in new_dataset.column_names for c in ["id", "filepath"])
assert all(c in new_dataset.column_names for c in ["new_id", "filename"])
def test_iterable_dataset_remove_columns(dataset_with_several_columns: IterableDataset):
new_dataset = dataset_with_several_columns.remove_columns("id")
assert list(new_dataset) == [
{k: v for k, v in example.items() if k != "id"} for example in dataset_with_several_columns
]
assert new_dataset.features is None
new_dataset = dataset_with_several_columns.remove_columns(["id", "filepath"])
assert list(new_dataset) == [
{k: v for k, v in example.items() if k != "id" and k != "filepath"} for example in dataset_with_several_columns
]
assert new_dataset.features is None
assert new_dataset.column_names is None
# remove the columns if ds.features was not None
new_dataset = dataset_with_several_columns._resolve_features().remove_columns(["id", "filepath"])
assert new_dataset.features is not None
assert new_dataset.column_names is not None
assert all(c not in new_dataset.features for c in ["id", "filepath"])
assert all(c not in new_dataset.column_names for c in ["id", "filepath"])
def test_iterable_dataset_select_columns(dataset_with_several_columns: IterableDataset):
new_dataset = dataset_with_several_columns.select_columns("id")
assert list(new_dataset) == [
{k: v for k, v in example.items() if k == "id"} for example in dataset_with_several_columns
]
assert new_dataset.features is None
new_dataset = dataset_with_several_columns.select_columns(["id", "filepath"])
assert list(new_dataset) == [
{k: v for k, v in example.items() if k in ("id", "filepath")} for example in dataset_with_several_columns
]
assert new_dataset.features is None
# select the columns if ds.features was not None
new_dataset = dataset_with_several_columns._resolve_features().select_columns(["id", "filepath"])
assert new_dataset.features is not None
assert new_dataset.column_names is not None
assert all(c in new_dataset.features for c in ["id", "filepath"])
assert all(c in new_dataset.column_names for c in ["id", "filepath"])
def test_iterable_dataset_cast_column():
ex_iterable = ExamplesIterable(generate_examples_fn, {"label": 10})
features = Features({"id": Value("int64"), "label": Value("int64")})
dataset = IterableDataset(ex_iterable, info=DatasetInfo(features=features))
casted_dataset = dataset.cast_column("label", Value("bool"))
casted_features = features.copy()
casted_features["label"] = Value("bool")
assert list(casted_dataset) == [casted_features.encode_example(ex) for _, ex in ex_iterable]
def test_iterable_dataset_cast():
ex_iterable = ExamplesIterable(generate_examples_fn, {"label": 10})
features = Features({"id": Value("int64"), "label": Value("int64")})
dataset = IterableDataset(ex_iterable, info=DatasetInfo(features=features))
new_features = Features({"id": Value("int64"), "label": Value("bool")})
casted_dataset = dataset.cast(new_features)
assert list(casted_dataset) == [new_features.encode_example(ex) for _, ex in ex_iterable]
def test_iterable_dataset_resolve_features():
ex_iterable = ExamplesIterable(generate_examples_fn, {})
dataset = IterableDataset(ex_iterable)
assert dataset.features is None
assert dataset.column_names is None
dataset = dataset._resolve_features()
assert dataset.features == Features(
{
"id": Value("int64"),
}
)
assert dataset.column_names == ["id"]
def test_iterable_dataset_resolve_features_keep_order():
def gen():
yield from zip(range(3), [{"a": 1}, {"c": 1}, {"b": 1}])
ex_iterable = ExamplesIterable(gen, {})
dataset = IterableDataset(ex_iterable)._resolve_features()
# columns appear in order of appearance in the dataset
assert list(dataset.features) == ["a", "c", "b"]
assert dataset.column_names == ["a", "c", "b"]
def test_iterable_dataset_with_features_fill_with_none():
def gen():
yield from zip(range(2), [{"a": 1}, {"b": 1}])
ex_iterable = ExamplesIterable(gen, {})
info = DatasetInfo(features=Features({"a": Value("int32"), "b": Value("int32")}))
dataset = IterableDataset(ex_iterable, info=info)
assert list(dataset) == [{"a": 1, "b": None}, {"b": 1, "a": None}]
def test_concatenate_datasets():
ex_iterable1 = ExamplesIterable(generate_examples_fn, {"label": 10})
dataset1 = IterableDataset(ex_iterable1)
ex_iterable2 = ExamplesIterable(generate_examples_fn, {"label": 5})
dataset2 = IterableDataset(ex_iterable2)
concatenated_dataset = concatenate_datasets([dataset1, dataset2])
assert list(concatenated_dataset) == list(dataset1) + list(dataset2)
def test_concatenate_datasets_resolves_features():
ex_iterable1 = ExamplesIterable(generate_examples_fn, {"label": 10})
dataset1 = IterableDataset(ex_iterable1)
ex_iterable2 = ExamplesIterable(generate_examples_fn, {"label": 5})
dataset2 = IterableDataset(ex_iterable2)
concatenated_dataset = concatenate_datasets([dataset1, dataset2])
assert concatenated_dataset.features is not None
assert sorted(concatenated_dataset.features) == ["id", "label"]
def test_concatenate_datasets_with_different_columns():
ex_iterable1 = ExamplesIterable(generate_examples_fn, {"label": 10})
dataset1 = IterableDataset(ex_iterable1)
ex_iterable2 = ExamplesIterable(generate_examples_fn, {})
dataset2 = IterableDataset(ex_iterable2)
# missing column "label" -> it should be replaced with nulls
extended_dataset2_list = [{"label": None, **x} for x in dataset2]
concatenated_dataset = concatenate_datasets([dataset1, dataset2])
assert list(concatenated_dataset) == list(dataset1) + extended_dataset2_list
# change order
concatenated_dataset = concatenate_datasets([dataset2, dataset1])
assert list(concatenated_dataset) == extended_dataset2_list + list(dataset1)
def test_concatenate_datasets_axis_1():
ex_iterable1 = ExamplesIterable(generate_examples_fn, {"label1": 10})
dataset1 = IterableDataset(ex_iterable1)
ex_iterable2 = ExamplesIterable(generate_examples_fn, {"label2": 5})
dataset2 = IterableDataset(ex_iterable2)
with pytest.raises(ValueError): # column "id" is duplicated -> raise an error
concatenate_datasets([dataset1, dataset2], axis=1)
concatenated_dataset = concatenate_datasets([dataset1, dataset2.remove_columns("id")], axis=1)
assert list(concatenated_dataset) == [{**x, **y} for x, y in zip(dataset1, dataset2)]
def test_concatenate_datasets_axis_1_resolves_features():
ex_iterable1 = ExamplesIterable(generate_examples_fn, {"label1": 10})
dataset1 = IterableDataset(ex_iterable1)
ex_iterable2 = ExamplesIterable(generate_examples_fn, {"label2": 5})
dataset2 = IterableDataset(ex_iterable2).remove_columns("id")
concatenated_dataset = concatenate_datasets([dataset1, dataset2], axis=1)
assert concatenated_dataset.features is not None
assert sorted(concatenated_dataset.features) == ["id", "label1", "label2"]
def test_concatenate_datasets_axis_1_with_different_lengths():
n1 = 10
ex_iterable1 = ExamplesIterable(generate_examples_fn, {"label1": 10, "n": n1})
dataset1 = IterableDataset(ex_iterable1)
n2 = 5
ex_iterable2 = ExamplesIterable(generate_examples_fn, {"label2": 5, "n": n2})
dataset2 = IterableDataset(ex_iterable2).remove_columns("id")
# missing rows -> they should be replaced with nulls
extended_dataset2_list = list(dataset2) + [{"label2": None}] * (n1 - n2)
concatenated_dataset = concatenate_datasets([dataset1, dataset2], axis=1)
assert list(concatenated_dataset) == [{**x, **y} for x, y in zip(dataset1, extended_dataset2_list)]
# change order
concatenated_dataset = concatenate_datasets([dataset2, dataset1], axis=1)
assert list(concatenated_dataset) == [{**x, **y} for x, y in zip(extended_dataset2_list, dataset1)]
@require_torch
@require_tf
@require_jax
@pytest.mark.parametrize(
"format_type", [None, "torch", "python", "tf", "tensorflow", "np", "numpy", "jax", "arrow", "pd", "pandas"]
)
def test_concatenate_datasets_with_format(dataset: IterableDataset, format_type):
formatted_dataset = dataset.with_format(format_type)
concatenated_dataset = concatenate_datasets([formatted_dataset])
assert concatenated_dataset._formatting.format_type == get_format_type_from_alias(format_type)
@pytest.mark.parametrize(
"probas, seed, expected_length, stopping_strategy",
[
(None, None, 3 * (DEFAULT_N_EXAMPLES - 1) + 1, "first_exhausted"),
([1, 0, 0], None, DEFAULT_N_EXAMPLES, "first_exhausted"),
([0, 1, 0], None, DEFAULT_N_EXAMPLES, "first_exhausted"),
([0.2, 0.5, 0.3], 42, None, "first_exhausted"),
([0.1, 0.1, 0.8], 1337, None, "first_exhausted"),
([0.5, 0.2, 0.3], 101010, None, "first_exhausted"),
(None, None, 3 * DEFAULT_N_EXAMPLES, "all_exhausted"),
([0.2, 0.5, 0.3], 42, None, "all_exhausted"),
([0.1, 0.1, 0.8], 1337, None, "all_exhausted"),
([0.5, 0.2, 0.3], 101010, None, "all_exhausted"),
],
)
def test_interleave_datasets(dataset: IterableDataset, probas, seed, expected_length, stopping_strategy):
d1 = dataset
d2 = dataset.map(lambda x: {"id+1": x["id"] + 1, **x})
d3 = dataset.with_format("python")
datasets = [d1, d2, d3]
merged_dataset = interleave_datasets(
datasets, probabilities=probas, seed=seed, stopping_strategy=stopping_strategy
)
def fill_default(example):
return {"id": None, "id+1": None, **example}
# Check the examples iterable
assert isinstance(
merged_dataset._ex_iterable, (CyclingMultiSourcesExamplesIterable, RandomlyCyclingMultiSourcesExamplesIterable)
)
# Check that it is deterministic
if seed is not None:
merged_dataset2 = interleave_datasets(
[d1, d2, d3], probabilities=probas, seed=seed, stopping_strategy=stopping_strategy
)
assert list(merged_dataset) == list(merged_dataset2)
# Check features
assert merged_dataset.features == Features({"id": Value("int64"), "id+1": Value("int64")})
# Check first example
if seed is not None:
rng = np.random.default_rng(seed)
i = next(iter(cycle(rng.choice(len(datasets), size=1000, p=probas))))
assert next(iter(merged_dataset)) == fill_default(next(iter(datasets[i])))
else:
assert any(next(iter(merged_dataset)) == fill_default(next(iter(dataset))) for dataset in datasets)
# Compute length it case it's random
if expected_length is None:
expected_length = 0
counts = np.array([len(list(d)) for d in datasets])
bool_strategy_func = np.all if stopping_strategy == "all_exhausted" else np.any
rng = np.random.default_rng(seed)
for i in cycle(rng.choice(len(datasets), size=1000, p=probas)):
counts[i] -= 1
expected_length += 1
if bool_strategy_func(counts <= 0):
break
# Check length
assert len(list(merged_dataset)) == expected_length
def test_interleave_datasets_with_features(
dataset: IterableDataset,
):
features = Features(
{
"id": Value("int64"),
"label": ClassLabel(names=["negative", "positive"]),
}
)
ex_iterable = ExamplesIterable(generate_examples_fn, {"label": 0})
dataset_with_features = IterableDataset(ex_iterable, info=DatasetInfo(features=features))
merged_dataset = interleave_datasets([dataset, dataset_with_features])
assert merged_dataset.features == features
def test_interleave_datasets_with_oversampling():
# Test hardcoded results
d1 = IterableDataset(ExamplesIterable((lambda: (yield from [(i, {"a": i}) for i in [0, 1, 2]])), {}))
d2 = IterableDataset(ExamplesIterable((lambda: (yield from [(i, {"a": i}) for i in [10, 11, 12, 13]])), {}))
d3 = IterableDataset(ExamplesIterable((lambda: (yield from [(i, {"a": i}) for i in [20, 21, 22, 23, 24]])), {}))
expected_values = [0, 10, 20, 1, 11, 21, 2, 12, 22, 0, 13, 23, 1, 10, 24]
# Check oversampling strategy without probabilities
assert [x["a"] for x in interleave_datasets([d1, d2, d3], stopping_strategy="all_exhausted")] == expected_values
# Check oversampling strategy with probabilities
expected_values = [20, 0, 21, 10, 1, 22, 23, 24, 2, 0, 1, 20, 11, 21, 2, 0, 12, 1, 22, 13]
values = [
x["a"]
for x in interleave_datasets(
[d1, d2, d3], probabilities=[0.5, 0.2, 0.3], seed=42, stopping_strategy="all_exhausted"
)
]
assert values == expected_values
@require_torch
def test_with_format_torch(dataset_with_several_columns: IterableDataset):
import torch
dset = dataset_with_several_columns.with_format(type="torch")
example = next(iter(dset))
batch = next(iter(dset.iter(batch_size=3)))
assert len(example) == 3
assert isinstance(example["id"], torch.Tensor)
assert list(example["id"].shape) == []
assert example["id"].item() == 0
assert isinstance(batch["id"], torch.Tensor)
assert isinstance(example["filepath"], list)
assert isinstance(example["filepath"][0], str)
assert example["filepath"][0] == "data0.txt"
assert isinstance(batch["filepath"], list)
assert isinstance(example["metadata"], dict)
assert isinstance(example["metadata"]["sources"], list)
assert isinstance(example["metadata"]["sources"][0], str)
assert isinstance(batch["metadata"], list)
@require_tf
def test_with_format_tf(dataset_with_several_columns: IterableDataset):
import tensorflow as tf
dset = dataset_with_several_columns.with_format(type="tensorflow")
example = next(iter(dset))
batch = next(iter(dset.iter(batch_size=3)))
assert isinstance(example["id"], tf.Tensor)
assert list(example["id"].shape) == []
assert example["id"].numpy().item() == 0
assert isinstance(batch["id"], tf.Tensor)
assert isinstance(example["filepath"], tf.Tensor)
assert example["filepath"][0] == b"data0.txt"
assert isinstance(batch["filepath"], tf.Tensor)
assert isinstance(example["metadata"], dict)
assert isinstance(example["metadata"]["sources"], tf.Tensor)
assert isinstance(batch["metadata"], list)
def test_map_array_are_not_converted_back_to_lists(dataset: IterableDataset):
def func(example):
return {"array": np.array([1, 2, 3])}
dset_test = dataset.map(func)
example = next(iter(dset_test))
# not aligned with Dataset.map because we don't convert back to lists after map()
assert isinstance(example["array"], np.ndarray)
def test_formatted_map(dataset: IterableDataset):
dataset = dataset.with_format("np")
assert isinstance(next(dataset.iter(batch_size=3))["id"], np.ndarray)
dataset = dataset.with_format(None)
assert isinstance(next(dataset.iter(batch_size=3))["id"], list)
def add_one_numpy(example):
assert isinstance(example["id"], np.ndarray)
return {"id": example["id"] + 1}
dataset = dataset.with_format("np")
dataset = dataset.map(add_one_numpy, batched=True)
assert isinstance(next(dataset.iter(batch_size=3))["id"], np.ndarray)
dataset = dataset.with_format(None)
assert isinstance(next(dataset.iter(batch_size=3))["id"], list)
def test_format_from_arrow():
python_arrow_extractor = Formatter.python_arrow_extractor
numpy_arrow_extractor = Formatter.numpy_arrow_extractor
with (
patch.object(Formatter, "python_arrow_extractor") as mock_python_arrow_extractor,
patch.object(Formatter, "numpy_arrow_extractor") as mock_numpy_arrow_extractor,
):
mock_python_arrow_extractor.side_effect = python_arrow_extractor
mock_numpy_arrow_extractor.side_effect = numpy_arrow_extractor
def g():
yield 0, pa.table({"a": range(10)})
ds = IterableDataset(ArrowExamplesIterable(g, {}))
ds = ds.with_format("np")
ds = ds.map(lambda x: x, batched=True)
next(iter(ds))
# we do arrow -> numpy -> python
mock_numpy_arrow_extractor.assert_called()
# we don't do any arrow -> python
mock_python_arrow_extractor.assert_not_called()
def test_format_arrow(dataset: IterableDataset):
ds = dataset.with_format("arrow")
assert isinstance(next(iter(ds)), pa.Table)
assert isinstance(next(iter(ds.iter(batch_size=4))), pa.Table)
assert len(next(iter(ds))) == 1
assert len(next(iter(ds.iter(batch_size=4)))) == 4
ds = ds.map(lambda t: t.append_column("new_col", pa.array([0] * len(t))))
ds = ds.map(lambda t: t.append_column("new_col_batched", pa.array([1] * len(t))), batched=True)
ds = ds.with_format(None)
assert next(iter(ds)) == {**next(iter(dataset)), "new_col": 0, "new_col_batched": 1}
def test_format_pandas(dataset: IterableDataset):
ds = dataset.with_format("pandas")
assert isinstance(next(iter(ds)), pd.DataFrame)
assert isinstance(next(iter(ds.iter(batch_size=4))), pd.DataFrame)
assert len(next(iter(ds))) == 1
assert len(next(iter(ds.iter(batch_size=4)))) == 4
ds = ds.map(lambda df: df.assign(new_col=[0] * len(df)))
ds = ds.map(lambda df: df.assign(new_col_batched=[1] * len(df)), batched=True)
ds = ds.with_format(None)
assert next(iter(ds)) == {**next(iter(dataset)), "new_col": 0, "new_col_batched": 1}
@require_polars
def test_format_polars(dataset: IterableDataset):
import polars as pl
ds = dataset.with_format("polars")
assert isinstance(next(iter(ds)), pl.DataFrame)
assert isinstance(next(iter(ds.iter(batch_size=4))), pl.DataFrame)
assert len(next(iter(ds))) == 1
assert len(next(iter(ds.iter(batch_size=4)))) == 4
ds = ds.map(lambda df: df.with_columns(pl.Series([0] * len(df)).alias("new_col")))
ds = ds.map(lambda df: df.with_columns(pl.Series([1] * len(df)).alias("new_col_batched")), batched=True)
ds = ds.with_format(None)
assert next(iter(ds)) == {**next(iter(dataset)), "new_col": 0, "new_col_batched": 1}
@pytest.mark.parametrize("num_shards1, num_shards2, num_workers", [(2, 1, 1), (2, 2, 2), (1, 3, 1), (4, 3, 3)])
def test_interleave_dataset_with_sharding(num_shards1, num_shards2, num_workers):
from torch.utils.data import DataLoader
ex_iterable1 = ExamplesIterable(generate_examples_fn, {"filepaths": [f"{i}-1.txt" for i in range(num_shards1)]})
dataset1 = IterableDataset(ex_iterable1).with_format("torch")
ex_iterable2 = ExamplesIterable(generate_examples_fn, {"filepaths": [f"{i}-2.txt" for i in range(num_shards2)]})
dataset2 = IterableDataset(ex_iterable2).with_format("torch")
dataset_merged = interleave_datasets([dataset1, dataset2], stopping_strategy="first_exhausted")
assert dataset_merged.num_shards == min(num_shards1, num_shards2)
dataloader = DataLoader(dataset_merged, batch_size=None, num_workers=num_workers)
result = list(dataloader)
expected_length = 2 * min(
len([example for _, example in ex_iterable1]), len([example for _, example in ex_iterable2])
)
# some samples may be missing because the stopping strategy is applied per process
assert expected_length - num_workers <= len(result) <= expected_length
assert len(result) == len({str(x) for x in result})
def filter_func(batch):
return batch["id"] == 4
def map_func(batch):
batch["id"] *= 2
return batch
def test_pickle_after_many_transforms(dataset_with_several_columns):
dataset = dataset_with_several_columns
dataset = dataset.remove_columns(["filepath"])
dataset = dataset.take(5)
dataset = dataset.map(map_func)
dataset = dataset.shuffle()
dataset = dataset.skip(1)
dataset = dataset.filter(filter_func)
dataset = dataset.add_column("additional_col", ["something"])
dataset = dataset.rename_column("metadata", "metadata1")
dataset = dataset.rename_columns({"id": "id1", "metadata1": "metadata2"})
dataset = dataset.select_columns(["id1", "additional_col"])
unpickled_dataset = pickle.loads(pickle.dumps(dataset))
assert list(unpickled_dataset) == list(dataset)
@require_torchdata_stateful_dataloader
def test_resume_dataloader(dataset: IterableDataset):
from torchdata.stateful_dataloader import StatefulDataLoader
dl = StatefulDataLoader(dataset)
remaining = []
for i, x in enumerate(dl):
if i == 2:
state_dict = dl.state_dict()
elif i > 2:
remaining.append(x)
dl = StatefulDataLoader(dataset)
dl.load_state_dict(state_dict)
assert remaining == list(dl)
def test_iterable_dataset_batch():
# Create a simple IterableDataset
data = [{"id": i, "text": f"Text {i}"} for i in range(10)]
ds = IterableDataset.from_generator(lambda: (x for x in data))
# Test with batch_size=3, drop_last_batch=False
batched_ds = ds.batch(batch_size=3, drop_last_batch=False)
batches = list(batched_ds)
assert len(batches) == 4 # 3 full batches and 1 partial batch
for i, batch in enumerate(batches[:3]): # Check full batches
assert len(batch["id"]) == 3
assert len(batch["text"]) == 3
assert batch["id"] == [3 * i, 3 * i + 1, 3 * i + 2]
assert batch["text"] == [f"Text {3 * i}", f"Text {3 * i + 1}", f"Text {3 * i + 2}"]
# Check last partial batch
assert len(batches[3]["id"]) == 1
assert len(batches[3]["text"]) == 1
assert batches[3]["id"] == [9]
assert batches[3]["text"] == ["Text 9"]
# Test with batch_size=3, drop_last_batch=True
batched_ds = ds.batch(batch_size=3, drop_last_batch=True)
batches = list(batched_ds)
assert len(batches) == 3 # Only full batches
for i, batch in enumerate(batches):
assert len(batch["id"]) == 3
assert len(batch["text"]) == 3
assert batch["id"] == [3 * i, 3 * i + 1, 3 * i + 2]
assert batch["text"] == [f"Text {3 * i}", f"Text {3 * i + 1}", f"Text {3 * i + 2}"]
# Test with batch_size=4 (doesn't evenly divide dataset size)
batched_ds = ds.batch(batch_size=4, drop_last_batch=False)
batches = list(batched_ds)
assert len(batches) == 3 # 2 full batches and 1 partial batch
for i, batch in enumerate(batches[:2]): # Check full batches
assert len(batch["id"]) == 4
assert len(batch["text"]) == 4
assert batch["id"] == [4 * i, 4 * i + 1, 4 * i + 2, 4 * i + 3]
assert batch["text"] == [f"Text {4 * i}", f"Text {4 * i + 1}", f"Text {4 * i + 2}", f"Text {4 * i + 3}"]
# Check last partial batch
assert len(batches[2]["id"]) == 2
assert len(batches[2]["text"]) == 2
assert batches[2]["id"] == [8, 9]
assert batches[2]["text"] == ["Text 8", "Text 9"]
# Test with features
batched_ds = ds._resolve_features().batch(batch_size=3)
batches = list(batched_ds)
assert batched_ds.features is not None
assert len(batches) == 4 # 3 full batches and 1 partial batch
for i, batch in enumerate(batches[:1]):
assert len(batch["id"]) == 3
assert len(batch["text"]) == 3
assert batch["id"] == [3 * i, 3 * i + 1, 3 * i + 2]
assert batch["text"] == [f"Text {3 * i}", f"Text {3 * i + 1}", f"Text {3 * i + 2}"]
@dataclass
class DecodableFeature:
decode_example_num_calls = 0
def __init__(self):
self.decode = True
def decode_example(self, example, token_per_repo_id=None):
type(self).decode_example_num_calls += 1
return "decoded" if self.decode else example
def __call__(self):
return pa.string()
def test_decode():
data = [{"i": str(i)} for i in range(10)]
features = Features({"i": DecodableFeature()})
ds = IterableDataset.from_generator(lambda: (x for x in data), features=features)
assert next(iter(ds)) == {"i": "decoded"}
assert DecodableFeature.decode_example_num_calls == 1
ds = ds.decode(False)
assert next(iter(ds)) == {"i": "0"}
assert DecodableFeature.decode_example_num_calls == 1
ds = ds.decode(True)
assert next(iter(ds)) == {"i": "decoded"}
assert DecodableFeature.decode_example_num_calls == 2
ds = ds.decode(num_threads=1)
assert next(iter(ds)) == {"i": "decoded"}
assert DecodableFeature.decode_example_num_calls == 4
############################
#
# IterableColumn tests
#
############################
class TestIterableColumn:
def test_simple_getitem(self):
def gen():
yield {"text": "Good", "label": 0}
yield {"text": "Bad", "label": 1}
ds = IterableDataset.from_generator(gen)
texts = ds["text"]
assert isinstance(texts, IterableColumn)
first_pass = list(texts)
assert first_pass == ["Good", "Bad"]
second_pass = list(texts)
assert second_pass == ["Good", "Bad"]
def test_chained_getitem(self):
def gen():
yield {"sample": {"text": "Good", "label": 0}}
yield {"sample": {"text": "Bad", "label": 1}}
ds = IterableDataset.from_generator(gen)
texts = ds["sample"]["text"]
assert isinstance(texts, IterableColumn)
first_pass = list(texts)
assert first_pass == ["Good", "Bad"]
second_pass = list(texts)
assert second_pass == ["Good", "Bad"]
def test_getitem_for_batched_dataset(self):
data = [
{"text": "Good", "label": 0},
{"text": "Bad", "label": 1},
{"text": "Good again", "label": 0},
{"text": "Bad again", "label": 1},
]
def gen():
yield from data
ds = IterableDataset.from_generator(gen).batch(batch_size=2)
texts = ds["text"]
assert isinstance(texts, IterableColumn)
assert list(texts) == [["Good", "Bad"], ["Good again", "Bad again"]]
| datasets/tests/test_iterable_dataset.py/0 | {
"file_path": "datasets/tests/test_iterable_dataset.py",
"repo_id": "datasets",
"token_count": 47494
} | 112 |
from functools import partial
import torch
from benchmarking_utils import BenchmarkMixin, BenchmarkScenario, model_init_fn
from diffusers import UNet2DConditionModel
from diffusers.utils.testing_utils import torch_device
CKPT_ID = "stabilityai/stable-diffusion-xl-base-1.0"
RESULT_FILENAME = "sdxl.csv"
def get_input_dict(**device_dtype_kwargs):
# height: 1024
# width: 1024
# max_sequence_length: 77
hidden_states = torch.randn(1, 4, 128, 128, **device_dtype_kwargs)
encoder_hidden_states = torch.randn(1, 77, 2048, **device_dtype_kwargs)
timestep = torch.tensor([1.0], **device_dtype_kwargs)
added_cond_kwargs = {
"text_embeds": torch.randn(1, 1280, **device_dtype_kwargs),
"time_ids": torch.ones(1, 6, **device_dtype_kwargs),
}
return {
"sample": hidden_states,
"encoder_hidden_states": encoder_hidden_states,
"timestep": timestep,
"added_cond_kwargs": added_cond_kwargs,
}
if __name__ == "__main__":
scenarios = [
BenchmarkScenario(
name=f"{CKPT_ID}-bf16",
model_cls=UNet2DConditionModel,
model_init_kwargs={
"pretrained_model_name_or_path": CKPT_ID,
"torch_dtype": torch.bfloat16,
"subfolder": "unet",
},
get_model_input_dict=partial(get_input_dict, device=torch_device, dtype=torch.bfloat16),
model_init_fn=model_init_fn,
compile_kwargs={"fullgraph": True},
),
BenchmarkScenario(
name=f"{CKPT_ID}-layerwise-upcasting",
model_cls=UNet2DConditionModel,
model_init_kwargs={
"pretrained_model_name_or_path": CKPT_ID,
"torch_dtype": torch.bfloat16,
"subfolder": "unet",
},
get_model_input_dict=partial(get_input_dict, device=torch_device, dtype=torch.bfloat16),
model_init_fn=partial(model_init_fn, layerwise_upcasting=True),
),
BenchmarkScenario(
name=f"{CKPT_ID}-group-offload-leaf",
model_cls=UNet2DConditionModel,
model_init_kwargs={
"pretrained_model_name_or_path": CKPT_ID,
"torch_dtype": torch.bfloat16,
"subfolder": "unet",
},
get_model_input_dict=partial(get_input_dict, device=torch_device, dtype=torch.bfloat16),
model_init_fn=partial(
model_init_fn,
group_offload_kwargs={
"onload_device": torch_device,
"offload_device": torch.device("cpu"),
"offload_type": "leaf_level",
"use_stream": True,
"non_blocking": True,
},
),
),
]
runner = BenchmarkMixin()
runner.run_bencmarks_and_collate(scenarios, filename=RESULT_FILENAME)
| diffusers/benchmarks/benchmarking_sdxl.py/0 | {
"file_path": "diffusers/benchmarks/benchmarking_sdxl.py",
"repo_id": "diffusers",
"token_count": 1510
} | 113 |
<!---
Copyright 2024- The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
-->
# Generating the documentation
To generate the documentation, you first have to build it. Several packages are necessary to build the doc,
you can install them with the following command, at the root of the code repository:
```bash
pip install -e ".[docs]"
```
Then you need to install our open source documentation builder tool:
```bash
pip install git+https://github.com/huggingface/doc-builder
```
---
**NOTE**
You only need to generate the documentation to inspect it locally (if you're planning changes and want to
check how they look before committing for instance). You don't have to commit the built documentation.
---
## Previewing the documentation
To preview the docs, first install the `watchdog` module with:
```bash
pip install watchdog
```
Then run the following command:
```bash
doc-builder preview {package_name} {path_to_docs}
```
For example:
```bash
doc-builder preview diffusers docs/source/en
```
The docs will be viewable at [http://localhost:3000](http://localhost:3000). You can also preview the docs once you have opened a PR. You will see a bot add a comment to a link where the documentation with your changes lives.
---
**NOTE**
The `preview` command only works with existing doc files. When you add a completely new file, you need to update `_toctree.yml` & restart `preview` command (`ctrl-c` to stop it & call `doc-builder preview ...` again).
---
## Adding a new element to the navigation bar
Accepted files are Markdown (.md).
Create a file with its extension and put it in the source directory. You can then link it to the toc-tree by putting
the filename without the extension in the [`_toctree.yml`](https://github.com/huggingface/diffusers/blob/main/docs/source/en/_toctree.yml) file.
## Renaming section headers and moving sections
It helps to keep the old links working when renaming the section header and/or moving sections from one document to another. This is because the old links are likely to be used in Issues, Forums, and Social media and it'd make for a much more superior user experience if users reading those months later could still easily navigate to the originally intended information.
Therefore, we simply keep a little map of moved sections at the end of the document where the original section was. The key is to preserve the original anchor.
So if you renamed a section from: "Section A" to "Section B", then you can add at the end of the file:
```md
Sections that were moved:
[ <a href="#section-b">Section A</a><a id="section-a"></a> ]
```
and of course, if you moved it to another file, then:
```md
Sections that were moved:
[ <a href="../new-file#section-b">Section A</a><a id="section-a"></a> ]
```
Use the relative style to link to the new file so that the versioned docs continue to work.
For an example of a rich moved section set please see the very end of [the transformers Trainer doc](https://github.com/huggingface/transformers/blob/main/docs/source/en/main_classes/trainer.md).
## Writing Documentation - Specification
The `huggingface/diffusers` documentation follows the
[Google documentation](https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html) style for docstrings,
although we can write them directly in Markdown.
### Adding a new tutorial
Adding a new tutorial or section is done in two steps:
- Add a new Markdown (.md) file under `docs/source/<languageCode>`.
- Link that file in `docs/source/<languageCode>/_toctree.yml` on the correct toc-tree.
Make sure to put your new file under the proper section. It's unlikely to go in the first section (*Get Started*), so
depending on the intended targets (beginners, more advanced users, or researchers) it should go in sections two, three, or four.
### Adding a new pipeline/scheduler
When adding a new pipeline:
- Create a file `xxx.md` under `docs/source/<languageCode>/api/pipelines` (don't hesitate to copy an existing file as template).
- Link that file in (*Diffusers Summary*) section in `docs/source/api/pipelines/overview.md`, along with the link to the paper, and a colab notebook (if available).
- Write a short overview of the diffusion model:
- Overview with paper & authors
- Paper abstract
- Tips and tricks and how to use it best
- Possible an end-to-end example of how to use it
- Add all the pipeline classes that should be linked in the diffusion model. These classes should be added using our Markdown syntax. By default as follows:
```
[[autodoc]] XXXPipeline
- all
- __call__
```
This will include every public method of the pipeline that is documented, as well as the `__call__` method that is not documented by default. If you just want to add additional methods that are not documented, you can put the list of all methods to add in a list that contains `all`.
```
[[autodoc]] XXXPipeline
- all
- __call__
- enable_attention_slicing
- disable_attention_slicing
- enable_xformers_memory_efficient_attention
- disable_xformers_memory_efficient_attention
```
You can follow the same process to create a new scheduler under the `docs/source/<languageCode>/api/schedulers` folder.
### Writing source documentation
Values that should be put in `code` should either be surrounded by backticks: \`like so\`. Note that argument names
and objects like True, None, or any strings should usually be put in `code`.
When mentioning a class, function, or method, it is recommended to use our syntax for internal links so that our tool
adds a link to its documentation with this syntax: \[\`XXXClass\`\] or \[\`function\`\]. This requires the class or
function to be in the main package.
If you want to create a link to some internal class or function, you need to
provide its path. For instance: \[\`pipelines.ImagePipelineOutput\`\]. This will be converted into a link with
`pipelines.ImagePipelineOutput` in the description. To get rid of the path and only keep the name of the object you are
linking to in the description, add a ~: \[\`~pipelines.ImagePipelineOutput\`\] will generate a link with `ImagePipelineOutput` in the description.
The same works for methods so you can either use \[\`XXXClass.method\`\] or \[\`~XXXClass.method\`\].
#### Defining arguments in a method
Arguments should be defined with the `Args:` (or `Arguments:` or `Parameters:`) prefix, followed by a line return and
an indentation. The argument should be followed by its type, with its shape if it is a tensor, a colon, and its
description:
```
Args:
n_layers (`int`): The number of layers of the model.
```
If the description is too long to fit in one line, another indentation is necessary before writing the description
after the argument.
Here's an example showcasing everything so far:
```
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`AlbertTokenizer`]. See [`~PreTrainedTokenizer.encode`] and
[`~PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
```
For optional arguments or arguments with defaults we follow the following syntax: imagine we have a function with the
following signature:
```py
def my_function(x: str=None, a: float=3.14):
```
then its documentation should look like this:
```
Args:
x (`str`, *optional*):
This argument controls ...
a (`float`, *optional*, defaults to `3.14`):
This argument is used to ...
```
Note that we always omit the "defaults to \`None\`" when None is the default for any argument. Also note that even
if the first line describing your argument type and its default gets long, you can't break it on several lines. You can
however write as many lines as you want in the indented description (see the example above with `input_ids`).
#### Writing a multi-line code block
Multi-line code blocks can be useful for displaying examples. They are done between two lines of three backticks as usual in Markdown:
````
```
# first line of code
# second line
# etc
```
````
#### Writing a return block
The return block should be introduced with the `Returns:` prefix, followed by a line return and an indentation.
The first line should be the type of the return, followed by a line return. No need to indent further for the elements
building the return.
Here's an example of a single value return:
```
Returns:
`List[int]`: A list of integers in the range [0, 1] --- 1 for a special token, 0 for a sequence token.
```
Here's an example of a tuple return, comprising several objects:
```
Returns:
`tuple(torch.Tensor)` comprising various elements depending on the configuration ([`BertConfig`]) and inputs:
- ** loss** (*optional*, returned when `masked_lm_labels` is provided) `torch.Tensor` of shape `(1,)` --
Total loss is the sum of the masked language modeling loss and the next sequence prediction (classification) loss.
- **prediction_scores** (`torch.Tensor` of shape `(batch_size, sequence_length, config.vocab_size)`) --
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
```
#### Adding an image
Due to the rapidly growing repository, it is important to make sure that no files that would significantly weigh down the repository are added. This includes images, videos, and other non-text files. We prefer to leverage a hf.co hosted `dataset` like
the ones hosted on [`hf-internal-testing`](https://huggingface.co/hf-internal-testing) in which to place these files and reference
them by URL. We recommend putting them in the following dataset: [huggingface/documentation-images](https://huggingface.co/datasets/huggingface/documentation-images).
If an external contribution, feel free to add the images to your PR and ask a Hugging Face member to migrate your images
to this dataset.
## Styling the docstring
We have an automatic script running with the `make style` command that will make sure that:
- the docstrings fully take advantage of the line width
- all code examples are formatted using black, like the code of the Transformers library
This script may have some weird failures if you made a syntax mistake or if you uncover a bug. Therefore, it's
recommended to commit your changes before running `make style`, so you can revert the changes done by that script
easily.
| diffusers/docs/README.md/0 | {
"file_path": "diffusers/docs/README.md",
"repo_id": "diffusers",
"token_count": 3142
} | 114 |
<!--Copyright 2025 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# SD3Transformer2D
This class is useful when *only* loading weights into a [`SD3Transformer2DModel`]. If you need to load weights into the text encoder or a text encoder and SD3Transformer2DModel, check [`SD3LoraLoaderMixin`](lora#diffusers.loaders.SD3LoraLoaderMixin) class instead.
The [`SD3Transformer2DLoadersMixin`] class currently only loads IP-Adapter weights, but will be used in the future to save weights and load LoRAs.
<Tip>
To learn more about how to load LoRA weights, see the [LoRA](../../using-diffusers/loading_adapters#lora) loading guide.
</Tip>
## SD3Transformer2DLoadersMixin
[[autodoc]] loaders.transformer_sd3.SD3Transformer2DLoadersMixin
- all
- _load_ip_adapter_weights | diffusers/docs/source/en/api/loaders/transformer_sd3.md/0 | {
"file_path": "diffusers/docs/source/en/api/loaders/transformer_sd3.md",
"repo_id": "diffusers",
"token_count": 379
} | 115 |
<!-- Copyright 2025 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License. -->
# AutoencoderKLLTXVideo
The 3D variational autoencoder (VAE) model with KL loss used in [LTX](https://huggingface.co/Lightricks/LTX-Video) was introduced by Lightricks.
The model can be loaded with the following code snippet.
```python
from diffusers import AutoencoderKLLTXVideo
vae = AutoencoderKLLTXVideo.from_pretrained("Lightricks/LTX-Video", subfolder="vae", torch_dtype=torch.float32).to("cuda")
```
## AutoencoderKLLTXVideo
[[autodoc]] AutoencoderKLLTXVideo
- decode
- encode
- all
## AutoencoderKLOutput
[[autodoc]] models.autoencoders.autoencoder_kl.AutoencoderKLOutput
## DecoderOutput
[[autodoc]] models.autoencoders.vae.DecoderOutput
| diffusers/docs/source/en/api/models/autoencoderkl_ltx_video.md/0 | {
"file_path": "diffusers/docs/source/en/api/models/autoencoderkl_ltx_video.md",
"repo_id": "diffusers",
"token_count": 386
} | 116 |
<!--Copyright 2025 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# AutoPipeline
The `AutoPipeline` is designed to make it easy to load a checkpoint for a task without needing to know the specific pipeline class. Based on the task, the `AutoPipeline` automatically retrieves the correct pipeline class from the checkpoint `model_index.json` file.
> [!TIP]
> Check out the [AutoPipeline](../../tutorials/autopipeline) tutorial to learn how to use this API!
## AutoPipelineForText2Image
[[autodoc]] AutoPipelineForText2Image
- all
- from_pretrained
- from_pipe
## AutoPipelineForImage2Image
[[autodoc]] AutoPipelineForImage2Image
- all
- from_pretrained
- from_pipe
## AutoPipelineForInpainting
[[autodoc]] AutoPipelineForInpainting
- all
- from_pretrained
- from_pipe
| 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
} | 117 |
<!--Copyright 2025 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# ControlNetUnion
<div class="flex flex-wrap space-x-1">
<img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
</div>
ControlNetUnionModel is an implementation of ControlNet for Stable Diffusion XL.
The ControlNet model was introduced in [ControlNetPlus](https://github.com/xinsir6/ControlNetPlus) by xinsir6. It supports multiple conditioning inputs without increasing computation.
*We design a new architecture that can support 10+ control types in condition text-to-image generation and can generate high resolution images visually comparable with midjourney. The network is based on the original ControlNet architecture, we propose two new modules to: 1 Extend the original ControlNet to support different image conditions using the same network parameter. 2 Support multiple conditions input without increasing computation offload, which is especially important for designers who want to edit image in detail, different conditions use the same condition encoder, without adding extra computations or parameters.*
## StableDiffusionXLControlNetUnionPipeline
[[autodoc]] StableDiffusionXLControlNetUnionPipeline
- all
- __call__
## StableDiffusionXLControlNetUnionImg2ImgPipeline
[[autodoc]] StableDiffusionXLControlNetUnionImg2ImgPipeline
- all
- __call__
## StableDiffusionXLControlNetUnionInpaintPipeline
[[autodoc]] StableDiffusionXLControlNetUnionInpaintPipeline
- all
- __call__
| diffusers/docs/source/en/api/pipelines/controlnet_union.md/0 | {
"file_path": "diffusers/docs/source/en/api/pipelines/controlnet_union.md",
"repo_id": "diffusers",
"token_count": 518
} | 118 |
<!--Copyright 2025 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# Pipelines
Pipelines provide a simple way to run state-of-the-art diffusion models in inference by bundling all of the necessary components (multiple independently-trained models, schedulers, and processors) into a single end-to-end class. Pipelines are flexible and they can be adapted to use different schedulers or even model components.
All pipelines are built from the base [`DiffusionPipeline`] class which provides basic functionality for loading, downloading, and saving all the components. Specific pipeline types (for example [`StableDiffusionPipeline`]) loaded with [`~DiffusionPipeline.from_pretrained`] are automatically detected and the pipeline components are loaded and passed to the `__init__` function of the pipeline.
<Tip warning={true}>
You shouldn't use the [`DiffusionPipeline`] class for training. Individual components (for example, [`UNet2DModel`] and [`UNet2DConditionModel`]) of diffusion pipelines are usually trained individually, so we suggest directly working with them instead.
<br>
Pipelines do not offer any training functionality. You'll notice PyTorch's autograd is disabled by decorating the [`~DiffusionPipeline.__call__`] method with a [`torch.no_grad`](https://pytorch.org/docs/stable/generated/torch.no_grad.html) decorator because pipelines should not be used for training. If you're interested in training, please take a look at the [Training](../../training/overview) guides instead!
</Tip>
The table below lists all the pipelines currently available in 🤗 Diffusers and the tasks they support. Click on a pipeline to view its abstract and published paper.
| Pipeline | Tasks |
|---|---|
| [aMUSEd](amused) | text2image |
| [AnimateDiff](animatediff) | text2video |
| [Attend-and-Excite](attend_and_excite) | text2image |
| [AudioLDM](audioldm) | text2audio |
| [AudioLDM2](audioldm2) | text2audio |
| [AuraFlow](auraflow) | text2image |
| [BLIP Diffusion](blip_diffusion) | text2image |
| [Bria 3.2](bria_3_2) | text2image |
| [CogVideoX](cogvideox) | text2video |
| [Consistency Models](consistency_models) | unconditional image generation |
| [ControlNet](controlnet) | text2image, image2image, inpainting |
| [ControlNet with Flux.1](controlnet_flux) | text2image |
| [ControlNet with Hunyuan-DiT](controlnet_hunyuandit) | text2image |
| [ControlNet with Stable Diffusion 3](controlnet_sd3) | text2image |
| [ControlNet with Stable Diffusion XL](controlnet_sdxl) | text2image |
| [ControlNet-XS](controlnetxs) | text2image |
| [ControlNet-XS with Stable Diffusion XL](controlnetxs_sdxl) | text2image |
| [Dance Diffusion](dance_diffusion) | unconditional audio generation |
| [DDIM](ddim) | unconditional image generation |
| [DDPM](ddpm) | unconditional image generation |
| [DeepFloyd IF](deepfloyd_if) | text2image, image2image, inpainting, super-resolution |
| [DiffEdit](diffedit) | inpainting |
| [DiT](dit) | text2image |
| [Flux](flux) | text2image |
| [Hunyuan-DiT](hunyuandit) | text2image |
| [I2VGen-XL](i2vgenxl) | image2video |
| [InstructPix2Pix](pix2pix) | image editing |
| [Kandinsky 2.1](kandinsky) | text2image, image2image, inpainting, interpolation |
| [Kandinsky 2.2](kandinsky_v22) | text2image, image2image, inpainting |
| [Kandinsky 3](kandinsky3) | text2image, image2image |
| [Kolors](kolors) | text2image |
| [Latent Consistency Models](latent_consistency_models) | text2image |
| [Latent Diffusion](latent_diffusion) | text2image, super-resolution |
| [Latte](latte) | text2image |
| [LEDITS++](ledits_pp) | image editing |
| [Lumina-T2X](lumina) | text2image |
| [Marigold](marigold) | depth-estimation, normals-estimation, intrinsic-decomposition |
| [MultiDiffusion](panorama) | text2image |
| [MusicLDM](musicldm) | text2audio |
| [PAG](pag) | text2image |
| [Paint by Example](paint_by_example) | inpainting |
| [PIA](pia) | image2video |
| [PixArt-α](pixart) | text2image |
| [PixArt-Σ](pixart_sigma) | text2image |
| [Self-Attention Guidance](self_attention_guidance) | text2image |
| [Semantic Guidance](semantic_stable_diffusion) | text2image |
| [Shap-E](shap_e) | text-to-3D, image-to-3D |
| [Stable Audio](stable_audio) | text2audio |
| [Stable Cascade](stable_cascade) | text2image |
| [Stable Diffusion](stable_diffusion/overview) | text2image, image2image, depth2image, inpainting, image variation, latent upscaler, super-resolution |
| [Stable Diffusion XL](stable_diffusion/stable_diffusion_xl) | text2image, image2image, inpainting |
| [Stable Diffusion XL Turbo](stable_diffusion/sdxl_turbo) | text2image, image2image, inpainting |
| [Stable unCLIP](stable_unclip) | text2image, image variation |
| [T2I-Adapter](stable_diffusion/adapter) | text2image |
| [Text2Video](text_to_video) | text2video, video2video |
| [Text2Video-Zero](text_to_video_zero) | text2video |
| [unCLIP](unclip) | text2image, image variation |
| [UniDiffuser](unidiffuser) | text2image, image2text, image variation, text variation, unconditional image generation, unconditional audio generation |
| [Value-guided planning](value_guided_sampling) | value guided sampling |
| [Wuerstchen](wuerstchen) | text2image |
| [VisualCloze](visualcloze) | text2image, image2image, subject driven generation, inpainting, style transfer, image restoration, image editing, [depth,normal,edge,pose]2image, [depth,normal,edge,pose]-estimation, virtual try-on, image relighting |
## DiffusionPipeline
[[autodoc]] DiffusionPipeline
- all
- __call__
- device
- to
- components
[[autodoc]] pipelines.StableDiffusionMixin.enable_freeu
[[autodoc]] pipelines.StableDiffusionMixin.disable_freeu
## FlaxDiffusionPipeline
[[autodoc]] pipelines.pipeline_flax_utils.FlaxDiffusionPipeline
## PushToHubMixin
[[autodoc]] utils.PushToHubMixin
## Callbacks
[[autodoc]] callbacks.PipelineCallback
[[autodoc]] callbacks.SDCFGCutoffCallback
[[autodoc]] callbacks.SDXLCFGCutoffCallback
[[autodoc]] callbacks.SDXLControlnetCFGCutoffCallback
[[autodoc]] callbacks.IPAdapterScaleCutoffCallback
[[autodoc]] callbacks.SD3CFGCutoffCallback
| diffusers/docs/source/en/api/pipelines/overview.md/0 | {
"file_path": "diffusers/docs/source/en/api/pipelines/overview.md",
"repo_id": "diffusers",
"token_count": 2149
} | 119 |
<!--Copyright 2025 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# Stable Cascade
This model is built upon the [Würstchen](https://openreview.net/forum?id=gU58d5QeGv) architecture and its main
difference to other models like Stable Diffusion is that it is working at a much smaller latent space. Why is this
important? The smaller the latent space, the **faster** you can run inference and the **cheaper** the training becomes.
How small is the latent space? Stable Diffusion uses a compression factor of 8, resulting in a 1024x1024 image being
encoded to 128x128. Stable Cascade achieves a compression factor of 42, meaning that it is possible to encode a
1024x1024 image to 24x24, while maintaining crisp reconstructions. The text-conditional model is then trained in the
highly compressed latent space. Previous versions of this architecture, achieved a 16x cost reduction over Stable
Diffusion 1.5.
Therefore, this kind of model is well suited for usages where efficiency is important. Furthermore, all known extensions
like finetuning, LoRA, ControlNet, IP-Adapter, LCM etc. are possible with this method as well.
The original codebase can be found at [Stability-AI/StableCascade](https://github.com/Stability-AI/StableCascade).
## Model Overview
Stable Cascade consists of three models: Stage A, Stage B and Stage C, representing a cascade to generate images,
hence the name "Stable Cascade".
Stage A & B are used to compress images, similar to what the job of the VAE is in Stable Diffusion.
However, with this setup, a much higher compression of images can be achieved. While the Stable Diffusion models use a
spatial compression factor of 8, encoding an image with resolution of 1024 x 1024 to 128 x 128, Stable Cascade achieves
a compression factor of 42. This encodes a 1024 x 1024 image to 24 x 24, while being able to accurately decode the
image. This comes with the great benefit of cheaper training and inference. Furthermore, Stage C is responsible
for generating the small 24 x 24 latents given a text prompt.
The Stage C model operates on the small 24 x 24 latents and denoises the latents conditioned on text prompts. The model is also the largest component in the Cascade pipeline and is meant to be used with the `StableCascadePriorPipeline`
The Stage B and Stage A models are used with the `StableCascadeDecoderPipeline` and are responsible for generating the final image given the small 24 x 24 latents.
<Tip warning={true}>
There are some restrictions on data types that can be used with the Stable Cascade models. The official checkpoints for the `StableCascadePriorPipeline` do not support the `torch.float16` data type. Please use `torch.bfloat16` instead.
In order to use the `torch.bfloat16` data type with the `StableCascadeDecoderPipeline` you need to have PyTorch 2.2.0 or higher installed. This also means that using the `StableCascadeCombinedPipeline` with `torch.bfloat16` requires PyTorch 2.2.0 or higher, since it calls the `StableCascadeDecoderPipeline` internally.
If it is not possible to install PyTorch 2.2.0 or higher in your environment, the `StableCascadeDecoderPipeline` can be used on its own with the `torch.float16` data type. You can download the full precision or `bf16` variant weights for the pipeline and cast the weights to `torch.float16`.
</Tip>
## Usage example
```python
import torch
from diffusers import StableCascadeDecoderPipeline, StableCascadePriorPipeline
prompt = "an image of a shiba inu, donning a spacesuit and helmet"
negative_prompt = ""
prior = StableCascadePriorPipeline.from_pretrained("stabilityai/stable-cascade-prior", variant="bf16", torch_dtype=torch.bfloat16)
decoder = StableCascadeDecoderPipeline.from_pretrained("stabilityai/stable-cascade", variant="bf16", torch_dtype=torch.float16)
prior.enable_model_cpu_offload()
prior_output = prior(
prompt=prompt,
height=1024,
width=1024,
negative_prompt=negative_prompt,
guidance_scale=4.0,
num_images_per_prompt=1,
num_inference_steps=20
)
decoder.enable_model_cpu_offload()
decoder_output = decoder(
image_embeddings=prior_output.image_embeddings.to(torch.float16),
prompt=prompt,
negative_prompt=negative_prompt,
guidance_scale=0.0,
output_type="pil",
num_inference_steps=10
).images[0]
decoder_output.save("cascade.png")
```
## Using the Lite Versions of the Stage B and Stage C models
```python
import torch
from diffusers import (
StableCascadeDecoderPipeline,
StableCascadePriorPipeline,
StableCascadeUNet,
)
prompt = "an image of a shiba inu, donning a spacesuit and helmet"
negative_prompt = ""
prior_unet = StableCascadeUNet.from_pretrained("stabilityai/stable-cascade-prior", subfolder="prior_lite")
decoder_unet = StableCascadeUNet.from_pretrained("stabilityai/stable-cascade", subfolder="decoder_lite")
prior = StableCascadePriorPipeline.from_pretrained("stabilityai/stable-cascade-prior", prior=prior_unet)
decoder = StableCascadeDecoderPipeline.from_pretrained("stabilityai/stable-cascade", decoder=decoder_unet)
prior.enable_model_cpu_offload()
prior_output = prior(
prompt=prompt,
height=1024,
width=1024,
negative_prompt=negative_prompt,
guidance_scale=4.0,
num_images_per_prompt=1,
num_inference_steps=20
)
decoder.enable_model_cpu_offload()
decoder_output = decoder(
image_embeddings=prior_output.image_embeddings,
prompt=prompt,
negative_prompt=negative_prompt,
guidance_scale=0.0,
output_type="pil",
num_inference_steps=10
).images[0]
decoder_output.save("cascade.png")
```
## Loading original checkpoints with `from_single_file`
Loading the original format checkpoints is supported via `from_single_file` method in the StableCascadeUNet.
```python
import torch
from diffusers import (
StableCascadeDecoderPipeline,
StableCascadePriorPipeline,
StableCascadeUNet,
)
prompt = "an image of a shiba inu, donning a spacesuit and helmet"
negative_prompt = ""
prior_unet = StableCascadeUNet.from_single_file(
"https://huggingface.co/stabilityai/stable-cascade/resolve/main/stage_c_bf16.safetensors",
torch_dtype=torch.bfloat16
)
decoder_unet = StableCascadeUNet.from_single_file(
"https://huggingface.co/stabilityai/stable-cascade/blob/main/stage_b_bf16.safetensors",
torch_dtype=torch.bfloat16
)
prior = StableCascadePriorPipeline.from_pretrained("stabilityai/stable-cascade-prior", prior=prior_unet, torch_dtype=torch.bfloat16)
decoder = StableCascadeDecoderPipeline.from_pretrained("stabilityai/stable-cascade", decoder=decoder_unet, torch_dtype=torch.bfloat16)
prior.enable_model_cpu_offload()
prior_output = prior(
prompt=prompt,
height=1024,
width=1024,
negative_prompt=negative_prompt,
guidance_scale=4.0,
num_images_per_prompt=1,
num_inference_steps=20
)
decoder.enable_model_cpu_offload()
decoder_output = decoder(
image_embeddings=prior_output.image_embeddings,
prompt=prompt,
negative_prompt=negative_prompt,
guidance_scale=0.0,
output_type="pil",
num_inference_steps=10
).images[0]
decoder_output.save("cascade-single-file.png")
```
## Uses
### Direct Use
The model is intended for research purposes for now. Possible research areas and tasks include
- Research on generative models.
- Safe deployment of models which have the potential to generate harmful content.
- Probing and understanding the limitations and biases of generative models.
- Generation of artworks and use in design and other artistic processes.
- Applications in educational or creative tools.
Excluded uses are described below.
### Out-of-Scope Use
The model was not trained to be factual or true representations of people or events,
and therefore using the model to generate such content is out-of-scope for the abilities of this model.
The model should not be used in any way that violates Stability AI's [Acceptable Use Policy](https://stability.ai/use-policy).
## Limitations and Bias
### Limitations
- Faces and people in general may not be generated properly.
- The autoencoding part of the model is lossy.
## StableCascadeCombinedPipeline
[[autodoc]] StableCascadeCombinedPipeline
- all
- __call__
## StableCascadePriorPipeline
[[autodoc]] StableCascadePriorPipeline
- all
- __call__
## StableCascadePriorPipelineOutput
[[autodoc]] pipelines.stable_cascade.pipeline_stable_cascade_prior.StableCascadePriorPipelineOutput
## StableCascadeDecoderPipeline
[[autodoc]] StableCascadeDecoderPipeline
- all
- __call__
| diffusers/docs/source/en/api/pipelines/stable_cascade.md/0 | {
"file_path": "diffusers/docs/source/en/api/pipelines/stable_cascade.md",
"repo_id": "diffusers",
"token_count": 2836
} | 120 |
<!--Copyright 2025 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# DDIMScheduler
[Denoising Diffusion Implicit Models](https://huggingface.co/papers/2010.02502) (DDIM) by Jiaming Song, Chenlin Meng and Stefano Ermon.
The abstract from the paper is:
*Denoising diffusion probabilistic models (DDPMs) have achieved high quality image generation without adversarial training, yet they require simulating a Markov chain for many steps to produce a sample.
To accelerate sampling, we present denoising diffusion implicit models (DDIMs), a more efficient class of iterative implicit probabilistic models
with the same training procedure as DDPMs. In DDPMs, the generative process is defined as the reverse of a Markovian diffusion process.
We construct a class of non-Markovian diffusion processes that lead to the same training objective, but whose reverse process can be much faster to sample from.
We empirically demonstrate that DDIMs can produce high quality samples 10× to 50× faster in terms of wall-clock time compared to DDPMs, allow us to trade off computation for sample quality, and can perform semantically meaningful image interpolation directly in the latent space.*
The original codebase of this paper can be found at [ermongroup/ddim](https://github.com/ermongroup/ddim), and you can contact the author on [tsong.me](https://tsong.me/).
## Tips
The paper [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) claims that a mismatch between the training and inference settings leads to suboptimal inference generation results for Stable Diffusion. To fix this, the authors propose:
<Tip warning={true}>
🧪 This is an experimental feature!
</Tip>
1. rescale the noise schedule to enforce zero terminal signal-to-noise ratio (SNR)
```py
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config, rescale_betas_zero_snr=True)
```
2. train a model with `v_prediction` (add the following argument to the [train_text_to_image.py](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py) or [train_text_to_image_lora.py](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_lora.py) scripts)
```bash
--prediction_type="v_prediction"
```
3. change the sampler to always start from the last timestep
```py
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
```
4. rescale classifier-free guidance to prevent over-exposure
```py
image = pipe(prompt, guidance_rescale=0.7).images[0]
```
For example:
```py
from diffusers import DiffusionPipeline, DDIMScheduler
import torch
pipe = DiffusionPipeline.from_pretrained("ptx0/pseudo-journey-v2", torch_dtype=torch.float16)
pipe.scheduler = DDIMScheduler.from_config(
pipe.scheduler.config, rescale_betas_zero_snr=True, timestep_spacing="trailing"
)
pipe.to("cuda")
prompt = "A lion in galaxies, spirals, nebulae, stars, smoke, iridescent, intricate detail, octane render, 8k"
image = pipe(prompt, guidance_rescale=0.7).images[0]
image
```
## DDIMScheduler
[[autodoc]] DDIMScheduler
## DDIMSchedulerOutput
[[autodoc]] schedulers.scheduling_ddim.DDIMSchedulerOutput
| diffusers/docs/source/en/api/schedulers/ddim.md/0 | {
"file_path": "diffusers/docs/source/en/api/schedulers/ddim.md",
"repo_id": "diffusers",
"token_count": 1122
} | 121 |
<!--Copyright 2025 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# Latent Consistency Model Multistep Scheduler
## Overview
Multistep and onestep scheduler (Algorithm 3) introduced alongside latent consistency models in the paper [Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference](https://huggingface.co/papers/2310.04378) by Simian Luo, Yiqin Tan, Longbo Huang, Jian Li, and Hang Zhao.
This scheduler should be able to generate good samples from [`LatentConsistencyModelPipeline`] in 1-8 steps.
## LCMScheduler
[[autodoc]] LCMScheduler
| diffusers/docs/source/en/api/schedulers/lcm.md/0 | {
"file_path": "diffusers/docs/source/en/api/schedulers/lcm.md",
"repo_id": "diffusers",
"token_count": 292
} | 122 |
<!--Copyright 2025 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# Guiders
[Classifier-free guidance](https://huggingface.co/papers/2207.12598) steers model generation that better match a prompt and is commonly used to improve generation quality, control, and adherence to prompts. There are different types of guidance methods, and in Diffusers, they are known as *guiders*. Like blocks, it is easy to switch and use different guiders for different use cases without rewriting the pipeline.
This guide will show you how to switch guiders, adjust guider parameters, and load and share them to the Hub.
## Switching guiders
[`ClassifierFreeGuidance`] is the default guider and created when a pipeline is initialized with [`~ModularPipelineBlocks.init_pipeline`]. It is created by `from_config` which means it doesn't require loading specifications from a modular repository. A guider won't be listed in `modular_model_index.json`.
Use [`~ModularPipeline.get_component_spec`] to inspect a guider.
```py
t2i_pipeline.get_component_spec("guider")
ComponentSpec(name='guider', type_hint=<class 'diffusers.guiders.classifier_free_guidance.ClassifierFreeGuidance'>, description=None, config=FrozenDict([('guidance_scale', 7.5), ('guidance_rescale', 0.0), ('use_original_formulation', False), ('start', 0.0), ('stop', 1.0), ('_use_default_values', ['start', 'guidance_rescale', 'stop', 'use_original_formulation'])]), repo=None, subfolder=None, variant=None, revision=None, default_creation_method='from_config')
```
Switch to a different guider by passing the new guider to [`~ModularPipeline.update_components`].
> [!TIP]
> Changing guiders will return text letting you know you're changing the guider type.
> ```bash
> ModularPipeline.update_components: adding guider with new type: PerturbedAttentionGuidance, previous type: ClassifierFreeGuidance
> ```
```py
from diffusers import LayerSkipConfig, PerturbedAttentionGuidance
config = LayerSkipConfig(indices=[2, 9], fqn="mid_block.attentions.0.transformer_blocks", skip_attention=False, skip_attention_scores=True, skip_ff=False)
guider = PerturbedAttentionGuidance(
guidance_scale=5.0, perturbed_guidance_scale=2.5, perturbed_guidance_config=config
)
t2i_pipeline.update_components(guider=guider)
```
Use [`~ModularPipeline.get_component_spec`] again to verify the guider type is different.
```py
t2i_pipeline.get_component_spec("guider")
ComponentSpec(name='guider', type_hint=<class 'diffusers.guiders.perturbed_attention_guidance.PerturbedAttentionGuidance'>, description=None, config=FrozenDict([('guidance_scale', 5.0), ('perturbed_guidance_scale', 2.5), ('perturbed_guidance_start', 0.01), ('perturbed_guidance_stop', 0.2), ('perturbed_guidance_layers', None), ('perturbed_guidance_config', LayerSkipConfig(indices=[2, 9], fqn='mid_block.attentions.0.transformer_blocks', skip_attention=False, skip_attention_scores=True, skip_ff=False, dropout=1.0)), ('guidance_rescale', 0.0), ('use_original_formulation', False), ('start', 0.0), ('stop', 1.0), ('_use_default_values', ['perturbed_guidance_start', 'use_original_formulation', 'perturbed_guidance_layers', 'stop', 'start', 'guidance_rescale', 'perturbed_guidance_stop']), ('_class_name', 'PerturbedAttentionGuidance'), ('_diffusers_version', '0.35.0.dev0')]), repo=None, subfolder=None, variant=None, revision=None, default_creation_method='from_config')
```
## Loading custom guiders
Guiders that are already saved on the Hub with a `modular_model_index.json` file are considered a `from_pretrained` component now instead of a `from_config` component.
```json
{
"guider": [
null,
null,
{
"repo": "YiYiXu/modular-loader-t2i-guider",
"revision": null,
"subfolder": "pag_guider",
"type_hint": [
"diffusers",
"PerturbedAttentionGuidance"
],
"variant": null
}
]
}
```
The guider is only created after calling [`~ModularPipeline.load_default_components`] based on the loading specification in `modular_model_index.json`.
```py
t2i_pipeline = t2i_blocks.init_pipeline("YiYiXu/modular-doc-guider")
# not created during init
assert t2i_pipeline.guider is None
t2i_pipeline.load_default_components()
# loaded as PAG guider
t2i_pipeline.guider
```
## Changing guider parameters
The guider parameters can be adjusted with either the [`~ComponentSpec.create`] method or with [`~ModularPipeline.update_components`]. The example below changes the `guidance_scale` value.
<hfoptions id="switch">
<hfoption id="create">
```py
guider_spec = t2i_pipeline.get_component_spec("guider")
guider = guider_spec.create(guidance_scale=10)
t2i_pipeline.update_components(guider=guider)
```
</hfoption>
<hfoption id="update_components">
```py
guider_spec = t2i_pipeline.get_component_spec("guider")
guider_spec.config["guidance_scale"] = 10
t2i_pipeline.update_components(guider=guider_spec)
```
</hfoption>
</hfoptions>
## Uploading custom guiders
Call the [`~utils.PushToHubMixin.push_to_hub`] method on a custom guider to share it to the Hub.
```py
guider.push_to_hub("YiYiXu/modular-loader-t2i-guider", subfolder="pag_guider")
```
To make this guider available to the pipeline, either modify the `modular_model_index.json` file or use the [`~ModularPipeline.update_components`] method.
<hfoptions id="upload">
<hfoption id="modular_model_index.json">
Edit the `modular_model_index.json` file and add a loading specification for the guider by pointing to a folder containing the guider config.
```json
{
"guider": [
"diffusers",
"PerturbedAttentionGuidance",
{
"repo": "YiYiXu/modular-loader-t2i-guider",
"revision": null,
"subfolder": "pag_guider",
"type_hint": [
"diffusers",
"PerturbedAttentionGuidance"
],
"variant": null
}
],
```
</hfoption>
<hfoption id="update_components">
Change the [`~ComponentSpec.default_creation_method`] to `from_pretrained` and use [`~ModularPipeline.update_components`] to update the guider and component specifications as well as the pipeline config.
> [!TIP]
> Changing the creation method will return text letting you know you're changing the creation type to `from_pretrained`.
> ```bash
> ModularPipeline.update_components: changing the default_creation_method of guider from from_config to from_pretrained.
> ```
```py
guider_spec = t2i_pipeline.get_component_spec("guider")
guider_spec.default_creation_method="from_pretrained"
guider_spec.repo="YiYiXu/modular-loader-t2i-guider"
guider_spec.subfolder="pag_guider"
pag_guider = guider_spec.load()
t2i_pipeline.update_components(guider=pag_guider)
```
To make it the default guider for a pipeline, call [`~utils.PushToHubMixin.push_to_hub`]. This is an optional step and not necessary if you are only experimenting locally.
