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Wuerstchen main (#876)

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* Wuerstchen main.

* More of the wuerstchen cli example.

* Paella creation.

* Build the prior model.

* Fix the weight file names.
This commit is contained in:
Laurent Mazare
2023-09-17 13:46:38 +02:00
committed by GitHub
parent 7f65af1f0d
commit db3e9dae04
3 changed files with 213 additions and 273 deletions
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335
candle-examples/examples/wuerstchen/main.rs
View File

@ -1,3 +1,5 @@
#![allow(unused)]
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
@ -5,6 +7,7 @@ extern crate accelerate_src;
extern crate intel_mkl_src;
use candle_transformers::models::stable_diffusion;
use candle_transformers::models::wuerstchen;
use anyhow::{Error as E, Result};
use candle::{DType, Device, IndexOp, Module, Tensor, D};
@ -42,17 +45,21 @@ struct Args {
#[arg(long)]
width: Option<usize>,
/// The UNet weight file, in .safetensors format.
/// The decoder weight file, in .safetensors format.
#[arg(long, value_name = "FILE")]
unet_weights: Option<String>,
decoder_weights: Option<String>,
/// The CLIP weight file, in .safetensors format.
#[arg(long, value_name = "FILE")]
clip_weights: Option<String>,
/// The VAE weight file, in .safetensors format.
/// The prior weight file, in .safetensors format.
#[arg(long, value_name = "FILE")]
vae_weights: Option<String>,
prior_weights: Option<String>,
/// The VQGAN weight file, in .safetensors format.
#[arg(long, value_name = "FILE")]
vqgan_weights: Option<String>,
#[arg(long, value_name = "FILE")]
/// The file specifying the tokenizer to used for tokenization.
@ -73,138 +80,31 @@ struct Args {
/// The name of the final image to generate.
#[arg(long, value_name = "FILE", default_value = "sd_final.png")]
final_image: String,
#[arg(long, value_enum, default_value = "v2-1")]
sd_version: StableDiffusionVersion,
/// Generate intermediary images at each step.
#[arg(long, action)]
intermediary_images: bool,
#[arg(long)]
use_flash_attn: bool,
#[arg(long)]
use_f16: bool,
#[arg(long, value_name = "FILE")]
img2img: Option<String>,
/// The strength, indicates how much to transform the initial image. The
/// value must be between 0 and 1, a value of 1 discards the initial image
/// information.
#[arg(long, default_value_t = 0.8)]
img2img_strength: f64,
}
#[derive(Debug, Clone, Copy, clap::ValueEnum)]
enum StableDiffusionVersion {
V1_5,
V2_1,
Xl,
}
#[allow(unused)]
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
enum ModelFile {
Tokenizer,
Tokenizer2,
Clip,
Clip2,
Unet,
Vae,
}
impl StableDiffusionVersion {
fn repo(&self) -> &'static str {
match self {
Self::Xl => "stabilityai/stable-diffusion-xl-base-1.0",
Self::V2_1 => "stabilityai/stable-diffusion-2-1",
Self::V1_5 => "runwayml/stable-diffusion-v1-5",
}
}
fn unet_file(&self, use_f16: bool) -> &'static str {
match self {
Self::V1_5 | Self::V2_1 | Self::Xl => {
if use_f16 {
"unet/diffusion_pytorch_model.fp16.safetensors"
} else {
"unet/diffusion_pytorch_model.safetensors"
}
}
}
}
fn vae_file(&self, use_f16: bool) -> &'static str {
match self {
