Automated PR - 2026-03-05

packages/ltx-pipelines/src/ltx_pipelines/distilled.py

@@ -12,7 +12,6 @@ from ltx_core.model.upsampler import upsample_video
 from ltx_core.model.video_vae import TilingConfig, get_video_chunks_number
 from ltx_core.model.video_vae import decode_video as vae_decode_video
 from ltx_core.quantization import QuantizationPolicy
-from ltx_core.text_encoders.gemma import encode_text
 from ltx_core.types import Audio, LatentState, VideoPixelShape
 from ltx_pipelines.utils import ModelLedger, euler_denoising_loop
 from ltx_pipelines.utils.args import (
@@ -28,10 +27,10 @@ from ltx_pipelines.utils.constants import (
 from ltx_pipelines.utils.helpers import (
     assert_resolution,
     cleanup_memory,
+    combined_image_conditionings,
     denoise_audio_video,
-    generate_enhanced_prompt,
+    encode_prompts,
     get_device,
-    image_conditionings_by_replacing_latent,
     simple_denoising_func,
 )
 from ltx_pipelines.utils.media_io import encode_video
@@ -93,15 +92,13 @@ class DistilledPipeline:
         stepper = EulerDiffusionStep()
         dtype = torch.bfloat16
 
-        text_encoder = self.model_ledger.text_encoder()
-        if enhance_prompt:
-            prompt = generate_enhanced_prompt(text_encoder, prompt, images[0][0] if len(images) > 0 else None)
-        context_p = encode_text(text_encoder, prompts=[prompt])[0]
-        video_context, audio_context = context_p
-
-        torch.cuda.synchronize()
-        del text_encoder
-        cleanup_memory()
+        (ctx_p,) = encode_prompts(
+            [prompt],
+            self.model_ledger,
+            enhance_first_prompt=enhance_prompt,
+            enhance_prompt_image=images[0][0] if len(images) > 0 else None,
+        )
+        video_context, audio_context = ctx_p.video_encoding, ctx_p.audio_encoding
 
         # Stage 1: Initial low resolution video generation.
         video_encoder = self.model_ledger.video_encoder()
@@ -130,7 +127,7 @@ class DistilledPipeline:
             height=height // 2,
             fps=frame_rate,
         )
-        stage_1_conditionings = image_conditionings_by_replacing_latent(
+        stage_1_conditionings = combined_image_conditionings(
             images=images,
             height=stage_1_output_shape.height,
             width=stage_1_output_shape.width,
@@ -161,7 +158,7 @@ class DistilledPipeline:
 
         stage_2_sigmas = torch.Tensor(STAGE_2_DISTILLED_SIGMA_VALUES).to(self.device)
         stage_2_output_shape = VideoPixelShape(batch=1, frames=num_frames, width=width, height=height, fps=frame_rate)
-        stage_2_conditionings = image_conditionings_by_replacing_latent(
+        stage_2_conditionings = combined_image_conditionings(
             images=images,
             height=stage_2_output_shape.height,
             width=stage_2_output_shape.width,
@@ -209,7 +206,7 @@ def main() -> None:
         distilled_checkpoint_path=args.distilled_checkpoint_path,
         spatial_upsampler_path=args.spatial_upsampler_path,
         gemma_root=args.gemma_root,
-        loras=args.lora,
+        loras=tuple(args.lora) if args.lora else (),
         quantization=args.quantization,
     )
     tiling_config = TilingConfig.default()