```py
t2i_pipeline.push_to_hub("YiYiXu/modular-doc-guider")
```
</hfoption>
</hfoptions> | diffusers/docs/source/en/modular_diffusers/guiders.md/0 | {
"file_path": "diffusers/docs/source/en/modular_diffusers/guiders.md",
"repo_id": "diffusers",
"token_count": 2575
} | 123 |
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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 writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# ONNX Runtime
🤗 [Optimum](https://github.com/huggingface/optimum) provides a Stable Diffusion pipeline compatible with ONNX Runtime. You'll need to install 🤗 Optimum with the following command for ONNX Runtime support:
```bash
pip install -q optimum["onnxruntime"]
```
This guide will show you how to use the Stable Diffusion and Stable Diffusion XL (SDXL) pipelines with ONNX Runtime.
## Stable Diffusion
To load and run inference, use the [`~optimum.onnxruntime.ORTStableDiffusionPipeline`]. If you want to load a PyTorch model and convert it to the ONNX format on-the-fly, set `export=True`:
```python
from optimum.onnxruntime import ORTStableDiffusionPipeline
model_id = "stable-diffusion-v1-5/stable-diffusion-v1-5"
pipeline = ORTStableDiffusionPipeline.from_pretrained(model_id, export=True)
prompt = "sailing ship in storm by Leonardo da Vinci"
image = pipeline(prompt).images[0]
pipeline.save_pretrained("./onnx-stable-diffusion-v1-5")
```
<Tip warning={true}>
Generating multiple prompts in a batch seems to take too much memory. While we look into it, you may need to iterate instead of batching.
</Tip>
To export the pipeline in the ONNX format offline and use it later for inference,
use the [`optimum-cli export`](https://huggingface.co/docs/optimum/main/en/exporters/onnx/usage_guides/export_a_model#exporting-a-model-to-onnx-using-the-cli) command:
```bash
optimum-cli export onnx --model stable-diffusion-v1-5/stable-diffusion-v1-5 sd_v15_onnx/
```
Then to perform inference (you don't have to specify `export=True` again):
```python
from optimum.onnxruntime import ORTStableDiffusionPipeline
model_id = "sd_v15_onnx"
pipeline = ORTStableDiffusionPipeline.from_pretrained(model_id)
prompt = "sailing ship in storm by Leonardo da Vinci"
image = pipeline(prompt).images[0]
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/optimum/documentation-images/resolve/main/onnxruntime/stable_diffusion_v1_5_ort_sail_boat.png">
</div>
You can find more examples in 🤗 Optimum [documentation](https://huggingface.co/docs/optimum/), and Stable Diffusion is supported for text-to-image, image-to-image, and inpainting.
## Stable Diffusion XL
To load and run inference with SDXL, use the [`~optimum.onnxruntime.ORTStableDiffusionXLPipeline`]:
```python
from optimum.onnxruntime import ORTStableDiffusionXLPipeline
model_id = "stabilityai/stable-diffusion-xl-base-1.0"
pipeline = ORTStableDiffusionXLPipeline.from_pretrained(model_id)
prompt = "sailing ship in storm by Leonardo da Vinci"
image = pipeline(prompt).images[0]
```
To export the pipeline in the ONNX format and use it later for inference, use the [`optimum-cli export`](https://huggingface.co/docs/optimum/main/en/exporters/onnx/usage_guides/export_a_model#exporting-a-model-to-onnx-using-the-cli) command:
```bash
optimum-cli export onnx --model stabilityai/stable-diffusion-xl-base-1.0 --task stable-diffusion-xl sd_xl_onnx/
```
SDXL in the ONNX format is supported for text-to-image and image-to-image.
| diffusers/docs/source/en/optimization/onnx.md/0 | {
"file_path": "diffusers/docs/source/en/optimization/onnx.md",
"repo_id": "diffusers",
"token_count": 1206
} | 124 |
# Adapt a model to a new task
Many diffusion systems share the same components, allowing you to adapt a pretrained model for one task to an entirely different task.
This guide will show you how to adapt a pretrained text-to-image model for inpainting by initializing and modifying the architecture of a pretrained [`UNet2DConditionModel`].
## Configure UNet2DConditionModel parameters
A [`UNet2DConditionModel`] by default accepts 4 channels in the [input sample](https://huggingface.co/docs/diffusers/v0.16.0/en/api/models#diffusers.UNet2DConditionModel.in_channels). For example, load a pretrained text-to-image model like [`stable-diffusion-v1-5/stable-diffusion-v1-5`](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) and take a look at the number of `in_channels`:
```py
from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", use_safetensors=True)
pipeline.unet.config["in_channels"]
4
```
Inpainting requires 9 channels in the input sample. You can check this value in a pretrained inpainting model like [`runwayml/stable-diffusion-inpainting`](https://huggingface.co/runwayml/stable-diffusion-inpainting):
```py
from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-inpainting", use_safetensors=True)
pipeline.unet.config["in_channels"]
9
```
To adapt your text-to-image model for inpainting, you'll need to change the number of `in_channels` from 4 to 9.
Initialize a [`UNet2DConditionModel`] with the pretrained text-to-image model weights, and change `in_channels` to 9. Changing the number of `in_channels` means you need to set `ignore_mismatched_sizes=True` and `low_cpu_mem_usage=False` to avoid a size mismatch error because the shape is different now.
```py
from diffusers import AutoModel
model_id = "stable-diffusion-v1-5/stable-diffusion-v1-5"
unet = AutoModel.from_pretrained(
model_id,
subfolder="unet",
in_channels=9,
low_cpu_mem_usage=False,
ignore_mismatched_sizes=True,
use_safetensors=True,
)
```
The pretrained weights of the other components from the text-to-image model are initialized from their checkpoints, but the input channel weights (`conv_in.weight`) of the `unet` are randomly initialized. It is important to finetune the model for inpainting because otherwise the model returns noise.
| diffusers/docs/source/en/training/adapt_a_model.md/0 | {
"file_path": "diffusers/docs/source/en/training/adapt_a_model.md",
"repo_id": "diffusers",
"token_count": 792
} | 125 |
<!--Copyright 2025 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# Textual Inversion
[Textual Inversion](https://hf.co/papers/2208.01618) is a training technique for personalizing image generation models with just a few example images of what you want it to learn. This technique works by learning and updating the text embeddings (the new embeddings are tied to a special word you must use in the prompt) to match the example images you provide.
If you're training on a GPU with limited vRAM, you should try enabling the `gradient_checkpointing` and `mixed_precision` parameters in the training command. You can also reduce your memory footprint by using memory-efficient attention with [xFormers](../optimization/xformers). JAX/Flax training is also supported for efficient training on TPUs and GPUs, but it doesn't support gradient checkpointing or xFormers. With the same configuration and setup as PyTorch, the Flax training script should be at least ~70% faster!
This guide will explore the [textual_inversion.py](https://github.com/huggingface/diffusers/blob/main/examples/textual_inversion/textual_inversion.py) script to help you become more familiar with it, and how you can adapt it for your own use-case.
Before running the script, make sure you install the library from source:
```bash
git clone https://github.com/huggingface/diffusers
cd diffusers
pip install .
```
Navigate to the example folder with the training script and install the required dependencies for the script you're using:
<hfoptions id="installation">
<hfoption id="PyTorch">
```bash
cd examples/textual_inversion
pip install -r requirements.txt
```
</hfoption>
<hfoption id="Flax">
```bash
cd examples/textual_inversion
pip install -r requirements_flax.txt
```
</hfoption>
</hfoptions>
<Tip>
🤗 Accelerate is a library for helping you train on multiple GPUs/TPUs or with mixed-precision. It'll automatically configure your training setup based on your hardware and environment. Take a look at the 🤗 Accelerate [Quick tour](https://huggingface.co/docs/accelerate/quicktour) to learn more.
</Tip>
Initialize an 🤗 Accelerate environment:
```bash
accelerate config
```
To setup a default 🤗 Accelerate environment without choosing any configurations:
```bash
accelerate config default
```
Or if your environment doesn't support an interactive shell, like a notebook, you can use:
```py
from accelerate.utils import write_basic_config
write_basic_config()
```
Lastly, if you want to train a model on your own dataset, take a look at the [Create a dataset for training](create_dataset) guide to learn how to create a dataset that works with the training script.
<Tip>
The following sections highlight parts of the training script that are important for understanding how to modify it, but it doesn't cover every aspect of the script in detail. If you're interested in learning more, feel free to read through the [script](https://github.com/huggingface/diffusers/blob/main/examples/textual_inversion/textual_inversion.py) and let us know if you have any questions or concerns.
</Tip>
## Script parameters
The training script has many parameters to help you tailor the training run to your needs. All of the parameters and their descriptions are listed in the [`parse_args()`](https://github.com/huggingface/diffusers/blob/839c2a5ece0af4e75530cb520d77bc7ed8acf474/examples/textual_inversion/textual_inversion.py#L176) function. Where applicable, Diffusers provides default values for each parameter such as the training batch size and learning rate, but feel free to change these values in the training command if you'd like.
For example, to increase the number of gradient accumulation steps above the default value of 1:
```bash
accelerate launch textual_inversion.py \
--gradient_accumulation_steps=4
```
Some other basic and important parameters to specify include:
- `--pretrained_model_name_or_path`: the name of the model on the Hub or a local path to the pretrained model
- `--train_data_dir`: path to a folder containing the training dataset (example images)
- `--output_dir`: where to save the trained model
- `--push_to_hub`: whether to push the trained model to the Hub
- `--checkpointing_steps`: frequency of saving a checkpoint as the model trains; this is useful if for some reason training is interrupted, you can continue training from that checkpoint by adding `--resume_from_checkpoint` to your training command
- `--num_vectors`: the number of vectors to learn the embeddings with; increasing this parameter helps the model learn better but it comes with increased training costs
- `--placeholder_token`: the special word to tie the learned embeddings to (you must use the word in your prompt for inference)
- `--initializer_token`: a single-word that roughly describes the object or style you're trying to train on
- `--learnable_property`: whether you're training the model to learn a new "style" (for example, Van Gogh's painting style) or "object" (for example, your dog)
## Training script
Unlike some of the other training scripts, textual_inversion.py has a custom dataset class, [`TextualInversionDataset`](https://github.com/huggingface/diffusers/blob/b81c69e489aad3a0ba73798c459a33990dc4379c/examples/textual_inversion/textual_inversion.py#L487) for creating a dataset. You can customize the image size, placeholder token, interpolation method, whether to crop the image, and more. If you need to change how the dataset is created, you can modify `TextualInversionDataset`.
Next, you'll find the dataset preprocessing code and training loop in the [`main()`](https://github.com/huggingface/diffusers/blob/839c2a5ece0af4e75530cb520d77bc7ed8acf474/examples/textual_inversion/textual_inversion.py#L573) function.
The script starts by loading the [tokenizer](https://github.com/huggingface/diffusers/blob/b81c69e489aad3a0ba73798c459a33990dc4379c/examples/textual_inversion/textual_inversion.py#L616), [scheduler and model](https://github.com/huggingface/diffusers/blob/b81c69e489aad3a0ba73798c459a33990dc4379c/examples/textual_inversion/textual_inversion.py#L622):
```py
# Load tokenizer
if args.tokenizer_name:
tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name)
elif args.pretrained_model_name_or_path:
tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
# Load scheduler and models
noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
text_encoder = CLIPTextModel.from_pretrained(
args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
)
vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision)
unet = UNet2DConditionModel.from_pretrained(
args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision
)
```
The special [placeholder token](https://github.com/huggingface/diffusers/blob/b81c69e489aad3a0ba73798c459a33990dc4379c/examples/textual_inversion/textual_inversion.py#L632) is added next to the tokenizer, and the embedding is readjusted to account for the new token.
Then, the script [creates a dataset](https://github.com/huggingface/diffusers/blob/b81c69e489aad3a0ba73798c459a33990dc4379c/examples/textual_inversion/textual_inversion.py#L716) from the `TextualInversionDataset`:
```py
train_dataset = TextualInversionDataset(
data_root=args.train_data_dir,
tokenizer=tokenizer,
size=args.resolution,
placeholder_token=(" ".join(tokenizer.convert_ids_to_tokens(placeholder_token_ids))),
repeats=args.repeats,
learnable_property=args.learnable_property,
center_crop=args.center_crop,
set="train",
)
train_dataloader = torch.utils.data.DataLoader(
train_dataset, batch_size=args.train_batch_size, shuffle=True, num_workers=args.dataloader_num_workers
)
```
Finally, the [training loop](https://github.com/huggingface/diffusers/blob/b81c69e489aad3a0ba73798c459a33990dc4379c/examples/textual_inversion/textual_inversion.py#L784) handles everything else from predicting the noisy residual to updating the embedding weights of the special placeholder token.
If you want to learn more about how the training loop works, check out the [Understanding pipelines, models and schedulers](../using-diffusers/write_own_pipeline) tutorial which breaks down the basic pattern of the denoising process.
## Launch the script
Once you've made all your changes or you're okay with the default configuration, you're ready to launch the training script! 🚀
For this guide, you'll download some images of a [cat toy](https://huggingface.co/datasets/diffusers/cat_toy_example) and store them in a directory. But remember, you can create and use your own dataset if you want (see the [Create a dataset for training](create_dataset) guide).
```py
from huggingface_hub import snapshot_download
local_dir = "./cat"
snapshot_download(
"diffusers/cat_toy_example", local_dir=local_dir, repo_type="dataset", ignore_patterns=".gitattributes"
)
```
Set the environment variable `MODEL_NAME` to a model id on the Hub or a path to a local model, and `DATA_DIR` to the path where you just downloaded the cat images to. The script creates and saves the following files to your repository:
- `learned_embeds.bin`: the learned embedding vectors corresponding to your example images
- `token_identifier.txt`: the special placeholder token
- `type_of_concept.txt`: the type of concept you're training on (either "object" or "style")
<Tip warning={true}>
A full training run takes ~1 hour on a single V100 GPU.
</Tip>
One more thing before you launch the script. If you're interested in following along with the training process, you can periodically save generated images as training progresses. Add the following parameters to the training command:
```bash
--validation_prompt="A <cat-toy> train"
--num_validation_images=4
--validation_steps=100
```
<hfoptions id="training-inference">
<hfoption id="PyTorch">
```bash
export MODEL_NAME="stable-diffusion-v1-5/stable-diffusion-v1-5"
export DATA_DIR="./cat"
accelerate launch textual_inversion.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--train_data_dir=$DATA_DIR \
--learnable_property="object" \
--placeholder_token="<cat-toy>" \
--initializer_token="toy" \
--resolution=512 \
--train_batch_size=1 \
--gradient_accumulation_steps=4 \
--max_train_steps=3000 \
--learning_rate=5.0e-04 \
--scale_lr \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--output_dir="textual_inversion_cat" \
--push_to_hub
```
</hfoption>
<hfoption id="Flax">
```bash
export MODEL_NAME="duongna/stable-diffusion-v1-4-flax"
export DATA_DIR="./cat"
python textual_inversion_flax.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--train_data_dir=$DATA_DIR \
--learnable_property="object" \
--placeholder_token="<cat-toy>" \
--initializer_token="toy" \
--resolution=512 \
--train_batch_size=1 \
--max_train_steps=3000 \
--learning_rate=5.0e-04 \
--scale_lr \
--output_dir="textual_inversion_cat" \
--push_to_hub
```
</hfoption>
</hfoptions>
After training is complete, you can use your newly trained model for inference like:
<hfoptions id="training-inference">
<hfoption id="PyTorch">
```py
from diffusers import StableDiffusionPipeline
import torch
pipeline = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16).to("cuda")
pipeline.load_textual_inversion("sd-concepts-library/cat-toy")
image = pipeline("A <cat-toy> train", num_inference_steps=50).images[0]
image.save("cat-train.png")
```
</hfoption>
<hfoption id="Flax">
Flax doesn't support the [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] method, but the textual_inversion_flax.py script [saves](https://github.com/huggingface/diffusers/blob/c0f058265161178f2a88849e92b37ffdc81f1dcc/examples/textual_inversion/textual_inversion_flax.py#L636C2-L636C2) the learned embeddings as a part of the model after training. This means you can use the model for inference like any other Flax model:
```py
import jax
import numpy as np
from flax.jax_utils import replicate
from flax.training.common_utils import shard
from diffusers import FlaxStableDiffusionPipeline
model_path = "path-to-your-trained-model"
pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(model_path, dtype=jax.numpy.bfloat16)
prompt = "A <cat-toy> train"
prng_seed = jax.random.PRNGKey(0)
num_inference_steps = 50
num_samples = jax.device_count()
prompt = num_samples * [prompt]
prompt_ids = pipeline.prepare_inputs(prompt)
# shard inputs and rng
params = replicate(params)
prng_seed = jax.random.split(prng_seed, jax.device_count())
prompt_ids = shard(prompt_ids)
images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images
images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))
image.save("cat-train.png")
```
</hfoption>
</hfoptions>
## Next steps
Congratulations on training your own Textual Inversion model! 🎉 To learn more about how to use your new model, the following guides may be helpful:
- Learn how to [load Textual Inversion embeddings](../using-diffusers/loading_adapters) and also use them as negative embeddings.
- Learn how to use [Textual Inversion](textual_inversion_inference) for inference with Stable Diffusion 1/2 and Stable Diffusion XL. | diffusers/docs/source/en/training/text_inversion.md/0 | {
"file_path": "diffusers/docs/source/en/training/text_inversion.md",
"repo_id": "diffusers",
"token_count": 4395
} | 126 |
<!--Copyright 2025 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# DreamBooth
[DreamBooth](https://huggingface.co/papers/2208.12242) is a method for generating personalized images of a specific instance. It works by fine-tuning the model on 3-5 images of the subject (for example, a cat) that is associated with a unique identifier (`sks cat`). This allows you to use `sks cat` in your prompt to trigger the model to generate images of your cat in different settings, lighting, poses, and styles.
DreamBooth checkpoints are typically a few GBs in size because it contains the full model weights.
Load the DreamBooth checkpoint with [`~DiffusionPipeline.from_pretrained`] and include the unique identifier in the prompt to activate its generation.
```py
import torch
from diffusers import AutoPipelineForText2Image
pipeline = AutoPipelineForText2Image.from_pretrained(
"sd-dreambooth-library/herge-style",
torch_dtype=torch.float16
).to("cuda")
prompt = "A cute sks herge_style brown bear eating a slice of pizza, stunning color scheme, masterpiece, illustration"
pipeline(prompt).images[0]
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/load_dreambooth.png" />
</div> | diffusers/docs/source/en/using-diffusers/dreambooth.md/0 | {
"file_path": "diffusers/docs/source/en/using-diffusers/dreambooth.md",
"repo_id": "diffusers",
"token_count": 500
} | 127 |
<!--Copyright 2025 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# Load schedulers and models
[[open-in-colab]]
Diffusion pipelines are a collection of interchangeable schedulers and models that can be mixed and matched to tailor a pipeline to a specific use case. The scheduler encapsulates the entire denoising process such as the number of denoising steps and the algorithm for finding the denoised sample. A scheduler is not parameterized or trained so they don't take very much memory. The model is usually only concerned with the forward pass of going from a noisy input to a less noisy sample.
This guide will show you how to load schedulers and models to customize a pipeline. You'll use the [stable-diffusion-v1-5/stable-diffusion-v1-5](https://hf.co/stable-diffusion-v1-5/stable-diffusion-v1-5) checkpoint throughout this guide, so let's load it first.
```py
import torch
from diffusers import DiffusionPipeline
pipeline = DiffusionPipeline.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True
).to("cuda")
```
You can see what scheduler this pipeline uses with the `pipeline.scheduler` attribute.
```py
pipeline.scheduler
PNDMScheduler {
"_class_name": "PNDMScheduler",
"_diffusers_version": "0.21.4",
"beta_end": 0.012,
"beta_schedule": "scaled_linear",
"beta_start": 0.00085,
"clip_sample": false,
"num_train_timesteps": 1000,
"set_alpha_to_one": false,
"skip_prk_steps": true,
"steps_offset": 1,
"timestep_spacing": "leading",
"trained_betas": null
}
```
## Load a scheduler
Schedulers are defined by a configuration file that can be used by a variety of schedulers. Load a scheduler with the [`SchedulerMixin.from_pretrained`] method, and specify the `subfolder` parameter to load the configuration file into the correct subfolder of the pipeline repository.
For example, to load the [`DDIMScheduler`]:
```py
from diffusers import DDIMScheduler, DiffusionPipeline
ddim = DDIMScheduler.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="scheduler")
```
Then you can pass the newly loaded scheduler to the pipeline.
```python
pipeline = DiffusionPipeline.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5", scheduler=ddim, torch_dtype=torch.float16, use_safetensors=True
).to("cuda")
```
## Compare schedulers
Schedulers have their own unique strengths and weaknesses, making it difficult to quantitatively compare which scheduler works best for a pipeline. You typically have to make a trade-off between denoising speed and denoising quality. We recommend trying out different schedulers to find one that works best for your use case. Call the `pipeline.scheduler.compatibles` attribute to see what schedulers are compatible with a pipeline.
Let's compare the [`LMSDiscreteScheduler`], [`EulerDiscreteScheduler`], [`EulerAncestralDiscreteScheduler`], and the [`DPMSolverMultistepScheduler`] on the following prompt and seed.
```py
import torch
from diffusers import DiffusionPipeline
pipeline = DiffusionPipeline.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True
).to("cuda")
prompt = "A photograph of an astronaut riding a horse on Mars, high resolution, high definition."
generator = torch.Generator(device="cuda").manual_seed(8)
```
To change the pipelines scheduler, use the [`~ConfigMixin.from_config`] method to load a different scheduler's `pipeline.scheduler.config` into the pipeline.
<hfoptions id="schedulers">
<hfoption id="LMSDiscreteScheduler">
[`LMSDiscreteScheduler`] typically generates higher quality images than the default scheduler.
```py
from diffusers import LMSDiscreteScheduler
pipeline.scheduler = LMSDiscreteScheduler.from_config(pipeline.scheduler.config)
image = pipeline(prompt, generator=generator).images[0]
image
```
</hfoption>
<hfoption id="EulerDiscreteScheduler">
[`EulerDiscreteScheduler`] can generate higher quality images in just 30 steps.
```py
from diffusers import EulerDiscreteScheduler
pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config)
image = pipeline(prompt, generator=generator).images[0]
image
```
</hfoption>
<hfoption id="EulerAncestralDiscreteScheduler">
[`EulerAncestralDiscreteScheduler`] can generate higher quality images in just 30 steps.
```py
from diffusers import EulerAncestralDiscreteScheduler
pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(pipeline.scheduler.config)
image = pipeline(prompt, generator=generator).images[0]
image
```
</hfoption>
<hfoption id="DPMSolverMultistepScheduler">
[`DPMSolverMultistepScheduler`] provides a balance between speed and quality and can generate higher quality images in just 20 steps.
```py
from diffusers import DPMSolverMultistepScheduler
pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
image = pipeline(prompt, generator=generator).images[0]
image
```
</hfoption>
</hfoptions>
<div class="flex gap-4">
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/diffusers_docs/astronaut_lms.png" />
<figcaption class="mt-2 text-center text-sm text-gray-500">LMSDiscreteScheduler</figcaption>
</div>
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/diffusers_docs/astronaut_euler_discrete.png" />
<figcaption class="mt-2 text-center text-sm text-gray-500">EulerDiscreteScheduler</figcaption>
</div>
</div>
<div class="flex gap-4">
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/diffusers_docs/astronaut_euler_ancestral.png" />
<figcaption class="mt-2 text-center text-sm text-gray-500">EulerAncestralDiscreteScheduler</figcaption>
</div>
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/diffusers_docs/astronaut_dpm.png" />
<figcaption class="mt-2 text-center text-sm text-gray-500">DPMSolverMultistepScheduler</figcaption>
</div>
</div>
Most images look very similar and are comparable in quality. Again, it often comes down to your specific use case so a good approach is to run multiple different schedulers and compare the results.
### Flax schedulers
To compare Flax schedulers, you need to additionally load the scheduler state into the model parameters. For example, let's change the default scheduler in [`FlaxStableDiffusionPipeline`] to use the super fast [`FlaxDPMSolverMultistepScheduler`].
> [!WARNING]
> The [`FlaxLMSDiscreteScheduler`] and [`FlaxDDPMScheduler`] are not compatible with the [`FlaxStableDiffusionPipeline`] yet.
```py
import jax
import numpy as np
from flax.jax_utils import replicate
from flax.training.common_utils import shard
from diffusers import FlaxStableDiffusionPipeline, FlaxDPMSolverMultistepScheduler
scheduler, scheduler_state = FlaxDPMSolverMultistepScheduler.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5",
subfolder="scheduler"
)
pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5",
scheduler=scheduler,
variant="bf16",
dtype=jax.numpy.bfloat16,
)
params["scheduler"] = scheduler_state
```
Then you can take advantage of Flax's compatibility with TPUs to generate a number of images in parallel. You'll need to make a copy of the model parameters for each available device and then split the inputs across them to generate your desired number of images.
```py
# Generate 1 image per parallel device (8 on TPUv2-8 or TPUv3-8)
prompt = "A photograph of an astronaut riding a horse on Mars, high resolution, high definition."
num_samples = jax.device_count()
prompt_ids = pipeline.prepare_inputs([prompt] * num_samples)
prng_seed = jax.random.PRNGKey(0)
num_inference_steps = 25
# shard inputs and rng
params = replicate(params)
prng_seed = jax.random.split(prng_seed, jax.device_count())
prompt_ids = shard(prompt_ids)
images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images
images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))
```
## Models
Models are loaded from the [`ModelMixin.from_pretrained`] method, which downloads and caches the latest version of the model weights and configurations. If the latest files are available in the local cache, [`~ModelMixin.from_pretrained`] reuses files in the cache instead of re-downloading them.
Models can be loaded from a subfolder with the `subfolder` argument. For example, the model weights for [stable-diffusion-v1-5/stable-diffusion-v1-5](https://hf.co/stable-diffusion-v1-5/stable-diffusion-v1-5) are stored in the [unet](https://hf.co/stable-diffusion-v1-5/stable-diffusion-v1-5/tree/main/unet) subfolder.
```python
from diffusers import UNet2DConditionModel
unet = UNet2DConditionModel.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="unet", use_safetensors=True)
```
They can also be directly loaded from a [repository](https://huggingface.co/google/ddpm-cifar10-32/tree/main).
```python
from diffusers import UNet2DModel
unet = UNet2DModel.from_pretrained("google/ddpm-cifar10-32", use_safetensors=True)
```
To load and save model variants, specify the `variant` argument in [`ModelMixin.from_pretrained`] and [`ModelMixin.save_pretrained`].
```python
from diffusers import UNet2DConditionModel
unet = UNet2DConditionModel.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="unet", variant="non_ema", use_safetensors=True
)
unet.save_pretrained("./local-unet", variant="non_ema")
```
Use the `torch_dtype` argument in [`~ModelMixin.from_pretrained`] to specify the dtype to load a model in.
```py
from diffusers import AutoModel
unet = AutoModel.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", subfolder="unet", torch_dtype=torch.float16
)
```
You can also use the [torch.Tensor.to](https://docs.pytorch.org/docs/stable/generated/torch.Tensor.to.html) method to convert to the specified dtype on the fly. It converts *all* weights unlike the `torch_dtype` argument that respects the `_keep_in_fp32_modules`. This is important for models whose layers must remain in fp32 for numerical stability and best generation quality (see example [here](https://github.com/huggingface/diffusers/blob/f864a9a352fa4a220d860bfdd1782e3e5af96382/src/diffusers/models/transformers/transformer_wan.py#L374)).
| diffusers/docs/source/en/using-diffusers/schedulers.md/0 | {
"file_path": "diffusers/docs/source/en/using-diffusers/schedulers.md",
"repo_id": "diffusers",
"token_count": 3684
} | 128 |
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# 効果的で効率的な拡散モデル
[[open-in-colab]]
[`DiffusionPipeline`]を使って特定のスタイルで画像を生成したり、希望する画像を生成したりするのは難しいことです。多くの場合、[`DiffusionPipeline`]を何度か実行してからでないと満足のいく画像は得られません。しかし、何もないところから何かを生成するにはたくさんの計算が必要です。生成を何度も何度も実行する場合、特にたくさんの計算量が必要になります。
そのため、パイプラインから*計算*（速度）と*メモリ*（GPU RAM）の効率を最大限に引き出し、生成サイクル間の時間を短縮することで、より高速な反復処理を行えるようにすることが重要です。
このチュートリアルでは、[`DiffusionPipeline`]を用いて、より速く、より良い計算を行う方法を説明します。
まず、[`stable-diffusion-v1-5/stable-diffusion-v1-5`](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5)モデルをロードします：
```python
from diffusers import DiffusionPipeline
model_id = "stable-diffusion-v1-5/stable-diffusion-v1-5"
pipeline = DiffusionPipeline.from_pretrained(model_id, use_safetensors=True)
```
ここで使用するプロンプトの例は年老いた戦士の長の肖像画ですが、ご自由に変更してください：
```python
prompt = "portrait photo of a old warrior chief"
```
## Speed
<Tip>
💡 GPUを利用できない場合は、[Colab](https://colab.research.google.com/)のようなGPUプロバイダーから無料で利用できます！
</Tip>
画像生成を高速化する最も簡単な方法の1つは、PyTorchモジュールと同じようにGPU上にパイプラインを配置することです：
```python
pipeline = pipeline.to("cuda")
```
同じイメージを使って改良できるようにするには、[`Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html)を使い、[reproducibility](./using-diffusers/reusing_seeds)の種を設定します：
```python
import torch
generator = torch.Generator("cuda").manual_seed(0)
```
これで画像を生成できます：
```python
image = pipeline(prompt, generator=generator).images[0]
image
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/stable_diffusion_101/sd_101_1.png">
</div>
この処理にはT4 GPUで~30秒かかりました（割り当てられているGPUがT4より優れている場合はもっと速いかもしれません）。デフォルトでは、[`DiffusionPipeline`]は完全な`float32`精度で生成を50ステップ実行します。float16`のような低い精度に変更するか、推論ステップ数を減らすことで高速化することができます。
まずは `float16` でモデルをロードして画像を生成してみましょう：
```python
import torch
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16, use_safetensors=True)
pipeline = pipeline.to("cuda")
generator = torch.Generator("cuda").manual_seed(0)
image = pipeline(prompt, generator=generator).images[0]
image
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/stable_diffusion_101/sd_101_2.png">
</div>
今回、画像生成にかかった時間はわずか11秒で、以前より3倍近く速くなりました！
<Tip>
💡 パイプラインは常に `float16` で実行することを強くお勧めします。
</Tip>
生成ステップ数を減らすという方法もあります。より効率的なスケジューラを選択することで、出力品質を犠牲にすることなくステップ数を減らすことができます。`compatibles`メソッドを呼び出すことで、[`DiffusionPipeline`]の現在のモデルと互換性のあるスケジューラを見つけることができます：
```python
pipeline.scheduler.compatibles
[
diffusers.schedulers.scheduling_lms_discrete.LMSDiscreteScheduler,
diffusers.schedulers.scheduling_unipc_multistep.UniPCMultistepScheduler,
diffusers.schedulers.scheduling_k_dpm_2_discrete.KDPM2DiscreteScheduler,
diffusers.schedulers.scheduling_deis_multistep.DEISMultistepScheduler,
diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler,
diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler,
diffusers.schedulers.scheduling_ddpm.DDPMScheduler,
diffusers.schedulers.scheduling_dpmsolver_singlestep.DPMSolverSinglestepScheduler,
diffusers.schedulers.scheduling_k_dpm_2_ancestral_discrete.KDPM2AncestralDiscreteScheduler,
diffusers.schedulers.scheduling_heun_discrete.HeunDiscreteScheduler,
diffusers.schedulers.scheduling_pndm.PNDMScheduler,
diffusers.schedulers.scheduling_euler_ancestral_discrete.EulerAncestralDiscreteScheduler,
diffusers.schedulers.scheduling_ddim.DDIMScheduler,
]
```
Stable Diffusionモデルはデフォルトで[`PNDMScheduler`]を使用します。このスケジューラは通常~50の推論ステップを必要としますが、[`DPMSolverMultistepScheduler`]のような高性能なスケジューラでは~20または25の推論ステップで済みます。[`ConfigMixin.from_config`]メソッドを使用すると、新しいスケジューラをロードすることができます：
```python
from diffusers import DPMSolverMultistepScheduler
pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
```
ここで `num_inference_steps` を20に設定します：
```python
generator = torch.Generator("cuda").manual_seed(0)
image = pipeline(prompt, generator=generator, num_inference_steps=20).images[0]
image
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/stable_diffusion_101/sd_101_3.png">
</div>
推論時間をわずか4秒に短縮することに成功した！⚡️
## メモリー
パイプラインのパフォーマンスを向上させるもう1つの鍵は、消費メモリを少なくすることです。一度に生成できる画像の数を確認する最も簡単な方法は、`OutOfMemoryError`（OOM）が発生するまで、さまざまなバッチサイズを試してみることです。
文章と `Generators` のリストから画像のバッチを生成する関数を作成します。各 `Generator` にシードを割り当てて、良い結果が得られた場合に再利用できるようにします。
```python
def get_inputs(batch_size=1):
generator = [torch.Generator("cuda").manual_seed(i) for i in range(batch_size)]
prompts = batch_size * [prompt]
num_inference_steps = 20
return {"prompt": prompts, "generator": generator, "num_inference_steps": num_inference_steps}
```
`batch_size=4`で開始し、どれだけメモリを消費したかを確認します：
```python
from diffusers.utils import make_image_grid
images = pipeline(**get_inputs(batch_size=4)).images
make_image_grid(images, 2, 2)
```
大容量のRAMを搭載したGPUでない限り、上記のコードはおそらく`OOM`エラーを返したはずです！メモリの大半はクロスアテンションレイヤーが占めています。この処理をバッチで実行する代わりに、逐次実行することでメモリを大幅に節約できます。必要なのは、[`~DiffusionPipeline.enable_attention_slicing`]関数を使用することだけです：
```python
pipeline.enable_attention_slicing()
```
今度は`batch_size`を8にしてみてください！
```python
images = pipeline(**get_inputs(batch_size=8)).images
make_image_grid(images, rows=2, cols=4)
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/stable_diffusion_101/sd_101_5.png">
</div>
以前は4枚の画像のバッチを生成することさえできませんでしたが、今では8枚の画像のバッチを1枚あたり～3.5秒で生成できます！これはおそらく、品質を犠牲にすることなくT4 GPUでできる最速の処理速度です。
## 品質
前の2つのセクションでは、`fp16` を使ってパイプラインの速度を最適化する方法、よりパフォーマン スなスケジューラーを使って生成ステップ数を減らす方法、アテンションスライスを有効 にしてメモリ消費量を減らす方法について学びました。今度は、生成される画像の品質を向上させる方法に焦点を当てます。
### より良いチェックポイント
最も単純なステップは、より良いチェックポイントを使うことです。Stable Diffusionモデルは良い出発点であり、公式発表以来、いくつかの改良版もリリースされています。しかし、新しいバージョンを使ったからといって、自動的に良い結果が得られるわけではありません。最良の結果を得るためには、自分でさまざまなチェックポイントを試してみたり、ちょっとした研究（[ネガティブプロンプト](https://minimaxir.com/2022/11/stable-diffusion-negative-prompt/)の使用など）をしたりする必要があります。
この分野が成長するにつれて、特定のスタイルを生み出すために微調整された、より質の高いチェックポイントが増えています。[Hub](https://huggingface.co/models?library=diffusers&sort=downloads)や[Diffusers Gallery](https://huggingface.co/spaces/huggingface-projects/diffusers-gallery)を探索して、興味のあるものを見つけてみてください！
### より良いパイプラインコンポーネント
現在のパイプラインコンポーネントを新しいバージョンに置き換えてみることもできます。Stability AIが提供する最新の[autodecoder](https://huggingface.co/stabilityai/stable-diffusion-2-1/tree/main/vae)をパイプラインにロードし、画像を生成してみましょう：
```python
from diffusers import AutoencoderKL
vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse", torch_dtype=torch.float16).to("cuda")
pipeline.vae = vae
images = pipeline(**get_inputs(batch_size=8)).images
make_image_grid(images, rows=2, cols=4)
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/stable_diffusion_101/sd_101_6.png">
</div>
### より良いプロンプト・エンジニアリング
画像を生成するために使用する文章は、*プロンプトエンジニアリング*と呼ばれる分野を作られるほど、非常に重要です。プロンプト・エンジニアリングで考慮すべき点は以下の通りです：
- 生成したい画像やその類似画像は、インターネット上にどのように保存されているか？
- 私が望むスタイルにモデルを誘導するために、どのような追加詳細を与えるべきか？
このことを念頭に置いて、プロンプトに色やより質の高いディテールを含めるように改良してみましょう：
```python
prompt += ", tribal panther make up, blue on red, side profile, looking away, serious eyes"
prompt += " 50mm portrait photography, hard rim lighting photography--beta --ar 2:3 --beta --upbeta"
```
新しいプロンプトで画像のバッチを生成しましょう：
```python
images = pipeline(**get_inputs(batch_size=8)).images
make_image_grid(images, rows=2, cols=4)
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/stable_diffusion_101/sd_101_7.png">
</div>
かなりいいです！種が`1`の`Generator`に対応する2番目の画像に、被写体の年齢に関するテキストを追加して、もう少し手を加えてみましょう：
```python
prompts = [
"portrait photo of the oldest warrior chief, tribal panther make up, blue on red, side profile, looking away, serious eyes 50mm portrait photography, hard rim lighting photography--beta --ar 2:3 --beta --upbeta",
"portrait photo of a old warrior chief, tribal panther make up, blue on red, side profile, looking away, serious eyes 50mm portrait photography, hard rim lighting photography--beta --ar 2:3 --beta --upbeta",
"portrait photo of a warrior chief, tribal panther make up, blue on red, side profile, looking away, serious eyes 50mm portrait photography, hard rim lighting photography--beta --ar 2:3 --beta --upbeta",
"portrait photo of a young warrior chief, tribal panther make up, blue on red, side profile, looking away, serious eyes 50mm portrait photography, hard rim lighting photography--beta --ar 2:3 --beta --upbeta",
]
generator = [torch.Generator("cuda").manual_seed(1) for _ in range(len(prompts))]
images = pipeline(prompt=prompts, generator=generator, num_inference_steps=25).images
make_image_grid(images, 2, 2)
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/stable_diffusion_101/sd_101_8.png">
</div>
## 次のステップ
このチュートリアルでは、[`DiffusionPipeline`]を最適化して計算効率とメモリ効率を向上させ、生成される出力の品質を向上させる方法を学びました。パイプラインをさらに高速化することに興味があれば、以下のリソースを参照してください：
- [PyTorch 2.0](./optimization/torch2.0)と[`torch.compile`](https://pytorch.org/docs/stable/generated/torch.compile.html)がどのように生成速度を5-300%高速化できるかを学んでください。A100 GPUの場合、画像生成は最大50%速くなります！
- PyTorch 2が使えない場合は、[xFormers](./optimization/xformers)をインストールすることをお勧めします。このライブラリのメモリ効率の良いアテンションメカニズムは PyTorch 1.13.1 と相性が良く、高速化とメモリ消費量の削減を同時に実現します。
- モデルのオフロードなど、その他の最適化テクニックは [this guide](./optimization/fp16) でカバーされています。
| diffusers/docs/source/ja/stable_diffusion.md/0 | {
"file_path": "diffusers/docs/source/ja/stable_diffusion.md",
"repo_id": "diffusers",
"token_count": 6259
} | 129 |
<!--Copyright 2025 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
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
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# 추론을 위해 ONNX 런타임을 사용하는 방법
🤗 Diffusers는 ONNX Runtime과 호환되는 Stable Diffusion 파이프라인을 제공합니다. 이를 통해 ONNX(CPU 포함)를 지원하고 PyTorch의 가속 버전을 사용할 수 없는 모든 하드웨어에서 Stable Diffusion을 실행할 수 있습니다.
## 설치
다음 명령어로 ONNX Runtime를 지원하는 🤗 Optimum를 설치합니다:
```sh
pip install optimum["onnxruntime"]
```
## Stable Diffusion 추론
아래 코드는 ONNX 런타임을 사용하는 방법을 보여줍니다. `StableDiffusionPipeline` 대신 `OnnxStableDiffusionPipeline`을 사용해야 합니다.
PyTorch 모델을 불러오고 즉시 ONNX 형식으로 변환하려는 경우 `export=True`로 설정합니다.
```python
from optimum.onnxruntime import ORTStableDiffusionPipeline
model_id = "stable-diffusion-v1-5/stable-diffusion-v1-5"
pipe = ORTStableDiffusionPipeline.from_pretrained(model_id, export=True)
prompt = "a photo of an astronaut riding a horse on mars"
images = pipe(prompt).images[0]
pipe.save_pretrained("./onnx-stable-diffusion-v1-5")
```
파이프라인을 ONNX 형식으로 오프라인으로 내보내고 나중에 추론에 사용하려는 경우,
[`optimum-cli export`](https://huggingface.co/docs/optimum/main/en/exporters/onnx/usage_guides/export_a_model#exporting-a-model-to-onnx-using-the-cli) 명령어를 사용할 수 있습니다:
```bash
optimum-cli export onnx --model stable-diffusion-v1-5/stable-diffusion-v1-5 sd_v15_onnx/
```
그 다음 추론을 수행합니다:
```python
from optimum.onnxruntime import ORTStableDiffusionPipeline
model_id = "sd_v15_onnx"
pipe = ORTStableDiffusionPipeline.from_pretrained(model_id)
prompt = "a photo of an astronaut riding a horse on mars"
images = pipe(prompt).images[0]
```
Notice that we didn't have to specify `export=True` above.
[Optimum 문서](https://huggingface.co/docs/optimum/)에서 더 많은 예시를 찾을 수 있습니다.
## 알려진 이슈들
- 여러 프롬프트를 배치로 생성하면 너무 많은 메모리가 사용되는 것 같습니다. 이를 조사하는 동안, 배치 대신 반복 방법이 필요할 수도 있습니다.
| diffusers/docs/source/ko/optimization/onnx.md/0 | {
"file_path": "diffusers/docs/source/ko/optimization/onnx.md",
"repo_id": "diffusers",
"token_count": 1448
} | 130 |
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
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# Text-to-image
<Tip warning={true}>
text-to-image 파인튜닝 스크립트는 experimental 상태입니다. 과적합하기 쉽고 치명적인 망각과 같은 문제에 부딪히기 쉽습니다. 자체 데이터셋에서 최상의 결과를 얻으려면 다양한 하이퍼파라미터를 탐색하는 것이 좋습니다.
</Tip>
Stable Diffusion과 같은 text-to-image 모델은 텍스트 프롬프트에서 이미지를 생성합니다. 이 가이드는 PyTorch 및 Flax를 사용하여 자체 데이터셋에서 [`CompVis/stable-diffusion-v1-4`](https://huggingface.co/CompVis/stable-diffusion-v1-4) 모델로 파인튜닝하는 방법을 보여줍니다. 이 가이드에 사용된 text-to-image 파인튜닝을 위한 모든 학습 스크립트에 관심이 있는 경우 이 [리포지토리](https://github.com/huggingface/diffusers/tree/main/examples/text_to_image)에서 자세히 찾을 수 있습니다.
스크립트를 실행하기 전에, 라이브러리의 학습 dependency들을 설치해야 합니다:
```bash
pip install git+https://github.com/huggingface/diffusers.git
pip install -U -r requirements.txt
```
그리고 [🤗Accelerate](https://github.com/huggingface/accelerate/) 환경을 초기화합니다:
```bash
accelerate config
```
리포지토리를 이미 복제한 경우, 이 단계를 수행할 필요가 없습니다. 대신, 로컬 체크아웃 경로를 학습 스크립트에 명시할 수 있으며 거기에서 로드됩니다.
### 하드웨어 요구 사항
`gradient_checkpointing` 및 `mixed_precision`을 사용하면 단일 24GB GPU에서 모델을 파인튜닝할 수 있습니다. 더 높은 `batch_size`와 더 빠른 훈련을 위해서는 GPU 메모리가 30GB 이상인 GPU를 사용하는 것이 좋습니다. TPU 또는 GPU에서 파인튜닝을 위해 JAX나 Flax를 사용할 수도 있습니다. 자세한 내용은 [아래](#flax-jax-finetuning)를 참조하세요.
xFormers로 memory efficient attention을 활성화하여 메모리 사용량 훨씬 더 줄일 수 있습니다. [xFormers가 설치](./optimization/xformers)되어 있는지 확인하고 `--enable_xformers_memory_efficient_attention`를 학습 스크립트에 명시합니다.
xFormers는 Flax에 사용할 수 없습니다.
## Hub에 모델 업로드하기
학습 스크립트에 다음 인수를 추가하여 모델을 허브에 저장합니다:
```bash
--push_to_hub
```
## 체크포인트 저장 및 불러오기
학습 중 발생할 수 있는 일에 대비하여 정기적으로 체크포인트를 저장해 두는 것이 좋습니다. 체크포인트를 저장하려면 학습 스크립트에 다음 인수를 명시합니다.
```bash
--checkpointing_steps=500
```
500스텝마다 전체 학습 state가 'output_dir'의 하위 폴더에 저장됩니다. 체크포인트는 'checkpoint-'에 지금까지 학습된 step 수입니다. 예를 들어 'checkpoint-1500'은 1500 학습 step 후에 저장된 체크포인트입니다.
학습을 재개하기 위해 체크포인트를 불러오려면 '--resume_from_checkpoint' 인수를 학습 스크립트에 명시하고 재개할 체크포인트를 지정하십시오. 예를 들어 다음 인수는 1500개의 학습 step 후에 저장된 체크포인트에서부터 훈련을 재개합니다.
```bash
--resume_from_checkpoint="checkpoint-1500"
```
## 파인튜닝
<frameworkcontent>
<pt>
다음과 같이 [Naruto BLIP 캡션](https://huggingface.co/datasets/lambdalabs/naruto-blip-captions) 데이터셋에서 파인튜닝 실행을 위해 [PyTorch 학습 스크립트](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py)를 실행합니다:
```bash
export MODEL_NAME="CompVis/stable-diffusion-v1-4"
export dataset_name="lambdalabs/naruto-blip-captions"
accelerate launch train_text_to_image.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--dataset_name=$dataset_name \
--use_ema \
--resolution=512 --center_crop --random_flip \
--train_batch_size=1 \
--gradient_accumulation_steps=4 \
--gradient_checkpointing \
--mixed_precision="fp16" \
--max_train_steps=15000 \
--learning_rate=1e-05 \
--max_grad_norm=1 \
--lr_scheduler="constant" --lr_warmup_steps=0 \
--output_dir="sd-naruto-model"
```
자체 데이터셋으로 파인튜닝하려면 🤗 [Datasets](https://huggingface.co/docs/datasets/index)에서 요구하는 형식에 따라 데이터셋을 준비하세요. [데이터셋을 허브에 업로드](https://huggingface.co/docs/datasets/image_dataset#upload-dataset-to-the-hub)하거나 [파일들이 있는 로컬 폴더를 준비](https ://huggingface.co/docs/datasets/image_dataset#imagefolder)할 수 있습니다.
사용자 커스텀 loading logic을 사용하려면 스크립트를 수정하십시오. 도움이 되도록 코드의 적절한 위치에 포인터를 남겼습니다. 🤗 아래 예제 스크립트는 `TRAIN_DIR`의 로컬 데이터셋으로를 파인튜닝하는 방법과 `OUTPUT_DIR`에서 모델을 저장할 위치를 보여줍니다:
```bash
export MODEL_NAME="CompVis/stable-diffusion-v1-4"
export TRAIN_DIR="path_to_your_dataset"
export OUTPUT_DIR="path_to_save_model"
accelerate launch train_text_to_image.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--train_data_dir=$TRAIN_DIR \
--use_ema \
--resolution=512 --center_crop --random_flip \
--train_batch_size=1 \
--gradient_accumulation_steps=4 \
--gradient_checkpointing \
--mixed_precision="fp16" \
--max_train_steps=15000 \
--learning_rate=1e-05 \
--max_grad_norm=1 \
--lr_scheduler="constant" --lr_warmup_steps=0 \
--output_dir=${OUTPUT_DIR}
```
</pt>
<jax>
[@duongna211](https://github.com/duongna21)의 기여로, Flax를 사용해 TPU 및 GPU에서 Stable Diffusion 모델을 더 빠르게 학습할 수 있습니다. 이는 TPU 하드웨어에서 매우 효율적이지만 GPU에서도 훌륭하게 작동합니다. Flax 학습 스크립트는 gradient checkpointing나 gradient accumulation과 같은 기능을 아직 지원하지 않으므로 메모리가 30GB 이상인 GPU 또는 TPU v3가 필요합니다.
스크립트를 실행하기 전에 요구 사항이 설치되어 있는지 확인하십시오:
```bash
pip install -U -r requirements_flax.txt
```
그러면 다음과 같이 [Flax 학습 스크립트](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_flax.py)를 실행할 수 있습니다.
```bash
export MODEL_NAME="stable-diffusion-v1-5/stable-diffusion-v1-5"
export dataset_name="lambdalabs/naruto-blip-captions"
python train_text_to_image_flax.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--dataset_name=$dataset_name \
--resolution=512 --center_crop --random_flip \
--train_batch_size=1 \
--max_train_steps=15000 \
--learning_rate=1e-05 \
--max_grad_norm=1 \
--output_dir="sd-naruto-model"
```
자체 데이터셋으로 파인튜닝하려면 🤗 [Datasets](https://huggingface.co/docs/datasets/index)에서 요구하는 형식에 따라 데이터셋을 준비하세요. [데이터셋을 허브에 업로드](https://huggingface.co/docs/datasets/image_dataset#upload-dataset-to-the-hub)하거나 [파일들이 있는 로컬 폴더를 준비](https ://huggingface.co/docs/datasets/image_dataset#imagefolder)할 수 있습니다.
사용자 커스텀 loading logic을 사용하려면 스크립트를 수정하십시오. 도움이 되도록 코드의 적절한 위치에 포인터를 남겼습니다. 🤗 아래 예제 스크립트는 `TRAIN_DIR`의 로컬 데이터셋으로를 파인튜닝하는 방법을 보여줍니다:
```bash
export MODEL_NAME="duongna/stable-diffusion-v1-4-flax"
export TRAIN_DIR="path_to_your_dataset"
python train_text_to_image_flax.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--train_data_dir=$TRAIN_DIR \
--resolution=512 --center_crop --random_flip \
--train_batch_size=1 \
--mixed_precision="fp16" \
--max_train_steps=15000 \
--learning_rate=1e-05 \
--max_grad_norm=1 \
--output_dir="sd-naruto-model"
```
</jax>
</frameworkcontent>
## LoRA
Text-to-image 모델 파인튜닝을 위해, 대규모 모델 학습을 가속화하기 위한 파인튜닝 기술인 LoRA(Low-Rank Adaptation of Large Language Models)를 사용할 수 있습니다. 자세한 내용은 [LoRA 학습](lora#text-to-image) 가이드를 참조하세요.
## 추론
허브의 모델 경로 또는 모델 이름을 [`StableDiffusionPipeline`]에 전달하여 추론을 위해 파인 튜닝된 모델을 불러올 수 있습니다:
<frameworkcontent>
<pt>
```python
from diffusers import StableDiffusionPipeline
model_path = "path_to_saved_model"
pipe = StableDiffusionPipeline.from_pretrained(model_path, torch_dtype=torch.float16)
pipe.to("cuda")
image = pipe(prompt="yoda").images[0]
image.save("yoda-naruto.png")
```
</pt>
<jax>
```python
import jax
import numpy as np
from flax.jax_utils import replicate
from flax.training.common_utils import shard
from diffusers import FlaxStableDiffusionPipeline
model_path = "path_to_saved_model"
pipe, params = FlaxStableDiffusionPipeline.from_pretrained(model_path, dtype=jax.numpy.bfloat16)
prompt = "yoda naruto"
prng_seed = jax.random.PRNGKey(0)
num_inference_steps = 50
num_samples = jax.device_count()
prompt = num_samples * [prompt]
prompt_ids = pipeline.prepare_inputs(prompt)
# shard inputs and rng
params = replicate(params)
prng_seed = jax.random.split(prng_seed, jax.device_count())
prompt_ids = shard(prompt_ids)
images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images
images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))
image.save("yoda-naruto.png")
```
</jax>
</frameworkcontent> | diffusers/docs/source/ko/training/text2image.md/0 | {
"file_path": "diffusers/docs/source/ko/training/text2image.md",
"repo_id": "diffusers",
"token_count": 6021
} | 131 |
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
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# 파일들을 Hub로 푸시하기
[[open-in-colab]]
🤗 Diffusers는 모델, 스케줄러 또는 파이프라인을 Hub에 업로드할 수 있는 [`~diffusers.utils.PushToHubMixin`]을 제공합니다. 이는 Hub에 당신의 파일을 저장하는 쉬운 방법이며, 다른 사람들과 작업을 공유할 수도 있습니다. 실제적으로 [`~diffusers.utils.PushToHubMixin`]가 동작하는 방식은 다음과 같습니다:
1. Hub에 리포지토리를 생성합니다.
2. 나중에 다시 불러올 수 있도록 모델, 스케줄러 또는 파이프라인 파일을 저장합니다.
3. 이러한 파일이 포함된 폴더를 Hub에 업로드합니다.
이 가이드는 [`~diffusers.utils.PushToHubMixin`]을 사용하여 Hub에 파일을 업로드하는 방법을 보여줍니다.
먼저 액세스 [토큰](https://huggingface.co/settings/tokens)으로 Hub 계정에 로그인해야 합니다:
```py
from huggingface_hub import notebook_login
notebook_login()
```
## 모델
모델을 허브에 푸시하려면 [`~diffusers.utils.PushToHubMixin.push_to_hub`]를 호출하고 Hub에 저장할 모델의 리포지토리 id를 지정합니다:
```py
from diffusers import ControlNetModel
controlnet = ControlNetModel(
block_out_channels=(32, 64),
layers_per_block=2,
in_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
cross_attention_dim=32,
conditioning_embedding_out_channels=(16, 32),
)
controlnet.push_to_hub("my-controlnet-model")
```
모델의 경우 Hub에 푸시할 가중치의 [*변형*](loading#checkpoint-variants)을 지정할 수도 있습니다. 예를 들어, `fp16` 가중치를 푸시하려면 다음과 같이 하세요:
```py
controlnet.push_to_hub("my-controlnet-model", variant="fp16")
```
[`~diffusers.utils.PushToHubMixin.push_to_hub`] 함수는 모델의 `config.json` 파일을 저장하고 가중치는 `safetensors` 형식으로 자동으로 저장됩니다.
이제 Hub의 리포지토리에서 모델을 다시 불러올 수 있습니다:
```py
model = ControlNetModel.from_pretrained("your-namespace/my-controlnet-model")
```
## 스케줄러
스케줄러를 허브에 푸시하려면 [`~diffusers.utils.PushToHubMixin.push_to_hub`]를 호출하고 Hub에 저장할 스케줄러의 리포지토리 id를 지정합니다:
```py
from diffusers import DDIMScheduler
scheduler = DDIMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
)
scheduler.push_to_hub("my-controlnet-scheduler")
```
[`~diffusers.utils.PushToHubMixin.push_to_hub`] 함수는 스케줄러의 `scheduler_config.json` 파일을 지정된 리포지토리에 저장합니다.
이제 허브의 리포지토리에서 스케줄러를 다시 불러올 수 있습니다:
```py
scheduler = DDIMScheduler.from_pretrained("your-namepsace/my-controlnet-scheduler")
```
## 파이프라인
모든 컴포넌트가 포함된 전체 파이프라인을 Hub로 푸시할 수도 있습니다. 예를 들어, 원하는 파라미터로 [`StableDiffusionPipeline`]의 컴포넌트들을 초기화합니다:
```py
from diffusers import (
UNet2DConditionModel,
AutoencoderKL,
DDIMScheduler,
StableDiffusionPipeline,
)
from transformers import CLIPTextModel, CLIPTextConfig, CLIPTokenizer
unet = UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=32,
)
scheduler = DDIMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
)
vae = AutoencoderKL(
block_out_channels=[32, 64],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
```
모든 컴포넌트들을 [`StableDiffusionPipeline`]에 전달하고 [`~diffusers.utils.PushToHubMixin.push_to_hub`]를 호출하여 파이프라인을 Hub로 푸시합니다:
```py
components = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
pipeline = StableDiffusionPipeline(**components)
pipeline.push_to_hub("my-pipeline")
```
[`~diffusers.utils.PushToHubMixin.push_to_hub`] 함수는 각 컴포넌트를 리포지토리의 하위 폴더에 저장합니다. 이제 Hub의 리포지토리에서 파이프라인을 다시 불러올 수 있습니다:
```py
pipeline = StableDiffusionPipeline.from_pretrained("your-namespace/my-pipeline")
```
## 비공개
모델, 스케줄러 또는 파이프라인 파일들을 비공개로 두려면 [`~diffusers.utils.PushToHubMixin.push_to_hub`] 함수에서 `private=True`를 설정하세요:
```py
controlnet.push_to_hub("my-controlnet-model-private", private=True)
```
비공개 리포지토리는 본인만 볼 수 있으며 다른 사용자는 리포지토리를 복제할 수 없고 리포지토리가 검색 결과에 표시되지 않습니다. 사용자가 비공개 리포지토리의 URL을 가지고 있더라도 `404 - Sorry, we can't find the page you are looking for`라는 메시지가 표시됩니다. 비공개 리포지토리에서 모델을 로드하려면 [로그인](https://huggingface.co/docs/huggingface_hub/quick-start#login) 상태여야 합니다. | diffusers/docs/source/ko/using-diffusers/push_to_hub.md/0 | {
"file_path": "diffusers/docs/source/ko/using-diffusers/push_to_hub.md",
"repo_id": "diffusers",
"token_count": 3793
} | 132 |
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-->
# 如何为Diffusers 🧨做贡献
我们❤️来自开源社区的贡献！欢迎所有人参与，所有类型的贡献——不仅仅是代码——都受到重视和赞赏。回答问题、帮助他人、主动交流以及改进文档对社区都极具价值，所以如果您愿意参与，请不要犹豫！
我们鼓励每个人先在公开Discord频道里打招呼👋。在那里我们讨论扩散模型的最新趋势、提出问题、展示个人项目、互相协助贡献，或者只是闲聊☕。<a href="https://Discord.gg/G7tWnz98XR"><img alt="加入Discord社区" src="https://img.shields.io/discord/823813159592001537?color=5865F2&logo=discord&logoColor=white"></a>
无论您选择以何种方式贡献，我们都致力于成为一个开放、友好、善良的社区。请阅读我们的[行为准则](https://github.com/huggingface/diffusers/blob/main/CODE_OF_CONDUCT.md)，并在互动时注意遵守。我们也建议您了解指导本项目的[伦理准则](https://huggingface.co/docs/diffusers/conceptual/ethical_guidelines)，并请您遵循同样的透明度和责任原则。
我们高度重视社区的反馈，所以如果您认为自己有能帮助改进库的有价值反馈，请不要犹豫说出来——每条消息、评论、issue和拉取请求（PR）都会被阅读和考虑。
## 概述
您可以通过多种方式做出贡献，从在issue和讨论区回答问题，到向核心库添加新的diffusion模型。
下面我们按难度升序列出不同的贡献方式，所有方式对社区都很有价值：
* 1. 在[Diffusers讨论论坛](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers)或[Discord](https://discord.gg/G7tWnz98XR)上提问和回答问题
* 2. 在[GitHub Issues标签页](https://github.com/huggingface/diffusers/issues/new/choose)提交新issue，或在[GitHub Discussions标签页](https://github.com/huggingface/diffusers/discussions/new/choose)发起新讨论
* 3. 在[GitHub Issues标签页](https://github.com/huggingface/diffusers/issues)解答issue，或在[GitHub Discussions标签页](https://github.com/huggingface/diffusers/discussions)参与讨论
* 4. 解决标记为"Good first issue"的简单问题，详见[此处](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22)
* 5. 参与[文档](https://github.com/huggingface/diffusers/tree/main/docs/source)建设
* 6. 贡献[社区Pipeline](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3Acommunity-examples)
* 7. 完善[示例代码](https://github.com/huggingface/diffusers/tree/main/examples)
* 8. 解决标记为"Good second issue"的中等难度问题，详见[此处](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22Good+second+issue%22)
* 9. 添加新pipeline/模型/调度器，参见["New Pipeline/Model"](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+pipeline%2Fmodel%22)和["New scheduler"](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+scheduler%22)类issue。此类贡献请先阅读[设计哲学](https://github.com/huggingface/diffusers/blob/main/PHILOSOPHY.md)
重申：**所有贡献对社区都具有重要价值。**下文将详细说明各类贡献方式。
对于4-9类贡献，您需要提交PR（拉取请求），具体操作详见[如何提交PR](#how-to-open-a-pr)章节。
### 1. 在Diffusers讨论区或Discord提问与解答
任何与Diffusers库相关的问题或讨论都可以发布在[官方论坛](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/)或[Discord频道](https://discord.gg/G7tWnz98XR)，包括但不限于：
- 分享训练/推理实验报告
- 展示个人项目
- 咨询非官方训练示例
- 项目提案
- 通用反馈
- 论文解读
- 基于Diffusers库的个人项目求助
- 一般性问题
- 关于diffusion模型的伦理讨论
- ...
