update

docs/source/en/api/pipelines/wan.md

@@ -45,6 +45,22 @@ pipe = WanPipeline.from_pretrained("Wan-AI/Wan2.1-T2V-1.3B-Diffusers", scheduler
 pipe.scheduler = <CUSTOM_SCHEDULER_HERE>
 ```
 
+### Using single file loading with Wan
+
+The `WanTransformer3DModel` and `AutoencoderKLWan` models support loading checkpoints in their original format via the `from_single_file` loading 
+method. 
+
+
+```python
+import torch
+from diffusers import WanPipeline, WanTransformer3DModel
+
+ckpt_path = "https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/blob/main/split_files/diffusion_models/wan2.1_t2v_1.3B_bf16.safetensors"
+transformer = WanTransformer3DModel.from_single_file(ckpt_path, torch_dtype=torch.bfloat16)
+
+pipe = WanPipeline.from_pretrained("Wan-AI/Wan2.1-T2V-1.3B-Diffusers", transformer=transformer)
+```
+
 ## WanPipeline
 
 [[autodoc]] WanPipeline

src/diffusers/loaders/single_file_model.py

@@ -39,6 +39,8 @@ from .single_file_utils import (
     convert_mochi_transformer_checkpoint_to_diffusers,
     convert_sd3_transformer_checkpoint_to_diffusers,
     convert_stable_cascade_unet_single_file_to_diffusers,
+    convert_wan_transformer_to_diffusers,
+    convert_wan_vae_to_diffusers,
     create_controlnet_diffusers_config_from_ldm,
     create_unet_diffusers_config_from_ldm,
     create_vae_diffusers_config_from_ldm,
@@ -117,6 +119,14 @@ SINGLE_FILE_LOADABLE_CLASSES = {
         "checkpoint_mapping_fn": convert_lumina2_to_diffusers,
         "default_subfolder": "transformer",
     },
+    "WanTransformer3DModel": {
+        "checkpoint_mapping_fn": convert_wan_transformer_to_diffusers,
+        "default_subfolder": "transformer",
+    },
+    "AutoencoderKLWan": {
+        "checkpoint_mapping_fn": convert_wan_vae_to_diffusers,
+        "default_subfolder": "vae",
+    },
 }
 
 

src/diffusers/loaders/single_file_utils.py

@@ -117,6 +117,8 @@ CHECKPOINT_KEY_NAMES = {
     "hunyuan-video": "txt_in.individual_token_refiner.blocks.0.adaLN_modulation.1.bias",
     "instruct-pix2pix": "model.diffusion_model.input_blocks.0.0.weight",
     "lumina2": ["model.diffusion_model.cap_embedder.0.weight", "cap_embedder.0.weight"],
+    "wan": ["model.diffusion_model.head.modulation", "head.modulation"],
+    "wan_vae": "decoder.middle.0.residual.0.gamma",
 }
 
 DIFFUSERS_DEFAULT_PIPELINE_PATHS = {
@@ -176,6 +178,9 @@ DIFFUSERS_DEFAULT_PIPELINE_PATHS = {
     "hunyuan-video": {"pretrained_model_name_or_path": "hunyuanvideo-community/HunyuanVideo"},
     "instruct-pix2pix": {"pretrained_model_name_or_path": "timbrooks/instruct-pix2pix"},
     "lumina2": {"pretrained_model_name_or_path": "Alpha-VLLM/Lumina-Image-2.0"},
+    "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"},
 }
 
 # Use to configure model sample size when original config is provided
@@ -664,6 +669,21 @@ def infer_diffusers_model_type(checkpoint):
     elif any(key in checkpoint for key in CHECKPOINT_KEY_NAMES["lumina2"]):
         model_type = "lumina2"
 
+    elif any(key in checkpoint for key in CHECKPOINT_KEY_NAMES["wan"]):
+        if "model.diffusion_model.patch_embedding.weight" in checkpoint:
+            target_key = "model.diffusion_model.patch_embedding.weight"
+        else:
+            target_key = "patch_embedding.weight"
+
+        if 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"
+        else:
+            model_type = "wan-i2v-14B"
+    elif CHECKPOINT_KEY_NAMES["wan_vae"] in checkpoint:
+        # All Wan models use the same VAE so we can use the same default model repo to fetch the config
+        model_type = "wan-t2v-14B"
     else:
         model_type = "v1"
 
