Add the flux model for image generation. (#2390)

* Add the flux autoencoder.

* Add the encoder down-blocks.

* Upsampling in the decoder.

* Sketch the flow matching model.

* More flux model.

* Add some of the positional embeddings.

* Add the rope embeddings.

* Add the sampling functions.

* Add the flux example.

* Fix the T5 bits.

* Proper T5 tokenizer.

* Clip encoder path fix.

* Get the clip embeddings.

* No configurable weights in layer norm.

* More weights related fixes.

* Yet another shape fix.

* DType fix.

* Fix a couple more shape issues.

* DType fixes.

* Fix the latent dims.

* Fix more shape issues.

* Autoencoder fixes.

* Get some generations out.

* Bugfix.

* T5 padding.

* Clippy fix.

* Add the decode only mode.

* Fix.

* More fixes.

* Finally get some generations to work.

* Add readme.

candle-examples/examples/flux/README.md

@@ -0,0 +1,19 @@
+# candle-flux: image generation with latent rectified flow transformers
+
+![rusty robot holding a candle](./assets/flux-robot.jpg)
+
+Flux is a 12B rectified flow transformer capable of generating images from text
+descriptions,
+[huggingface](https://huggingface.co/black-forest-labs/FLUX.1-schnell),
+[github](https://github.com/black-forest-labs/flux),
+[blog post](https://blackforestlabs.ai/announcing-black-forest-labs/).
+
+
+## Running the model
+
+```bash
+cargo run --features cuda --example flux -r -- \
+    --height 1024 --width 1024
+    --prompt "a rusty robot walking on a beach holding a small torch, the robot has the word "rust" written on it, high quality, 4k"
+```
+

candle-examples/examples/flux/main.rs

@@ -0,0 +1,182 @@
+#[cfg(feature = "accelerate")]
+extern crate accelerate_src;
+
+#[cfg(feature = "mkl")]
+extern crate intel_mkl_src;
+
+use candle_transformers::models::{clip, flux, t5};
+
+use anyhow::{Error as E, Result};
+use candle::{IndexOp, Module, Tensor};
+use candle_nn::VarBuilder;
+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 rusty robot walking on a beach")]
+    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>,
+
+    #[arg(long)]
+    decode_only: Option<String>,
+}
+
+fn run(args: Args) -> Result<()> {
+    use tracing_chrome::ChromeLayerBuilder;
+    use tracing_subscriber::prelude::*;
+
+    let Args {
+        prompt,
+        cpu,
+        height,
+        width,
+        tracing,
+        decode_only,
+    } = args;
+    let width = width.unwrap_or(1360);
+    let height = height.unwrap_or(768);
+
+    let _guard = if tracing {
+        let (chrome_layer, guard) = ChromeLayerBuilder::new().build();
+        tracing_subscriber::registry().with(chrome_layer).init();
+        Some(guard)
+    } else {
+        None
+    };
+
+    let api = hf_hub::api::sync::Api::new()?;
+    let bf_repo = api.repo(hf_hub::Repo::model(
+        "black-forest-labs/FLUX.1-schnell".to_string(),
+    ));
+    let device = candle_examples::device(cpu)?;
+    let dtype = device.bf16_default_to_f32();
+    let img = match decode_only {
+        None => {
+            let t5_emb = {
+                let repo = api.repo(hf_hub::Repo::with_revision(
+                    "google/t5-v1_1-xxl".to_string(),
+                    hf_hub::RepoType::Model,
+                    "refs/pr/2".to_string(),
+                ));
+                let model_file = repo.get("model.safetensors")?;
+                let vb =
+                    unsafe { VarBuilder::from_mmaped_safetensors(&[model_file], dtype, &device)? };
+                let config_filename = repo.get("config.json")?;
+                let config = std::fs::read_to_string(config_filename)?;
+                let config: t5::Config = serde_json::from_str(&config)?;
+                let mut model = t5::T5EncoderModel::load(vb, &config)?;
+                let tokenizer_filename = api
+                    .model("lmz/mt5-tokenizers".to_string())
+                    .get("t5-v1_1-xxl.tokenizer.json")?;
+                let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
+                let mut tokens = tokenizer
+                    .encode(prompt.as_str(), true)
+                    .map_err(E::msg)?
+                    .get_ids()
+                    .to_vec();
+                tokens.resize(256, 0);
+                let input_token_ids = Tensor::new(&tokens[..], &device)?.unsqueeze(0)?;
+                println!("{input_token_ids}");
+                model.forward(&input_token_ids)?
+            };
+            println!("T5\n{t5_emb}");
+            let clip_emb = {
+                let repo = api.repo(hf_hub::Repo::model(
+                    "openai/clip-vit-large-patch14".to_string(),
+                ));
+                let model_file = repo.get("model.safetensors")?;
+                let vb =
+                    unsafe { VarBuilder::from_mmaped_safetensors(&[model_file], dtype, &device)? };
+                // https://huggingface.co/openai/clip-vit-large-patch14/blob/main/config.json
+                let config = clip::text_model::ClipTextConfig {
+                    vocab_size: 49408,
+                    projection_dim: 768,
+                    activation: clip::text_model::Activation::QuickGelu,
+                    intermediate_size: 3072,
+                    embed_dim: 768,
+                    max_position_embeddings: 77,
+                    pad_with: None,
+                    num_hidden_layers: 12,
+                    num_attention_heads: 12,
+                };
+                let model =
+                    clip::text_model::ClipTextTransformer::new(vb.pp("text_model"), &config)?;
+                let tokenizer_filename = repo.get("tokenizer.json")?;
+                let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
+                let tokens = tokenizer
+                    .encode(prompt.as_str(), true)
+                    .map_err(E::msg)?
+                    .get_ids()
+                    .to_vec();
+                let input_token_ids = Tensor::new(&tokens[..], &device)?.unsqueeze(0)?;
+                println!("{input_token_ids}");
+                model.forward(&input_token_ids)?
+            };
+            println!("CLIP\n{clip_emb}");
+            let img = {
+                let model_file = bf_repo.get("flux1-schnell.sft")?;
+                let vb =
+                    unsafe { VarBuilder::from_mmaped_safetensors(&[model_file], dtype, &device)? };
+                let cfg = flux::model::Config::schnell();
+                let model = flux::model::Flux::new(&cfg, vb)?;
+
+                let img = flux::sampling::get_noise(1, height, width, &device)?.to_dtype(dtype)?;
+                let state = flux::sampling::State::new(&t5_emb, &clip_emb, &img)?;
+                println!("{state:?}");
+                let timesteps = flux::sampling::get_schedule(4, None); // no shift for flux-schnell
+                println!("{timesteps:?}");
+                flux::sampling::denoise(
+                    &model,
+                    &state.img,
+                    &state.img_ids,
+                    &state.txt,
+                    &state.txt_ids,
+                    &state.vec,
+                    &timesteps,
+                    4.,
+                )?
+            };
+            flux::sampling::unpack(&img, height, width)?
+        }
+        Some(file) => {
+            let mut st = candle::safetensors::load(file, &device)?;
+            st.remove("img").unwrap().to_dtype(dtype)?
+        }
+    };
+    println!("latent img\n{img}");
+
+    let img = {
+        let model_file = bf_repo.get("ae.sft")?;
+        let vb = unsafe { VarBuilder::from_mmaped_safetensors(&[model_file], dtype, &device)? };
+        let cfg = flux::autoencoder::Config::schnell();
+        let model = flux::autoencoder::AutoEncoder::new(&cfg, vb)?;
+        model.decode(&img)?
+    };
+    println!("img\n{img}");
+    let img = ((img.clamp(-1f32, 1f32)? + 1.0)? * 127.5)?.to_dtype(candle::DType::U8)?;
+    candle_examples::save_image(&img.i(0)?, "out.jpg")?;
+    Ok(())
+}
+
+fn main() -> Result<()> {
+    let args = Args::parse();
+    run(args)
+}

