mirror of
https://github.com/huggingface/candle.git
synced 2025-06-15 18:28:24 +00:00
37bf1ed012
* Add support for SD Turbo * Set Leading as default in euler_ancestral discrete * Use the appropriate default values for n_steps and guidance_scale. --------- Co-authored-by: Laurent <laurent.mazare@gmail.com>
568 lines
18 KiB
Rust
568 lines
18 KiB
Rust
#[cfg(feature = "accelerate")]
|
|
extern crate accelerate_src;
|
|
|
|
#[cfg(feature = "mkl")]
|
|
extern crate intel_mkl_src;
|
|
|
|
use candle_transformers::models::stable_diffusion;
|
|
|
|
use anyhow::{Error as E, Result};
|
|
use candle::{DType, Device, IndexOp, Module, Tensor, D};
|
|
use clap::Parser;
|
|
use tokenizers::Tokenizer;
|
|
|
|
#[derive(Parser)]
|
|
#[command(author, version, about, long_about = None)]
|
|
struct Args {
|
|
/// The prompt to be used for image generation.
|
|
#[arg(
|
|
long,
|
|
default_value = "A very realistic photo of a rusty robot walking on a sandy beach"
|
|
)]
|
|
prompt: String,
|
|
|
|
#[arg(long, default_value = "")]
|
|
uncond_prompt: String,
|
|
|
|
/// Run on CPU rather than on GPU.
|
|
#[arg(long)]
|
|
cpu: bool,
|
|
|
|
/// Enable tracing (generates a trace-timestamp.json file).
|
|
#[arg(long)]
|
|
tracing: bool,
|
|
|
|
/// The height in pixels of the generated image.
|
|
#[arg(long)]
|
|
height: Option<usize>,
|
|
|
|
/// The width in pixels of the generated image.
|
|
#[arg(long)]
|
|
width: Option<usize>,
|
|
|
|
/// The UNet weight file, in .safetensors format.
|
|
#[arg(long, value_name = "FILE")]
|
|
unet_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.
|
|
#[arg(long, value_name = "FILE")]
|
|
vae_weights: Option<String>,
|
|
|
|
#[arg(long, value_name = "FILE")]
|
|
/// The file specifying the tokenizer to used for tokenization.
|
|
tokenizer: Option<String>,
|
|
|
|
/// The size of the sliced attention or 0 for automatic slicing (disabled by default)
|
|
#[arg(long)]
|
|
sliced_attention_size: Option<usize>,
|
|
|
|
/// The number of steps to run the diffusion for.
|
|
#[arg(long)]
|
|
n_steps: Option<usize>,
|
|
|
|
/// The number of samples to generate.
|
|
#[arg(long, default_value_t = 1)]
|
|
num_samples: i64,
|
|
|
|
/// 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)]
|
|
guidance_scale: Option<f64>,
|
|
|
|
#[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, PartialEq, Eq)]
|
|
enum StableDiffusionVersion {
|
|
V1_5,
|
|
V2_1,
|
|
Xl,
|
|
Turbo,
|
|
}
|
|
|
|
#[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",
|
|
Self::Turbo => "stabilityai/sdxl-turbo",
|
|
}
|
|
}
|
|
|
|
fn unet_file(&self, use_f16: bool) -> &'static str {
|
|
match self {
|
|
Self::V1_5 | Self::V2_1 | Self::Xl | Self::Turbo => {
|
|
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 | Self::Turbo => {
|
|
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 | Self::Turbo => {
|
|
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 | Self::Turbo => {
|
|
if use_f16 {
|
|
"text_encoder_2/model.fp16.safetensors"
|
|
} else {
|
|
"text_encoder_2/model.safetensors"
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
impl ModelFile {
|
|
fn get(
|
|
&self,
|
|
filename: Option<String>,
|
|
version: StableDiffusionVersion,
|
|
use_f16: bool,
|
|
) -> Result<std::path::PathBuf> {
|
|
use hf_hub::api::sync::Api;
|
|
match filename {
|
|
Some(filename) => Ok(std::path::PathBuf::from(filename)),
|
|
None => {
|
|
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 | StableDiffusionVersion::Turbo => {
|
|
// 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 => {
|
|
// Override for SDXL when using f16 weights.