@@ -2470,7 +2490,7 @@ def convert_autoencoder_dc_checkpoint_to_diffusers(checkpoint, **kwargs):
 
 
 def convert_mochi_transformer_checkpoint_to_diffusers(checkpoint, **kwargs):
-    new_state_dict = {}
+    converted_state_dict = {}
 
     # Comfy checkpoints add this prefix
     keys = list(checkpoint.keys())
@@ -2479,22 +2499,22 @@ def convert_mochi_transformer_checkpoint_to_diffusers(checkpoint, **kwargs):
             checkpoint[k.replace("model.diffusion_model.", "")] = checkpoint.pop(k)
 
     # Convert patch_embed
-    new_state_dict["patch_embed.proj.weight"] = checkpoint.pop("x_embedder.proj.weight")
-    new_state_dict["patch_embed.proj.bias"] = checkpoint.pop("x_embedder.proj.bias")
+    converted_state_dict["patch_embed.proj.weight"] = checkpoint.pop("x_embedder.proj.weight")
+    converted_state_dict["patch_embed.proj.bias"] = checkpoint.pop("x_embedder.proj.bias")
 
     # Convert time_embed
-    new_state_dict["time_embed.timestep_embedder.linear_1.weight"] = checkpoint.pop("t_embedder.mlp.0.weight")
-    new_state_dict["time_embed.timestep_embedder.linear_1.bias"] = checkpoint.pop("t_embedder.mlp.0.bias")
-    new_state_dict["time_embed.timestep_embedder.linear_2.weight"] = checkpoint.pop("t_embedder.mlp.2.weight")
-    new_state_dict["time_embed.timestep_embedder.linear_2.bias"] = checkpoint.pop("t_embedder.mlp.2.bias")
-    new_state_dict["time_embed.pooler.to_kv.weight"] = checkpoint.pop("t5_y_embedder.to_kv.weight")
-    new_state_dict["time_embed.pooler.to_kv.bias"] = checkpoint.pop("t5_y_embedder.to_kv.bias")
-    new_state_dict["time_embed.pooler.to_q.weight"] = checkpoint.pop("t5_y_embedder.to_q.weight")
-    new_state_dict["time_embed.pooler.to_q.bias"] = checkpoint.pop("t5_y_embedder.to_q.bias")
-    new_state_dict["time_embed.pooler.to_out.weight"] = checkpoint.pop("t5_y_embedder.to_out.weight")
-    new_state_dict["time_embed.pooler.to_out.bias"] = checkpoint.pop("t5_y_embedder.to_out.bias")
-    new_state_dict["time_embed.caption_proj.weight"] = checkpoint.pop("t5_yproj.weight")
-    new_state_dict["time_embed.caption_proj.bias"] = checkpoint.pop("t5_yproj.bias")
+    converted_state_dict["time_embed.timestep_embedder.linear_1.weight"] = checkpoint.pop("t_embedder.mlp.0.weight")
+    converted_state_dict["time_embed.timestep_embedder.linear_1.bias"] = checkpoint.pop("t_embedder.mlp.0.bias")
+    converted_state_dict["time_embed.timestep_embedder.linear_2.weight"] = checkpoint.pop("t_embedder.mlp.2.weight")
+    converted_state_dict["time_embed.timestep_embedder.linear_2.bias"] = checkpoint.pop("t_embedder.mlp.2.bias")
+    converted_state_dict["time_embed.pooler.to_kv.weight"] = checkpoint.pop("t5_y_embedder.to_kv.weight")
+    converted_state_dict["time_embed.pooler.to_kv.bias"] = checkpoint.pop("t5_y_embedder.to_kv.bias")
+    converted_state_dict["time_embed.pooler.to_q.weight"] = checkpoint.pop("t5_y_embedder.to_q.weight")
+    converted_state_dict["time_embed.pooler.to_q.bias"] = checkpoint.pop("t5_y_embedder.to_q.bias")
+    converted_state_dict["time_embed.pooler.to_out.weight"] = checkpoint.pop("t5_y_embedder.to_out.weight")
+    converted_state_dict["time_embed.pooler.to_out.bias"] = checkpoint.pop("t5_y_embedder.to_out.bias")
+    converted_state_dict["time_embed.caption_proj.weight"] = checkpoint.pop("t5_yproj.weight")
+    converted_state_dict["time_embed.caption_proj.bias"] = checkpoint.pop("t5_yproj.bias")
 
     # Convert transformer blocks
     num_layers = 48
@@ -2503,68 +2523,84 @@ def convert_mochi_transformer_checkpoint_to_diffusers(checkpoint, **kwargs):
         old_prefix = f"blocks.{i}."
