[CI] Refactor Wan Model Tests

src/diffusers/models/transformers/transformer_chronoedit.py

@@ -43,7 +43,7 @@ def _get_qkv_projections(attn: "WanAttention", hidden_states: torch.Tensor, enco
         encoder_hidden_states = hidden_states
 
     if attn.fused_projections:
-        if attn.cross_attention_dim_head is None:
+        if not attn.is_cross_attention:
             # In self-attention layers, we can fuse the entire QKV projection into a single linear
             query, key, value = attn.to_qkv(hidden_states).chunk(3, dim=-1)
         else:
@@ -219,15 +219,18 @@ def __init__(
             self.add_v_proj = torch.nn.Linear(added_kv_proj_dim, self.inner_dim, bias=True)
             self.norm_added_k = torch.nn.RMSNorm(dim_head * heads, eps=eps)
 
-        self.is_cross_attention = cross_attention_dim_head is not None
+        if is_cross_attention is not None:
+            self.is_cross_attention = is_cross_attention
+        else:
+            self.is_cross_attention = cross_attention_dim_head is not None
 
         self.set_processor(processor)
 
     def fuse_projections(self):
         if getattr(self, "fused_projections", False):
             return
 
-        if self.cross_attention_dim_head is None:
+        if not self.is_cross_attention:
             concatenated_weights = torch.cat([self.to_q.weight.data, self.to_k.weight.data, self.to_v.weight.data])
             concatenated_bias = torch.cat([self.to_q.bias.data, self.to_k.bias.data, self.to_v.bias.data])
             out_features, in_features = concatenated_weights.shape

src/diffusers/models/transformers/transformer_wan.py

@@ -42,7 +42,7 @@ def _get_qkv_projections(attn: "WanAttention", hidden_states: torch.Tensor, enco
         encoder_hidden_states = hidden_states
 
     if attn.fused_projections:
-        if attn.cross_attention_dim_head is None:
+        if not attn.is_cross_attention:
             # In self-attention layers, we can fuse the entire QKV projection into a single linear
             query, key, value = attn.to_qkv(hidden_states).chunk(3, dim=-1)
         else:
@@ -214,15 +214,18 @@ def __init__(
             self.add_v_proj = torch.nn.Linear(added_kv_proj_dim, self.inner_dim, bias=True)
             self.norm_added_k = torch.nn.RMSNorm(dim_head * heads, eps=eps)
 
-        self.is_cross_attention = cross_attention_dim_head is not None
+        if is_cross_attention is not None:
+            self.is_cross_attention = is_cross_attention
+        else:
+            self.is_cross_attention = cross_attention_dim_head is not None
 
         self.set_processor(processor)
 
     def fuse_projections(self):
         if getattr(self, "fused_projections", False):
             return
 
-        if self.cross_attention_dim_head is None:
+        if not self.is_cross_attention:
             concatenated_weights = torch.cat([self.to_q.weight.data, self.to_k.weight.data, self.to_v.weight.data])
             concatenated_bias = torch.cat([self.to_q.bias.data, self.to_k.bias.data, self.to_v.bias.data])
             out_features, in_features = concatenated_weights.shape

src/diffusers/models/transformers/transformer_wan_animate.py

@@ -54,7 +54,7 @@ def _get_qkv_projections(attn: "WanAttention", hidden_states: torch.Tensor, enco
         encoder_hidden_states = hidden_states
 
     if attn.fused_projections:
-        if attn.cross_attention_dim_head is None:
+        if not attn.is_cross_attention:
             # In self-attention layers, we can fuse the entire QKV projection into a single linear
             query, key, value = attn.to_qkv(hidden_states).chunk(3, dim=-1)
         else:
@@ -502,24 +502,27 @@ def __init__(
         dim_head: int = 64,
         eps: float = 1e-6,
         cross_attention_dim_head: Optional[int] = None,
+        bias: bool = True,
         processor=None,
     ):
         super().__init__()
         self.inner_dim = dim_head * heads
         self.heads = heads
-        self.cross_attention_head_dim = cross_attention_dim_head
+        self.cross_attention_dim_head = cross_attention_dim_head
         self.kv_inner_dim = self.inner_dim if cross_attention_dim_head is None else cross_attention_dim_head * heads
+        self.use_bias = bias
+        self.is_cross_attention = cross_attention_dim_head is not None
 