论坛/Discord上的每个问题都能促使社区公开分享知识，很可能帮助未来遇到相同问题的初学者。请务必提出您的疑问。
同样地，通过回答问题您也在为社区创造公共知识文档，这种贡献极具价值。
**请注意**：提问/回答时投入的精力越多，产生的公共知识质量就越高。精心构建的问题与专业解答能形成高质量知识库，而表述不清的问题则可能降低讨论价值。
低质量的问题或回答会降低公共知识库的整体质量。
简而言之，高质量的问题或回答应具备*精确性*、*简洁性*、*相关性*、*易于理解*、*可访问性*和*格式规范/表述清晰*等特质。更多详情请参阅[如何提交优质议题](#how-to-write-a-good-issue)章节。
**关于渠道的说明**：
[*论坛*](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/63)的内容能被谷歌等搜索引擎更好地收录，且帖子按热度而非时间排序，便于查找历史问答。此外，论坛内容更容易被直接链接引用。
而*Discord*采用即时聊天模式，适合快速交流。虽然在Discord上可能更快获得解答，但信息会随时间淹没，且难以回溯历史讨论。因此我们强烈建议在论坛发布优质问答，以构建可持续的社区知识库。若Discord讨论产生有价值结论，建议将成果整理发布至论坛以惠及更多读者。
### 2. 在GitHub议题页提交新议题
🧨 Diffusers库的稳健性离不开用户的问题反馈，感谢您的报错。
请注意：GitHub议题仅限处理与Diffusers库代码直接相关的技术问题、错误报告、功能请求或库设计反馈。
简言之，**与Diffusers库代码（含文档）无关**的内容应发布至[论坛](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/63)或[Discord](https://discord.gg/G7tWnz98XR)。
**提交新议题时请遵循以下准则**：
- 确认是否已有类似议题（使用GitHub议题页的搜索栏）
- 请勿在现有议题下追加新问题。若存在高度关联议题，应新建议题并添加相关链接
- 确保使用英文提交。非英语用户可通过[DeepL](https://www.deepl.com/translator)等免费工具翻译
- 检查升级至最新Diffusers版本是否能解决问题。提交前请确认`python -c "import diffusers; print(diffusers.__version__)"`显示的版本号不低于最新版本
- 记请记住，你在提交新issue时投入的精力越多，得到的回答质量就越高，Diffusers项目的整体issue质量也会越好。
新issue通常包含以下内容：
#### 2.1 可复现的最小化错误报告
错误报告应始终包含可复现的代码片段，并尽可能简洁明了。具体而言：
- 尽量缩小问题范围，**不要直接粘贴整个代码文件**
- 规范代码格式
- 除Diffusers依赖库外，不要包含其他外部库
- **务必**提供环境信息：可在终端运行`diffusers-cli env`命令，然后将显示的信息复制到issue中
- 详细说明问题。如果读者不清楚问题所在及其影响，就无法解决问题
- **确保**读者能以最小成本复现问题。如果代码片段因缺少库或未定义变量而无法运行，读者将无法提供帮助。请确保提供的可复现代码尽可能精简，可直接复制到Python shell运行
- 如需特定模型/数据集复现问题，请确保读者能获取这些资源。可将模型/数据集上传至[Hub](https://huggingface.co)便于下载。尽量保持模型和数据集体积最小化，降低复现难度
更多信息请参阅[如何撰写优质issue](#how-to-write-a-good-issue)章节。
提交错误报告请点击[此处](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=bug&projects=&template=bug-report.yml)。
#### 2.2 功能请求
优质的功能请求应包含以下要素：
1. 首先说明动机：
* 是否与库的使用痛点相关？若是，请解释原因，最好提供演示问题的代码片段
* 是否因项目需求产生？我们很乐意了解详情！
* 是否是你已实现且认为对社区有价值的功能？请说明它为你解决了什么问题
2. 用**完整段落**描述功能特性
3. 提供**代码片段**演示预期用法
4. 如涉及论文，请附上链接
5. 可补充任何有助于理解的辅助材料（示意图、截图等）
提交功能请求请点击[此处](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=&template=feature_request.md&title=)。
#### 2.3 设计反馈
关于库设计的反馈（无论正面还是负面）能极大帮助核心维护者打造更友好的库。要了解当前设计理念，请参阅[此文档](https://huggingface.co/docs/diffusers/conceptual/philosophy)如果您认为某个设计选择与当前理念不符，请说明原因及改进建议。如果某个设计选择因过度遵循理念而限制了使用场景，也请解释原因并提出调整方案。
若某个设计对您特别实用，请同样留下备注——这对未来的设计决策极具参考价值。
您可通过[此链接](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=&template=feedback.md&title=)提交设计反馈。
#### 2.4 技术问题
技术问题主要涉及库代码的实现逻辑或特定功能模块的作用。提问时请务必：
- 附上相关代码链接
- 详细说明难以理解的具体原因
技术问题提交入口：[点击此处](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=bug&template=bug-report.yml)
#### 2.5 新模型/调度器/pipeline提案
若diffusion模型社区发布了您希望集成到Diffusers库的新模型、pipeline或调度器，请提供以下信息：
* 简要说明并附论文或发布链接
* 开源实现链接（如有）
* 模型权重下载链接（如已公开）
若您愿意参与开发，请告知我们以便指导。另请尝试通过GitHub账号标记原始组件作者。
提案提交地址：[新建请求](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=New+model%2Fpipeline%2Fscheduler&template=new-model-addition.yml)
### 3. 解答GitHub问题
回答GitHub问题可能需要Diffusers的技术知识，但我们鼓励所有人尝试参与——即使您对答案不完全正确。高质量回答的建议：
- 保持简洁精炼
- 严格聚焦问题本身
- 提供代码/论文等佐证材料
- 优先用代码说话：若代码片段能解决问题，请提供完整可复现代码
许多问题可能存在离题、重复或无关情况。您可以通过以下方式协助维护者：
- 引导提问者精确描述问题
- 标记重复issue并附原链接
- 推荐用户至[论坛](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/63)或[Discord](https://discord.gg/G7tWnz98XR)
在确认提交的Bug报告正确且需要修改源代码后，请继续阅读以下章节内容。
以下所有贡献都需要提交PR（拉取请求）。具体操作步骤详见[如何提交PR](#how-to-open-a-pr)章节。
### 4. 修复"Good first issue"类问题
标有[Good first issue](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22)标签的问题通常已说明解决方案建议，便于修复。若该问题尚未关闭且您想尝试解决，只需留言"我想尝试解决这个问题"。通常有三种情况：
- a.) 问题描述已提出解决方案。若您认可该方案，可直接提交PR或草稿PR进行修复
- b.) 问题描述未提出解决方案。您可询问修复建议，Diffusers团队会尽快回复。若有成熟解决方案，也可直接提交PR
- c.) 已有PR但问题未关闭。若原PR停滞，可新开PR并关联原PR（开源社区常见现象）。若PR仍活跃，您可通过建议、审查或协作等方式帮助原作者
### 5. 文档贡献
优秀库**必然**拥有优秀文档！官方文档是新用户的首要接触点，因此文档贡献具有**极高价值**。贡献形式包括：
- 修正拼写/语法错误
- 修复文档字符串格式错误（如显示异常或链接失效）
- 修正文档字符串中张量的形状/维度描述
- 优化晦涩或错误的说明
- 更新过时代码示例
- 文档翻译
[官方文档页面](https://huggingface.co/docs/diffusers/index)所有内容均属可修改范围，对应[文档源文件](https://github.com/huggingface/diffusers/tree/main/docs/source)可进行编辑。修改前请查阅[验证说明](https://github.com/huggingface/diffusers/tree/main/docs)。
### 6. 贡献社区流程
> [!TIP]
> 阅读[社区流程](../using-diffusers/custom_pipeline_overview#community-pipelines)指南了解GitHub与Hugging Face Hub社区流程的区别。若想了解我们设立社区流程的原因，请查看GitHub Issue [#841](https://github.com/huggingface/diffusers/issues/841)（简而言之，我们无法维护diffusion模型所有可能的推理使用方式，但也不希望限制社区构建这些流程）。
贡献社区流程是向社区分享创意与成果的绝佳方式。您可以在[`DiffusionPipeline`]基础上构建流程，任何人都能通过设置`custom_pipeline`参数加载使用。本节将指导您创建一个简单的"单步"流程——UNet仅执行单次前向传播并调用调度器一次。
1. 为社区流程创建one_step_unet.py文件。只要用户已安装相关包，该文件可包含任意所需包。确保仅有一个继承自[`DiffusionPipeline`]的流程类，用于从Hub加载模型权重和调度器配置。在`__init__`函数中添加UNet和调度器。
同时添加`register_modules`函数，确保您的流程及其组件可通过[`~DiffusionPipeline.save_pretrained`]保存。
```py
from diffusers import DiffusionPipeline
import torch
class UnetSchedulerOneForwardPipeline(DiffusionPipeline):
def __init__(self, unet, scheduler):
super().__init__()
self.register_modules(unet=unet, scheduler=scheduler)
```
2. 在前向传播中（建议定义为`__call__`），可添加任意功能。对于"单步"流程，创建随机图像并通过设置`timestep=1`调用UNet和调度器一次。
```py
from diffusers import DiffusionPipeline
import torch
class UnetSchedulerOneForwardPipeline(DiffusionPipeline):
def __init__(self, unet, scheduler):
super().__init__()
self.register_modules(unet=unet, scheduler=scheduler)
def __call__(self):
image = torch.randn(
(1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size),
)
timestep = 1
model_output = self.unet(image, timestep).sample
scheduler_output = self.scheduler.step(model_output, timestep, image).prev_sample
return scheduler_output
```
现在您可以通过传入UNet和调度器来运行流程，若流程结构相同也可加载预训练权重。
```python
from diffusers import DDPMScheduler, UNet2DModel
scheduler = DDPMScheduler()
unet = UNet2DModel()
pipeline = UnetSchedulerOneForwardPipeline(unet=unet, scheduler=scheduler)
output = pipeline()
# 加载预训练权重
pipeline = UnetSchedulerOneForwardPipeline.from_pretrained("google/ddpm-cifar10-32", use_safetensors=True)
output = pipeline()
```
您可以选择将pipeline作为GitHub社区pipeline或Hub社区pipeline进行分享。
<hfoptions id="pipeline类型">
<hfoption id="GitHub pipeline">
通过向Diffusers[代码库](https://github.com/huggingface/diffusers)提交拉取请求来分享GitHub pipeline，将one_step_unet.py文件添加到[examples/community](https://github.com/huggingface/diffusers/tree/main/examples/community)子文件夹中。
</hfoption>
<hfoption id="Hub pipeline">
通过在Hub上创建模型仓库并上传one_step_unet.py文件来分享Hub pipeline。
</hfoption>
</hfoptions>
### 7. 贡献训练示例
Diffusers训练示例是位于[examples](https://github.com/huggingface/diffusers/tree/main/examples)目录下的训练脚本集合。
我们支持两种类型的训练示例：
- 官方训练示例
- 研究型训练示例
研究型训练示例位于[examples/research_projects](https://github.com/huggingface/diffusers/tree/main/examples/research_projects)，而官方训练示例包含[examples](https://github.com/huggingface/diffusers/tree/main/examples)目录下除`research_projects`和`community`外的所有文件夹。
官方训练示例由Diffusers核心维护者维护，研究型训练示例则由社区维护。
这与[6. 贡献社区pipeline](#6-contribute-a-community-pipeline)中关于官方pipeline与社区pipeline的原因相同：核心维护者不可能维护diffusion模型的所有可能训练方法。
如果Diffusers核心维护者和社区认为某种训练范式过于实验性或不够普及，相应训练代码应放入`research_projects`文件夹并由作者维护。
官方训练和研究型示例都包含一个目录，其中含有一个或多个训练脚本、`requirements.txt`文件和`README.md`文件。用户使用时需要先克隆代码库：
```bash
git clone https://github.com/huggingface/diffusers
```
并安装训练所需的所有额外依赖：
```bash
cd diffusers
pip install -r examples/<your-example-folder>/requirements.txt
```
因此添加示例时，`requirements.txt`文件应定义训练示例所需的所有pip依赖项，安装完成后用户即可运行示例训练脚本。可参考[DreamBooth的requirements.txt文件](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/requirements.txt)。
- 运行示例所需的所有代码应集中在单个Python文件中
- 用户应能通过命令行`python <your-example>.py --args`直接运行示例
- **示例**应保持简洁，主要展示如何使用Diffusers进行训练。示例脚本的目的**不是**创建最先进的diffusion模型，而是复现已知训练方案，避免添加过多自定义逻辑。因此，这些示例也力求成为优质的教学材料。
提交示例时，强烈建议参考现有示例（如[dreambooth](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth.py)）来了解规范格式。
我们强烈建议贡献者使用[Accelerate库](https://github.com/huggingface/accelerate)，因其与Diffusers深度集成。
当示例脚本完成后，请确保添加详细的`README.md`说明使用方法，包括：
- 运行示例的具体命令（示例参见[此处](https://github.com/huggingface/diffusers/tree/main/examples/dreambooth#running-locally-with-pytorch)）
- 训练结果链接（日志/模型等），展示用户可预期的效果（示例参见[此处](https://api.wandb.ai/report/patrickvonplaten/xm6cd5q5)）
- 若添加非官方/研究性训练示例，**必须注明**维护者信息（含Git账号），格式参照[此处](https://github.com/huggingface/diffusers/tree/main/examples/research_projects/intel_opts#diffusers-examples-with-intel-optimizations)
贡献官方训练示例时，还需在对应目录添加测试文件（如[examples/dreambooth/test_dreambooth.py](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/test_dreambooth.py)），非官方示例无需此步骤。
### 8. 处理"Good second issue"类问题
标有[Good second issue](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22Good+second+issue%22)标签的问题通常比[Good first issues](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22)更复杂。
这类问题的描述通常不会提供详细解决指引，需要贡献者对库有较深理解。
若您想解决此类问题，可直接提交PR并关联对应issue。若已有未合并的PR，请分析原因后提交改进版。需注意，Good second issue类PR的合并难度通常高于good first issues。在需要帮助的时候请不要犹豫，大胆的向核心维护者询问。
### 9. 添加管道、模型和调度器
管道（pipelines）、模型（models）和调度器（schedulers）是Diffusers库中最重要的组成部分。它们提供了对最先进diffusion技术的便捷访问，使得社区能够构建强大的生成式AI应用。
通过添加新的模型、管道或调度器，您可能为依赖Diffusers的任何用户界面开启全新的强大用例，这对整个生成式AI生态系统具有巨大价值。
Diffusers针对这三类组件都有一些开放的功能请求——如果您还不确定要添加哪个具体组件，可以浏览以下链接：
- [模型或管道](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+pipeline%2Fmodel%22)
- [调度器](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+scheduler%22)
在添加任何组件之前，强烈建议您阅读[设计哲学指南](philosophy)，以更好地理解这三类组件的设计理念。请注意，如果添加的模型、调度器或管道与我们的设计理念存在严重分歧，我们将无法合并，因为这会导致API不一致。如果您从根本上不同意某个设计选择，请改为提交[反馈问题](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=&template=feedback.md&title=)，以便讨论是否应该更改库中的特定设计模式/选择，以及是否更新我们的设计哲学。保持库内的一致性对我们非常重要。
请确保在PR中添加原始代码库/论文的链接，并最好直接在PR中@原始作者，以便他们可以跟踪进展并在有疑问时提供帮助。
如果您在PR过程中遇到不确定或卡住的情况，请随时留言请求初步审查或帮助。
#### 复制机制（Copied from）
在添加任何管道、模型或调度器代码时，理解`# Copied from`机制是独特且重要的。您会在整个Diffusers代码库中看到这种机制，我们使用它的原因是为了保持代码库易于理解和维护。用`# Copied from`机制标记代码会强制标记的代码与复制来源的代码完全相同。这使得每当您运行`make fix-copies`时，可以轻松更新并将更改传播到多个文件。
例如，在下面的代码示例中，[`~diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput`]是原始代码，而`AltDiffusionPipelineOutput`使用`# Copied from`机制来复制它。唯一的区别是将类前缀从`Stable`改为`Alt`。
```py
# 从 diffusers.pipelines.stable_diffusion.pipeline_output.StableDiffusionPipelineOutput 复制并将 Stable 替换为 Alt
class AltDiffusionPipelineOutput(BaseOutput):
"""
Output class for Alt Diffusion pipelines.
Args:
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,
num_channels)`.
nsfw_content_detected (`List[bool]`)
List indicating whether the corresponding generated image contains "not-safe-for-work" (nsfw) content or
`None` if safety checking could not be performed.
"""
```
要了解更多信息，请阅读[~不要~重复自己*](https://huggingface.co/blog/transformers-design-philosophy#4-machine-learning-models-are-static)博客文章的相应部分。
## 如何撰写优质问题
**问题描述越清晰，被快速解决的可能性就越高。**
1. 确保使用了正确的issue模板。您可以选择*错误报告*、*功能请求*、*API设计反馈*、*新模型/流水线/调度器添加*、*论坛*或空白issue。在[新建issue](https://github.com/huggingface/diffusers/issues/new/choose)时务必选择正确的模板。
2. **精确描述**：为issue起一个恰当的标题。尽量用最简练的语言描述问题。提交issue时越精确，理解问题和潜在解决方案所需的时间就越少。确保一个issue只针对一个问题，不要将多个问题放在同一个issue中。如果发现多个问题，请分别创建多个issue。如果是错误报告，请尽可能精确描述错误类型——不应只写"diffusers出错"。
3. **可复现性**：无法复现的代码片段 == 无法解决问题。如果遇到错误，维护人员必须能够**复现**它。确保包含一个可以复制粘贴到Python解释器中复现问题的代码片段。确保您的代码片段是可运行的，即没有缺少导入或图像链接等问题。issue应包含错误信息和可直接复制粘贴以复现相同错误的代码片段。如果issue涉及本地模型权重或无法被读者访问的本地数据，则问题无法解决。如果无法共享数据或模型，请尝试创建虚拟模型或虚拟数据。
4. **最小化原则**：通过尽可能简洁的描述帮助读者快速理解问题。删除所有与问题无关的代码/信息。如果发现错误，请创建最简单的代码示例来演示问题，不要一发现错误就把整个工作流程都转储到issue中。例如，如果在训练模型时某个阶段出现错误或训练过程中遇到问题时，应首先尝试理解训练代码的哪部分导致了错误，并用少量代码尝试复现。建议使用模拟数据替代完整数据集进行测试。
5. 添加引用链接。当提及特定命名、方法或模型时，请务必提供引用链接以便读者理解。若涉及具体PR或issue，请确保添加对应链接。不要假设读者了解你所指内容。issue中引用链接越丰富越好。
6. 规范格式。请确保规范格式化issue内容：Python代码使用代码语法块，错误信息使用标准代码语法。详见[GitHub官方格式文档](https://docs.github.com/en/get-started/writing-on-github/getting-started-with-writing-and-formatting-on-github/basic-writing-and-formatting-syntax)。
7. 请将issue视为百科全书的精美词条，而非待解决的工单。每个规范撰写的issue不仅是向维护者有效传递问题的方式，更是帮助社区深入理解库特性的公共知识贡献。
## 优质PR编写规范
1. 保持风格统一。理解现有设计模式和语法规范，确保新增代码与代码库现有结构无缝衔接。显著偏离现有设计模式或用户界面的PR将不予合并。
2. 聚焦单一问题。每个PR应当只解决一个明确问题，避免"顺手修复其他问题"的陷阱。包含多个无关修改的PR会极大增加审查难度。
3. 如适用，建议添加代码片段演示新增功能的使用方法。
4. PR标题应准确概括其核心贡献。
5. 若PR针对某个issue，请在描述中注明issue编号以建立关联（也让关注该issue的用户知晓有人正在处理）；
6. 进行中的PR请在标题添加`[WIP]`前缀。这既能避免重复劳动，也可与待合并PR明确区分；
7. 文本表述与格式要求请参照[优质issue编写规范](#how-to-write-a-good-issue)；
8. 确保现有测试用例全部通过；
9. 必须添加高覆盖率测试。未经充分测试的代码不予合并。
- 若新增`@slow`测试，请使用`RUN_SLOW=1 python -m pytest tests/test_my_new_model.py`确保通过。
CircleCI不执行慢速测试，但GitHub Actions会每日夜间运行！
10. 所有公开方法必须包含格式规范、兼容markdown的说明文档。可参考[`pipeline_latent_diffusion.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py)
11. 由于代码库快速增长，必须确保不会添加明显增加仓库体积的文件（如图片、视频等非文本文件）。建议优先使用托管在hf.co的`dataset`（例如[`hf-internal-testing`](https://huggingface.co/hf-internal-testing)或[huggingface/documentation-images](https://huggingface.co/datasets/huggingface/documentation-images)）存放这类文件。若为外部贡献，可将图片添加到PR中并请Hugging Face成员将其迁移至该数据集。
## 提交PR流程
编写代码前，强烈建议先搜索现有PR或issue，确认没有重复工作。如有疑问，建议先创建issue获取反馈。
贡献至🧨 Diffusers需要基本的`git`技能。虽然`git`学习曲线较高，但其拥有最完善的手册。在终端输入`git --help`即可查阅，或参考书籍[Pro Git](https://git-scm.com/book/en/v2)。
请按以下步骤操作（[支持的Python版本](https://github.com/huggingface/diffusers/blob/83bc6c94eaeb6f7704a2a428931cf2d9ad973ae9/setup.py#L270)）：
1. 在[仓库页面](https://github.com/huggingface/diffusers)点击"Fork"按钮创建代码副本至您的GitHub账户
2. 克隆fork到本地，并添加主仓库为远程源：
```bash
$ git clone git@github.com:<您的GitHub账号>/diffusers.git
$ cd diffusers
$ git remote add upstream https://github.com/huggingface/diffusers.git
```
3. 创建新分支进行开发：
```bash
$ git checkout -b 您的开发分支名称
```
**禁止**直接在`main`分支上修改
4. 在虚拟环境中运行以下命令配置开发环境：
```bash
$ pip install -e ".[dev]"
```
若已克隆仓库，可能需要先执行`git pull`获取最新代码
5. 在您的分支上开发功能
开发过程中应确保测试通过。可运行受影响测试：
```bash
$ pytest tests/<待测文件>.py
```
执行测试前请安装测试依赖：
```bash
$ pip install -e ".[test]"
```
也可运行完整测试套件（需高性能机器）：
```bash
$ make test
```
🧨 Diffusers使用`black`和`isort`工具保持代码风格统一。修改后请执行自动化格式校正与代码验证，以下内容无法通过以下命令一次性自动化完成：
```bash
$ make style
```
🧨 Diffusers 还使用 `ruff` 和一些自定义脚本来检查代码错误。虽然质量控制流程会在 CI 中运行，但您也可以通过以下命令手动执行相同的检查：
```bash
$ make quality
```
当您对修改满意后，使用 `git add` 添加更改的文件，并通过 `git commit` 在本地记录这些更改：
```bash
$ git add modified_file.py
$ git commit -m "关于您所做更改的描述性信息。"
```
定期将您的代码副本与原始仓库同步是一个好习惯。这样可以快速适应上游变更：
```bash
$ git pull upstream main
```
使用以下命令将更改推送到您的账户：
```bash
$ git push -u origin 此处替换为您的描述性分支名称
```
6. 确认无误后，请访问您 GitHub 账户中的派生仓库页面。点击「Pull request」将您的更改提交给项目维护者审核。
7. 如果维护者要求修改，这很正常——核心贡献者也会遇到这种情况！为了让所有人能在 Pull request 中看到变更，请在本地分支继续工作并将修改推送到您的派生仓库，这些变更会自动出现在 Pull request 中。
### 测试
我们提供了全面的测试套件来验证库行为和多个示例。库测试位于 [tests 文件夹](https://github.com/huggingface/diffusers/tree/main/tests)。
我们推荐使用 `pytest` 和 `pytest-xdist`，因为它们速度更快。在仓库根目录下运行以下命令执行库测试：
```bash
$ python -m pytest -n auto --dist=loadfile -s -v ./tests/
```
实际上，这就是 `make test` 的实现方式！
您可以指定更小的测试范围来仅验证您正在开发的功能。
默认情况下会跳过耗时测试。设置 `RUN_SLOW` 环境变量为 `yes` 可运行这些测试。注意：这将下载数十 GB 的模型文件——请确保您有足够的磁盘空间、良好的网络连接或充足的耐心！
```bash
$ RUN_SLOW=yes python -m pytest -n auto --dist=loadfile -s -v ./tests/
```
我们也完全支持 `unittest`，运行方式如下：
```bash
$ python -m unittest discover -s tests -t . -v
$ python -m unittest discover -s examples -t examples -v
```
### 将派生仓库的 main 分支与上游（HuggingFace）main 分支同步
为避免向上游仓库发送引用通知（这会给相关 PR 添加注释并向开发者发送不必要的通知），在同步派生仓库的 main 分支时，请遵循以下步骤：
1. 尽可能避免通过派生仓库的分支和 PR 来同步上游，而是直接合并到派生仓库的 main 分支
2. 如果必须使用 PR，请在检出分支后执行以下操作：
```bash
$ git checkout -b 您的同步分支名称
$ git pull --squash --no-commit upstream main
$ git commit -m '提交信息（不要包含 GitHub 引用）'
$ git push --set-upstream origin 您的分支名称
```
### 风格指南
对于文档字符串，🧨 Diffusers 遵循 [Google 风格指南](https://google.github.io/styleguide/pyguide.html)。
| diffusers/docs/source/zh/conceptual/contribution.md/0 | {
"file_path": "diffusers/docs/source/zh/conceptual/contribution.md",
"repo_id": "diffusers",
"token_count": 20804
} | 133 |
<!--版权 2025 The HuggingFace Team。保留所有权利。
根据Apache许可证2.0版（“许可证”）授权；除非符合许可证，否则不得使用此文件。您可以在
http://www.apache.org/licenses/LICENSE-2.0
获取许可证的副本。
除非适用法律要求或书面同意，根据许可证分发的软件是基于“按原样”基础分发的，没有任何明示或暗示的保证或条件。请参阅许可证了解特定语言管理权限和限制。
-->
# ModularPipelineBlocks
[`~modular_pipelines.ModularPipelineBlocks`] 是构建 [`ModularPipeline`] 的基本块。它定义了管道中特定步骤应执行的组件、输入/输出和计算。一个 [`~modular_pipelines.ModularPipelineBlocks`] 与其他块连接，使用 [状态](./modular_diffusers_states)，以实现工作流的模块化构建。
单独的 [`~modular_pipelines.ModularPipelineBlocks`] 无法执行。它是管道中步骤应执行的操作的蓝图。要实际运行和执行管道，需要将 [`~modular_pipelines.ModularPipelineBlocks`] 转换为 [`ModularPipeline`]。
本指南将向您展示如何创建 [`~modular_pipelines.ModularPipelineBlocks`]。
## 输入和输出
> [!TIP]
> 如果您不熟悉Modular Diffusers中状态的工作原理，请参考 [States](./modular_diffusers_states) 指南。
一个 [`~modular_pipelines.ModularPipelineBlocks`] 需要 `inputs` 和 `intermediate_outputs`。
- `inputs` 是由用户提供并从 [`~modular_pipelines.PipelineState`] 中检索的值。这很有用，因为某些工作流会调整图像大小，但仍需要原始图像。 [`~modular_pipelines.PipelineState`] 维护原始图像。
使用 `InputParam` 定义 `inputs`。
```py
from diffusers.modular_pipelines import InputParam
user_inputs = [
InputParam(name="image", type_hint="PIL.Image", description="要处理的原始输入图像")
]
```
- `intermediate_inputs` 通常由前一个块创建的值，但如果前面的块没有生成它们，也可以直接提供。与 `inputs` 不同，`intermediate_inputs` 可以被修改。
使用 `InputParam` 定义 `intermediate_inputs`。
```py
user_intermediate_inputs = [
InputParam(name="processed_image", type_hint="torch.Tensor", description="image that has been preprocessed and normalized"),
]
```
- `intermediate_outputs` 是由块创建并添加到 [`~modular_pipelines.PipelineState`] 的新值。`intermediate_outputs` 可作为后续块的 `intermediate_inputs` 使用，或作为运行管道的最终输出使用。
使用 `OutputParam` 定义 `intermediate_outputs`。
```py
from diffusers.modular_pipelines import OutputParam
user_intermediate_outputs = [
OutputParam(name="image_latents", description="latents representing the image")
]
```
中间输入和输出共享数据以连接块。它们可以在任何时候访问，允许你跟踪工作流的进度。
## 计算逻辑
一个块执行的计算在`__call__`方法中定义，它遵循特定的结构。
1. 检索[`~modular_pipelines.BlockState`]以获取`inputs`和`intermediate_inputs`的局部视图。
2. 在`inputs`和`intermediate_inputs`上实现计算逻辑。
3. 更新[`~modular_pipelines.PipelineState`]以将局部[`~modular_pipelines.BlockState`]的更改推送回全局[`~modular_pipelines.PipelineState`]。
4. 返回对下一个块可用的组件和状态。
```py
def __call__(self, components, state):
# 获取该块需要的状态变量的局部视图
block_state = self.get_block_state(state)
# 你的计算逻辑在这里
# block_state包含你所有的inputs和intermediate_inputs
# 像这样访问它们: block_state.image, block_state.processed_image
# 用你更新的block_states更新管道状态
self.set_block_state(state, block_state)
return components, state
```
### 组件和配置
块需要的组件和管道级别的配置在[`ComponentSpec`]和[`~modular_pipelines.ConfigSpec`]中指定。
- [`ComponentSpec`]包含块使用的预期组件。你需要组件的`name`和理想情况下指定组件确切是什么的`type_hint`。
- [`~modular_pipelines.ConfigSpec`]包含控制所有块行为的管道级别设置。
```py
from diffusers import ComponentSpec, ConfigSpec
expected_components = [
ComponentSpec(name="unet", type_hint=UNet2DConditionModel),
ComponentSpec(name="scheduler", type_hint=EulerDiscreteScheduler)
]
expected_config = [
ConfigSpec("force_zeros_for_empty_prompt", True)
]
```
当块被转换为管道时，组件作为`__call__`中的第一个参数对块可用。
```py
def __call__(self, components, state):
# 使用点符号访问组件
unet = components.unet
vae = components.vae
scheduler = components.scheduler
```
| diffusers/docs/source/zh/modular_diffusers/pipeline_block.md/0 | {
"file_path": "diffusers/docs/source/zh/modular_diffusers/pipeline_block.md",
"repo_id": "diffusers",
"token_count": 2632
} | 134 |
# T-GATE
[T-GATE](https://github.com/HaozheLiu-ST/T-GATE/tree/main) 通过跳过交叉注意力计算一旦收敛，加速了 [Stable Diffusion](../api/pipelines/stable_diffusion/overview)、[PixArt](../api/pipelines/pixart) 和 [Latency Consistency Model](../api/pipelines/latent_consistency_models.md) 管道的推理。此方法不需要任何额外训练，可以将推理速度提高 10-50%。T-GATE 还与 [DeepCache](./deepcache) 等其他优化方法兼容。
开始之前，请确保安装 T-GATE。
```bash
pip install tgate
pip install -U torch diffusers transformers accelerate DeepCache
```
要使用 T-GATE 与管道，您需要使用其对应的加载器。
| 管道 | T-GATE 加载器 |
|---|---|
| PixArt | TgatePixArtLoader |
| Stable Diffusion XL | TgateSDXLLoader |
| Stable Diffusion XL + DeepCache | TgateSDXLDeepCacheLoader |
| Stable Diffusion | TgateSDLoader |
| Stable Diffusion + DeepCache | TgateSDDeepCacheLoader |
接下来，创建一个 `TgateLoader`，包含管道、门限步骤（停止计算交叉注意力的时间步）和推理步骤数。然后在管道上调用 `tgate` 方法，提供提示、门限步骤和推理步骤数。
让我们看看如何为几个不同的管道启用此功能。
<hfoptions id="pipelines">
<hfoption id="PixArt">
使用 T-GATE 加速 `PixArtAlphaPipeline`：
```py
import torch
from diffusers import PixArtAlphaPipeline
from tgate import TgatePixArtLoader
pipe = PixArtAlphaPipeline.from_pretrained("PixArt-alpha/PixArt-XL-2-1024-MS", torch_dtype=torch.float16)
gate_step = 8
inference_step = 25
pipe = TgatePixArtLoader(
pipe,
gate_step=gate_step,
num_inference_steps=inference_step,
).to("cuda")
image = pipe.tgate(
"An alpaca made of colorful building blocks, cyberpunk.",
gate_step=gate_step,
num_inference_steps=inference_step,
).images[0]
```
</hfoption>
<hfoption id="Stable Diffusion XL">
使用 T-GATE 加速 `StableDiffusionXLPipeline`：
```py
import torch
from diffusers import StableDiffusionXLPipeline
from diffusers import DPMSolverMultistepScheduler
from tgate import TgateSDXLLoader
pipe = StableDiffusionXLPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
torch_dtype=torch.float16,
variant="fp16",
use_safetensors=True,
)
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
gate_step = 10
inference_step = 25
pipe = TgateSDXLLoader(
pipe,
gate_step=gate_step,
num_inference_steps=inference_step,
).to("cuda")
image = pipe.tgate(
"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k.",
gate_step=gate_step,
num_inference_steps=inference_step
).images[0]
```
</hfoption>
<hfoption id="StableDiffusionXL with DeepCache">
使用 [DeepCache](https://github.co 加速 `StableDiffusionXLPipeline`
m/horseee/DeepCache) 和 T-GATE：
```py
import torch
from diffusers import StableDiffusionXLPipeline
from diffusers import DPMSolverMultistepScheduler
from tgate import TgateSDXLDeepCacheLoader
pipe = StableDiffusionXLPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
torch_dtype=torch.float16,
variant="fp16",
use_safetensors=True,
)
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
gate_step = 10
inference_step = 25
pipe = TgateSDXLDeepCacheLoader(
pipe,
cache_interval=3,
cache_branch_id=0,
).to("cuda")
image = pipe.tgate(
"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k.",
gate_step=gate_step,
num_inference_steps=inference_step
).images[0]
```
</hfoption>
<hfoption id="Latent Consistency Model">
使用 T-GATE 加速 `latent-consistency/lcm-sdxl`：
```py
import torch
from diffusers import StableDiffusionXLPipeline
from diffusers import UNet2DConditionModel, LCMScheduler
from diffusers import DPMSolverMultistepScheduler
from tgate import TgateSDXLLoader
unet = UNet2DConditionModel.from_pretrained(
"latent-consistency/lcm-sdxl",
torch_dtype=torch.float16,
variant="fp16",
)
pipe = StableDiffusionXLPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
unet=unet,
torch_dtype=torch.float16,
variant="fp16",
)
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
gate_step = 1
inference_step = 4
pipe = TgateSDXLLoader(
pipe,
gate_step=gate_step,
num_inference_steps=inference_step,
lcm=True
).to("cuda")
image = pipe.tgate(
"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k.",
gate_step=gate_step,
num_inference_steps=inference_step
).images[0]
```
</hfoption>
</hfoptions>
T-GATE 还支持 [`StableDiffusionPipeline`] 和 [PixArt-alpha/PixArt-LCM-XL-2-1024-MS](https://hf.co/PixArt-alpha/PixArt-LCM-XL-2-1024-MS)。
## 基准测试
| 模型 | MACs | 参数 | 延迟 | 零样本 10K-FID on MS-COCO |
|-----------------------|----------|-----------|---------|---------------------------|
| SD-1.5 | 16.938T | 859.520M | 7.032s | 23.927 |
| SD-1.5 w/ T-GATE | 9.875T | 815.557M | 4.313s | 20.789 |
| SD-2.1 | 38.041T | 865.785M | 16.121s | 22.609 |
| SD-2.1 w/ T-GATE | 22.208T | 815.433 M | 9.878s | 19.940 |
| SD-XL | 149.438T | 2.570B | 53.187s | 24.628 |
| SD-XL w/ T-GATE | 84.438T | 2.024B | 27.932s | 22.738 |
| Pixart-Alpha | 107.031T | 611.350M | 61.502s | 38.669 |
| Pixart-Alpha w/ T-GATE | 65.318T | 462.585M | 37.867s | 35.825 |
| DeepCache (SD-XL) | 57.888T | - | 19.931s | 23.755 |
| DeepCache 配合 T-GATE | 43.868T | - | 14.666秒 | 23.999 |
| LCM (SD-XL) | 11.955T | 2.570B | 3.805秒 | 25.044 |
| LCM 配合 T-GATE | 11.171T | 2.024B | 3.533秒 | 25.028 |
| LCM (Pixart-Alpha) | 8.563T | 611.350M | 4.733秒 | 36.086 |
| LCM 配合 T-GATE | 7.623T | 462.585M | 4.543秒 | 37.048 |
延迟测试基于 NVIDIA 1080TI，MACs 和 Params 使用 [calflops](https://github.com/MrYxJ/calculate-flops.pytorch) 计算，FID 使用 [PytorchFID](https://github.com/mseitzer/pytorch-fid) 计算。 | diffusers/docs/source/zh/optimization/tgate.md/0 | {
"file_path": "diffusers/docs/source/zh/optimization/tgate.md",
"repo_id": "diffusers",
"token_count": 3346
} | 135 |
<!--Copyright 2025 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# Wuerstchen
[Wuerstchen](https://hf.co/papers/2306.00637) 模型通过将潜在空间压缩 42 倍，在不影响图像质量的情况下大幅降低计算成本并加速推理。在训练过程中，Wuerstchen 使用两个模型（VQGAN + 自动编码器）来压缩潜在表示，然后第三个模型（文本条件潜在扩散模型）在这个高度压缩的空间上进行条件化以生成图像。
为了将先验模型放入 GPU 内存并加速训练，尝试分别启用 `gradient_accumulation_steps`、`gradient_checkpointing` 和 `mixed_precision`。
本指南探讨 [train_text_to_image_prior.py](https://github.com/huggingface/diffusers/blob/main/examples/wuerstchen/text_to_image/train_text_to_image_prior.py) 脚本，帮助您更熟悉它，以及如何根据您的用例进行适配。
在运行脚本之前，请确保从源代码安装库：
```bash
git clone https://github.com/huggingface/diffusers
cd diffusers
pip install .
```
然后导航到包含训练脚本的示例文件夹，并安装脚本所需的依赖项：
```bash
cd examples/wuerstchen/text_to_image
pip install -r requirements.txt
```
<Tip>
🤗 Accelerate 是一个帮助您在多个 GPU/TPU 上或使用混合精度进行训练的库。它会根据您的硬件和环境自动配置训练设置。查看 🤗 Accelerate [快速入门](https://huggingface.co/docs/accelerate/quicktour) 以了解更多信息。
</Tip>
初始化一个 🤗 Accelerate 环境：
```bash
accelerate config
```
要设置一个默认的 🤗 Accelerate 环境而不选择任何配置：
```bash
accelerate config default
```
或者，如果您的环境不支持交互式 shell，例如笔记本，您可以使用：
```py
from accelerate.utils import write_basic_config
write_basic_config()
```
最后，如果您想在自己的数据集上训练模型，请查看 [创建训练数据集](create_dataset) 指南，了解如何创建与训练脚本兼容的数据集。
<Tip>
以下部分重点介绍了训练脚本中对于理解如何修改它很重要的部分，但并未涵盖 [脚本](https://github.com/huggingface/diffusers/blob/main/examples/wuerstchen/text_to_image/train_text_to_image_prior.py) 的详细信息。如果您有兴趣了解更多，请随时阅读脚本，并告诉我们您是否有任何问题或疑虑。
</Tip>
## 脚本参数
训练脚本提供了许多参数来帮助您自定义训练运行。所有参数及其描述都可以在 [`parse_args()`](https://github.com/huggingface/diffusers/blob/6e68c71503682c8693cb5b06a4da4911dfd655ee/examples/wuerstchen/text_to_image/train_text_to_image_prior.py#L192) 函数中找到。它为每个参数提供了默认值，例如训练批次大小和学习率，但如果您愿意，也可以在训练命令中设置自己的值。
例如，要使用 fp16 格式的混合精度加速训练，请在训练命令中添加 `--mixed_precision` 参数：
```bash
accelerate launch train_text_to_image_prior.py \
--mixed_precision="fp16"
```
大多数参数与 [文本到图像](text2image#script-parameters) 训练指南中的参数相同，因此让我们直接深入 Wuerstchen 训练脚本！
## 训练脚本
训练脚本也与 [文本到图像](text2image#training-script) 训练指南类似，但已修改以支持 Wuerstchen。本指南重点介绍 Wuerstchen 训练脚本中独特的代码。
[`main()`](https://github.com/huggingface/diffusers/blob/6e68c71503682c8693cb5b06a4da4911dfd655ee/examples/wuerstchen/text_to_image/train_text_to_image_prior.py#L441) 函数首先初始化图像编码器 - 一个 [EfficientNet](https://github.com/huggingface/diffusers/blob/main/examples/wuerstchen/text_to_image/modeling_efficient_net_encoder.py) - 以及通常的调度器和分词器。
```py
with ContextManagers(deepspeed_zero_init_disabled_context_manager()):
pretrained_checkpoint_file = hf_hub_download("dome272/wuerstchen", filename="model_v2_stage_b.pt")
state_dict = torch.load(pretrained_checkpoint_file, map_location="cpu")
image_encoder = EfficientNetEncoder()
image_encoder.load_state_dict(state_dict["effnet_state_dict"])
image_encoder.eval()
```
您还将加载 [`WuerstchenPrior`] 模型以进行优化。
```py
prior = WuerstchenPrior.from_pretrained(args.pretrained_prior_model_name_or_path, subfolder="prior")
optimizer = optimizer_cls(
prior.parameters(),
lr=args.learning_rate,
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
)
```
接下来，您将对图像应用一些 [transforms](https://github.com/huggingface/diffusers/blob/65ef7a0c5c594b4f84092e328fbdd73183613b30/examples/wuerstchen/text_to_image/train_text_to_image_prior.py#L656) 并对标题进行 [tokenize](https://github.com/huggingface/diffusers/blob/65ef7a0c5c594b4f84092e328fbdd73183613b30/examples/wuerstchen/text_to_image/train_text_to_image_prior.py#L637)：
```py
def preprocess_train(examples):
images = [image.conver
t("RGB") for image in examples[image_column]]
examples["effnet_pixel_values"] = [effnet_transforms(image) for image in images]
examples["text_input_ids"], examples["text_mask"] = tokenize_captions(examples)
return examples
```
最后，[训练循环](https://github.com/huggingface/diffusers/blob/65ef7a0c5c594b4f84092e328fbdd73183613b30/examples/wuerstchen/text_to_image/train_text_to_image_prior.py#L656)处理使用`EfficientNetEncoder`将图像压缩到潜在空间，向潜在表示添加噪声，并使用[`WuerstchenPrior`]模型预测噪声残差。
```py
pred_noise = prior(noisy_latents, timesteps, prompt_embeds)
```
如果您想了解更多关于训练循环的工作原理，请查看[理解管道、模型和调度器](../using-diffusers/write_own_pipeline)教程，该教程分解了去噪过程的基本模式。
## 启动脚本
一旦您完成了所有更改或对默认配置满意，就可以启动训练脚本了！🚀
设置`DATASET_NAME`环境变量为Hub中的数据集名称。本指南使用[Naruto BLIP captions](https://huggingface.co/datasets/lambdalabs/naruto-blip-captions)数据集，但您也可以创建和训练自己的数据集（参见[创建用于训练的数据集](create_dataset)指南）。
<Tip>
要使用Weights & Biases监控训练进度，请在训练命令中添加`--report_to=wandb`参数。您还需要在训练命令中添加`--validation_prompt`以跟踪结果。这对于调试模型和查看中间结果非常有用。
</Tip>
```bash
export DATASET_NAME="lambdalabs/naruto-blip-captions"
accelerate launch train_text_to_image_prior.py \
--mixed_precision="fp16" \
--dataset_name=$DATASET_NAME \
--resolution=768 \
--train_batch_size=4 \
--gradient_accumulation_steps=4 \
--gradient_checkpointing \
--dataloader_num_workers=4 \
--max_train_steps=15000 \
--learning_rate=1e-05 \
--max_grad_norm=1 \
--checkpoints_total_limit=3 \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--validation_prompts="A robot naruto, 4k photo" \
--report_to="wandb" \
--push_to_hub \
--output_dir="wuerstchen-prior-naruto-model"
```
训练完成后，您可以使用新训练的模型进行推理！
```py
import torch
from diffusers import AutoPipelineForText2Image
from diffusers.pipelines.wuerstchen import DEFAULT_STAGE_C_TIMESTEPS
pipeline = AutoPipelineForText2Image.from_pretrained("path/to/saved/model", torch_dtype=torch.float16).to("cuda")
caption = "A cute bird naruto holding a shield"
images = pipeline(
caption,
width=1024,
height=1536,
prior_timesteps=DEFAULT_STAGE_C_TIMESTEPS,
prior_guidance_scale=4.0,
num_images_per_prompt=2,
).images
```
## 下一步
恭喜您训练了一个Wuerstchen模型！要了解更多关于如何使用您的新模型的信息，请参
以下内容可能有所帮助：
- 查看 [Wuerstchen](../api/pipelines/wuerstchen#text-to-image-generation) API 文档，了解更多关于如何使用该管道进行文本到图像生成及其限制的信息。 | diffusers/docs/source/zh/training/wuerstchen.md/0 | {
"file_path": "diffusers/docs/source/zh/training/wuerstchen.md",
"repo_id": "diffusers",
"token_count": 4604
} | 136 |
# Copyright 2025 The HuggingFace Team.
# All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import logging
import math
import os
import random
import shutil
from datetime import timedelta
from pathlib import Path
from typing import List, Optional, Tuple, Union
import torch
import transformers
from accelerate import Accelerator, DistributedType
from accelerate.logging import get_logger
from accelerate.utils import DistributedDataParallelKwargs, InitProcessGroupKwargs, ProjectConfiguration, set_seed
from huggingface_hub import create_repo, upload_folder
from peft import LoraConfig, get_peft_model_state_dict, set_peft_model_state_dict
from torch.utils.data import DataLoader, Dataset
from torchvision import transforms
from tqdm.auto import tqdm
from transformers import AutoTokenizer, T5EncoderModel, T5Tokenizer
import diffusers
from diffusers import (
AutoencoderKLCogVideoX,
CogVideoXDPMScheduler,
CogVideoXImageToVideoPipeline,
CogVideoXTransformer3DModel,
)
from diffusers.models.embeddings import get_3d_rotary_pos_embed
from diffusers.optimization import get_scheduler
from diffusers.pipelines.cogvideo.pipeline_cogvideox import get_resize_crop_region_for_grid
from diffusers.training_utils import cast_training_params, free_memory
from diffusers.utils import (
check_min_version,
convert_unet_state_dict_to_peft,
export_to_video,
is_wandb_available,
load_image,
)
from diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card
from diffusers.utils.torch_utils import is_compiled_module
if is_wandb_available():
import wandb
# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
check_min_version("0.36.0.dev0")
logger = get_logger(__name__)
def get_args():
parser = argparse.ArgumentParser(description="Simple example of a training script for CogVideoX.")
# Model information
parser.add_argument(
"--pretrained_model_name_or_path",
type=str,
default=None,
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--revision",
type=str,
default=None,
required=False,
help="Revision of pretrained model identifier from huggingface.co/models.",
)
parser.add_argument(
"--variant",
type=str,
default=None,
help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16",
)
parser.add_argument(
"--cache_dir",
type=str,
default=None,
help="The directory where the downloaded models and datasets will be stored.",
)
# Dataset information
parser.add_argument(
"--dataset_name",
type=str,
default=None,
help=(
"The name of the Dataset (from the HuggingFace hub) containing the training data of instance images (could be your own, possibly private,"
" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
" or to a folder containing files that 🤗 Datasets can understand."
),
)
parser.add_argument(
"--dataset_config_name",
type=str,
default=None,
help="The config of the Dataset, leave as None if there's only one config.",
)
parser.add_argument(
"--instance_data_root",
type=str,
default=None,
help=("A folder containing the training data."),
)
parser.add_argument(
"--video_column",
type=str,
default="video",
help="The column of the dataset containing videos. Or, the name of the file in `--instance_data_root` folder containing the line-separated path to video data.",
)
parser.add_argument(
"--caption_column",
type=str,
default="text",
help="The column of the dataset containing the instance prompt for each video. Or, the name of the file in `--instance_data_root` folder containing the line-separated instance prompts.",
)
parser.add_argument(
"--id_token", type=str, default=None, help="Identifier token appended to the start of each prompt if provided."
)
parser.add_argument(
"--dataloader_num_workers",
type=int,
default=0,
help=(
"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
),
)
# Validation
parser.add_argument(
"--validation_prompt",
type=str,
default=None,
help="One or more prompt(s) that is used during validation to verify that the model is learning. Multiple validation prompts should be separated by the '--validation_prompt_seperator' string.",
)
parser.add_argument(
"--validation_images",
type=str,
default=None,
help="One or more image path(s) that is used during validation to verify that the model is learning. Multiple validation paths should be separated by the '--validation_prompt_seperator' string. These should correspond to the order of the validation prompts.",
)
parser.add_argument(
"--validation_prompt_separator",
type=str,
default=":::",
help="String that separates multiple validation prompts",
)
parser.add_argument(
"--num_validation_videos",
type=int,
default=1,
help="Number of videos that should be generated during validation per `validation_prompt`.",
)
parser.add_argument(
"--validation_epochs",
type=int,
default=50,
help=(
"Run validation every X epochs. Validation consists of running the prompt `args.validation_prompt` multiple times: `args.num_validation_videos`."
),
)
parser.add_argument(
"--guidance_scale",
type=float,
default=6,
help="The guidance scale to use while sampling validation videos.",
)
parser.add_argument(
"--use_dynamic_cfg",
action="store_true",
default=False,
help="Whether or not to use the default cosine dynamic guidance schedule when sampling validation videos.",
)
# Training information
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
parser.add_argument(
"--rank",
type=int,
default=128,
help=("The dimension of the LoRA update matrices."),
)
parser.add_argument(
"--lora_alpha",
type=float,
default=128,
help=("The scaling factor to scale LoRA weight update. The actual scaling factor is `lora_alpha / rank`"),
)
parser.add_argument(
"--mixed_precision",
type=str,
default=None,
choices=["no", "fp16", "bf16"],
help=(
"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the"
" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
),
)
parser.add_argument(
"--output_dir",
type=str,
default="cogvideox-i2v-lora",
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument(
"--height",
type=int,
default=480,
help="All input videos are resized to this height.",
)
parser.add_argument(
"--width",
type=int,
default=720,
help="All input videos are resized to this width.",
)
parser.add_argument("--fps", type=int, default=8, help="All input videos will be used at this FPS.")
parser.add_argument(
"--max_num_frames", type=int, default=49, help="All input videos will be truncated to these many frames."
)
parser.add_argument(
"--skip_frames_start",
type=int,
default=0,
help="Number of frames to skip from the beginning of each input video. Useful if training data contains intro sequences.",
)
parser.add_argument(
"--skip_frames_end",
type=int,
default=0,
help="Number of frames to skip from the end of each input video. Useful if training data contains outro sequences.",
)
parser.add_argument(
"--random_flip",
action="store_true",
help="whether to randomly flip videos horizontally",
)
parser.add_argument(
"--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
)
parser.add_argument("--num_train_epochs", type=int, default=1)
parser.add_argument(
"--max_train_steps",
type=int,
default=None,
help="Total number of training steps to perform. If provided, overrides `--num_train_epochs`.",
)
parser.add_argument(
"--checkpointing_steps",
type=int,
default=500,
help=(
"Save a checkpoint of the training state every X updates. These checkpoints can be used both as final"
" checkpoints in case they are better than the last checkpoint, and are also suitable for resuming"
" training using `--resume_from_checkpoint`."
),
)
parser.add_argument(
"--checkpoints_total_limit",
type=int,
default=None,
help=("Max number of checkpoints to store."),
)
parser.add_argument(
"--resume_from_checkpoint",
type=str,
default=None,
help=(
"Whether training should be resumed from a previous checkpoint. Use a path saved by"
' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
),
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--gradient_checkpointing",
action="store_true",
help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=1e-4,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument(
"--scale_lr",
action="store_true",
default=False,
help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
)
parser.add_argument(
"--lr_scheduler",
type=str,
default="constant",
help=(
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
' "constant", "constant_with_warmup"]'
),
)
parser.add_argument(
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument(
"--lr_num_cycles",
type=int,
default=1,
help="Number of hard resets of the lr in cosine_with_restarts scheduler.",
)
parser.add_argument("--lr_power", type=float, default=1.0, help="Power factor of the polynomial scheduler.")
parser.add_argument(
"--enable_slicing",
action="store_true",
default=False,
help="Whether or not to use VAE slicing for saving memory.",
)
parser.add_argument(
"--enable_tiling",
action="store_true",
default=False,
help="Whether or not to use VAE tiling for saving memory.",
)
parser.add_argument(
"--noised_image_dropout",
type=float,
default=0.05,
help="Image condition dropout probability.",
)
# Optimizer
parser.add_argument(
"--optimizer",
type=lambda s: s.lower(),
default="adam",
choices=["adam", "adamw", "prodigy"],
help=("The optimizer type to use."),
)
parser.add_argument(
"--use_8bit_adam",
action="store_true",
help="Whether or not to use 8-bit Adam from bitsandbytes. Ignored if optimizer is not set to AdamW",
)
parser.add_argument(
"--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam and Prodigy optimizers."
)
parser.add_argument(
"--adam_beta2", type=float, default=0.95, help="The beta2 parameter for the Adam and Prodigy optimizers."
)
parser.add_argument(
"--prodigy_beta3",
type=float,
default=None,
help="Coefficients for computing the Prodigy optimizer's stepsize using running averages. If set to None, uses the value of square root of beta2.",
)
parser.add_argument("--prodigy_decouple", action="store_true", help="Use AdamW style decoupled weight decay")
parser.add_argument("--adam_weight_decay", type=float, default=1e-04, help="Weight decay to use for unet params")
parser.add_argument(
"--adam_epsilon",
type=float,
default=1e-08,
help="Epsilon value for the Adam optimizer and Prodigy optimizers.",
)
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
parser.add_argument("--prodigy_use_bias_correction", action="store_true", help="Turn on Adam's bias correction.")
parser.add_argument(
"--prodigy_safeguard_warmup",
action="store_true",
help="Remove lr from the denominator of D estimate to avoid issues during warm-up stage.",
)
# Other information
parser.add_argument("--tracker_name", type=str, default=None, help="Project tracker name")
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
parser.add_argument(
"--hub_model_id",
type=str,
default=None,
help="The name of the repository to keep in sync with the local `output_dir`.",
)
parser.add_argument(
"--logging_dir",
type=str,
default="logs",
help="Directory where logs are stored.",
)
parser.add_argument(
"--allow_tf32",
action="store_true",
help=(
"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
),
)
parser.add_argument(
"--report_to",
type=str,
default=None,
help=(
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
),
)
parser.add_argument("--nccl_timeout", type=int, default=600, help="NCCL backend timeout in seconds.")
return parser.parse_args()
class VideoDataset(Dataset):
def __init__(
self,
instance_data_root: Optional[str] = None,
dataset_name: Optional[str] = None,
dataset_config_name: Optional[str] = None,
caption_column: str = "text",
video_column: str = "video",
height: int = 480,
width: int = 720,
fps: int = 8,
max_num_frames: int = 49,
skip_frames_start: int = 0,
skip_frames_end: int = 0,
cache_dir: Optional[str] = None,
id_token: Optional[str] = None,
) -> None:
super().__init__()
self.instance_data_root = Path(instance_data_root) if instance_data_root is not None else None
self.dataset_name = dataset_name
self.dataset_config_name = dataset_config_name
self.caption_column = caption_column
self.video_column = video_column
self.height = height
self.width = width
self.fps = fps
self.max_num_frames = max_num_frames
self.skip_frames_start = skip_frames_start
self.skip_frames_end = skip_frames_end
self.cache_dir = cache_dir
self.id_token = id_token or ""
if dataset_name is not None:
self.instance_prompts, self.instance_video_paths = self._load_dataset_from_hub()
else:
self.instance_prompts, self.instance_video_paths = self._load_dataset_from_local_path()
self.instance_prompts = [self.id_token + prompt for prompt in self.instance_prompts]
self.num_instance_videos = len(self.instance_video_paths)
if self.num_instance_videos != len(self.instance_prompts):
raise ValueError(
f"Expected length of instance prompts and videos to be the same but found {len(self.instance_prompts)=} and {len(self.instance_video_paths)=}. Please ensure that the number of caption prompts and videos match in your dataset."
)
self.instance_videos = self._preprocess_data()
def __len__(self):
return self.num_instance_videos
def __getitem__(self, index):
return {
"instance_prompt": self.instance_prompts[index],
"instance_video": self.instance_videos[index],
}
def _load_dataset_from_hub(self):
try:
from datasets import load_dataset
except ImportError:
raise ImportError(
"You are trying to load your data using the datasets library. If you wish to train using custom "
"captions please install the datasets library: `pip install datasets`. If you wish to load a "
"local folder containing images only, specify --instance_data_root instead."
)
# Downloading and loading a dataset from the hub. See more about loading custom images at
# https://huggingface.co/docs/datasets/v2.0.0/en/dataset_script
dataset = load_dataset(
self.dataset_name,
self.dataset_config_name,
cache_dir=self.cache_dir,
)
column_names = dataset["train"].column_names
if self.video_column is None:
video_column = column_names[0]
logger.info(f"`video_column` defaulting to {video_column}")
else:
video_column = self.video_column
if video_column not in column_names:
raise ValueError(
f"`--video_column` value '{video_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}"
)
if self.caption_column is None:
caption_column = column_names[1]
logger.info(f"`caption_column` defaulting to {caption_column}")
else:
caption_column = self.caption_column
if self.caption_column not in column_names:
raise ValueError(
f"`--caption_column` value '{self.caption_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}"
)
instance_prompts = dataset["train"][caption_column]
instance_videos = [Path(self.instance_data_root, filepath) for filepath in dataset["train"][video_column]]
return instance_prompts, instance_videos
def _load_dataset_from_local_path(self):
if not self.instance_data_root.exists():
raise ValueError("Instance videos root folder does not exist")
prompt_path = self.instance_data_root.joinpath(self.caption_column)
video_path = self.instance_data_root.joinpath(self.video_column)
if not prompt_path.exists() or not prompt_path.is_file():
raise ValueError(
"Expected `--caption_column` to be path to a file in `--instance_data_root` containing line-separated text prompts."
)
if not video_path.exists() or not video_path.is_file():
raise ValueError(
"Expected `--video_column` to be path to a file in `--instance_data_root` containing line-separated paths to video data in the same directory."
)
with open(prompt_path, "r", encoding="utf-8") as file:
instance_prompts = [line.strip() for line in file.readlines() if len(line.strip()) > 0]
with open(video_path, "r", encoding="utf-8") as file:
instance_videos = [
self.instance_data_root.joinpath(line.strip()) for line in file.readlines() if len(line.strip()) > 0
]
if any(not path.is_file() for path in instance_videos):
raise ValueError(
"Expected '--video_column' to be a path to a file in `--instance_data_root` containing line-separated paths to video data but found at least one path that is not a valid file."
)
return instance_prompts, instance_videos
def _preprocess_data(self):
try:
import decord
except ImportError:
raise ImportError(
"The `decord` package is required for loading the video dataset. Install with `pip install decord`"
)
decord.bridge.set_bridge("torch")
videos = []
train_transforms = transforms.Compose(
[
transforms.Lambda(lambda x: x / 255.0 * 2.0 - 1.0),
]
)
for filename in self.instance_video_paths:
video_reader = decord.VideoReader(uri=filename.as_posix(), width=self.width, height=self.height)
video_num_frames = len(video_reader)
start_frame = min(self.skip_frames_start, video_num_frames)
end_frame = max(0, video_num_frames - self.skip_frames_end)
if end_frame <= start_frame:
frames = video_reader.get_batch([start_frame])
elif end_frame - start_frame <= self.max_num_frames:
frames = video_reader.get_batch(list(range(start_frame, end_frame)))
else:
indices = list(range(start_frame, end_frame, (end_frame - start_frame) // self.max_num_frames))
frames = video_reader.get_batch(indices)
# Ensure that we don't go over the limit
frames = frames[: self.max_num_frames]
selected_num_frames = frames.shape[0]
# Choose first (4k + 1) frames as this is how many is required by the VAE
remainder = (3 + (selected_num_frames % 4)) % 4
if remainder != 0:
frames = frames[:-remainder]
selected_num_frames = frames.shape[0]
assert (selected_num_frames - 1) % 4 == 0
# Training transforms
frames = frames.float()
frames = torch.stack([train_transforms(frame) for frame in frames], dim=0)
videos.append(frames.permute(0, 3, 1, 2).contiguous()) # [F, C, H, W]
return videos
def save_model_card(
repo_id: str,
videos=None,
base_model: str = None,
validation_prompt=None,
repo_folder=None,
fps=8,
):
widget_dict = []
if videos is not None:
for i, video in enumerate(videos):
video_path = f"final_video_{i}.mp4"
export_to_video(video, os.path.join(repo_folder, video_path, fps=fps))
widget_dict.append(
{"text": validation_prompt if validation_prompt else " ", "output": {"url": video_path}},
)
model_description = f"""
# CogVideoX LoRA - {repo_id}
<Gallery />
## Model description
These are {repo_id} LoRA weights for {base_model}.
The weights were trained using the [CogVideoX Diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/cogvideo/train_cogvideox_image_to_video_lora.py).
Was LoRA for the text encoder enabled? No.
## Download model
[Download the *.safetensors LoRA]({repo_id}/tree/main) in the Files & versions tab.
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
import torch
from diffusers import CogVideoXImageToVideoPipeline
from diffusers.utils import load_image, export_to_video
pipe = CogVideoXImageToVideoPipeline.from_pretrained("THUDM/CogVideoX-5b", torch_dtype=torch.bfloat16).to("cuda")
pipe.load_lora_weights("{repo_id}", weight_name="pytorch_lora_weights.safetensors", adapter_name=["cogvideox-i2v-lora"])
# The LoRA adapter weights are determined by what was used for training.
# In this case, we assume `--lora_alpha` is 32 and `--rank` is 64.
# It can be made lower or higher from what was used in training to decrease or amplify the effect
# of the LoRA upto a tolerance, beyond which one might notice no effect at all or overflows.
pipe.set_adapters(["cogvideox-i2v-lora"], [32 / 64])
image = load_image("/path/to/image")
video = pipe(image=image, "{validation_prompt}", guidance_scale=6, use_dynamic_cfg=True).frames[0]
export_to_video(video, "output.mp4", fps=8)
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## License
Please adhere to the licensing terms as described [here](https://huggingface.co/THUDM/CogVideoX-5b-I2V/blob/main/LICENSE).
"""
model_card = load_or_create_model_card(
repo_id_or_path=repo_id,
from_training=True,
license="other",
base_model=base_model,
prompt=validation_prompt,
model_description=model_description,
widget=widget_dict,
)
tags = [
"image-to-video",
"diffusers-training",
"diffusers",
"lora",
"cogvideox",
"cogvideox-diffusers",
"template:sd-lora",
]
model_card = populate_model_card(model_card, tags=tags)
model_card.save(os.path.join(repo_folder, "README.md"))
def log_validation(
pipe,
args,
accelerator,
pipeline_args,
epoch,
is_final_validation: bool = False,
):
logger.info(
f"Running validation... \n Generating {args.num_validation_videos} videos with prompt: {pipeline_args['prompt']}."
)
# We train on the simplified learning objective. If we were previously predicting a variance, we need the scheduler to ignore it
scheduler_args = {}
if "variance_type" in pipe.scheduler.config:
variance_type = pipe.scheduler.config.variance_type
if variance_type in ["learned", "learned_range"]:
variance_type = "fixed_small"
scheduler_args["variance_type"] = variance_type
pipe.scheduler = CogVideoXDPMScheduler.from_config(pipe.scheduler.config, **scheduler_args)
pipe = pipe.to(accelerator.device)
# pipe.set_progress_bar_config(disable=True)
# run inference
generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed is not None else None
videos = []
for _ in range(args.num_validation_videos):
video = pipe(**pipeline_args, generator=generator, output_type="np").frames[0]
videos.append(video)
for tracker in accelerator.trackers:
phase_name = "test" if is_final_validation else "validation"
if tracker.name == "wandb":
video_filenames = []
for i, video in enumerate(videos):
prompt = (
pipeline_args["prompt"][:25]
.replace(" ", "_")
.replace(" ", "_")
.replace("'", "_")
.replace('"', "_")
.replace("/", "_")
)
filename = os.path.join(args.output_dir, f"{phase_name}_video_{i}_{prompt}.mp4")
export_to_video(video, filename, fps=8)
video_filenames.append(filename)
tracker.log(
{
phase_name: [
wandb.Video(filename, caption=f"{i}: {pipeline_args['prompt']}")
for i, filename in enumerate(video_filenames)
]
}
)
del pipe
free_memory()
return videos
def _get_t5_prompt_embeds(
tokenizer: T5Tokenizer,
text_encoder: T5EncoderModel,
prompt: Union[str, List[str]],
num_videos_per_prompt: int = 1,
max_sequence_length: int = 226,
device: Optional[torch.device] = None,
dtype: Optional[torch.dtype] = None,
text_input_ids=None,
):
prompt = [prompt] if isinstance(prompt, str) else prompt
batch_size = len(prompt)
if tokenizer is not None:
text_inputs = tokenizer(
prompt,
padding="max_length",
max_length=max_sequence_length,
truncation=True,
add_special_tokens=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
else:
if text_input_ids is None:
raise ValueError("`text_input_ids` must be provided when the tokenizer is not specified.")
prompt_embeds = text_encoder(text_input_ids.to(device))[0]
prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
# duplicate text embeddings for each generation per prompt, using mps friendly method
_, seq_len, _ = prompt_embeds.shape
prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1)
prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt, seq_len, -1)
return prompt_embeds
def encode_prompt(
tokenizer: T5Tokenizer,
text_encoder: T5EncoderModel,
prompt: Union[str, List[str]],
num_videos_per_prompt: int = 1,
max_sequence_length: int = 226,
device: Optional[torch.device] = None,
dtype: Optional[torch.dtype] = None,
text_input_ids=None,
):
prompt = [prompt] if isinstance(prompt, str) else prompt
prompt_embeds = _get_t5_prompt_embeds(
tokenizer,
text_encoder,
prompt=prompt,
num_videos_per_prompt=num_videos_per_prompt,
max_sequence_length=max_sequence_length,
device=device,
dtype=dtype,
text_input_ids=text_input_ids,
)
return prompt_embeds
def compute_prompt_embeddings(
tokenizer, text_encoder, prompt, max_sequence_length, device, dtype, requires_grad: bool = False
):
if requires_grad:
prompt_embeds = encode_prompt(
tokenizer,
text_encoder,
prompt,
num_videos_per_prompt=1,
max_sequence_length=max_sequence_length,
device=device,
dtype=dtype,
)
else:
with torch.no_grad():
prompt_embeds = encode_prompt(
tokenizer,
text_encoder,
prompt,
num_videos_per_prompt=1,
max_sequence_length=max_sequence_length,
device=device,
dtype=dtype,
)
return prompt_embeds
def prepare_rotary_positional_embeddings(
height: int,
width: int,
num_frames: int,
vae_scale_factor_spatial: int = 8,
patch_size: int = 2,
attention_head_dim: int = 64,
device: Optional[torch.device] = None,
base_height: int = 480,
base_width: int = 720,
) -> Tuple[torch.Tensor, torch.Tensor]:
grid_height = height // (vae_scale_factor_spatial * patch_size)
grid_width = width // (vae_scale_factor_spatial * patch_size)
base_size_width = base_width // (vae_scale_factor_spatial * patch_size)
base_size_height = base_height // (vae_scale_factor_spatial * patch_size)
grid_crops_coords = get_resize_crop_region_for_grid((grid_height, grid_width), base_size_width, base_size_height)
freqs_cos, freqs_sin = get_3d_rotary_pos_embed(
embed_dim=attention_head_dim,
crops_coords=grid_crops_coords,
grid_size=(grid_height, grid_width),
temporal_size=num_frames,
device=device,
)
return freqs_cos, freqs_sin
def get_optimizer(args, params_to_optimize, use_deepspeed: bool = False):
# Use DeepSpeed optimizer
if use_deepspeed:
from accelerate.utils import DummyOptim
return DummyOptim(
params_to_optimize,
lr=args.learning_rate,
betas=(args.adam_beta1, args.adam_beta2),
eps=args.adam_epsilon,
weight_decay=args.adam_weight_decay,
)
# Optimizer creation
supported_optimizers = ["adam", "adamw", "prodigy"]
if args.optimizer not in supported_optimizers:
logger.warning(
f"Unsupported choice of optimizer: {args.optimizer}. Supported optimizers include {supported_optimizers}. Defaulting to AdamW"
)
args.optimizer = "adamw"
if args.use_8bit_adam and args.optimizer.lower() not in ["adam", "adamw"]:
logger.warning(
f"use_8bit_adam is ignored when optimizer is not set to 'Adam' or 'AdamW'. Optimizer was "
f"set to {args.optimizer.lower()}"
)
if args.use_8bit_adam:
try:
import bitsandbytes as bnb
except ImportError:
raise ImportError(
"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`."
)
if args.optimizer.lower() == "adamw":
optimizer_class = bnb.optim.AdamW8bit if args.use_8bit_adam else torch.optim.AdamW
optimizer = optimizer_class(
params_to_optimize,
betas=(args.adam_beta1, args.adam_beta2),
eps=args.adam_epsilon,
weight_decay=args.adam_weight_decay,
)
elif args.optimizer.lower() == "adam":
optimizer_class = bnb.optim.Adam8bit if args.use_8bit_adam else torch.optim.Adam
optimizer = optimizer_class(
params_to_optimize,
betas=(args.adam_beta1, args.adam_beta2),
eps=args.adam_epsilon,
weight_decay=args.adam_weight_decay,
)
elif args.optimizer.lower() == "prodigy":
try:
import prodigyopt
except ImportError:
raise ImportError("To use Prodigy, please install the prodigyopt library: `pip install prodigyopt`")
optimizer_class = prodigyopt.Prodigy
if args.learning_rate <= 0.1:
logger.warning(
"Learning rate is too low. When using prodigy, it's generally better to set learning rate around 1.0"
)
optimizer = optimizer_class(
params_to_optimize,
betas=(args.adam_beta1, args.adam_beta2),
beta3=args.prodigy_beta3,
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
decouple=args.prodigy_decouple,
use_bias_correction=args.prodigy_use_bias_correction,
safeguard_warmup=args.prodigy_safeguard_warmup,
)
return optimizer
def main(args):
if args.report_to == "wandb" and args.hub_token is not None:
raise ValueError(
"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
" Please use `hf auth login` to authenticate with the Hub."
)
if torch.backends.mps.is_available() and args.mixed_precision == "bf16":
# due to pytorch#99272, MPS does not yet support bfloat16.
raise ValueError(
"Mixed precision training with bfloat16 is not supported on MPS. Please use fp16 (recommended) or fp32 instead."
)
logging_dir = Path(args.output_dir, args.logging_dir)
accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
ddp_kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)
init_kwargs = InitProcessGroupKwargs(backend="nccl", timeout=timedelta(seconds=args.nccl_timeout))
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
mixed_precision=args.mixed_precision,
log_with=args.report_to,
project_config=accelerator_project_config,
kwargs_handlers=[ddp_kwargs, init_kwargs],
)
# Disable AMP for MPS.
if torch.backends.mps.is_available():
accelerator.native_amp = False
if args.report_to == "wandb":
if not is_wandb_available():
raise ImportError("Make sure to install wandb if you want to use it for logging during training.")
