Quantized version of flux.

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

@@ -0,0 +1,626 @@
+use crate::quantized_nn::{linear, linear_b, Linear};
+use crate::quantized_var_builder::VarBuilder;
+use candle::{DType, IndexOp, Result, Tensor, D};
+use candle_nn::{LayerNorm, RmsNorm};
+
+// 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, DType::F32, 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 = linear(in_sz, h_sz, vb.pp("in_layer"))?;
+        let out_layer = 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")?.dequantize(vb.device())?;
+        let query_norm = RmsNorm::new(query_norm, 1e-6);
+        let key_norm = vb.get(dim, "key_norm.scale")?.dequantize(vb.device())?;
+        let key_norm = RmsNorm::new(key_norm, 1e-6);
+        Ok(Self {
+            query_norm,
+            key_norm,
+        })
+    }
+}
+
+struct ModulationOut {
+    shift: Tensor,
+    scale: Tensor,
+    gate: Tensor,
+}
+
+impl ModulationOut {
+    fn scale_shift(&self, xs: &Tensor) -> Result<Tensor> {
+        xs.broadcast_mul(&(&self.scale + 1.)?)?
+            .broadcast_add(&self.shift)
+    }
+
+    fn gate(&self, xs: &Tensor) -> Result<Tensor> {
+        self.gate.broadcast_mul(xs)
+    }
+}
+
+#[derive(Debug, Clone)]
+struct Modulation1 {
+    lin: Linear,
+}
+
+impl Modulation1 {
+    fn new(dim: usize, vb: VarBuilder) -> Result<Self> {
+        let lin = linear(dim, 3 * dim, vb.pp("lin"))?;
+        Ok(Self { lin })
+    }
+
+    fn forward(&self, vec_: &Tensor) -> Result<ModulationOut> {
+        let ys = vec_
+            .silu()?
+            .apply(&self.lin)?
+            .unsqueeze(1)?
+            .chunk(3, D::Minus1)?;
+        if ys.len() != 3 {
+            candle::bail!("unexpected len from chunk {ys:?}")
+        }
+        Ok(ModulationOut {
+            shift: ys[0].clone(),
+            scale: ys[1].clone(),
+            gate: ys[2].clone(),
+        })
+    }
+}
+
+#[derive(Debug, Clone)]
+struct Modulation2 {
+    lin: Linear,
+}
+
+impl Modulation2 {
+    fn new(dim: usize, vb: VarBuilder) -> Result<Self> {
+        let lin = linear(dim, 6 * dim, vb.pp("lin"))?;
+        Ok(Self { lin })
+    }
+
+    fn forward(&self, vec_: &Tensor) -> Result<(ModulationOut, ModulationOut)> {
+        let ys = vec_
+            .silu()?
+            .apply(&self.lin)?
+            .unsqueeze(1)?
+            .chunk(6, D::Minus1)?;
+        if ys.len() != 6 {
+            candle::bail!("unexpected len from chunk {ys:?}")
+        }
+        let mod1 = ModulationOut {
+            shift: ys[0].clone(),
+            scale: ys[1].clone(),
+            gate: ys[2].clone(),
+        };
+        let mod2 = ModulationOut {
+            shift: ys[3].clone(),
+            scale: ys[4].clone(),
+            gate: ys[5].clone(),
+        };
+        Ok((mod1, mod2))
+    }
+}
+
+#[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 = linear_b(dim, dim * 3, qkv_bias, vb.pp("qkv"))?;
+        let norm = QkNorm::new(head_dim, vb.pp("norm"))?;
+        let proj = 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 = linear(in_sz, mlp_sz, vb.pp("0"))?;
+        let lin2 = 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: Modulation2,
+    img_norm1: LayerNorm,
+    img_attn: SelfAttention,
+    img_norm2: LayerNorm,
+    img_mlp: Mlp,
+    txt_mod: Modulation2,
+    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 = Modulation2::new(h_sz, 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 = Modulation2::new(h_sz, 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_mod1, img_mod2) = self.img_mod.forward(vec_)?; // shift, scale, gate
+        let (txt_mod1, txt_mod2) = self.txt_mod.forward(vec_)?; // shift, scale, gate
+        let img_modulated = img.apply(&self.img_norm1)?;
+        let img_modulated = img_mod1.scale_shift(&img_modulated)?;
+        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_mod1.scale_shift(&txt_modulated)?;
+        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_mod1.gate(&img_attn.apply(&self.img_attn.proj)?))?;
+        let img = (&img
+            + img_mod2.gate(
+                &img_mod2
+                    .scale_shift(&img.apply(&self.img_norm2)?)?
+                    .apply(&self.img_mlp)?,
+            )?)?;
+
+        let txt = (txt + txt_mod1.gate(&txt_attn.apply(&self.txt_attn.proj)?))?;
+        let txt = (&txt
+            + txt_mod2.gate(
+                &txt_mod2
+                    .scale_shift(&txt.apply(&self.txt_norm2)?)?
+                    .apply(&self.txt_mlp)?,
+            )?)?;
+
+        Ok((img, txt))
+    }
+}
+
+#[derive(Debug, Clone)]
+pub struct SingleStreamBlock {
+    linear1: Linear,
+    linear2: Linear,
+    norm: QkNorm,
+    pre_norm: LayerNorm,
+    modulation: Modulation1,
+    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 = linear(h_sz, h_sz * 3 + mlp_sz, vb.pp("linear1"))?;
+        let linear2 = 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 = Modulation1::new(h_sz, 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 x_mod = mod_.scale_shift(&xs.apply(&self.pre_norm)?)?;
+        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 + mod_.gate(&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_ = linear(h_sz, p_sz * p_sz * out_c, vb.pp("linear"))?;
+        let ada_ln_modulation = linear(h_sz, 2 * h_sz, vb.pp("adaLN_modulation.1"))?;
+        Ok(Self {
+            norm_final,
+            linear: 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 = linear(cfg.in_channels, cfg.hidden_size, vb.pp("img_in"))?;
+        let txt_in = 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_)
+    }
+}