candle-transformers/src/models/flux/autoencoder.rs

@@ -0,0 +1,440 @@
+use candle::{Result, Tensor, D};
+use candle_nn::{conv2d, group_norm, Conv2d, GroupNorm, VarBuilder};
+
+// https://github.com/black-forest-labs/flux/blob/727e3a71faf37390f318cf9434f0939653302b60/src/flux/modules/autoencoder.py#L9
+#[derive(Debug, Clone)]
+pub struct Config {
+    pub resolution: usize,
+    pub in_channels: usize,
+    pub ch: usize,
+    pub out_ch: usize,
+    pub ch_mult: Vec<usize>,
+    pub num_res_blocks: usize,
+    pub z_channels: usize,
+    pub scale_factor: f64,
+    pub shift_factor: f64,
+}
+
+impl Config {
+    // https://github.com/black-forest-labs/flux/blob/727e3a71faf37390f318cf9434f0939653302b60/src/flux/util.py#L47
+    pub fn dev() -> Self {
+        Self {
+            resolution: 256,
+            in_channels: 3,
+            ch: 128,
+            out_ch: 3,
+            ch_mult: vec![1, 2, 4, 4],
+            num_res_blocks: 2,
+            z_channels: 16,
+            scale_factor: 0.3611,
+            shift_factor: 0.1159,
+        }
+    }
+
+    // https://github.com/black-forest-labs/flux/blob/727e3a71faf37390f318cf9434f0939653302b60/src/flux/util.py#L79
+    pub fn schnell() -> Self {
+        Self {
+            resolution: 256,
+            in_channels: 3,
+            ch: 128,
+            out_ch: 3,
+            ch_mult: vec![1, 2, 4, 4],
+            num_res_blocks: 2,
+            z_channels: 16,
+            scale_factor: 0.3611,
+            shift_factor: 0.1159,
+        }
+    }
+}
+
+fn scaled_dot_product_attention(q: &Tensor, k: &Tensor, v: &Tensor) -> Result<Tensor> {
+    let dim = q.dim(D::Minus1)?;
+    let scale_factor = 1.0 / (dim as f64).sqrt();
+    let attn_weights = (q.matmul(&k.t()?)? * scale_factor)?;
+    candle_nn::ops::softmax_last_dim(&attn_weights)?.matmul(v)
+}
+
+#[derive(Debug, Clone)]
+struct AttnBlock {
+    q: Conv2d,
+    k: Conv2d,
+    v: Conv2d,
+    proj_out: Conv2d,
+    norm: GroupNorm,
+}
+
+impl AttnBlock {
+    fn new(in_c: usize, vb: VarBuilder) -> Result<Self> {
+        let q = conv2d(in_c, in_c, 1, Default::default(), vb.pp("q"))?;
+        let k = conv2d(in_c, in_c, 1, Default::default(), vb.pp("k"))?;
+        let v = conv2d(in_c, in_c, 1, Default::default(), vb.pp("v"))?;
+        let proj_out = conv2d(in_c, in_c, 1, Default::default(), vb.pp("proj_out"))?;
+        let norm = group_norm(32, in_c, 1e-6, vb.pp("norm"))?;
+        Ok(Self {
+            q,
+            k,
+            v,
+            proj_out,
+            norm,
+        })
+    }
+}
+
+impl candle::Module for AttnBlock {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let init_xs = xs;
+        let xs = xs.apply(&self.norm)?;
+        let q = xs.apply(&self.q)?;
+        let k = xs.apply(&self.k)?;
+        let v = xs.apply(&self.v)?;
+        let (b, c, h, w) = q.dims4()?;
+        let q = q.flatten_from(2)?.t()?.unsqueeze(1)?;
+        let k = k.flatten_from(2)?.t()?.unsqueeze(1)?;
+        let v = v.flatten_from(2)?.t()?.unsqueeze(1)?;
+        let xs = scaled_dot_product_attention(&q, &k, &v)?;
+        let xs = xs.squeeze(1)?.t()?.reshape((b, c, h, w))?;
+        xs.apply(&self.proj_out)? + init_xs
+    }
+}
+
+#[derive(Debug, Clone)]
+struct ResnetBlock {
+    norm1: GroupNorm,
+    conv1: Conv2d,
+    norm2: GroupNorm,
+    conv2: Conv2d,
+    nin_shortcut: Option<Conv2d>,
+}
+
+impl ResnetBlock {
+    fn new(in_c: usize, out_c: usize, vb: VarBuilder) -> Result<Self> {
+        let conv_cfg = candle_nn::Conv2dConfig {
+            padding: 1,
+            ..Default::default()
+        };
+        let norm1 = group_norm(32, in_c, 1e-6, vb.pp("norm1"))?;
+        let conv1 = conv2d(in_c, out_c, 3, conv_cfg, vb.pp("conv1"))?;
+        let norm2 = group_norm(32, out_c, 1e-6, vb.pp("norm2"))?;
+        let conv2 = conv2d(out_c, out_c, 3, conv_cfg, vb.pp("conv2"))?;
+        let nin_shortcut = if in_c == out_c {
+            None
+        } else {
+            Some(conv2d(
+                in_c,
+                out_c,
+                1,
+                Default::default(),
+                vb.pp("nin_shortcut"),
+            )?)
+        };
+        Ok(Self {
+            norm1,
+            conv1,
+            norm2,
+            conv2,
+            nin_shortcut,
+        })
+    }
+}
+
+impl candle::Module for ResnetBlock {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let h = xs
+            .apply(&self.norm1)?
+            .apply(&candle_nn::Activation::Swish)?
+            .apply(&self.conv1)?
+            .apply(&self.norm2)?
+            .apply(&candle_nn::Activation::Swish)?
+            .apply(&self.conv2)?;
+        match self.nin_shortcut.as_ref() {
+            None => xs + h,
+            Some(c) => xs.apply(c)? + h,
+        }
+    }
+}
+
+#[derive(Debug, Clone)]
+struct Downsample {
+    conv: Conv2d,
+}
+
+impl Downsample {
+    fn new(in_c: usize, vb: VarBuilder) -> Result<Self> {
+        let conv_cfg = candle_nn::Conv2dConfig {
+            stride: 2,
+            ..Default::default()
+        };
+        let conv = conv2d(in_c, in_c, 3, conv_cfg, vb.pp("conv"))?;
+        Ok(Self { conv })
+    }
+}
+
+impl candle::Module for Downsample {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let xs = xs.pad_with_zeros(D::Minus1, 0, 1)?;
+        let xs = xs.pad_with_zeros(D::Minus2, 0, 1)?;
+        xs.apply(&self.conv)
+    }
+}
+
+#[derive(Debug, Clone)]
+struct Upsample {
+    conv: Conv2d,
+}
+
+impl Upsample {
+    fn new(in_c: usize, vb: VarBuilder) -> Result<Self> {
+        let conv_cfg = candle_nn::Conv2dConfig {
+            padding: 1,
+            ..Default::default()
+        };
+        let conv = conv2d(in_c, in_c, 3, conv_cfg, vb.pp("conv"))?;
+        Ok(Self { conv })
+    }
+}
+
+impl candle::Module for Upsample {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let (_, _, h, w) = xs.dims4()?;
+        xs.upsample_nearest2d(h * 2, w * 2)?.apply(&self.conv)
+    }
+}
+
+#[derive(Debug, Clone)]
+struct DownBlock {
+    block: Vec<ResnetBlock>,
+    downsample: Option<Downsample>,
+}
+
+#[derive(Debug, Clone)]
+pub struct Encoder {
+    conv_in: Conv2d,
+    mid_block_1: ResnetBlock,
+    mid_attn_1: AttnBlock,
+    mid_block_2: ResnetBlock,
+    norm_out: GroupNorm,
+    conv_out: Conv2d,
+    down: Vec<DownBlock>,
+}
+
+impl Encoder {
+    pub fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
+        let conv_cfg = candle_nn::Conv2dConfig {
+            padding: 1,
+            ..Default::default()
+        };
+        let mut block_in = cfg.ch;
+        let conv_in = conv2d(cfg.in_channels, block_in, 3, conv_cfg, vb.pp("conv_in"))?;
+
+        let mut down = Vec::with_capacity(cfg.ch_mult.len());
+        let vb_d = vb.pp("down");
+        for (i_level, ch_mult) in cfg.ch_mult.iter().enumerate() {