Self::V1_5 | Self::V2_1 | Self::Xl => {
if use_f16 {
"vae/diffusion_pytorch_model.fp16.safetensors"
} else {
"vae/diffusion_pytorch_model.safetensors"
}
}
}
}
fn clip_file(&self, use_f16: bool) -> &'static str {
match self {
Self::V1_5 | Self::V2_1 | Self::Xl => {
if use_f16 {
"text_encoder/model.fp16.safetensors"
} else {
"text_encoder/model.safetensors"
}
}
}
}
fn clip2_file(&self, use_f16: bool) -> &'static str {
match self {
Self::V1_5 | Self::V2_1 | Self::Xl => {
if use_f16 {
"text_encoder_2/model.fp16.safetensors"
} else {
"text_encoder_2/model.safetensors"
}
}
}
}
Decoder,
VqGan,
Prior,
}
impl ModelFile {
fn get(
&self,
filename: Option<String>,
version: StableDiffusionVersion,
use_f16: bool,
) -> Result<std::path::PathBuf> {
fn get(&self, filename: Option<String>) -> Result<std::path::PathBuf> {
use hf_hub::api::sync::Api;
match filename {
Some(filename) => Ok(std::path::PathBuf::from(filename)),
None => {
let repo_main = "warp-ai/wuerstchen";
let repo_prior = "warp-ai/wuerstchen-prior";
let (repo, path) = match self {
Self::Tokenizer => {
let tokenizer_repo = match version {
StableDiffusionVersion::V1_5 | StableDiffusionVersion::V2_1 => {
"openai/clip-vit-base-patch32"
}
StableDiffusionVersion::Xl => {
// This seems similar to the patch32 version except some very small
// difference in the split regex.
"openai/clip-vit-large-patch14"
}
};
(tokenizer_repo, "tokenizer.json")
}
Self::Tokenizer2 => {
("laion/CLIP-ViT-bigG-14-laion2B-39B-b160k", "tokenizer.json")
}
Self::Clip => (version.repo(), version.clip_file(use_f16)),
Self::Clip2 => (version.repo(), version.clip2_file(use_f16)),
Self::Unet => (version.repo(), version.unet_file(use_f16)),
Self::Vae => (version.repo(), version.vae_file(use_f16)),
Self::Tokenizer => (repo_main, "tokenizer/tokenizer.json"),
Self::Clip => (repo_main, "text_encoder/model.safetensors"),
Self::Decoder => (repo_main, "decoder/diffusion_pytorch_model.safetensors"),
Self::VqGan => (repo_main, "vqgan/diffusion_pytorch_model.safetensors"),
Self::Prior => (repo_prior, "prior/diffusion_pytorch_model.safetensors"),
};
let filename = Api::new()?.model(repo.to_string()).get(path)?;
Ok(filename)
@ -240,27 +140,17 @@ fn output_filename(
}
}
#[allow(clippy::too_many_arguments)]
fn text_embeddings(
fn encode_prompt(
prompt: &str,
uncond_prompt: &str,
tokenizer: Option<String>,
clip_weights: Option<String>,
sd_version: StableDiffusionVersion,
sd_config: &stable_diffusion::StableDiffusionConfig,
use_f16: bool,
clip_config: stable_diffusion::clip::Config,
device: &Device,
dtype: DType,
first: bool,
) -> Result<Tensor> {
let tokenizer_file = if first {
ModelFile::Tokenizer
} else {
ModelFile::Tokenizer2
};
let tokenizer = tokenizer_file.get(tokenizer, sd_version, use_f16)?;
let tokenizer = ModelFile::Tokenizer.get(tokenizer)?;
let tokenizer = Tokenizer::from_file(tokenizer).map_err(E::msg)?;
let pad_id = match &sd_config.clip.pad_with {
let pad_id = match &clip_config.pad_with {
Some(padding) => *tokenizer.get_vocab(true).get(padding.as_str()).unwrap(),
None => *tokenizer.get_vocab(true).get("<|endoftext|>").unwrap(),
};
@ -270,7 +160,7 @@ fn text_embeddings(
.map_err(E::msg)?