|
|
// See https://github.com/huggingface/candle/issues/1060
|
|
if matches!(
|
|
version,
|
|
StableDiffusionVersion::Xl | StableDiffusionVersion::Turbo,
|
|
) && use_f16
|
|
{
|
|
(
|
|
"madebyollin/sdxl-vae-fp16-fix",
|
|
"diffusion_pytorch_model.safetensors",
|
|
)
|
|
} else {
|
|
(version.repo(), version.vae_file(use_f16))
|
|
}
|
|
}
|
|
};
|
|
let filename = Api::new()?.model(repo.to_string()).get(path)?;
|
|
Ok(filename)
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
fn output_filename(
|
|
basename: &str,
|
|
sample_idx: i64,
|
|
num_samples: i64,
|
|
timestep_idx: Option<usize>,
|
|
) -> String {
|
|
let filename = if num_samples > 1 {
|
|
match basename.rsplit_once('.') {
|
|
None => format!("{basename}.{sample_idx}.png"),
|
|
Some((filename_no_extension, extension)) => {
|
|
format!("{filename_no_extension}.{sample_idx}.{extension}")
|
|
}
|
|
}
|
|
} else {
|
|
basename.to_string()
|
|
};
|
|
match timestep_idx {
|
|
None => filename,
|
|
Some(timestep_idx) => match filename.rsplit_once('.') {
|
|
None => format!("{filename}-{timestep_idx}.png"),
|
|
Some((filename_no_extension, extension)) => {
|
|
format!("{filename_no_extension}-{timestep_idx}.{extension}")
|
|
}
|
|
},
|
|
}
|
|
}
|
|
|
|
#[allow(clippy::too_many_arguments)]
|
|
fn text_embeddings(
|
|
prompt: &str,
|
|
uncond_prompt: &str,
|
|
tokenizer: Option<String>,
|
|
clip_weights: Option<String>,
|
|
sd_version: StableDiffusionVersion,
|
|
sd_config: &stable_diffusion::StableDiffusionConfig,
|
|
use_f16: bool,
|
|
device: &Device,
|
|
dtype: DType,
|
|
use_guide_scale: bool,
|
|
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 = Tokenizer::from_file(tokenizer).map_err(E::msg)?;
|
|
let pad_id = match &sd_config.clip.pad_with {
|
|
Some(padding) => *tokenizer.get_vocab(true).get(padding.as_str()).unwrap(),
|
|
None => *tokenizer.get_vocab(true).get("<|endoftext|>").unwrap(),
|
|
};
|
|
println!("Running with prompt \"{prompt}\".");
|
|
let mut tokens = tokenizer
|
|
.encode(prompt, true)
|
|
.map_err(E::msg)?
|
|
.get_ids()
|
|
.to_vec();
|
|
while tokens.len() < sd_config.clip.max_position_embeddings {
|
|
tokens.push(pad_id)
|
|
}
|
|
let tokens = Tensor::new(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 text_model =
|
|
stable_diffusion::build_clip_transformer(clip_config, clip_weights, device, DType::F32)?;
|
|
let text_embeddings = text_model.forward(&tokens)?;
|
|
|
|
let text_embeddings = if use_guide_scale {
|
|
let mut uncond_tokens = tokenizer
|
|
.encode(uncond_prompt, true)
|
|
.map_err(E::msg)?
|
|
.get_ids()
|
|
.to_vec();
|
|
while uncond_tokens.len() < sd_config.clip.max_position_embeddings {
|
|
uncond_tokens.push(pad_id)
|
|
}
|
|
|
|
let uncond_tokens = Tensor::new(uncond_tokens.as_slice(), device)?.unsqueeze(0)?;
|
|
let uncond_embeddings = text_model.forward(&uncond_tokens)?;
|
|
|
|
Tensor::cat(&[uncond_embeddings, text_embeddings], 0)?.to_dtype(dtype)?
|
|
} else {
|
|
text_embeddings.to_dtype(dtype)?