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.info(accelerator.state, main_process_only=False)
if accelerator.is_local_main_process:
transformers.utils.logging.set_verbosity_warning()
diffusers.utils.logging.set_verbosity_info()
else:
transformers.utils.logging.set_verbosity_error()
diffusers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Handle the repository creation
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
if args.push_to_hub:
repo_id = create_repo(
repo_id=args.hub_model_id or Path(args.output_dir).name,
exist_ok=True,
).repo_id
# Prepare models and scheduler
tokenizer = AutoTokenizer.from_pretrained(
args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision
)
text_encoder = T5EncoderModel.from_pretrained(
args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
)
# CogVideoX-2b weights are stored in float16
# CogVideoX-5b and CogVideoX-5b-I2V weights are stored in bfloat16
load_dtype = torch.bfloat16 if "5b" in args.pretrained_model_name_or_path.lower() else torch.float16
transformer = CogVideoXTransformer3DModel.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="transformer",
torch_dtype=load_dtype,
revision=args.revision,
variant=args.variant,
)
vae = AutoencoderKLCogVideoX.from_pretrained(
args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision, variant=args.variant
)
scheduler = CogVideoXDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
if args.enable_slicing:
vae.enable_slicing()
if args.enable_tiling:
vae.enable_tiling()
# We only train the additional adapter LoRA layers
text_encoder.requires_grad_(False)
transformer.requires_grad_(False)
vae.requires_grad_(False)
# For mixed precision training we cast all non-trainable weights (vae, text_encoder and transformer) to half-precision
# as these weights are only used for inference, keeping weights in full precision is not required.
weight_dtype = torch.float32
if accelerator.state.deepspeed_plugin:
# DeepSpeed is handling precision, use what's in the DeepSpeed config
if (
"fp16" in accelerator.state.deepspeed_plugin.deepspeed_config
and accelerator.state.deepspeed_plugin.deepspeed_config["fp16"]["enabled"]
):
weight_dtype = torch.float16
if (
"bf16" in accelerator.state.deepspeed_plugin.deepspeed_config
and accelerator.state.deepspeed_plugin.deepspeed_config["bf16"]["enabled"]
):
weight_dtype = torch.float16
else:
if accelerator.mixed_precision == "fp16":
weight_dtype = torch.float16
elif accelerator.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
if torch.backends.mps.is_available() and weight_dtype == torch.bfloat16:
# due to pytorch#99272, MPS does not yet support bfloat16.
raise ValueError(
"Mixed precision training with bfloat16 is not supported on MPS. Please use fp16 (recommended) or fp32 instead."
)
text_encoder.to(accelerator.device, dtype=weight_dtype)
transformer.to(accelerator.device, dtype=weight_dtype)
vae.to(accelerator.device, dtype=weight_dtype)
if args.gradient_checkpointing:
transformer.enable_gradient_checkpointing()
# now we will add new LoRA weights to the attention layers
transformer_lora_config = LoraConfig(
r=args.rank,
lora_alpha=args.lora_alpha,
init_lora_weights=True,
target_modules=["to_k", "to_q", "to_v", "to_out.0"],
)
transformer.add_adapter(transformer_lora_config)
def unwrap_model(model):
model = accelerator.unwrap_model(model)
model = model._orig_mod if is_compiled_module(model) else model
return model
# create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
def save_model_hook(models, weights, output_dir):
if accelerator.is_main_process:
transformer_lora_layers_to_save = None
for model in models:
if isinstance(model, type(unwrap_model(transformer))):
transformer_lora_layers_to_save = get_peft_model_state_dict(model)
else:
raise ValueError(f"unexpected save model: {model.__class__}")
# make sure to pop weight so that corresponding model is not saved again
weights.pop()
CogVideoXImageToVideoPipeline.save_lora_weights(
output_dir,
transformer_lora_layers=transformer_lora_layers_to_save,
)
def load_model_hook(models, input_dir):
transformer_ = None
while len(models) > 0:
model = models.pop()
if isinstance(model, type(unwrap_model(transformer))):
transformer_ = model
else:
raise ValueError(f"Unexpected save model: {model.__class__}")
lora_state_dict = CogVideoXImageToVideoPipeline.lora_state_dict(input_dir)
transformer_state_dict = {
f"{k.replace('transformer.', '')}": v for k, v in lora_state_dict.items() if k.startswith("transformer.")
}
transformer_state_dict = convert_unet_state_dict_to_peft(transformer_state_dict)
incompatible_keys = set_peft_model_state_dict(transformer_, transformer_state_dict, adapter_name="default")
if incompatible_keys is not None:
# check only for unexpected keys
unexpected_keys = getattr(incompatible_keys, "unexpected_keys", None)
if unexpected_keys:
logger.warning(
f"Loading adapter weights from state_dict led to unexpected keys not found in the model: "
f" {unexpected_keys}. "
)
# Make sure the trainable params are in float32. This is again needed since the base models
# are in `weight_dtype`. More details:
# https://github.com/huggingface/diffusers/pull/6514#discussion_r1449796804
if args.mixed_precision == "fp16":
# only upcast trainable parameters (LoRA) into fp32
cast_training_params([transformer_])
accelerator.register_save_state_pre_hook(save_model_hook)
accelerator.register_load_state_pre_hook(load_model_hook)
# Enable TF32 for faster training on Ampere GPUs,
# cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
if args.allow_tf32 and torch.cuda.is_available():
torch.backends.cuda.matmul.allow_tf32 = True
if args.scale_lr:
args.learning_rate = (
args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
)
# Make sure the trainable params are in float32.
if args.mixed_precision == "fp16":
# only upcast trainable parameters (LoRA) into fp32
cast_training_params([transformer], dtype=torch.float32)
transformer_lora_parameters = list(filter(lambda p: p.requires_grad, transformer.parameters()))
# Optimization parameters
transformer_parameters_with_lr = {"params": transformer_lora_parameters, "lr": args.learning_rate}
params_to_optimize = [transformer_parameters_with_lr]
use_deepspeed_optimizer = (
accelerator.state.deepspeed_plugin is not None
and "optimizer" in accelerator.state.deepspeed_plugin.deepspeed_config
)
use_deepspeed_scheduler = (
accelerator.state.deepspeed_plugin is not None
and "scheduler" in accelerator.state.deepspeed_plugin.deepspeed_config
)
optimizer = get_optimizer(args, params_to_optimize, use_deepspeed=use_deepspeed_optimizer)
# Dataset and DataLoader
train_dataset = VideoDataset(
instance_data_root=args.instance_data_root,
dataset_name=args.dataset_name,
dataset_config_name=args.dataset_config_name,
caption_column=args.caption_column,
video_column=args.video_column,
height=args.height,
width=args.width,
fps=args.fps,
max_num_frames=args.max_num_frames,
skip_frames_start=args.skip_frames_start,
skip_frames_end=args.skip_frames_end,
cache_dir=args.cache_dir,
id_token=args.id_token,
)
def encode_video(video):
video = video.to(accelerator.device, dtype=vae.dtype).unsqueeze(0)
video = video.permute(0, 2, 1, 3, 4) # [B, C, F, H, W]
image = video[:, :, :1].clone()
latent_dist = vae.encode(video).latent_dist
image_noise_sigma = torch.normal(mean=-3.0, std=0.5, size=(1,), device=image.device)
image_noise_sigma = torch.exp(image_noise_sigma).to(dtype=image.dtype)
noisy_image = image + torch.randn_like(image) * image_noise_sigma[:, None, None, None, None]
image_latent_dist = vae.encode(noisy_image).latent_dist
return latent_dist, image_latent_dist
train_dataset.instance_prompts = [
compute_prompt_embeddings(
tokenizer,
text_encoder,
[prompt],
transformer.config.max_text_seq_length,
accelerator.device,
weight_dtype,
requires_grad=False,
)
for prompt in train_dataset.instance_prompts
]
train_dataset.instance_videos = [encode_video(video) for video in train_dataset.instance_videos]
def collate_fn(examples):
videos = []
images = []
for example in examples:
latent_dist, image_latent_dist = example["instance_video"]
video_latents = latent_dist.sample() * vae.config.scaling_factor
image_latents = image_latent_dist.sample() * vae.config.scaling_factor
video_latents = video_latents.permute(0, 2, 1, 3, 4)
image_latents = image_latents.permute(0, 2, 1, 3, 4)
padding_shape = (video_latents.shape[0], video_latents.shape[1] - 1, *video_latents.shape[2:])
latent_padding = image_latents.new_zeros(padding_shape)
image_latents = torch.cat([image_latents, latent_padding], dim=1)
if random.random() < args.noised_image_dropout:
image_latents = torch.zeros_like(image_latents)
videos.append(video_latents)
images.append(image_latents)
videos = torch.cat(videos)
images = torch.cat(images)
videos = videos.to(memory_format=torch.contiguous_format).float()
images = images.to(memory_format=torch.contiguous_format).float()
prompts = [example["instance_prompt"] for example in examples]
prompts = torch.cat(prompts)
return {
"videos": (videos, images),
"prompts": prompts,
}
train_dataloader = DataLoader(
train_dataset,
batch_size=args.train_batch_size,
shuffle=True,
collate_fn=collate_fn,
num_workers=args.dataloader_num_workers,
)
# Scheduler and math around the number of training steps.
overrode_max_train_steps = False
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if args.max_train_steps is None:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
overrode_max_train_steps = True
if use_deepspeed_scheduler:
from accelerate.utils import DummyScheduler
lr_scheduler = DummyScheduler(
name=args.lr_scheduler,
optimizer=optimizer,
total_num_steps=args.max_train_steps * accelerator.num_processes,
num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
)
else:
lr_scheduler = get_scheduler(
args.lr_scheduler,
optimizer=optimizer,
num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
num_training_steps=args.max_train_steps * accelerator.num_processes,
num_cycles=args.lr_num_cycles,
power=args.lr_power,
)
# Prepare everything with our `accelerator`.
transformer, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
transformer, optimizer, train_dataloader, lr_scheduler
)
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if overrode_max_train_steps:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
# Afterwards we recalculate our number of training epochs
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process.
if accelerator.is_main_process:
tracker_name = args.tracker_name or "cogvideox-i2v-lora"
accelerator.init_trackers(tracker_name, config=vars(args))
# Train!
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
num_trainable_parameters = sum(param.numel() for model in params_to_optimize for param in model["params"])
logger.info("***** Running training *****")
logger.info(f" Num trainable parameters = {num_trainable_parameters}")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num batches each epoch = {len(train_dataloader)}")
logger.info(f" Num epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient accumulation steps = {args.gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {args.max_train_steps}")
global_step = 0
first_epoch = 0
# Potentially load in the weights and states from a previous save
if not args.resume_from_checkpoint:
initial_global_step = 0
else:
if args.resume_from_checkpoint != "latest":
path = os.path.basename(args.resume_from_checkpoint)
else:
# Get the mos recent checkpoint
dirs = os.listdir(args.output_dir)
dirs = [d for d in dirs if d.startswith("checkpoint")]
dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
path = dirs[-1] if len(dirs) > 0 else None
if path is None:
accelerator.print(
f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
)
args.resume_from_checkpoint = None
initial_global_step = 0
else:
accelerator.print(f"Resuming from checkpoint {path}")
accelerator.load_state(os.path.join(args.output_dir, path))
global_step = int(path.split("-")[1])
initial_global_step = global_step
first_epoch = global_step // num_update_steps_per_epoch
progress_bar = tqdm(
range(0, args.max_train_steps),
initial=initial_global_step,
desc="Steps",
# Only show the progress bar once on each machine.
disable=not accelerator.is_local_main_process,
)
vae_scale_factor_spatial = 2 ** (len(vae.config.block_out_channels) - 1)
# For DeepSpeed training
model_config = transformer.module.config if hasattr(transformer, "module") else transformer.config
for epoch in range(first_epoch, args.num_train_epochs):
transformer.train()
for step, batch in enumerate(train_dataloader):
models_to_accumulate = [transformer]
with accelerator.accumulate(models_to_accumulate):
video_latents, image_latents = batch["videos"]
prompt_embeds = batch["prompts"]
video_latents = video_latents.to(dtype=weight_dtype) # [B, F, C, H, W]
image_latents = image_latents.to(dtype=weight_dtype) # [B, F, C, H, W]
batch_size, num_frames, num_channels, height, width = video_latents.shape
# Sample a random timestep for each image
timesteps = torch.randint(
0, scheduler.config.num_train_timesteps, (batch_size,), device=video_latents.device
)
timesteps = timesteps.long()
# Sample noise that will be added to the latents
noise = torch.randn_like(video_latents)
# Add noise to the model input according to the noise magnitude at each timestep
# (this is the forward diffusion process)
noisy_video_latents = scheduler.add_noise(video_latents, noise, timesteps)
noisy_model_input = torch.cat([noisy_video_latents, image_latents], dim=2)
# Prepare rotary embeds
image_rotary_emb = (
prepare_rotary_positional_embeddings(
height=args.height,
width=args.width,
num_frames=num_frames,
vae_scale_factor_spatial=vae_scale_factor_spatial,
patch_size=model_config.patch_size,
attention_head_dim=model_config.attention_head_dim,
device=accelerator.device,
)
if model_config.use_rotary_positional_embeddings
else None
)
# Predict the noise residual
model_output = transformer(
hidden_states=noisy_model_input,
encoder_hidden_states=prompt_embeds,
timestep=timesteps,
image_rotary_emb=image_rotary_emb,
return_dict=False,
)[0]
model_pred = scheduler.get_velocity(model_output, noisy_video_latents, timesteps)
alphas_cumprod = scheduler.alphas_cumprod[timesteps]
weights = 1 / (1 - alphas_cumprod)
while len(weights.shape) < len(model_pred.shape):
weights = weights.unsqueeze(-1)
target = video_latents
loss = torch.mean((weights * (model_pred - target) ** 2).reshape(batch_size, -1), dim=1)
loss = loss.mean()
accelerator.backward(loss)
if accelerator.sync_gradients:
params_to_clip = transformer.parameters()
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
if accelerator.state.deepspeed_plugin is None:
optimizer.step()
optimizer.zero_grad()
lr_scheduler.step()
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
progress_bar.update(1)
global_step += 1
if accelerator.is_main_process or accelerator.distributed_type == DistributedType.DEEPSPEED:
if global_step % args.checkpointing_steps == 0:
# _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
if args.checkpoints_total_limit is not None:
checkpoints = os.listdir(args.output_dir)
checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))
# before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
if len(checkpoints) >= args.checkpoints_total_limit:
num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
removing_checkpoints = checkpoints[0:num_to_remove]
logger.info(
f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
)
logger.info(f"Removing checkpoints: {', '.join(removing_checkpoints)}")
for removing_checkpoint in removing_checkpoints:
removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)
shutil.rmtree(removing_checkpoint)
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
accelerator.save_state(save_path)
logger.info(f"Saved state to {save_path}")
logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
progress_bar.set_postfix(**logs)
accelerator.log(logs, step=global_step)
if global_step >= args.max_train_steps:
break
if accelerator.is_main_process:
if args.validation_prompt is not None and (epoch + 1) % args.validation_epochs == 0:
# Create pipeline
pipe = CogVideoXImageToVideoPipeline.from_pretrained(
args.pretrained_model_name_or_path,
transformer=unwrap_model(transformer),
scheduler=scheduler,
revision=args.revision,
variant=args.variant,
torch_dtype=weight_dtype,
)
validation_prompts = args.validation_prompt.split(args.validation_prompt_separator)
validation_images = args.validation_images.split(args.validation_prompt_separator)
for validation_image, validation_prompt in zip(validation_images, validation_prompts):
pipeline_args = {
"image": load_image(validation_image),
"prompt": validation_prompt,
"guidance_scale": args.guidance_scale,
"use_dynamic_cfg": args.use_dynamic_cfg,
"height": args.height,
"width": args.width,
}
validation_outputs = log_validation(
pipe=pipe,
args=args,
accelerator=accelerator,
pipeline_args=pipeline_args,
epoch=epoch,
)
# Save the lora layers
accelerator.wait_for_everyone()
if accelerator.is_main_process:
transformer = unwrap_model(transformer)
dtype = (
torch.float16
if args.mixed_precision == "fp16"
else torch.bfloat16
if args.mixed_precision == "bf16"
else torch.float32
)
transformer = transformer.to(dtype)
transformer_lora_layers = get_peft_model_state_dict(transformer)
CogVideoXImageToVideoPipeline.save_lora_weights(
save_directory=args.output_dir,
transformer_lora_layers=transformer_lora_layers,
)
# Cleanup trained models to save memory
del transformer
free_memory()
# Final test inference
pipe = CogVideoXImageToVideoPipeline.from_pretrained(
args.pretrained_model_name_or_path,
revision=args.revision,
variant=args.variant,
torch_dtype=weight_dtype,
)
pipe.scheduler = CogVideoXDPMScheduler.from_config(pipe.scheduler.config)
if args.enable_slicing:
pipe.vae.enable_slicing()
if args.enable_tiling:
pipe.vae.enable_tiling()
# Load LoRA weights
lora_scaling = args.lora_alpha / args.rank
pipe.load_lora_weights(args.output_dir, adapter_name="cogvideox-i2v-lora")
pipe.set_adapters(["cogvideox-i2v-lora"], [lora_scaling])
# Run inference
validation_outputs = []
if args.validation_prompt and args.num_validation_videos > 0:
validation_prompts = args.validation_prompt.split(args.validation_prompt_separator)
validation_images = args.validation_images.split(args.validation_prompt_separator)
for validation_image, validation_prompt in zip(validation_images, validation_prompts):
pipeline_args = {
"image": load_image(validation_image),
"prompt": validation_prompt,
"guidance_scale": args.guidance_scale,
"use_dynamic_cfg": args.use_dynamic_cfg,
"height": args.height,
"width": args.width,
}
video = log_validation(
pipe=pipe,
args=args,
accelerator=accelerator,
pipeline_args=pipeline_args,
epoch=epoch,
is_final_validation=True,
)
validation_outputs.extend(video)
if args.push_to_hub:
validation_prompt = args.validation_prompt or ""
validation_prompt = validation_prompt.split(args.validation_prompt_separator)[0]
save_model_card(
repo_id,
videos=validation_outputs,
base_model=args.pretrained_model_name_or_path,
validation_prompt=validation_prompt,
repo_folder=args.output_dir,
fps=args.fps,
)
upload_folder(
repo_id=repo_id,
folder_path=args.output_dir,
commit_message="End of training",
ignore_patterns=["step_*", "epoch_*"],
)
accelerator.end_training()
if __name__ == "__main__":
args = get_args()
main(args)
| diffusers/examples/cogvideo/train_cogvideox_image_to_video_lora.py/0 | {
"file_path": "diffusers/examples/cogvideo/train_cogvideox_image_to_video_lora.py",
"repo_id": "diffusers",
"token_count": 28143
} | 137 |
# Copyright 2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from math import pi
from typing import Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from PIL import Image
from diffusers import DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNet2DModel
from diffusers.utils.torch_utils import randn_tensor
class DPSPipeline(DiffusionPipeline):
r"""
Pipeline for Diffusion Posterior Sampling.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
Parameters:
unet ([`UNet2DModel`]):
A `UNet2DModel` to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image. Can be one of
[`DDPMScheduler`], or [`DDIMScheduler`].
"""
model_cpu_offload_seq = "unet"
def __init__(self, unet, scheduler):
super().__init__()
self.register_modules(unet=unet, scheduler=scheduler)
@torch.no_grad()
def __call__(
self,
measurement: torch.Tensor,
operator: torch.nn.Module,
loss_fn: Callable[[torch.Tensor, torch.Tensor], torch.Tensor],
batch_size: int = 1,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
num_inference_steps: int = 1000,
output_type: Optional[str] = "pil",
return_dict: bool = True,
zeta: float = 0.3,
) -> Union[ImagePipelineOutput, Tuple]:
r"""
The call function to the pipeline for generation.
Args:
measurement (`torch.Tensor`, *required*):
A 'torch.Tensor', the corrupted image
operator (`torch.nn.Module`, *required*):
A 'torch.nn.Module', the operator generating the corrupted image
loss_fn (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`, *required*):
A 'Callable[[torch.Tensor, torch.Tensor], torch.Tensor]', the loss function used
between the measurements, for most of the cases using RMSE is fine.
batch_size (`int`, *optional*, defaults to 1):
The number of images to generate.
generator (`torch.Generator`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
num_inference_steps (`int`, *optional*, defaults to 1000):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
Example:
```py
>>> from diffusers import DDPMPipeline
>>> # load model and scheduler
>>> pipe = DDPMPipeline.from_pretrained("google/ddpm-cat-256")
>>> # run pipeline in inference (sample random noise and denoise)
>>> image = pipe().images[0]
>>> # save image
>>> image.save("ddpm_generated_image.png")
```
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
returned where the first element is a list with the generated images
"""
# Sample gaussian noise to begin loop
if isinstance(self.unet.config.sample_size, int):
image_shape = (
batch_size,
self.unet.config.in_channels,
self.unet.config.sample_size,
self.unet.config.sample_size,
)
else:
image_shape = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)
if self.device.type == "mps":
# randn does not work reproducibly on mps
image = randn_tensor(image_shape, generator=generator)
image = image.to(self.device)
else:
image = randn_tensor(image_shape, generator=generator, device=self.device)
# set step values
self.scheduler.set_timesteps(num_inference_steps)
for t in self.progress_bar(self.scheduler.timesteps):
with torch.enable_grad():
# 1. predict noise model_output
image = image.requires_grad_()
model_output = self.unet(image, t).sample
# 2. compute previous image x'_{t-1} and original prediction x0_{t}
scheduler_out = self.scheduler.step(model_output, t, image, generator=generator)
image_pred, origi_pred = scheduler_out.prev_sample, scheduler_out.pred_original_sample
# 3. compute y'_t = f(x0_{t})
measurement_pred = operator(origi_pred)
# 4. compute loss = d(y, y'_t-1)
loss = loss_fn(measurement, measurement_pred)
loss.backward()
print("distance: {0:.4f}".format(loss.item()))
with torch.no_grad():
image_pred = image_pred - zeta * image.grad
image = image_pred.detach()
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image)
if __name__ == "__main__":
import scipy
from torch import nn
from torchvision.utils import save_image
# defining the operators f(.) of y = f(x)
# super-resolution operator
class SuperResolutionOperator(nn.Module):
def __init__(self, in_shape, scale_factor):
super().__init__()
# Resizer local class, do not use outiside the SR operator class
class Resizer(nn.Module):
def __init__(self, in_shape, scale_factor=None, output_shape=None, kernel=None, antialiasing=True):
super(Resizer, self).__init__()
# First standardize values and fill missing arguments (if needed) by deriving scale from output shape or vice versa
scale_factor, output_shape = self.fix_scale_and_size(in_shape, output_shape, scale_factor)
# Choose interpolation method, each method has the matching kernel size
def cubic(x):
absx = np.abs(x)
absx2 = absx**2
absx3 = absx**3
return (1.5 * absx3 - 2.5 * absx2 + 1) * (absx <= 1) + (
-0.5 * absx3 + 2.5 * absx2 - 4 * absx + 2
) * ((1 < absx) & (absx <= 2))
def lanczos2(x):
return (
(np.sin(pi * x) * np.sin(pi * x / 2) + np.finfo(np.float32).eps)
/ ((pi**2 * x**2 / 2) + np.finfo(np.float32).eps)
) * (abs(x) < 2)
def box(x):
return ((-0.5 <= x) & (x < 0.5)) * 1.0
def lanczos3(x):
return (
(np.sin(pi * x) * np.sin(pi * x / 3) + np.finfo(np.float32).eps)
/ ((pi**2 * x**2 / 3) + np.finfo(np.float32).eps)
) * (abs(x) < 3)
def linear(x):
return (x + 1) * ((-1 <= x) & (x < 0)) + (1 - x) * ((0 <= x) & (x <= 1))
method, kernel_width = {
"cubic": (cubic, 4.0),
"lanczos2": (lanczos2, 4.0),
"lanczos3": (lanczos3, 6.0),
"box": (box, 1.0),
"linear": (linear, 2.0),
None: (cubic, 4.0), # set default interpolation method as cubic
}.get(kernel)
# Antialiasing is only used when downscaling
antialiasing *= np.any(np.array(scale_factor) < 1)
# Sort indices of dimensions according to scale of each dimension. since we are going dim by dim this is efficient
sorted_dims = np.argsort(np.array(scale_factor))
self.sorted_dims = [int(dim) for dim in sorted_dims if scale_factor[dim] != 1]
# Iterate over dimensions to calculate local weights for resizing and resize each time in one direction
field_of_view_list = []
weights_list = []
for dim in self.sorted_dims:
# for each coordinate (along 1 dim), calculate which coordinates in the input image affect its result and the
# weights that multiply the values there to get its result.
weights, field_of_view = self.contributions(
in_shape[dim], output_shape[dim], scale_factor[dim], method, kernel_width, antialiasing
)
# convert to torch tensor
weights = torch.tensor(weights.T, dtype=torch.float32)
# We add singleton dimensions to the weight matrix so we can multiply it with the big tensor we get for
# tmp_im[field_of_view.T], (bsxfun style)
weights_list.append(
nn.Parameter(
torch.reshape(weights, list(weights.shape) + (len(scale_factor) - 1) * [1]),
requires_grad=False,
)
)
field_of_view_list.append(
nn.Parameter(
torch.tensor(field_of_view.T.astype(np.int32), dtype=torch.long), requires_grad=False
)
)
self.field_of_view = nn.ParameterList(field_of_view_list)
self.weights = nn.ParameterList(weights_list)
def forward(self, in_tensor):
x = in_tensor
# Use the affecting position values and the set of weights to calculate the result of resizing along this 1 dim
for dim, fov, w in zip(self.sorted_dims, self.field_of_view, self.weights):
# To be able to act on each dim, we swap so that dim 0 is the wanted dim to resize
x = torch.transpose(x, dim, 0)
# This is a bit of a complicated multiplication: x[field_of_view.T] is a tensor of order image_dims+1.
# for each pixel in the output-image it matches the positions the influence it from the input image (along 1 dim
# only, this is why it only adds 1 dim to 5the shape). We then multiply, for each pixel, its set of positions with
# the matching set of weights. we do this by this big tensor element-wise multiplication (MATLAB bsxfun style:
# matching dims are multiplied element-wise while singletons mean that the matching dim is all multiplied by the
# same number
x = torch.sum(x[fov] * w, dim=0)
# Finally we swap back the axes to the original order
x = torch.transpose(x, dim, 0)
return x
def fix_scale_and_size(self, input_shape, output_shape, scale_factor):
# First fixing the scale-factor (if given) to be standardized the function expects (a list of scale factors in the
# same size as the number of input dimensions)
if scale_factor is not None:
# By default, if scale-factor is a scalar we assume 2d resizing and duplicate it.
if np.isscalar(scale_factor) and len(input_shape) > 1:
scale_factor = [scale_factor, scale_factor]
# We extend the size of scale-factor list to the size of the input by assigning 1 to all the unspecified scales
scale_factor = list(scale_factor)
scale_factor = [1] * (len(input_shape) - len(scale_factor)) + scale_factor
# Fixing output-shape (if given): extending it to the size of the input-shape, by assigning the original input-size
# to all the unspecified dimensions
if output_shape is not None:
output_shape = list(input_shape[len(output_shape) :]) + list(np.uint(np.array(output_shape)))
# Dealing with the case of non-give scale-factor, calculating according to output-shape. note that this is
# sub-optimal, because there can be different scales to the same output-shape.
if scale_factor is None:
scale_factor = 1.0 * np.array(output_shape) / np.array(input_shape)
# Dealing with missing output-shape. calculating according to scale-factor
if output_shape is None:
output_shape = np.uint(np.ceil(np.array(input_shape) * np.array(scale_factor)))
return scale_factor, output_shape
def contributions(self, in_length, out_length, scale, kernel, kernel_width, antialiasing):
# This function calculates a set of 'filters' and a set of field_of_view that will later on be applied
# such that each position from the field_of_view will be multiplied with a matching filter from the
# 'weights' based on the interpolation method and the distance of the sub-pixel location from the pixel centers
# around it. This is only done for one dimension of the image.
# When anti-aliasing is activated (default and only for downscaling) the receptive field is stretched to size of
# 1/sf. this means filtering is more 'low-pass filter'.
fixed_kernel = (lambda arg: scale * kernel(scale * arg)) if antialiasing else kernel
kernel_width *= 1.0 / scale if antialiasing else 1.0
# These are the coordinates of the output image
out_coordinates = np.arange(1, out_length + 1)
# since both scale-factor and output size can be provided simultaneously, preserving the center of the image requires shifting
# the output coordinates. the deviation is because out_length doesn't necessary equal in_length*scale.
# to keep the center we need to subtract half of this deviation so that we get equal margins for both sides and center is preserved.
shifted_out_coordinates = out_coordinates - (out_length - in_length * scale) / 2
# These are the matching positions of the output-coordinates on the input image coordinates.
# Best explained by example: say we have 4 horizontal pixels for HR and we downscale by SF=2 and get 2 pixels:
# [1,2,3,4] -> [1,2]. Remember each pixel number is the middle of the pixel.
# The scaling is done between the distances and not pixel numbers (the right boundary of pixel 4 is transformed to
# the right boundary of pixel 2. pixel 1 in the small image matches the boundary between pixels 1 and 2 in the big
# one and not to pixel 2. This means the position is not just multiplication of the old pos by scale-factor).
# So if we measure distance from the left border, middle of pixel 1 is at distance d=0.5, border between 1 and 2 is
# at d=1, and so on (d = p - 0.5). we calculate (d_new = d_old / sf) which means:
# (p_new-0.5 = (p_old-0.5) / sf) -> p_new = p_old/sf + 0.5 * (1-1/sf)
match_coordinates = shifted_out_coordinates / scale + 0.5 * (1 - 1 / scale)
# This is the left boundary to start multiplying the filter from, it depends on the size of the filter
left_boundary = np.floor(match_coordinates - kernel_width / 2)
# Kernel width needs to be enlarged because when covering has sub-pixel borders, it must 'see' the pixel centers
# of the pixels it only covered a part from. So we add one pixel at each side to consider (weights can zeroize them)
expanded_kernel_width = np.ceil(kernel_width) + 2
# Determine a set of field_of_view for each each output position, these are the pixels in the input image
# that the pixel in the output image 'sees'. We get a matrix whose horizontal dim is the output pixels (big) and the
# vertical dim is the pixels it 'sees' (kernel_size + 2)
field_of_view = np.squeeze(
np.int16(np.expand_dims(left_boundary, axis=1) + np.arange(expanded_kernel_width) - 1)
)
# Assign weight to each pixel in the field of view. A matrix whose horizontal dim is the output pixels and the
# vertical dim is a list of weights matching to the pixel in the field of view (that are specified in
# 'field_of_view')
weights = fixed_kernel(1.0 * np.expand_dims(match_coordinates, axis=1) - field_of_view - 1)
# Normalize weights to sum up to 1. be careful from dividing by 0
sum_weights = np.sum(weights, axis=1)
sum_weights[sum_weights == 0] = 1.0
weights = 1.0 * weights / np.expand_dims(sum_weights, axis=1)
# We use this mirror structure as a trick for reflection padding at the boundaries
mirror = np.uint(np.concatenate((np.arange(in_length), np.arange(in_length - 1, -1, step=-1))))
field_of_view = mirror[np.mod(field_of_view, mirror.shape[0])]
# Get rid of weights and pixel positions that are of zero weight
non_zero_out_pixels = np.nonzero(np.any(weights, axis=0))
weights = np.squeeze(weights[:, non_zero_out_pixels])
field_of_view = np.squeeze(field_of_view[:, non_zero_out_pixels])
# Final products are the relative positions and the matching weights, both are output_size X fixed_kernel_size
return weights, field_of_view
self.down_sample = Resizer(in_shape, 1 / scale_factor)
for param in self.parameters():
param.requires_grad = False
def forward(self, data, **kwargs):
return self.down_sample(data)
# Gaussian blurring operator
class GaussialBlurOperator(nn.Module):
def __init__(self, kernel_size, intensity):
super().__init__()
class Blurkernel(nn.Module):
def __init__(self, blur_type="gaussian", kernel_size=31, std=3.0):
super().__init__()
self.blur_type = blur_type
self.kernel_size = kernel_size
self.std = std
self.seq = nn.Sequential(
nn.ReflectionPad2d(self.kernel_size // 2),
nn.Conv2d(3, 3, self.kernel_size, stride=1, padding=0, bias=False, groups=3),
)
self.weights_init()
def forward(self, x):
return self.seq(x)
def weights_init(self):
if self.blur_type == "gaussian":
n = np.zeros((self.kernel_size, self.kernel_size))
n[self.kernel_size // 2, self.kernel_size // 2] = 1
k = scipy.ndimage.gaussian_filter(n, sigma=self.std)
k = torch.from_numpy(k)
self.k = k
for name, f in self.named_parameters():
f.data.copy_(k)
def update_weights(self, k):
if not torch.is_tensor(k):
k = torch.from_numpy(k)
for name, f in self.named_parameters():
f.data.copy_(k)
def get_kernel(self):
return self.k
self.kernel_size = kernel_size
self.conv = Blurkernel(blur_type="gaussian", kernel_size=kernel_size, std=intensity)
self.kernel = self.conv.get_kernel()
self.conv.update_weights(self.kernel.type(torch.float32))
for param in self.parameters():
param.requires_grad = False
def forward(self, data, **kwargs):
return self.conv(data)
def transpose(self, data, **kwargs):
return data
def get_kernel(self):
return self.kernel.view(1, 1, self.kernel_size, self.kernel_size)
# assuming the forward process y = f(x) is polluted by Gaussian noise, use l2 norm
def RMSELoss(yhat, y):
return torch.sqrt(torch.sum((yhat - y) ** 2))
# set up source image
src = Image.open("sample.png")
# read image into [1,3,H,W]
src = torch.from_numpy(np.array(src, dtype=np.float32)).permute(2, 0, 1)[None]
# normalize image to [-1,1]
src = (src / 127.5) - 1.0
src = src.to("cuda")
# set up operator and measurement
# operator = SuperResolutionOperator(in_shape=src.shape, scale_factor=4).to("cuda")
operator = GaussialBlurOperator(kernel_size=61, intensity=3.0).to("cuda")
measurement = operator(src)
# set up scheduler
scheduler = DDPMScheduler.from_pretrained("google/ddpm-celebahq-256")
scheduler.set_timesteps(1000)
# set up model
model = UNet2DModel.from_pretrained("google/ddpm-celebahq-256").to("cuda")
save_image((src + 1.0) / 2.0, "dps_src.png")
save_image((measurement + 1.0) / 2.0, "dps_mea.png")
# finally, the pipeline
dpspipe = DPSPipeline(model, scheduler)
image = dpspipe(
measurement=measurement,
operator=operator,
loss_fn=RMSELoss,
zeta=1.0,
).images[0]
image.save("dps_generated_image.png")
| diffusers/examples/community/dps_pipeline.py/0 | {
"file_path": "diffusers/examples/community/dps_pipeline.py",
"repo_id": "diffusers",
"token_count": 11130
} | 138 |
import inspect
import re
from typing import Any, Callable, Dict, List, Optional, Union
import numpy as np
import PIL.Image
import torch
from packaging import version
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.image_processor import VaeImageProcessor
from diffusers.loaders import FromSingleFileMixin, StableDiffusionLoraLoaderMixin, TextualInversionLoaderMixin
from diffusers.models import AutoencoderKL, UNet2DConditionModel
from diffusers.models.lora import adjust_lora_scale_text_encoder
from diffusers.pipelines.pipeline_utils import StableDiffusionMixin
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput, StableDiffusionSafetyChecker
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import (
PIL_INTERPOLATION,
USE_PEFT_BACKEND,
deprecate,
logging,
scale_lora_layers,
unscale_lora_layers,
)
from diffusers.utils.torch_utils import randn_tensor
# ------------------------------------------------------------------------------
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
re_attention = re.compile(
r"""
\\\(|
\\\)|
\\\[|
\\]|
\\\\|
\\|
\(|
\[|
:([+-]?[.\d]+)\)|
\)|
]|
[^\\()\[\]:]+|
:
""",
re.X,
)
def parse_prompt_attention(text):
"""
Parses a string with attention tokens and returns a list of pairs: text and its associated weight.
Accepted tokens are:
(abc) - increases attention to abc by a multiplier of 1.1
(abc:3.12) - increases attention to abc by a multiplier of 3.12
[abc] - decreases attention to abc by a multiplier of 1.1
\\( - literal character '('
\\[ - literal character '['
\\) - literal character ')'
\\] - literal character ']'
\\ - literal character '\'
anything else - just text
>>> parse_prompt_attention('normal text')
[['normal text', 1.0]]
>>> parse_prompt_attention('an (important) word')
[['an ', 1.0], ['important', 1.1], [' word', 1.0]]
>>> parse_prompt_attention('(unbalanced')
[['unbalanced', 1.1]]
>>> parse_prompt_attention('\\(literal\\]')
[['(literal]', 1.0]]
>>> parse_prompt_attention('(unnecessary)(parens)')
[['unnecessaryparens', 1.1]]
>>> parse_prompt_attention('a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).')
[['a ', 1.0],
['house', 1.5730000000000004],
[' ', 1.1],
['on', 1.0],
[' a ', 1.1],
['hill', 0.55],
[', sun, ', 1.1],
['sky', 1.4641000000000006],
['.', 1.1]]
"""
res = []
round_brackets = []
square_brackets = []
round_bracket_multiplier = 1.1
square_bracket_multiplier = 1 / 1.1
def multiply_range(start_position, multiplier):
for p in range(start_position, len(res)):
res[p][1] *= multiplier
for m in re_attention.finditer(text):
text = m.group(0)
weight = m.group(1)
if text.startswith("\\"):
res.append([text[1:], 1.0])
elif text == "(":
round_brackets.append(len(res))
elif text == "[":
square_brackets.append(len(res))
elif weight is not None and len(round_brackets) > 0:
multiply_range(round_brackets.pop(), float(weight))
elif text == ")" and len(round_brackets) > 0:
multiply_range(round_brackets.pop(), round_bracket_multiplier)
elif text == "]" and len(square_brackets) > 0:
multiply_range(square_brackets.pop(), square_bracket_multiplier)
else:
res.append([text, 1.0])
for pos in round_brackets:
multiply_range(pos, round_bracket_multiplier)
for pos in square_brackets:
multiply_range(pos, square_bracket_multiplier)
if len(res) == 0:
res = [["", 1.0]]
# merge runs of identical weights
i = 0
while i + 1 < len(res):
if res[i][1] == res[i + 1][1]:
res[i][0] += res[i + 1][0]
res.pop(i + 1)
else:
i += 1
return res
def get_prompts_with_weights(pipe: DiffusionPipeline, prompt: List[str], max_length: int):
r"""
Tokenize a list of prompts and return its tokens with weights of each token.
No padding, starting or ending token is included.
"""
tokens = []
weights = []
truncated = False
for text in prompt:
texts_and_weights = parse_prompt_attention(text)
text_token = []
text_weight = []
for word, weight in texts_and_weights:
# tokenize and discard the starting and the ending token
token = pipe.tokenizer(word).input_ids[1:-1]
text_token += token
# copy the weight by length of token
text_weight += [weight] * len(token)
# stop if the text is too long (longer than truncation limit)
if len(text_token) > max_length:
truncated = True
break
# truncate
if len(text_token) > max_length:
truncated = True
text_token = text_token[:max_length]
text_weight = text_weight[:max_length]
tokens.append(text_token)
weights.append(text_weight)
if truncated:
logger.warning("Prompt was truncated. Try to shorten the prompt or increase max_embeddings_multiples")
return tokens, weights
def pad_tokens_and_weights(tokens, weights, max_length, bos, eos, pad, no_boseos_middle=True, chunk_length=77):
r"""
Pad the tokens (with starting and ending tokens) and weights (with 1.0) to max_length.
"""
max_embeddings_multiples = (max_length - 2) // (chunk_length - 2)
weights_length = max_length if no_boseos_middle else max_embeddings_multiples * chunk_length
for i in range(len(tokens)):
tokens[i] = [bos] + tokens[i] + [pad] * (max_length - 1 - len(tokens[i]) - 1) + [eos]
if no_boseos_middle:
weights[i] = [1.0] + weights[i] + [1.0] * (max_length - 1 - len(weights[i]))
else:
w = []
if len(weights[i]) == 0:
w = [1.0] * weights_length
else:
for j in range(max_embeddings_multiples):
w.append(1.0) # weight for starting token in this chunk
w += weights[i][j * (chunk_length - 2) : min(len(weights[i]), (j + 1) * (chunk_length - 2))]
w.append(1.0) # weight for ending token in this chunk
w += [1.0] * (weights_length - len(w))
weights[i] = w[:]
return tokens, weights
def get_unweighted_text_embeddings(
pipe: DiffusionPipeline,
text_input: torch.Tensor,
chunk_length: int,
no_boseos_middle: Optional[bool] = True,
clip_skip: Optional[int] = None,
):
"""
When the length of tokens is a multiple of the capacity of the text encoder,
it should be split into chunks and sent to the text encoder individually.
"""
max_embeddings_multiples = (text_input.shape[1] - 2) // (chunk_length - 2)
if max_embeddings_multiples > 1:
text_embeddings = []
for i in range(max_embeddings_multiples):
# extract the i-th chunk
text_input_chunk = text_input[:, i * (chunk_length - 2) : (i + 1) * (chunk_length - 2) + 2].clone()
# cover the head and the tail by the starting and the ending tokens
text_input_chunk[:, 0] = text_input[0, 0]
text_input_chunk[:, -1] = text_input[0, -1]
if clip_skip is None:
prompt_embeds = pipe.text_encoder(text_input_chunk.to(pipe.device))
text_embedding = prompt_embeds[0]
else:
prompt_embeds = pipe.text_encoder(text_input_chunk.to(pipe.device), output_hidden_states=True)
# Access the `hidden_states` first, that contains a tuple of
# all the hidden states from the encoder layers. Then index into
# the tuple to access the hidden states from the desired layer.
prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]
# We also need to apply the final LayerNorm here to not mess with the
# representations. The `last_hidden_states` that we typically use for
# obtaining the final prompt representations passes through the LayerNorm
# layer.
text_embedding = pipe.text_encoder.text_model.final_layer_norm(prompt_embeds)
if no_boseos_middle:
if i == 0:
# discard the ending token
text_embedding = text_embedding[:, :-1]
elif i == max_embeddings_multiples - 1:
# discard the starting token
text_embedding = text_embedding[:, 1:]
else:
# discard both starting and ending tokens
text_embedding = text_embedding[:, 1:-1]
text_embeddings.append(text_embedding)
text_embeddings = torch.concat(text_embeddings, axis=1)
else:
if clip_skip is None:
clip_skip = 0
prompt_embeds = pipe.text_encoder(text_input, output_hidden_states=True)[-1][-(clip_skip + 1)]
text_embeddings = pipe.text_encoder.text_model.final_layer_norm(prompt_embeds)
return text_embeddings
def get_weighted_text_embeddings(
pipe: DiffusionPipeline,
prompt: Union[str, List[str]],
uncond_prompt: Optional[Union[str, List[str]]] = None,
max_embeddings_multiples: Optional[int] = 3,
no_boseos_middle: Optional[bool] = False,
skip_parsing: Optional[bool] = False,
skip_weighting: Optional[bool] = False,
clip_skip=None,
lora_scale=None,
):
r"""
Prompts can be assigned with local weights using brackets. For example,
prompt 'A (very beautiful) masterpiece' highlights the words 'very beautiful',
and the embedding tokens corresponding to the words get multiplied by a constant, 1.1.
Also, to regularize of the embedding, the weighted embedding would be scaled to preserve the original mean.
Args:
pipe (`DiffusionPipeline`):
Pipe to provide access to the tokenizer and the text encoder.
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
uncond_prompt (`str` or `List[str]`):
The unconditional prompt or prompts for guide the image generation. If unconditional prompt
is provided, the embeddings of prompt and uncond_prompt are concatenated.
max_embeddings_multiples (`int`, *optional*, defaults to `3`):
The max multiple length of prompt embeddings compared to the max output length of text encoder.
no_boseos_middle (`bool`, *optional*, defaults to `False`):
If the length of text token is multiples of the capacity of text encoder, whether reserve the starting and
ending token in each of the chunk in the middle.
skip_parsing (`bool`, *optional*, defaults to `False`):
Skip the parsing of brackets.
skip_weighting (`bool`, *optional*, defaults to `False`):
Skip the weighting. When the parsing is skipped, it is forced True.
"""
# set lora scale so that monkey patched LoRA
# function of text encoder can correctly access it
if lora_scale is not None and isinstance(pipe, StableDiffusionLoraLoaderMixin):
pipe._lora_scale = lora_scale
# dynamically adjust the LoRA scale
if not USE_PEFT_BACKEND:
adjust_lora_scale_text_encoder(pipe.text_encoder, lora_scale)
else:
scale_lora_layers(pipe.text_encoder, lora_scale)
max_length = (pipe.tokenizer.model_max_length - 2) * max_embeddings_multiples + 2
if isinstance(prompt, str):
prompt = [prompt]
if not skip_parsing:
prompt_tokens, prompt_weights = get_prompts_with_weights(pipe, prompt, max_length - 2)
if uncond_prompt is not None:
if isinstance(uncond_prompt, str):
uncond_prompt = [uncond_prompt]
uncond_tokens, uncond_weights = get_prompts_with_weights(pipe, uncond_prompt, max_length - 2)
else:
prompt_tokens = [
token[1:-1] for token in pipe.tokenizer(prompt, max_length=max_length, truncation=True).input_ids
]
prompt_weights = [[1.0] * len(token) for token in prompt_tokens]
if uncond_prompt is not None:
if isinstance(uncond_prompt, str):
uncond_prompt = [uncond_prompt]
uncond_tokens = [
token[1:-1]
for token in pipe.tokenizer(uncond_prompt, max_length=max_length, truncation=True).input_ids
]
uncond_weights = [[1.0] * len(token) for token in uncond_tokens]
# round up the longest length of tokens to a multiple of (model_max_length - 2)
max_length = max([len(token) for token in prompt_tokens])
if uncond_prompt is not None:
max_length = max(max_length, max([len(token) for token in uncond_tokens]))
max_embeddings_multiples = min(
max_embeddings_multiples,
(max_length - 1) // (pipe.tokenizer.model_max_length - 2) + 1,
)
max_embeddings_multiples = max(1, max_embeddings_multiples)
max_length = (pipe.tokenizer.model_max_length - 2) * max_embeddings_multiples + 2
# pad the length of tokens and weights
bos = pipe.tokenizer.bos_token_id
eos = pipe.tokenizer.eos_token_id
pad = getattr(pipe.tokenizer, "pad_token_id", eos)
prompt_tokens, prompt_weights = pad_tokens_and_weights(
prompt_tokens,
prompt_weights,
max_length,
bos,
eos,
pad,
no_boseos_middle=no_boseos_middle,
chunk_length=pipe.tokenizer.model_max_length,
)
prompt_tokens = torch.tensor(prompt_tokens, dtype=torch.long, device=pipe.device)
if uncond_prompt is not None:
uncond_tokens, uncond_weights = pad_tokens_and_weights(
uncond_tokens,
uncond_weights,
max_length,
bos,
eos,
pad,
no_boseos_middle=no_boseos_middle,
chunk_length=pipe.tokenizer.model_max_length,
)
uncond_tokens = torch.tensor(uncond_tokens, dtype=torch.long, device=pipe.device)
# get the embeddings
text_embeddings = get_unweighted_text_embeddings(
pipe, prompt_tokens, pipe.tokenizer.model_max_length, no_boseos_middle=no_boseos_middle, clip_skip=clip_skip
)
prompt_weights = torch.tensor(prompt_weights, dtype=text_embeddings.dtype, device=text_embeddings.device)
if uncond_prompt is not None:
uncond_embeddings = get_unweighted_text_embeddings(
pipe,
uncond_tokens,
pipe.tokenizer.model_max_length,
no_boseos_middle=no_boseos_middle,
clip_skip=clip_skip,
)
uncond_weights = torch.tensor(uncond_weights, dtype=uncond_embeddings.dtype, device=uncond_embeddings.device)
# assign weights to the prompts and normalize in the sense of mean
# TODO: should we normalize by chunk or in a whole (current implementation)?
if (not skip_parsing) and (not skip_weighting):
previous_mean = text_embeddings.float().mean(axis=[-2, -1]).to(text_embeddings.dtype)
text_embeddings *= prompt_weights.unsqueeze(-1)
current_mean = text_embeddings.float().mean(axis=[-2, -1]).to(text_embeddings.dtype)
text_embeddings *= (previous_mean / current_mean).unsqueeze(-1).unsqueeze(-1)
if uncond_prompt is not None:
previous_mean = uncond_embeddings.float().mean(axis=[-2, -1]).to(uncond_embeddings.dtype)
uncond_embeddings *= uncond_weights.unsqueeze(-1)
current_mean = uncond_embeddings.float().mean(axis=[-2, -1]).to(uncond_embeddings.dtype)
uncond_embeddings *= (previous_mean / current_mean).unsqueeze(-1).unsqueeze(-1)
if pipe.text_encoder is not None:
if isinstance(pipe, StableDiffusionLoraLoaderMixin) and USE_PEFT_BACKEND:
# Retrieve the original scale by scaling back the LoRA layers
unscale_lora_layers(pipe.text_encoder, lora_scale)
if uncond_prompt is not None:
return text_embeddings, uncond_embeddings
return text_embeddings, None
def preprocess_image(image, batch_size):
w, h = image.size
w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8
image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"])
image = np.array(image).astype(np.float32) / 255.0
image = np.vstack([image[None].transpose(0, 3, 1, 2)] * batch_size)
image = torch.from_numpy(image)
return 2.0 * image - 1.0
def preprocess_mask(mask, batch_size, scale_factor=8):
if not isinstance(mask, torch.Tensor):
mask = mask.convert("L")
w, h = mask.size
w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8
mask = mask.resize((w // scale_factor, h // scale_factor), resample=PIL_INTERPOLATION["nearest"])
mask = np.array(mask).astype(np.float32) / 255.0
mask = np.tile(mask, (4, 1, 1))
mask = np.vstack([mask[None]] * batch_size)
mask = 1 - mask # repaint white, keep black
mask = torch.from_numpy(mask)
return mask
else:
valid_mask_channel_sizes = [1, 3]
# if mask channel is fourth tensor dimension, permute dimensions to pytorch standard (B, C, H, W)
if mask.shape[3] in valid_mask_channel_sizes:
mask = mask.permute(0, 3, 1, 2)
elif mask.shape[1] not in valid_mask_channel_sizes:
raise ValueError(
f"Mask channel dimension of size in {valid_mask_channel_sizes} should be second or fourth dimension,"
f" but received mask of shape {tuple(mask.shape)}"
)
# (potentially) reduce mask channel dimension from 3 to 1 for broadcasting to latent shape
mask = mask.mean(dim=1, keepdim=True)
h, w = mask.shape[-2:]
h, w = (x - x % 8 for x in (h, w)) # resize to integer multiple of 8
mask = torch.nn.functional.interpolate(mask, (h // scale_factor, w // scale_factor))
return mask
class StableDiffusionLongPromptWeightingPipeline(
DiffusionPipeline,
StableDiffusionMixin,
TextualInversionLoaderMixin,
StableDiffusionLoraLoaderMixin,
FromSingleFileMixin,
):
r"""
Pipeline for text-to-image generation using Stable Diffusion without tokens length limit, and support parsing
weighting in prompt.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]):
Frozen text-encoder. Stable Diffusion uses the text portion of
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
feature_extractor ([`CLIPImageProcessor`]):
Model that extracts features from generated images to be used as inputs for the `safety_checker`.
"""
model_cpu_offload_seq = "text_encoder-->unet->vae"
_optional_components = ["safety_checker", "feature_extractor"]
_exclude_from_cpu_offload = ["safety_checker"]
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: KarrasDiffusionSchedulers,
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPImageProcessor,
requires_safety_checker: bool = True,
):
super().__init__()
if scheduler is not None and getattr(scheduler.config, "steps_offset", 1) != 1:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
"to update the config accordingly as leaving `steps_offset` might led to incorrect results"
" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
" file"
)
deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["steps_offset"] = 1
scheduler._internal_dict = FrozenDict(new_config)
if scheduler is not None and getattr(scheduler.config, "clip_sample", False) is True:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
" `clip_sample` should be set to False in the configuration file. Please make sure to update the"
" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
)
deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["clip_sample"] = False
scheduler._internal_dict = FrozenDict(new_config)
if safety_checker is None and requires_safety_checker:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
if safety_checker is not None and feature_extractor is None:
raise ValueError(
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
)
is_unet_version_less_0_9_0 = (
unet is not None
and hasattr(unet.config, "_diffusers_version")
and version.parse(version.parse(unet.config._diffusers_version).base_version) < version.parse("0.9.0.dev0")
)
is_unet_sample_size_less_64 = (
unet is not None and hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
)
if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
deprecation_message = (
"The configuration file of the unet has set the default `sample_size` to smaller than"
" 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
" following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
" \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
" in the config might lead to incorrect results in future versions. If you have downloaded this"
" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
" the `unet/config.json` file"
)
deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(unet.config)
new_config["sample_size"] = 64
unet._internal_dict = FrozenDict(new_config)
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
self.register_to_config(
requires_safety_checker=requires_safety_checker,
)
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
max_embeddings_multiples=3,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
clip_skip: Optional[int] = None,
lora_scale: Optional[float] = None,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `list(int)`):
prompt to be encoded
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
max_embeddings_multiples (`int`, *optional*, defaults to `3`):
The max multiple length of prompt embeddings compared to the max output length of text encoder.
"""
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if negative_prompt_embeds is None:
if negative_prompt is None:
negative_prompt = [""] * batch_size
elif isinstance(negative_prompt, str):
negative_prompt = [negative_prompt] * batch_size
if batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
if prompt_embeds is None or negative_prompt_embeds is None:
if isinstance(self, TextualInversionLoaderMixin):
prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
if do_classifier_free_guidance and negative_prompt_embeds is None:
negative_prompt = self.maybe_convert_prompt(negative_prompt, self.tokenizer)
prompt_embeds1, negative_prompt_embeds1 = get_weighted_text_embeddings(
pipe=self,
prompt=prompt,
uncond_prompt=negative_prompt if do_classifier_free_guidance else None,
max_embeddings_multiples=max_embeddings_multiples,
clip_skip=clip_skip,
lora_scale=lora_scale,
)
if prompt_embeds is None:
prompt_embeds = prompt_embeds1
if negative_prompt_embeds is None:
negative_prompt_embeds = negative_prompt_embeds1
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
if do_classifier_free_guidance:
bs_embed, seq_len, _ = negative_prompt_embeds.shape
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
return prompt_embeds
def check_inputs(
self,
prompt,
height,
width,
strength,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
):
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if strength < 0 or strength > 1:
raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
)
def get_timesteps(self, num_inference_steps, strength, device, is_text2img):
if is_text2img:
return self.scheduler.timesteps.to(device), num_inference_steps
else:
# get the original timestep using init_timestep
init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
t_start = max(num_inference_steps - init_timestep, 0)
timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
return timesteps, num_inference_steps - t_start
def run_safety_checker(self, image, device, dtype):
if self.safety_checker is not None:
safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
image, has_nsfw_concept = self.safety_checker(
images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
)
else:
has_nsfw_concept = None
return image, has_nsfw_concept
def decode_latents(self, latents):
latents = 1 / self.vae.config.scaling_factor * latents
image = self.vae.decode(latents).sample
image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
return image
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://huggingface.co/papers/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
def prepare_latents(
self,
image,
timestep,
num_images_per_prompt,
batch_size,
num_channels_latents,
height,
width,
dtype,
device,
generator,
latents=None,
):
if image is None:
batch_size = batch_size * num_images_per_prompt
shape = (
batch_size,
num_channels_latents,
int(height) // self.vae_scale_factor,
int(width) // self.vae_scale_factor,
)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
latents = latents.to(device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents, None, None
else:
image = image.to(device=self.device, dtype=dtype)
init_latent_dist = self.vae.encode(image).latent_dist
init_latents = init_latent_dist.sample(generator=generator)
init_latents = self.vae.config.scaling_factor * init_latents
# Expand init_latents for batch_size and num_images_per_prompt
init_latents = torch.cat([init_latents] * num_images_per_prompt, dim=0)
init_latents_orig = init_latents
# add noise to latents using the timesteps
noise = randn_tensor(init_latents.shape, generator=generator, device=self.device, dtype=dtype)
init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
latents = init_latents
return latents, init_latents_orig, noise
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]],
negative_prompt: Optional[Union[str, List[str]]] = None,
image: Union[torch.Tensor, PIL.Image.Image] = None,
mask_image: Union[torch.Tensor, PIL.Image.Image] = None,
height: int = 512,
width: int = 512,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
strength: float = 0.8,
num_images_per_prompt: Optional[int] = 1,
add_predicted_noise: Optional[bool] = False,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.Tensor] = None,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
max_embeddings_multiples: Optional[int] = 3,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,
is_cancelled_callback: Optional[Callable[[], bool]] = None,
clip_skip: Optional[int] = None,
callback_steps: int = 1,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
):
r"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
image (`torch.Tensor` or `PIL.Image.Image`):
`Image`, or tensor representing an image batch, that will be used as the starting point for the
process.
mask_image (`torch.Tensor` or `PIL.Image.Image`):
`Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
replaced by noise and therefore repainted, while black pixels will be preserved. If `mask_image` is a
PIL image, it will be converted to a single channel (luminance) before use. If it's a tensor, it should
contain one color channel (L) instead of 3, so the expected shape would be `(B, H, W, 1)`.
height (`int`, *optional*, defaults to 512):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to 512):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://huggingface.co/papers/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://huggingface.co/papers/2205.11487). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
strength (`float`, *optional*, defaults to 0.8):
Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1.
`image` will be used as a starting point, adding more noise to it the larger the `strength`. The
number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added
noise will be maximum and the denoising process will run for the full number of iterations specified in
`num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
add_predicted_noise (`bool`, *optional*, defaults to True):
Use predicted noise instead of random noise when constructing noisy versions of the original image in
the reverse diffusion process
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://huggingface.co/papers/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
latents (`torch.Tensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
max_embeddings_multiples (`int`, *optional*, defaults to `3`):
The max multiple length of prompt embeddings compared to the max output length of text encoder.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.
is_cancelled_callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. If the function returns
`True`, the inference will be cancelled.
clip_skip (`int`, *optional*):
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
the output of the pre-final layer will be used for computing the prompt embeddings.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
`self.processor` in
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
Returns:
`None` if cancelled by `is_cancelled_callback`,
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
When returning a tuple, the first element is a list with the generated images, and the second element is a
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
(nsfw) content, according to the `safety_checker`.
"""
# 0. Default height and width to unet
height = height or self.unet.config.sample_size * self.vae_scale_factor
width = width or self.unet.config.sample_size * self.vae_scale_factor
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt, height, width, strength, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds
)
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://huggingface.co/papers/2205.11487 . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
lora_scale = cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
# 3. Encode input prompt
prompt_embeds = self._encode_prompt(
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt,
max_embeddings_multiples,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
clip_skip=clip_skip,
lora_scale=lora_scale,
)
dtype = prompt_embeds.dtype
# 4. Preprocess image and mask
if isinstance(image, PIL.Image.Image):
image = preprocess_image(image, batch_size)
if image is not None:
image = image.to(device=self.device, dtype=dtype)
if isinstance(mask_image, PIL.Image.Image):
mask_image = preprocess_mask(mask_image, batch_size, self.vae_scale_factor)
if mask_image is not None:
mask = mask_image.to(device=self.device, dtype=dtype)
mask = torch.cat([mask] * num_images_per_prompt)
else:
mask = None
# 5. set timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device, image is None)
latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
# 6. Prepare latent variables
latents, init_latents_orig, noise = self.prepare_latents(
image,
latent_timestep,
num_images_per_prompt,
batch_size,
self.unet.config.in_channels,
height,
width,
dtype,
device,
generator,
latents,
)
# 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 8. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs,
).sample
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
if mask is not None:
# masking
if add_predicted_noise:
init_latents_proper = self.scheduler.add_noise(
init_latents_orig, noise_pred_uncond, torch.tensor([t])
)
else:
init_latents_proper = self.scheduler.add_noise(init_latents_orig, noise, torch.tensor([t]))
latents = (init_latents_proper * mask) + (latents * (1 - mask))
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if i % callback_steps == 0:
if callback is not None:
step_idx = i // getattr(self.scheduler, "order", 1)
callback(step_idx, t, latents)
if is_cancelled_callback is not None and is_cancelled_callback():
return None
if output_type == "latent":
image = latents
has_nsfw_concept = None
elif output_type == "pil":
# 9. Post-processing
image = self.decode_latents(latents)
# 10. Run safety checker
image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
# 11. Convert to PIL
image = self.numpy_to_pil(image)
else:
# 9. Post-processing
image = self.decode_latents(latents)
# 10. Run safety checker
image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
# Offload last model to CPU
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
self.final_offload_hook.offload()
if not return_dict:
return image, has_nsfw_concept
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
def text2img(
self,
prompt: Union[str, List[str]],
negative_prompt: Optional[Union[str, List[str]]] = None,
height: int = 512,
width: int = 512,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.Tensor] = None,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
max_embeddings_multiples: Optional[int] = 3,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,
is_cancelled_callback: Optional[Callable[[], bool]] = None,
clip_skip=None,
callback_steps: int = 1,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
):
r"""
Function for text-to-image generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
height (`int`, *optional*, defaults to 512):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to 512):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://huggingface.co/papers/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://huggingface.co/papers/2205.11487). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://huggingface.co/papers/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
latents (`torch.Tensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
max_embeddings_multiples (`int`, *optional*, defaults to `3`):
The max multiple length of prompt embeddings compared to the max output length of text encoder.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.
is_cancelled_callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. If the function returns
`True`, the inference will be cancelled.
clip_skip (`int`, *optional*):
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
the output of the pre-final layer will be used for computing the prompt embeddings.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
`self.processor` in
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
Returns:
`None` if cancelled by `is_cancelled_callback`,
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
When returning a tuple, the first element is a list with the generated images, and the second element is a
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
(nsfw) content, according to the `safety_checker`.
"""
return self.__call__(
prompt=prompt,
negative_prompt=negative_prompt,
height=height,
width=width,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
num_images_per_prompt=num_images_per_prompt,
eta=eta,
generator=generator,
latents=latents,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
max_embeddings_multiples=max_embeddings_multiples,
output_type=output_type,
return_dict=return_dict,
callback=callback,
is_cancelled_callback=is_cancelled_callback,
clip_skip=clip_skip,
callback_steps=callback_steps,
cross_attention_kwargs=cross_attention_kwargs,
)
def img2img(
self,
image: Union[torch.Tensor, PIL.Image.Image],
prompt: Union[str, List[str]],
negative_prompt: Optional[Union[str, List[str]]] = None,
strength: float = 0.8,
num_inference_steps: Optional[int] = 50,
guidance_scale: Optional[float] = 7.5,
num_images_per_prompt: Optional[int] = 1,
eta: Optional[float] = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
max_embeddings_multiples: Optional[int] = 3,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,
is_cancelled_callback: Optional[Callable[[], bool]] = None,
callback_steps: int = 1,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
):
r"""
Function for image-to-image generation.
Args:
image (`torch.Tensor` or `PIL.Image.Image`):
`Image`, or tensor representing an image batch, that will be used as the starting point for the
process.
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
strength (`float`, *optional*, defaults to 0.8):
Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1.
`image` will be used as a starting point, adding more noise to it the larger the `strength`. The
number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added
noise will be maximum and the denoising process will run for the full number of iterations specified in
`num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference. This parameter will be modulated by `strength`.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://huggingface.co/papers/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://huggingface.co/papers/2205.11487). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://huggingface.co/papers/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
max_embeddings_multiples (`int`, *optional*, defaults to `3`):
The max multiple length of prompt embeddings compared to the max output length of text encoder.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.
is_cancelled_callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. If the function returns
`True`, the inference will be cancelled.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
`self.processor` in
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
Returns:
`None` if cancelled by `is_cancelled_callback`,
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
When returning a tuple, the first element is a list with the generated images, and the second element is a
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
(nsfw) content, according to the `safety_checker`.
"""
return self.__call__(
prompt=prompt,
negative_prompt=negative_prompt,
image=image,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
strength=strength,
num_images_per_prompt=num_images_per_prompt,
eta=eta,
generator=generator,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
max_embeddings_multiples=max_embeddings_multiples,
output_type=output_type,
return_dict=return_dict,
callback=callback,
is_cancelled_callback=is_cancelled_callback,
callback_steps=callback_steps,
cross_attention_kwargs=cross_attention_kwargs,
)
def inpaint(
self,
image: Union[torch.Tensor, PIL.Image.Image],
mask_image: Union[torch.Tensor, PIL.Image.Image],
prompt: Union[str, List[str]],
negative_prompt: Optional[Union[str, List[str]]] = None,
strength: float = 0.8,
num_inference_steps: Optional[int] = 50,
guidance_scale: Optional[float] = 7.5,
num_images_per_prompt: Optional[int] = 1,
add_predicted_noise: Optional[bool] = False,
eta: Optional[float] = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
max_embeddings_multiples: Optional[int] = 3,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,
is_cancelled_callback: Optional[Callable[[], bool]] = None,
callback_steps: int = 1,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
):
r"""
Function for inpaint.
Args:
image (`torch.Tensor` or `PIL.Image.Image`):
`Image`, or tensor representing an image batch, that will be used as the starting point for the
process. This is the image whose masked region will be inpainted.
mask_image (`torch.Tensor` or `PIL.Image.Image`):
`Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
replaced by noise and therefore repainted, while black pixels will be preserved. If `mask_image` is a
PIL image, it will be converted to a single channel (luminance) before use. If it's a tensor, it should
contain one color channel (L) instead of 3, so the expected shape would be `(B, H, W, 1)`.