 
         # norm1
-        new_state_dict[block_prefix + "norm1.linear.weight"] = checkpoint.pop(old_prefix + "mod_x.weight")
-        new_state_dict[block_prefix + "norm1.linear.bias"] = checkpoint.pop(old_prefix + "mod_x.bias")
+        converted_state_dict[block_prefix + "norm1.linear.weight"] = checkpoint.pop(old_prefix + "mod_x.weight")
+        converted_state_dict[block_prefix + "norm1.linear.bias"] = checkpoint.pop(old_prefix + "mod_x.bias")
         if i < num_layers - 1:
-            new_state_dict[block_prefix + "norm1_context.linear.weight"] = checkpoint.pop(old_prefix + "mod_y.weight")
-            new_state_dict[block_prefix + "norm1_context.linear.bias"] = checkpoint.pop(old_prefix + "mod_y.bias")
-        else:
-            new_state_dict[block_prefix + "norm1_context.linear_1.weight"] = checkpoint.pop(
+            converted_state_dict[block_prefix + "norm1_context.linear.weight"] = checkpoint.pop(
                 old_prefix + "mod_y.weight"
             )
-            new_state_dict[block_prefix + "norm1_context.linear_1.bias"] = checkpoint.pop(old_prefix + "mod_y.bias")
+            converted_state_dict[block_prefix + "norm1_context.linear.bias"] = checkpoint.pop(
+                old_prefix + "mod_y.bias"
+            )
+        else:
+            converted_state_dict[block_prefix + "norm1_context.linear_1.weight"] = checkpoint.pop(
+                old_prefix + "mod_y.weight"
+            )
+            converted_state_dict[block_prefix + "norm1_context.linear_1.bias"] = checkpoint.pop(
+                old_prefix + "mod_y.bias"
+            )
 
         # Visual attention
         qkv_weight = checkpoint.pop(old_prefix + "attn.qkv_x.weight")
         q, k, v = qkv_weight.chunk(3, dim=0)
 
-        new_state_dict[block_prefix + "attn1.to_q.weight"] = q
-        new_state_dict[block_prefix + "attn1.to_k.weight"] = k
-        new_state_dict[block_prefix + "attn1.to_v.weight"] = v
-        new_state_dict[block_prefix + "attn1.norm_q.weight"] = checkpoint.pop(old_prefix + "attn.q_norm_x.weight")
-        new_state_dict[block_prefix + "attn1.norm_k.weight"] = checkpoint.pop(old_prefix + "attn.k_norm_x.weight")
-        new_state_dict[block_prefix + "attn1.to_out.0.weight"] = checkpoint.pop(old_prefix + "attn.proj_x.weight")
-        new_state_dict[block_prefix + "attn1.to_out.0.bias"] = checkpoint.pop(old_prefix + "attn.proj_x.bias")
+        converted_state_dict[block_prefix + "attn1.to_q.weight"] = q
+        converted_state_dict[block_prefix + "attn1.to_k.weight"] = k
+        converted_state_dict[block_prefix + "attn1.to_v.weight"] = v
+        converted_state_dict[block_prefix + "attn1.norm_q.weight"] = checkpoint.pop(
+            old_prefix + "attn.q_norm_x.weight"
+        )
+        converted_state_dict[block_prefix + "attn1.norm_k.weight"] = checkpoint.pop(
+            old_prefix + "attn.k_norm_x.weight"
+        )
+        converted_state_dict[block_prefix + "attn1.to_out.0.weight"] = checkpoint.pop(
+            old_prefix + "attn.proj_x.weight"
+        )
+        converted_state_dict[block_prefix + "attn1.to_out.0.bias"] = checkpoint.pop(old_prefix + "attn.proj_x.bias")
 
         # Context attention
         qkv_weight = checkpoint.pop(old_prefix + "attn.qkv_y.weight")
         q, k, v = qkv_weight.chunk(3, dim=0)
 
-        new_state_dict[block_prefix + "attn1.add_q_proj.weight"] = q
-        new_state_dict[block_prefix + "attn1.add_k_proj.weight"] = k
-        new_state_dict[block_prefix + "attn1.add_v_proj.weight"] = v
-        new_state_dict[block_prefix + "attn1.norm_added_q.weight"] = checkpoint.pop(
+        converted_state_dict[block_prefix + "attn1.add_q_proj.weight"] = q
+        converted_state_dict[block_prefix + "attn1.add_k_proj.weight"] = k
+        converted_state_dict[block_prefix + "attn1.add_v_proj.weight"] = v
+        converted_state_dict[block_prefix + "attn1.norm_added_q.weight"] = checkpoint.pop(
             old_prefix + "attn.q_norm_y.weight"
         )
-        new_state_dict[block_prefix + "attn1.norm_added_k.weight"] = checkpoint.pop(
+        converted_state_dict[block_prefix + "attn1.norm_added_k.weight"] = checkpoint.pop(
             old_prefix + "attn.k_norm_y.weight"
         )
         if i < num_layers - 1:
-            new_state_dict[block_prefix + "attn1.to_add_out.weight"] = checkpoint.pop(
+            converted_state_dict[block_prefix + "attn1.to_add_out.weight"] = checkpoint.pop(
                 old_prefix + "attn.proj_y.weight"
             )