|
|
};
|
|
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::*;
|
|
|
|
let Args {
|
|
prompt,
|
|
uncond_prompt,
|
|
cpu,
|
|
height,
|
|
width,
|
|
n_steps,
|
|
tokenizer,
|
|
final_image,
|
|
sliced_attention_size,
|
|
num_samples,
|
|
sd_version,
|
|
clip_weights,
|
|
vae_weights,
|
|
unet_weights,
|
|
tracing,
|
|
use_f16,
|
|
guidance_scale,
|
|
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();
|
|
Some(guard)
|
|
} else {
|
|
None
|
|
};
|
|
|
|
let guidance_scale = match guidance_scale {
|
|
Some(guidance_scale) => guidance_scale,
|
|
None => match sd_version {
|
|
StableDiffusionVersion::V1_5
|
|
| StableDiffusionVersion::V2_1
|
|
| StableDiffusionVersion::Xl => 7.5,
|
|
StableDiffusionVersion::Turbo => 0.,
|
|
},
|
|
};
|
|
let n_steps = match n_steps {
|
|
Some(n_steps) => n_steps,
|
|
None => match sd_version {
|
|
StableDiffusionVersion::V1_5
|
|
| StableDiffusionVersion::V2_1
|
|
| StableDiffusionVersion::Xl => 30,
|
|
StableDiffusionVersion::Turbo => 1,
|
|
},
|
|
};
|
|
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)
|
|
}
|
|
StableDiffusionVersion::Turbo => stable_diffusion::StableDiffusionConfig::sdxl_turbo(
|
|
sliced_attention_size,
|
|
height,
|
|
width,
|
|
),
|
|
};
|
|
|
|
let scheduler = sd_config.build_scheduler(n_steps)?;
|
|
let device = candle_examples::device(cpu)?;
|
|
let use_guide_scale = guidance_scale > 1.0;
|
|
|
|
let which = match sd_version {
|
|
StableDiffusionVersion::Xl | StableDiffusionVersion::Turbo => vec![true, false],
|
|
_ => vec![true],
|
|
};
|
|
let text_embeddings = which
|
|
.iter()
|
|
.map(|first| {
|
|
text_embeddings(
|
|
&prompt,
|
|
&uncond_prompt,
|
|
tokenizer.clone(),
|
|
clip_weights.clone(),
|
|
sd_version,
|
|
&sd_config,
|
|
use_f16,
|
|
&device,
|
|
dtype,
|
|
use_guide_scale,
|
|
*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 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
|
|
};
|
|
let bsize = 1;
|
|
|
|
let vae_scale = match sd_version {
|
|
StableDiffusionVersion::V1_5
|
|
| StableDiffusionVersion::V2_1
|
|
| StableDiffusionVersion::Xl => 0.18215,
|
|
StableDiffusionVersion::Turbo => 0.13025,
|
|
};
|
|
|
|
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()? * vae_scale)?.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,
|
|
(bsize, 4, sd_config.height / 8, sd_config.width / 8),
|
|
&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)?;
|
|
|
|
println!("starting sampling");
|
|
for (timestep_index, ×tep) in timesteps.iter().enumerate() {
|
|
if timestep_index < t_start {
|
|
continue;
|
|
}
|
|
let start_time = std::time::Instant::now();
|
|
let latent_model_input = if use_guide_scale {
|
|
Tensor::cat(&[&latents, &latents], 0)?
|
|
} else {
|
|
latents.clone()
|
|
};
|
|
|
|
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)?;
|
|
|
|
let noise_pred = if use_guide_scale {
|
|
let noise_pred = noise_pred.chunk(2, 0)?;
|
|
let (noise_pred_uncond, noise_pred_text) = (&noise_pred[0], &noise_pred[1]);
|
|
|
|
(noise_pred_uncond + ((noise_pred_text - noise_pred_uncond)? * guidance_scale)?)?
|
|
} else {
|
|
noise_pred
|
|
};
|
|
|
|
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 / vae_scale)?)?;
|
|
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 / vae_scale)?)?;
|
|
let image = ((image / 2.)? + 0.5)?.to_device(&Device::Cpu)?;
|
|
let image = (image.clamp(0f32, 1.)? * 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(())
|
|
}
|
|
|
|
fn main() -> Result<()> {
|
|
let args = Args::parse();
|
|
run(args)
|
|
}
|