@@ -136,6 +136,10 @@
         "checkpoint_mapping_fn": convert_wan_transformer_to_diffusers,
         "default_subfolder": "transformer",
     },
+    "WanVACETransformer3DModel": {
+        "checkpoint_mapping_fn": convert_wan_transformer_to_diffusers,
+        "default_subfolder": "transformer",
+    },
     "AutoencoderKLWan": {
         "checkpoint_mapping_fn": convert_wan_vae_to_diffusers,
         "default_subfolder": "vae",
 
@@ -126,6 +126,7 @@
     ],
     "wan": ["model.diffusion_model.head.modulation", "head.modulation"],
     "wan_vae": "decoder.middle.0.residual.0.gamma",
+    "wan_vace": "vace_blocks.0.after_proj.bias",
     "hidream": "double_stream_blocks.0.block.adaLN_modulation.1.bias",
     "cosmos-1.0": [
         "net.x_embedder.proj.1.weight",
@@ -202,6 +203,8 @@
     "wan-t2v-1.3B": {"pretrained_model_name_or_path": "Wan-AI/Wan2.1-T2V-1.3B-Diffusers"},
     "wan-t2v-14B": {"pretrained_model_name_or_path": "Wan-AI/Wan2.1-T2V-14B-Diffusers"},
     "wan-i2v-14B": {"pretrained_model_name_or_path": "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers"},
+    "wan-vace-1.3B": {"pretrained_model_name_or_path": "Wan-AI/Wan2.1-VACE-1.3B-diffusers"},
+    "wan-vace-14B": {"pretrained_model_name_or_path": "Wan-AI/Wan2.1-VACE-14B-diffusers"},
     "hidream": {"pretrained_model_name_or_path": "HiDream-ai/HiDream-I1-Dev"},
     "cosmos-1.0-t2w-7B": {"pretrained_model_name_or_path": "nvidia/Cosmos-1.0-Diffusion-7B-Text2World"},
     "cosmos-1.0-t2w-14B": {"pretrained_model_name_or_path": "nvidia/Cosmos-1.0-Diffusion-14B-Text2World"},
@@ -716,7 +719,13 @@ def infer_diffusers_model_type(checkpoint):
         else:
             target_key = "patch_embedding.weight"
 
-        if checkpoint[target_key].shape[0] == 1536:
+        if CHECKPOINT_KEY_NAMES["wan_vace"] in checkpoint:
+            if checkpoint[target_key].shape[0] == 1536:
+                model_type = "wan-vace-1.3B"
+            elif checkpoint[target_key].shape[0] == 5120:
+                model_type = "wan-vace-14B"
+
+        elif checkpoint[target_key].shape[0] == 1536:
             model_type = "wan-t2v-1.3B"
         elif checkpoint[target_key].shape[0] == 5120 and checkpoint[target_key].shape[1] == 16:
             model_type = "wan-t2v-14B"
@@ -3132,6 +3141,9 @@ def convert_wan_transformer_to_diffusers(checkpoint, **kwargs):
         "img_emb.proj.1": "condition_embedder.image_embedder.ff.net.0.proj",
         "img_emb.proj.3": "condition_embedder.image_embedder.ff.net.2",
         "img_emb.proj.4": "condition_embedder.image_embedder.norm2",
+        # For the VACE model
+        "before_proj": "proj_in",
+        "after_proj": "proj_out",
     }
 
     for key in list(checkpoint.keys()):
 
@@ -15,6 +15,8 @@
     HiDreamImageTransformer2DModel,
     SD3Transformer2DModel,
     StableDiffusion3Pipeline,
+    WanTransformer3DModel,
+    WanVACETransformer3DModel,
 )
 from diffusers.utils import load_image
 from diffusers.utils.testing_utils import (
@@ -577,3 +579,71 @@ def get_dummy_inputs(self):
             ).to(torch_device, self.torch_dtype),
             "timesteps": torch.tensor([1]).to(torch_device, self.torch_dtype),
         }
+
+
+class WanGGUFTexttoVideoSingleFileTests(GGUFSingleFileTesterMixin, unittest.TestCase):
+    ckpt_path = "https://huggingface.co/city96/Wan2.1-T2V-14B-gguf/blob/main/wan2.1-t2v-14b-Q3_K_S.gguf"
+    torch_dtype = torch.bfloat16
+    model_cls = WanTransformer3DModel
+    expected_memory_use_in_gb = 9
+
+    def get_dummy_inputs(self):
+        return {
+            "hidden_states": torch.randn((1, 36, 2, 64, 64), generator=torch.Generator("cpu").manual_seed(0)).to(
+                torch_device, self.torch_dtype
+            ),
+            "encoder_hidden_states": torch.randn(
+                (1, 512, 4096),
+                generator=torch.Generator("cpu").manual_seed(0),
+            ).to(torch_device, self.torch_dtype),
+            "timestep": torch.tensor([1]).to(torch_device, self.torch_dtype),
+        }
+
+
+class WanGGUFImagetoVideoSingleFileTests(GGUFSingleFileTesterMixin, unittest.TestCase):
+    ckpt_path = "https://huggingface.co/city96/Wan2.1-I2V-14B-480P-gguf/blob/main/wan2.1-i2v-14b-480p-Q3_K_S.gguf"
+    torch_dtype = torch.bfloat16
+    model_cls = WanTransformer3DModel
+    expected_memory_use_in_gb = 9
+
+    def get_dummy_inputs(self):
+        return {
+            "hidden_states": torch.randn((1, 36, 2, 64, 64), generator=torch.Generator("cpu").manual_seed(0)).to(
+                torch_device, self.torch_dtype
+            ),
+            "encoder_hidden_states": torch.randn(
+                (1, 512, 4096),
+                generator=torch.Generator("cpu").manual_seed(0),
+            ).to(torch_device, self.torch_dtype),
+            "encoder_hidden_states_image": torch.randn(
+                (1, 257, 1280), generator=torch.Generator("cpu").manual_seed(0)
+            ).to(torch_device, self.torch_dtype),
+            "timestep": torch.tensor([1]).to(torch_device, self.torch_dtype),
+        }
+
+
+class WanVACEGGUFSingleFileTests(GGUFSingleFileTesterMixin, unittest.TestCase):
+    ckpt_path = "https://huggingface.co/QuantStack/Wan2.1_14B_VACE-GGUF/blob/main/Wan2.1_14B_VACE-Q3_K_S.gguf"
+    torch_dtype = torch.bfloat16
+    model_cls = WanVACETransformer3DModel
+    expected_memory_use_in_gb = 9
+
+    def get_dummy_inputs(self):
+        return {
+            "hidden_states": torch.randn((1, 16, 2, 64, 64), generator=torch.Generator("cpu").manual_seed(0)).to(
+                torch_device, self.torch_dtype
+            ),
+            "encoder_hidden_states": torch.randn(
+                (1, 512, 4096),
+                generator=torch.Generator("cpu").manual_seed(0),
+            ).to(torch_device, self.torch_dtype),
+            "control_hidden_states": torch.randn(
+                (1, 96, 2, 64, 64),
+                generator=torch.Generator("cpu").manual_seed(0),
+            ).to(torch_device, self.torch_dtype),
+            "control_hidden_states_scale": torch.randn(
+                (8,),
+                generator=torch.Generator("cpu").manual_seed(0),
+            ).to(torch_device, self.torch_dtype),
+            "timestep": torch.tensor([1]).to(torch_device, self.torch_dtype),
+        }