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
strength (`float`, *optional*, defaults to 0.8):
Conceptually, indicates how much to inpaint the masked area. Must be between 0 and 1. When `strength`
is 1, the denoising process will be run on the masked area for the full number of iterations specified
in `num_inference_steps`. `image` will be used as a reference for the masked area, adding more
noise to that region the larger the `strength`. If `strength` is 0, no inpainting will occur.
num_inference_steps (`int`, *optional*, defaults to 50):
The reference number of denoising steps. More denoising steps usually lead to a higher quality image at
the expense of slower inference. This parameter will be modulated by `strength`, as explained above.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://huggingface.co/papers/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://huggingface.co/papers/2205.11487). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
add_predicted_noise (`bool`, *optional*, defaults to True):
Use predicted noise instead of random noise when constructing noisy versions of the original image in
the reverse diffusion process
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://huggingface.co/papers/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
max_embeddings_multiples (`int`, *optional*, defaults to `3`):
The max multiple length of prompt embeddings compared to the max output length of text encoder.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.
is_cancelled_callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. If the function returns
`True`, the inference will be cancelled.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
`self.processor` in
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
Returns:
`None` if cancelled by `is_cancelled_callback`,
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
When returning a tuple, the first element is a list with the generated images, and the second element is a
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
(nsfw) content, according to the `safety_checker`.
"""
return self.__call__(
prompt=prompt,
negative_prompt=negative_prompt,
image=image,
mask_image=mask_image,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
strength=strength,
num_images_per_prompt=num_images_per_prompt,
add_predicted_noise=add_predicted_noise,
eta=eta,
generator=generator,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
max_embeddings_multiples=max_embeddings_multiples,
output_type=output_type,
return_dict=return_dict,
callback=callback,
is_cancelled_callback=is_cancelled_callback,
callback_steps=callback_steps,
cross_attention_kwargs=cross_attention_kwargs,
)
| diffusers/examples/community/lpw_stable_diffusion.py/0 | {
"file_path": "diffusers/examples/community/lpw_stable_diffusion.py",
"repo_id": "diffusers",
"token_count": 32377
} | 139 |
# Copyright 2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
import abc
import inspect
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import numpy as np
import torch
import torch.nn.functional as F
from packaging import version
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DiffusionPipeline, UNet2DConditionModel
from diffusers.configuration_utils import FrozenDict, deprecate
from diffusers.image_processor import VaeImageProcessor
from diffusers.loaders import (
FromSingleFileMixin,
IPAdapterMixin,
StableDiffusionLoraLoaderMixin,
TextualInversionLoaderMixin,
)
from diffusers.models.attention import Attention
from diffusers.models.lora import adjust_lora_scale_text_encoder
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import (
StableDiffusionSafetyChecker,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import (
USE_PEFT_BACKEND,
logging,
scale_lora_layers,
unscale_lora_layers,
)
from diffusers.utils.torch_utils import randn_tensor
logger = logging.get_logger(__name__)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg
def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
"""
Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
Sample Steps are Flawed](https://huggingface.co/papers/2305.08891). See Section 3.4
"""
std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
# rescale the results from guidance (fixes overexposure)
noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
# mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
return noise_cfg
class Prompt2PromptPipeline(
DiffusionPipeline,
TextualInversionLoaderMixin,
StableDiffusionLoraLoaderMixin,
IPAdapterMixin,
FromSingleFileMixin,
):
r"""
Pipeline for text-to-image generation using Stable Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
The pipeline also inherits the following loading methods:
- [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
- [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
- [`~loaders.StableDiffusionLoraLoaderMixin.save_lora_weights`] for saving LoRA weights
- [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
- [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`~transformers.CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
tokenizer ([`~transformers.CLIPTokenizer`]):
A `CLIPTokenizer` to tokenize text.
unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
"""
model_cpu_offload_seq = "text_encoder->image_encoder->unet->vae"
_exclude_from_cpu_offload = ["safety_checker"]
_callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
_optional_components = ["safety_checker", "feature_extractor"]
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: KarrasDiffusionSchedulers,
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPImageProcessor,
image_encoder: CLIPVisionModelWithProjection = None,
requires_safety_checker: bool = True,
):
super().__init__()
if scheduler is not None and getattr(scheduler.config, "steps_offset", 1) != 1:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
"to update the config accordingly as leaving `steps_offset` might led to incorrect results"
" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
" file"
)
deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["steps_offset"] = 1
scheduler._internal_dict = FrozenDict(new_config)
if scheduler is not None and getattr(scheduler.config, "clip_sample", False) is True:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
" `clip_sample` should be set to False in the configuration file. Please make sure to update the"
" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
)
deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["clip_sample"] = False
scheduler._internal_dict = FrozenDict(new_config)
if safety_checker is None and requires_safety_checker:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
if safety_checker is not None and feature_extractor is None:
raise ValueError(
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
)
is_unet_version_less_0_9_0 = (
unet is not None
and hasattr(unet.config, "_diffusers_version")
and version.parse(version.parse(unet.config._diffusers_version).base_version) < version.parse("0.9.0.dev0")
)
is_unet_sample_size_less_64 = (
unet is not None and hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
)
if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
deprecation_message = (
"The configuration file of the unet has set the default `sample_size` to smaller than"
" 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
" following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
" \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
" in the config might lead to incorrect results in future versions. If you have downloaded this"
" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
" the `unet/config.json` file"
)
deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(unet.config)
new_config["sample_size"] = 64
unet._internal_dict = FrozenDict(new_config)
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
image_encoder=image_encoder,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
self.register_to_config(requires_safety_checker=requires_safety_checker)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
lora_scale: Optional[float] = None,
**kwargs,
):
deprecation_message = "`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple."
deprecate("_encode_prompt()", "1.0.0", deprecation_message, standard_warn=False)
prompt_embeds_tuple = self.encode_prompt(
prompt=prompt,
device=device,
num_images_per_prompt=num_images_per_prompt,
do_classifier_free_guidance=do_classifier_free_guidance,
negative_prompt=negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
lora_scale=lora_scale,
**kwargs,
)
# concatenate for backwards comp
prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])
return prompt_embeds
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt
def encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
lora_scale: Optional[float] = None,
clip_skip: Optional[int] = None,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
lora_scale (`float`, *optional*):
A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
clip_skip (`int`, *optional*):
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
the output of the pre-final layer will be used for computing the prompt embeddings.
"""
# set lora scale so that monkey patched LoRA
# function of text encoder can correctly access it
if lora_scale is not None and isinstance(self, StableDiffusionLoraLoaderMixin):
self._lora_scale = lora_scale
# dynamically adjust the LoRA scale
if not USE_PEFT_BACKEND:
adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
else:
scale_lora_layers(self.text_encoder, lora_scale)
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if prompt_embeds is None:
# textual inversion: process multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
text_input_ids, untruncated_ids
):
removed_text = self.tokenizer.batch_decode(
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
)
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = text_inputs.attention_mask.to(device)
else:
attention_mask = None
if clip_skip is None:
prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)
prompt_embeds = prompt_embeds[0]
else:
prompt_embeds = self.text_encoder(
text_input_ids.to(device),
attention_mask=attention_mask,
output_hidden_states=True,
)
# Access the `hidden_states` first, that contains a tuple of
# all the hidden states from the encoder layers. Then index into
# the tuple to access the hidden states from the desired layer.
prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]
# We also need to apply the final LayerNorm here to not mess with the
# representations. The `last_hidden_states` that we typically use for
# obtaining the final prompt representations passes through the LayerNorm
# layer.
prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)
if self.text_encoder is not None:
prompt_embeds_dtype = self.text_encoder.dtype
elif self.unet is not None:
prompt_embeds_dtype = self.unet.dtype
else:
prompt_embeds_dtype = prompt_embeds.dtype
prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif prompt is not None and type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
# textual inversion: process multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = uncond_input.attention_mask.to(device)
else:
attention_mask = None
negative_prompt_embeds = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
negative_prompt_embeds = negative_prompt_embeds[0]
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
if isinstance(self, StableDiffusionLoraLoaderMixin) and USE_PEFT_BACKEND:
# Retrieve the original scale by scaling back the LoRA layers
unscale_lora_layers(self.text_encoder, lora_scale)
return prompt_embeds, negative_prompt_embeds
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
def run_safety_checker(self, image, device, dtype):
if self.safety_checker is None:
has_nsfw_concept = None
else:
if torch.is_tensor(image):
feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
else:
feature_extractor_input = self.image_processor.numpy_to_pil(image)
safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
image, has_nsfw_concept = self.safety_checker(
images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
)
return image, has_nsfw_concept
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://huggingface.co/papers/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs
def check_inputs(
self,
prompt,
height,
width,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
ip_adapter_image=None,
ip_adapter_image_embeds=None,
callback_on_step_end_tensor_inputs=None,
):
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if callback_on_step_end_tensor_inputs is not None and not all(
k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
):
raise ValueError(
f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
)
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
)
if ip_adapter_image is not None and ip_adapter_image_embeds is not None:
raise ValueError(
"Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined."
)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
def prepare_latents(
self,
batch_size,
num_channels_latents,
height,
width,
dtype,
device,
generator,
latents=None,
):
shape = (
batch_size,
num_channels_latents,
height // self.vae_scale_factor,
width // self.vae_scale_factor,
)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
latents = latents.to(device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]],
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.Tensor] = None,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,
callback_steps: Optional[int] = 1,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
guidance_rescale: float = 0.0,
):
r"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://huggingface.co/papers/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://huggingface.co/papers/2205.11487). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://huggingface.co/papers/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`torch.Generator`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
latents (`torch.Tensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
The keyword arguments to configure the edit are:
- edit_type (`str`). The edit type to apply. Can be either of `replace`, `refine`, `reweight`.
- n_cross_replace (`int`): Number of diffusion steps in which cross attention should be replaced
- n_self_replace (`int`): Number of diffusion steps in which self attention should be replaced
- local_blend_words(`List[str]`, *optional*, default to `None`): Determines which area should be
changed. If None, then the whole image can be changed.
- equalizer_words(`List[str]`, *optional*, default to `None`): Required for edit type `reweight`.
Determines which words should be enhanced.
- equalizer_strengths (`List[float]`, *optional*, default to `None`) Required for edit type `reweight`.
Determines which how much the words in `equalizer_words` should be enhanced.
guidance_rescale (`float`, *optional*, defaults to 0.0):
Guidance rescale factor from [Common Diffusion Noise Schedules and Sample Steps are
Flawed](https://huggingface.co/papers/2305.08891). Guidance rescale factor should fix overexposure when
using zero terminal SNR.
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
When returning a tuple, the first element is a list with the generated images, and the second element is a
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
(nsfw) content, according to the `safety_checker`.
"""
self.controller = create_controller(
prompt,
cross_attention_kwargs,
num_inference_steps,
tokenizer=self.tokenizer,
device=self.device,
)
self.register_attention_control(self.controller) # add attention controller
# 0. Default height and width to unet
height = height or self.unet.config.sample_size * self.vae_scale_factor
width = width or self.unet.config.sample_size * self.vae_scale_factor
# 1. Check inputs. Raise error if not correct
self.check_inputs(prompt, height, width, callback_steps)
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://huggingface.co/papers/2205.11487 . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# 3. Encode input prompt
text_encoder_lora_scale = (
cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
)
prompt_embeds = self._encode_prompt(
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
lora_scale=text_encoder_lora_scale,
)
# 4. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
# 5. Prepare latent variables
num_channels_latents = self.unet.config.in_channels
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
prompt_embeds.dtype,
device,
generator,
latents,
)
# 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 7. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=prompt_embeds).sample
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
if do_classifier_free_guidance and guidance_rescale > 0.0:
# Based on 3.4. in https://huggingface.co/papers/2305.08891
noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
# step callback
latents = self.controller.step_callback(latents)
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
step_idx = i // getattr(self.scheduler, "order", 1)
callback(step_idx, t, latents)
# 8. Post-processing
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
else:
image = latents
has_nsfw_concept = None
# 9. Run safety checker
if has_nsfw_concept is None:
do_denormalize = [True] * image.shape[0]
else:
do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
# Offload last model to CPU
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
self.final_offload_hook.offload()
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
def register_attention_control(self, controller):
attn_procs = {}
cross_att_count = 0
for name in self.unet.attn_processors.keys():
(None if name.endswith("attn1.processor") else self.unet.config.cross_attention_dim)
if name.startswith("mid_block"):
self.unet.config.block_out_channels[-1]
place_in_unet = "mid"
elif name.startswith("up_blocks"):
block_id = int(name[len("up_blocks.")])
list(reversed(self.unet.config.block_out_channels))[block_id]
place_in_unet = "up"
elif name.startswith("down_blocks"):
block_id = int(name[len("down_blocks.")])
self.unet.config.block_out_channels[block_id]
place_in_unet = "down"
else:
continue
cross_att_count += 1
attn_procs[name] = P2PCrossAttnProcessor(controller=controller, place_in_unet=place_in_unet)
self.unet.set_attn_processor(attn_procs)
controller.num_att_layers = cross_att_count
class P2PCrossAttnProcessor:
def __init__(self, controller, place_in_unet):
super().__init__()
self.controller = controller
self.place_in_unet = place_in_unet
def __call__(
self,
attn: Attention,
hidden_states,
encoder_hidden_states=None,
attention_mask=None,
):
batch_size, sequence_length, _ = hidden_states.shape
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
query = attn.to_q(hidden_states)
is_cross = encoder_hidden_states is not None
encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states
key = attn.to_k(encoder_hidden_states)
value = attn.to_v(encoder_hidden_states)
query = attn.head_to_batch_dim(query)
key = attn.head_to_batch_dim(key)
value = attn.head_to_batch_dim(value)
attention_probs = attn.get_attention_scores(query, key, attention_mask)
# one line change
self.controller(attention_probs, is_cross, self.place_in_unet)
hidden_states = torch.bmm(attention_probs, value)
hidden_states = attn.batch_to_head_dim(hidden_states)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
return hidden_states
def create_controller(
prompts: List[str],
cross_attention_kwargs: Dict,
num_inference_steps: int,
tokenizer,
device,
) -> AttentionControl:
edit_type = cross_attention_kwargs.get("edit_type", None)
local_blend_words = cross_attention_kwargs.get("local_blend_words", None)
equalizer_words = cross_attention_kwargs.get("equalizer_words", None)
equalizer_strengths = cross_attention_kwargs.get("equalizer_strengths", None)
n_cross_replace = cross_attention_kwargs.get("n_cross_replace", 0.4)
n_self_replace = cross_attention_kwargs.get("n_self_replace", 0.4)
# only replace
if edit_type == "replace" and local_blend_words is None:
return AttentionReplace(
prompts,
num_inference_steps,
n_cross_replace,
n_self_replace,
tokenizer=tokenizer,
device=device,
)
# replace + localblend
if edit_type == "replace" and local_blend_words is not None:
lb = LocalBlend(prompts, local_blend_words, tokenizer=tokenizer, device=device)
return AttentionReplace(
prompts,
num_inference_steps,
n_cross_replace,
n_self_replace,
lb,
tokenizer=tokenizer,
device=device,
)
# only refine
if edit_type == "refine" and local_blend_words is None:
return AttentionRefine(
prompts,
num_inference_steps,
n_cross_replace,
n_self_replace,
tokenizer=tokenizer,
device=device,
)
# refine + localblend
if edit_type == "refine" and local_blend_words is not None:
lb = LocalBlend(prompts, local_blend_words, tokenizer=tokenizer, device=device)
return AttentionRefine(
prompts,
num_inference_steps,
n_cross_replace,
n_self_replace,
lb,
tokenizer=tokenizer,
device=device,
)
# reweight
if edit_type == "reweight":
assert equalizer_words is not None and equalizer_strengths is not None, (
"To use reweight edit, please specify equalizer_words and equalizer_strengths."
)
assert len(equalizer_words) == len(equalizer_strengths), (
"equalizer_words and equalizer_strengths must be of same length."
)
equalizer = get_equalizer(prompts[1], equalizer_words, equalizer_strengths, tokenizer=tokenizer)
return AttentionReweight(
prompts,
num_inference_steps,
n_cross_replace,
n_self_replace,
tokenizer=tokenizer,
device=device,
equalizer=equalizer,
)
raise ValueError(f"Edit type {edit_type} not recognized. Use one of: replace, refine, reweight.")
class AttentionControl(abc.ABC):
def step_callback(self, x_t):
return x_t
def between_steps(self):
return
@property
def num_uncond_att_layers(self):
return 0
@abc.abstractmethod
def forward(self, attn, is_cross: bool, place_in_unet: str):
raise NotImplementedError
def __call__(self, attn, is_cross: bool, place_in_unet: str):
if self.cur_att_layer >= self.num_uncond_att_layers:
h = attn.shape[0]
attn[h // 2 :] = self.forward(attn[h // 2 :], is_cross, place_in_unet)
self.cur_att_layer += 1
if self.cur_att_layer == self.num_att_layers + self.num_uncond_att_layers:
self.cur_att_layer = 0
self.cur_step += 1
self.between_steps()
return attn
def reset(self):
self.cur_step = 0
self.cur_att_layer = 0
def __init__(self):
self.cur_step = 0
self.num_att_layers = -1
self.cur_att_layer = 0
class EmptyControl(AttentionControl):
def forward(self, attn, is_cross: bool, place_in_unet: str):
return attn
class AttentionStore(AttentionControl):
@staticmethod
def get_empty_store():
return {
"down_cross": [],
"mid_cross": [],
"up_cross": [],
"down_self": [],
"mid_self": [],
"up_self": [],
}
def forward(self, attn, is_cross: bool, place_in_unet: str):
key = f"{place_in_unet}_{'cross' if is_cross else 'self'}"
if attn.shape[1] <= 32**2: # avoid memory overhead
self.step_store[key].append(attn)
return attn
def between_steps(self):
if len(self.attention_store) == 0:
self.attention_store = self.step_store
else:
for key in self.attention_store:
for i in range(len(self.attention_store[key])):
self.attention_store[key][i] += self.step_store[key][i]
self.step_store = self.get_empty_store()
def get_average_attention(self):
average_attention = {
key: [item / self.cur_step for item in self.attention_store[key]] for key in self.attention_store
}
return average_attention
def reset(self):
super(AttentionStore, self).reset()
self.step_store = self.get_empty_store()
self.attention_store = {}
def __init__(self):
super(AttentionStore, self).__init__()
self.step_store = self.get_empty_store()
self.attention_store = {}
class LocalBlend:
def __call__(self, x_t, attention_store):
k = 1
maps = attention_store["down_cross"][2:4] + attention_store["up_cross"][:3]
maps = [item.reshape(self.alpha_layers.shape[0], -1, 1, 16, 16, self.max_num_words) for item in maps]
maps = torch.cat(maps, dim=1)
maps = (maps * self.alpha_layers).sum(-1).mean(1)
mask = F.max_pool2d(maps, (k * 2 + 1, k * 2 + 1), (1, 1), padding=(k, k))
mask = F.interpolate(mask, size=(x_t.shape[2:]))
mask = mask / mask.max(2, keepdims=True)[0].max(3, keepdims=True)[0]
mask = mask.gt(self.threshold)
mask = (mask[:1] + mask[1:]).float()
x_t = x_t[:1] + mask * (x_t - x_t[:1])
return x_t
def __init__(
self,
prompts: List[str],
words: [List[List[str]]],
tokenizer,
device,
threshold=0.3,
max_num_words=77,
):
self.max_num_words = 77
alpha_layers = torch.zeros(len(prompts), 1, 1, 1, 1, self.max_num_words)
for i, (prompt, words_) in enumerate(zip(prompts, words)):
if isinstance(words_, str):
words_ = [words_]
for word in words_:
ind = get_word_inds(prompt, word, tokenizer)
alpha_layers[i, :, :, :, :, ind] = 1
self.alpha_layers = alpha_layers.to(device)
self.threshold = threshold
class AttentionControlEdit(AttentionStore, abc.ABC):
def step_callback(self, x_t):
if self.local_blend is not None:
x_t = self.local_blend(x_t, self.attention_store)
return x_t
def replace_self_attention(self, attn_base, att_replace):
if att_replace.shape[2] <= 16**2:
return attn_base.unsqueeze(0).expand(att_replace.shape[0], *attn_base.shape)
else:
return att_replace
@abc.abstractmethod
def replace_cross_attention(self, attn_base, att_replace):
raise NotImplementedError
def forward(self, attn, is_cross: bool, place_in_unet: str):
super(AttentionControlEdit, self).forward(attn, is_cross, place_in_unet)
# FIXME not replace correctly
if is_cross or (self.num_self_replace[0] <= self.cur_step < self.num_self_replace[1]):
h = attn.shape[0] // (self.batch_size)
attn = attn.reshape(self.batch_size, h, *attn.shape[1:])
attn_base, attn_repalce = attn[0], attn[1:]
if is_cross:
alpha_words = self.cross_replace_alpha[self.cur_step]
attn_repalce_new = (
self.replace_cross_attention(attn_base, attn_repalce) * alpha_words
+ (1 - alpha_words) * attn_repalce
)
attn[1:] = attn_repalce_new
else:
attn[1:] = self.replace_self_attention(attn_base, attn_repalce)
attn = attn.reshape(self.batch_size * h, *attn.shape[2:])
return attn
def __init__(
self,
prompts,
num_steps: int,
cross_replace_steps: Union[float, Tuple[float, float], Dict[str, Tuple[float, float]]],
self_replace_steps: Union[float, Tuple[float, float]],
local_blend: Optional[LocalBlend],
tokenizer,
device,
):
super(AttentionControlEdit, self).__init__()
# add tokenizer and device here
self.tokenizer = tokenizer
self.device = device
self.batch_size = len(prompts)
self.cross_replace_alpha = get_time_words_attention_alpha(
prompts, num_steps, cross_replace_steps, self.tokenizer
).to(self.device)
if isinstance(self_replace_steps, float):
self_replace_steps = 0, self_replace_steps
self.num_self_replace = int(num_steps * self_replace_steps[0]), int(num_steps * self_replace_steps[1])
self.local_blend = local_blend # 在外面定义后传进来
class AttentionReplace(AttentionControlEdit):
def replace_cross_attention(self, attn_base, att_replace):
return torch.einsum("hpw,bwn->bhpn", attn_base, self.mapper)
def __init__(
self,
prompts,
num_steps: int,
cross_replace_steps: float,
self_replace_steps: float,
local_blend: Optional[LocalBlend] = None,
tokenizer=None,
device=None,
):
super(AttentionReplace, self).__init__(
prompts,
num_steps,
cross_replace_steps,
self_replace_steps,
local_blend,
tokenizer,
device,
)
self.mapper = get_replacement_mapper(prompts, self.tokenizer).to(self.device)
class AttentionRefine(AttentionControlEdit):
def replace_cross_attention(self, attn_base, att_replace):
attn_base_replace = attn_base[:, :, self.mapper].permute(2, 0, 1, 3)
attn_replace = attn_base_replace * self.alphas + att_replace * (1 - self.alphas)
return attn_replace
def __init__(
self,
prompts,
num_steps: int,
cross_replace_steps: float,
self_replace_steps: float,
local_blend: Optional[LocalBlend] = None,
tokenizer=None,
device=None,
):
super(AttentionRefine, self).__init__(
prompts,
num_steps,
cross_replace_steps,
self_replace_steps,
local_blend,
tokenizer,
device,
)
self.mapper, alphas = get_refinement_mapper(prompts, self.tokenizer)
self.mapper, alphas = self.mapper.to(self.device), alphas.to(self.device)
self.alphas = alphas.reshape(alphas.shape[0], 1, 1, alphas.shape[1])
class AttentionReweight(AttentionControlEdit):
def replace_cross_attention(self, attn_base, att_replace):
if self.prev_controller is not None:
attn_base = self.prev_controller.replace_cross_attention(attn_base, att_replace)
attn_replace = attn_base[None, :, :, :] * self.equalizer[:, None, None, :]
return attn_replace
def __init__(
self,
prompts,
num_steps: int,
cross_replace_steps: float,
self_replace_steps: float,
equalizer,
local_blend: Optional[LocalBlend] = None,
controller: Optional[AttentionControlEdit] = None,
tokenizer=None,
device=None,
):
super(AttentionReweight, self).__init__(
prompts,
num_steps,
cross_replace_steps,
self_replace_steps,
local_blend,
tokenizer,
device,
)
self.equalizer = equalizer.to(self.device)
self.prev_controller = controller
### util functions for all Edits
def update_alpha_time_word(
alpha,
bounds: Union[float, Tuple[float, float]],
prompt_ind: int,
word_inds: Optional[torch.Tensor] = None,
):
if isinstance(bounds, float):
bounds = 0, bounds
start, end = int(bounds[0] * alpha.shape[0]), int(bounds[1] * alpha.shape[0])
if word_inds is None:
word_inds = torch.arange(alpha.shape[2])
alpha[:start, prompt_ind, word_inds] = 0
alpha[start:end, prompt_ind, word_inds] = 1
alpha[end:, prompt_ind, word_inds] = 0
return alpha
def get_time_words_attention_alpha(
prompts,
num_steps,
cross_replace_steps: Union[float, Dict[str, Tuple[float, float]]],
tokenizer,
max_num_words=77,
):
if not isinstance(cross_replace_steps, dict):
cross_replace_steps = {"default_": cross_replace_steps}
if "default_" not in cross_replace_steps:
cross_replace_steps["default_"] = (0.0, 1.0)
alpha_time_words = torch.zeros(num_steps + 1, len(prompts) - 1, max_num_words)
for i in range(len(prompts) - 1):
alpha_time_words = update_alpha_time_word(alpha_time_words, cross_replace_steps["default_"], i)
for key, item in cross_replace_steps.items():
if key != "default_":
inds = [get_word_inds(prompts[i], key, tokenizer) for i in range(1, len(prompts))]
for i, ind in enumerate(inds):
if len(ind) > 0:
alpha_time_words = update_alpha_time_word(alpha_time_words, item, i, ind)
alpha_time_words = alpha_time_words.reshape(num_steps + 1, len(prompts) - 1, 1, 1, max_num_words)
return alpha_time_words
### util functions for LocalBlend and ReplacementEdit
def get_word_inds(text: str, word_place: int, tokenizer):
split_text = text.split(" ")
if isinstance(word_place, str):
word_place = [i for i, word in enumerate(split_text) if word_place == word]
elif isinstance(word_place, int):
word_place = [word_place]
out = []
if len(word_place) > 0:
words_encode = [tokenizer.decode([item]).strip("#") for item in tokenizer.encode(text)][1:-1]
cur_len, ptr = 0, 0
for i in range(len(words_encode)):
cur_len += len(words_encode[i])
if ptr in word_place:
out.append(i + 1)
if cur_len >= len(split_text[ptr]):
ptr += 1
cur_len = 0
return np.array(out)
### util functions for ReplacementEdit
def get_replacement_mapper_(x: str, y: str, tokenizer, max_len=77):
words_x = x.split(" ")
words_y = y.split(" ")
if len(words_x) != len(words_y):
raise ValueError(
f"attention replacement edit can only be applied on prompts with the same length"
f" but prompt A has {len(words_x)} words and prompt B has {len(words_y)} words."
)
inds_replace = [i for i in range(len(words_y)) if words_y[i] != words_x[i]]
inds_source = [get_word_inds(x, i, tokenizer) for i in inds_replace]
inds_target = [get_word_inds(y, i, tokenizer) for i in inds_replace]
mapper = np.zeros((max_len, max_len))
i = j = 0
cur_inds = 0
while i < max_len and j < max_len:
if cur_inds < len(inds_source) and inds_source[cur_inds][0] == i:
inds_source_, inds_target_ = inds_source[cur_inds], inds_target[cur_inds]
if len(inds_source_) == len(inds_target_):
mapper[inds_source_, inds_target_] = 1
else:
ratio = 1 / len(inds_target_)
for i_t in inds_target_:
mapper[inds_source_, i_t] = ratio
cur_inds += 1
i += len(inds_source_)
j += len(inds_target_)
elif cur_inds < len(inds_source):
mapper[i, j] = 1
i += 1
j += 1
else:
mapper[j, j] = 1
i += 1
j += 1
return torch.from_numpy(mapper).float()
def get_replacement_mapper(prompts, tokenizer, max_len=77):
x_seq = prompts[0]
mappers = []
for i in range(1, len(prompts)):
mapper = get_replacement_mapper_(x_seq, prompts[i], tokenizer, max_len)
mappers.append(mapper)
return torch.stack(mappers)
### util functions for ReweightEdit
def get_equalizer(
text: str,
word_select: Union[int, Tuple[int, ...]],
values: Union[List[float], Tuple[float, ...]],
tokenizer,
):
if isinstance(word_select, (int, str)):
word_select = (word_select,)
equalizer = torch.ones(len(values), 77)
values = torch.tensor(values, dtype=torch.float32)
for word in word_select:
inds = get_word_inds(text, word, tokenizer)
equalizer[:, inds] = values
return equalizer
### util functions for RefinementEdit
class ScoreParams:
def __init__(self, gap, match, mismatch):
self.gap = gap
self.match = match
self.mismatch = mismatch
def mis_match_char(self, x, y):
if x != y:
return self.mismatch
else:
return self.match
def get_matrix(size_x, size_y, gap):
matrix = np.zeros((size_x + 1, size_y + 1), dtype=np.int32)
matrix[0, 1:] = (np.arange(size_y) + 1) * gap
matrix[1:, 0] = (np.arange(size_x) + 1) * gap
return matrix
def get_traceback_matrix(size_x, size_y):
matrix = np.zeros((size_x + 1, size_y + 1), dtype=np.int32)
matrix[0, 1:] = 1
matrix[1:, 0] = 2
matrix[0, 0] = 4
return matrix
def global_align(x, y, score):
matrix = get_matrix(len(x), len(y), score.gap)
trace_back = get_traceback_matrix(len(x), len(y))
for i in range(1, len(x) + 1):
for j in range(1, len(y) + 1):
left = matrix[i, j - 1] + score.gap
up = matrix[i - 1, j] + score.gap
diag = matrix[i - 1, j - 1] + score.mis_match_char(x[i - 1], y[j - 1])
matrix[i, j] = max(left, up, diag)
if matrix[i, j] == left:
trace_back[i, j] = 1
elif matrix[i, j] == up:
trace_back[i, j] = 2
else:
trace_back[i, j] = 3
return matrix, trace_back
def get_aligned_sequences(x, y, trace_back):
x_seq = []
y_seq = []
i = len(x)
j = len(y)
mapper_y_to_x = []
while i > 0 or j > 0:
if trace_back[i, j] == 3:
x_seq.append(x[i - 1])
y_seq.append(y[j - 1])
i = i - 1
j = j - 1
mapper_y_to_x.append((j, i))
elif trace_back[i][j] == 1:
x_seq.append("-")
y_seq.append(y[j - 1])
j = j - 1
mapper_y_to_x.append((j, -1))
elif trace_back[i][j] == 2:
x_seq.append(x[i - 1])
y_seq.append("-")
i = i - 1
elif trace_back[i][j] == 4:
break
mapper_y_to_x.reverse()
return x_seq, y_seq, torch.tensor(mapper_y_to_x, dtype=torch.int64)
def get_mapper(x: str, y: str, tokenizer, max_len=77):
x_seq = tokenizer.encode(x)
y_seq = tokenizer.encode(y)
score = ScoreParams(0, 1, -1)
matrix, trace_back = global_align(x_seq, y_seq, score)
mapper_base = get_aligned_sequences(x_seq, y_seq, trace_back)[-1]
alphas = torch.ones(max_len)
alphas[: mapper_base.shape[0]] = mapper_base[:, 1].ne(-1).float()
mapper = torch.zeros(max_len, dtype=torch.int64)
mapper[: mapper_base.shape[0]] = mapper_base[:, 1]
mapper[mapper_base.shape[0] :] = len(y_seq) + torch.arange(max_len - len(y_seq))
return mapper, alphas
def get_refinement_mapper(prompts, tokenizer, max_len=77):
x_seq = prompts[0]
mappers, alphas = [], []
for i in range(1, len(prompts)):
mapper, alpha = get_mapper(x_seq, prompts[i], tokenizer, max_len)
mappers.append(mapper)
alphas.append(alpha)
return torch.stack(mappers), torch.stack(alphas)
| diffusers/examples/community/pipeline_prompt2prompt.py/0 | {
"file_path": "diffusers/examples/community/pipeline_prompt2prompt.py",
"repo_id": "diffusers",
"token_count": 28058
} | 140 |
# Copyright 2025 The HunyuanVideo Team and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
import types
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import numpy as np
import torch
from transformers import CLIPTextModel, CLIPTokenizer, LlamaModel, LlamaTokenizerFast
from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback
from diffusers.loaders import HunyuanVideoLoraLoaderMixin
from diffusers.models import AutoencoderKLHunyuanVideo, HunyuanVideoTransformer3DModel
from diffusers.pipelines.hunyuan_video.pipeline_output import HunyuanVideoPipelineOutput
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
from diffusers.utils import is_torch_xla_available, logging, replace_example_docstring
from diffusers.utils.torch_utils import randn_tensor
from diffusers.video_processor import VideoProcessor
if is_torch_xla_available():
import torch_xla.core.xla_model as xm
XLA_AVAILABLE = True
else:
XLA_AVAILABLE = False
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
EXAMPLE_DOC_STRING = """
Examples:
```python
>>> import torch
>>> from diffusers.utils import export_to_video
>>> from diffusers import HunyuanVideoTransformer3DModel
>>> from examples.community.pipeline_stg_hunyuan_video import HunyuanVideoSTGPipeline
>>> model_id = "hunyuanvideo-community/HunyuanVideo"
>>> transformer = HunyuanVideoTransformer3DModel.from_pretrained(
... model_id, subfolder="transformer", torch_dtype=torch.bfloat16
... )
>>> pipe = HunyuanVideoSTGPipeline.from_pretrained(model_id, transformer=transformer, torch_dtype=torch.float16)
>>> pipe.vae.enable_tiling()
>>> pipe.to("cuda")
>>> # Configure STG mode options
>>> stg_applied_layers_idx = [2] # Layer indices from 0 to 41
>>> stg_scale = 1.0 # Set 0.0 for CFG
>>> output = pipe(
... prompt="A wolf howling at the moon, with the moon subtly resembling a giant clock face, realistic style.",
... height=320,
... width=512,
... num_frames=61,
... num_inference_steps=30,
... stg_applied_layers_idx=stg_applied_layers_idx,
... stg_scale=stg_scale,
>>> ).frames[0]
>>> export_to_video(output, "output.mp4", fps=15)
```
"""
DEFAULT_PROMPT_TEMPLATE = {
"template": (
"<|start_header_id|>system<|end_header_id|>\n\nDescribe the video by detailing the following aspects: "
"1. The main content and theme of the video."
"2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects."
"3. Actions, events, behaviors temporal relationships, physical movement changes of the objects."
"4. background environment, light, style and atmosphere."
"5. camera angles, movements, and transitions used in the video:<|eot_id|>"
"<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>"
),
"crop_start": 95,
}
def forward_with_stg(
self,
hidden_states: torch.Tensor,
encoder_hidden_states: torch.Tensor,
temb: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
freqs_cis: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
) -> Tuple[torch.Tensor, torch.Tensor]:
return hidden_states, encoder_hidden_states
def forward_without_stg(
self,
hidden_states: torch.Tensor,
encoder_hidden_states: torch.Tensor,
temb: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
freqs_cis: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
) -> Tuple[torch.Tensor, torch.Tensor]:
# 1. Input normalization
norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(hidden_states, emb=temb)
norm_encoder_hidden_states, c_gate_msa, c_shift_mlp, c_scale_mlp, c_gate_mlp = self.norm1_context(
encoder_hidden_states, emb=temb
)
# 2. Joint attention
attn_output, context_attn_output = self.attn(
hidden_states=norm_hidden_states,
encoder_hidden_states=norm_encoder_hidden_states,
attention_mask=attention_mask,
image_rotary_emb=freqs_cis,
)
# 3. Modulation and residual connection
hidden_states = hidden_states + attn_output * gate_msa.unsqueeze(1)
encoder_hidden_states = encoder_hidden_states + context_attn_output * c_gate_msa.unsqueeze(1)
norm_hidden_states = self.norm2(hidden_states)
norm_encoder_hidden_states = self.norm2_context(encoder_hidden_states)
norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
norm_encoder_hidden_states = norm_encoder_hidden_states * (1 + c_scale_mlp[:, None]) + c_shift_mlp[:, None]
# 4. Feed-forward
ff_output = self.ff(norm_hidden_states)
context_ff_output = self.ff_context(norm_encoder_hidden_states)
hidden_states = hidden_states + gate_mlp.unsqueeze(1) * ff_output
encoder_hidden_states = encoder_hidden_states + c_gate_mlp.unsqueeze(1) * context_ff_output
return hidden_states, encoder_hidden_states
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
def retrieve_timesteps(
scheduler,
num_inference_steps: Optional[int] = None,
device: Optional[Union[str, torch.device]] = None,
timesteps: Optional[List[int]] = None,
sigmas: Optional[List[float]] = None,
**kwargs,
):
r"""
Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
Args:
scheduler (`SchedulerMixin`):
The scheduler to get timesteps from.
num_inference_steps (`int`):
The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
must be `None`.
device (`str` or `torch.device`, *optional*):
The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
timesteps (`List[int]`, *optional*):
Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
`num_inference_steps` and `sigmas` must be `None`.
sigmas (`List[float]`, *optional*):
Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
`num_inference_steps` and `timesteps` must be `None`.
Returns:
`Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
second element is the number of inference steps.
"""
if timesteps is not None and sigmas is not None:
raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
if timesteps is not None:
accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
if not accepts_timesteps:
raise ValueError(
f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
f" timestep schedules. Please check whether you are using the correct scheduler."
)
scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
timesteps = scheduler.timesteps
num_inference_steps = len(timesteps)
elif sigmas is not None:
accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
if not accept_sigmas:
raise ValueError(
f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
f" sigmas schedules. Please check whether you are using the correct scheduler."
)
scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
timesteps = scheduler.timesteps
num_inference_steps = len(timesteps)
else:
scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
timesteps = scheduler.timesteps
return timesteps, num_inference_steps
class HunyuanVideoSTGPipeline(DiffusionPipeline, HunyuanVideoLoraLoaderMixin):
r"""
Pipeline for text-to-video generation using HunyuanVideo.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
Args:
text_encoder ([`LlamaModel`]):
[Llava Llama3-8B](https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-transformers).
tokenizer (`LlamaTokenizer`):
Tokenizer from [Llava Llama3-8B](https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-transformers).
transformer ([`HunyuanVideoTransformer3DModel`]):
Conditional Transformer to denoise the encoded image latents.
scheduler ([`FlowMatchEulerDiscreteScheduler`]):
A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
vae ([`AutoencoderKLHunyuanVideo`]):
Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations.
text_encoder_2 ([`CLIPTextModel`]):
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
tokenizer_2 (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/en/model_doc/clip#transformers.CLIPTokenizer).
"""
model_cpu_offload_seq = "text_encoder->text_encoder_2->transformer->vae"
_callback_tensor_inputs = ["latents", "prompt_embeds"]
def __init__(
self,
text_encoder: LlamaModel,
tokenizer: LlamaTokenizerFast,
transformer: HunyuanVideoTransformer3DModel,
vae: AutoencoderKLHunyuanVideo,
scheduler: FlowMatchEulerDiscreteScheduler,
text_encoder_2: CLIPTextModel,
tokenizer_2: CLIPTokenizer,
):
super().__init__()
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
transformer=transformer,
scheduler=scheduler,
text_encoder_2=text_encoder_2,
tokenizer_2=tokenizer_2,
)
self.vae_scale_factor_temporal = self.vae.temporal_compression_ratio if getattr(self, "vae", None) else 4
self.vae_scale_factor_spatial = self.vae.spatial_compression_ratio if getattr(self, "vae", None) else 8
self.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor_spatial)
def _get_llama_prompt_embeds(
self,
prompt: Union[str, List[str]],
prompt_template: Dict[str, Any],
num_videos_per_prompt: int = 1,
device: Optional[torch.device] = None,
dtype: Optional[torch.dtype] = None,
max_sequence_length: int = 256,
num_hidden_layers_to_skip: int = 2,
) -> Tuple[torch.Tensor, torch.Tensor]:
device = device or self._execution_device
dtype = dtype or self.text_encoder.dtype
prompt = [prompt] if isinstance(prompt, str) else prompt
batch_size = len(prompt)
prompt = [prompt_template["template"].format(p) for p in prompt]
crop_start = prompt_template.get("crop_start", None)
if crop_start is None:
prompt_template_input = self.tokenizer(
prompt_template["template"],
padding="max_length",
return_tensors="pt",
return_length=False,
return_overflowing_tokens=False,
return_attention_mask=False,
)
crop_start = prompt_template_input["input_ids"].shape[-1]
# Remove <|eot_id|> token and placeholder {}
crop_start -= 2
max_sequence_length += crop_start
text_inputs = self.tokenizer(
prompt,
max_length=max_sequence_length,
padding="max_length",
truncation=True,
return_tensors="pt",
return_length=False,
return_overflowing_tokens=False,
return_attention_mask=True,
)
text_input_ids = text_inputs.input_ids.to(device=device)
prompt_attention_mask = text_inputs.attention_mask.to(device=device)
prompt_embeds = self.text_encoder(
input_ids=text_input_ids,
attention_mask=prompt_attention_mask,
output_hidden_states=True,
).hidden_states[-(num_hidden_layers_to_skip + 1)]
prompt_embeds = prompt_embeds.to(dtype=dtype)
if crop_start is not None and crop_start > 0:
prompt_embeds = prompt_embeds[:, crop_start:]
prompt_attention_mask = prompt_attention_mask[:, crop_start:]
# duplicate text embeddings for each generation per prompt, using mps friendly method
_, seq_len, _ = prompt_embeds.shape
prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1)
prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt, seq_len, -1)
prompt_attention_mask = prompt_attention_mask.repeat(1, num_videos_per_prompt)
prompt_attention_mask = prompt_attention_mask.view(batch_size * num_videos_per_prompt, seq_len)
return prompt_embeds, prompt_attention_mask
def _get_clip_prompt_embeds(
self,
prompt: Union[str, List[str]],
num_videos_per_prompt: int = 1,
device: Optional[torch.device] = None,
dtype: Optional[torch.dtype] = None,
max_sequence_length: int = 77,
) -> torch.Tensor:
device = device or self._execution_device
dtype = dtype or self.text_encoder_2.dtype
prompt = [prompt] if isinstance(prompt, str) else prompt
batch_size = len(prompt)
text_inputs = self.tokenizer_2(
prompt,
padding="max_length",
max_length=max_sequence_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer_2(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
removed_text = self.tokenizer_2.batch_decode(untruncated_ids[:, max_sequence_length - 1 : -1])
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {max_sequence_length} tokens: {removed_text}"
)
prompt_embeds = self.text_encoder_2(text_input_ids.to(device), output_hidden_states=False).pooler_output
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt)
prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt, -1)
return prompt_embeds
def encode_prompt(
self,
prompt: Union[str, List[str]],
prompt_2: Union[str, List[str]] = None,
prompt_template: Dict[str, Any] = DEFAULT_PROMPT_TEMPLATE,
num_videos_per_prompt: int = 1,
prompt_embeds: Optional[torch.Tensor] = None,
pooled_prompt_embeds: Optional[torch.Tensor] = None,
prompt_attention_mask: Optional[torch.Tensor] = None,
device: Optional[torch.device] = None,
dtype: Optional[torch.dtype] = None,
max_sequence_length: int = 256,
):
if prompt_embeds is None:
prompt_embeds, prompt_attention_mask = self._get_llama_prompt_embeds(
prompt,
prompt_template,
num_videos_per_prompt,
device=device,
dtype=dtype,
max_sequence_length=max_sequence_length,
)
if pooled_prompt_embeds is None:
if prompt_2 is None and pooled_prompt_embeds is None:
prompt_2 = prompt
pooled_prompt_embeds = self._get_clip_prompt_embeds(
prompt,
num_videos_per_prompt,
device=device,
dtype=dtype,
max_sequence_length=77,
)
return prompt_embeds, pooled_prompt_embeds, prompt_attention_mask
def check_inputs(
self,
prompt,
prompt_2,
height,
width,
prompt_embeds=None,
callback_on_step_end_tensor_inputs=None,
prompt_template=None,
):
if height % 16 != 0 or width % 16 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 16 but are {height} and {width}.")
if callback_on_step_end_tensor_inputs is not None and not all(
k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
):
raise ValueError(
f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
)
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt_2 is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):
raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}")
if prompt_template is not None:
if not isinstance(prompt_template, dict):
raise ValueError(f"`prompt_template` has to be of type `dict` but is {type(prompt_template)}")
if "template" not in prompt_template:
raise ValueError(
f"`prompt_template` has to contain a key `template` but only found {prompt_template.keys()}"
)
def prepare_latents(
self,
batch_size: int,
num_channels_latents: 32,
height: int = 720,
width: int = 1280,
num_frames: int = 129,
dtype: Optional[torch.dtype] = None,
device: Optional[torch.device] = None,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.Tensor] = None,
) -> torch.Tensor:
if latents is not None:
return latents.to(device=device, dtype=dtype)
shape = (
batch_size,
num_channels_latents,
num_frames,
int(height) // self.vae_scale_factor_spatial,
int(width) // self.vae_scale_factor_spatial,
)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
return latents
def enable_vae_slicing(self):
r"""
Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
"""
self.vae.enable_slicing()
def disable_vae_slicing(self):
r"""
Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
computing decoding in one step.
"""
self.vae.disable_slicing()
def enable_vae_tiling(self):
r"""
Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
processing larger images.
"""
self.vae.enable_tiling()
def disable_vae_tiling(self):
r"""
Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
computing decoding in one step.
"""
self.vae.disable_tiling()
@property
def guidance_scale(self):
return self._guidance_scale
@property
def do_spatio_temporal_guidance(self):
return self._stg_scale > 0.0
@property
def num_timesteps(self):
return self._num_timesteps
@property
def attention_kwargs(self):
return self._attention_kwargs
@property
def current_timestep(self):
return self._current_timestep
@property
def interrupt(self):
return self._interrupt
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
prompt_2: Union[str, List[str]] = None,
height: int = 720,
width: int = 1280,
num_frames: int = 129,
num_inference_steps: int = 50,
sigmas: List[float] = None,
guidance_scale: float = 6.0,
num_videos_per_prompt: Optional[int] = 1,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.Tensor] = None,
prompt_embeds: Optional[torch.Tensor] = None,
pooled_prompt_embeds: Optional[torch.Tensor] = None,
prompt_attention_mask: Optional[torch.Tensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
attention_kwargs: Optional[Dict[str, Any]] = None,
callback_on_step_end: Optional[
Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
] = None,
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
prompt_template: Dict[str, Any] = DEFAULT_PROMPT_TEMPLATE,
max_sequence_length: int = 256,
stg_applied_layers_idx: Optional[List[int]] = [2],
stg_scale: Optional[float] = 0.0,
):
r"""
The call function to the pipeline for generation.
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
instead.
prompt_2 (`str` or `List[str]`, *optional*):
The prompt or prompts to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
will be used instead.
height (`int`, defaults to `720`):
The height in pixels of the generated image.
width (`int`, defaults to `1280`):
The width in pixels of the generated image.
num_frames (`int`, defaults to `129`):
The number of frames in the generated video.
num_inference_steps (`int`, defaults to `50`):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
sigmas (`List[float]`, *optional*):
Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
will be used.
guidance_scale (`float`, defaults to `6.0`):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://huggingface.co/papers/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://huggingface.co/papers/2205.11487). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality. Note that the only available HunyuanVideo model is
CFG-distilled, which means that traditional guidance between unconditional and conditional latent is
not applied.
num_videos_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
latents (`torch.Tensor`, *optional*):
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random `generator`.
prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
provided, text embeddings are generated from the `prompt` input argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`HunyuanVideoPipelineOutput`] instead of a plain tuple.
attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
`self.processor` in
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
clip_skip (`int`, *optional*):
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
the output of the pre-final layer will be used for computing the prompt embeddings.
callback_on_step_end (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*):
A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of
each denoising step during the inference. with the following arguments: `callback_on_step_end(self:
DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a
list of all tensors as specified by `callback_on_step_end_tensor_inputs`.
callback_on_step_end_tensor_inputs (`List`, *optional*):
The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
`._callback_tensor_inputs` attribute of your pipeline class.
Examples:
Returns:
[`~HunyuanVideoPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`HunyuanVideoPipelineOutput`] is returned, otherwise a `tuple` is returned
where the first element is a list with the generated images and the second element is a list of `bool`s
indicating whether the corresponding generated image contains "not-safe-for-work" (nsfw) content.
"""
if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt,
prompt_2,
height,
width,
prompt_embeds,
callback_on_step_end_tensor_inputs,
prompt_template,
)
self._stg_scale = stg_scale
self._guidance_scale = guidance_scale
self._attention_kwargs = attention_kwargs
self._current_timestep = None
self._interrupt = False
device = self._execution_device
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
# 3. Encode input prompt
prompt_embeds, pooled_prompt_embeds, prompt_attention_mask = self.encode_prompt(
prompt=prompt,
prompt_2=prompt_2,
prompt_template=prompt_template,
num_videos_per_prompt=num_videos_per_prompt,
prompt_embeds=prompt_embeds,
pooled_prompt_embeds=pooled_prompt_embeds,
prompt_attention_mask=prompt_attention_mask,
device=device,
max_sequence_length=max_sequence_length,
)
transformer_dtype = self.transformer.dtype
prompt_embeds = prompt_embeds.to(transformer_dtype)
prompt_attention_mask = prompt_attention_mask.to(transformer_dtype)
if pooled_prompt_embeds is not None:
pooled_prompt_embeds = pooled_prompt_embeds.to(transformer_dtype)
# 4. Prepare timesteps
sigmas = np.linspace(1.0, 0.0, num_inference_steps + 1)[:-1] if sigmas is None else sigmas
timesteps, num_inference_steps = retrieve_timesteps(
self.scheduler,
num_inference_steps,
device,
sigmas=sigmas,
)
# 5. Prepare latent variables
num_channels_latents = self.transformer.config.in_channels
num_latent_frames = (num_frames - 1) // self.vae_scale_factor_temporal + 1
latents = self.prepare_latents(
batch_size * num_videos_per_prompt,
num_channels_latents,
height,
width,
num_latent_frames,
torch.float32,
device,
generator,
latents,
)
# 6. Prepare guidance condition
guidance = torch.tensor([guidance_scale] * latents.shape[0], dtype=transformer_dtype, device=device) * 1000.0
# 7. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
self._num_timesteps = len(timesteps)
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
if self.interrupt:
continue
self._current_timestep = t
latent_model_input = latents.to(transformer_dtype)
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
timestep = t.expand(latents.shape[0]).to(latents.dtype)
if self.do_spatio_temporal_guidance:
for i in stg_applied_layers_idx:
self.transformer.transformer_blocks[i].forward = types.MethodType(
forward_without_stg, self.transformer.transformer_blocks[i]
)
noise_pred = self.transformer(
hidden_states=latent_model_input,
timestep=timestep,
encoder_hidden_states=prompt_embeds,
encoder_attention_mask=prompt_attention_mask,
pooled_projections=pooled_prompt_embeds,
guidance=guidance,
attention_kwargs=attention_kwargs,
return_dict=False,
)[0]
if self.do_spatio_temporal_guidance:
for i in stg_applied_layers_idx:
self.transformer.transformer_blocks[i].forward = types.MethodType(
forward_with_stg, self.transformer.transformer_blocks[i]
)
noise_pred_perturb = self.transformer(
hidden_states=latent_model_input,
timestep=timestep,
encoder_hidden_states=prompt_embeds,
encoder_attention_mask=prompt_attention_mask,
pooled_projections=pooled_prompt_embeds,
guidance=guidance,
attention_kwargs=attention_kwargs,
return_dict=False,
)[0]
noise_pred = noise_pred + self._stg_scale * (noise_pred - noise_pred_perturb)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
if callback_on_step_end is not None:
callback_kwargs = {}
for k in callback_on_step_end_tensor_inputs:
callback_kwargs[k] = locals()[k]
callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
latents = callback_outputs.pop("latents", latents)
prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if XLA_AVAILABLE:
xm.mark_step()
self._current_timestep = None
if not output_type == "latent":
latents = latents.to(self.vae.dtype) / self.vae.config.scaling_factor
video = self.vae.decode(latents, return_dict=False)[0]
video = self.video_processor.postprocess_video(video, output_type=output_type)
else:
video = latents
# Offload all models
self.maybe_free_model_hooks()
if not return_dict:
return (video,)
return HunyuanVideoPipelineOutput(frames=video)
| diffusers/examples/community/pipeline_stg_hunyuan_video.py/0 | {
"file_path": "diffusers/examples/community/pipeline_stg_hunyuan_video.py",
"repo_id": "diffusers",
"token_count": 15854
} | 141 |
# Inspired by: https://github.com/haofanwang/ControlNet-for-Diffusers/
import inspect
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import numpy as np
import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, ControlNetModel, UNet2DConditionModel, logging
from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput, StableDiffusionSafetyChecker
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import (
PIL_INTERPOLATION,
replace_example_docstring,
)
from diffusers.utils.torch_utils import randn_tensor
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
EXAMPLE_DOC_STRING = """
Examples:
```py
>>> import numpy as np
>>> import torch
>>> from PIL import Image
>>> from diffusers import ControlNetModel, UniPCMultistepScheduler
>>> from diffusers.utils import load_image
>>> input_image = load_image("https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png")
>>> controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16)
>>> pipe_controlnet = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",
controlnet=controlnet,
safety_checker=None,
torch_dtype=torch.float16
)
>>> pipe_controlnet.scheduler = UniPCMultistepScheduler.from_config(pipe_controlnet.scheduler.config)
>>> pipe_controlnet.enable_xformers_memory_efficient_attention()
>>> pipe_controlnet.enable_model_cpu_offload()
# using image with edges for our canny controlnet
>>> control_image = load_image(
"https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/vermeer_canny_edged.png")
>>> result_img = pipe_controlnet(controlnet_conditioning_image=control_image,
image=input_image,
prompt="an android robot, cyberpank, digitl art masterpiece",
num_inference_steps=20).images[0]
>>> result_img.show()
```
"""
def prepare_image(image):
if isinstance(image, torch.Tensor):
# Batch single image
if image.ndim == 3:
image = image.unsqueeze(0)
image = image.to(dtype=torch.float32)
else:
# preprocess image
if isinstance(image, (PIL.Image.Image, np.ndarray)):
image = [image]
if isinstance(image, list) and isinstance(image[0], PIL.Image.Image):
image = [np.array(i.convert("RGB"))[None, :] for i in image]
image = np.concatenate(image, axis=0)
elif isinstance(image, list) and isinstance(image[0], np.ndarray):
image = np.concatenate([i[None, :] for i in image], axis=0)
image = image.transpose(0, 3, 1, 2)
image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
return image
def prepare_controlnet_conditioning_image(
controlnet_conditioning_image,
width,
height,
batch_size,
num_images_per_prompt,
device,
dtype,
do_classifier_free_guidance,
):
if not isinstance(controlnet_conditioning_image, torch.Tensor):
if isinstance(controlnet_conditioning_image, PIL.Image.Image):
controlnet_conditioning_image = [controlnet_conditioning_image]
if isinstance(controlnet_conditioning_image[0], PIL.Image.Image):
controlnet_conditioning_image = [
np.array(i.resize((width, height), resample=PIL_INTERPOLATION["lanczos"]))[None, :]
for i in controlnet_conditioning_image
]
controlnet_conditioning_image = np.concatenate(controlnet_conditioning_image, axis=0)
controlnet_conditioning_image = np.array(controlnet_conditioning_image).astype(np.float32) / 255.0
controlnet_conditioning_image = controlnet_conditioning_image.transpose(0, 3, 1, 2)
controlnet_conditioning_image = torch.from_numpy(controlnet_conditioning_image)
elif isinstance(controlnet_conditioning_image[0], torch.Tensor):
controlnet_conditioning_image = torch.cat(controlnet_conditioning_image, dim=0)
image_batch_size = controlnet_conditioning_image.shape[0]
if image_batch_size == 1:
repeat_by = batch_size
else:
# image batch size is the same as prompt batch size
repeat_by = num_images_per_prompt
controlnet_conditioning_image = controlnet_conditioning_image.repeat_interleave(repeat_by, dim=0)
controlnet_conditioning_image = controlnet_conditioning_image.to(device=device, dtype=dtype)
if do_classifier_free_guidance:
controlnet_conditioning_image = torch.cat([controlnet_conditioning_image] * 2)
return controlnet_conditioning_image
class StableDiffusionControlNetImg2ImgPipeline(DiffusionPipeline, StableDiffusionMixin):
"""
Inspired by: https://github.com/haofanwang/ControlNet-for-Diffusers/
"""
_optional_components = ["safety_checker", "feature_extractor"]
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
controlnet: Union[ControlNetModel, List[ControlNetModel], Tuple[ControlNetModel], MultiControlNetModel],
scheduler: KarrasDiffusionSchedulers,
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPImageProcessor,
requires_safety_checker: bool = True,
):
super().__init__()
if safety_checker is None and requires_safety_checker:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
if safety_checker is not None and feature_extractor is None:
raise ValueError(
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
)
if isinstance(controlnet, (list, tuple)):
controlnet = MultiControlNetModel(controlnet)
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
controlnet=controlnet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
self.register_to_config(requires_safety_checker=requires_safety_checker)
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
"""
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if prompt_embeds is None:
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
text_input_ids, untruncated_ids
):
removed_text = self.tokenizer.batch_decode(
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
)
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = text_inputs.attention_mask.to(device)
else:
attention_mask = None
prompt_embeds = self.text_encoder(
text_input_ids.to(device),
attention_mask=attention_mask,
)
prompt_embeds = prompt_embeds[0]
prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = uncond_input.attention_mask.to(device)
else:
attention_mask = None
negative_prompt_embeds = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
negative_prompt_embeds = negative_prompt_embeds[0]
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
return prompt_embeds
def run_safety_checker(self, image, device, dtype):
if self.safety_checker is not None:
safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
image, has_nsfw_concept = self.safety_checker(
images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
)
else:
has_nsfw_concept = None
return image, has_nsfw_concept
def decode_latents(self, latents):
latents = 1 / self.vae.config.scaling_factor * latents
image = self.vae.decode(latents).sample
image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
return image
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://huggingface.co/papers/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
def check_controlnet_conditioning_image(self, image, prompt, prompt_embeds):
image_is_pil = isinstance(image, PIL.Image.Image)
image_is_tensor = isinstance(image, torch.Tensor)
image_is_pil_list = isinstance(image, list) and isinstance(image[0], PIL.Image.Image)
image_is_tensor_list = isinstance(image, list) and isinstance(image[0], torch.Tensor)
if not image_is_pil and not image_is_tensor and not image_is_pil_list and not image_is_tensor_list:
raise TypeError(
"image must be passed and be one of PIL image, torch tensor, list of PIL images, or list of torch tensors"
)
if image_is_pil:
image_batch_size = 1
elif image_is_tensor:
image_batch_size = image.shape[0]
elif image_is_pil_list:
image_batch_size = len(image)
elif image_is_tensor_list:
image_batch_size = len(image)
else:
raise ValueError("controlnet condition image is not valid")
if prompt is not None and isinstance(prompt, str):
prompt_batch_size = 1
elif prompt is not None and isinstance(prompt, list):
prompt_batch_size = len(prompt)
elif prompt_embeds is not None:
prompt_batch_size = prompt_embeds.shape[0]
else:
raise ValueError("prompt or prompt_embeds are not valid")
if image_batch_size != 1 and image_batch_size != prompt_batch_size:
raise ValueError(
f"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}"
)
def check_inputs(
self,
prompt,
image,
controlnet_conditioning_image,
height,
width,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
strength=None,
controlnet_guidance_start=None,
controlnet_guidance_end=None,
controlnet_conditioning_scale=None,
):
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
)
# check controlnet condition image
if isinstance(self.controlnet, ControlNetModel):
self.check_controlnet_conditioning_image(controlnet_conditioning_image, prompt, prompt_embeds)
elif isinstance(self.controlnet, MultiControlNetModel):
if not isinstance(controlnet_conditioning_image, list):
raise TypeError("For multiple controlnets: `image` must be type `list`")
if len(controlnet_conditioning_image) != len(self.controlnet.nets):
raise ValueError(
"For multiple controlnets: `image` must have the same length as the number of controlnets."
)
for image_ in controlnet_conditioning_image:
self.check_controlnet_conditioning_image(image_, prompt, prompt_embeds)
else:
assert False
# Check `controlnet_conditioning_scale`
if isinstance(self.controlnet, ControlNetModel):
if not isinstance(controlnet_conditioning_scale, float):
raise TypeError("For single controlnet: `controlnet_conditioning_scale` must be type `float`.")
elif isinstance(self.controlnet, MultiControlNetModel):
if isinstance(controlnet_conditioning_scale, list) and len(controlnet_conditioning_scale) != len(
self.controlnet.nets
):
raise ValueError(
"For multiple controlnets: When `controlnet_conditioning_scale` is specified as `list`, it must have"
" the same length as the number of controlnets"
)
else:
assert False
if isinstance(image, torch.Tensor):
if image.ndim != 3 and image.ndim != 4:
raise ValueError("`image` must have 3 or 4 dimensions")
if image.ndim == 3:
image_batch_size = 1
image_channels, image_height, image_width = image.shape
elif image.ndim == 4:
image_batch_size, image_channels, image_height, image_width = image.shape
else:
assert False
if image_channels != 3:
raise ValueError("`image` must have 3 channels")
if image.min() < -1 or image.max() > 1:
raise ValueError("`image` should be in range [-1, 1]")
if self.vae.config.latent_channels != self.unet.config.in_channels:
raise ValueError(
f"The config of `pipeline.unet` expects {self.unet.config.in_channels} but received"
f" latent channels: {self.vae.config.latent_channels},"
f" Please verify the config of `pipeline.unet` and the `pipeline.vae`"
)
if strength < 0 or strength > 1:
raise ValueError(f"The value of `strength` should in [0.0, 1.0] but is {strength}")
if controlnet_guidance_start < 0 or controlnet_guidance_start > 1:
raise ValueError(
f"The value of `controlnet_guidance_start` should in [0.0, 1.0] but is {controlnet_guidance_start}"
)
if controlnet_guidance_end < 0 or controlnet_guidance_end > 1:
raise ValueError(
f"The value of `controlnet_guidance_end` should in [0.0, 1.0] but is {controlnet_guidance_end}"
)
if controlnet_guidance_start > controlnet_guidance_end:
raise ValueError(
"The value of `controlnet_guidance_start` should be less than `controlnet_guidance_end`, but got"
f" `controlnet_guidance_start` {controlnet_guidance_start} >= `controlnet_guidance_end` {controlnet_guidance_end}"
)
def get_timesteps(self, num_inference_steps, strength, device):
# get the original timestep using init_timestep
init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
t_start = max(num_inference_steps - init_timestep, 0)
timesteps = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None):
if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
raise ValueError(
f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
)
image = image.to(device=device, dtype=dtype)
batch_size = batch_size * num_images_per_prompt
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
if isinstance(generator, list):
init_latents = [
self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)
]
init_latents = torch.cat(init_latents, dim=0)
else:
init_latents = self.vae.encode(image).latent_dist.sample(generator)
init_latents = self.vae.config.scaling_factor * init_latents
if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:
raise ValueError(
f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
)
else:
init_latents = torch.cat([init_latents], dim=0)
shape = init_latents.shape
noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
# get latents
init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
latents = init_latents
return latents
def _default_height_width(self, height, width, image):
if isinstance(image, list):
image = image[0]
if height is None:
if isinstance(image, PIL.Image.Image):
height = image.height
elif isinstance(image, torch.Tensor):
height = image.shape[3]
height = (height // 8) * 8 # round down to nearest multiple of 8
if width is None:
if isinstance(image, PIL.Image.Image):
width = image.width
elif isinstance(image, torch.Tensor):
width = image.shape[2]
width = (width // 8) * 8 # round down to nearest multiple of 8
return height, width
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
image: Union[torch.Tensor, PIL.Image.Image] = None,
controlnet_conditioning_image: Union[
torch.Tensor, PIL.Image.Image, List[torch.Tensor], List[PIL.Image.Image]
] = None,
strength: float = 0.8,
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.Tensor] = None,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,
callback_steps: int = 1,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
controlnet_conditioning_scale: Union[float, List[float]] = 1.0,
controlnet_guidance_start: float = 0.0,
controlnet_guidance_end: float = 1.0,
):
r"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
instead.
image (`torch.Tensor` or `PIL.Image.Image`):
`Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will
be masked out with `mask_image` and repainted according to `prompt`.
controlnet_conditioning_image (`torch.Tensor`, `PIL.Image.Image`, `List[torch.Tensor]` or `List[PIL.Image.Image]`):
The ControlNet input condition. ControlNet uses this input condition to generate guidance to Unet. If
the type is specified as `torch.Tensor`, it is passed to ControlNet as is. PIL.Image.Image` can
also be accepted as an image. The control image is automatically resized to fit the output image.
strength (`float`, *optional*):
Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`
will be used as a starting point, adding more noise to it the larger the `strength`. The number of
denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will
be maximum and the denoising process will run for the full number of iterations specified in
`num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://huggingface.co/papers/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://huggingface.co/papers/2205.11487). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://huggingface.co/papers/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
latents (`torch.Tensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
`self.processor` in
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
controlnet_conditioning_scale (`float`, *optional*, defaults to 1.0):
The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added
to the residual in the original unet.
controlnet_guidance_start ('float', *optional*, defaults to 0.0):
The percentage of total steps the controlnet starts applying. Must be between 0 and 1.
controlnet_guidance_end ('float', *optional*, defaults to 1.0):
The percentage of total steps the controlnet ends applying. Must be between 0 and 1. Must be greater
than `controlnet_guidance_start`.
Examples:
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
When returning a tuple, the first element is a list with the generated images, and the second element is a
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
(nsfw) content, according to the `safety_checker`.