-            new_state_dict[block_prefix + "attn1.to_add_out.bias"] = checkpoint.pop(old_prefix + "attn.proj_y.bias")
+            converted_state_dict[block_prefix + "attn1.to_add_out.bias"] = checkpoint.pop(
+                old_prefix + "attn.proj_y.bias"
+            )
 
         # MLP
-        new_state_dict[block_prefix + "ff.net.0.proj.weight"] = swap_proj_gate(
+        converted_state_dict[block_prefix + "ff.net.0.proj.weight"] = swap_proj_gate(
             checkpoint.pop(old_prefix + "mlp_x.w1.weight")
         )
-        new_state_dict[block_prefix + "ff.net.2.weight"] = checkpoint.pop(old_prefix + "mlp_x.w2.weight")
+        converted_state_dict[block_prefix + "ff.net.2.weight"] = checkpoint.pop(old_prefix + "mlp_x.w2.weight")
         if i < num_layers - 1:
-            new_state_dict[block_prefix + "ff_context.net.0.proj.weight"] = swap_proj_gate(
+            converted_state_dict[block_prefix + "ff_context.net.0.proj.weight"] = swap_proj_gate(
                 checkpoint.pop(old_prefix + "mlp_y.w1.weight")
             )
-            new_state_dict[block_prefix + "ff_context.net.2.weight"] = checkpoint.pop(old_prefix + "mlp_y.w2.weight")
+            converted_state_dict[block_prefix + "ff_context.net.2.weight"] = checkpoint.pop(
+                old_prefix + "mlp_y.w2.weight"
+            )
 
     # Output layers
-    new_state_dict["norm_out.linear.weight"] = swap_scale_shift(checkpoint.pop("final_layer.mod.weight"), dim=0)
-    new_state_dict["norm_out.linear.bias"] = swap_scale_shift(checkpoint.pop("final_layer.mod.bias"), dim=0)
-    new_state_dict["proj_out.weight"] = checkpoint.pop("final_layer.linear.weight")
-    new_state_dict["proj_out.bias"] = checkpoint.pop("final_layer.linear.bias")
+    converted_state_dict["norm_out.linear.weight"] = swap_scale_shift(checkpoint.pop("final_layer.mod.weight"), dim=0)
+    converted_state_dict["norm_out.linear.bias"] = swap_scale_shift(checkpoint.pop("final_layer.mod.bias"), dim=0)
+    converted_state_dict["proj_out.weight"] = checkpoint.pop("final_layer.linear.weight")
+    converted_state_dict["proj_out.bias"] = checkpoint.pop("final_layer.linear.bias")
 
-    new_state_dict["pos_frequencies"] = checkpoint.pop("pos_frequencies")
+    converted_state_dict["pos_frequencies"] = checkpoint.pop("pos_frequencies")
 
-    return new_state_dict
+    return converted_state_dict
 
 
 def convert_hunyuan_video_transformer_to_diffusers(checkpoint, **kwargs):
@@ -2859,3 +2895,252 @@ def convert_lumina2_to_diffusers(checkpoint, **kwargs):
             converted_state_dict[diffusers_key] = checkpoint.pop(key)
 
     return converted_state_dict
+
+
+def convert_wan_transformer_to_diffusers(checkpoint, **kwargs):
+    converted_state_dict = {}
+
+    keys = list(checkpoint.keys())
+    for k in keys:
+        if "model.diffusion_model." in k:
+            checkpoint[k.replace("model.diffusion_model.", "")] = checkpoint.pop(k)
+
+    TRANSFORMER_KEYS_RENAME_DICT = {
+        "time_embedding.0": "condition_embedder.time_embedder.linear_1",
+        "time_embedding.2": "condition_embedder.time_embedder.linear_2",
+        "text_embedding.0": "condition_embedder.text_embedder.linear_1",
+        "text_embedding.2": "condition_embedder.text_embedder.linear_2",
+        "time_projection.1": "condition_embedder.time_proj",
+        "cross_attn": "attn2",
+        "self_attn": "attn1",
+        ".o.": ".to_out.0.",
+        ".q.": ".to_q.",
+        ".k.": ".to_k.",
+        ".v.": ".to_v.",
+        ".k_img.": ".add_k_proj.",
+        ".v_img.": ".add_v_proj.",
+        ".norm_k_img.": ".norm_added_k.",
+        "head.modulation": "scale_shift_table",
+        "head.head": "proj_out",
+        "modulation": "scale_shift_table",
+        "ffn.0": "ffn.net.0.proj",
+        "ffn.2": "ffn.net.2",
+        # Hack to swap the layer names
+        # The original model calls the norms in following order: norm1, norm3, norm2
+        # We convert it to: norm1, norm2, norm3
+        "norm2": "norm__placeholder",
+        "norm3": "norm2",
+        "norm__placeholder": "norm3",
+        # For the I2V model
+        "img_emb.proj.0": "condition_embedder.image_embedder.norm1",
+        "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 key in list(checkpoint.keys()):
+        new_key = key[:]
+        for replace_key, rename_key in TRANSFORMER_KEYS_RENAME_DICT.items():
+            new_key = new_key.replace(replace_key, rename_key)
+
+        converted_state_dict[new_key] = checkpoint.pop(key)
+
+    return converted_state_dict
+
+