         # 1. Pre-Attention Norms for the hidden_states (video latents) and encoder_hidden_states (motion vector).
         # NOTE: this is not used in "vanilla" WanAttention
         self.pre_norm_q = nn.LayerNorm(dim, eps, elementwise_affine=False)
         self.pre_norm_kv = nn.LayerNorm(dim, eps, elementwise_affine=False)
 
         # 2. QKV and Output Projections
-        self.to_q = torch.nn.Linear(dim, self.inner_dim, bias=True)
-        self.to_k = torch.nn.Linear(dim, self.kv_inner_dim, bias=True)
-        self.to_v = torch.nn.Linear(dim, self.kv_inner_dim, bias=True)
-        self.to_out = torch.nn.Linear(self.inner_dim, dim, bias=True)
+        self.to_q = torch.nn.Linear(dim, self.inner_dim, bias=bias)
+        self.to_k = torch.nn.Linear(dim, self.kv_inner_dim, bias=bias)
+        self.to_v = torch.nn.Linear(dim, self.kv_inner_dim, bias=bias)
+        self.to_out = torch.nn.Linear(self.inner_dim, dim, bias=bias)
 
         # 3. QK Norm
         # NOTE: this is applied after the reshape, so only over dim_head rather than dim_head * heads
@@ -682,15 +685,18 @@ def __init__(
             self.add_v_proj = torch.nn.Linear(added_kv_proj_dim, self.inner_dim, bias=True)
             self.norm_added_k = torch.nn.RMSNorm(dim_head * heads, eps=eps)
 
-        self.is_cross_attention = cross_attention_dim_head is not None
+        if is_cross_attention is not None:
+            self.is_cross_attention = is_cross_attention
+        else:
+            self.is_cross_attention = cross_attention_dim_head is not None
 
         self.set_processor(processor)
 
     def fuse_projections(self):
         if getattr(self, "fused_projections", False):
             return
 
-        if self.cross_attention_dim_head is None:
+        if not self.is_cross_attention:
             concatenated_weights = torch.cat([self.to_q.weight.data, self.to_k.weight.data, self.to_v.weight.data])
             concatenated_bias = torch.cat([self.to_q.bias.data, self.to_k.bias.data, self.to_v.bias.data])
             out_features, in_features = concatenated_weights.shape

src/diffusers/models/transformers/transformer_wan_vace.py

@@ -76,6 +76,7 @@ def __init__(
             eps=eps,
             added_kv_proj_dim=added_kv_proj_dim,
             processor=WanAttnProcessor(),
+            is_cross_attention=True,
         )
         self.norm2 = FP32LayerNorm(dim, eps, elementwise_affine=True) if cross_attn_norm else nn.Identity()
 
@@ -178,6 +179,7 @@ class WanVACETransformer3DModel(
     _no_split_modules = ["WanTransformerBlock", "WanVACETransformerBlock"]
     _keep_in_fp32_modules = ["time_embedder", "scale_shift_table", "norm1", "norm2", "norm3"]
     _keys_to_ignore_on_load_unexpected = ["norm_added_q"]
+    _repeated_blocks = ["WanTransformerBlock", "WanVACETransformerBlock"]
 