.get_ids()
.to_vec();
while tokens.len() < sd_config.clip.max_position_embeddings {
while tokens.len() < clip_config.max_position_embeddings {
tokens.push(pad_id)
}
let tokens = Tensor::new(tokens.as_slice(), device)?.unsqueeze(0)?;
@ -280,51 +170,21 @@ fn text_embeddings(
.map_err(E::msg)?
.get_ids()
.to_vec();
while uncond_tokens.len() < sd_config.clip.max_position_embeddings {
while uncond_tokens.len() < clip_config.max_position_embeddings {
uncond_tokens.push(pad_id)
}
let uncond_tokens = Tensor::new(uncond_tokens.as_slice(), device)?.unsqueeze(0)?;
println!("Building the Clip transformer.");
let clip_weights_file = if first {
ModelFile::Clip
} else {
ModelFile::Clip2
};
let clip_weights = clip_weights_file.get(clip_weights, sd_version, false)?;
let clip_config = if first {
&sd_config.clip
} else {
sd_config.clip2.as_ref().unwrap()
};
let clip_weights = ModelFile::Clip.get(clip_weights)?;
let text_model =
stable_diffusion::build_clip_transformer(clip_config, clip_weights, device, DType::F32)?;
stable_diffusion::build_clip_transformer(&clip_config, clip_weights, device, DType::F32)?;
let text_embeddings = text_model.forward(&tokens)?;
let uncond_embeddings = text_model.forward(&uncond_tokens)?;
let text_embeddings = Tensor::cat(&[uncond_embeddings, text_embeddings], 0)?.to_dtype(dtype)?;
let text_embeddings = Tensor::cat(&[uncond_embeddings, text_embeddings], 0)?;
Ok(text_embeddings)
}
fn image_preprocess<T: AsRef<std::path::Path>>(path: T) -> anyhow::Result<Tensor> {
let img = image::io::Reader::open(path)?.decode()?;
let (height, width) = (img.height() as usize, img.width() as usize);
let height = height - height % 32;
let width = width - width % 32;
let img = img.resize_to_fill(
width as u32,
height as u32,
image::imageops::FilterType::CatmullRom,
);
let img = img.to_rgb8();
let img = img.into_raw();
let img = Tensor::from_vec(img, (height, width, 3), &Device::Cpu)?
.permute((2, 0, 1))?
.to_dtype(DType::F32)?
.affine(2. / 255., -1.)?
.unsqueeze(0)?;
Ok(img)
}
fn run(args: Args) -> Result<()> {
use tracing_chrome::ChromeLayerBuilder;
use tracing_subscriber::prelude::*;
@ -340,22 +200,14 @@ fn run(args: Args) -> Result<()> {
final_image,
sliced_attention_size,
num_samples,
sd_version,
clip_weights,
vae_weights,
unet_weights,
prior_weights,
vqgan_weights,
decoder_weights,
tracing,
use_f16,
use_flash_attn,
img2img,
img2img_strength,
..
} = args;
if !(0. ..=1.).contains(&img2img_strength) {
anyhow::bail!("img2img-strength should be between 0 and 1, got {img2img_strength}")
}
let _guard = if tracing {
let (chrome_layer, guard) = ChromeLayerBuilder::new().build();
tracing_subscriber::registry().with(chrome_layer).init();
@ -364,79 +216,58 @@ fn run(args: Args) -> Result<()> {
None
};
let dtype = if use_f16 { DType::F16 } else { DType::F32 };
let sd_config = match sd_version {
StableDiffusionVersion::V1_5 => {
stable_diffusion::StableDiffusionConfig::v1_5(sliced_attention_size, height, width)
}
StableDiffusionVersion::V2_1 => {
stable_diffusion::StableDiffusionConfig::v2_1(sliced_attention_size, height, width)
}
StableDiffusionVersion::Xl => {
stable_diffusion::StableDiffusionConfig::sdxl(sliced_attention_size, height, width)
}
};
let scheduler = sd_config.build_scheduler(n_steps)?;
let device = candle_examples::device(cpu)?;
let which = match sd_version {
StableDiffusionVersion::Xl => vec![true, false],
_ => vec![true],
};
let text_embeddings = which
.iter()
.map(|first| {
text_embeddings(
let text_embeddings = encode_prompt(
&prompt,
&uncond_prompt,
tokenizer.clone(),
clip_weights.clone(),
sd_version,
&sd_config,
use_f16,
stable_diffusion::clip::Config::wuerstchen(),
&device,
dtype,
*first,
)
})
.collect::<Result<Vec<_>>>()?;
let text_embeddings = Tensor::cat(&text_embeddings, D::Minus1)?;
);
println!("{text_embeddings:?}");
println!("Building the autoencoder.");
let vae_weights = ModelFile::Vae.get(vae_weights, sd_version, use_f16)?;
let vae = sd_config.build_vae(&vae_weights, &device, dtype)?;
let init_latent_dist = match &img2img {
None => None,
Some(image) => {
let image = image_preprocess(image)?.to_device(&device)?;
Some(vae.encode(&image)?)