+def convert_wan_vae_to_diffusers(checkpoint, **kwargs):
+    converted_state_dict = {}
+
+    # Create mappings for specific components
+    middle_key_mapping = {
+        # Encoder middle block
+        "encoder.middle.0.residual.0.gamma": "encoder.mid_block.resnets.0.norm1.gamma",
+        "encoder.middle.0.residual.2.bias": "encoder.mid_block.resnets.0.conv1.bias",
+        "encoder.middle.0.residual.2.weight": "encoder.mid_block.resnets.0.conv1.weight",
+        "encoder.middle.0.residual.3.gamma": "encoder.mid_block.resnets.0.norm2.gamma",
+        "encoder.middle.0.residual.6.bias": "encoder.mid_block.resnets.0.conv2.bias",
+        "encoder.middle.0.residual.6.weight": "encoder.mid_block.resnets.0.conv2.weight",
+        "encoder.middle.2.residual.0.gamma": "encoder.mid_block.resnets.1.norm1.gamma",
+        "encoder.middle.2.residual.2.bias": "encoder.mid_block.resnets.1.conv1.bias",
+        "encoder.middle.2.residual.2.weight": "encoder.mid_block.resnets.1.conv1.weight",
+        "encoder.middle.2.residual.3.gamma": "encoder.mid_block.resnets.1.norm2.gamma",
+        "encoder.middle.2.residual.6.bias": "encoder.mid_block.resnets.1.conv2.bias",
+        "encoder.middle.2.residual.6.weight": "encoder.mid_block.resnets.1.conv2.weight",
+        # Decoder middle block
+        "decoder.middle.0.residual.0.gamma": "decoder.mid_block.resnets.0.norm1.gamma",
+        "decoder.middle.0.residual.2.bias": "decoder.mid_block.resnets.0.conv1.bias",
+        "decoder.middle.0.residual.2.weight": "decoder.mid_block.resnets.0.conv1.weight",
+        "decoder.middle.0.residual.3.gamma": "decoder.mid_block.resnets.0.norm2.gamma",
+        "decoder.middle.0.residual.6.bias": "decoder.mid_block.resnets.0.conv2.bias",
+        "decoder.middle.0.residual.6.weight": "decoder.mid_block.resnets.0.conv2.weight",
+        "decoder.middle.2.residual.0.gamma": "decoder.mid_block.resnets.1.norm1.gamma",
+        "decoder.middle.2.residual.2.bias": "decoder.mid_block.resnets.1.conv1.bias",
+        "decoder.middle.2.residual.2.weight": "decoder.mid_block.resnets.1.conv1.weight",
+        "decoder.middle.2.residual.3.gamma": "decoder.mid_block.resnets.1.norm2.gamma",
+        "decoder.middle.2.residual.6.bias": "decoder.mid_block.resnets.1.conv2.bias",
+        "decoder.middle.2.residual.6.weight": "decoder.mid_block.resnets.1.conv2.weight",
+    }
+
+    # Create a mapping for attention blocks
+    attention_mapping = {
+        # Encoder middle attention
+        "encoder.middle.1.norm.gamma": "encoder.mid_block.attentions.0.norm.gamma",
+        "encoder.middle.1.to_qkv.weight": "encoder.mid_block.attentions.0.to_qkv.weight",
+        "encoder.middle.1.to_qkv.bias": "encoder.mid_block.attentions.0.to_qkv.bias",
+        "encoder.middle.1.proj.weight": "encoder.mid_block.attentions.0.proj.weight",
+        "encoder.middle.1.proj.bias": "encoder.mid_block.attentions.0.proj.bias",
+        # Decoder middle attention
+        "decoder.middle.1.norm.gamma": "decoder.mid_block.attentions.0.norm.gamma",
+        "decoder.middle.1.to_qkv.weight": "decoder.mid_block.attentions.0.to_qkv.weight",
+        "decoder.middle.1.to_qkv.bias": "decoder.mid_block.attentions.0.to_qkv.bias",
+        "decoder.middle.1.proj.weight": "decoder.mid_block.attentions.0.proj.weight",
+        "decoder.middle.1.proj.bias": "decoder.mid_block.attentions.0.proj.bias",
+    }
+
+    # Create a mapping for the head components
+    head_mapping = {
+        # Encoder head
+        "encoder.head.0.gamma": "encoder.norm_out.gamma",
+        "encoder.head.2.bias": "encoder.conv_out.bias",
+        "encoder.head.2.weight": "encoder.conv_out.weight",
+        # Decoder head
+        "decoder.head.0.gamma": "decoder.norm_out.gamma",
+        "decoder.head.2.bias": "decoder.conv_out.bias",
+        "decoder.head.2.weight": "decoder.conv_out.weight",
+    }
+
+    # Create a mapping for the quant components
+    quant_mapping = {
+        "conv1.weight": "quant_conv.weight",
+        "conv1.bias": "quant_conv.bias",
+        "conv2.weight": "post_quant_conv.weight",
+        "conv2.bias": "post_quant_conv.bias",
+    }
+
+    # Process each key in the state dict
+    for key, value in checkpoint.items():
+        # Handle middle block keys using the mapping
+        if key in middle_key_mapping:
+            new_key = middle_key_mapping[key]
+            converted_state_dict[new_key] = value
+        # Handle attention blocks using the mapping
+        elif key in attention_mapping:
+            new_key = attention_mapping[key]
