Fixed some issues with imports and the original lora.py code.

finetrainers/ltx_video/full_finetune_condition.py

@@ -3,21 +3,14 @@ from typing import Dict, List, Optional
 import torch
 import torch.nn as nn
 
-from diffusers import FlowMatchEulerDiscreteScheduler, LTXVideoTransformer3DModel
+from diffusers import FlowMatchEulerDiscreteScheduler, LTXVideoTransformer3DModel, AutoencoderKLLTXVideo
 from finetrainers.conditioning.LTXVideoConditionedTransformer3DModel import LTXVideoConditionedTransformer3DModel
 from finetrainers.conditioning.conditioned_pipeline import LTXConditionedPipeline
 from PIL import Image
-from diffusers import LTXVideoConditionedTransformer3DModel, FlowMatchEulerDiscreteScheduler
+from transformers import T5Tokenizer, T5EncoderModel
-from finetrainers.conditioning.LTXVideoConditionedTransformer3DModel import LTXVideoConditionedTransformer3DModel
-from finetrainers.conditioning.conditioned_pipeline import LTXConditionedPipeline
-from PIL import Image
-from transformers import T5Tokenizer, T5EncoderModel,AutoencoderKLLTXVideo
-
 
 # Exisiting Pipeline
-from .lora import (
+from finetrainers.ltx_video.lora import (
-    initialize_pipeline,
-    load_conditioned_diffusion_models, # use this for conditioning
     load_latent_models,
     post_latent_preparation,
     prepare_conditions,

finetrainers/ltx_video/lora.py

@@ -44,11 +44,12 @@ def load_condition_models(
     cache_dir: Optional[str] = None,
     **kwargs,
 ) -> Dict[str, nn.Module]:
-    transformer = LTXVideoConditionedTransformer3DModel.from_pretrained(
+    tokenizer = T5Tokenizer.from_pretrained(model_id, subfolder="tokenizer", revision=revision, cache_dir=cache_dir)
-        model_id, subfolder="transformer", torch_dtype=transformer_dtype, revision=revision, cache_dir=cache_dir
+    text_encoder = T5EncoderModel.from_pretrained(
+        model_id, subfolder="text_encoder", torch_dtype=text_encoder_dtype, revision=revision, cache_dir=cache_dir
     )
-    scheduler = FlowMatchEulerDiscreteScheduler()
+    return {"tokenizer": tokenizer, "text_encoder": text_encoder}
-    return {"transformer": transformer, "scheduler": scheduler}
+
 
 
 def load_diffusion_models(