     @register_to_config
     def __init__(

src/diffusers/quantizers/gguf/gguf_quantizer.py

@@ -41,7 +41,7 @@ def __init__(self, quantization_config, **kwargs):
 
         self.compute_dtype = quantization_config.compute_dtype
         self.pre_quantized = quantization_config.pre_quantized
-        self.modules_to_not_convert = quantization_config.modules_to_not_convert
+        self.modules_to_not_convert = quantization_config.modules_to_not_convert or []
 
         if not isinstance(self.modules_to_not_convert, list):
             self.modules_to_not_convert = [self.modules_to_not_convert]

tests/models/testing_utils/common.py

@@ -446,16 +446,17 @@ def test_getattr_is_correct(self, caplog):
         torch_device not in ["cuda", "xpu"],
         reason="float16 and bfloat16 can only be used with an accelerator",
     )
-    def test_keep_in_fp32_modules(self):
+    def test_keep_in_fp32_modules(self, tmp_path):
         model = self.model_class(**self.get_init_dict())
         fp32_modules = model._keep_in_fp32_modules
 
         if fp32_modules is None or len(fp32_modules) == 0:
             pytest.skip("Model does not have _keep_in_fp32_modules defined.")
 
-        # Test with float16
-        model.to(torch_device)
-        model.to(torch.float16)
+        # Save the model and reload with float16 dtype
+        # _keep_in_fp32_modules is only enforced during from_pretrained loading
+        model.save_pretrained(tmp_path)
+        model = self.model_class.from_pretrained(tmp_path, torch_dtype=torch.float16).to(torch_device)
 
         for name, param in model.named_parameters():
             if any(module_to_keep_in_fp32 in name.split(".") for module_to_keep_in_fp32 in fp32_modules):
@@ -470,7 +471,7 @@ def test_keep_in_fp32_modules(self):
     )
     @pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16], ids=["fp16", "bf16"])
     @torch.no_grad()
-    def test_from_save_pretrained_dtype_inference(self, tmp_path, dtype):
+    def test_from_save_pretrained_dtype_inference(self, tmp_path, dtype, atol=1e-4, rtol=0):
         model = self.model_class(**self.get_init_dict())
         model.to(torch_device)
         fp32_modules = model._keep_in_fp32_modules or []
@@ -490,10 +491,6 @@ def test_from_save_pretrained_dtype_inference(self, tmp_path, dtype):
         output = model(**inputs, return_dict=False)[0]
         output_loaded = model_loaded(**inputs, return_dict=False)[0]
 
-        self._check_dtype_inference_output(output, output_loaded, dtype)
-
-    def _check_dtype_inference_output(self, output, output_loaded, dtype, atol=1e-4, rtol=0):
-        """Check dtype inference output with configurable tolerance."""
         assert_tensors_close(
             output, output_loaded, atol=atol, rtol=rtol, msg=f"Loaded model output differs for {dtype}"
         )

tests/models/testing_utils/quantization.py

@@ -176,15 +176,7 @@ def _test_quantization_inference(self, config_kwargs):
         model_quantized = self._create_quantized_model(config_kwargs)
         model_quantized.to(torch_device)
 
-        # Get model dtype from first parameter
-        model_dtype = next(model_quantized.parameters()).dtype
-
         inputs = self.get_dummy_inputs()
-        # Cast inputs to model dtype
-        inputs = {
-            k: v.to(model_dtype) if isinstance(v, torch.Tensor) and v.is_floating_point() else v
-            for k, v in inputs.items()
-        }
         output = model_quantized(**inputs, return_dict=False)[0]
 
         assert output is not None, "Model output is None"
@@ -229,6 +221,8 @@ def _test_quantization_lora_inference(self, config_kwargs):
             init_lora_weights=False,
         )
         model.add_adapter(lora_config)
+        # Move LoRA adapter weights to device (they default to CPU)
+        model.to(torch_device)
 
         inputs = self.get_dummy_inputs()
         output = model(**inputs, return_dict=False)[0]
@@ -1021,9 +1015,6 @@ def test_gguf_dequantize(self):
         """Test that dequantize() works correctly."""
         self._test_dequantize({"compute_dtype": torch.bfloat16})
 
-    def test_gguf_quantized_layers(self):
-        self._test_quantized_layers({"compute_dtype": torch.bfloat16})
-
 
 @is_quantization
 @is_modelopt