+            let mut block = Vec::with_capacity(cfg.num_res_blocks);
+            let vb_d = vb_d.pp(i_level);
+            let vb_b = vb_d.pp("block");
+            let in_ch_mult = if i_level == 0 {
+                1
+            } else {
+                cfg.ch_mult[i_level - 1]
+            };
+            block_in = cfg.ch * in_ch_mult;
+            let block_out = cfg.ch * ch_mult;
+            for i_block in 0..cfg.num_res_blocks {
+                let b = ResnetBlock::new(block_in, block_out, vb_b.pp(i_block))?;
+                block.push(b);
+                block_in = block_out;
+            }
+            let downsample = if i_level != cfg.ch_mult.len() - 1 {
+                Some(Downsample::new(block_in, vb_d.pp("downsample"))?)
+            } else {
+                None
+            };
+            let block = DownBlock { block, downsample };
+            down.push(block)
+        }
+
+        let mid_block_1 = ResnetBlock::new(block_in, block_in, vb.pp("mid.block_1"))?;
+        let mid_attn_1 = AttnBlock::new(block_in, vb.pp("mid.attn_1"))?;
+        let mid_block_2 = ResnetBlock::new(block_in, block_in, vb.pp("mid.block_2"))?;
+        let conv_out = conv2d(block_in, 2 * cfg.z_channels, 3, conv_cfg, vb.pp("conv_out"))?;
+        let norm_out = group_norm(32, block_in, 1e-6, vb.pp("norm_out"))?;
+        Ok(Self {
+            conv_in,
+            mid_block_1,
+            mid_attn_1,
+            mid_block_2,
+            norm_out,
+            conv_out,
+            down,
+        })
+    }
+}
+
+impl candle_nn::Module for Encoder {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let mut h = xs.apply(&self.conv_in)?;
+        for block in self.down.iter() {
+            for b in block.block.iter() {
+                h = h.apply(b)?
+            }
+            if let Some(ds) = block.downsample.as_ref() {
+                h = h.apply(ds)?
+            }
+        }
+        h.apply(&self.mid_block_1)?
+            .apply(&self.mid_attn_1)?
+            .apply(&self.mid_block_2)?
+            .apply(&self.norm_out)?
+            .apply(&candle_nn::Activation::Swish)?
+            .apply(&self.conv_out)
+    }
+}
+
+#[derive(Debug, Clone)]
+struct UpBlock {
+    block: Vec<ResnetBlock>,
+    upsample: Option<Upsample>,
+}
+
+#[derive(Debug, Clone)]
+pub struct Decoder {
+    conv_in: Conv2d,
+    mid_block_1: ResnetBlock,
+    mid_attn_1: AttnBlock,
+    mid_block_2: ResnetBlock,
+    norm_out: GroupNorm,
+    conv_out: Conv2d,
+    up: Vec<UpBlock>,
+}
+
+impl Decoder {
+    pub fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
+        let conv_cfg = candle_nn::Conv2dConfig {
+            padding: 1,
+            ..Default::default()
+        };
+        let mut block_in = cfg.ch * cfg.ch_mult.last().unwrap_or(&1);
+        let conv_in = conv2d(cfg.z_channels, block_in, 3, conv_cfg, vb.pp("conv_in"))?;
+        let mid_block_1 = ResnetBlock::new(block_in, block_in, vb.pp("mid.block_1"))?;
+        let mid_attn_1 = AttnBlock::new(block_in, vb.pp("mid.attn_1"))?;
+        let mid_block_2 = ResnetBlock::new(block_in, block_in, vb.pp("mid.block_2"))?;
+
+        let mut up = Vec::with_capacity(cfg.ch_mult.len());
+        let vb_u = vb.pp("up");
+        for (i_level, ch_mult) in cfg.ch_mult.iter().enumerate().rev() {
+            let block_out = cfg.ch * ch_mult;
+            let vb_u = vb_u.pp(i_level);
+            let vb_b = vb_u.pp("block");
+            let mut block = Vec::with_capacity(cfg.num_res_blocks + 1);
+            for i_block in 0..=cfg.num_res_blocks {
+                let b = ResnetBlock::new(block_in, block_out, vb_b.pp(i_block))?;
+                block.push(b);
+                block_in = block_out;
+            }
+            let upsample = if i_level != 0 {
+                Some(Upsample::new(block_in, vb_u.pp("upsample"))?)
+            } else {
+                None
+            };
+            let block = UpBlock { block, upsample };
+            up.push(block)
+        }
+        up.reverse();
+
+        let norm_out = group_norm(32, block_in, 1e-6, vb.pp("norm_out"))?;
+        let conv_out = conv2d(block_in, cfg.out_ch, 3, conv_cfg, vb.pp("conv_out"))?;
+        Ok(Self {
+            conv_in,
+            mid_block_1,
+            mid_attn_1,
+            mid_block_2,
+            norm_out,
+            conv_out,
+            up,
+        })
+    }
+}
+
+impl candle_nn::Module for Decoder {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let h = xs.apply(&self.conv_in)?;
+        let mut h = h
+            .apply(&self.mid_block_1)?
+            .apply(&self.mid_attn_1)?
+            .apply(&self.mid_block_2)?;
+        for block in self.up.iter().rev() {
+            for b in block.block.iter() {
+                h = h.apply(b)?
+            }
+            if let Some(us) = block.upsample.as_ref() {
+                h = h.apply(us)?
+            }
+        }
+        h.apply(&self.norm_out)?
+            .apply(&candle_nn::Activation::Swish)?
+            .apply(&self.conv_out)
+    }
+}
+
+#[derive(Debug, Clone)]
+pub struct DiagonalGaussian {
+    sample: bool,
+    chunk_dim: usize,
+}
+
+impl DiagonalGaussian {
+    pub fn new(sample: bool, chunk_dim: usize) -> Result<Self> {
+        Ok(Self { sample, chunk_dim })
+    }
+}
+
+impl candle_nn::Module for DiagonalGaussian {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let chunks = xs.chunk(2, self.chunk_dim)?;
+        if self.sample {
+            let std = (&chunks[1] * 0.5)?.exp()?;
+            &chunks[0] + (std * chunks[0].randn_like(0., 1.))?
+        } else {
+            Ok(chunks[0].clone())
+        }
+    }
+}
+
+#[derive(Debug, Clone)]
+pub struct AutoEncoder {
+    encoder: Encoder,
+    decoder: Decoder,
+    reg: DiagonalGaussian,
+    shift_factor: f64,
+    scale_factor: f64,
+}
+
+impl AutoEncoder {
+    pub fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
+        let encoder = Encoder::new(cfg, vb.pp("encoder"))?;
+        let decoder = Decoder::new(cfg, vb.pp("decoder"))?;
+        let reg = DiagonalGaussian::new(true, 1)?;
+        Ok(Self {
+            encoder,
+            decoder,
+            reg,
+            scale_factor: cfg.scale_factor,
+            shift_factor: cfg.shift_factor,
+        })
+    }
+
+    pub fn encode(&self, xs: &Tensor) -> Result<Tensor> {
+        let z = xs.apply(&self.encoder)?.apply(&self.reg)?;
+        (z - self.shift_factor)? * self.scale_factor
+    }
+    pub fn decode(&self, xs: &Tensor) -> Result<Tensor> {
+        let xs = ((xs / self.scale_factor)? + self.shift_factor)?;
+        xs.apply(&self.decoder)
+    }
+}
+
+impl candle::Module for AutoEncoder {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        self.decode(&self.encode(xs)?)
+    }
+}

