Refactor Wan model imports and update script paths in pyproject.toml; transition from wan to wan2 module structure for improved organization and clarity.

mlx_video/models/wan2/generate.py

@@ -11,15 +11,15 @@ import mlx.core as mx
 import numpy as np
 from tqdm import tqdm
 
-from mlx_video.models.wan.i2v_utils import build_i2v_mask, preprocess_image
-from mlx_video.models.wan.loading import (
+from mlx_video.models.wan2.i2v_utils import build_i2v_mask, preprocess_image
+from mlx_video.models.wan2.utils import (
     encode_text,
     load_t5_encoder,
     load_vae_decoder,
     load_vae_encoder,
     load_wan_model,
 )
-from mlx_video.models.wan.postprocess import save_video
+from mlx_video.models.wan2.postprocess import save_video
 
 
 class Colors:
@@ -121,8 +121,8 @@ def generate_video(
     """
     import json
 
-    from mlx_video.models.wan.config import WanModelConfig
-    from mlx_video.models.wan.scheduler import (
+    from mlx_video.models.wan2.config import WanModelConfig
+    from mlx_video.models.wan2.scheduler import (
         FlowDPMPP2MScheduler,
         FlowMatchEulerScheduler,
         FlowUniPCScheduler,
@@ -729,7 +729,7 @@ def generate_video(
     # the CausalConv3d zero-padding artifacts fall on the prefix (which we crop).
     # This gives the first real frame a full temporal receptive field of real data.
     # Select tiling configuration
-    from mlx_video.models.ltx.video_vae.tiling import TilingConfig
+    from mlx_video.models.ltx_2.video_vae.tiling import TilingConfig
 
     if tiling == "none":
         tiling_config = None
@@ -767,7 +767,7 @@ def generate_video(
         )
 
     if is_wan22_vae:
-        from mlx_video.models.wan.vae22 import denormalize_latents
+        from mlx_video.models.wan2.vae22 import denormalize_latents
 
         # latents: [C, T, H, W] → [1, T, H, W, C] (channels-last for Wan2.2 VAE)
         z = latents.transpose(1, 2, 3, 0)[None]