"""
# 0. Default height and width to unet
height, width = self._default_height_width(height, width, controlnet_conditioning_image)
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt,
image,
controlnet_conditioning_image,
height,
width,
callback_steps,
negative_prompt,
prompt_embeds,
negative_prompt_embeds,
strength,
controlnet_guidance_start,
controlnet_guidance_end,
controlnet_conditioning_scale,
)
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://huggingface.co/papers/2205.11487 . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
if isinstance(self.controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):
controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(self.controlnet.nets)
# 3. Encode input prompt
prompt_embeds = self._encode_prompt(
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
)
# 4. Prepare image, and controlnet_conditioning_image
image = prepare_image(image)
# condition image(s)
if isinstance(self.controlnet, ControlNetModel):
controlnet_conditioning_image = prepare_controlnet_conditioning_image(
controlnet_conditioning_image=controlnet_conditioning_image,
width=width,
height=height,
batch_size=batch_size * num_images_per_prompt,
num_images_per_prompt=num_images_per_prompt,
device=device,
dtype=self.controlnet.dtype,
do_classifier_free_guidance=do_classifier_free_guidance,
)
elif isinstance(self.controlnet, MultiControlNetModel):
controlnet_conditioning_images = []
for image_ in controlnet_conditioning_image:
image_ = prepare_controlnet_conditioning_image(
controlnet_conditioning_image=image_,
width=width,
height=height,
batch_size=batch_size * num_images_per_prompt,
num_images_per_prompt=num_images_per_prompt,
device=device,
dtype=self.controlnet.dtype,
do_classifier_free_guidance=do_classifier_free_guidance,
)
controlnet_conditioning_images.append(image_)
controlnet_conditioning_image = controlnet_conditioning_images
else:
assert False
# 5. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
# 6. Prepare latent variables
if latents is None:
latents = self.prepare_latents(
image,
latent_timestep,
batch_size,
num_images_per_prompt,
prompt_embeds.dtype,
device,
generator,
)
# 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 8. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# compute the percentage of total steps we are at
current_sampling_percent = i / len(timesteps)
if (
current_sampling_percent < controlnet_guidance_start
or current_sampling_percent > controlnet_guidance_end
):
# do not apply the controlnet
down_block_res_samples = None
mid_block_res_sample = None
else:
# apply the controlnet
down_block_res_samples, mid_block_res_sample = self.controlnet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
controlnet_cond=controlnet_conditioning_image,
conditioning_scale=controlnet_conditioning_scale,
return_dict=False,
)
# predict the noise residual
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs,
down_block_additional_residuals=down_block_res_samples,
mid_block_additional_residual=mid_block_res_sample,
).sample
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
step_idx = i // getattr(self.scheduler, "order", 1)
callback(step_idx, t, latents)
# If we do sequential model offloading, let's offload unet and controlnet
# manually for max memory savings
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
self.unet.to("cpu")
self.controlnet.to("cpu")
torch.cuda.empty_cache()
if output_type == "latent":
image = latents
has_nsfw_concept = None
elif output_type == "pil":
# 8. Post-processing
image = self.decode_latents(latents)
# 9. Run safety checker
image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
# 10. Convert to PIL
image = self.numpy_to_pil(image)
else:
# 8. Post-processing
image = self.decode_latents(latents)
# 9. Run safety checker
image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
# Offload last model to CPU
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
self.final_offload_hook.offload()
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
| diffusers/examples/community/stable_diffusion_controlnet_img2img.py/0 | {
"file_path": "diffusers/examples/community/stable_diffusion_controlnet_img2img.py",
"repo_id": "diffusers",
"token_count": 19383
} | 142 |
import inspect
from typing import List, Optional, Union
import PIL.Image
import torch
from torch.nn import functional as F
from transformers import (
CLIPImageProcessor,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionModelWithProjection,
)
from diffusers import (
DiffusionPipeline,
ImagePipelineOutput,
UnCLIPScheduler,
UNet2DConditionModel,
UNet2DModel,
)
from diffusers.pipelines.unclip import UnCLIPTextProjModel
from diffusers.utils import logging
from diffusers.utils.torch_utils import randn_tensor
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
def slerp(val, low, high):
"""
Find the interpolation point between the 'low' and 'high' values for the given 'val'. See https://en.wikipedia.org/wiki/Slerp for more details on the topic.
"""
low_norm = low / torch.norm(low)
high_norm = high / torch.norm(high)
omega = torch.acos((low_norm * high_norm))
so = torch.sin(omega)
res = (torch.sin((1.0 - val) * omega) / so) * low + (torch.sin(val * omega) / so) * high
return res
class UnCLIPImageInterpolationPipeline(DiffusionPipeline):
"""
Pipeline to generate variations from an input image using unCLIP
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Args:
text_encoder ([`CLIPTextModelWithProjection`]):
Frozen text-encoder.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
feature_extractor ([`CLIPImageProcessor`]):
Model that extracts features from generated images to be used as inputs for the `image_encoder`.
image_encoder ([`CLIPVisionModelWithProjection`]):
Frozen CLIP image-encoder. unCLIP Image Variation uses the vision portion of
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPVisionModelWithProjection),
specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
text_proj ([`UnCLIPTextProjModel`]):
Utility class to prepare and combine the embeddings before they are passed to the decoder.
decoder ([`UNet2DConditionModel`]):
The decoder to invert the image embedding into an image.
super_res_first ([`UNet2DModel`]):
Super resolution unet. Used in all but the last step of the super resolution diffusion process.
super_res_last ([`UNet2DModel`]):
Super resolution unet. Used in the last step of the super resolution diffusion process.
decoder_scheduler ([`UnCLIPScheduler`]):
Scheduler used in the decoder denoising process. Just a modified DDPMScheduler.
super_res_scheduler ([`UnCLIPScheduler`]):
Scheduler used in the super resolution denoising process. Just a modified DDPMScheduler.
"""
decoder: UNet2DConditionModel
text_proj: UnCLIPTextProjModel
text_encoder: CLIPTextModelWithProjection
tokenizer: CLIPTokenizer
feature_extractor: CLIPImageProcessor
image_encoder: CLIPVisionModelWithProjection
super_res_first: UNet2DModel
super_res_last: UNet2DModel
decoder_scheduler: UnCLIPScheduler
super_res_scheduler: UnCLIPScheduler
# Copied from diffusers.pipelines.unclip.pipeline_unclip_image_variation.UnCLIPImageVariationPipeline.__init__
def __init__(
self,
decoder: UNet2DConditionModel,
text_encoder: CLIPTextModelWithProjection,
tokenizer: CLIPTokenizer,
text_proj: UnCLIPTextProjModel,
feature_extractor: CLIPImageProcessor,
image_encoder: CLIPVisionModelWithProjection,
super_res_first: UNet2DModel,
super_res_last: UNet2DModel,
decoder_scheduler: UnCLIPScheduler,
super_res_scheduler: UnCLIPScheduler,
):
super().__init__()
self.register_modules(
decoder=decoder,
text_encoder=text_encoder,
tokenizer=tokenizer,
text_proj=text_proj,
feature_extractor=feature_extractor,
image_encoder=image_encoder,
super_res_first=super_res_first,
super_res_last=super_res_last,
decoder_scheduler=decoder_scheduler,
super_res_scheduler=super_res_scheduler,
)
# Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.prepare_latents
def prepare_latents(self, shape, dtype, device, generator, latents, scheduler):
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
if latents.shape != shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
latents = latents.to(device)
latents = latents * scheduler.init_noise_sigma
return latents
# Copied from diffusers.pipelines.unclip.pipeline_unclip_image_variation.UnCLIPImageVariationPipeline._encode_prompt
def _encode_prompt(self, prompt, device, num_images_per_prompt, do_classifier_free_guidance):
batch_size = len(prompt) if isinstance(prompt, list) else 1
# get prompt text embeddings
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
text_mask = text_inputs.attention_mask.bool().to(device)
text_encoder_output = self.text_encoder(text_input_ids.to(device))
prompt_embeds = text_encoder_output.text_embeds
text_encoder_hidden_states = text_encoder_output.last_hidden_state
prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0)
text_encoder_hidden_states = text_encoder_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
text_mask = text_mask.repeat_interleave(num_images_per_prompt, dim=0)
if do_classifier_free_guidance:
uncond_tokens = [""] * batch_size
max_length = text_input_ids.shape[-1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
uncond_text_mask = uncond_input.attention_mask.bool().to(device)
negative_prompt_embeds_text_encoder_output = self.text_encoder(uncond_input.input_ids.to(device))
negative_prompt_embeds = negative_prompt_embeds_text_encoder_output.text_embeds
uncond_text_encoder_hidden_states = negative_prompt_embeds_text_encoder_output.last_hidden_state
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len)
seq_len = uncond_text_encoder_hidden_states.shape[1]
uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.repeat(1, num_images_per_prompt, 1)
uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.view(
batch_size * num_images_per_prompt, seq_len, -1
)
uncond_text_mask = uncond_text_mask.repeat_interleave(num_images_per_prompt, dim=0)
# done duplicates
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
text_encoder_hidden_states = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states])
text_mask = torch.cat([uncond_text_mask, text_mask])
return prompt_embeds, text_encoder_hidden_states, text_mask
# Copied from diffusers.pipelines.unclip.pipeline_unclip_image_variation.UnCLIPImageVariationPipeline._encode_image
def _encode_image(self, image, device, num_images_per_prompt, image_embeddings: Optional[torch.Tensor] = None):
dtype = next(self.image_encoder.parameters()).dtype
if image_embeddings is None:
if not isinstance(image, torch.Tensor):
image = self.feature_extractor(images=image, return_tensors="pt").pixel_values
image = image.to(device=device, dtype=dtype)
image_embeddings = self.image_encoder(image).image_embeds
image_embeddings = image_embeddings.repeat_interleave(num_images_per_prompt, dim=0)
return image_embeddings
@torch.no_grad()
def __call__(
self,
image: Optional[Union[List[PIL.Image.Image], torch.Tensor]] = None,
steps: int = 5,
decoder_num_inference_steps: int = 25,
super_res_num_inference_steps: int = 7,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
image_embeddings: Optional[torch.Tensor] = None,
decoder_latents: Optional[torch.Tensor] = None,
super_res_latents: Optional[torch.Tensor] = None,
decoder_guidance_scale: float = 8.0,
output_type: Optional[str] = "pil",
return_dict: bool = True,
):
"""
Function invoked when calling the pipeline for generation.
Args:
image (`List[PIL.Image.Image]` or `torch.Tensor`):
The images to use for the image interpolation. Only accepts a list of two PIL Images or If you provide a tensor, it needs to comply with the
configuration of
[this](https://huggingface.co/fusing/karlo-image-variations-diffusers/blob/main/feature_extractor/preprocessor_config.json)
`CLIPImageProcessor` while still having a shape of two in the 0th dimension. Can be left to `None` only when `image_embeddings` are passed.
steps (`int`, *optional*, defaults to 5):
The number of interpolation images to generate.
decoder_num_inference_steps (`int`, *optional*, defaults to 25):
The number of denoising steps for the decoder. More denoising steps usually lead to a higher quality
image at the expense of slower inference.
super_res_num_inference_steps (`int`, *optional*, defaults to 7):
The number of denoising steps for super resolution. More denoising steps usually lead to a higher
quality image at the expense of slower inference.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
image_embeddings (`torch.Tensor`, *optional*):
Pre-defined image embeddings that can be derived from the image encoder. Pre-defined image embeddings
can be passed for tasks like image interpolations. `image` can the be left to `None`.
decoder_latents (`torch.Tensor` of shape (batch size, channels, height, width), *optional*):
Pre-generated noisy latents to be used as inputs for the decoder.
super_res_latents (`torch.Tensor` of shape (batch size, channels, super res height, super res width), *optional*):
Pre-generated noisy latents to be used as inputs for the decoder.
decoder_guidance_scale (`float`, *optional*, defaults to 4.0):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://huggingface.co/papers/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://huggingface.co/papers/2205.11487). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
"""
batch_size = steps
device = self._execution_device
if isinstance(image, List):
if len(image) != 2:
raise AssertionError(
f"Expected 'image' List to be of size 2, but passed 'image' length is {len(image)}"
)
elif not (isinstance(image[0], PIL.Image.Image) and isinstance(image[0], PIL.Image.Image)):
raise AssertionError(
f"Expected 'image' List to contain PIL.Image.Image, but passed 'image' contents are {type(image[0])} and {type(image[1])}"
)
elif isinstance(image, torch.Tensor):
if image.shape[0] != 2:
raise AssertionError(
f"Expected 'image' to be torch.Tensor of shape 2 in 0th dimension, but passed 'image' size is {image.shape[0]}"
)
elif isinstance(image_embeddings, torch.Tensor):
if image_embeddings.shape[0] != 2:
raise AssertionError(
f"Expected 'image_embeddings' to be torch.Tensor of shape 2 in 0th dimension, but passed 'image_embeddings' shape is {image_embeddings.shape[0]}"
)
else:
raise AssertionError(
f"Expected 'image' or 'image_embeddings' to be not None with types List[PIL.Image] or torch.Tensor respectively. Received {type(image)} and {type(image_embeddings)} respectively"
)
original_image_embeddings = self._encode_image(
image=image, device=device, num_images_per_prompt=1, image_embeddings=image_embeddings
)
image_embeddings = []
for interp_step in torch.linspace(0, 1, steps):
temp_image_embeddings = slerp(
interp_step, original_image_embeddings[0], original_image_embeddings[1]
).unsqueeze(0)
image_embeddings.append(temp_image_embeddings)
image_embeddings = torch.cat(image_embeddings).to(device)
do_classifier_free_guidance = decoder_guidance_scale > 1.0
prompt_embeds, text_encoder_hidden_states, text_mask = self._encode_prompt(
prompt=["" for i in range(steps)],
device=device,
num_images_per_prompt=1,
do_classifier_free_guidance=do_classifier_free_guidance,
)
text_encoder_hidden_states, additive_clip_time_embeddings = self.text_proj(
image_embeddings=image_embeddings,
prompt_embeds=prompt_embeds,
text_encoder_hidden_states=text_encoder_hidden_states,
do_classifier_free_guidance=do_classifier_free_guidance,
)
if device.type == "mps":
# HACK: MPS: There is a panic when padding bool tensors,
# so cast to int tensor for the pad and back to bool afterwards
text_mask = text_mask.type(torch.int)
decoder_text_mask = F.pad(text_mask, (self.text_proj.clip_extra_context_tokens, 0), value=1)
decoder_text_mask = decoder_text_mask.type(torch.bool)
else:
decoder_text_mask = F.pad(text_mask, (self.text_proj.clip_extra_context_tokens, 0), value=True)
self.decoder_scheduler.set_timesteps(decoder_num_inference_steps, device=device)
decoder_timesteps_tensor = self.decoder_scheduler.timesteps
num_channels_latents = self.decoder.config.in_channels
height = self.decoder.config.sample_size
width = self.decoder.config.sample_size
# Get the decoder latents for 1 step and then repeat the same tensor for the entire batch to keep same noise across all interpolation steps.
decoder_latents = self.prepare_latents(
(1, num_channels_latents, height, width),
text_encoder_hidden_states.dtype,
device,
generator,
decoder_latents,
self.decoder_scheduler,
)
decoder_latents = decoder_latents.repeat((batch_size, 1, 1, 1))
for i, t in enumerate(self.progress_bar(decoder_timesteps_tensor)):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([decoder_latents] * 2) if do_classifier_free_guidance else decoder_latents
noise_pred = self.decoder(
sample=latent_model_input,
timestep=t,
encoder_hidden_states=text_encoder_hidden_states,
class_labels=additive_clip_time_embeddings,
attention_mask=decoder_text_mask,
).sample
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred_uncond, _ = noise_pred_uncond.split(latent_model_input.shape[1], dim=1)
noise_pred_text, predicted_variance = noise_pred_text.split(latent_model_input.shape[1], dim=1)
noise_pred = noise_pred_uncond + decoder_guidance_scale * (noise_pred_text - noise_pred_uncond)
noise_pred = torch.cat([noise_pred, predicted_variance], dim=1)
if i + 1 == decoder_timesteps_tensor.shape[0]:
prev_timestep = None
else:
prev_timestep = decoder_timesteps_tensor[i + 1]
# compute the previous noisy sample x_t -> x_t-1
decoder_latents = self.decoder_scheduler.step(
noise_pred, t, decoder_latents, prev_timestep=prev_timestep, generator=generator
).prev_sample
decoder_latents = decoder_latents.clamp(-1, 1)
image_small = decoder_latents
# done decoder
# super res
self.super_res_scheduler.set_timesteps(super_res_num_inference_steps, device=device)
super_res_timesteps_tensor = self.super_res_scheduler.timesteps
channels = self.super_res_first.config.in_channels // 2
height = self.super_res_first.config.sample_size
width = self.super_res_first.config.sample_size
super_res_latents = self.prepare_latents(
(batch_size, channels, height, width),
image_small.dtype,
device,
generator,
super_res_latents,
self.super_res_scheduler,
)
if device.type == "mps":
# MPS does not support many interpolations
image_upscaled = F.interpolate(image_small, size=[height, width])
else:
interpolate_antialias = {}
if "antialias" in inspect.signature(F.interpolate).parameters:
interpolate_antialias["antialias"] = True
image_upscaled = F.interpolate(
image_small, size=[height, width], mode="bicubic", align_corners=False, **interpolate_antialias
)
for i, t in enumerate(self.progress_bar(super_res_timesteps_tensor)):
# no classifier free guidance
if i == super_res_timesteps_tensor.shape[0] - 1:
unet = self.super_res_last
else:
unet = self.super_res_first
latent_model_input = torch.cat([super_res_latents, image_upscaled], dim=1)
noise_pred = unet(
sample=latent_model_input,
timestep=t,
).sample
if i + 1 == super_res_timesteps_tensor.shape[0]:
prev_timestep = None
else:
prev_timestep = super_res_timesteps_tensor[i + 1]
# compute the previous noisy sample x_t -> x_t-1
super_res_latents = self.super_res_scheduler.step(
noise_pred, t, super_res_latents, prev_timestep=prev_timestep, generator=generator
).prev_sample
image = super_res_latents
# done super res
# post processing
image = image * 0.5 + 0.5
image = image.clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image)
| diffusers/examples/community/unclip_image_interpolation.py/0 | {
"file_path": "diffusers/examples/community/unclip_image_interpolation.py",
"repo_id": "diffusers",
"token_count": 9322
} | 143 |
# ControlNet training example for Stable Diffusion XL (SDXL)
The `train_controlnet_sdxl.py` script shows how to implement the ControlNet training procedure and adapt it for [Stable Diffusion XL](https://huggingface.co/papers/2307.01952).
## Running locally with PyTorch
### Installing the dependencies
Before running the scripts, make sure to install the library's training dependencies:
**Important**
To make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:
```bash
git clone https://github.com/huggingface/diffusers
cd diffusers
pip install -e .
```
Then cd in the `examples/controlnet` folder and run
```bash
pip install -r requirements_sdxl.txt
```
And initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:
```bash
accelerate config
```
Or for a default accelerate configuration without answering questions about your environment
```bash
accelerate config default
```
Or if your environment doesn't support an interactive shell (e.g., a notebook)
```python
from accelerate.utils import write_basic_config
write_basic_config()
```
When running `accelerate config`, if we specify torch compile mode to True there can be dramatic speedups.
## Circle filling dataset
The original dataset is hosted in the [ControlNet repo](https://huggingface.co/lllyasviel/ControlNet/blob/main/training/fill50k.zip). We re-uploaded it to be compatible with `datasets` [here](https://huggingface.co/datasets/fusing/fill50k). Note that `datasets` handles dataloading within the training script.
## Training
Our training examples use two test conditioning images. They can be downloaded by running
```sh
wget https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_1.png
wget https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_2.png
```
Then run `hf auth login` to log into your Hugging Face account. This is needed to be able to push the trained ControlNet parameters to Hugging Face Hub.
```bash
export MODEL_DIR="stabilityai/stable-diffusion-xl-base-1.0"
export OUTPUT_DIR="path to save model"
accelerate launch train_controlnet_sdxl.py \
--pretrained_model_name_or_path=$MODEL_DIR \
--output_dir=$OUTPUT_DIR \
--dataset_name=fusing/fill50k \
--mixed_precision="fp16" \
--resolution=1024 \
--learning_rate=1e-5 \
--max_train_steps=15000 \
--validation_image "./conditioning_image_1.png" "./conditioning_image_2.png" \
--validation_prompt "red circle with blue background" "cyan circle with brown floral background" \
--validation_steps=100 \
--train_batch_size=1 \
--gradient_accumulation_steps=4 \
--report_to="wandb" \
--seed=42 \
--push_to_hub
```
To better track our training experiments, we're using the following flags in the command above:
* `report_to="wandb` will ensure the training runs are tracked on Weights and Biases. To use it, be sure to install `wandb` with `pip install wandb`.
* `validation_image`, `validation_prompt`, and `validation_steps` to allow the script to do a few validation inference runs. This allows us to qualitatively check if the training is progressing as expected.
Our experiments were conducted on a single 40GB A100 GPU.
### Inference
Once training is done, we can perform inference like so:
```python
from diffusers import StableDiffusionXLControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
from diffusers.utils import load_image
import torch
base_model_path = "stabilityai/stable-diffusion-xl-base-1.0"
controlnet_path = "path to controlnet"
controlnet = ControlNetModel.from_pretrained(controlnet_path, torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
base_model_path, controlnet=controlnet, torch_dtype=torch.float16
)
# speed up diffusion process with faster scheduler and memory optimization
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
# remove following line if xformers is not installed or when using Torch 2.0.
pipe.enable_xformers_memory_efficient_attention()
# memory optimization.
pipe.enable_model_cpu_offload()
control_image = load_image("./conditioning_image_1.png").resize((1024, 1024))
prompt = "pale golden rod circle with old lace background"
# generate image
generator = torch.manual_seed(0)
image = pipe(
prompt, num_inference_steps=20, generator=generator, image=control_image
).images[0]
image.save("./output.png")
```
## Notes
### Specifying a better VAE
SDXL's VAE is known to suffer from numerical instability issues. This is why we also expose a CLI argument namely `--pretrained_vae_model_name_or_path` that lets you specify the location of an alternative VAE (such as [`madebyollin/sdxl-vae-fp16-fix`](https://huggingface.co/madebyollin/sdxl-vae-fp16-fix)).
If you're using this VAE during training, you need to ensure you're using it during inference too. You do so by:
```diff
+ vae = AutoencoderKL.from_pretrained(vae_path_or_repo_id, torch_dtype=torch.float16)
controlnet = ControlNetModel.from_pretrained(controlnet_path, torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
base_model_path, controlnet=controlnet, torch_dtype=torch.float16,
+ vae=vae,
)
| diffusers/examples/controlnet/README_sdxl.md/0 | {
"file_path": "diffusers/examples/controlnet/README_sdxl.md",
"repo_id": "diffusers",
"token_count": 1695
} | 144 |
#!/usr/bin/env python
# coding=utf-8
# Copyright 2025 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://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import itertools
import json
import logging
import math
import os
import random
import shutil
import warnings
from pathlib import Path
import numpy as np
import safetensors
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import transformers
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import ProjectConfiguration, set_seed
from huggingface_hub import HfApi, create_repo
from huggingface_hub.utils import insecure_hashlib
from packaging import version
from PIL import Image
from torch.utils.data import Dataset
from torchvision import transforms
from tqdm.auto import tqdm
from transformers import AutoTokenizer, PretrainedConfig
import diffusers
from diffusers import (
AutoencoderKL,
DDPMScheduler,
DiffusionPipeline,
DPMSolverMultistepScheduler,
UNet2DConditionModel,
)
from diffusers.loaders import AttnProcsLayers
from diffusers.models.attention_processor import (
CustomDiffusionAttnProcessor,
CustomDiffusionAttnProcessor2_0,
CustomDiffusionXFormersAttnProcessor,
)
from diffusers.optimization import get_scheduler
from diffusers.utils import check_min_version, is_wandb_available
from diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card
from diffusers.utils.import_utils import is_xformers_available
# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
check_min_version("0.36.0.dev0")
logger = get_logger(__name__)
def freeze_params(params):
for param in params:
param.requires_grad = False
def save_model_card(repo_id: str, images=None, base_model=str, prompt=str, repo_folder=None):
img_str = ""
for i, image in enumerate(images):
image.save(os.path.join(repo_folder, f"image_{i}.png"))
img_str += f"\n"
model_description = f"""
# Custom Diffusion - {repo_id}
These are Custom Diffusion adaption weights for {base_model}. The weights were trained on {prompt} using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following. \n
{img_str}
\nFor more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion).
"""
model_card = load_or_create_model_card(
repo_id_or_path=repo_id,
from_training=True,
license="creativeml-openrail-m",
base_model=base_model,
prompt=prompt,
model_description=model_description,
inference=True,
)
tags = [
"text-to-image",
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"custom-diffusion",
"diffusers-training",
]
model_card = populate_model_card(model_card, tags=tags)
model_card.save(os.path.join(repo_folder, "README.md"))
def import_model_class_from_model_name_or_path(pretrained_model_name_or_path: str, revision: str):
text_encoder_config = PretrainedConfig.from_pretrained(
pretrained_model_name_or_path,
subfolder="text_encoder",
revision=revision,
)
model_class = text_encoder_config.architectures[0]
if model_class == "CLIPTextModel":
from transformers import CLIPTextModel
return CLIPTextModel
elif model_class == "RobertaSeriesModelWithTransformation":
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import RobertaSeriesModelWithTransformation
return RobertaSeriesModelWithTransformation
else:
raise ValueError(f"{model_class} is not supported.")
def collate_fn(examples, with_prior_preservation):
input_ids = [example["instance_prompt_ids"] for example in examples]
pixel_values = [example["instance_images"] for example in examples]
mask = [example["mask"] for example in examples]
# Concat class and instance examples for prior preservation.
# We do this to avoid doing two forward passes.
if with_prior_preservation:
input_ids += [example["class_prompt_ids"] for example in examples]
pixel_values += [example["class_images"] for example in examples]
mask += [example["class_mask"] for example in examples]
input_ids = torch.cat(input_ids, dim=0)
pixel_values = torch.stack(pixel_values)
mask = torch.stack(mask)
pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
mask = mask.to(memory_format=torch.contiguous_format).float()
batch = {"input_ids": input_ids, "pixel_values": pixel_values, "mask": mask.unsqueeze(1)}
return batch
class PromptDataset(Dataset):
"""A simple dataset to prepare the prompts to generate class images on multiple GPUs."""
def __init__(self, prompt, num_samples):
self.prompt = prompt
self.num_samples = num_samples
def __len__(self):
return self.num_samples
def __getitem__(self, index):
example = {}
example["prompt"] = self.prompt
example["index"] = index
return example
class CustomDiffusionDataset(Dataset):
"""
A dataset to prepare the instance and class images with the prompts for fine-tuning the model.
It pre-processes the images and the tokenizes prompts.
"""
def __init__(
self,
concepts_list,
tokenizer,
size=512,
mask_size=64,
center_crop=False,
with_prior_preservation=False,
num_class_images=200,
hflip=False,
aug=True,
):
self.size = size
self.mask_size = mask_size
self.center_crop = center_crop
self.tokenizer = tokenizer
self.interpolation = Image.BILINEAR
self.aug = aug
self.instance_images_path = []
self.class_images_path = []
self.with_prior_preservation = with_prior_preservation
for concept in concepts_list:
inst_img_path = [
(x, concept["instance_prompt"]) for x in Path(concept["instance_data_dir"]).iterdir() if x.is_file()
]
self.instance_images_path.extend(inst_img_path)
if with_prior_preservation:
class_data_root = Path(concept["class_data_dir"])
if os.path.isdir(class_data_root):
class_images_path = list(class_data_root.iterdir())
class_prompt = [concept["class_prompt"] for _ in range(len(class_images_path))]
else:
with open(class_data_root, "r") as f:
class_images_path = f.read().splitlines()
with open(concept["class_prompt"], "r") as f:
class_prompt = f.read().splitlines()
class_img_path = list(zip(class_images_path, class_prompt))
self.class_images_path.extend(class_img_path[:num_class_images])
random.shuffle(self.instance_images_path)
self.num_instance_images = len(self.instance_images_path)
self.num_class_images = len(self.class_images_path)
self._length = max(self.num_class_images, self.num_instance_images)
self.flip = transforms.RandomHorizontalFlip(0.5 * hflip)
self.image_transforms = transforms.Compose(
[
self.flip,
transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),
transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
def __len__(self):
return self._length
def preprocess(self, image, scale, resample):
outer, inner = self.size, scale
factor = self.size // self.mask_size
if scale > self.size:
outer, inner = scale, self.size
top, left = np.random.randint(0, outer - inner + 1), np.random.randint(0, outer - inner + 1)
image = image.resize((scale, scale), resample=resample)
image = np.array(image).astype(np.uint8)
image = (image / 127.5 - 1.0).astype(np.float32)
instance_image = np.zeros((self.size, self.size, 3), dtype=np.float32)
mask = np.zeros((self.size // factor, self.size // factor))
if scale > self.size:
instance_image = image[top : top + inner, left : left + inner, :]
mask = np.ones((self.size // factor, self.size // factor))
else:
instance_image[top : top + inner, left : left + inner, :] = image
mask[
top // factor + 1 : (top + scale) // factor - 1, left // factor + 1 : (left + scale) // factor - 1
] = 1.0
return instance_image, mask
def __getitem__(self, index):
example = {}
instance_image, instance_prompt = self.instance_images_path[index % self.num_instance_images]
instance_image = Image.open(instance_image)
if not instance_image.mode == "RGB":
instance_image = instance_image.convert("RGB")
instance_image = self.flip(instance_image)
# apply resize augmentation and create a valid image region mask
random_scale = self.size
if self.aug:
random_scale = (
np.random.randint(self.size // 3, self.size + 1)
if np.random.uniform() < 0.66
else np.random.randint(int(1.2 * self.size), int(1.4 * self.size))
)
instance_image, mask = self.preprocess(instance_image, random_scale, self.interpolation)
if random_scale < 0.6 * self.size:
instance_prompt = np.random.choice(["a far away ", "very small "]) + instance_prompt
elif random_scale > self.size:
instance_prompt = np.random.choice(["zoomed in ", "close up "]) + instance_prompt
example["instance_images"] = torch.from_numpy(instance_image).permute(2, 0, 1)
example["mask"] = torch.from_numpy(mask)
example["instance_prompt_ids"] = self.tokenizer(
instance_prompt,
truncation=True,
padding="max_length",
max_length=self.tokenizer.model_max_length,
return_tensors="pt",
).input_ids
if self.with_prior_preservation:
class_image, class_prompt = self.class_images_path[index % self.num_class_images]
class_image = Image.open(class_image)
if not class_image.mode == "RGB":
class_image = class_image.convert("RGB")
example["class_images"] = self.image_transforms(class_image)
example["class_mask"] = torch.ones_like(example["mask"])
example["class_prompt_ids"] = self.tokenizer(
class_prompt,
truncation=True,
padding="max_length",
max_length=self.tokenizer.model_max_length,
return_tensors="pt",
).input_ids
return example
def save_new_embed(text_encoder, modifier_token_id, accelerator, args, output_dir, safe_serialization=True):
"""Saves the new token embeddings from the text encoder."""
logger.info("Saving embeddings")
learned_embeds = accelerator.unwrap_model(text_encoder).get_input_embeddings().weight
for x, y in zip(modifier_token_id, args.modifier_token):
learned_embeds_dict = {}
learned_embeds_dict[y] = learned_embeds[x]
if safe_serialization:
filename = f"{output_dir}/{y}.safetensors"
safetensors.torch.save_file(learned_embeds_dict, filename, metadata={"format": "pt"})
else:
filename = f"{output_dir}/{y}.bin"
torch.save(learned_embeds_dict, filename)
def parse_args(input_args=None):
parser = argparse.ArgumentParser(description="Custom Diffusion training script.")
parser.add_argument(
"--pretrained_model_name_or_path",
type=str,
default=None,
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--revision",
type=str,
default=None,
required=False,
help="Revision of pretrained model identifier from huggingface.co/models.",
)
parser.add_argument(
"--variant",
type=str,
default=None,
help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16",
)
parser.add_argument(
"--tokenizer_name",
type=str,
default=None,
help="Pretrained tokenizer name or path if not the same as model_name",
)
parser.add_argument(
"--instance_data_dir",
type=str,
default=None,
help="A folder containing the training data of instance images.",
)
parser.add_argument(
"--class_data_dir",
type=str,
default=None,
help="A folder containing the training data of class images.",
)
parser.add_argument(
"--instance_prompt",
type=str,
default=None,
help="The prompt with identifier specifying the instance",
)
parser.add_argument(
"--class_prompt",
type=str,
default=None,
help="The prompt to specify images in the same class as provided instance images.",
)
parser.add_argument(
"--validation_prompt",
type=str,
default=None,
help="A prompt that is used during validation to verify that the model is learning.",
)
parser.add_argument(
"--num_validation_images",
type=int,
default=2,
help="Number of images that should be generated during validation with `validation_prompt`.",
)
parser.add_argument(
"--validation_steps",
type=int,
default=50,
help=(
"Run dreambooth validation every X epochs. Dreambooth validation consists of running the prompt"
" `args.validation_prompt` multiple times: `args.num_validation_images`."
),
)
parser.add_argument(
"--with_prior_preservation",
default=False,
action="store_true",
help="Flag to add prior preservation loss.",
)
parser.add_argument(
"--real_prior",
default=False,
action="store_true",
help="real images as prior.",
)
parser.add_argument("--prior_loss_weight", type=float, default=1.0, help="The weight of prior preservation loss.")
parser.add_argument(
"--num_class_images",
type=int,
default=200,
help=(
"Minimal class images for prior preservation loss. If there are not enough images already present in"
" class_data_dir, additional images will be sampled with class_prompt."
),
)
parser.add_argument(
"--output_dir",
type=str,
default="custom-diffusion-model",
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument("--seed", type=int, default=42, help="A seed for reproducible training.")
parser.add_argument(
"--resolution",
type=int,
default=512,
help=(
"The resolution for input images, all the images in the train/validation dataset will be resized to this"
" resolution"
),
)
parser.add_argument(
"--center_crop",
default=False,
action="store_true",
help=(
"Whether to center crop the input images to the resolution. If not set, the images will be randomly"
" cropped. The images will be resized to the resolution first before cropping."
),
)
parser.add_argument(
"--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
)
parser.add_argument(
"--sample_batch_size", type=int, default=4, help="Batch size (per device) for sampling images."
)
parser.add_argument("--num_train_epochs", type=int, default=1)
parser.add_argument(
"--max_train_steps",
type=int,
default=None,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument(
"--checkpointing_steps",
type=int,
default=250,
help=(
"Save a checkpoint of the training state every X updates. These checkpoints can be used both as final"
" checkpoints in case they are better than the last checkpoint, and are also suitable for resuming"
" training using `--resume_from_checkpoint`."
),
)
parser.add_argument(
"--checkpoints_total_limit",
type=int,
default=None,
help=("Max number of checkpoints to store."),
)
parser.add_argument(
"--resume_from_checkpoint",
type=str,
default=None,
help=(
"Whether training should be resumed from a previous checkpoint. Use a path saved by"
' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
),
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--gradient_checkpointing",
action="store_true",
help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=1e-5,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument(
"--scale_lr",
action="store_true",
default=False,
help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
)
parser.add_argument(
"--dataloader_num_workers",
type=int,
default=2,
help=(
"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
),
)
parser.add_argument(
"--freeze_model",
type=str,
default="crossattn_kv",
choices=["crossattn_kv", "crossattn"],
help="crossattn to enable fine-tuning of all params in the cross attention",
)
parser.add_argument(
"--lr_scheduler",
type=str,
default="constant",
help=(
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
' "constant", "constant_with_warmup"]'
),
)
parser.add_argument(
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument(
"--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
)
parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
parser.add_argument(
"--hub_model_id",
type=str,
default=None,
help="The name of the repository to keep in sync with the local `output_dir`.",
)
parser.add_argument(
"--logging_dir",
type=str,
default="logs",
help=(
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
),
)
parser.add_argument(
"--allow_tf32",
action="store_true",
help=(
"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
),
)
parser.add_argument(
"--report_to",
type=str,
default="tensorboard",
help=(
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
),
)
parser.add_argument(
"--mixed_precision",
type=str,
default=None,
choices=["no", "fp16", "bf16"],
help=(
"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the"
" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
),
)
parser.add_argument(
"--prior_generation_precision",
type=str,
default=None,
choices=["no", "fp32", "fp16", "bf16"],
help=(
"Choose prior generation precision between fp32, fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
" 1.10.and an Nvidia Ampere GPU. Default to fp16 if a GPU is available else fp32."
),
)
parser.add_argument(
"--concepts_list",
type=str,
default=None,
help="Path to json containing multiple concepts, will overwrite parameters like instance_prompt, class_prompt, etc.",
)
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
parser.add_argument(
"--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
)
parser.add_argument(
"--set_grads_to_none",
action="store_true",
help=(
"Save more memory by using setting grads to None instead of zero. Be aware, that this changes certain"
" behaviors, so disable this argument if it causes any problems. More info:"
" https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html"
),
)
parser.add_argument(
"--modifier_token",
type=str,
default=None,
help="A token to use as a modifier for the concept.",
)
parser.add_argument(
"--initializer_token", type=str, default="ktn+pll+ucd", help="A token to use as initializer word."
)
parser.add_argument("--hflip", action="store_true", help="Apply horizontal flip data augmentation.")
parser.add_argument(
"--noaug",
action="store_true",
help="Dont apply augmentation during data augmentation when this flag is enabled.",
)
parser.add_argument(
"--no_safe_serialization",
action="store_true",
help="If specified save the checkpoint not in `safetensors` format, but in original PyTorch format instead.",
)
if input_args is not None:
args = parser.parse_args(input_args)
else:
args = parser.parse_args()
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != args.local_rank:
args.local_rank = env_local_rank
if args.with_prior_preservation:
if args.concepts_list is None:
if args.class_data_dir is None:
raise ValueError("You must specify a data directory for class images.")
if args.class_prompt is None:
raise ValueError("You must specify prompt for class images.")
else:
# logger is not available yet
if args.class_data_dir is not None:
warnings.warn("You need not use --class_data_dir without --with_prior_preservation.")
if args.class_prompt is not None:
warnings.warn("You need not use --class_prompt without --with_prior_preservation.")
return args
def main(args):
if args.report_to == "wandb" and args.hub_token is not None:
raise ValueError(
"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
" Please use `hf auth login` to authenticate with the Hub."
)
logging_dir = Path(args.output_dir, args.logging_dir)
accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
mixed_precision=args.mixed_precision,
log_with=args.report_to,
project_config=accelerator_project_config,
)
# Disable AMP for MPS.
if torch.backends.mps.is_available():
accelerator.native_amp = False
if args.report_to == "wandb":
if not is_wandb_available():
raise ImportError("Make sure to install wandb if you want to use it for logging during training.")
import wandb
# Currently, it's not possible to do gradient accumulation when training two models with accelerate.accumulate
# This will be enabled soon in accelerate. For now, we don't allow gradient accumulation when training two models.
# TODO (patil-suraj): Remove this check when gradient accumulation with two models is enabled in accelerate.
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.info(accelerator.state, main_process_only=False)
if accelerator.is_local_main_process:
transformers.utils.logging.set_verbosity_warning()
diffusers.utils.logging.set_verbosity_info()
else:
transformers.utils.logging.set_verbosity_error()
diffusers.utils.logging.set_verbosity_error()
# We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process.
if accelerator.is_main_process:
accelerator.init_trackers("custom-diffusion", config=vars(args))
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
if args.concepts_list is None:
args.concepts_list = [
{
"instance_prompt": args.instance_prompt,
"class_prompt": args.class_prompt,
"instance_data_dir": args.instance_data_dir,
"class_data_dir": args.class_data_dir,
}
]
else:
with open(args.concepts_list, "r") as f:
args.concepts_list = json.load(f)
# Generate class images if prior preservation is enabled.
if args.with_prior_preservation:
for i, concept in enumerate(args.concepts_list):
class_images_dir = Path(concept["class_data_dir"])
if not class_images_dir.exists():
class_images_dir.mkdir(parents=True, exist_ok=True)
if args.real_prior:
assert (class_images_dir / "images").exists(), (
f'Please run: python retrieve.py --class_prompt "{concept["class_prompt"]}" --class_data_dir {class_images_dir} --num_class_images {args.num_class_images}'
)
assert len(list((class_images_dir / "images").iterdir())) == args.num_class_images, (
f'Please run: python retrieve.py --class_prompt "{concept["class_prompt"]}" --class_data_dir {class_images_dir} --num_class_images {args.num_class_images}'
)
assert (class_images_dir / "caption.txt").exists(), (
f'Please run: python retrieve.py --class_prompt "{concept["class_prompt"]}" --class_data_dir {class_images_dir} --num_class_images {args.num_class_images}'
)
assert (class_images_dir / "images.txt").exists(), (
f'Please run: python retrieve.py --class_prompt "{concept["class_prompt"]}" --class_data_dir {class_images_dir} --num_class_images {args.num_class_images}'
)
concept["class_prompt"] = os.path.join(class_images_dir, "caption.txt")
concept["class_data_dir"] = os.path.join(class_images_dir, "images.txt")
args.concepts_list[i] = concept
accelerator.wait_for_everyone()
else:
cur_class_images = len(list(class_images_dir.iterdir()))
if cur_class_images < args.num_class_images:
torch_dtype = torch.float16 if accelerator.device.type == "cuda" else torch.float32
if args.prior_generation_precision == "fp32":
torch_dtype = torch.float32
elif args.prior_generation_precision == "fp16":
torch_dtype = torch.float16
elif args.prior_generation_precision == "bf16":
torch_dtype = torch.bfloat16
pipeline = DiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path,
torch_dtype=torch_dtype,
safety_checker=None,
revision=args.revision,
variant=args.variant,
)
pipeline.set_progress_bar_config(disable=True)
num_new_images = args.num_class_images - cur_class_images
logger.info(f"Number of class images to sample: {num_new_images}.")
sample_dataset = PromptDataset(concept["class_prompt"], num_new_images)
sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size)
sample_dataloader = accelerator.prepare(sample_dataloader)
pipeline.to(accelerator.device)
for example in tqdm(
sample_dataloader,
desc="Generating class images",
disable=not accelerator.is_local_main_process,
):
images = pipeline(example["prompt"]).images
for i, image in enumerate(images):
hash_image = insecure_hashlib.sha1(image.tobytes()).hexdigest()
image_filename = (
class_images_dir / f"{example['index'][i] + cur_class_images}-{hash_image}.jpg"
)
image.save(image_filename)
del pipeline
if torch.cuda.is_available():
torch.cuda.empty_cache()
# Handle the repository creation
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
if args.push_to_hub:
repo_id = create_repo(
repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
).repo_id
# Load the tokenizer
if args.tokenizer_name:
tokenizer = AutoTokenizer.from_pretrained(
args.tokenizer_name,
revision=args.revision,
use_fast=False,
)
elif args.pretrained_model_name_or_path:
tokenizer = AutoTokenizer.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="tokenizer",
revision=args.revision,
use_fast=False,
)
# import correct text encoder class
text_encoder_cls = import_model_class_from_model_name_or_path(args.pretrained_model_name_or_path, args.revision)
# Load scheduler and models
noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
text_encoder = text_encoder_cls.from_pretrained(
args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, variant=args.variant
)
vae = AutoencoderKL.from_pretrained(
args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision, variant=args.variant
)
unet = UNet2DConditionModel.from_pretrained(
args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision, variant=args.variant
)
# Adding a modifier token which is optimized ####
# Code taken from https://github.com/huggingface/diffusers/blob/main/examples/textual_inversion/textual_inversion.py
modifier_token_id = []
initializer_token_id = []
if args.modifier_token is not None:
args.modifier_token = args.modifier_token.split("+")
args.initializer_token = args.initializer_token.split("+")
if len(args.modifier_token) > len(args.initializer_token):
raise ValueError("You must specify + separated initializer token for each modifier token.")
for modifier_token, initializer_token in zip(
args.modifier_token, args.initializer_token[: len(args.modifier_token)]
):
# Add the placeholder token in tokenizer
num_added_tokens = tokenizer.add_tokens(modifier_token)
if num_added_tokens == 0:
raise ValueError(
f"The tokenizer already contains the token {modifier_token}. Please pass a different"
" `modifier_token` that is not already in the tokenizer."
)
# Convert the initializer_token, placeholder_token to ids
token_ids = tokenizer.encode([initializer_token], add_special_tokens=False)
print(token_ids)
# Check if initializer_token is a single token or a sequence of tokens
if len(token_ids) > 1:
raise ValueError("The initializer token must be a single token.")
initializer_token_id.append(token_ids[0])
modifier_token_id.append(tokenizer.convert_tokens_to_ids(modifier_token))
# Resize the token embeddings as we are adding new special tokens to the tokenizer
text_encoder.resize_token_embeddings(len(tokenizer))
# Initialise the newly added placeholder token with the embeddings of the initializer token
token_embeds = text_encoder.get_input_embeddings().weight.data
for x, y in zip(modifier_token_id, initializer_token_id):
token_embeds[x] = token_embeds[y]
# Freeze all parameters except for the token embeddings in text encoder
params_to_freeze = itertools.chain(
text_encoder.text_model.encoder.parameters(),
text_encoder.text_model.final_layer_norm.parameters(),
text_encoder.text_model.embeddings.position_embedding.parameters(),
)
freeze_params(params_to_freeze)
########################################################
########################################################
vae.requires_grad_(False)
if args.modifier_token is None:
text_encoder.requires_grad_(False)
unet.requires_grad_(False)
# For mixed precision training we cast the text_encoder and vae weights to half-precision
# as these models are only used for inference, keeping weights in full precision is not required.
weight_dtype = torch.float32
if accelerator.mixed_precision == "fp16":
weight_dtype = torch.float16
elif accelerator.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
# Move unet, vae and text_encoder to device and cast to weight_dtype
if accelerator.mixed_precision != "fp16" and args.modifier_token is not None:
text_encoder.to(accelerator.device, dtype=weight_dtype)
unet.to(accelerator.device, dtype=weight_dtype)
vae.to(accelerator.device, dtype=weight_dtype)
attention_class = (
CustomDiffusionAttnProcessor2_0 if hasattr(F, "scaled_dot_product_attention") else CustomDiffusionAttnProcessor
)
if args.enable_xformers_memory_efficient_attention:
if is_xformers_available():
import xformers
xformers_version = version.parse(xformers.__version__)
if xformers_version == version.parse("0.0.16"):
logger.warning(
"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
)
attention_class = CustomDiffusionXFormersAttnProcessor
else:
raise ValueError("xformers is not available. Make sure it is installed correctly")
# now we will add new Custom Diffusion weights to the attention layers
# It's important to realize here how many attention weights will be added and of which sizes
# The sizes of the attention layers consist only of two different variables:
# 1) - the "hidden_size", which is increased according to `unet.config.block_out_channels`.
# 2) - the "cross attention size", which is set to `unet.config.cross_attention_dim`.
# Let's first see how many attention processors we will have to set.
# For Stable Diffusion, it should be equal to:
# - down blocks (2x attention layers) * (2x transformer layers) * (3x down blocks) = 12
# - mid blocks (2x attention layers) * (1x transformer layers) * (1x mid blocks) = 2
# - up blocks (2x attention layers) * (3x transformer layers) * (3x down blocks) = 18
# => 32 layers
# Only train key, value projection layers if freeze_model = 'crossattn_kv' else train all params in the cross attention layer
train_kv = True
train_q_out = False if args.freeze_model == "crossattn_kv" else True
custom_diffusion_attn_procs = {}
st = unet.state_dict()
for name, _ in unet.attn_processors.items():
cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
if name.startswith("mid_block"):
hidden_size = unet.config.block_out_channels[-1]
elif name.startswith("up_blocks"):
block_id = int(name[len("up_blocks.")])
hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
elif name.startswith("down_blocks"):
block_id = int(name[len("down_blocks.")])
hidden_size = unet.config.block_out_channels[block_id]
layer_name = name.split(".processor")[0]
weights = {
"to_k_custom_diffusion.weight": st[layer_name + ".to_k.weight"],
"to_v_custom_diffusion.weight": st[layer_name + ".to_v.weight"],
}
if train_q_out:
weights["to_q_custom_diffusion.weight"] = st[layer_name + ".to_q.weight"]
weights["to_out_custom_diffusion.0.weight"] = st[layer_name + ".to_out.0.weight"]
weights["to_out_custom_diffusion.0.bias"] = st[layer_name + ".to_out.0.bias"]
if cross_attention_dim is not None:
custom_diffusion_attn_procs[name] = attention_class(
train_kv=train_kv,
train_q_out=train_q_out,
hidden_size=hidden_size,
cross_attention_dim=cross_attention_dim,
).to(unet.device)
custom_diffusion_attn_procs[name].load_state_dict(weights)
else:
custom_diffusion_attn_procs[name] = attention_class(
train_kv=False,
train_q_out=False,
hidden_size=hidden_size,
cross_attention_dim=cross_attention_dim,
)
del st
unet.set_attn_processor(custom_diffusion_attn_procs)
custom_diffusion_layers = AttnProcsLayers(unet.attn_processors)
accelerator.register_for_checkpointing(custom_diffusion_layers)
if args.gradient_checkpointing:
unet.enable_gradient_checkpointing()
if args.modifier_token is not None:
text_encoder.gradient_checkpointing_enable()
# Enable TF32 for faster training on Ampere GPUs,
# cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
if args.allow_tf32:
torch.backends.cuda.matmul.allow_tf32 = True
if args.scale_lr:
args.learning_rate = (
args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
)
if args.with_prior_preservation:
args.learning_rate = args.learning_rate * 2.0
# Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs
if args.use_8bit_adam:
try:
import bitsandbytes as bnb
except ImportError:
raise ImportError(
"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`."
)
optimizer_class = bnb.optim.AdamW8bit
else:
optimizer_class = torch.optim.AdamW
# Optimizer creation
optimizer = optimizer_class(
itertools.chain(text_encoder.get_input_embeddings().parameters(), custom_diffusion_layers.parameters())
if args.modifier_token is not None
else custom_diffusion_layers.parameters(),
lr=args.learning_rate,
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
)
# Dataset and DataLoaders creation:
train_dataset = CustomDiffusionDataset(
concepts_list=args.concepts_list,
tokenizer=tokenizer,
with_prior_preservation=args.with_prior_preservation,
size=args.resolution,
mask_size=vae.encode(
torch.randn(1, 3, args.resolution, args.resolution).to(dtype=weight_dtype).to(accelerator.device)
)
.latent_dist.sample()
.size()[-1],
center_crop=args.center_crop,
num_class_images=args.num_class_images,
hflip=args.hflip,
aug=not args.noaug,
)
train_dataloader = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.train_batch_size,
shuffle=True,
collate_fn=lambda examples: collate_fn(examples, args.with_prior_preservation),
num_workers=args.dataloader_num_workers,
)
# Scheduler and math around the number of training steps.
# Check the PR https://github.com/huggingface/diffusers/pull/8312 for detailed explanation.
num_warmup_steps_for_scheduler = args.lr_warmup_steps * accelerator.num_processes
if args.max_train_steps is None:
len_train_dataloader_after_sharding = math.ceil(len(train_dataloader) / accelerator.num_processes)
num_update_steps_per_epoch = math.ceil(len_train_dataloader_after_sharding / args.gradient_accumulation_steps)
num_training_steps_for_scheduler = (
args.num_train_epochs * num_update_steps_per_epoch * accelerator.num_processes
)
else:
num_training_steps_for_scheduler = args.max_train_steps * accelerator.num_processes
lr_scheduler = get_scheduler(
args.lr_scheduler,
optimizer=optimizer,
num_warmup_steps=num_warmup_steps_for_scheduler,
num_training_steps=num_training_steps_for_scheduler,
)
# Prepare everything with our `accelerator`.
if args.modifier_token is not None:
custom_diffusion_layers, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
custom_diffusion_layers, text_encoder, optimizer, train_dataloader, lr_scheduler
)
else:
custom_diffusion_layers, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
custom_diffusion_layers, optimizer, train_dataloader, lr_scheduler
)
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if args.max_train_steps is None:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
if num_training_steps_for_scheduler != args.max_train_steps * accelerator.num_processes:
logger.warning(
f"The length of the 'train_dataloader' after 'accelerator.prepare' ({len(train_dataloader)}) does not match "
f"the expected length ({len_train_dataloader_after_sharding}) when the learning rate scheduler was created. "
f"This inconsistency may result in the learning rate scheduler not functioning properly."
)
# Afterwards we recalculate our number of training epochs
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# Train!
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num batches each epoch = {len(train_dataloader)}")
logger.info(f" Num Epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {args.max_train_steps}")
global_step = 0
first_epoch = 0
# Potentially load in the weights and states from a previous save
if args.resume_from_checkpoint:
if args.resume_from_checkpoint != "latest":
path = os.path.basename(args.resume_from_checkpoint)
else:
# Get the most recent checkpoint
dirs = os.listdir(args.output_dir)
dirs = [d for d in dirs if d.startswith("checkpoint")]
dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
path = dirs[-1] if len(dirs) > 0 else None
if path is None:
accelerator.print(
f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
)
args.resume_from_checkpoint = None
initial_global_step = 0
else:
accelerator.print(f"Resuming from checkpoint {path}")
accelerator.load_state(os.path.join(args.output_dir, path))
global_step = int(path.split("-")[1])
initial_global_step = global_step
first_epoch = global_step // num_update_steps_per_epoch
else:
initial_global_step = 0
progress_bar = tqdm(
range(0, args.max_train_steps),
initial=initial_global_step,
desc="Steps",
# Only show the progress bar once on each machine.
disable=not accelerator.is_local_main_process,
)
for epoch in range(first_epoch, args.num_train_epochs):
unet.train()
if args.modifier_token is not None:
text_encoder.train()
for step, batch in enumerate(train_dataloader):
with accelerator.accumulate(unet), accelerator.accumulate(text_encoder):
# Convert images to latent space
latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample()
latents = latents * vae.config.scaling_factor
# Sample noise that we'll add to the latents
noise = torch.randn_like(latents)
bsz = latents.shape[0]
# Sample a random timestep for each image
timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
timesteps = timesteps.long()
# Add noise to the latents according to the noise magnitude at each timestep
# (this is the forward diffusion process)
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
# Get the text embedding for conditioning
encoder_hidden_states = text_encoder(batch["input_ids"])[0]
# Predict the noise residual
model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
# Get the target for loss depending on the prediction type
if noise_scheduler.config.prediction_type == "epsilon":
target = noise
elif noise_scheduler.config.prediction_type == "v_prediction":
target = noise_scheduler.get_velocity(latents, noise, timesteps)
else:
raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
if args.with_prior_preservation:
# Chunk the noise and model_pred into two parts and compute the loss on each part separately.
model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0)
target, target_prior = torch.chunk(target, 2, dim=0)
mask = torch.chunk(batch["mask"], 2, dim=0)[0]
# Compute instance loss
loss = F.mse_loss(model_pred.float(), target.float(), reduction="none")
loss = ((loss * mask).sum([1, 2, 3]) / mask.sum([1, 2, 3])).mean()
# Compute prior loss
prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction="mean")
# Add the prior loss to the instance loss.
loss = loss + args.prior_loss_weight * prior_loss
else:
mask = batch["mask"]
loss = F.mse_loss(model_pred.float(), target.float(), reduction="none")
loss = ((loss * mask).sum([1, 2, 3]) / mask.sum([1, 2, 3])).mean()
accelerator.backward(loss)
# Zero out the gradients for all token embeddings except the newly added
# embeddings for the concept, as we only want to optimize the concept embeddings
if args.modifier_token is not None:
if accelerator.num_processes > 1:
grads_text_encoder = text_encoder.module.get_input_embeddings().weight.grad
else:
grads_text_encoder = text_encoder.get_input_embeddings().weight.grad
# Get the index for tokens that we want to zero the grads for
index_grads_to_zero = torch.arange(len(tokenizer)) != modifier_token_id[0]
for i in range(1, len(modifier_token_id)):
index_grads_to_zero = index_grads_to_zero & (
torch.arange(len(tokenizer)) != modifier_token_id[i]
)
grads_text_encoder.data[index_grads_to_zero, :] = grads_text_encoder.data[
index_grads_to_zero, :
].fill_(0)
if accelerator.sync_gradients:
params_to_clip = (
itertools.chain(text_encoder.parameters(), custom_diffusion_layers.parameters())
if args.modifier_token is not None
else custom_diffusion_layers.parameters()
)
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad(set_to_none=args.set_grads_to_none)
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
progress_bar.update(1)
global_step += 1
if global_step % args.checkpointing_steps == 0:
if accelerator.is_main_process:
# _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
if args.checkpoints_total_limit is not None:
checkpoints = os.listdir(args.output_dir)
checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))
# before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
if len(checkpoints) >= args.checkpoints_total_limit:
num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
removing_checkpoints = checkpoints[0:num_to_remove]
logger.info(
f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
)
logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")
for removing_checkpoint in removing_checkpoints:
removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)
shutil.rmtree(removing_checkpoint)
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
accelerator.save_state(save_path)
logger.info(f"Saved state to {save_path}")
logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
progress_bar.set_postfix(**logs)
accelerator.log(logs, step=global_step)
if global_step >= args.max_train_steps:
break
if accelerator.is_main_process:
images = []
if args.validation_prompt is not None and global_step % args.validation_steps == 0:
logger.info(
f"Running validation... \n Generating {args.num_validation_images} images with prompt:"
f" {args.validation_prompt}."
)
# create pipeline
pipeline = DiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path,
unet=accelerator.unwrap_model(unet),
text_encoder=accelerator.unwrap_model(text_encoder),
tokenizer=tokenizer,
revision=args.revision,
variant=args.variant,
torch_dtype=weight_dtype,
)
pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
pipeline = pipeline.to(accelerator.device)
pipeline.set_progress_bar_config(disable=True)
# run inference
generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)
images = [
pipeline(args.validation_prompt, num_inference_steps=25, generator=generator, eta=1.0).images[
0
]
for _ in range(args.num_validation_images)
]
for tracker in accelerator.trackers:
if tracker.name == "tensorboard":
np_images = np.stack([np.asarray(img) for img in images])
tracker.writer.add_images("validation", np_images, epoch, dataformats="NHWC")
if tracker.name == "wandb":
tracker.log(
{
"validation": [
wandb.Image(image, caption=f"{i}: {args.validation_prompt}")
for i, image in enumerate(images)
]
}
)
del pipeline
torch.cuda.empty_cache()
# Save the custom diffusion layers
accelerator.wait_for_everyone()
if accelerator.is_main_process:
unet = unet.to(torch.float32)
unet.save_attn_procs(args.output_dir, safe_serialization=not args.no_safe_serialization)
save_new_embed(
text_encoder,
modifier_token_id,
accelerator,
args,
args.output_dir,
safe_serialization=not args.no_safe_serialization,
)
# Final inference
# Load previous pipeline
pipeline = DiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path, revision=args.revision, variant=args.variant, torch_dtype=weight_dtype
)
pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
pipeline = pipeline.to(accelerator.device)
# load attention processors
weight_name = (
"pytorch_custom_diffusion_weights.safetensors"
if not args.no_safe_serialization
else "pytorch_custom_diffusion_weights.bin"
)
pipeline.unet.load_attn_procs(args.output_dir, weight_name=weight_name)
for token in args.modifier_token:
token_weight_name = f"{token}.safetensors" if not args.no_safe_serialization else f"{token}.bin"
pipeline.load_textual_inversion(args.output_dir, weight_name=token_weight_name)
# run inference
if args.validation_prompt and args.num_validation_images > 0:
generator = (
torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed is not None else None
)
images = [
pipeline(args.validation_prompt, num_inference_steps=25, generator=generator, eta=1.0).images[0]
for _ in range(args.num_validation_images)
]
for tracker in accelerator.trackers:
if tracker.name == "tensorboard":
np_images = np.stack([np.asarray(img) for img in images])
tracker.writer.add_images("test", np_images, epoch, dataformats="NHWC")
if tracker.name == "wandb":
tracker.log(
{
"test": [
wandb.Image(image, caption=f"{i}: {args.validation_prompt}")
for i, image in enumerate(images)
]
}
)
if args.push_to_hub:
save_model_card(
repo_id,
images=images,
base_model=args.pretrained_model_name_or_path,
prompt=args.instance_prompt,
repo_folder=args.output_dir,
)
api = HfApi(token=args.hub_token)
api.upload_folder(
repo_id=repo_id,
folder_path=args.output_dir,
commit_message="End of training",
ignore_patterns=["step_*", "epoch_*"],
)
accelerator.end_training()
if __name__ == "__main__":
args = parse_args()
main(args)
| diffusers/examples/custom_diffusion/train_custom_diffusion.py/0 | {
"file_path": "diffusers/examples/custom_diffusion/train_custom_diffusion.py",
"repo_id": "diffusers",
"token_count": 27069
} | 145 |
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
"The `inpainting.py` script is outdated. Please use directly `from diffusers import"
" StableDiffusionInpaintPipeline` instead."
)
| diffusers/examples/inference/inpainting.py/0 | {
"file_path": "diffusers/examples/inference/inpainting.py",
"repo_id": "diffusers",
"token_count": 89
} | 146 |
## Diffusion-based Policy Learning for RL
`diffusion_policy` implements [Diffusion Policy](https://diffusion-policy.cs.columbia.edu/), a diffusion model that predicts robot action sequences in reinforcement learning tasks.
This example implements a robot control model for pushing a T-shaped block into a target area. The model takes in current state observations as input, and outputs a trajectory of subsequent steps to follow.
To execute the script, run `diffusion_policy.py`
## Diffuser Locomotion
These examples show how to run [Diffuser](https://huggingface.co/papers/2205.09991) in Diffusers.
There are two ways to use the script, `run_diffuser_locomotion.py`.
The key option is a change of the variable `n_guide_steps`.
When `n_guide_steps=0`, the trajectories are sampled from the diffusion model, but not fine-tuned to maximize reward in the environment.
By default, `n_guide_steps=2` to match the original implementation.
You will need some RL specific requirements to run the examples:
```sh
pip install -f https://download.pytorch.org/whl/torch_stable.html \
free-mujoco-py \
einops \
gym==0.24.1 \
protobuf==3.20.1 \
git+https://github.com/rail-berkeley/d4rl.git \
mediapy \
Pillow==9.0.0
```
| diffusers/examples/reinforcement_learning/README.md/0 | {
"file_path": "diffusers/examples/reinforcement_learning/README.md",
"repo_id": "diffusers",
"token_count": 466
} | 147 |
#!/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/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import contextlib
import gc
import logging
import math
import os
import shutil
from pathlib import Path
import accelerate
import lpips
import numpy as np
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import torchvision
import transformers
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import ProjectConfiguration, set_seed
from datasets import load_dataset
from huggingface_hub import create_repo, upload_folder
from packaging import version
from PIL import Image
from taming.modules.losses.vqperceptual import NLayerDiscriminator, hinge_d_loss, vanilla_d_loss, weights_init
from torchvision import transforms
from tqdm.auto import tqdm
import diffusers
from diffusers import AutoencoderKL
from diffusers.optimization import get_scheduler
from diffusers.training_utils import EMAModel
from diffusers.utils import check_min_version, is_wandb_available, make_image_grid
from diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.torch_utils import is_compiled_module
if is_wandb_available():
import wandb
# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
check_min_version("0.33.0.dev0")
logger = get_logger(__name__)
@torch.no_grad()
def log_validation(vae, args, accelerator, weight_dtype, step, is_final_validation=False):
logger.info("Running validation... ")
if not is_final_validation:
vae = accelerator.unwrap_model(vae)
else:
vae = AutoencoderKL.from_pretrained(args.output_dir, torch_dtype=weight_dtype)
images = []
inference_ctx = contextlib.nullcontext() if is_final_validation else torch.autocast("cuda")
image_transforms = transforms.Compose(
[
transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),
transforms.CenterCrop(args.resolution),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
for i, validation_image in enumerate(args.validation_image):
validation_image = Image.open(validation_image).convert("RGB")
targets = image_transforms(validation_image).to(accelerator.device, weight_dtype)
targets = targets.unsqueeze(0)
with inference_ctx:
reconstructions = vae(targets).sample
images.append(torch.cat([targets.cpu(), reconstructions.cpu()], axis=0))
tracker_key = "test" if is_final_validation else "validation"
for tracker in accelerator.trackers:
if tracker.name == "tensorboard":
np_images = np.stack([np.asarray(img) for img in images])
tracker.writer.add_images(f"{tracker_key}: Original (left), Reconstruction (right)", np_images, step)
elif tracker.name == "wandb":
tracker.log(
{
f"{tracker_key}: Original (left), Reconstruction (right)": [
wandb.Image(torchvision.utils.make_grid(image)) for _, image in enumerate(images)
]
}
)
else:
logger.warn(f"image logging not implemented for {tracker.name}")
gc.collect()
torch.cuda.empty_cache()
return images
def save_model_card(repo_id: str, images=None, base_model=str, repo_folder=None):
img_str = ""
if images is not None:
img_str = "You can find some example images below.\n\n"
make_image_grid(images, 1, len(images)).save(os.path.join(repo_folder, "images.png"))
img_str += "\n"
model_description = f"""
# autoencoderkl-{repo_id}
These are autoencoderkl weights trained on {base_model} with new type of conditioning.
{img_str}
"""
model_card = load_or_create_model_card(
repo_id_or_path=repo_id,
from_training=True,
license="creativeml-openrail-m",
base_model=base_model,
model_description=model_description,
inference=True,
)
tags = [
"stable-diffusion",
"stable-diffusion-diffusers",
"image-to-image",
"diffusers",
"autoencoderkl",
"diffusers-training",
]
model_card = populate_model_card(model_card, tags=tags)
model_card.save(os.path.join(repo_folder, "README.md"))
def parse_args(input_args=None):
parser = argparse.ArgumentParser(description="Simple example of a AutoencoderKL training script.")
parser.add_argument(
"--pretrained_model_name_or_path",
type=str,
default=None,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--model_config_name_or_path",
type=str,
default=None,
help="The config of the VAE model to train, leave as None to use standard VAE model configuration.",
)
parser.add_argument(
"--revision",
type=str,
default=None,
required=False,
help="Revision of pretrained model identifier from huggingface.co/models.",
)
parser.add_argument(
"--output_dir",
type=str,
default="autoencoderkl-model",
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument(
"--cache_dir",
type=str,
default=None,
help="The directory where the downloaded models and datasets will be stored.",
)
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
parser.add_argument(
"--resolution",
type=int,
default=512,
help=(
"The resolution for input images, all the images in the train/validation dataset will be resized to this"
" resolution"
),
)
parser.add_argument(
"--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
)
parser.add_argument("--num_train_epochs", type=int, default=1)
parser.add_argument(
"--max_train_steps",
type=int,
default=None,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument(
"--checkpointing_steps",
type=int,
default=500,
help=(
"Save a checkpoint of the training state every X updates. Checkpoints can be used for resuming training via `--resume_from_checkpoint`. "
"In the case that the checkpoint is better than the final trained model, the checkpoint can also be used for inference."
"Using a checkpoint for inference requires separate loading of the original pipeline and the individual checkpointed model components."
"See https://huggingface.co/docs/diffusers/main/en/training/dreambooth#performing-inference-using-a-saved-checkpoint for step by step"
"instructions."