candle-transformers/src/models/flux/mod.rs

@@ -0,0 +1,3 @@
+pub mod autoencoder;
+pub mod model;
+pub mod sampling;

candle-transformers/src/models/flux/model.rs

@@ -0,0 +1,582 @@
+use candle::{DType, IndexOp, Result, Tensor, D};
+use candle_nn::{LayerNorm, Linear, RmsNorm, VarBuilder};
+
+// https://github.com/black-forest-labs/flux/blob/727e3a71faf37390f318cf9434f0939653302b60/src/flux/model.py#L12
+#[derive(Debug, Clone)]
+pub struct Config {
+    pub in_channels: usize,
+    pub vec_in_dim: usize,
+    pub context_in_dim: usize,
+    pub hidden_size: usize,
+    pub mlp_ratio: f64,
+    pub num_heads: usize,
+    pub depth: usize,
+    pub depth_single_blocks: usize,
+    pub axes_dim: Vec<usize>,
+    pub theta: usize,
+    pub qkv_bias: bool,
+    pub guidance_embed: bool,
+}
+
+impl Config {
+    // https://github.com/black-forest-labs/flux/blob/727e3a71faf37390f318cf9434f0939653302b60/src/flux/util.py#L32
+    pub fn dev() -> Self {
+        Self {
+            in_channels: 64,
+            vec_in_dim: 768,
+            context_in_dim: 4096,
+            hidden_size: 3072,
+            mlp_ratio: 4.0,
+            num_heads: 24,
+            depth: 19,
+            depth_single_blocks: 38,
+            axes_dim: vec![16, 56, 56],
+            theta: 10_000,
+            qkv_bias: true,
+            guidance_embed: true,
+        }
+    }
+
+    // https://github.com/black-forest-labs/flux/blob/727e3a71faf37390f318cf9434f0939653302b60/src/flux/util.py#L64
+    pub fn schnell() -> Self {
+        Self {
+            in_channels: 64,
+            vec_in_dim: 768,
+            context_in_dim: 4096,
+            hidden_size: 3072,
+            mlp_ratio: 4.0,
+            num_heads: 24,
+            depth: 19,
+            depth_single_blocks: 38,
+            axes_dim: vec![16, 56, 56],
+            theta: 10_000,
+            qkv_bias: true,
+            guidance_embed: false,
+        }
+    }
+}
+
+fn layer_norm(dim: usize, vb: VarBuilder) -> Result<LayerNorm> {
+    let ws = Tensor::ones(dim, vb.dtype(), vb.device())?;
+    Ok(LayerNorm::new_no_bias(ws, 1e-6))
+}
+
+fn scaled_dot_product_attention(q: &Tensor, k: &Tensor, v: &Tensor) -> Result<Tensor> {
+    let dim = q.dim(D::Minus1)?;
+    let scale_factor = 1.0 / (dim as f64).sqrt();
+    let mut batch_dims = q.dims().to_vec();
+    batch_dims.pop();
+    batch_dims.pop();
+    let q = q.flatten_to(batch_dims.len() - 1)?;
+    let k = k.flatten_to(batch_dims.len() - 1)?;
+    let v = v.flatten_to(batch_dims.len() - 1)?;
+    let attn_weights = (q.matmul(&k.t()?)? * scale_factor)?;
+    let attn_scores = candle_nn::ops::softmax_last_dim(&attn_weights)?.matmul(&v)?;
+    batch_dims.push(attn_scores.dim(D::Minus2)?);
+    batch_dims.push(attn_scores.dim(D::Minus1)?);
+    attn_scores.reshape(batch_dims)
+}
+
+fn rope(pos: &Tensor, dim: usize, theta: usize) -> Result<Tensor> {
+    if dim % 2 == 1 {
+        candle::bail!("dim {dim} is odd")
+    }
+    let dev = pos.device();
+    let theta = theta as f64;
+    let inv_freq: Vec<_> = (0..dim)
+        .step_by(2)
+        .map(|i| 1f32 / 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, 1, inv_freq_len), dev)?;
+    let inv_freq = inv_freq.to_dtype(pos.dtype())?;
+    let freqs = pos.unsqueeze(2)?.broadcast_mul(&inv_freq)?;
+    let cos = freqs.cos()?;
+    let sin = freqs.sin()?;
+    let out = Tensor::stack(&[&cos, &sin.neg()?, &sin, &cos], 3)?;
+    let (b, n, d, _ij) = out.dims4()?;
+    out.reshape((b, n, d, 2, 2))
+}
+
+fn apply_rope(x: &Tensor, freq_cis: &Tensor) -> Result<Tensor> {
+    let dims = x.dims();
+    let (b_sz, n_head, seq_len, n_embd) = x.dims4()?;
+    let x = x.reshape((b_sz, n_head, seq_len, n_embd / 2, 2))?;
+    let x0 = x.narrow(D::Minus1, 0, 1)?;
+    let x1 = x.narrow(D::Minus1, 1, 1)?;
+    let fr0 = freq_cis.get_on_dim(D::Minus1, 0)?;
+    let fr1 = freq_cis.get_on_dim(D::Minus1, 1)?;
+    (fr0.broadcast_mul(&x0)? + fr1.broadcast_mul(&x1)?)?.reshape(dims.to_vec())
+}
+
+fn attention(q: &Tensor, k: &Tensor, v: &Tensor, pe: &Tensor) -> Result<Tensor> {
+    let q = apply_rope(q, pe)?.contiguous()?;
+    let k = apply_rope(k, pe)?.contiguous()?;
+    let x = scaled_dot_product_attention(&q, &k, v)?;
+    x.transpose(1, 2)?.flatten_from(2)
+}
+
+fn timestep_embedding(t: &Tensor, dim: usize, dtype: DType) -> Result<Tensor> {
+    const TIME_FACTOR: f64 = 1000.;
+    const MAX_PERIOD: f64 = 10000.;
+    if dim % 2 == 1 {
+        candle::bail!("{dim} is odd")
+    }
+    let dev = t.device();
+    let half = dim / 2;
+    let t = (t * TIME_FACTOR)?;
+    let arange = Tensor::arange(0, half as u32, dev)?.to_dtype(candle::DType::F32)?;
+    let freqs = (arange * (-MAX_PERIOD.ln() / half as f64))?.exp()?;
+    let args = t
+        .unsqueeze(1)?
+        .to_dtype(candle::DType::F32)?
+        .broadcast_mul(&freqs.unsqueeze(0)?)?;
+    let emb = Tensor::cat(&[args.cos()?, args.sin()?], D::Minus1)?.to_dtype(dtype)?;
