Add image-to-video (I2V) conditioning support

- Introduced `load_image`, `prepare_image_for_encoding`, and `apply_conditioning` functions for handling image inputs and conditioning during video generation.
- Enhanced `generate_video` and `denoise_av` functions to accept optional image inputs for I2V conditioning.
- Updated command-line interface to include parameters for image conditioning, such as `--image`, `--image-strength`, and `--image-frame-idx`.
- Added new `VideoConditionByLatentIndex` and `LatentState` classes for managing latent states with conditioning.
- Implemented VAE encoder loading and image encoding for conditioning in the video generation process.d

mlx_video/models/ltx/video_vae/resnet.py

@@ -141,9 +141,10 @@ class UNetMidBlock3D(nn.Module):
 
         self.num_layers = num_layers
 
-        # Create ResNet blocks
-        self.resnets = [
-            ResnetBlock3D(
+        # Create ResNet blocks - use dict for MLX parameter tracking
+        # Named res_blocks to match PyTorch weight keys
+        self.res_blocks = {
+            i: ResnetBlock3D(
                 dims=dims,
                 in_channels=in_channels,
                 out_channels=in_channels,
@@ -154,8 +155,8 @@ class UNetMidBlock3D(nn.Module):
                 timestep_conditioning=timestep_conditioning,
                 spatial_padding_mode=spatial_padding_mode,
             )
-            for _ in range(num_layers)
-        ]
+            for i in range(num_layers)
+        }
 
     def __call__(
         self,
@@ -164,8 +165,8 @@ class UNetMidBlock3D(nn.Module):
         timestep: Optional[mx.array] = None,
         generator: Optional[int] = None,
     ) -> mx.array:
-        
-        for resnet in self.resnets:
+
+        for resnet in self.res_blocks.values():
             x = resnet(x, causal=causal, generator=generator)
 
         return x