+            converted_state_dict[new_key] = value
+        # Handle head keys using the mapping
+        elif key in head_mapping:
+            new_key = head_mapping[key]
+            converted_state_dict[new_key] = value
+        # Handle quant keys using the mapping
+        elif key in quant_mapping:
+            new_key = quant_mapping[key]
+            converted_state_dict[new_key] = value
+        # Handle encoder conv1
+        elif key == "encoder.conv1.weight":
+            converted_state_dict["encoder.conv_in.weight"] = value
+        elif key == "encoder.conv1.bias":
+            converted_state_dict["encoder.conv_in.bias"] = value
+        # Handle decoder conv1
+        elif key == "decoder.conv1.weight":
+            converted_state_dict["decoder.conv_in.weight"] = value
+        elif key == "decoder.conv1.bias":
+            converted_state_dict["decoder.conv_in.bias"] = value
+        # Handle encoder downsamples
+        elif key.startswith("encoder.downsamples."):
+            # Convert to down_blocks
+            new_key = key.replace("encoder.downsamples.", "encoder.down_blocks.")
+
+            # Convert residual block naming but keep the original structure
+            if ".residual.0.gamma" in new_key:
+                new_key = new_key.replace(".residual.0.gamma", ".norm1.gamma")
+            elif ".residual.2.bias" in new_key:
+                new_key = new_key.replace(".residual.2.bias", ".conv1.bias")
+            elif ".residual.2.weight" in new_key:
+                new_key = new_key.replace(".residual.2.weight", ".conv1.weight")
+            elif ".residual.3.gamma" in new_key:
+                new_key = new_key.replace(".residual.3.gamma", ".norm2.gamma")
+            elif ".residual.6.bias" in new_key:
+                new_key = new_key.replace(".residual.6.bias", ".conv2.bias")
+            elif ".residual.6.weight" in new_key:
+                new_key = new_key.replace(".residual.6.weight", ".conv2.weight")
+            elif ".shortcut.bias" in new_key:
+                new_key = new_key.replace(".shortcut.bias", ".conv_shortcut.bias")
+            elif ".shortcut.weight" in new_key:
+                new_key = new_key.replace(".shortcut.weight", ".conv_shortcut.weight")
+
+            converted_state_dict[new_key] = value
+
+        # Handle decoder upsamples
+        elif key.startswith("decoder.upsamples."):
+            # Convert to up_blocks
+            parts = key.split(".")
+            block_idx = int(parts[2])
+
+            # Group residual blocks
+            if "residual" in key:
+                if block_idx in [0, 1, 2]:
+                    new_block_idx = 0
+                    resnet_idx = block_idx
+                elif block_idx in [4, 5, 6]:
+                    new_block_idx = 1
+                    resnet_idx = block_idx - 4
+                elif block_idx in [8, 9, 10]:
+                    new_block_idx = 2
+                    resnet_idx = block_idx - 8
+                elif block_idx in [12, 13, 14]:
+                    new_block_idx = 3
+                    resnet_idx = block_idx - 12
+                else:
+                    # Keep as is for other blocks
+                    converted_state_dict[key] = value
+                    continue
+
+                # Convert residual block naming
+                if ".residual.0.gamma" in key:
+                    new_key = f"decoder.up_blocks.{new_block_idx}.resnets.{resnet_idx}.norm1.gamma"
+                elif ".residual.2.bias" in key:
+                    new_key = f"decoder.up_blocks.{new_block_idx}.resnets.{resnet_idx}.conv1.bias"
+                elif ".residual.2.weight" in key:
+                    new_key = f"decoder.up_blocks.{new_block_idx}.resnets.{resnet_idx}.conv1.weight"
+                elif ".residual.3.gamma" in key:
+                    new_key = f"decoder.up_blocks.{new_block_idx}.resnets.{resnet_idx}.norm2.gamma"
+                elif ".residual.6.bias" in key:
+                    new_key = f"decoder.up_blocks.{new_block_idx}.resnets.{resnet_idx}.conv2.bias"
+                elif ".residual.6.weight" in key:
+                    new_key = f"decoder.up_blocks.{new_block_idx}.resnets.{resnet_idx}.conv2.weight"
+                else:
+                    new_key = key
+
+                converted_state_dict[new_key] = value
+
+            # Handle shortcut connections
+            elif ".shortcut." in key:
+                if block_idx == 4:
+                    new_key = key.replace(".shortcut.", ".resnets.0.conv_shortcut.")