}
println!("Building the prior.");
// https://huggingface.co/warp-ai/wuerstchen-prior/blob/main/prior/config.json
let _prior = {
let prior_weights = ModelFile::Prior.get(prior_weights)?;
let weights = unsafe { candle::safetensors::MmapedFile::new(prior_weights)? };
let weights = weights.deserialize()?;
let vb = candle_nn::VarBuilder::from_safetensors(vec![weights], DType::F32, &device);
wuerstchen::prior::WPrior::new(
/* c_in */ 16, /* c */ 1536, /* c_cond */ 1280, /* c_r */ 64,
/* depth */ 32, /* nhead */ 24, vb,
)
};
println!("Building the unet.");
let unet_weights = ModelFile::Unet.get(unet_weights, sd_version, use_f16)?;
let unet = sd_config.build_unet(&unet_weights, &device, 4, use_flash_attn, dtype)?;
let t_start = if img2img.is_some() {
n_steps - (n_steps as f64 * img2img_strength) as usize
} else {
0
println!("Building the vqgan.");
let _vqgan = {
let vqgan_weights = ModelFile::VqGan.get(vqgan_weights)?;
let weights = unsafe { candle::safetensors::MmapedFile::new(vqgan_weights)? };
let weights = weights.deserialize()?;
let vb = candle_nn::VarBuilder::from_safetensors(vec![weights], DType::F32, &device);
wuerstchen::paella_vq::PaellaVQ::new(vb)?
};
let bsize = 1;
println!("Building the decoder.");
// https://huggingface.co/warp-ai/wuerstchen/blob/main/decoder/config.json
let _decoder = {
let decoder_weights = ModelFile::Decoder.get(decoder_weights)?;
let weights = unsafe { candle::safetensors::MmapedFile::new(decoder_weights)? };
let weights = weights.deserialize()?;
let vb = candle_nn::VarBuilder::from_safetensors(vec![weights], DType::F32, &device);
wuerstchen::diffnext::WDiffNeXt::new(
/* c_in */ 4, /* c_out */ 4, /* c_r */ 64, /* c_cond */ 1024,
/* clip_embd */ 1024, /* patch_size */ 2, vb,
)?
};
let _bsize = 1;
for idx in 0..num_samples {
/*
let timesteps = scheduler.timesteps();
let latents = match &init_latent_dist {
Some(init_latent_dist) => {
let latents = (init_latent_dist.sample()? * 0.18215)?.to_device(&device)?;
if t_start < timesteps.len() {
let noise = latents.randn_like(0f64, 1f64)?;
scheduler.add_noise(&latents, noise, timesteps[t_start])?
} else {
latents
}
}
None => {
let latents = Tensor::randn(
0f32,
1f32,
@ -444,22 +275,16 @@ fn run(args: Args) -> Result<()> {
&device,
)?;
// scale the initial noise by the standard deviation required by the scheduler
(latents * scheduler.init_noise_sigma())?