+    Ok(emb)
+}
+
+#[derive(Debug, Clone)]
+pub struct EmbedNd {
+    #[allow(unused)]
+    dim: usize,
+    theta: usize,
+    axes_dim: Vec<usize>,
+}
+
+impl EmbedNd {
+    fn new(dim: usize, theta: usize, axes_dim: Vec<usize>) -> Self {
+        Self {
+            dim,
+            theta,
+            axes_dim,
+        }
+    }
+}
+
+impl candle::Module for EmbedNd {
+    fn forward(&self, ids: &Tensor) -> Result<Tensor> {
+        let n_axes = ids.dim(D::Minus1)?;
+        let mut emb = Vec::with_capacity(n_axes);
+        for idx in 0..n_axes {
+            let r = rope(
+                &ids.get_on_dim(D::Minus1, idx)?,
+                self.axes_dim[idx],
+                self.theta,
+            )?;
+            emb.push(r)
+        }
+        let emb = Tensor::cat(&emb, 2)?;
+        emb.unsqueeze(1)
+    }
+}
+
+#[derive(Debug, Clone)]
+pub struct MlpEmbedder {
+    in_layer: Linear,
+    out_layer: Linear,
+}
+
+impl MlpEmbedder {
+    fn new(in_sz: usize, h_sz: usize, vb: VarBuilder) -> Result<Self> {
+        let in_layer = candle_nn::linear(in_sz, h_sz, vb.pp("in_layer"))?;
+        let out_layer = candle_nn::linear(h_sz, h_sz, vb.pp("out_layer"))?;
+        Ok(Self {
+            in_layer,
+            out_layer,
+        })
+    }
+}
+
+impl candle::Module for MlpEmbedder {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        xs.apply(&self.in_layer)?.silu()?.apply(&self.out_layer)
+    }
+}
+
+#[derive(Debug, Clone)]
+pub struct QkNorm {
+    query_norm: RmsNorm,
+    key_norm: RmsNorm,
+}
+
+impl QkNorm {
+    fn new(dim: usize, vb: VarBuilder) -> Result<Self> {
+        let query_norm = vb.get(dim, "query_norm.scale")?;
+        let query_norm = RmsNorm::new(query_norm, 1e-6);
+        let key_norm = vb.get(dim, "key_norm.scale")?;
+        let key_norm = RmsNorm::new(key_norm, 1e-6);
+        Ok(Self {
+            query_norm,
+            key_norm,
+        })
+    }
+}
+
+#[derive(Debug, Clone)]
+pub struct Modulation {
+    lin: Linear,
+    multiplier: usize,
+}
+
+impl Modulation {
+    fn new(dim: usize, double: bool, vb: VarBuilder) -> Result<Self> {
+        let multiplier = if double { 6 } else { 3 };
+        let lin = candle_nn::linear(dim, multiplier * dim, vb.pp("lin"))?;
+        Ok(Self { lin, multiplier })
+    }
+
+    fn forward(&self, vec_: &Tensor) -> Result<Vec<Tensor>> {
+        vec_.silu()?
+            .apply(&self.lin)?
+            .unsqueeze(1)?
+            .chunk(self.multiplier, D::Minus1)
+    }
+}
+
+#[derive(Debug, Clone)]
+pub struct SelfAttention {
+    qkv: Linear,
+    norm: QkNorm,
+    proj: Linear,
+    num_heads: usize,
+}
+
+impl SelfAttention {
+    fn new(dim: usize, num_heads: usize, qkv_bias: bool, vb: VarBuilder) -> Result<Self> {
+        let head_dim = dim / num_heads;
+        let qkv = candle_nn::linear_b(dim, dim * 3, qkv_bias, vb.pp("qkv"))?;
+        let norm = QkNorm::new(head_dim, vb.pp("norm"))?;
+        let proj = candle_nn::linear(dim, dim, vb.pp("proj"))?;
+        Ok(Self {
+            qkv,
+            norm,
+            proj,
+            num_heads,
+        })
+    }
+
+    fn qkv(&self, xs: &Tensor) -> Result<(Tensor, Tensor, Tensor)> {
+        let qkv = xs.apply(&self.qkv)?;
+        let (b, l, _khd) = qkv.dims3()?;
+        let qkv = qkv.reshape((b, l, 3, self.num_heads, ()))?;
+        let q = qkv.i((.., .., 0))?.transpose(1, 2)?;
+        let k = qkv.i((.., .., 1))?.transpose(1, 2)?;
+        let v = qkv.i((.., .., 2))?.transpose(1, 2)?;
+        let q = q.apply(&self.norm.query_norm)?;
+        let k = k.apply(&self.norm.key_norm)?;
+        Ok((q, k, v))
+    }
+
+    #[allow(unused)]
+    fn forward(&self, xs: &Tensor, pe: &Tensor) -> Result<Tensor> {
+        let (q, k, v) = self.qkv(xs)?;
+        attention(&q, &k, &v, pe)?.apply(&self.proj)
+    }
+}
+
+#[derive(Debug, Clone)]
+struct Mlp {
+    lin1: Linear,
+    lin2: Linear,
+}
+
+impl Mlp {
+    fn new(in_sz: usize, mlp_sz: usize, vb: VarBuilder) -> Result<Self> {
+        let lin1 = candle_nn::linear(in_sz, mlp_sz, vb.pp("0"))?;
+        let lin2 = candle_nn::linear(mlp_sz, in_sz, vb.pp("2"))?;
+        Ok(Self { lin1, lin2 })
+    }
+}
+
+impl candle::Module for Mlp {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        xs.apply(&self.lin1)?.gelu()?.apply(&self.lin2)
+    }
+}
+
+#[derive(Debug, Clone)]
+pub struct DoubleStreamBlock {
+    img_mod: Modulation,
+    img_norm1: LayerNorm,
+    img_attn: SelfAttention,
+    img_norm2: LayerNorm,
+    img_mlp: Mlp,
+    txt_mod: Modulation,
+    txt_norm1: LayerNorm,
+    txt_attn: SelfAttention,
+    txt_norm2: LayerNorm,
+    txt_mlp: Mlp,
+}
+
+impl DoubleStreamBlock {
+    fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
+        let h_sz = cfg.hidden_size;
+        let mlp_sz = (h_sz as f64 * cfg.mlp_ratio) as usize;
+        let img_mod = Modulation::new(h_sz, true, vb.pp("img_mod"))?;