+                    new_key = new_key.replace("decoder.upsamples.4", "decoder.up_blocks.1")
+                else:
+                    new_key = key.replace("decoder.upsamples.", "decoder.up_blocks.")
+                    new_key = new_key.replace(".shortcut.", ".conv_shortcut.")
+
+                converted_state_dict[new_key] = value
+
+            # Handle upsamplers
+            elif ".resample." in key or ".time_conv." in key:
+                if block_idx == 3:
+                    new_key = key.replace(f"decoder.upsamples.{block_idx}", "decoder.up_blocks.0.upsamplers.0")
+                elif block_idx == 7:
+                    new_key = key.replace(f"decoder.upsamples.{block_idx}", "decoder.up_blocks.1.upsamplers.0")
+                elif block_idx == 11:
+                    new_key = key.replace(f"decoder.upsamples.{block_idx}", "decoder.up_blocks.2.upsamplers.0")
+                else:
+                    new_key = key.replace("decoder.upsamples.", "decoder.up_blocks.")
+
+                converted_state_dict[new_key] = value
+            else:
+                new_key = key.replace("decoder.upsamples.", "decoder.up_blocks.")
+                converted_state_dict[new_key] = value
+        else:
+            # Keep other keys unchanged
+            converted_state_dict[key] = value
+
+    return converted_state_dict

src/diffusers/models/attention_processor.py

@@ -284,8 +284,9 @@ class Attention(nn.Module):
                 self.norm_added_q = RMSNorm(dim_head, eps=eps)
                 self.norm_added_k = RMSNorm(dim_head, eps=eps)
             elif qk_norm == "rms_norm_across_heads":
-                # Wanx applies qk norm across all heads
-                self.norm_added_q = RMSNorm(dim_head * heads, eps=eps)
+                # Wan applies qk norm across all heads
+                # Wan also doesn't apply a q norm
+                self.norm_added_q = None
                 self.norm_added_k = RMSNorm(dim_head * kv_heads, eps=eps)
             else:
                 raise ValueError(

src/diffusers/models/autoencoders/autoencoder_kl_wan.py

@@ -20,6 +20,7 @@ import torch.nn.functional as F
 import torch.utils.checkpoint
 
 from ...configuration_utils import ConfigMixin, register_to_config
+from ...loaders import FromOriginalModelMixin
 from ...utils import logging
 from ...utils.accelerate_utils import apply_forward_hook
 from ..activations import get_activation
@@ -655,7 +656,7 @@ class WanDecoder3d(nn.Module):
         return x
 
 
-class AutoencoderKLWan(ModelMixin, ConfigMixin):
+class AutoencoderKLWan(ModelMixin, ConfigMixin, FromOriginalModelMixin):
     r"""
     A VAE model with KL loss for encoding videos into latents and decoding latent representations into videos.
     Introduced in [Wan 2.1].

src/diffusers/models/transformers/transformer_wan.py

@@ -20,7 +20,7 @@ import torch.nn as nn
 import torch.nn.functional as F
 
 from ...configuration_utils import ConfigMixin, register_to_config
-from ...loaders import PeftAdapterMixin
+from ...loaders import FromOriginalModelMixin, PeftAdapterMixin
 from ...utils import USE_PEFT_BACKEND, logging, scale_lora_layers, unscale_lora_layers
 from ..attention import FeedForward
 from ..attention_processor import Attention
@@ -288,7 +288,7 @@ class WanTransformerBlock(nn.Module):
         return hidden_states
 
 
-class WanTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
+class WanTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin):
     r"""
     A Transformer model for video-like data used in the Wan model.
