fix the cross-attention num_head bug.

Gemma/nodes.py

@@ -109,11 +109,10 @@ class GemmaTextEncode:
                 return_tensors="pt"
             ).to(text_encoder.device)
             
-            cond = text_encoder(tokens.input_ids, tokens.attention_mask)[0][:, None]
+            cond = text_encoder(tokens.input_ids, tokens.attention_mask)[0]
             emb_masks = tokens.attention_mask
             
-        # 利用emb_masks将有效的cond选出来，其他置零
+        cond = cond * emb_masks.unsqueeze(-1)
-        # cond = cond * emb_masks.unsqueeze(-1)
 
         return ([[cond, {}]], )
 

Sana/conf.py

@@ -14,7 +14,7 @@ sana_conf = {
 			"depth": 28,
 			"hidden_size": 1152,
 			"patch_size": 1,
-			"num_heads": 36,
+			"num_heads": 16,
 			"linear_head_dim": 32,
 			"model_max_length": 300,
 			"y_norm": True,
@@ -36,7 +36,7 @@ sana_conf = {
 			"depth": 20,
 			"hidden_size": 2240,
 			"patch_size": 1,
-			"num_heads": 70,
+			"num_heads": 20,
 			"linear_head_dim": 32,
 			"model_max_length": 300,
 			"y_norm": True,

Sana/loader.py

@@ -48,7 +48,7 @@ class EXM_Sana_Model(comfy.model_base.BaseModel):
 		return out
 
 
-def load_sana(model_path, model_conf, dtype):
+def load_sana(model_path, model_conf):
 	state_dict = comfy.utils.load_torch_file(model_path)
 	state_dict = state_dict.get("model", state_dict)
 
@@ -62,7 +62,7 @@ def load_sana(model_path, model_conf, dtype):
 		state_dict = convert_state_dict(state_dict) # Diffusers
 
 	parameters = comfy.utils.calculate_parameters(state_dict)
-	unet_dtype = dtype
+	unet_dtype = comfy.model_management.unet_dtype()
 	load_device = comfy.model_management.get_torch_device()
 	offload_device = comfy.model_management.unet_offload_device()
 

Sana/models/sana.py

@@ -159,7 +159,7 @@ class Sana(nn.Module):
         caption_channels=2304,
         pe_interpolation=1.0,
         config=None,
-        model_max_length=120,
+        model_max_length=300,
         qk_norm=False,
         y_norm=False,
         norm_eps=1e-5,
@@ -182,6 +182,7 @@ class Sana(nn.Module):
         self.depth = depth
         self.use_pe = use_pe
         self.y_norm = y_norm
+        self.model_max_length = model_max_length
         self.fp32_attention = kwargs.get("use_fp32_attention", False)
 
         kernel_size = patch_embed_kernel or patch_size

Sana/models/sana_multi_scale.py

@@ -296,20 +296,19 @@ class SanaMS(Sana):
 
         t = self.t_embedder(timestep)  # (N, D)
 
+        y_lens = ((y != 0).sum(dim=3) > 0).sum(dim=2).squeeze().tolist()
+        y_lens = [y_lens[1]] * bs
+
+        mask = torch.zeros((len(y_lens), self.model_max_length), dtype=torch.int).to(x.device)
+        for i, count in enumerate(y_lens):
+            mask[i, :count] = 1
+
         t0 = self.t_block(t)
         y = self.y_embedder(y, self.training, mask=mask)  # (N, D)
         if self.y_norm:
             y = self.attention_y_norm(y)
 
-        if mask is not None:
+        y = y.squeeze(1).masked_select(mask.unsqueeze(-1).bool()).view(1, -1, y.shape[-1])
-            if mask.shape[0] != y.shape[0]:
-                mask = mask.repeat(y.shape[0] // mask.shape[0], 1)
-            mask = mask.squeeze(1).squeeze(1)
-            y = y.squeeze(1).masked_select(mask.unsqueeze(-1) != 0).view(1, -1, x.shape[-1])
-            y_lens = mask.sum(dim=1).tolist()
-        else:
-            y_lens = [y.shape[2]] * y.shape[0]
-            y = y.squeeze(1).view(1, -1, x.shape[-1])
 
         for block in self.blocks:
             x = auto_grad_checkpoint(

Sana/nodes.py

@@ -23,7 +23,6 @@ class SanaCheckpointLoader:
 			"required": {
 				"ckpt_name": (folder_paths.get_filename_list("checkpoints"),),
 				"model": (list(sana_conf.keys()),),
-				"dtype": (dtypes,),
 			}
 		}
 	RETURN_TYPES = ("MODEL",)
@@ -32,13 +31,12 @@ class SanaCheckpointLoader:
 	CATEGORY = "ExtraModels/Sana"
 	TITLE = "Sana Checkpoint Loader"
 
-	def load_checkpoint(self, ckpt_name, model, dtype):
+	def load_checkpoint(self, ckpt_name, model):
 		ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
 		model_conf = sana_conf[model]
 		model = load_sana(
 			model_path = ckpt_path,
 			model_conf = model_conf,
-			dtype = string_to_dtype(dtype, "text_encoder")
 		)
 		return (model,)
 
@@ -132,8 +130,6 @@ class SanaTextEncode:
 			emb_masks = tokens.attention_mask[:, select_idx]
 		# 利用emb_masks将有效的embs选出来，其他置零
 		embs = embs * emb_masks.unsqueeze(-1)
-		# import IPython
-		# IPython.embed()
 			
 		return ([[embs, {}]], )