+        let img_norm1 = layer_norm(h_sz, vb.pp("img_norm1"))?;
+        let img_attn = SelfAttention::new(h_sz, cfg.num_heads, cfg.qkv_bias, vb.pp("img_attn"))?;
+        let img_norm2 = layer_norm(h_sz, vb.pp("img_norm2"))?;
+        let img_mlp = Mlp::new(h_sz, mlp_sz, vb.pp("img_mlp"))?;
+        let txt_mod = Modulation::new(h_sz, true, vb.pp("txt_mod"))?;
+        let txt_norm1 = layer_norm(h_sz, vb.pp("txt_norm1"))?;
+        let txt_attn = SelfAttention::new(h_sz, cfg.num_heads, cfg.qkv_bias, vb.pp("txt_attn"))?;
+        let txt_norm2 = layer_norm(h_sz, vb.pp("txt_norm2"))?;
+        let txt_mlp = Mlp::new(h_sz, mlp_sz, vb.pp("txt_mlp"))?;
+        Ok(Self {
+            img_mod,
+            img_norm1,
+            img_attn,
+            img_norm2,
+            img_mlp,
+            txt_mod,
+            txt_norm1,
+            txt_attn,
+            txt_norm2,
+            txt_mlp,
+        })
+    }
+
+    fn forward(
+        &self,
+        img: &Tensor,
+        txt: &Tensor,
+        vec_: &Tensor,
+        pe: &Tensor,
+    ) -> Result<(Tensor, Tensor)> {
+        let img_mod = self.img_mod.forward(vec_)?; // shift, scale, gate
+        let txt_mod = self.txt_mod.forward(vec_)?; // shift, scale, gate
+        let img_modulated = img.apply(&self.img_norm1)?;
+        let img_modulated = img_modulated
+            .broadcast_mul(&(&img_mod[1] + 1.)?)?
+            .broadcast_add(&img_mod[0])?;
+        let (img_q, img_k, img_v) = self.img_attn.qkv(&img_modulated)?;
+
+        let txt_modulated = txt.apply(&self.txt_norm1)?;
+        let txt_modulated = txt_modulated
+            .broadcast_mul(&(&txt_mod[1] + 1.)?)?
+            .broadcast_add(&txt_mod[0])?;
+        let (txt_q, txt_k, txt_v) = self.txt_attn.qkv(&txt_modulated)?;
+
+        let q = Tensor::cat(&[txt_q, img_q], 2)?;
+        let k = Tensor::cat(&[txt_k, img_k], 2)?;
+        let v = Tensor::cat(&[txt_v, img_v], 2)?;
+
+        let attn = attention(&q, &k, &v, pe)?;
+        let txt_attn = attn.narrow(1, 0, txt.dim(1)?)?;
+        let img_attn = attn.narrow(1, txt.dim(1)?, attn.dim(1)? - txt.dim(1)?)?;
+
+        let img = (img
+            + img_attn
+                .apply(&self.img_attn.proj)?
+                .broadcast_mul(&img_mod[2]))?;
+        let img = (&img
+            + &img_mod[5].broadcast_mul(
+                &img.apply(&self.img_norm2)?
+                    .broadcast_mul(&(&img_mod[4] + 1.0)?)?
+                    .broadcast_add(&img_mod[3])?
+                    .apply(&self.img_mlp)?,
+            )?)?;
+
+        let txt = (txt
+            + txt_attn
+                .apply(&self.txt_attn.proj)?
+                .broadcast_mul(&txt_mod[2]))?;
+        let txt = (&txt
+            + &txt_mod[5].broadcast_mul(
+                &txt.apply(&self.txt_norm2)?
+                    .broadcast_mul(&(&txt_mod[4] + 1.0)?)?
+                    .broadcast_add(&txt_mod[3])?
+                    .apply(&self.txt_mlp)?,
+            )?)?;
+
+        Ok((img, txt))
+    }
+}
+
+#[derive(Debug, Clone)]
+pub struct SingleStreamBlock {
+    linear1: Linear,
+    linear2: Linear,
+    norm: QkNorm,
+    pre_norm: LayerNorm,
+    modulation: Modulation,
+    h_sz: usize,
+    mlp_sz: usize,
+    num_heads: usize,
+}
+
+impl SingleStreamBlock {
+    fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
+        let h_sz = cfg.hidden_size;
+        let mlp_sz = (h_sz as f64 * cfg.mlp_ratio) as usize;
+        let head_dim = h_sz / cfg.num_heads;
+        let linear1 = candle_nn::linear(h_sz, h_sz * 3 + mlp_sz, vb.pp("linear1"))?;
+        let linear2 = candle_nn::linear(h_sz + mlp_sz, h_sz, vb.pp("linear2"))?;
+        let norm = QkNorm::new(head_dim, vb.pp("norm"))?;
+        let pre_norm = layer_norm(h_sz, vb.pp("pre_norm"))?;
+        let modulation = Modulation::new(h_sz, false, vb.pp("modulation"))?;
+        Ok(Self {
+            linear1,
+            linear2,
+            norm,
+            pre_norm,
+            modulation,
+            h_sz,
+            mlp_sz,
+            num_heads: cfg.num_heads,
+        })
+    }
+
+    fn forward(&self, xs: &Tensor, vec_: &Tensor, pe: &Tensor) -> Result<Tensor> {
+        let mod_ = self.modulation.forward(vec_)?;
+        let (shift, scale, gate) = (&mod_[0], &mod_[1], &mod_[2]);
+        let x_mod = xs
+            .apply(&self.pre_norm)?
+            .broadcast_mul(&(scale + 1.0)?)?
+            .broadcast_add(shift)?;
+        let x_mod = x_mod.apply(&self.linear1)?;
+        let qkv = x_mod.narrow(D::Minus1, 0, 3 * self.h_sz)?;
+        let (b, l, _khd) = qkv.dims3()?;
+        let qkv = qkv.reshape((b, l, 3, self.num_heads, ()))?;
+        let q = qkv.i((.., .., 0))?.transpose(1, 2)?;
+        let k = qkv.i((.., .., 1))?.transpose(1, 2)?;
+        let v = qkv.i((.., .., 2))?.transpose(1, 2)?;
+        let mlp = x_mod.narrow(D::Minus1, 3 * self.h_sz, self.mlp_sz)?;
+        let q = q.apply(&self.norm.query_norm)?;