 
@@ -329,6 +329,7 @@ class WanTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
     _skip_layerwise_casting_patterns = ["patch_embedding", "condition_embedder", "norm"]
     _no_split_modules = ["WanTransformerBlock"]
     _keep_in_fp32_modules = ["time_embedder", "scale_shift_table", "norm1", "norm2", "norm3"]
+    _keys_to_ignore_on_load_unexpected = ["norm_added_q"]
 
     @register_to_config
     def __init__(

tests/single_file/test_model_wan_autoencoder_single_file.py

@@ -0,0 +1,61 @@
+# coding=utf-8
+# Copyright 2025 HuggingFace Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import gc
+import unittest
+
+from diffusers import (
+    AutoencoderKLWan,
+)
+from diffusers.utils.testing_utils import (
+    backend_empty_cache,
+    enable_full_determinism,
+    require_torch_accelerator,
+    torch_device,
+)
+
+
+enable_full_determinism()
+
+
+@require_torch_accelerator
+class AutoencoderKLWanSingleFileTests(unittest.TestCase):
+    model_class = AutoencoderKLWan
+    ckpt_path = (
+        "https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/blob/main/split_files/vae/wan_2.1_vae.safetensors"
+    )
+    repo_id = "Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
+
+    def setUp(self):
+        super().setUp()
+        gc.collect()
+        backend_empty_cache(torch_device)
+
+    def tearDown(self):
+        super().tearDown()
+        gc.collect()
+        backend_empty_cache(torch_device)
+
+    def test_single_file_components(self):
+        model = self.model_class.from_pretrained(self.repo_id, subfolder="vae")
+        model_single_file = self.model_class.from_single_file(self.ckpt_path)
+
+        PARAMS_TO_IGNORE = ["torch_dtype", "_name_or_path", "_use_default_values", "_diffusers_version"]
+        for param_name, param_value in model_single_file.config.items():
+            if param_name in PARAMS_TO_IGNORE:
+                continue
+            assert (
+                model.config[param_name] == param_value
+            ), f"{param_name} differs between single file loading and pretrained loading"

tests/single_file/test_model_wan_transformer3d_single_file.py

@@ -0,0 +1,93 @@
+# coding=utf-8
+# Copyright 2025 HuggingFace Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import gc
+import unittest
+
+import torch
+
+from diffusers import (
+    WanTransformer3DModel,
+)
+from diffusers.utils.testing_utils import (
+    backend_empty_cache,
+    enable_full_determinism,
+    require_big_gpu_with_torch_cuda,
+    require_torch_accelerator,
+    torch_device,
+)
+
+
+enable_full_determinism()
+
+
+@require_torch_accelerator
+class WanTransformer3DModelText2VideoSingleFileTest(unittest.TestCase):
+    model_class = WanTransformer3DModel
+    ckpt_path = "https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/blob/main/split_files/diffusion_models/wan2.1_t2v_1.3B_bf16.safetensors"
+    repo_id = "Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
+
+    def setUp(self):
+        super().setUp()
+        gc.collect()
+        backend_empty_cache(torch_device)
+
+    def tearDown(self):
+        super().tearDown()
+        gc.collect()
+        backend_empty_cache(torch_device)
+
+    def test_single_file_components(self):
+        model = self.model_class.from_pretrained(self.repo_id, subfolder="transformer")
+        model_single_file = self.model_class.from_single_file(self.ckpt_path)
+
+        PARAMS_TO_IGNORE = ["torch_dtype", "_name_or_path", "_use_default_values", "_diffusers_version"]
+        for param_name, param_value in model_single_file.config.items():
+            if param_name in PARAMS_TO_IGNORE:
+                continue
+            assert (
+                model.config[param_name] == param_value
+            ), f"{param_name} differs between single file loading and pretrained loading"
+
+
+@require_big_gpu_with_torch_cuda
+@require_torch_accelerator
+class WanTransformer3DModelImage2VideoSingleFileTest(unittest.TestCase):
+    model_class = WanTransformer3DModel
+    ckpt_path = "https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/blob/main/split_files/diffusion_models/wan2.1_i2v_480p_14B_fp8_e4m3fn.safetensors"
+    repo_id = "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers"
+    torch_dtype = torch.float8_e4m3fn
+
+    def setUp(self):
+        super().setUp()
+        gc.collect()
+        backend_empty_cache(torch_device)
+
+    def tearDown(self):
+        super().tearDown()
+        gc.collect()
+        backend_empty_cache(torch_device)
+
+    def test_single_file_components(self):
+        model = self.model_class.from_pretrained(self.repo_id, subfolder="transformer", torch_dtype=self.torch_dtype)
+        model_single_file = self.model_class.from_single_file(self.ckpt_path, torch_dtype=self.torch_dtype)
+
+        PARAMS_TO_IGNORE = ["torch_dtype", "_name_or_path", "_use_default_values", "_diffusers_version"]
+        for param_name, param_value in model_single_file.config.items():
+            if param_name in PARAMS_TO_IGNORE:
+                continue
+            assert (
+                model.config[param_name] == param_value
+            ), f"{param_name} differs between single file loading and pretrained loading"