Stable diffusion 3.5 support. (#2578)

* Stable diffusion 3.5 support.

* Clippy fixes.

* CFG fix.

* Remove some unnecessary clones.

* Avoid duplicating some of the code.

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

@@ -36,6 +36,20 @@ impl Config {
             frequency_embedding_size: 256,
         }
     }
+
+    pub fn sd3_5_large() -> Self {
+        Self {
+            patch_size: 2,
+            in_channels: 16,
+            out_channels: 16,
+            depth: 38,
+            head_size: 64,
+            adm_in_channels: 2048,
+            pos_embed_max_size: 192,
+            context_embed_size: 4096,
+            frequency_embedding_size: 256,
+        }
+    }
 }
 
 pub struct MMDiT {

candle-transformers/src/models/mmdit/projections.rs

@@ -56,6 +56,8 @@ impl QkvOnlyAttnProjections {
 pub struct AttnProjections {
     head_dim: usize,
     qkv: nn::Linear,
+    ln_k: Option<candle_nn::RmsNorm>,
+    ln_q: Option<candle_nn::RmsNorm>,
     proj: nn::Linear,
 }
 
@@ -64,16 +66,42 @@ impl AttnProjections {
         let head_dim = dim / num_heads;
         let qkv = nn::linear(dim, dim * 3, vb.pp("qkv"))?;
         let proj = nn::linear(dim, dim, vb.pp("proj"))?;
+        let (ln_k, ln_q) = if vb.contains_tensor("ln_k.weight") {
+            let ln_k = candle_nn::rms_norm(head_dim, 1e-6, vb.pp("ln_k"))?;
+            let ln_q = candle_nn::rms_norm(head_dim, 1e-6, vb.pp("ln_q"))?;
+            (Some(ln_k), Some(ln_q))
+        } else {
+            (None, None)
+        };
         Ok(Self {
             head_dim,
             qkv,
             proj,
+            ln_k,
+            ln_q,
         })
     }
 
     pub fn pre_attention(&self, x: &Tensor) -> Result<Qkv> {
         let qkv = self.qkv.forward(x)?;
-        split_qkv(&qkv, self.head_dim)
+        let Qkv { q, k, v } = split_qkv(&qkv, self.head_dim)?;
+        let q = match self.ln_q.as_ref() {
+            None => q,
+            Some(l) => {
+                let (b, t, h) = q.dims3()?;
+                l.forward(&q.reshape((b, t, (), self.head_dim))?)?
+                    .reshape((b, t, h))?
+            }
+        };
+        let k = match self.ln_k.as_ref() {
+            None => k,
+            Some(l) => {
+                let (b, t, h) = k.dims3()?;
+                l.forward(&k.reshape((b, t, (), self.head_dim))?)?
+                    .reshape((b, t, h))?
+            }
+        };
+        Ok(Qkv { q, k, v })
     }
 
     pub fn post_attention(&self, x: &Tensor) -> Result<Tensor> {