+        let k = k.apply(&self.norm.key_norm)?;
+        let attn = attention(&q, &k, &v, pe)?;
+        let output = Tensor::cat(&[attn, mlp.gelu()?], 2)?.apply(&self.linear2)?;
+        xs + gate.broadcast_mul(&output)
+    }
+}
+
+#[derive(Debug, Clone)]
+pub struct LastLayer {
+    norm_final: LayerNorm,
+    linear: Linear,
+    ada_ln_modulation: Linear,
+}
+
+impl LastLayer {
+    fn new(h_sz: usize, p_sz: usize, out_c: usize, vb: VarBuilder) -> Result<Self> {
+        let norm_final = layer_norm(h_sz, vb.pp("norm_final"))?;
+        let linear = candle_nn::linear(h_sz, p_sz * p_sz * out_c, vb.pp("linear"))?;
+        let ada_ln_modulation = candle_nn::linear(h_sz, 2 * h_sz, vb.pp("adaLN_modulation.1"))?;
+        Ok(Self {
+            norm_final,
+            linear,
+            ada_ln_modulation,
+        })
+    }
+
+    fn forward(&self, xs: &Tensor, vec: &Tensor) -> Result<Tensor> {
+        let chunks = vec.silu()?.apply(&self.ada_ln_modulation)?.chunk(2, 1)?;
+        let (shift, scale) = (&chunks[0], &chunks[1]);
+        let xs = xs
+            .apply(&self.norm_final)?
+            .broadcast_mul(&(scale.unsqueeze(1)? + 1.0)?)?
+            .broadcast_add(&shift.unsqueeze(1)?)?;
+        xs.apply(&self.linear)
+    }
+}
+
+#[derive(Debug, Clone)]
+pub struct Flux {
+    img_in: Linear,
+    txt_in: Linear,
+    time_in: MlpEmbedder,
+    vector_in: MlpEmbedder,
+    guidance_in: Option<MlpEmbedder>,
+    pe_embedder: EmbedNd,
+    double_blocks: Vec<DoubleStreamBlock>,
+    single_blocks: Vec<SingleStreamBlock>,
+    final_layer: LastLayer,
+}
+
+impl Flux {
+    pub fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
+        let img_in = candle_nn::linear(cfg.in_channels, cfg.hidden_size, vb.pp("img_in"))?;
+        let txt_in = candle_nn::linear(cfg.context_in_dim, cfg.hidden_size, vb.pp("txt_in"))?;
+        let mut double_blocks = Vec::with_capacity(cfg.depth);
+        let vb_d = vb.pp("double_blocks");
+        for idx in 0..cfg.depth {
+            let db = DoubleStreamBlock::new(cfg, vb_d.pp(idx))?;
+            double_blocks.push(db)
+        }
+        let mut single_blocks = Vec::with_capacity(cfg.depth_single_blocks);
+        let vb_s = vb.pp("single_blocks");
+        for idx in 0..cfg.depth_single_blocks {
+            let sb = SingleStreamBlock::new(cfg, vb_s.pp(idx))?;
+            single_blocks.push(sb)
+        }
+        let time_in = MlpEmbedder::new(256, cfg.hidden_size, vb.pp("time_in"))?;
+        let vector_in = MlpEmbedder::new(cfg.vec_in_dim, cfg.hidden_size, vb.pp("vector_in"))?;
+        let guidance_in = if cfg.guidance_embed {
+            let mlp = MlpEmbedder::new(256, cfg.hidden_size, vb.pp("guidance_in"))?;
+            Some(mlp)
+        } else {
+            None
+        };
+        let final_layer =
+            LastLayer::new(cfg.hidden_size, 1, cfg.in_channels, vb.pp("final_layer"))?;
+        let pe_dim = cfg.hidden_size / cfg.num_heads;
+        let pe_embedder = EmbedNd::new(pe_dim, cfg.theta, cfg.axes_dim.to_vec());
+        Ok(Self {
+            img_in,
+            txt_in,
+            time_in,
+            vector_in,
+            guidance_in,
+            pe_embedder,
+            double_blocks,
+            single_blocks,
+            final_layer,
+        })
+    }
+
+    #[allow(clippy::too_many_arguments)]
+    pub fn forward(
+        &self,
+        img: &Tensor,
+        img_ids: &Tensor,
+        txt: &Tensor,
+        txt_ids: &Tensor,
+        timesteps: &Tensor,
+        y: &Tensor,
+        guidance: Option<&Tensor>,
+    ) -> Result<Tensor> {
+        if txt.rank() != 3 {
+            candle::bail!("unexpected shape for txt {:?}", txt.shape())
+        }
+        if img.rank() != 3 {
+            candle::bail!("unexpected shape for img {:?}", img.shape())
+        }
+        let dtype = img.dtype();
+        let pe = {
+            let ids = Tensor::cat(&[txt_ids, img_ids], 1)?;
+            ids.apply(&self.pe_embedder)?
+        };
+        let mut txt = txt.apply(&self.txt_in)?;
+        let mut img = img.apply(&self.img_in)?;
+        let vec_ = timestep_embedding(timesteps, 256, dtype)?.apply(&self.time_in)?;
+        let vec_ = match (self.guidance_in.as_ref(), guidance) {
+            (Some(g_in), Some(guidance)) => {
+                (vec_ + timestep_embedding(guidance, 256, dtype)?.apply(g_in))?
+            }
+            _ => vec_,
+        };
+        let vec_ = (vec_ + y.apply(&self.vector_in))?;
+
+        // Double blocks
+        for block in self.double_blocks.iter() {
+            (img, txt) = block.forward(&img, &txt, &vec_, &pe)?
+        }
+        // Single blocks
+        let mut img = Tensor::cat(&[&txt, &img], 1)?;
+        for block in self.single_blocks.iter() {
+            img = block.forward(&img, &vec_, &pe)?;
+        }
+        let img = img.i((.., txt.dim(1)?..))?;
+        self.final_layer.forward(&img, &vec_)
+    }
+}