),
)
parser.add_argument(
"--checkpoints_total_limit",
type=int,
default=None,
help=("Max number of checkpoints to store."),
)
parser.add_argument(
"--resume_from_checkpoint",
type=str,
default=None,
help=(
"Whether training should be resumed from a previous checkpoint. Use a path saved by"
' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
),
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--gradient_checkpointing",
action="store_true",
help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=4.5e-6,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument(
"--disc_learning_rate",
type=float,
default=4.5e-6,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument(
"--scale_lr",
action="store_true",
default=False,
help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
)
parser.add_argument(
"--lr_scheduler",
type=str,
default="constant",
help=(
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
' "constant", "constant_with_warmup"]'
),
)
parser.add_argument(
"--disc_lr_scheduler",
type=str,
default="constant",
help=(
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
' "constant", "constant_with_warmup"]'
),
)
parser.add_argument(
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument(
"--lr_num_cycles",
type=int,
default=1,
help="Number of hard resets of the lr in cosine_with_restarts scheduler.",
)
parser.add_argument("--lr_power", type=float, default=1.0, help="Power factor of the polynomial scheduler.")
parser.add_argument(
"--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
)
parser.add_argument("--use_ema", action="store_true", help="Whether to use EMA model.")
parser.add_argument(
"--dataloader_num_workers",
type=int,
default=0,
help=(
"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
),
)
parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
parser.add_argument(
"--hub_model_id",
type=str,
default=None,
help="The name of the repository to keep in sync with the local `output_dir`.",
)
parser.add_argument(
"--logging_dir",
type=str,
default="logs",
help=(
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
),
)
parser.add_argument(
"--allow_tf32",
action="store_true",
help=(
"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
),
)
parser.add_argument(
"--report_to",
type=str,
default="tensorboard",
help=(
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
),
)
parser.add_argument(
"--mixed_precision",
type=str,
default=None,
choices=["no", "fp16", "bf16"],
help=(
"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the"
" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
),
)
parser.add_argument(
"--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
)
parser.add_argument(
"--set_grads_to_none",
action="store_true",
help=(
"Save more memory by using setting grads to None instead of zero. Be aware, that this changes certain"
" behaviors, so disable this argument if it causes any problems. More info:"
" https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html"
),
)
parser.add_argument(
"--dataset_name",
type=str,
default=None,
help=(
"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"
" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
" or to a folder containing files that 🤗 Datasets can understand."
),
)
parser.add_argument(
"--dataset_config_name",
type=str,
default=None,
help="The config of the Dataset, leave as None if there's only one config.",
)
parser.add_argument(
"--train_data_dir",
type=str,
default=None,
help=(
"A folder containing the training data. Folder contents must follow the structure described in"
" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file"
" must exist to provide the captions for the images. Ignored if `dataset_name` is specified."
),
)
parser.add_argument(
"--image_column", type=str, default="image", help="The column of the dataset containing the target image."
)
parser.add_argument(
"--max_train_samples",
type=int,
default=None,
help=(
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
),
)
parser.add_argument(
"--validation_image",
type=str,
default=None,
nargs="+",
help="A set of paths to the image be evaluated every `--validation_steps` and logged to `--report_to`.",
)
parser.add_argument(
"--validation_steps",
type=int,
default=100,
help=(
"Run validation every X steps. Validation consists of running the prompt"
" `args.validation_prompt` multiple times: `args.num_validation_images`"
" and logging the images."
),
)
parser.add_argument(
"--tracker_project_name",
type=str,
default="train_autoencoderkl",
help=(
"The `project_name` argument passed to Accelerator.init_trackers for"
" more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator"
),
)
parser.add_argument(
"--rec_loss",
type=str,
default="l2",
help="The loss function for VAE reconstruction loss.",
)
parser.add_argument(
"--kl_scale",
type=float,
default=1e-6,
help="Scaling factor for the Kullback-Leibler divergence penalty term.",
)
parser.add_argument(
"--perceptual_scale",
type=float,
default=0.5,
help="Scaling factor for the LPIPS metric",
)
parser.add_argument(
"--disc_start",
type=int,
default=50001,
help="Start for the discriminator",
)
parser.add_argument(
"--disc_factor",
type=float,
default=1.0,
help="Scaling factor for the discriminator",
)
parser.add_argument(
"--disc_scale",
type=float,
default=1.0,
help="Scaling factor for the discriminator",
)
parser.add_argument(
"--disc_loss",
type=str,
default="hinge",
help="Loss function for the discriminator",
)
parser.add_argument(
"--decoder_only",
action="store_true",
help="Only train the VAE decoder.",
)
if input_args is not None:
args = parser.parse_args(input_args)
else:
args = parser.parse_args()
if args.pretrained_model_name_or_path is not None and args.model_config_name_or_path is not None:
raise ValueError("Cannot specify both `--pretrained_model_name_or_path` and `--model_config_name_or_path`")
if args.dataset_name is None and args.train_data_dir is None:
raise ValueError("Specify either `--dataset_name` or `--train_data_dir`")
if args.resolution % 8 != 0:
raise ValueError(
"`--resolution` must be divisible by 8 for consistently sized encoded images between the VAE and the diffusion model."
)
return args
def make_train_dataset(args, accelerator):
# Get the datasets: you can either provide your own training and evaluation files (see below)
# or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
if args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
dataset = load_dataset(
args.dataset_name,
args.dataset_config_name,
cache_dir=args.cache_dir,
data_dir=args.train_data_dir,
)
else:
data_files = {}
if args.train_data_dir is not None:
data_files["train"] = os.path.join(args.train_data_dir, "**")
dataset = load_dataset(
"imagefolder",
data_files=data_files,
cache_dir=args.cache_dir,
)
# See more about loading custom images at
# https://huggingface.co/docs/datasets/v2.0.0/en/dataset_script
# Preprocessing the datasets.
# We need to tokenize inputs and targets.
column_names = dataset["train"].column_names
# 6. Get the column names for input/target.
if args.image_column is None:
image_column = column_names[0]
logger.info(f"image column defaulting to {image_column}")
else:
image_column = args.image_column
if image_column not in column_names:
raise ValueError(
f"`--image_column` value '{args.image_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}"
)
image_transforms = transforms.Compose(
[
transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),
transforms.CenterCrop(args.resolution),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
def preprocess_train(examples):
images = [image.convert("RGB") for image in examples[image_column]]
images = [image_transforms(image) for image in images]
examples["pixel_values"] = images
return examples
with accelerator.main_process_first():
if args.max_train_samples is not None:
dataset["train"] = dataset["train"].shuffle(seed=args.seed).select(range(args.max_train_samples))
# Set the training transforms
train_dataset = dataset["train"].with_transform(preprocess_train)
return train_dataset
def collate_fn(examples):
pixel_values = torch.stack([example["pixel_values"] for example in examples])
pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
return {"pixel_values": pixel_values}
def main(args):
if args.report_to == "wandb" and args.hub_token is not None:
raise ValueError(
"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
" Please use `hf auth login` to authenticate with the Hub."
)
logging_dir = Path(args.output_dir, args.logging_dir)
accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
mixed_precision=args.mixed_precision,
log_with=args.report_to,
project_config=accelerator_project_config,
)
# Disable AMP for MPS.
if torch.backends.mps.is_available():
accelerator.native_amp = False
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.info(accelerator.state, main_process_only=False)
if accelerator.is_local_main_process:
transformers.utils.logging.set_verbosity_warning()
diffusers.utils.logging.set_verbosity_info()
else:
transformers.utils.logging.set_verbosity_error()
diffusers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Handle the repository creation
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
if args.push_to_hub:
repo_id = create_repo(
repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
).repo_id
# Load AutoencoderKL
if args.pretrained_model_name_or_path is None and args.model_config_name_or_path is None:
config = AutoencoderKL.load_config("stabilityai/sd-vae-ft-mse")
vae = AutoencoderKL.from_config(config)
elif args.pretrained_model_name_or_path is not None:
vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, revision=args.revision)
else:
config = AutoencoderKL.load_config(args.model_config_name_or_path)
vae = AutoencoderKL.from_config(config)
if args.use_ema:
ema_vae = EMAModel(vae.parameters(), model_cls=AutoencoderKL, model_config=vae.config)
perceptual_loss = lpips.LPIPS(net="vgg").eval()
discriminator = NLayerDiscriminator(input_nc=3, n_layers=3, use_actnorm=False).apply(weights_init)
discriminator = torch.nn.SyncBatchNorm.convert_sync_batchnorm(discriminator)
# Taken from [Sayak Paul's Diffusers PR #6511](https://github.com/huggingface/diffusers/pull/6511/files)
def unwrap_model(model):
model = accelerator.unwrap_model(model)
model = model._orig_mod if is_compiled_module(model) else model
return model
# `accelerate` 0.16.0 will have better support for customized saving
if version.parse(accelerate.__version__) >= version.parse("0.16.0"):
# create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
def save_model_hook(models, weights, output_dir):
if accelerator.is_main_process:
if args.use_ema:
sub_dir = "autoencoderkl_ema"
ema_vae.save_pretrained(os.path.join(output_dir, sub_dir))
i = len(weights) - 1
while len(weights) > 0:
weights.pop()
model = models[i]
if isinstance(model, AutoencoderKL):
sub_dir = "autoencoderkl"
model.save_pretrained(os.path.join(output_dir, sub_dir))
else:
sub_dir = "discriminator"
os.makedirs(os.path.join(output_dir, sub_dir), exist_ok=True)
torch.save(model.state_dict(), os.path.join(output_dir, sub_dir, "pytorch_model.bin"))
i -= 1
def load_model_hook(models, input_dir):
while len(models) > 0:
if args.use_ema:
sub_dir = "autoencoderkl_ema"
load_model = EMAModel.from_pretrained(os.path.join(input_dir, sub_dir), AutoencoderKL)
ema_vae.load_state_dict(load_model.state_dict())
ema_vae.to(accelerator.device)
del load_model
# pop models so that they are not loaded again
model = models.pop()
load_model = NLayerDiscriminator(input_nc=3, n_layers=3, use_actnorm=False).load_state_dict(
os.path.join(input_dir, "discriminator", "pytorch_model.bin")
)
model.load_state_dict(load_model.state_dict())
del load_model
model = models.pop()
load_model = AutoencoderKL.from_pretrained(input_dir, subfolder="autoencoderkl")
model.register_to_config(**load_model.config)
model.load_state_dict(load_model.state_dict())
del load_model
accelerator.register_save_state_pre_hook(save_model_hook)
accelerator.register_load_state_pre_hook(load_model_hook)
vae.requires_grad_(True)
if args.decoder_only:
vae.encoder.requires_grad_(False)
if getattr(vae, "quant_conv", None):
vae.quant_conv.requires_grad_(False)
vae.train()
discriminator.requires_grad_(True)
discriminator.train()
if args.enable_xformers_memory_efficient_attention:
if is_xformers_available():
import xformers
xformers_version = version.parse(xformers.__version__)
if xformers_version == version.parse("0.0.16"):
logger.warning(
"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
)
vae.enable_xformers_memory_efficient_attention()
else:
raise ValueError("xformers is not available. Make sure it is installed correctly")
if args.gradient_checkpointing:
vae.enable_gradient_checkpointing()
# Check that all trainable models are in full precision
low_precision_error_string = (
" Please make sure to always have all model weights in full float32 precision when starting training - even if"
" doing mixed precision training, copy of the weights should still be float32."
)
if unwrap_model(vae).dtype != torch.float32:
raise ValueError(f"VAE loaded as datatype {unwrap_model(vae).dtype}. {low_precision_error_string}")
# Enable TF32 for faster training on Ampere GPUs,
# cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
if args.allow_tf32:
torch.backends.cuda.matmul.allow_tf32 = True
if args.scale_lr:
args.learning_rate = (
args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
)
# Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs
if args.use_8bit_adam:
try:
import bitsandbytes as bnb
except ImportError:
raise ImportError(
"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`."
)
optimizer_class = bnb.optim.AdamW8bit
else:
optimizer_class = torch.optim.AdamW
params_to_optimize = filter(lambda p: p.requires_grad, vae.parameters())
disc_params_to_optimize = filter(lambda p: p.requires_grad, discriminator.parameters())
optimizer = optimizer_class(
params_to_optimize,
lr=args.learning_rate,
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
)
disc_optimizer = optimizer_class(
disc_params_to_optimize,
lr=args.disc_learning_rate,
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
)
train_dataset = make_train_dataset(args, accelerator)
train_dataloader = torch.utils.data.DataLoader(
train_dataset,
shuffle=True,
collate_fn=collate_fn,
batch_size=args.train_batch_size,
num_workers=args.dataloader_num_workers,
)
# Scheduler and math around the number of training steps.
overrode_max_train_steps = False
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if args.max_train_steps is None:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
overrode_max_train_steps = True
lr_scheduler = get_scheduler(
args.lr_scheduler,
optimizer=optimizer,
num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
num_training_steps=args.max_train_steps * accelerator.num_processes,
num_cycles=args.lr_num_cycles,
power=args.lr_power,
)
disc_lr_scheduler = get_scheduler(
args.disc_lr_scheduler,
optimizer=disc_optimizer,
num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
num_training_steps=args.max_train_steps * accelerator.num_processes,
num_cycles=args.lr_num_cycles,
power=args.lr_power,
)
# Prepare everything with our `accelerator`.
(
vae,
discriminator,
optimizer,
disc_optimizer,
train_dataloader,
lr_scheduler,
disc_lr_scheduler,
) = accelerator.prepare(
vae, discriminator, optimizer, disc_optimizer, train_dataloader, lr_scheduler, disc_lr_scheduler
)
# For mixed precision training we cast the text_encoder and vae weights to half-precision
# as these models are only used for inference, keeping weights in full precision is not required.
weight_dtype = torch.float32
if accelerator.mixed_precision == "fp16":
weight_dtype = torch.float16
elif accelerator.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
# Move VAE, perceptual loss and discriminator to device and cast to weight_dtype
vae.to(accelerator.device, dtype=weight_dtype)
perceptual_loss.to(accelerator.device, dtype=weight_dtype)
discriminator.to(accelerator.device, dtype=weight_dtype)
if args.use_ema:
ema_vae.to(accelerator.device, dtype=weight_dtype)
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if overrode_max_train_steps:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
# Afterwards we recalculate our number of training epochs
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process.
if accelerator.is_main_process:
tracker_config = dict(vars(args))
accelerator.init_trackers(args.tracker_project_name, config=tracker_config)
# Train!
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num batches each epoch = {len(train_dataloader)}")
logger.info(f" Num Epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {args.max_train_steps}")
global_step = 0
first_epoch = 0
# Potentially load in the weights and states from a previous save
if args.resume_from_checkpoint:
if args.resume_from_checkpoint != "latest":
path = os.path.basename(args.resume_from_checkpoint)
else:
# Get the most recent checkpoint
dirs = os.listdir(args.output_dir)
dirs = [d for d in dirs if d.startswith("checkpoint")]
dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
path = dirs[-1] if len(dirs) > 0 else None
if path is None:
accelerator.print(
f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
)
args.resume_from_checkpoint = None
initial_global_step = 0
else:
accelerator.print(f"Resuming from checkpoint {path}")
accelerator.load_state(os.path.join(args.output_dir, path))
global_step = int(path.split("-")[1])
initial_global_step = global_step
first_epoch = global_step // num_update_steps_per_epoch
else:
initial_global_step = 0
progress_bar = tqdm(
range(0, args.max_train_steps),
initial=initial_global_step,
desc="Steps",
# Only show the progress bar once on each machine.
disable=not accelerator.is_local_main_process,
)
image_logs = None
for epoch in range(first_epoch, args.num_train_epochs):
vae.train()
discriminator.train()
for step, batch in enumerate(train_dataloader):
# Convert images to latent space and reconstruct from them
targets = batch["pixel_values"].to(dtype=weight_dtype)
posterior = accelerator.unwrap_model(vae).encode(targets).latent_dist
latents = posterior.sample()
reconstructions = accelerator.unwrap_model(vae).decode(latents).sample
if (step // args.gradient_accumulation_steps) % 2 == 0 or global_step < args.disc_start:
with accelerator.accumulate(vae):
# reconstruction loss. Pixel level differences between input vs output
if args.rec_loss == "l2":
rec_loss = F.mse_loss(reconstructions.float(), targets.float(), reduction="none")
elif args.rec_loss == "l1":
rec_loss = F.l1_loss(reconstructions.float(), targets.float(), reduction="none")
else:
raise ValueError(f"Invalid reconstruction loss type: {args.rec_loss}")
# perceptual loss. The high level feature mean squared error loss
with torch.no_grad():
p_loss = perceptual_loss(reconstructions, targets)
rec_loss = rec_loss + args.perceptual_scale * p_loss
nll_loss = rec_loss
nll_loss = torch.sum(nll_loss) / nll_loss.shape[0]
kl_loss = posterior.kl()
kl_loss = torch.sum(kl_loss) / kl_loss.shape[0]
logits_fake = discriminator(reconstructions)
g_loss = -torch.mean(logits_fake)
last_layer = accelerator.unwrap_model(vae).decoder.conv_out.weight
nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0]
g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0]
disc_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4)
disc_weight = torch.clamp(disc_weight, 0.0, 1e4).detach()
disc_weight = disc_weight * args.disc_scale
disc_factor = args.disc_factor if global_step >= args.disc_start else 0.0
loss = nll_loss + args.kl_scale * kl_loss + disc_weight * disc_factor * g_loss
logs = {
"loss": loss.detach().mean().item(),
"nll_loss": nll_loss.detach().mean().item(),
"rec_loss": rec_loss.detach().mean().item(),
"p_loss": p_loss.detach().mean().item(),
"kl_loss": kl_loss.detach().mean().item(),
"disc_weight": disc_weight.detach().mean().item(),
"disc_factor": disc_factor,
"g_loss": g_loss.detach().mean().item(),
"lr": lr_scheduler.get_last_lr()[0],
}
accelerator.backward(loss)
if accelerator.sync_gradients:
params_to_clip = vae.parameters()
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad(set_to_none=args.set_grads_to_none)
else:
with accelerator.accumulate(discriminator):
logits_real = discriminator(targets)
logits_fake = discriminator(reconstructions)
disc_loss = hinge_d_loss if args.disc_loss == "hinge" else vanilla_d_loss
disc_factor = args.disc_factor if global_step >= args.disc_start else 0.0
d_loss = disc_factor * disc_loss(logits_real, logits_fake)
logs = {
"disc_loss": d_loss.detach().mean().item(),
"logits_real": logits_real.detach().mean().item(),
"logits_fake": logits_fake.detach().mean().item(),
"disc_lr": disc_lr_scheduler.get_last_lr()[0],
}
accelerator.backward(d_loss)
if accelerator.sync_gradients:
params_to_clip = discriminator.parameters()
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
disc_optimizer.step()
disc_lr_scheduler.step()
disc_optimizer.zero_grad(set_to_none=args.set_grads_to_none)
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
progress_bar.update(1)
global_step += 1
if args.use_ema:
ema_vae.step(vae.parameters())
if accelerator.is_main_process:
if global_step % args.checkpointing_steps == 0:
# _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
if args.checkpoints_total_limit is not None:
checkpoints = os.listdir(args.output_dir)
checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))
# before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
if len(checkpoints) >= args.checkpoints_total_limit:
num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
removing_checkpoints = checkpoints[0:num_to_remove]
logger.info(
f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
)
logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")
for removing_checkpoint in removing_checkpoints:
removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)
shutil.rmtree(removing_checkpoint)
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
accelerator.save_state(save_path)
logger.info(f"Saved state to {save_path}")
if global_step == 1 or global_step % args.validation_steps == 0:
if args.use_ema:
ema_vae.store(vae.parameters())
ema_vae.copy_to(vae.parameters())
image_logs = log_validation(
vae,
args,
accelerator,
weight_dtype,
global_step,
)
if args.use_ema:
ema_vae.restore(vae.parameters())
progress_bar.set_postfix(**logs)
accelerator.log(logs, step=global_step)
if global_step >= args.max_train_steps:
break
# Create the pipeline using using the trained modules and save it.
accelerator.wait_for_everyone()
if accelerator.is_main_process:
vae = accelerator.unwrap_model(vae)
discriminator = accelerator.unwrap_model(discriminator)
if args.use_ema:
ema_vae.copy_to(vae.parameters())
vae.save_pretrained(args.output_dir)
torch.save(discriminator.state_dict(), os.path.join(args.output_dir, "pytorch_model.bin"))
# Run a final round of validation.
image_logs = None
image_logs = log_validation(
vae=vae,
args=args,
accelerator=accelerator,
weight_dtype=weight_dtype,
step=global_step,
is_final_validation=True,
)
if args.push_to_hub:
save_model_card(
repo_id,
image_logs=image_logs,
base_model=args.pretrained_model_name_or_path,
repo_folder=args.output_dir,
)
upload_folder(
repo_id=repo_id,
folder_path=args.output_dir,
commit_message="End of training",
ignore_patterns=["step_*", "epoch_*"],
)
accelerator.end_training()
if __name__ == "__main__":
args = parse_args()
main(args)
| diffusers/examples/research_projects/autoencoderkl/train_autoencoderkl.py/0 | {
"file_path": "diffusers/examples/research_projects/autoencoderkl/train_autoencoderkl.py",
"repo_id": "diffusers",
"token_count": 19416
} | 148 |
import argparse
import os
import random
import torch
import torchvision
import torchvision.transforms as TS
from PIL import Image
from ram import inference_ram
from ram.models import ram
from tqdm import tqdm
from transformers import (
AutoModelForZeroShotObjectDetection,
AutoProcessor,
Blip2ForConditionalGeneration,
Blip2Processor,
CLIPTextModel,
CLIPTokenizer,
)
torch.autograd.set_grad_enabled(False)
if __name__ == "__main__":
parser = argparse.ArgumentParser("Caption Generation script", add_help=False)
parser.add_argument("--data_root", type=str, required=True, help="path to COCO")
parser.add_argument("--save_root", type=str, required=True, help="path to save")
parser.add_argument("--ram_checkpoint", type=str, required=True, help="path to save")
args = parser.parse_args()
# ram_checkpoint = '/root/.cache/huggingface/hub/models--xinyu1205--recognize_anything_model/snapshots/ebc52dc741e86466202a5ab8ab22eae6e7d48bf1/ram_swin_large_14m.pth'
# data_root = '/mnt/workspace/workgroup/zhizhonghuang/dataset/COCO/train2017'
# save_root = '/root/gligen_data'
box_threshold = 0.25
text_threshold = 0.2
import torch.distributed as dist
dist.init_process_group(backend="nccl", init_method="env://")
local_rank = torch.distributed.get_rank() % torch.cuda.device_count()
device = f"cuda:{local_rank}"
torch.cuda.set_device(local_rank)
ram_model = ram(pretrained=args.ram_checkpoint, image_size=384, vit="swin_l").cuda().eval()
ram_processor = TS.Compose(
[TS.Resize((384, 384)), TS.ToTensor(), TS.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])]
)
grounding_dino_processor = AutoProcessor.from_pretrained("IDEA-Research/grounding-dino-base")
grounding_dino_model = AutoModelForZeroShotObjectDetection.from_pretrained(
"IDEA-Research/grounding-dino-base"
).cuda()
blip2_processor = Blip2Processor.from_pretrained("Salesforce/blip2-flan-t5-xxl")
blip2_model = Blip2ForConditionalGeneration.from_pretrained(
"Salesforce/blip2-flan-t5-xxl", torch_dtype=torch.float16
).cuda()
clip_text_encoder = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14").cuda()
clip_tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
image_paths = [os.path.join(args.data_root, x) for x in os.listdir(args.data_root)]
random.shuffle(image_paths)
for image_path in tqdm.tqdm(image_paths):
pth_path = os.path.join(args.save_root, os.path.basename(image_path))
if os.path.exists(pth_path):
continue
sample = {"file_path": os.path.basename(image_path), "annos": []}
raw_image = Image.open(image_path).convert("RGB")
res = inference_ram(ram_processor(raw_image).unsqueeze(0).cuda(), ram_model)
text = res[0].replace(" |", ".")
inputs = grounding_dino_processor(images=raw_image, text=text, return_tensors="pt")
inputs = {k: v.cuda() for k, v in inputs.items()}
outputs = grounding_dino_model(**inputs)
results = grounding_dino_processor.post_process_grounded_object_detection(
outputs,
inputs["input_ids"],
box_threshold=box_threshold,
text_threshold=text_threshold,
target_sizes=[raw_image.size[::-1]],
)
boxes = results[0]["boxes"]
labels = results[0]["labels"]
scores = results[0]["scores"]
indices = torchvision.ops.nms(boxes, scores, 0.5)
boxes = boxes[indices]
category_names = [labels[i] for i in indices]
for i, bbox in enumerate(boxes):
bbox = bbox.tolist()
inputs = blip2_processor(images=raw_image.crop(bbox), return_tensors="pt")
inputs = {k: v.cuda().to(torch.float16) for k, v in inputs.items()}
outputs = blip2_model.generate(**inputs)
caption = blip2_processor.decode(outputs[0], skip_special_tokens=True)
inputs = clip_tokenizer(
caption,
padding="max_length",
max_length=clip_tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
inputs = {k: v.cuda() for k, v in inputs.items()}
text_embeddings_before_projection = clip_text_encoder(**inputs).pooler_output.squeeze(0)
sample["annos"].append(
{
"caption": caption,
"bbox": bbox,
"text_embeddings_before_projection": text_embeddings_before_projection,
}
)
torch.save(sample, pth_path)
| diffusers/examples/research_projects/gligen/make_datasets.py/0 | {
"file_path": "diffusers/examples/research_projects/gligen/make_datasets.py",
"repo_id": "diffusers",
"token_count": 2102
} | 149 |
import argparse
import itertools
import json
import os
import random
import time
from pathlib import Path
import torch
import torch.nn.functional as F
from accelerate import Accelerator
from accelerate.utils import ProjectConfiguration
from ip_adapter.attention_processor_faceid import LoRAAttnProcessor, LoRAIPAttnProcessor
from ip_adapter.ip_adapter_faceid import MLPProjModel
from PIL import Image
from torchvision import transforms
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDPMScheduler, UNet2DConditionModel
# Dataset
class MyDataset(torch.utils.data.Dataset):
def __init__(
self, json_file, tokenizer, size=512, t_drop_rate=0.05, i_drop_rate=0.05, ti_drop_rate=0.05, image_root_path=""
):
super().__init__()
self.tokenizer = tokenizer
self.size = size
self.i_drop_rate = i_drop_rate
self.t_drop_rate = t_drop_rate
self.ti_drop_rate = ti_drop_rate
self.image_root_path = image_root_path
self.data = json.load(
open(json_file)
) # list of dict: [{"image_file": "1.png", "id_embed_file": "faceid.bin"}]
self.transform = transforms.Compose(
[
transforms.Resize(self.size, interpolation=transforms.InterpolationMode.BILINEAR),
transforms.CenterCrop(self.size),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
def __getitem__(self, idx):
item = self.data[idx]
text = item["text"]
image_file = item["image_file"]
# read image
raw_image = Image.open(os.path.join(self.image_root_path, image_file))
image = self.transform(raw_image.convert("RGB"))
face_id_embed = torch.load(item["id_embed_file"], map_location="cpu")
face_id_embed = torch.from_numpy(face_id_embed)
# drop
drop_image_embed = 0
rand_num = random.random()
if rand_num < self.i_drop_rate:
drop_image_embed = 1
elif rand_num < (self.i_drop_rate + self.t_drop_rate):
text = ""
elif rand_num < (self.i_drop_rate + self.t_drop_rate + self.ti_drop_rate):
text = ""
drop_image_embed = 1
if drop_image_embed:
face_id_embed = torch.zeros_like(face_id_embed)
# get text and tokenize
text_input_ids = self.tokenizer(
text,
max_length=self.tokenizer.model_max_length,
padding="max_length",
truncation=True,
return_tensors="pt",
).input_ids
return {
"image": image,
"text_input_ids": text_input_ids,
"face_id_embed": face_id_embed,
"drop_image_embed": drop_image_embed,
}
def __len__(self):
return len(self.data)
def collate_fn(data):
images = torch.stack([example["image"] for example in data])
text_input_ids = torch.cat([example["text_input_ids"] for example in data], dim=0)
face_id_embed = torch.stack([example["face_id_embed"] for example in data])
drop_image_embeds = [example["drop_image_embed"] for example in data]
return {
"images": images,
"text_input_ids": text_input_ids,
"face_id_embed": face_id_embed,
"drop_image_embeds": drop_image_embeds,
}
class IPAdapter(torch.nn.Module):
"""IP-Adapter"""
def __init__(self, unet, image_proj_model, adapter_modules, ckpt_path=None):
super().__init__()
self.unet = unet
self.image_proj_model = image_proj_model
self.adapter_modules = adapter_modules
if ckpt_path is not None:
self.load_from_checkpoint(ckpt_path)
def forward(self, noisy_latents, timesteps, encoder_hidden_states, image_embeds):
ip_tokens = self.image_proj_model(image_embeds)
encoder_hidden_states = torch.cat([encoder_hidden_states, ip_tokens], dim=1)
# Predict the noise residual
noise_pred = self.unet(noisy_latents, timesteps, encoder_hidden_states).sample
return noise_pred
def load_from_checkpoint(self, ckpt_path: str):
# Calculate original checksums
orig_ip_proj_sum = torch.sum(torch.stack([torch.sum(p) for p in self.image_proj_model.parameters()]))
orig_adapter_sum = torch.sum(torch.stack([torch.sum(p) for p in self.adapter_modules.parameters()]))
state_dict = torch.load(ckpt_path, map_location="cpu")
# Load state dict for image_proj_model and adapter_modules
self.image_proj_model.load_state_dict(state_dict["image_proj"], strict=True)
self.adapter_modules.load_state_dict(state_dict["ip_adapter"], strict=True)
# Calculate new checksums
new_ip_proj_sum = torch.sum(torch.stack([torch.sum(p) for p in self.image_proj_model.parameters()]))
new_adapter_sum = torch.sum(torch.stack([torch.sum(p) for p in self.adapter_modules.parameters()]))
# Verify if the weights have changed
assert orig_ip_proj_sum != new_ip_proj_sum, "Weights of image_proj_model did not change!"
assert orig_adapter_sum != new_adapter_sum, "Weights of adapter_modules did not change!"
print(f"Successfully loaded weights from checkpoint {ckpt_path}")
def parse_args():
parser = argparse.ArgumentParser(description="Simple example of a training script.")
parser.add_argument(
"--pretrained_model_name_or_path",
type=str,
default=None,
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--pretrained_ip_adapter_path",
type=str,
default=None,
help="Path to pretrained ip adapter model. If not specified weights are initialized randomly.",
)
parser.add_argument(
"--data_json_file",
type=str,
default=None,
required=True,
help="Training data",
)
parser.add_argument(
"--data_root_path",
type=str,
default="",
required=True,
help="Training data root path",
)
parser.add_argument(
"--image_encoder_path",
type=str,
default=None,
required=True,
help="Path to CLIP image encoder",
)
parser.add_argument(
"--output_dir",
type=str,
default="sd-ip_adapter",
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument(
"--logging_dir",
type=str,
default="logs",
help=(
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
),
)
parser.add_argument(
"--resolution",
type=int,
default=512,
help=("The resolution for input images"),
)
parser.add_argument(
"--learning_rate",
type=float,
default=1e-4,
help="Learning rate to use.",
)
parser.add_argument("--weight_decay", type=float, default=1e-2, help="Weight decay to use.")
parser.add_argument("--num_train_epochs", type=int, default=100)
parser.add_argument(
"--train_batch_size", type=int, default=8, help="Batch size (per device) for the training dataloader."
)
parser.add_argument(
"--dataloader_num_workers",
type=int,
default=0,
help=(
"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
),
)
parser.add_argument(
"--save_steps",
type=int,
default=2000,
help=("Save a checkpoint of the training state every X updates"),
)
parser.add_argument(
"--mixed_precision",
type=str,
default=None,
choices=["no", "fp16", "bf16"],
help=(
"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the"
" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
),
)
parser.add_argument(
"--report_to",
type=str,
default="tensorboard",
help=(
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
),
)
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
args = parser.parse_args()
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != args.local_rank:
args.local_rank = env_local_rank
return args
def main():
args = parse_args()
logging_dir = Path(args.output_dir, args.logging_dir)
accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
accelerator = Accelerator(
mixed_precision=args.mixed_precision,
log_with=args.report_to,
project_config=accelerator_project_config,
)
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
# Load scheduler, tokenizer and models.
noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder")
vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae")
unet = UNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="unet")
# image_encoder = CLIPVisionModelWithProjection.from_pretrained(args.image_encoder_path)
# freeze parameters of models to save more memory
unet.requires_grad_(False)
vae.requires_grad_(False)
text_encoder.requires_grad_(False)
# image_encoder.requires_grad_(False)
# ip-adapter
image_proj_model = MLPProjModel(
cross_attention_dim=unet.config.cross_attention_dim,
id_embeddings_dim=512,
num_tokens=4,
)
# init adapter modules
lora_rank = 128
attn_procs = {}
unet_sd = unet.state_dict()
for name in unet.attn_processors.keys():
cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
if name.startswith("mid_block"):
hidden_size = unet.config.block_out_channels[-1]
elif name.startswith("up_blocks"):
block_id = int(name[len("up_blocks.")])
hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
elif name.startswith("down_blocks"):
block_id = int(name[len("down_blocks.")])
hidden_size = unet.config.block_out_channels[block_id]
if cross_attention_dim is None:
attn_procs[name] = LoRAAttnProcessor(
hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, rank=lora_rank
)
else:
layer_name = name.split(".processor")[0]
weights = {
"to_k_ip.weight": unet_sd[layer_name + ".to_k.weight"],
"to_v_ip.weight": unet_sd[layer_name + ".to_v.weight"],
}
attn_procs[name] = LoRAIPAttnProcessor(
hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, rank=lora_rank
)
attn_procs[name].load_state_dict(weights, strict=False)
unet.set_attn_processor(attn_procs)
adapter_modules = torch.nn.ModuleList(unet.attn_processors.values())
ip_adapter = IPAdapter(unet, image_proj_model, adapter_modules, args.pretrained_ip_adapter_path)
weight_dtype = torch.float32
if accelerator.mixed_precision == "fp16":
weight_dtype = torch.float16
elif accelerator.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
# unet.to(accelerator.device, dtype=weight_dtype)
vae.to(accelerator.device, dtype=weight_dtype)
text_encoder.to(accelerator.device, dtype=weight_dtype)
# image_encoder.to(accelerator.device, dtype=weight_dtype)
# optimizer
params_to_opt = itertools.chain(ip_adapter.image_proj_model.parameters(), ip_adapter.adapter_modules.parameters())
optimizer = torch.optim.AdamW(params_to_opt, lr=args.learning_rate, weight_decay=args.weight_decay)
# dataloader
train_dataset = MyDataset(
args.data_json_file, tokenizer=tokenizer, size=args.resolution, image_root_path=args.data_root_path
)
train_dataloader = torch.utils.data.DataLoader(
train_dataset,
shuffle=True,
collate_fn=collate_fn,
batch_size=args.train_batch_size,
num_workers=args.dataloader_num_workers,
)
# Prepare everything with our `accelerator`.
ip_adapter, optimizer, train_dataloader = accelerator.prepare(ip_adapter, optimizer, train_dataloader)
global_step = 0
for epoch in range(0, args.num_train_epochs):
begin = time.perf_counter()
for step, batch in enumerate(train_dataloader):
load_data_time = time.perf_counter() - begin
with accelerator.accumulate(ip_adapter):
# Convert images to latent space
with torch.no_grad():
latents = vae.encode(
batch["images"].to(accelerator.device, dtype=weight_dtype)
).latent_dist.sample()
latents = latents * vae.config.scaling_factor
# Sample noise that we'll add to the latents
noise = torch.randn_like(latents)
bsz = latents.shape[0]
# Sample a random timestep for each image
timesteps = torch.randint(0, noise_scheduler.num_train_timesteps, (bsz,), device=latents.device)
timesteps = timesteps.long()
# Add noise to the latents according to the noise magnitude at each timestep
# (this is the forward diffusion process)
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
image_embeds = batch["face_id_embed"].to(accelerator.device, dtype=weight_dtype)
with torch.no_grad():
encoder_hidden_states = text_encoder(batch["text_input_ids"].to(accelerator.device))[0]
noise_pred = ip_adapter(noisy_latents, timesteps, encoder_hidden_states, image_embeds)
loss = F.mse_loss(noise_pred.float(), noise.float(), reduction="mean")
# Gather the losses across all processes for logging (if we use distributed training).
avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean().item()
# Backpropagate
accelerator.backward(loss)
optimizer.step()
optimizer.zero_grad()
if accelerator.is_main_process:
print(
"Epoch {}, step {}, data_time: {}, time: {}, step_loss: {}".format(
epoch, step, load_data_time, time.perf_counter() - begin, avg_loss
)
)
global_step += 1
if global_step % args.save_steps == 0:
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
accelerator.save_state(save_path)
begin = time.perf_counter()
if __name__ == "__main__":
main()
| diffusers/examples/research_projects/ip_adapter/tutorial_train_faceid.py/0 | {
"file_path": "diffusers/examples/research_projects/ip_adapter/tutorial_train_faceid.py",
"repo_id": "diffusers",
"token_count": 7145
} | 150 |
# RealFill
[RealFill](https://huggingface.co/papers/2309.16668) is a method to personalize text2image inpainting models like stable diffusion inpainting given just a few(1~5) images of a scene.
The `train_realfill.py` script shows how to implement the training procedure for stable diffusion inpainting.
## Running locally with PyTorch
### Installing the dependencies
Before running the scripts, make sure to install the library's training dependencies:
cd to the realfill folder and run
```bash
cd realfill
pip install -r requirements.txt
```
And initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:
```bash
accelerate config
```
Or for a default accelerate configuration without answering questions about your environment
```bash
accelerate config default
```
Or if your environment doesn't support an interactive shell e.g. a notebook
```python
from accelerate.utils import write_basic_config
write_basic_config()
```
When running `accelerate config`, if we specify torch compile mode to True there can be dramatic speedups.
### Toy example
Now let's fill the real. For this example, we will use some images of the flower girl example from the paper.
We already provide some images for testing in [this link](https://github.com/thuanz123/realfill/tree/main/data/flowerwoman)
You only have to launch the training using:
```bash
export MODEL_NAME="stabilityai/stable-diffusion-2-inpainting"
export TRAIN_DIR="data/flowerwoman"
export OUTPUT_DIR="flowerwoman-model"
accelerate launch train_realfill.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--train_data_dir=$TRAIN_DIR \
--output_dir=$OUTPUT_DIR \
--resolution=512 \
--train_batch_size=16 \
--gradient_accumulation_steps=1 \
--unet_learning_rate=2e-4 \
--text_encoder_learning_rate=4e-5 \
--lr_scheduler="constant" \
--lr_warmup_steps=100 \
--max_train_steps=2000 \
--lora_rank=8 \
--lora_dropout=0.1 \
--lora_alpha=16 \
```
### Training on a low-memory GPU:
It is possible to run realfill on a low-memory GPU by using the following optimizations:
- [gradient checkpointing and the 8-bit optimizer](#training-with-gradient-checkpointing-and-8-bit-optimizers)
- [xformers](#training-with-xformers)
- [setting grads to none](#set-grads-to-none)
```bash
export MODEL_NAME="stabilityai/stable-diffusion-2-inpainting"
export TRAIN_DIR="data/flowerwoman"
export OUTPUT_DIR="flowerwoman-model"
accelerate launch train_realfill.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--train_data_dir=$TRAIN_DIR \
--output_dir=$OUTPUT_DIR \
--resolution=512 \
--train_batch_size=16 \
--gradient_accumulation_steps=1 --gradient_checkpointing \
--use_8bit_adam \
--enable_xformers_memory_efficient_attention \
--set_grads_to_none \
--unet_learning_rate=2e-4 \
--text_encoder_learning_rate=4e-5 \
--lr_scheduler="constant" \
--lr_warmup_steps=100 \
--max_train_steps=2000 \
--lora_rank=8 \
--lora_dropout=0.1 \
--lora_alpha=16 \
```
### Training with gradient checkpointing and 8-bit optimizers:
With the help of gradient checkpointing and the 8-bit optimizer from bitsandbytes it's possible to run train realfill on a 16GB GPU.
To install `bitsandbytes` please refer to this [readme](https://github.com/TimDettmers/bitsandbytes#requirements--installation).
### Training with xformers:
You can enable memory efficient attention by [installing xFormers](https://github.com/facebookresearch/xformers#installing-xformers) and padding the `--enable_xformers_memory_efficient_attention` argument to the script.
### Set grads to none
To save even more memory, pass the `--set_grads_to_none` argument to the script. This will set grads to None instead of zero. However, be aware that it changes certain behaviors, so if you start experiencing any problems, remove this argument.
More info: https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html
## Acknowledge
This repo is built upon the code of DreamBooth from diffusers and we thank the developers for their great works and efforts to release source code. Furthermore, a special "thank you" to RealFill's authors for publishing such an amazing work.
| diffusers/examples/research_projects/realfill/README.md/0 | {
"file_path": "diffusers/examples/research_projects/realfill/README.md",
"repo_id": "diffusers",
"token_count": 1322
} | 151 |
#!/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/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import glob
import hashlib
import pandas as pd
import torch
from transformers import T5EncoderModel
from diffusers import StableDiffusion3Pipeline
PROMPT = "a photo of sks dog"
MAX_SEQ_LENGTH = 77
LOCAL_DATA_DIR = "dog"
OUTPUT_PATH = "sample_embeddings.parquet"
def bytes_to_giga_bytes(bytes):
return bytes / 1024 / 1024 / 1024
def generate_image_hash(image_path):
with open(image_path, "rb") as f:
img_data = f.read()
return hashlib.sha256(img_data).hexdigest()
def load_sd3_pipeline():
id = "stabilityai/stable-diffusion-3-medium-diffusers"
text_encoder = T5EncoderModel.from_pretrained(id, subfolder="text_encoder_3", load_in_8bit=True, device_map="auto")
pipeline = StableDiffusion3Pipeline.from_pretrained(
id, text_encoder_3=text_encoder, transformer=None, vae=None, device_map="balanced"
)
return pipeline
@torch.no_grad()
def compute_embeddings(pipeline, prompt, max_sequence_length):
(
prompt_embeds,
negative_prompt_embeds,
pooled_prompt_embeds,
negative_pooled_prompt_embeds,
) = pipeline.encode_prompt(prompt=prompt, prompt_2=None, prompt_3=None, max_sequence_length=max_sequence_length)
print(
f"{prompt_embeds.shape=}, {negative_prompt_embeds.shape=}, {pooled_prompt_embeds.shape=}, {negative_pooled_prompt_embeds.shape}"
)
max_memory = bytes_to_giga_bytes(torch.cuda.max_memory_allocated())
print(f"Max memory allocated: {max_memory:.3f} GB")
return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
def run(args):
pipeline = load_sd3_pipeline()
prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds = compute_embeddings(
pipeline, args.prompt, args.max_sequence_length
)
# Assumes that the images within `args.local_image_dir` have a JPEG extension. Change
# as needed.
image_paths = glob.glob(f"{args.local_data_dir}/*.jpeg")
data = []
for image_path in image_paths:
img_hash = generate_image_hash(image_path)
data.append(
(img_hash, prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds)
)
# Create a DataFrame
embedding_cols = [
"prompt_embeds",
"negative_prompt_embeds",
"pooled_prompt_embeds",
"negative_pooled_prompt_embeds",
]
df = pd.DataFrame(
data,
columns=["image_hash"] + embedding_cols,
)
# Convert embedding lists to arrays (for proper storage in parquet)
for col in embedding_cols:
df[col] = df[col].apply(lambda x: x.cpu().numpy().flatten().tolist())
# Save the dataframe to a parquet file
df.to_parquet(args.output_path)
print(f"Data successfully serialized to {args.output_path}")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--prompt", type=str, default=PROMPT, help="The instance prompt.")
parser.add_argument(
"--max_sequence_length",
type=int,
default=MAX_SEQ_LENGTH,
help="Maximum sequence length to use for computing the embeddings. The more the higher computational costs.",
)
parser.add_argument(
"--local_data_dir", type=str, default=LOCAL_DATA_DIR, help="Path to the directory containing instance images."
)
parser.add_argument("--output_path", type=str, default=OUTPUT_PATH, help="Path to serialize the parquet file.")
args = parser.parse_args()
run(args)
| diffusers/examples/research_projects/sd3_lora_colab/compute_embeddings.py/0 | {
"file_path": "diffusers/examples/research_projects/sd3_lora_colab/compute_embeddings.py",
"repo_id": "diffusers",
"token_count": 1605
} | 152 |
#!/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/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import logging
import math
import os
import random
from pathlib import Path
import jax
import jax.numpy as jnp
import numpy as np
import optax
import torch
import torch.utils.checkpoint
import transformers
from datasets import load_dataset
from flax import jax_utils
from flax.training import train_state
from flax.training.common_utils import shard
from huggingface_hub import create_repo, upload_folder
from torchvision import transforms
from tqdm.auto import tqdm
from transformers import CLIPImageProcessor, CLIPTokenizer, FlaxCLIPTextModel, set_seed
from diffusers import (
FlaxAutoencoderKL,
FlaxDDPMScheduler,
FlaxPNDMScheduler,
FlaxStableDiffusionPipeline,
FlaxUNet2DConditionModel,
)
from diffusers.pipelines.stable_diffusion import FlaxStableDiffusionSafetyChecker
from diffusers.utils import check_min_version
# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
check_min_version("0.36.0.dev0")
logger = logging.getLogger(__name__)
def parse_args():
parser = argparse.ArgumentParser(description="Simple example of a training script.")
parser.add_argument(
"--pretrained_model_name_or_path",
type=str,
default=None,
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--revision",
type=str,
default=None,
required=False,
help="Revision of pretrained model identifier from huggingface.co/models.",
)
parser.add_argument(
"--variant",
type=str,
default=None,
help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16",
)
parser.add_argument(
"--dataset_name",
type=str,
default=None,
help=(
"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"
" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
" or to a folder containing files that 🤗 Datasets can understand."
),
)
parser.add_argument(
"--dataset_config_name",
type=str,
default=None,
help="The config of the Dataset, leave as None if there's only one config.",
)
parser.add_argument(
"--train_data_dir",
type=str,
default=None,
help=(
"A folder containing the training data. Folder contents must follow the structure described in"
" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file"
" must exist to provide the captions for the images. Ignored if `dataset_name` is specified."
),
)
parser.add_argument(
"--image_column", type=str, default="image", help="The column of the dataset containing an image."
)
parser.add_argument(
"--caption_column",
type=str,
default="text",
help="The column of the dataset containing a caption or a list of captions.",
)
parser.add_argument(
"--max_train_samples",
type=int,
default=None,
help=(
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
),
)
parser.add_argument(
"--output_dir",
type=str,
default="sd-model-finetuned",
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument(
"--cache_dir",
type=str,
default=None,
help="The directory where the downloaded models and datasets will be stored.",
)
parser.add_argument("--seed", type=int, default=0, help="A seed for reproducible training.")
parser.add_argument(
"--resolution",
type=int,
default=512,
help=(
"The resolution for input images, all the images in the train/validation dataset will be resized to this"
" resolution"
),
)
parser.add_argument(
"--center_crop",
default=False,
action="store_true",
help=(
"Whether to center crop the input images to the resolution. If not set, the images will be randomly"
" cropped. The images will be resized to the resolution first before cropping."
),
)
parser.add_argument(
"--random_flip",
action="store_true",
help="whether to randomly flip images horizontally",
)
parser.add_argument(
"--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
)
parser.add_argument("--num_train_epochs", type=int, default=100)
parser.add_argument(
"--max_train_steps",
type=int,
default=None,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=1e-4,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument(
"--scale_lr",
action="store_true",
default=False,
help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
)
parser.add_argument(
"--lr_scheduler",
type=str,
default="constant",
help=(
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
' "constant", "constant_with_warmup"]'
),
)
parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
parser.add_argument(
"--hub_model_id",
type=str,
default=None,
help="The name of the repository to keep in sync with the local `output_dir`.",
)
parser.add_argument(
"--logging_dir",
type=str,
default="logs",
help=(
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
),
)
parser.add_argument(
"--report_to",
type=str,
default="tensorboard",
help=(
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
),
)
parser.add_argument(
"--mixed_precision",
type=str,
default="no",
choices=["no", "fp16", "bf16"],
help=(
"Whether to use mixed precision. Choose"
"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
"and an Nvidia Ampere GPU."
),
)
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
parser.add_argument(
"--from_pt",
action="store_true",
default=False,
help="Flag to indicate whether to convert models from PyTorch.",
)
args = parser.parse_args()
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != args.local_rank:
args.local_rank = env_local_rank
# Sanity checks
if args.dataset_name is None and args.train_data_dir is None:
raise ValueError("Need either a dataset name or a training folder.")
return args
dataset_name_mapping = {
"lambdalabs/naruto-blip-captions": ("image", "text"),
}
def get_params_to_save(params):
return jax.device_get(jax.tree_util.tree_map(lambda x: x[0], params))
def main():
args = parse_args()
if args.report_to == "wandb" and args.hub_token is not None:
raise ValueError(
"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
" Please use `hf auth login` to authenticate with the Hub."
)
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
# Setup logging, we only want one process per machine to log things on the screen.
logger.setLevel(logging.INFO if jax.process_index() == 0 else logging.ERROR)
if jax.process_index() == 0:
transformers.utils.logging.set_verbosity_info()
else:
transformers.utils.logging.set_verbosity_error()
if args.seed is not None:
set_seed(args.seed)
# Handle the repository creation
if jax.process_index() == 0:
if args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
if args.push_to_hub:
repo_id = create_repo(
repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
).repo_id
# Get the datasets: you can either provide your own training and evaluation files (see below)
# or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
if args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
dataset = load_dataset(
args.dataset_name, args.dataset_config_name, cache_dir=args.cache_dir, data_dir=args.train_data_dir
)
else:
data_files = {}
if args.train_data_dir is not None:
data_files["train"] = os.path.join(args.train_data_dir, "**")
dataset = load_dataset(
"imagefolder",
data_files=data_files,
cache_dir=args.cache_dir,
)
# See more about loading custom images at
# https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder
# Preprocessing the datasets.
# We need to tokenize inputs and targets.
column_names = dataset["train"].column_names
# 6. Get the column names for input/target.
dataset_columns = dataset_name_mapping.get(args.dataset_name, None)
if args.image_column is None:
image_column = dataset_columns[0] if dataset_columns is not None else column_names[0]
else:
image_column = args.image_column
if image_column not in column_names:
raise ValueError(
f"--image_column' value '{args.image_column}' needs to be one of: {', '.join(column_names)}"
)
if args.caption_column is None:
caption_column = dataset_columns[1] if dataset_columns is not None else column_names[1]
else:
caption_column = args.caption_column
if caption_column not in column_names:
raise ValueError(
f"--caption_column' value '{args.caption_column}' needs to be one of: {', '.join(column_names)}"
)
# Preprocessing the datasets.
# We need to tokenize input captions and transform the images.
def tokenize_captions(examples, is_train=True):
captions = []
for caption in examples[caption_column]:
if isinstance(caption, str):
captions.append(caption)
elif isinstance(caption, (list, np.ndarray)):
# take a random caption if there are multiple
captions.append(random.choice(caption) if is_train else caption[0])
else:
raise ValueError(
f"Caption column `{caption_column}` should contain either strings or lists of strings."
)
inputs = tokenizer(captions, max_length=tokenizer.model_max_length, padding="do_not_pad", truncation=True)
input_ids = inputs.input_ids
return input_ids
train_transforms = transforms.Compose(
[
transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),
transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution),
transforms.RandomHorizontalFlip() if args.random_flip else transforms.Lambda(lambda x: x),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
def preprocess_train(examples):
images = [image.convert("RGB") for image in examples[image_column]]
examples["pixel_values"] = [train_transforms(image) for image in images]
examples["input_ids"] = tokenize_captions(examples)
return examples
if args.max_train_samples is not None:
dataset["train"] = dataset["train"].shuffle(seed=args.seed).select(range(args.max_train_samples))
# Set the training transforms
train_dataset = dataset["train"].with_transform(preprocess_train)
def collate_fn(examples):
pixel_values = torch.stack([example["pixel_values"] for example in examples])
pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
input_ids = [example["input_ids"] for example in examples]
padded_tokens = tokenizer.pad(
{"input_ids": input_ids}, padding="max_length", max_length=tokenizer.model_max_length, return_tensors="pt"
)
batch = {
"pixel_values": pixel_values,
"input_ids": padded_tokens.input_ids,
}
batch = {k: v.numpy() for k, v in batch.items()}
return batch
total_train_batch_size = args.train_batch_size * jax.local_device_count()
train_dataloader = torch.utils.data.DataLoader(
train_dataset, shuffle=True, collate_fn=collate_fn, batch_size=total_train_batch_size, drop_last=True
)
weight_dtype = jnp.float32
if args.mixed_precision == "fp16":
weight_dtype = jnp.float16
elif args.mixed_precision == "bf16":
weight_dtype = jnp.bfloat16
# Load models and create wrapper for stable diffusion
tokenizer = CLIPTokenizer.from_pretrained(
args.pretrained_model_name_or_path,
from_pt=args.from_pt,
revision=args.revision,
subfolder="tokenizer",
)
text_encoder = FlaxCLIPTextModel.from_pretrained(
args.pretrained_model_name_or_path,
from_pt=args.from_pt,
revision=args.revision,
subfolder="text_encoder",
dtype=weight_dtype,
)
vae, vae_params = FlaxAutoencoderKL.from_pretrained(
args.pretrained_model_name_or_path,
from_pt=args.from_pt,
revision=args.revision,
subfolder="vae",
dtype=weight_dtype,
)
unet, unet_params = FlaxUNet2DConditionModel.from_pretrained(
args.pretrained_model_name_or_path,
from_pt=args.from_pt,
revision=args.revision,
subfolder="unet",
dtype=weight_dtype,
)
# Optimization
if args.scale_lr:
args.learning_rate = args.learning_rate * total_train_batch_size
constant_scheduler = optax.constant_schedule(args.learning_rate)
adamw = optax.adamw(
learning_rate=constant_scheduler,
b1=args.adam_beta1,
b2=args.adam_beta2,
eps=args.adam_epsilon,
weight_decay=args.adam_weight_decay,
)
optimizer = optax.chain(
optax.clip_by_global_norm(args.max_grad_norm),
adamw,
)
state = train_state.TrainState.create(apply_fn=unet.__call__, params=unet_params, tx=optimizer)
noise_scheduler = FlaxDDPMScheduler(
beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000
)
noise_scheduler_state = noise_scheduler.create_state()
# Initialize our training
rng = jax.random.PRNGKey(args.seed)
train_rngs = jax.random.split(rng, jax.local_device_count())
def train_step(state, text_encoder_params, vae_params, batch, train_rng):
dropout_rng, sample_rng, new_train_rng = jax.random.split(train_rng, 3)
def compute_loss(params):
# Convert images to latent space
vae_outputs = vae.apply(
{"params": vae_params}, batch["pixel_values"], deterministic=True, method=vae.encode
)
latents = vae_outputs.latent_dist.sample(sample_rng)
# (NHWC) -> (NCHW)
latents = jnp.transpose(latents, (0, 3, 1, 2))
latents = latents * vae.config.scaling_factor
# Sample noise that we'll add to the latents
noise_rng, timestep_rng = jax.random.split(sample_rng)
noise = jax.random.normal(noise_rng, latents.shape)
# Sample a random timestep for each image
bsz = latents.shape[0]
timesteps = jax.random.randint(
timestep_rng,
(bsz,),
0,
noise_scheduler.config.num_train_timesteps,
)
# Add noise to the latents according to the noise magnitude at each timestep
# (this is the forward diffusion process)
noisy_latents = noise_scheduler.add_noise(noise_scheduler_state, latents, noise, timesteps)
# Get the text embedding for conditioning
encoder_hidden_states = text_encoder(
batch["input_ids"],
params=text_encoder_params,
train=False,
)[0]
# Predict the noise residual and compute loss
model_pred = unet.apply(
{"params": params}, noisy_latents, timesteps, encoder_hidden_states, train=True
).sample
# Get the target for loss depending on the prediction type
if noise_scheduler.config.prediction_type == "epsilon":
target = noise
elif noise_scheduler.config.prediction_type == "v_prediction":
target = noise_scheduler.get_velocity(noise_scheduler_state, latents, noise, timesteps)
else:
raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
loss = (target - model_pred) ** 2
loss = loss.mean()
return loss
grad_fn = jax.value_and_grad(compute_loss)
loss, grad = grad_fn(state.params)
grad = jax.lax.pmean(grad, "batch")
new_state = state.apply_gradients(grads=grad)
metrics = {"loss": loss}
metrics = jax.lax.pmean(metrics, axis_name="batch")
return new_state, metrics, new_train_rng
# Create parallel version of the train step
p_train_step = jax.pmap(train_step, "batch", donate_argnums=(0,))
# Replicate the train state on each device
state = jax_utils.replicate(state)
text_encoder_params = jax_utils.replicate(text_encoder.params)
vae_params = jax_utils.replicate(vae_params)
# Train!
num_update_steps_per_epoch = math.ceil(len(train_dataloader))
# Scheduler and math around the number of training steps.
if args.max_train_steps is None:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num Epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
logger.info(f" Total train batch size (w. parallel & distributed) = {total_train_batch_size}")
logger.info(f" Total optimization steps = {args.max_train_steps}")
global_step = 0
epochs = tqdm(range(args.num_train_epochs), desc="Epoch ... ", position=0)
for epoch in epochs:
# ======================== Training ================================
train_metrics = []
steps_per_epoch = len(train_dataset) // total_train_batch_size
train_step_progress_bar = tqdm(total=steps_per_epoch, desc="Training...", position=1, leave=False)
# train
for batch in train_dataloader:
batch = shard(batch)
state, train_metric, train_rngs = p_train_step(state, text_encoder_params, vae_params, batch, train_rngs)
train_metrics.append(train_metric)
train_step_progress_bar.update(1)
global_step += 1
if global_step >= args.max_train_steps:
break
train_metric = jax_utils.unreplicate(train_metric)
train_step_progress_bar.close()
epochs.write(f"Epoch... ({epoch + 1}/{args.num_train_epochs} | Loss: {train_metric['loss']})")
# Create the pipeline using using the trained modules and save it.
if jax.process_index() == 0:
scheduler = FlaxPNDMScheduler(
beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", skip_prk_steps=True
)
safety_checker = FlaxStableDiffusionSafetyChecker.from_pretrained(
"CompVis/stable-diffusion-safety-checker", from_pt=True
)
pipeline = FlaxStableDiffusionPipeline(
text_encoder=text_encoder,
vae=vae,
unet=unet,
tokenizer=tokenizer,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=CLIPImageProcessor.from_pretrained("openai/clip-vit-base-patch32"),
)
pipeline.save_pretrained(
args.output_dir,
params={
"text_encoder": get_params_to_save(text_encoder_params),
"vae": get_params_to_save(vae_params),
"unet": get_params_to_save(state.params),
"safety_checker": safety_checker.params,
},
)
if args.push_to_hub:
upload_folder(
repo_id=repo_id,
folder_path=args.output_dir,
commit_message="End of training",
ignore_patterns=["step_*", "epoch_*"],
)
if __name__ == "__main__":
main()
| diffusers/examples/text_to_image/train_text_to_image_flax.py/0 | {
"file_path": "diffusers/examples/text_to_image/train_text_to_image_flax.py",
"repo_id": "diffusers",
"token_count": 10027
} | 153 |
"""
This script requires you to build `LAVIS` from source, since the pip version doesn't have BLIP Diffusion. Follow instructions here: https://github.com/salesforce/LAVIS/tree/main.