}
};
let mut latents = latents.to_dtype(dtype)?;
let mut latents = latents * scheduler.init_noise_sigma()?;
println!("starting sampling");
for (timestep_index, &timestep) in timesteps.iter().enumerate() {
if timestep_index < t_start {
continue;
}
let start_time = std::time::Instant::now();
let latent_model_input = Tensor::cat(&[&latents, &latents], 0)?;
let latent_model_input = scheduler.scale_model_input(latent_model_input, timestep)?;
let noise_pred =
unet.forward(&latent_model_input, timestep as f64, &text_embeddings)?;
decoder.forward(&latent_model_input, timestep as f64, &text_embeddings)?;
let noise_pred = noise_pred.chunk(2, 0)?;
let (noise_pred_uncond, noise_pred_text) = (&noise_pred[0], &noise_pred[1]);
let noise_pred =
@ -467,28 +292,22 @@ fn run(args: Args) -> Result<()> {
latents = scheduler.step(&noise_pred, timestep, &latents)?;
let dt = start_time.elapsed().as_secs_f32();
println!("step {}/{n_steps} done, {:.2}s", timestep_index + 1, dt);
if args.intermediary_images {
let image = vae.decode(&(&latents / 0.18215)?)?;
let image = ((image / 2.)? + 0.5)?.to_device(&Device::Cpu)?;
let image = (image * 255.)?.to_dtype(DType::U8)?.i(0)?;
let image_filename =
output_filename(&final_image, idx + 1, num_samples, Some(timestep_index + 1));
candle_examples::save_image(&image, image_filename)?
}
}
*/
println!(
"Generating the final image for sample {}/{}.",
idx + 1,
num_samples
);
/*
let image = vae.decode(&(&latents / 0.18215)?)?;
// TODO: Add the clamping between 0 and 1.
let image = ((image / 2.)? + 0.5)?.to_device(&Device::Cpu)?;
let image = (image * 255.)?.to_dtype(DType::U8)?.i(0)?;
let image_filename = output_filename(&final_image, idx + 1, num_samples, None);
candle_examples::save_image(&image, image_filename)?
*/
}
Ok(())
}

15
candle-transformers/src/models/stable_diffusion/clip.rs
View File

@ -99,6 +99,21 @@ impl Config {
activation: Activation::Gelu,
}
}
// https://huggingface.co/warp-ai/wuerstchen/blob/main/text_encoder/config.json
pub fn wuerstchen() -> Self {
Self {
vocab_size: 49408,
embed_dim: 1024,
intermediate_size: 4096,
max_position_embeddings: 77,
pad_with: Some("!".to_string()),
num_hidden_layers: 24,
num_attention_heads: 16,
projection_dim: 1024,
activation: Activation::Gelu,
}
}
}
// CLIP Text Model

112
candle-transformers/src/models/wuerstchen/paella_vq.rs
View File

@ -65,17 +65,121 @@ impl Module for MixingResidualBlock {
}
#[derive(Debug)]
struct PaellaVQ {
pub struct PaellaVQ {
in_block_conv: candle_nn::Conv2d,
out_block_conv: candle_nn::Conv2d,
down_blocks: Vec<(Option<candle_nn::Conv2d>, MixingResidualBlock)>,
down_blocks_conv: candle_nn::Conv2d,
down_blocks_bn: candle_nn::BatchNorm,
up_blocks_conv: candle_nn::Conv2d,
up_blocks: Vec<(MixingResidualBlock, Option<candle_nn::ConvTranspose2d>)>,
