replaced the pipeline functions.

time to test this then test the weight changes.

finetrainers/conditioning/LTXVideoConditionedTransformer3DModel.py

@@ -36,7 +36,6 @@ class LTXVideoConditionedTransformer3DModel(LTXVideoTransformer3DModel):
             adapter_dropout=0.1,
             adapter_init_scale=1e-3
         )
-        self.adapter.requires_grad_(True)
 
         super().__init__(
             in_channels=in_channels,
@@ -59,24 +58,21 @@ class LTXVideoConditionedTransformer3DModel(LTXVideoTransformer3DModel):
     @classmethod
     def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
         model = super().from_pretrained(pretrained_model_name_or_path, **kwargs)
-        # model is now a LTXVideoTransformer3DModel instance; 
-        # but we want an instance of LTXVideoTransformer3DModelWithAdapter
-        # so we often do something like:
         new_model = cls(**model.config.__dict__)
         new_model.load_state_dict(model.state_dict())
         return new_model
 
-    def save_pretrained(self, save_directory: str, **kwargs):
-        """
-        Saves model weights (including adapter) + config to disk
-        in a format compatible with .from_pretrained()
-        """
-        # 1) Save config
-        self.config.save_pretrained(save_directory)
+    # def save_pretrained(self, save_directory: str, **kwargs):
+    #     """
+    #     Saves model weights (including adapter) + config to disk
+    #     in a format compatible with .from_pretrained()
+    #     """
+    #     # 1) Save config
+    #     self.config.save_pretrained(save_directory)
 
-        # 2) Save PyTorch state dict
-        state_dict = self.state_dict()
-        torch.save(state_dict, os.path.join(save_directory, "pytorch_model.bin"))
+    #     # 2) Save PyTorch state dict
+    #     state_dict = self.state_dict()
+    #     torch.save(state_dict, os.path.join(save_directory, "pytorch_model.bin"))
 
 def forward(
         self,

finetrainers/ltx_video/full_finetune.py

@@ -6,6 +6,7 @@ from .lora import (
     initialize_pipeline,
     load_condition_models,
     load_diffusion_models,
+    load_conditioned_diffusion_models,
     load_latent_models,
     post_latent_preparation,
     prepare_conditions,
@@ -20,7 +21,7 @@ LTX_VIDEO_T2V_FULL_FINETUNE_CONFIG = {
     "pipeline_cls": LTXPipeline,
     "load_condition_models": load_condition_models,
     "load_latent_models": load_latent_models,
-    "load_diffusion_models": load_diffusion_models,
+    "load_diffusion_models": load_conditioned_diffusion_models,
     "initialize_pipeline": initialize_pipeline,
     "prepare_conditions": prepare_conditions,
     "prepare_latents": prepare_latents,

finetrainers/ltx_video/lora.py

@@ -4,6 +4,7 @@ import torch
 import torch.nn as nn
 from accelerate.logging import get_logger
 from diffusers import AutoencoderKLLTXVideo, FlowMatchEulerDiscreteScheduler, LTXPipeline, LTXVideoTransformer3DModel
+from finetrainers.conditioning import LTXVideoConditionedTransformer3DModel
 from PIL import Image
 from transformers import T5EncoderModel, T5Tokenizer
 
@@ -37,6 +38,19 @@ def load_latent_models(
     )
     return {"vae": vae}
 
+def load_conditioned_diffusion_models(
+    model_id: str = "Lightricks/LTX-Video",
+    transformer_dtype: torch.dtype = torch.bfloat16,
+    revision: Optional[str] = None,
+    cache_dir: Optional[str] = None,
+    **kwargs,
+) -> Dict[str, nn.Module]:
+    transformer = LTXVideoConditionedTransformer3DModel.from_pretrained(
+        model_id, subfolder="transformer", torch_dtype=transformer_dtype, revision=revision, cache_dir=cache_dir
+    )
+    scheduler = FlowMatchEulerDiscreteScheduler()
+    return {"transformer": transformer, "scheduler": scheduler}
+
 
 def load_diffusion_models(
     model_id: str = "Lightricks/LTX-Video",