candle-transformers/src/models/flux/sampling.rs

@@ -0,0 +1,119 @@
+use candle::{Device, Result, Tensor};
+
+pub fn get_noise(
+    num_samples: usize,
+    height: usize,
+    width: usize,
+    device: &Device,
+) -> Result<Tensor> {
+    let height = (height + 15) / 16 * 2;
+    let width = (width + 15) / 16 * 2;
+    Tensor::randn(0f32, 1., (num_samples, 16, height, width), device)
+}
+
+#[derive(Debug, Clone)]
+pub struct State {
+    pub img: Tensor,
+    pub img_ids: Tensor,
+    pub txt: Tensor,
+    pub txt_ids: Tensor,
+    pub vec: Tensor,
+}
+
+impl State {
+    pub fn new(t5_emb: &Tensor, clip_emb: &Tensor, img: &Tensor) -> Result<Self> {
+        let dtype = img.dtype();
+        let (bs, c, h, w) = img.dims4()?;
+        let dev = img.device();
+        let img = img.reshape((bs, c, h / 2, 2, w / 2, 2))?; // (b, c, h, ph, w, pw)
+        let img = img.permute((0, 2, 4, 1, 3, 5))?; // (b, h, w, c, ph, pw)
+        let img = img.reshape((bs, h / 2 * w / 2, c * 4))?;
+        let img_ids = Tensor::stack(
+            &[
+                Tensor::full(0u32, (h / 2, w / 2), dev)?,
+                Tensor::arange(0u32, h as u32 / 2, dev)?
+                    .reshape(((), 1))?
+                    .broadcast_as((h / 2, w / 2))?,
+                Tensor::arange(0u32, w as u32 / 2, dev)?
+                    .reshape((1, ()))?
+                    .broadcast_as((h / 2, w / 2))?,
+            ],
+            2,
+        )?
+        .to_dtype(dtype)?;
+        let img_ids = img_ids.reshape((1, h / 2 * w / 2, 3))?;
+        let img_ids = img_ids.repeat((bs, 1, 1))?;
+        let txt = t5_emb.repeat(bs)?;
+        let txt_ids = Tensor::zeros((bs, txt.dim(1)?, 3), dtype, dev)?;
+        let vec = clip_emb.repeat(bs)?;
+        Ok(Self {
+            img,
+            img_ids,
+            txt,
+            txt_ids,
+            vec,
+        })
+    }
+}
+
+fn time_shift(mu: f64, sigma: f64, t: f64) -> f64 {
+    let e = mu.exp();
+    e / (e + (1. / t - 1.).powf(sigma))
+}
+
+/// `shift` is a triple `(image_seq_len, base_shift, max_shift)`.
+pub fn get_schedule(num_steps: usize, shift: Option<(usize, f64, f64)>) -> Vec<f64> {
+    let timesteps: Vec<f64> = (0..=num_steps)
+        .map(|v| v as f64 / num_steps as f64)
+        .rev()
+        .collect();
+    match shift {
+        None => timesteps,
+        Some((image_seq_len, y1, y2)) => {
+            let (x1, x2) = (256., 4096.);
+            let m = (y2 - y1) / (x2 - x1);
+            let b = y1 - m * x1;
+            let mu = m * image_seq_len as f64 + b;
+            timesteps
+                .into_iter()
+                .map(|v| time_shift(mu, 1., v))
+                .collect()
+        }
+    }
+}
+
+pub fn unpack(xs: &Tensor, height: usize, width: usize) -> Result<Tensor> {
+    let (b, _h_w, c_ph_pw) = xs.dims3()?;
+    let height = (height + 15) / 16;
+    let width = (width + 15) / 16;
+    xs.reshape((b, height, width, c_ph_pw / 4, 2, 2))? // (b, h, w, c, ph, pw)
+        .permute((0, 3, 1, 4, 2, 5))? // (b, c, h, ph, w, pw)
+        .reshape((b, c_ph_pw / 4, height * 2, width * 2))
+}
+
+#[allow(clippy::too_many_arguments)]
+pub fn denoise(
+    model: &super::model::Flux,
+    img: &Tensor,
+    img_ids: &Tensor,
+    txt: &Tensor,
+    txt_ids: &Tensor,
+    vec_: &Tensor,
+    timesteps: &[f64],
+    guidance: f64,
+) -> Result<Tensor> {
+    let b_sz = img.dim(0)?;
+    let dev = img.device();
+    let guidance = Tensor::full(guidance as f32, b_sz, dev)?;
+    let mut img = img.clone();
+    for window in timesteps.windows(2) {
+        let (t_curr, t_prev) = match window {
+            [a, b] => (a, b),
+            _ => continue,
+        };
+        let t_vec = Tensor::full(*t_curr as f32, b_sz, dev)?;
+        let pred = model.forward(&img, img_ids, txt, txt_ids, &t_vec, vec_, Some(&guidance))?;
+        img = (img + pred * (t_prev - t_curr))?
+    }
+    Ok(img)
+}

candle-transformers/src/models/mod.rs

@@ -17,6 +17,7 @@ pub mod efficientvit;
 pub mod encodec;
 pub mod eva2;
 pub mod falcon;
+pub mod flux;
 pub mod gemma;
 pub mod hiera;
 pub mod jina_bert;