"""
import argparse
import os
import tempfile
import torch
from lavis.models import load_model_and_preprocess
from transformers import CLIPTokenizer
from transformers.models.blip_2.configuration_blip_2 import Blip2Config
from diffusers import (
AutoencoderKL,
PNDMScheduler,
UNet2DConditionModel,
)
from diffusers.pipelines import BlipDiffusionPipeline
from diffusers.pipelines.blip_diffusion.blip_image_processing import BlipImageProcessor
from diffusers.pipelines.blip_diffusion.modeling_blip2 import Blip2QFormerModel
from diffusers.pipelines.blip_diffusion.modeling_ctx_clip import ContextCLIPTextModel
BLIP2_CONFIG = {
"vision_config": {
"hidden_size": 1024,
"num_hidden_layers": 23,
"num_attention_heads": 16,
"image_size": 224,
"patch_size": 14,
"intermediate_size": 4096,
"hidden_act": "quick_gelu",
},
"qformer_config": {
"cross_attention_frequency": 1,
"encoder_hidden_size": 1024,
"vocab_size": 30523,
},
"num_query_tokens": 16,
}
blip2config = Blip2Config(**BLIP2_CONFIG)
def qformer_model_from_original_config():
qformer = Blip2QFormerModel(blip2config)
return qformer
def embeddings_from_original_checkpoint(model, diffuser_embeddings_prefix, original_embeddings_prefix):
embeddings = {}
embeddings.update(
{
f"{diffuser_embeddings_prefix}.word_embeddings.weight": model[
f"{original_embeddings_prefix}.word_embeddings.weight"
]
}
)
embeddings.update(
{
f"{diffuser_embeddings_prefix}.position_embeddings.weight": model[
f"{original_embeddings_prefix}.position_embeddings.weight"
]
}
)
embeddings.update(
{f"{diffuser_embeddings_prefix}.LayerNorm.weight": model[f"{original_embeddings_prefix}.LayerNorm.weight"]}
)
embeddings.update(
{f"{diffuser_embeddings_prefix}.LayerNorm.bias": model[f"{original_embeddings_prefix}.LayerNorm.bias"]}
)
return embeddings
def proj_layer_from_original_checkpoint(model, diffuser_proj_prefix, original_proj_prefix):
proj_layer = {}
proj_layer.update({f"{diffuser_proj_prefix}.dense1.weight": model[f"{original_proj_prefix}.dense1.weight"]})
proj_layer.update({f"{diffuser_proj_prefix}.dense1.bias": model[f"{original_proj_prefix}.dense1.bias"]})
proj_layer.update({f"{diffuser_proj_prefix}.dense2.weight": model[f"{original_proj_prefix}.dense2.weight"]})
proj_layer.update({f"{diffuser_proj_prefix}.dense2.bias": model[f"{original_proj_prefix}.dense2.bias"]})
proj_layer.update({f"{diffuser_proj_prefix}.LayerNorm.weight": model[f"{original_proj_prefix}.LayerNorm.weight"]})
proj_layer.update({f"{diffuser_proj_prefix}.LayerNorm.bias": model[f"{original_proj_prefix}.LayerNorm.bias"]})
return proj_layer
def attention_from_original_checkpoint(model, diffuser_attention_prefix, original_attention_prefix):
attention = {}
attention.update(
{
f"{diffuser_attention_prefix}.attention.query.weight": model[
f"{original_attention_prefix}.self.query.weight"
]
}
)
attention.update(
{f"{diffuser_attention_prefix}.attention.query.bias": model[f"{original_attention_prefix}.self.query.bias"]}
)
attention.update(
{f"{diffuser_attention_prefix}.attention.key.weight": model[f"{original_attention_prefix}.self.key.weight"]}
)
attention.update(
{f"{diffuser_attention_prefix}.attention.key.bias": model[f"{original_attention_prefix}.self.key.bias"]}
)
attention.update(
{
f"{diffuser_attention_prefix}.attention.value.weight": model[
f"{original_attention_prefix}.self.value.weight"
]
}
)
attention.update(
{f"{diffuser_attention_prefix}.attention.value.bias": model[f"{original_attention_prefix}.self.value.bias"]}
)
attention.update(
{f"{diffuser_attention_prefix}.output.dense.weight": model[f"{original_attention_prefix}.output.dense.weight"]}
)
attention.update(
{f"{diffuser_attention_prefix}.output.dense.bias": model[f"{original_attention_prefix}.output.dense.bias"]}
)
attention.update(
{
f"{diffuser_attention_prefix}.output.LayerNorm.weight": model[
f"{original_attention_prefix}.output.LayerNorm.weight"
]
}
)
attention.update(
{
f"{diffuser_attention_prefix}.output.LayerNorm.bias": model[
f"{original_attention_prefix}.output.LayerNorm.bias"
]
}
)
return attention
def output_layers_from_original_checkpoint(model, diffuser_output_prefix, original_output_prefix):
output_layers = {}
output_layers.update({f"{diffuser_output_prefix}.dense.weight": model[f"{original_output_prefix}.dense.weight"]})
output_layers.update({f"{diffuser_output_prefix}.dense.bias": model[f"{original_output_prefix}.dense.bias"]})
output_layers.update(
{f"{diffuser_output_prefix}.LayerNorm.weight": model[f"{original_output_prefix}.LayerNorm.weight"]}
)
output_layers.update(
{f"{diffuser_output_prefix}.LayerNorm.bias": model[f"{original_output_prefix}.LayerNorm.bias"]}
)
return output_layers
def encoder_from_original_checkpoint(model, diffuser_encoder_prefix, original_encoder_prefix):
encoder = {}
for i in range(blip2config.qformer_config.num_hidden_layers):
encoder.update(
attention_from_original_checkpoint(
model, f"{diffuser_encoder_prefix}.{i}.attention", f"{original_encoder_prefix}.{i}.attention"
)
)
encoder.update(
attention_from_original_checkpoint(
model, f"{diffuser_encoder_prefix}.{i}.crossattention", f"{original_encoder_prefix}.{i}.crossattention"
)
)
encoder.update(
{
f"{diffuser_encoder_prefix}.{i}.intermediate.dense.weight": model[
f"{original_encoder_prefix}.{i}.intermediate.dense.weight"
]
}
)
encoder.update(
{
f"{diffuser_encoder_prefix}.{i}.intermediate.dense.bias": model[
f"{original_encoder_prefix}.{i}.intermediate.dense.bias"
]
}
)
encoder.update(
{
f"{diffuser_encoder_prefix}.{i}.intermediate_query.dense.weight": model[
f"{original_encoder_prefix}.{i}.intermediate_query.dense.weight"
]
}
)
encoder.update(
{
f"{diffuser_encoder_prefix}.{i}.intermediate_query.dense.bias": model[
f"{original_encoder_prefix}.{i}.intermediate_query.dense.bias"
]
}
)
encoder.update(
output_layers_from_original_checkpoint(
model, f"{diffuser_encoder_prefix}.{i}.output", f"{original_encoder_prefix}.{i}.output"
)
)
encoder.update(
output_layers_from_original_checkpoint(
model, f"{diffuser_encoder_prefix}.{i}.output_query", f"{original_encoder_prefix}.{i}.output_query"
)
)
return encoder
def visual_encoder_layer_from_original_checkpoint(model, diffuser_prefix, original_prefix):
visual_encoder_layer = {}
visual_encoder_layer.update({f"{diffuser_prefix}.layer_norm1.weight": model[f"{original_prefix}.ln_1.weight"]})
visual_encoder_layer.update({f"{diffuser_prefix}.layer_norm1.bias": model[f"{original_prefix}.ln_1.bias"]})
visual_encoder_layer.update({f"{diffuser_prefix}.layer_norm2.weight": model[f"{original_prefix}.ln_2.weight"]})
visual_encoder_layer.update({f"{diffuser_prefix}.layer_norm2.bias": model[f"{original_prefix}.ln_2.bias"]})
visual_encoder_layer.update(
{f"{diffuser_prefix}.self_attn.qkv.weight": model[f"{original_prefix}.attn.in_proj_weight"]}
)
visual_encoder_layer.update(
{f"{diffuser_prefix}.self_attn.qkv.bias": model[f"{original_prefix}.attn.in_proj_bias"]}
)
visual_encoder_layer.update(
{f"{diffuser_prefix}.self_attn.projection.weight": model[f"{original_prefix}.attn.out_proj.weight"]}
)
visual_encoder_layer.update(
{f"{diffuser_prefix}.self_attn.projection.bias": model[f"{original_prefix}.attn.out_proj.bias"]}
)
visual_encoder_layer.update({f"{diffuser_prefix}.mlp.fc1.weight": model[f"{original_prefix}.mlp.c_fc.weight"]})
visual_encoder_layer.update({f"{diffuser_prefix}.mlp.fc1.bias": model[f"{original_prefix}.mlp.c_fc.bias"]})
visual_encoder_layer.update({f"{diffuser_prefix}.mlp.fc2.weight": model[f"{original_prefix}.mlp.c_proj.weight"]})
visual_encoder_layer.update({f"{diffuser_prefix}.mlp.fc2.bias": model[f"{original_prefix}.mlp.c_proj.bias"]})
return visual_encoder_layer
def visual_encoder_from_original_checkpoint(model, diffuser_prefix, original_prefix):
visual_encoder = {}
visual_encoder.update(
{
f"{diffuser_prefix}.embeddings.class_embedding": model[f"{original_prefix}.class_embedding"]
.unsqueeze(0)
.unsqueeze(0)
}
)
visual_encoder.update(
{
f"{diffuser_prefix}.embeddings.position_embedding": model[
f"{original_prefix}.positional_embedding"
].unsqueeze(0)
}
)
visual_encoder.update(
{f"{diffuser_prefix}.embeddings.patch_embedding.weight": model[f"{original_prefix}.conv1.weight"]}
)
visual_encoder.update({f"{diffuser_prefix}.pre_layernorm.weight": model[f"{original_prefix}.ln_pre.weight"]})
visual_encoder.update({f"{diffuser_prefix}.pre_layernorm.bias": model[f"{original_prefix}.ln_pre.bias"]})
for i in range(blip2config.vision_config.num_hidden_layers):
visual_encoder.update(
visual_encoder_layer_from_original_checkpoint(
model, f"{diffuser_prefix}.encoder.layers.{i}", f"{original_prefix}.transformer.resblocks.{i}"
)
)
visual_encoder.update({f"{diffuser_prefix}.post_layernorm.weight": model["blip.ln_vision.weight"]})
visual_encoder.update({f"{diffuser_prefix}.post_layernorm.bias": model["blip.ln_vision.bias"]})
return visual_encoder
def qformer_original_checkpoint_to_diffusers_checkpoint(model):
qformer_checkpoint = {}
qformer_checkpoint.update(embeddings_from_original_checkpoint(model, "embeddings", "blip.Qformer.bert.embeddings"))
qformer_checkpoint.update({"query_tokens": model["blip.query_tokens"]})
qformer_checkpoint.update(proj_layer_from_original_checkpoint(model, "proj_layer", "proj_layer"))
qformer_checkpoint.update(
encoder_from_original_checkpoint(model, "encoder.layer", "blip.Qformer.bert.encoder.layer")
)
qformer_checkpoint.update(visual_encoder_from_original_checkpoint(model, "visual_encoder", "blip.visual_encoder"))
return qformer_checkpoint
def get_qformer(model):
print("loading qformer")
qformer = qformer_model_from_original_config()
qformer_diffusers_checkpoint = qformer_original_checkpoint_to_diffusers_checkpoint(model)
load_checkpoint_to_model(qformer_diffusers_checkpoint, qformer)
print("done loading qformer")
return qformer
def load_checkpoint_to_model(checkpoint, model):
with tempfile.NamedTemporaryFile(delete=False) as file:
torch.save(checkpoint, file.name)
del checkpoint
model.load_state_dict(torch.load(file.name), strict=False)
os.remove(file.name)
def save_blip_diffusion_model(model, args):
qformer = get_qformer(model)
qformer.eval()
text_encoder = ContextCLIPTextModel.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="text_encoder"
)
vae = AutoencoderKL.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="vae")
unet = UNet2DConditionModel.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="unet")
vae.eval()
text_encoder.eval()
scheduler = PNDMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
set_alpha_to_one=False,
skip_prk_steps=True,
)
tokenizer = CLIPTokenizer.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="tokenizer")
image_processor = BlipImageProcessor()
blip_diffusion = BlipDiffusionPipeline(
tokenizer=tokenizer,
text_encoder=text_encoder,
vae=vae,
unet=unet,
scheduler=scheduler,
qformer=qformer,
image_processor=image_processor,
)
blip_diffusion.save_pretrained(args.checkpoint_path)
def main(args):
model, _, _ = load_model_and_preprocess("blip_diffusion", "base", device="cpu", is_eval=True)
save_blip_diffusion_model(model.state_dict(), args)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.")
args = parser.parse_args()
main(args)
| diffusers/scripts/convert_blipdiffusion_to_diffusers.py/0 | {
"file_path": "diffusers/scripts/convert_blipdiffusion_to_diffusers.py",
"repo_id": "diffusers",
"token_count": 5958
} | 154 |
import argparse
from contextlib import nullcontext
import safetensors.torch
from accelerate import init_empty_weights
from huggingface_hub import hf_hub_download
from diffusers.utils.import_utils import is_accelerate_available, is_transformers_available
if is_transformers_available():
from transformers import CLIPVisionModelWithProjection
vision = True
else:
vision = False
"""
python scripts/convert_flux_xlabs_ipadapter_to_diffusers.py \
--original_state_dict_repo_id "XLabs-AI/flux-ip-adapter" \
--filename "flux-ip-adapter.safetensors"
--output_path "flux-ip-adapter-hf/"
"""
CTX = init_empty_weights if is_accelerate_available else nullcontext
parser = argparse.ArgumentParser()
parser.add_argument("--original_state_dict_repo_id", default=None, type=str)
parser.add_argument("--filename", default="flux.safetensors", type=str)
parser.add_argument("--checkpoint_path", default=None, type=str)
parser.add_argument("--output_path", type=str)
parser.add_argument("--vision_pretrained_or_path", default="openai/clip-vit-large-patch14", type=str)
args = parser.parse_args()
def load_original_checkpoint(args):
if args.original_state_dict_repo_id is not None:
ckpt_path = hf_hub_download(repo_id=args.original_state_dict_repo_id, filename=args.filename)
elif args.checkpoint_path is not None:
ckpt_path = args.checkpoint_path
else:
raise ValueError(" please provide either `original_state_dict_repo_id` or a local `checkpoint_path`")
original_state_dict = safetensors.torch.load_file(ckpt_path)
return original_state_dict
def convert_flux_ipadapter_checkpoint_to_diffusers(original_state_dict, num_layers):
converted_state_dict = {}
# image_proj
## norm
converted_state_dict["image_proj.norm.weight"] = original_state_dict.pop("ip_adapter_proj_model.norm.weight")
converted_state_dict["image_proj.norm.bias"] = original_state_dict.pop("ip_adapter_proj_model.norm.bias")
## proj
converted_state_dict["image_proj.proj.weight"] = original_state_dict.pop("ip_adapter_proj_model.norm.weight")
converted_state_dict["image_proj.proj.bias"] = original_state_dict.pop("ip_adapter_proj_model.norm.bias")
# double transformer blocks
for i in range(num_layers):
block_prefix = f"ip_adapter.{i}."
# to_k_ip
converted_state_dict[f"{block_prefix}to_k_ip.bias"] = original_state_dict.pop(
f"double_blocks.{i}.processor.ip_adapter_double_stream_k_proj.bias"
)
converted_state_dict[f"{block_prefix}to_k_ip.weight"] = original_state_dict.pop(
f"double_blocks.{i}.processor.ip_adapter_double_stream_k_proj.weight"
)
# to_v_ip
converted_state_dict[f"{block_prefix}to_v_ip.bias"] = original_state_dict.pop(
f"double_blocks.{i}.processor.ip_adapter_double_stream_v_proj.bias"
)
converted_state_dict[f"{block_prefix}to_k_ip.weight"] = original_state_dict.pop(
f"double_blocks.{i}.processor.ip_adapter_double_stream_v_proj.weight"
)
return converted_state_dict
def main(args):
original_ckpt = load_original_checkpoint(args)
num_layers = 19
converted_ip_adapter_state_dict = convert_flux_ipadapter_checkpoint_to_diffusers(original_ckpt, num_layers)
print("Saving Flux IP-Adapter in Diffusers format.")
safetensors.torch.save_file(converted_ip_adapter_state_dict, f"{args.output_path}/model.safetensors")
if vision:
model = CLIPVisionModelWithProjection.from_pretrained(args.vision_pretrained_or_path)
model.save_pretrained(f"{args.output_path}/image_encoder")
if __name__ == "__main__":
main(args)
| diffusers/scripts/convert_flux_xlabs_ipadapter_to_diffusers.py/0 | {
"file_path": "diffusers/scripts/convert_flux_xlabs_ipadapter_to_diffusers.py",
"repo_id": "diffusers",
"token_count": 1463
} | 155 |
import json
import os
import torch
from diffusers import UNet1DModel
os.makedirs("hub/hopper-medium-v2/unet/hor32", exist_ok=True)
os.makedirs("hub/hopper-medium-v2/unet/hor128", exist_ok=True)
os.makedirs("hub/hopper-medium-v2/value_function", exist_ok=True)
def unet(hor):
if hor == 128:
down_block_types = ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
block_out_channels = (32, 128, 256)
up_block_types = ("UpResnetBlock1D", "UpResnetBlock1D")
elif hor == 32:
down_block_types = ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
block_out_channels = (32, 64, 128, 256)
up_block_types = ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
model = torch.load(f"/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch")
state_dict = model.state_dict()
config = {
"down_block_types": down_block_types,
"block_out_channels": block_out_channels,
"up_block_types": up_block_types,
"layers_per_block": 1,
"use_timestep_embedding": True,
"out_block_type": "OutConv1DBlock",
"norm_num_groups": 8,
"downsample_each_block": False,
"in_channels": 14,
"out_channels": 14,
"extra_in_channels": 0,
"time_embedding_type": "positional",
"flip_sin_to_cos": False,
"freq_shift": 1,
"sample_size": 65536,
"mid_block_type": "MidResTemporalBlock1D",
"act_fn": "mish",
}
hf_value_function = UNet1DModel(**config)
print(f"length of state dict: {len(state_dict.keys())}")
print(f"length of value function dict: {len(hf_value_function.state_dict().keys())}")
mapping = dict(zip(model.state_dict().keys(), hf_value_function.state_dict().keys()))
for k, v in mapping.items():
state_dict[v] = state_dict.pop(k)
hf_value_function.load_state_dict(state_dict)
torch.save(hf_value_function.state_dict(), f"hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin")
with open(f"hub/hopper-medium-v2/unet/hor{hor}/config.json", "w") as f:
json.dump(config, f)
def value_function():
config = {
"in_channels": 14,
"down_block_types": ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"),
"up_block_types": (),
"out_block_type": "ValueFunction",
"mid_block_type": "ValueFunctionMidBlock1D",
"block_out_channels": (32, 64, 128, 256),
"layers_per_block": 1,
"downsample_each_block": True,
"sample_size": 65536,
"out_channels": 14,
"extra_in_channels": 0,
"time_embedding_type": "positional",
"use_timestep_embedding": True,
"flip_sin_to_cos": False,
"freq_shift": 1,
"norm_num_groups": 8,
"act_fn": "mish",
}
model = torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch")
state_dict = model
hf_value_function = UNet1DModel(**config)
print(f"length of state dict: {len(state_dict.keys())}")
print(f"length of value function dict: {len(hf_value_function.state_dict().keys())}")
mapping = dict(zip(state_dict.keys(), hf_value_function.state_dict().keys()))
for k, v in mapping.items():
state_dict[v] = state_dict.pop(k)
hf_value_function.load_state_dict(state_dict)
torch.save(hf_value_function.state_dict(), "hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin")
with open("hub/hopper-medium-v2/value_function/config.json", "w") as f:
json.dump(config, f)
if __name__ == "__main__":
unet(32)
# unet(128)
value_function()
| diffusers/scripts/convert_models_diffuser_to_diffusers.py/0 | {
"file_path": "diffusers/scripts/convert_models_diffuser_to_diffusers.py",
"repo_id": "diffusers",
"token_count": 1700
} | 156 |
import argparse
from contextlib import nullcontext
import safetensors.torch
import torch
from accelerate import init_empty_weights
from diffusers import AutoencoderKL, SD3Transformer2DModel
from diffusers.loaders.single_file_utils import convert_ldm_vae_checkpoint
from diffusers.models.modeling_utils import load_model_dict_into_meta
from diffusers.utils.import_utils import is_accelerate_available
CTX = init_empty_weights if is_accelerate_available() else nullcontext
parser = argparse.ArgumentParser()
parser.add_argument("--checkpoint_path", type=str)
parser.add_argument("--output_path", type=str)
parser.add_argument("--dtype", type=str)
args = parser.parse_args()
def load_original_checkpoint(ckpt_path):
original_state_dict = safetensors.torch.load_file(ckpt_path)
keys = list(original_state_dict.keys())
for k in keys:
if "model.diffusion_model." in k:
original_state_dict[k.replace("model.diffusion_model.", "")] = original_state_dict.pop(k)
return original_state_dict
# in SD3 original implementation of AdaLayerNormContinuous, it split linear projection output into shift, scale;
# while in diffusers it split into scale, shift. Here we swap the linear projection weights in order to be able to use diffusers implementation
def swap_scale_shift(weight, dim):
shift, scale = weight.chunk(2, dim=0)
new_weight = torch.cat([scale, shift], dim=0)
return new_weight
def convert_sd3_transformer_checkpoint_to_diffusers(
original_state_dict, num_layers, caption_projection_dim, dual_attention_layers, has_qk_norm
):
converted_state_dict = {}
# Positional and patch embeddings.
converted_state_dict["pos_embed.pos_embed"] = original_state_dict.pop("pos_embed")
converted_state_dict["pos_embed.proj.weight"] = original_state_dict.pop("x_embedder.proj.weight")
converted_state_dict["pos_embed.proj.bias"] = original_state_dict.pop("x_embedder.proj.bias")
# Timestep embeddings.
converted_state_dict["time_text_embed.timestep_embedder.linear_1.weight"] = original_state_dict.pop(
"t_embedder.mlp.0.weight"
)
converted_state_dict["time_text_embed.timestep_embedder.linear_1.bias"] = original_state_dict.pop(
"t_embedder.mlp.0.bias"
)
converted_state_dict["time_text_embed.timestep_embedder.linear_2.weight"] = original_state_dict.pop(
"t_embedder.mlp.2.weight"
)
converted_state_dict["time_text_embed.timestep_embedder.linear_2.bias"] = original_state_dict.pop(
"t_embedder.mlp.2.bias"
)
# Context projections.
converted_state_dict["context_embedder.weight"] = original_state_dict.pop("context_embedder.weight")
converted_state_dict["context_embedder.bias"] = original_state_dict.pop("context_embedder.bias")
# Pooled context projection.
converted_state_dict["time_text_embed.text_embedder.linear_1.weight"] = original_state_dict.pop(
"y_embedder.mlp.0.weight"
)
converted_state_dict["time_text_embed.text_embedder.linear_1.bias"] = original_state_dict.pop(
"y_embedder.mlp.0.bias"
)
converted_state_dict["time_text_embed.text_embedder.linear_2.weight"] = original_state_dict.pop(
"y_embedder.mlp.2.weight"
)
converted_state_dict["time_text_embed.text_embedder.linear_2.bias"] = original_state_dict.pop(
"y_embedder.mlp.2.bias"
)
# Transformer blocks 🎸.
for i in range(num_layers):
# Q, K, V
sample_q, sample_k, sample_v = torch.chunk(
original_state_dict.pop(f"joint_blocks.{i}.x_block.attn.qkv.weight"), 3, dim=0
)
context_q, context_k, context_v = torch.chunk(
original_state_dict.pop(f"joint_blocks.{i}.context_block.attn.qkv.weight"), 3, dim=0
)
sample_q_bias, sample_k_bias, sample_v_bias = torch.chunk(
original_state_dict.pop(f"joint_blocks.{i}.x_block.attn.qkv.bias"), 3, dim=0
)
context_q_bias, context_k_bias, context_v_bias = torch.chunk(
original_state_dict.pop(f"joint_blocks.{i}.context_block.attn.qkv.bias"), 3, dim=0
)
converted_state_dict[f"transformer_blocks.{i}.attn.to_q.weight"] = torch.cat([sample_q])
converted_state_dict[f"transformer_blocks.{i}.attn.to_q.bias"] = torch.cat([sample_q_bias])
converted_state_dict[f"transformer_blocks.{i}.attn.to_k.weight"] = torch.cat([sample_k])
converted_state_dict[f"transformer_blocks.{i}.attn.to_k.bias"] = torch.cat([sample_k_bias])
converted_state_dict[f"transformer_blocks.{i}.attn.to_v.weight"] = torch.cat([sample_v])
converted_state_dict[f"transformer_blocks.{i}.attn.to_v.bias"] = torch.cat([sample_v_bias])
converted_state_dict[f"transformer_blocks.{i}.attn.add_q_proj.weight"] = torch.cat([context_q])
converted_state_dict[f"transformer_blocks.{i}.attn.add_q_proj.bias"] = torch.cat([context_q_bias])
converted_state_dict[f"transformer_blocks.{i}.attn.add_k_proj.weight"] = torch.cat([context_k])
converted_state_dict[f"transformer_blocks.{i}.attn.add_k_proj.bias"] = torch.cat([context_k_bias])
converted_state_dict[f"transformer_blocks.{i}.attn.add_v_proj.weight"] = torch.cat([context_v])
converted_state_dict[f"transformer_blocks.{i}.attn.add_v_proj.bias"] = torch.cat([context_v_bias])
# qk norm
if has_qk_norm:
converted_state_dict[f"transformer_blocks.{i}.attn.norm_q.weight"] = original_state_dict.pop(
f"joint_blocks.{i}.x_block.attn.ln_q.weight"
)
converted_state_dict[f"transformer_blocks.{i}.attn.norm_k.weight"] = original_state_dict.pop(
f"joint_blocks.{i}.x_block.attn.ln_k.weight"
)
converted_state_dict[f"transformer_blocks.{i}.attn.norm_added_q.weight"] = original_state_dict.pop(
f"joint_blocks.{i}.context_block.attn.ln_q.weight"
)
converted_state_dict[f"transformer_blocks.{i}.attn.norm_added_k.weight"] = original_state_dict.pop(
f"joint_blocks.{i}.context_block.attn.ln_k.weight"
)
# output projections.
converted_state_dict[f"transformer_blocks.{i}.attn.to_out.0.weight"] = original_state_dict.pop(
f"joint_blocks.{i}.x_block.attn.proj.weight"
)
converted_state_dict[f"transformer_blocks.{i}.attn.to_out.0.bias"] = original_state_dict.pop(
f"joint_blocks.{i}.x_block.attn.proj.bias"
)
if not (i == num_layers - 1):
converted_state_dict[f"transformer_blocks.{i}.attn.to_add_out.weight"] = original_state_dict.pop(
f"joint_blocks.{i}.context_block.attn.proj.weight"
)
converted_state_dict[f"transformer_blocks.{i}.attn.to_add_out.bias"] = original_state_dict.pop(
f"joint_blocks.{i}.context_block.attn.proj.bias"
)
# attn2
if i in dual_attention_layers:
# Q, K, V
sample_q2, sample_k2, sample_v2 = torch.chunk(
original_state_dict.pop(f"joint_blocks.{i}.x_block.attn2.qkv.weight"), 3, dim=0
)
sample_q2_bias, sample_k2_bias, sample_v2_bias = torch.chunk(
original_state_dict.pop(f"joint_blocks.{i}.x_block.attn2.qkv.bias"), 3, dim=0
)
converted_state_dict[f"transformer_blocks.{i}.attn2.to_q.weight"] = torch.cat([sample_q2])
converted_state_dict[f"transformer_blocks.{i}.attn2.to_q.bias"] = torch.cat([sample_q2_bias])
converted_state_dict[f"transformer_blocks.{i}.attn2.to_k.weight"] = torch.cat([sample_k2])
converted_state_dict[f"transformer_blocks.{i}.attn2.to_k.bias"] = torch.cat([sample_k2_bias])
converted_state_dict[f"transformer_blocks.{i}.attn2.to_v.weight"] = torch.cat([sample_v2])
converted_state_dict[f"transformer_blocks.{i}.attn2.to_v.bias"] = torch.cat([sample_v2_bias])
# qk norm
if has_qk_norm:
converted_state_dict[f"transformer_blocks.{i}.attn2.norm_q.weight"] = original_state_dict.pop(
f"joint_blocks.{i}.x_block.attn2.ln_q.weight"
)
converted_state_dict[f"transformer_blocks.{i}.attn2.norm_k.weight"] = original_state_dict.pop(
f"joint_blocks.{i}.x_block.attn2.ln_k.weight"
)
# output projections.
converted_state_dict[f"transformer_blocks.{i}.attn2.to_out.0.weight"] = original_state_dict.pop(
f"joint_blocks.{i}.x_block.attn2.proj.weight"
)
converted_state_dict[f"transformer_blocks.{i}.attn2.to_out.0.bias"] = original_state_dict.pop(
f"joint_blocks.{i}.x_block.attn2.proj.bias"
)
# norms.
converted_state_dict[f"transformer_blocks.{i}.norm1.linear.weight"] = original_state_dict.pop(
f"joint_blocks.{i}.x_block.adaLN_modulation.1.weight"
)
converted_state_dict[f"transformer_blocks.{i}.norm1.linear.bias"] = original_state_dict.pop(
f"joint_blocks.{i}.x_block.adaLN_modulation.1.bias"
)
if not (i == num_layers - 1):
converted_state_dict[f"transformer_blocks.{i}.norm1_context.linear.weight"] = original_state_dict.pop(
f"joint_blocks.{i}.context_block.adaLN_modulation.1.weight"
)
converted_state_dict[f"transformer_blocks.{i}.norm1_context.linear.bias"] = original_state_dict.pop(
f"joint_blocks.{i}.context_block.adaLN_modulation.1.bias"
)
else:
converted_state_dict[f"transformer_blocks.{i}.norm1_context.linear.weight"] = swap_scale_shift(
original_state_dict.pop(f"joint_blocks.{i}.context_block.adaLN_modulation.1.weight"),
dim=caption_projection_dim,
)
converted_state_dict[f"transformer_blocks.{i}.norm1_context.linear.bias"] = swap_scale_shift(
original_state_dict.pop(f"joint_blocks.{i}.context_block.adaLN_modulation.1.bias"),
dim=caption_projection_dim,
)
# ffs.
converted_state_dict[f"transformer_blocks.{i}.ff.net.0.proj.weight"] = original_state_dict.pop(
f"joint_blocks.{i}.x_block.mlp.fc1.weight"
)
converted_state_dict[f"transformer_blocks.{i}.ff.net.0.proj.bias"] = original_state_dict.pop(
f"joint_blocks.{i}.x_block.mlp.fc1.bias"
)
converted_state_dict[f"transformer_blocks.{i}.ff.net.2.weight"] = original_state_dict.pop(
f"joint_blocks.{i}.x_block.mlp.fc2.weight"
)
converted_state_dict[f"transformer_blocks.{i}.ff.net.2.bias"] = original_state_dict.pop(
f"joint_blocks.{i}.x_block.mlp.fc2.bias"
)
if not (i == num_layers - 1):
converted_state_dict[f"transformer_blocks.{i}.ff_context.net.0.proj.weight"] = original_state_dict.pop(
f"joint_blocks.{i}.context_block.mlp.fc1.weight"
)
converted_state_dict[f"transformer_blocks.{i}.ff_context.net.0.proj.bias"] = original_state_dict.pop(
f"joint_blocks.{i}.context_block.mlp.fc1.bias"
)
converted_state_dict[f"transformer_blocks.{i}.ff_context.net.2.weight"] = original_state_dict.pop(
f"joint_blocks.{i}.context_block.mlp.fc2.weight"
)
converted_state_dict[f"transformer_blocks.{i}.ff_context.net.2.bias"] = original_state_dict.pop(
f"joint_blocks.{i}.context_block.mlp.fc2.bias"
)
# Final blocks.
converted_state_dict["proj_out.weight"] = original_state_dict.pop("final_layer.linear.weight")
converted_state_dict["proj_out.bias"] = original_state_dict.pop("final_layer.linear.bias")
converted_state_dict["norm_out.linear.weight"] = swap_scale_shift(
original_state_dict.pop("final_layer.adaLN_modulation.1.weight"), dim=caption_projection_dim
)
converted_state_dict["norm_out.linear.bias"] = swap_scale_shift(
original_state_dict.pop("final_layer.adaLN_modulation.1.bias"), dim=caption_projection_dim
)
return converted_state_dict
def is_vae_in_checkpoint(original_state_dict):
return ("first_stage_model.decoder.conv_in.weight" in original_state_dict) and (
"first_stage_model.encoder.conv_in.weight" in original_state_dict
)
def get_attn2_layers(state_dict):
attn2_layers = []
for key in state_dict.keys():
if "attn2." in key:
# Extract the layer number from the key
layer_num = int(key.split(".")[1])
attn2_layers.append(layer_num)
return tuple(sorted(set(attn2_layers)))
def get_pos_embed_max_size(state_dict):
num_patches = state_dict["pos_embed"].shape[1]
pos_embed_max_size = int(num_patches**0.5)
return pos_embed_max_size
def get_caption_projection_dim(state_dict):
caption_projection_dim = state_dict["context_embedder.weight"].shape[0]
return caption_projection_dim
def main(args):
original_ckpt = load_original_checkpoint(args.checkpoint_path)
original_dtype = next(iter(original_ckpt.values())).dtype
# Initialize dtype with a default value
dtype = None
if args.dtype is None:
dtype = original_dtype
elif args.dtype == "fp16":
dtype = torch.float16
elif args.dtype == "bf16":
dtype = torch.bfloat16
elif args.dtype == "fp32":
dtype = torch.float32
else:
raise ValueError(f"Unsupported dtype: {args.dtype}")
if dtype != original_dtype:
print(
f"Checkpoint dtype {original_dtype} does not match requested dtype {dtype}. This can lead to unexpected results, proceed with caution."
)
num_layers = list(set(int(k.split(".", 2)[1]) for k in original_ckpt if "joint_blocks" in k))[-1] + 1 # noqa: C401
caption_projection_dim = get_caption_projection_dim(original_ckpt)
# () for sd3.0; (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12) for sd3.5
attn2_layers = get_attn2_layers(original_ckpt)
# sd3.5 use qk norm("rms_norm")
has_qk_norm = any("ln_q" in key for key in original_ckpt.keys())
# sd3.5 2b use pox_embed_max_size=384 and sd3.0 and sd3.5 8b use 192
pos_embed_max_size = get_pos_embed_max_size(original_ckpt)
converted_transformer_state_dict = convert_sd3_transformer_checkpoint_to_diffusers(
original_ckpt, num_layers, caption_projection_dim, attn2_layers, has_qk_norm
)
with CTX():
transformer = SD3Transformer2DModel(
sample_size=128,
patch_size=2,
in_channels=16,
joint_attention_dim=4096,
num_layers=num_layers,
caption_projection_dim=caption_projection_dim,
num_attention_heads=num_layers,
pos_embed_max_size=pos_embed_max_size,
qk_norm="rms_norm" if has_qk_norm else None,
dual_attention_layers=attn2_layers,
)
if is_accelerate_available():
load_model_dict_into_meta(transformer, converted_transformer_state_dict)
else:
transformer.load_state_dict(converted_transformer_state_dict, strict=True)
print("Saving SD3 Transformer in Diffusers format.")
transformer.to(dtype).save_pretrained(f"{args.output_path}/transformer")
if is_vae_in_checkpoint(original_ckpt):
with CTX():
vae = AutoencoderKL.from_config(
"stabilityai/stable-diffusion-xl-base-1.0",
subfolder="vae",
latent_channels=16,
use_post_quant_conv=False,
use_quant_conv=False,
scaling_factor=1.5305,
shift_factor=0.0609,
)
converted_vae_state_dict = convert_ldm_vae_checkpoint(original_ckpt, vae.config)
if is_accelerate_available():
load_model_dict_into_meta(vae, converted_vae_state_dict)
else:
vae.load_state_dict(converted_vae_state_dict, strict=True)
print("Saving SD3 Autoencoder in Diffusers format.")
vae.to(dtype).save_pretrained(f"{args.output_path}/vae")
if __name__ == "__main__":
main(args)
| diffusers/scripts/convert_sd3_to_diffusers.py/0 | {
"file_path": "diffusers/scripts/convert_sd3_to_diffusers.py",
"repo_id": "diffusers",
"token_count": 7598
} | 157 |
"""
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/Improved-VQ-Diffusion_model_release/ithq_vqvae.pth?sv=2020-10-02&st=2022-05-30T15%3A17%3A18Z&se=2030-05-31T15%3A17%3A00Z&sr=b&sp=r&sig=1jVavHFPpUjDs%2FTO1V3PTezaNbPp2Nx8MxiWI7y6fEY%3D -O ithq_vqvae.pth
# Download the VQVAE config
# NOTE that in VQ-diffusion the documented file is `configs/ithq.yaml` but the target class
# `image_synthesis.modeling.codecs.image_codec.ema_vqvae.PatchVQVAE`
# loads `OUTPUT/pretrained_model/taming_dvae/config.yaml`
$ wget https://raw.githubusercontent.com/microsoft/VQ-Diffusion/main/OUTPUT/pretrained_model/taming_dvae/config.yaml -O ithq_vqvae.yaml
# Download the main model checkpoint
$ wget https://facevcstandard.blob.core.windows.net/v-zhictang/Improved-VQ-Diffusion_model_release/ithq_learnable.pth?sv=2020-10-02&st=2022-05-30T10%3A22%3A06Z&se=2030-05-31T10%3A22%3A00Z&sr=b&sp=r&sig=GOE%2Bza02%2FPnGxYVOOPtwrTR4RA3%2F5NVgMxdW4kjaEZ8%3D -O ithq_learnable.pth
# Download the main model config
$ wget https://raw.githubusercontent.com/microsoft/VQ-Diffusion/main/configs/ithq.yaml -O ithq.yaml
# run the convert script
$ python ./scripts/convert_vq_diffusion_to_diffusers.py \
--checkpoint_path ./ithq_learnable.pth \
--original_config_file ./ithq.yaml \
--vqvae_checkpoint_path ./ithq_vqvae.pth \
--vqvae_original_config_file ./ithq_vqvae.yaml \
--dump_path <path to save pre-trained `VQDiffusionPipeline`>
```
"""
import argparse
import tempfile
import torch
import yaml
from accelerate import init_empty_weights, load_checkpoint_and_dispatch
from transformers import CLIPTextModel, CLIPTokenizer
from yaml.loader import FullLoader
from diffusers import Transformer2DModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
# vqvae model
PORTED_VQVAES = ["image_synthesis.modeling.codecs.image_codec.patch_vqgan.PatchVQGAN"]
def vqvae_model_from_original_config(original_config):
assert original_config["target"] in PORTED_VQVAES, (
f"{original_config['target']} has not yet been ported to diffusers."
)
original_config = original_config["params"]
original_encoder_config = original_config["encoder_config"]["params"]
original_decoder_config = original_config["decoder_config"]["params"]
in_channels = original_encoder_config["in_channels"]
out_channels = original_decoder_config["out_ch"]
down_block_types = get_down_block_types(original_encoder_config)
up_block_types = get_up_block_types(original_decoder_config)
assert original_encoder_config["ch"] == original_decoder_config["ch"]
assert original_encoder_config["ch_mult"] == original_decoder_config["ch_mult"]
block_out_channels = tuple(
[original_encoder_config["ch"] * a_ch_mult for a_ch_mult in original_encoder_config["ch_mult"]]
)
assert original_encoder_config["num_res_blocks"] == original_decoder_config["num_res_blocks"]
layers_per_block = original_encoder_config["num_res_blocks"]
assert original_encoder_config["z_channels"] == original_decoder_config["z_channels"]
latent_channels = original_encoder_config["z_channels"]
num_vq_embeddings = original_config["n_embed"]
# Hard coded value for ResnetBlock.GoupNorm(num_groups) in VQ-diffusion
norm_num_groups = 32
e_dim = original_config["embed_dim"]
model = VQModel(
in_channels=in_channels,
out_channels=out_channels,
down_block_types=down_block_types,
up_block_types=up_block_types,
block_out_channels=block_out_channels,
layers_per_block=layers_per_block,
latent_channels=latent_channels,
num_vq_embeddings=num_vq_embeddings,
norm_num_groups=norm_num_groups,
vq_embed_dim=e_dim,
)
return model
def get_down_block_types(original_encoder_config):
attn_resolutions = coerce_attn_resolutions(original_encoder_config["attn_resolutions"])
num_resolutions = len(original_encoder_config["ch_mult"])
resolution = coerce_resolution(original_encoder_config["resolution"])
curr_res = resolution
down_block_types = []
for _ in range(num_resolutions):
if curr_res in attn_resolutions:
down_block_type = "AttnDownEncoderBlock2D"
else:
down_block_type = "DownEncoderBlock2D"
down_block_types.append(down_block_type)
curr_res = [r // 2 for r in curr_res]
return down_block_types
def get_up_block_types(original_decoder_config):
attn_resolutions = coerce_attn_resolutions(original_decoder_config["attn_resolutions"])
num_resolutions = len(original_decoder_config["ch_mult"])
resolution = coerce_resolution(original_decoder_config["resolution"])
curr_res = [r // 2 ** (num_resolutions - 1) for r in resolution]
up_block_types = []
for _ in reversed(range(num_resolutions)):
if curr_res in attn_resolutions:
up_block_type = "AttnUpDecoderBlock2D"
else:
up_block_type = "UpDecoderBlock2D"
up_block_types.append(up_block_type)
curr_res = [r * 2 for r in curr_res]
return up_block_types
def coerce_attn_resolutions(attn_resolutions):
attn_resolutions = list(attn_resolutions)
attn_resolutions_ = []
for ar in attn_resolutions:
if isinstance(ar, (list, tuple)):
attn_resolutions_.append(list(ar))
else:
attn_resolutions_.append([ar, ar])
return attn_resolutions_
def coerce_resolution(resolution):
if isinstance(resolution, int):
resolution = [resolution, resolution] # H, W
elif isinstance(resolution, (tuple, list)):
resolution = list(resolution)
else:
raise ValueError("Unknown type of resolution:", resolution)
return resolution
# done vqvae model
# vqvae checkpoint
def vqvae_original_checkpoint_to_diffusers_checkpoint(model, checkpoint):
diffusers_checkpoint = {}
diffusers_checkpoint.update(vqvae_encoder_to_diffusers_checkpoint(model, checkpoint))
# quant_conv
diffusers_checkpoint.update(
{
"quant_conv.weight": checkpoint["quant_conv.weight"],
"quant_conv.bias": checkpoint["quant_conv.bias"],
}
)
# quantize
diffusers_checkpoint.update({"quantize.embedding.weight": checkpoint["quantize.embedding"]})
# post_quant_conv
diffusers_checkpoint.update(
{
"post_quant_conv.weight": checkpoint["post_quant_conv.weight"],
"post_quant_conv.bias": checkpoint["post_quant_conv.bias"],
}
)
# decoder
diffusers_checkpoint.update(vqvae_decoder_to_diffusers_checkpoint(model, checkpoint))
return diffusers_checkpoint
def vqvae_encoder_to_diffusers_checkpoint(model, checkpoint):
diffusers_checkpoint = {}
# conv_in
diffusers_checkpoint.update(
{
"encoder.conv_in.weight": checkpoint["encoder.conv_in.weight"],
"encoder.conv_in.bias": checkpoint["encoder.conv_in.bias"],
}
)
# down_blocks
for down_block_idx, down_block in enumerate(model.encoder.down_blocks):
diffusers_down_block_prefix = f"encoder.down_blocks.{down_block_idx}"
down_block_prefix = f"encoder.down.{down_block_idx}"
# resnets
for resnet_idx, resnet in enumerate(down_block.resnets):
diffusers_resnet_prefix = f"{diffusers_down_block_prefix}.resnets.{resnet_idx}"
resnet_prefix = f"{down_block_prefix}.block.{resnet_idx}"
diffusers_checkpoint.update(
vqvae_resnet_to_diffusers_checkpoint(
resnet, checkpoint, diffusers_resnet_prefix=diffusers_resnet_prefix, resnet_prefix=resnet_prefix
)
)
# downsample
# do not include the downsample when on the last down block
# There is no downsample on the last down block
if down_block_idx != len(model.encoder.down_blocks) - 1:
# There's a single downsample in the original checkpoint but a list of downsamples
# in the diffusers model.
diffusers_downsample_prefix = f"{diffusers_down_block_prefix}.downsamplers.0.conv"
downsample_prefix = f"{down_block_prefix}.downsample.conv"
diffusers_checkpoint.update(
{
f"{diffusers_downsample_prefix}.weight": checkpoint[f"{downsample_prefix}.weight"],
f"{diffusers_downsample_prefix}.bias": checkpoint[f"{downsample_prefix}.bias"],
}
)
# attentions
if hasattr(down_block, "attentions"):
for attention_idx, _ in enumerate(down_block.attentions):
diffusers_attention_prefix = f"{diffusers_down_block_prefix}.attentions.{attention_idx}"
attention_prefix = f"{down_block_prefix}.attn.{attention_idx}"
diffusers_checkpoint.update(
vqvae_attention_to_diffusers_checkpoint(
checkpoint,
diffusers_attention_prefix=diffusers_attention_prefix,
attention_prefix=attention_prefix,
)
)
# mid block
# mid block attentions
# There is a single hardcoded attention block in the middle of the VQ-diffusion encoder
diffusers_attention_prefix = "encoder.mid_block.attentions.0"
attention_prefix = "encoder.mid.attn_1"
diffusers_checkpoint.update(
vqvae_attention_to_diffusers_checkpoint(
checkpoint, diffusers_attention_prefix=diffusers_attention_prefix, attention_prefix=attention_prefix
)
)
# mid block resnets
for diffusers_resnet_idx, resnet in enumerate(model.encoder.mid_block.resnets):
diffusers_resnet_prefix = f"encoder.mid_block.resnets.{diffusers_resnet_idx}"
# the hardcoded prefixes to `block_` are 1 and 2
orig_resnet_idx = diffusers_resnet_idx + 1
# There are two hardcoded resnets in the middle of the VQ-diffusion encoder
resnet_prefix = f"encoder.mid.block_{orig_resnet_idx}"
diffusers_checkpoint.update(
vqvae_resnet_to_diffusers_checkpoint(
resnet, checkpoint, diffusers_resnet_prefix=diffusers_resnet_prefix, resnet_prefix=resnet_prefix
)
)
diffusers_checkpoint.update(
{
# conv_norm_out
"encoder.conv_norm_out.weight": checkpoint["encoder.norm_out.weight"],
"encoder.conv_norm_out.bias": checkpoint["encoder.norm_out.bias"],
# conv_out
"encoder.conv_out.weight": checkpoint["encoder.conv_out.weight"],
"encoder.conv_out.bias": checkpoint["encoder.conv_out.bias"],
}
)
return diffusers_checkpoint
def vqvae_decoder_to_diffusers_checkpoint(model, checkpoint):
diffusers_checkpoint = {}
# conv in
diffusers_checkpoint.update(
{
"decoder.conv_in.weight": checkpoint["decoder.conv_in.weight"],
"decoder.conv_in.bias": checkpoint["decoder.conv_in.bias"],
}
)
# up_blocks
for diffusers_up_block_idx, up_block in enumerate(model.decoder.up_blocks):
# up_blocks are stored in reverse order in the VQ-diffusion checkpoint
orig_up_block_idx = len(model.decoder.up_blocks) - 1 - diffusers_up_block_idx
diffusers_up_block_prefix = f"decoder.up_blocks.{diffusers_up_block_idx}"
up_block_prefix = f"decoder.up.{orig_up_block_idx}"
# resnets
for resnet_idx, resnet in enumerate(up_block.resnets):
diffusers_resnet_prefix = f"{diffusers_up_block_prefix}.resnets.{resnet_idx}"
resnet_prefix = f"{up_block_prefix}.block.{resnet_idx}"
diffusers_checkpoint.update(
vqvae_resnet_to_diffusers_checkpoint(
resnet, checkpoint, diffusers_resnet_prefix=diffusers_resnet_prefix, resnet_prefix=resnet_prefix
)
)
# upsample
# there is no up sample on the last up block
if diffusers_up_block_idx != len(model.decoder.up_blocks) - 1:
# There's a single upsample in the VQ-diffusion checkpoint but a list of downsamples
# in the diffusers model.
diffusers_downsample_prefix = f"{diffusers_up_block_prefix}.upsamplers.0.conv"
downsample_prefix = f"{up_block_prefix}.upsample.conv"
diffusers_checkpoint.update(
{
f"{diffusers_downsample_prefix}.weight": checkpoint[f"{downsample_prefix}.weight"],
f"{diffusers_downsample_prefix}.bias": checkpoint[f"{downsample_prefix}.bias"],
}
)
# attentions
if hasattr(up_block, "attentions"):
for attention_idx, _ in enumerate(up_block.attentions):
diffusers_attention_prefix = f"{diffusers_up_block_prefix}.attentions.{attention_idx}"
attention_prefix = f"{up_block_prefix}.attn.{attention_idx}"
diffusers_checkpoint.update(
vqvae_attention_to_diffusers_checkpoint(
checkpoint,
diffusers_attention_prefix=diffusers_attention_prefix,
attention_prefix=attention_prefix,
)
)
# mid block
# mid block attentions
# There is a single hardcoded attention block in the middle of the VQ-diffusion decoder
diffusers_attention_prefix = "decoder.mid_block.attentions.0"
attention_prefix = "decoder.mid.attn_1"
diffusers_checkpoint.update(
vqvae_attention_to_diffusers_checkpoint(
checkpoint, diffusers_attention_prefix=diffusers_attention_prefix, attention_prefix=attention_prefix
)
)
# mid block resnets
for diffusers_resnet_idx, resnet in enumerate(model.encoder.mid_block.resnets):
diffusers_resnet_prefix = f"decoder.mid_block.resnets.{diffusers_resnet_idx}"
# the hardcoded prefixes to `block_` are 1 and 2
orig_resnet_idx = diffusers_resnet_idx + 1
# There are two hardcoded resnets in the middle of the VQ-diffusion decoder
resnet_prefix = f"decoder.mid.block_{orig_resnet_idx}"
diffusers_checkpoint.update(
vqvae_resnet_to_diffusers_checkpoint(
resnet, checkpoint, diffusers_resnet_prefix=diffusers_resnet_prefix, resnet_prefix=resnet_prefix
)
)
diffusers_checkpoint.update(
{
# conv_norm_out
"decoder.conv_norm_out.weight": checkpoint["decoder.norm_out.weight"],
"decoder.conv_norm_out.bias": checkpoint["decoder.norm_out.bias"],
# conv_out
"decoder.conv_out.weight": checkpoint["decoder.conv_out.weight"],
"decoder.conv_out.bias": checkpoint["decoder.conv_out.bias"],
}
)
return diffusers_checkpoint
def vqvae_resnet_to_diffusers_checkpoint(resnet, checkpoint, *, diffusers_resnet_prefix, resnet_prefix):
rv = {
# norm1
f"{diffusers_resnet_prefix}.norm1.weight": checkpoint[f"{resnet_prefix}.norm1.weight"],
f"{diffusers_resnet_prefix}.norm1.bias": checkpoint[f"{resnet_prefix}.norm1.bias"],
# conv1
f"{diffusers_resnet_prefix}.conv1.weight": checkpoint[f"{resnet_prefix}.conv1.weight"],
f"{diffusers_resnet_prefix}.conv1.bias": checkpoint[f"{resnet_prefix}.conv1.bias"],
# norm2
f"{diffusers_resnet_prefix}.norm2.weight": checkpoint[f"{resnet_prefix}.norm2.weight"],
f"{diffusers_resnet_prefix}.norm2.bias": checkpoint[f"{resnet_prefix}.norm2.bias"],
# conv2
f"{diffusers_resnet_prefix}.conv2.weight": checkpoint[f"{resnet_prefix}.conv2.weight"],
f"{diffusers_resnet_prefix}.conv2.bias": checkpoint[f"{resnet_prefix}.conv2.bias"],
}
if resnet.conv_shortcut is not None:
rv.update(
{
f"{diffusers_resnet_prefix}.conv_shortcut.weight": checkpoint[f"{resnet_prefix}.nin_shortcut.weight"],
f"{diffusers_resnet_prefix}.conv_shortcut.bias": checkpoint[f"{resnet_prefix}.nin_shortcut.bias"],
}
)
return rv
def vqvae_attention_to_diffusers_checkpoint(checkpoint, *, diffusers_attention_prefix, attention_prefix):
return {
# group_norm
f"{diffusers_attention_prefix}.group_norm.weight": checkpoint[f"{attention_prefix}.norm.weight"],
f"{diffusers_attention_prefix}.group_norm.bias": checkpoint[f"{attention_prefix}.norm.bias"],
# query
f"{diffusers_attention_prefix}.query.weight": checkpoint[f"{attention_prefix}.q.weight"][:, :, 0, 0],
f"{diffusers_attention_prefix}.query.bias": checkpoint[f"{attention_prefix}.q.bias"],
# key
f"{diffusers_attention_prefix}.key.weight": checkpoint[f"{attention_prefix}.k.weight"][:, :, 0, 0],
f"{diffusers_attention_prefix}.key.bias": checkpoint[f"{attention_prefix}.k.bias"],
# value
f"{diffusers_attention_prefix}.value.weight": checkpoint[f"{attention_prefix}.v.weight"][:, :, 0, 0],
f"{diffusers_attention_prefix}.value.bias": checkpoint[f"{attention_prefix}.v.bias"],
# proj_attn
f"{diffusers_attention_prefix}.proj_attn.weight": checkpoint[f"{attention_prefix}.proj_out.weight"][
:, :, 0, 0
],
f"{diffusers_attention_prefix}.proj_attn.bias": checkpoint[f"{attention_prefix}.proj_out.bias"],
}
# done vqvae checkpoint
# transformer model
PORTED_DIFFUSIONS = ["image_synthesis.modeling.transformers.diffusion_transformer.DiffusionTransformer"]
PORTED_TRANSFORMERS = ["image_synthesis.modeling.transformers.transformer_utils.Text2ImageTransformer"]
PORTED_CONTENT_EMBEDDINGS = ["image_synthesis.modeling.embeddings.dalle_mask_image_embedding.DalleMaskImageEmbedding"]
def transformer_model_from_original_config(
original_diffusion_config, original_transformer_config, original_content_embedding_config
):
assert original_diffusion_config["target"] in PORTED_DIFFUSIONS, (
f"{original_diffusion_config['target']} has not yet been ported to diffusers."
)
assert original_transformer_config["target"] in PORTED_TRANSFORMERS, (
f"{original_transformer_config['target']} has not yet been ported to diffusers."
)
assert original_content_embedding_config["target"] in PORTED_CONTENT_EMBEDDINGS, (
f"{original_content_embedding_config['target']} has not yet been ported to diffusers."
)
original_diffusion_config = original_diffusion_config["params"]
original_transformer_config = original_transformer_config["params"]
original_content_embedding_config = original_content_embedding_config["params"]
inner_dim = original_transformer_config["n_embd"]
n_heads = original_transformer_config["n_head"]
# VQ-Diffusion gives dimension of the multi-headed attention layers as the
# number of attention heads times the sequence length (the dimension) of a
# single head. We want to specify our attention blocks with those values
# specified separately
assert inner_dim % n_heads == 0
d_head = inner_dim // n_heads
depth = original_transformer_config["n_layer"]
context_dim = original_transformer_config["condition_dim"]
num_embed = original_content_embedding_config["num_embed"]
# the number of embeddings in the transformer includes the mask embedding.
# the content embedding (the vqvae) does not include the mask embedding.
num_embed = num_embed + 1
height = original_transformer_config["content_spatial_size"][0]
width = original_transformer_config["content_spatial_size"][1]
assert width == height, "width has to be equal to height"
dropout = original_transformer_config["resid_pdrop"]
num_embeds_ada_norm = original_diffusion_config["diffusion_step"]
model_kwargs = {
"attention_bias": True,
"cross_attention_dim": context_dim,
"attention_head_dim": d_head,
"num_layers": depth,
"dropout": dropout,
"num_attention_heads": n_heads,
"num_vector_embeds": num_embed,
"num_embeds_ada_norm": num_embeds_ada_norm,
"norm_num_groups": 32,
"sample_size": width,
"activation_fn": "geglu-approximate",
}
model = Transformer2DModel(**model_kwargs)
return model
# done transformer model
# transformer checkpoint
def transformer_original_checkpoint_to_diffusers_checkpoint(model, checkpoint):
diffusers_checkpoint = {}
transformer_prefix = "transformer.transformer"
diffusers_latent_image_embedding_prefix = "latent_image_embedding"
latent_image_embedding_prefix = f"{transformer_prefix}.content_emb"
# DalleMaskImageEmbedding
diffusers_checkpoint.update(
{
f"{diffusers_latent_image_embedding_prefix}.emb.weight": checkpoint[
f"{latent_image_embedding_prefix}.emb.weight"
],
f"{diffusers_latent_image_embedding_prefix}.height_emb.weight": checkpoint[
f"{latent_image_embedding_prefix}.height_emb.weight"
],
f"{diffusers_latent_image_embedding_prefix}.width_emb.weight": checkpoint[
f"{latent_image_embedding_prefix}.width_emb.weight"
],
}
)
# transformer blocks
for transformer_block_idx, transformer_block in enumerate(model.transformer_blocks):
diffusers_transformer_block_prefix = f"transformer_blocks.{transformer_block_idx}"
transformer_block_prefix = f"{transformer_prefix}.blocks.{transformer_block_idx}"
# ada norm block
diffusers_ada_norm_prefix = f"{diffusers_transformer_block_prefix}.norm1"
ada_norm_prefix = f"{transformer_block_prefix}.ln1"
diffusers_checkpoint.update(
transformer_ada_norm_to_diffusers_checkpoint(
checkpoint, diffusers_ada_norm_prefix=diffusers_ada_norm_prefix, ada_norm_prefix=ada_norm_prefix
)
)
# attention block
diffusers_attention_prefix = f"{diffusers_transformer_block_prefix}.attn1"
attention_prefix = f"{transformer_block_prefix}.attn1"
diffusers_checkpoint.update(
transformer_attention_to_diffusers_checkpoint(
checkpoint, diffusers_attention_prefix=diffusers_attention_prefix, attention_prefix=attention_prefix
)
)
# ada norm block
diffusers_ada_norm_prefix = f"{diffusers_transformer_block_prefix}.norm2"
ada_norm_prefix = f"{transformer_block_prefix}.ln1_1"
diffusers_checkpoint.update(
transformer_ada_norm_to_diffusers_checkpoint(
checkpoint, diffusers_ada_norm_prefix=diffusers_ada_norm_prefix, ada_norm_prefix=ada_norm_prefix
)
)
# attention block
diffusers_attention_prefix = f"{diffusers_transformer_block_prefix}.attn2"
attention_prefix = f"{transformer_block_prefix}.attn2"
diffusers_checkpoint.update(
transformer_attention_to_diffusers_checkpoint(
checkpoint, diffusers_attention_prefix=diffusers_attention_prefix, attention_prefix=attention_prefix
)
)
# norm block
diffusers_norm_block_prefix = f"{diffusers_transformer_block_prefix}.norm3"
norm_block_prefix = f"{transformer_block_prefix}.ln2"
diffusers_checkpoint.update(
{
f"{diffusers_norm_block_prefix}.weight": checkpoint[f"{norm_block_prefix}.weight"],
f"{diffusers_norm_block_prefix}.bias": checkpoint[f"{norm_block_prefix}.bias"],
}
)
# feedforward block
diffusers_feedforward_prefix = f"{diffusers_transformer_block_prefix}.ff"
feedforward_prefix = f"{transformer_block_prefix}.mlp"
diffusers_checkpoint.update(
transformer_feedforward_to_diffusers_checkpoint(
checkpoint,
diffusers_feedforward_prefix=diffusers_feedforward_prefix,
feedforward_prefix=feedforward_prefix,
)
)
# to logits
diffusers_norm_out_prefix = "norm_out"
norm_out_prefix = f"{transformer_prefix}.to_logits.0"
diffusers_checkpoint.update(
{
f"{diffusers_norm_out_prefix}.weight": checkpoint[f"{norm_out_prefix}.weight"],
f"{diffusers_norm_out_prefix}.bias": checkpoint[f"{norm_out_prefix}.bias"],
}
)
diffusers_out_prefix = "out"
out_prefix = f"{transformer_prefix}.to_logits.1"
diffusers_checkpoint.update(
{
f"{diffusers_out_prefix}.weight": checkpoint[f"{out_prefix}.weight"],
f"{diffusers_out_prefix}.bias": checkpoint[f"{out_prefix}.bias"],
}
)
return diffusers_checkpoint
def transformer_ada_norm_to_diffusers_checkpoint(checkpoint, *, diffusers_ada_norm_prefix, ada_norm_prefix):
return {
f"{diffusers_ada_norm_prefix}.emb.weight": checkpoint[f"{ada_norm_prefix}.emb.weight"],
f"{diffusers_ada_norm_prefix}.linear.weight": checkpoint[f"{ada_norm_prefix}.linear.weight"],
f"{diffusers_ada_norm_prefix}.linear.bias": checkpoint[f"{ada_norm_prefix}.linear.bias"],
}
def transformer_attention_to_diffusers_checkpoint(checkpoint, *, diffusers_attention_prefix, attention_prefix):
return {
# key
f"{diffusers_attention_prefix}.to_k.weight": checkpoint[f"{attention_prefix}.key.weight"],
f"{diffusers_attention_prefix}.to_k.bias": checkpoint[f"{attention_prefix}.key.bias"],
# query
f"{diffusers_attention_prefix}.to_q.weight": checkpoint[f"{attention_prefix}.query.weight"],
f"{diffusers_attention_prefix}.to_q.bias": checkpoint[f"{attention_prefix}.query.bias"],
# value
f"{diffusers_attention_prefix}.to_v.weight": checkpoint[f"{attention_prefix}.value.weight"],
f"{diffusers_attention_prefix}.to_v.bias": checkpoint[f"{attention_prefix}.value.bias"],
# linear out
f"{diffusers_attention_prefix}.to_out.0.weight": checkpoint[f"{attention_prefix}.proj.weight"],
f"{diffusers_attention_prefix}.to_out.0.bias": checkpoint[f"{attention_prefix}.proj.bias"],
}
def transformer_feedforward_to_diffusers_checkpoint(checkpoint, *, diffusers_feedforward_prefix, feedforward_prefix):
return {
f"{diffusers_feedforward_prefix}.net.0.proj.weight": checkpoint[f"{feedforward_prefix}.0.weight"],
f"{diffusers_feedforward_prefix}.net.0.proj.bias": checkpoint[f"{feedforward_prefix}.0.bias"],
f"{diffusers_feedforward_prefix}.net.2.weight": checkpoint[f"{feedforward_prefix}.2.weight"],
f"{diffusers_feedforward_prefix}.net.2.bias": checkpoint[f"{feedforward_prefix}.2.bias"],
}
# done transformer checkpoint
def read_config_file(filename):
# The yaml file contains annotations that certain values should
# loaded as tuples.
with open(filename) as f:
original_config = yaml.load(f, FullLoader)
return original_config
# We take separate arguments for the vqvae because the ITHQ vqvae config file
# is separate from the config file for the rest of the model.
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--vqvae_checkpoint_path",
default=None,
type=str,
required=True,
help="Path to the vqvae checkpoint to convert.",
)
parser.add_argument(
"--vqvae_original_config_file",
default=None,
type=str,
required=True,
help="The YAML config file corresponding to the original architecture for the vqvae.",
)
parser.add_argument(
"--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
)
parser.add_argument(
"--original_config_file",
default=None,
type=str,
required=True,
help="The YAML config file corresponding to the original architecture.",
)
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
parser.add_argument(
"--checkpoint_load_device",
default="cpu",
type=str,
required=False,
help="The device passed to `map_location` when loading checkpoints.",
)
# See link for how ema weights are always selected
# https://github.com/microsoft/VQ-Diffusion/blob/3c98e77f721db7c787b76304fa2c96a36c7b00af/inference_VQ_Diffusion.py#L65
parser.add_argument(
"--no_use_ema",
action="store_true",
required=False,
help=(
"Set to not use the ema weights from the original VQ-Diffusion checkpoint. You probably do not want to set"
" it as the original VQ-Diffusion always uses the ema weights when loading models."
),
)
args = parser.parse_args()
use_ema = not args.no_use_ema
print(f"loading checkpoints to {args.checkpoint_load_device}")
checkpoint_map_location = torch.device(args.checkpoint_load_device)
# vqvae_model
print(f"loading vqvae, config: {args.vqvae_original_config_file}, checkpoint: {args.vqvae_checkpoint_path}")
vqvae_original_config = read_config_file(args.vqvae_original_config_file).model
vqvae_checkpoint = torch.load(args.vqvae_checkpoint_path, map_location=checkpoint_map_location)["model"]
with init_empty_weights():
vqvae_model = vqvae_model_from_original_config(vqvae_original_config)
vqvae_diffusers_checkpoint = vqvae_original_checkpoint_to_diffusers_checkpoint(vqvae_model, vqvae_checkpoint)
with tempfile.NamedTemporaryFile() as vqvae_diffusers_checkpoint_file:
torch.save(vqvae_diffusers_checkpoint, vqvae_diffusers_checkpoint_file.name)
del vqvae_diffusers_checkpoint
del vqvae_checkpoint
load_checkpoint_and_dispatch(vqvae_model, vqvae_diffusers_checkpoint_file.name, device_map="auto")
print("done loading vqvae")
# done vqvae_model
# transformer_model
print(
f"loading transformer, config: {args.original_config_file}, checkpoint: {args.checkpoint_path}, use ema:"
f" {use_ema}"
)
original_config = read_config_file(args.original_config_file).model
diffusion_config = original_config["params"]["diffusion_config"]
transformer_config = original_config["params"]["diffusion_config"]["params"]["transformer_config"]
content_embedding_config = original_config["params"]["diffusion_config"]["params"]["content_emb_config"]
pre_checkpoint = torch.load(args.checkpoint_path, map_location=checkpoint_map_location)
if use_ema:
if "ema" in pre_checkpoint:
checkpoint = {}
for k, v in pre_checkpoint["model"].items():
checkpoint[k] = v
for k, v in pre_checkpoint["ema"].items():
# The ema weights are only used on the transformer. To mimic their key as if they came
# from the state_dict for the top level model, we prefix with an additional "transformer."
# See the source linked in the args.use_ema config for more information.
checkpoint[f"transformer.{k}"] = v
else:
print("attempted to load ema weights but no ema weights are specified in the loaded checkpoint.")
checkpoint = pre_checkpoint["model"]
else:
checkpoint = pre_checkpoint["model"]
del pre_checkpoint
with init_empty_weights():
transformer_model = transformer_model_from_original_config(
diffusion_config, transformer_config, content_embedding_config
)
diffusers_transformer_checkpoint = transformer_original_checkpoint_to_diffusers_checkpoint(
transformer_model, checkpoint
)
# classifier free sampling embeddings interlude
# The learned embeddings are stored on the transformer in the original VQ-diffusion. We store them on a separate
# model, so we pull them off the checkpoint before the checkpoint is deleted.
learnable_classifier_free_sampling_embeddings = diffusion_config["params"].learnable_cf
if learnable_classifier_free_sampling_embeddings:
learned_classifier_free_sampling_embeddings_embeddings = checkpoint["transformer.empty_text_embed"]
else:
learned_classifier_free_sampling_embeddings_embeddings = None
# done classifier free sampling embeddings interlude
with tempfile.NamedTemporaryFile() as diffusers_transformer_checkpoint_file:
torch.save(diffusers_transformer_checkpoint, diffusers_transformer_checkpoint_file.name)
del diffusers_transformer_checkpoint
del checkpoint
load_checkpoint_and_dispatch(transformer_model, diffusers_transformer_checkpoint_file.name, device_map="auto")
print("done loading transformer")
# done transformer_model
# text encoder
print("loading CLIP text encoder")
clip_name = "openai/clip-vit-base-patch32"
# The original VQ-Diffusion specifies the pad value by the int used in the
# returned tokens. Each model uses `0` as the pad value. The transformers clip api
# specifies the pad value via the token before it has been tokenized. The `!` pad
# token is the same as padding with the `0` pad value.
pad_token = "!"
tokenizer_model = CLIPTokenizer.from_pretrained(clip_name, pad_token=pad_token, device_map="auto")
assert tokenizer_model.convert_tokens_to_ids(pad_token) == 0
text_encoder_model = CLIPTextModel.from_pretrained(
clip_name,
# `CLIPTextModel` does not support device_map="auto"
# device_map="auto"
)
print("done loading CLIP text encoder")
# done text encoder
# scheduler
scheduler_model = VQDiffusionScheduler(
# the scheduler has the same number of embeddings as the transformer
num_vec_classes=transformer_model.num_vector_embeds
)
# done scheduler
# learned classifier free sampling embeddings
with init_empty_weights():
learned_classifier_free_sampling_embeddings_model = LearnedClassifierFreeSamplingEmbeddings(
learnable_classifier_free_sampling_embeddings,
hidden_size=text_encoder_model.config.hidden_size,
length=tokenizer_model.model_max_length,
)
learned_classifier_free_sampling_checkpoint = {
"embeddings": learned_classifier_free_sampling_embeddings_embeddings.float()
}
with tempfile.NamedTemporaryFile() as learned_classifier_free_sampling_checkpoint_file:
torch.save(learned_classifier_free_sampling_checkpoint, learned_classifier_free_sampling_checkpoint_file.name)
del learned_classifier_free_sampling_checkpoint
del learned_classifier_free_sampling_embeddings_embeddings
load_checkpoint_and_dispatch(
learned_classifier_free_sampling_embeddings_model,
learned_classifier_free_sampling_checkpoint_file.name,
device_map="auto",
)
# done learned classifier free sampling embeddings
print(f"saving VQ diffusion model, path: {args.dump_path}")
pipe = VQDiffusionPipeline(
vqvae=vqvae_model,
transformer=transformer_model,
tokenizer=tokenizer_model,
text_encoder=text_encoder_model,
learned_classifier_free_sampling_embeddings=learned_classifier_free_sampling_embeddings_model,
scheduler=scheduler_model,
)
pipe.save_pretrained(args.dump_path)
print("done writing VQ diffusion model")
| diffusers/scripts/convert_vq_diffusion_to_diffusers.py/0 | {
"file_path": "diffusers/scripts/convert_vq_diffusion_to_diffusers.py",
"repo_id": "diffusers",
"token_count": 14920
} | 158 |
# THIS FILE HAS BEEN AUTOGENERATED. To update:
# 1. modify the `_deps` dict in setup.py
# 2. run `make deps_table_update`
deps = {
"Pillow": "Pillow",
"accelerate": "accelerate>=0.31.0",
"compel": "compel==0.1.8",
"datasets": "datasets",
"filelock": "filelock",
"flax": "flax>=0.4.1",
"hf-doc-builder": "hf-doc-builder>=0.3.0",
"huggingface-hub": "huggingface-hub>=0.34.0",
"requests-mock": "requests-mock==1.10.0",
"importlib_metadata": "importlib_metadata",
"invisible-watermark": "invisible-watermark>=0.2.0",
"isort": "isort>=5.5.4",
"jax": "jax>=0.4.1",
"jaxlib": "jaxlib>=0.4.1",
"Jinja2": "Jinja2",
"k-diffusion": "k-diffusion==0.0.12",
"torchsde": "torchsde",
"note_seq": "note_seq",
"librosa": "librosa",
"numpy": "numpy",
"parameterized": "parameterized",
"peft": "peft>=0.17.0",
"protobuf": "protobuf>=3.20.3,<4",
"pytest": "pytest",
"pytest-timeout": "pytest-timeout",
"pytest-xdist": "pytest-xdist",
"python": "python>=3.8.0",
"ruff": "ruff==0.9.10",
"safetensors": "safetensors>=0.3.1",
"sentencepiece": "sentencepiece>=0.1.91,!=0.1.92",
"GitPython": "GitPython<3.1.19",
"scipy": "scipy",
"onnx": "onnx",
"optimum_quanto": "optimum_quanto>=0.2.6",
"gguf": "gguf>=0.10.0",
"torchao": "torchao>=0.7.0",
"bitsandbytes": "bitsandbytes>=0.43.3",
"regex": "regex!=2019.12.17",
"requests": "requests",
"tensorboard": "tensorboard",
"tiktoken": "tiktoken>=0.7.0",
"torch": "torch>=1.4",
"torchvision": "torchvision",
"transformers": "transformers>=4.41.2",
"urllib3": "urllib3<=2.0.0",
"black": "black",
"phonemizer": "phonemizer",
"opencv-python": "opencv-python",
}
| diffusers/src/diffusers/dependency_versions_table.py/0 | {
"file_path": "diffusers/src/diffusers/dependency_versions_table.py",
"repo_id": "diffusers",
"token_count": 926
} | 159 |
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