up_blocks: Vec<(Vec<MixingResidualBlock>, Option<candle_nn::ConvTranspose2d>)>,
}
impl PaellaVQ {
pub fn new(vb: VarBuilder) -> Result<Self> {
const IN_CHANNELS: usize = 3;
const OUT_CHANNELS: usize = 3;
const LATENT_CHANNELS: usize = 4;
const EMBED_DIM: usize = 384;
const BOTTLENECK_BLOCKS: usize = 12;
const C_LEVELS: [usize; 2] = [EMBED_DIM / 2, EMBED_DIM];
let in_block_conv = candle_nn::conv2d(
IN_CHANNELS * 4,
C_LEVELS[0],
1,
Default::default(),
vb.pp("in_block.1"),
)?;
let out_block_conv = candle_nn::conv2d(
C_LEVELS[0],
OUT_CHANNELS * 4,
1,
Default::default(),
vb.pp("out_block.0"),
)?;
let mut down_blocks = Vec::new();
let vb_d = vb.pp("down_blocks");
let mut d_idx = 0;
for (i, &c_level) in C_LEVELS.iter().enumerate() {
let conv_block = if i > 0 {
let cfg = candle_nn::Conv2dConfig {
padding: 1,
stride: 2,
..Default::default()
};
let block =
candle_nn::conv2d_no_bias(C_LEVELS[i - 1], c_level, 4, cfg, vb_d.pp(d_idx))?;
d_idx += 1;
Some(block)
} else {
None
};
let res_block = MixingResidualBlock::new(c_level, c_level * 4, vb_d.pp(d_idx))?;
d_idx += 1;
down_blocks.push((conv_block, res_block))
}
let down_blocks_conv = candle_nn::conv2d_no_bias(
C_LEVELS[1],
LATENT_CHANNELS,
1,
Default::default(),
vb_d.pp(d_idx),
)?;
d_idx += 1;
let down_blocks_bn = candle_nn::batch_norm(LATENT_CHANNELS, 1e-5, vb_d.pp(d_idx))?;
let mut up_blocks = Vec::new();
let vb_u = vb.pp("up_blocks");
let mut u_idx = 0;
let up_blocks_conv = candle_nn::conv2d_no_bias(
LATENT_CHANNELS,
C_LEVELS[1],
1,
Default::default(),
vb_u.pp(u_idx),
)?;
u_idx += 1;
for (i, &c_level) in C_LEVELS.iter().rev().enumerate() {
let mut res_blocks = Vec::new();
let n_bottleneck_blocks = if i == 0 { BOTTLENECK_BLOCKS } else { 1 };
for _j in 0..n_bottleneck_blocks {
let res_block = MixingResidualBlock::new(c_level, c_level * 4, vb_u.pp(u_idx))?;
u_idx += 1;
res_blocks.push(res_block)
}
let conv_block = if i < C_LEVELS.len() - 1 {
let cfg = candle_nn::ConvTranspose2dConfig {
padding: 1,
stride: 2,
..Default::default()
};
let block = candle_nn::conv_transpose2d_no_bias(
c_level,
C_LEVELS[i - 1],
4,
cfg,
vb_u.pp(u_idx),
)?;
u_idx += 1;
Some(block)
} else {
None
};
up_blocks.push((res_blocks, conv_block))
}
Ok(Self {
in_block_conv,
down_blocks,
down_blocks_conv,
down_blocks_bn,
up_blocks,
up_blocks_conv,
out_block_conv,
})
}
pub fn encode(&self, xs: &Tensor) -> Result<Tensor> {
let mut xs = candle_nn::ops::pixel_unshuffle(xs, 2)?.apply(&self.in_block_conv)?;
for down_block in self.down_blocks.iter() {
@ -92,7 +196,9 @@ impl PaellaVQ {
// TODO: quantizer if we want to support `force_not_quantize=False`.
let mut xs = xs.apply(&self.up_blocks_conv)?;
for up_block in self.up_blocks.iter() {
xs = xs.apply(&up_block.0)?;
for b in up_block.0.iter() {
xs = xs.apply(b)?;
}
if let Some(conv) = &up_block.1 {
xs = xs.apply(conv)?
}