Update app.py

app.py

@@ -275,8 +275,8 @@ PENDING_LORA_STATUS: str = "No LoRA state prepared yet."
 weights_dir = Path("weights")
 weights_dir.mkdir(exist_ok=True)
 checkpoint_path = hf_hub_download(
-    repo_id="ibyteohdear/Lightricks-LTX-2.3-DISTILLED-10-Eros-v1.2",
-    filename="LTX2.3_DISTILLED_1.1_BAKED_v12_LoRA_fro99-avgrank91.safetensors",
+    repo_id="TenStrip/LTX2.3-10Eros",
+    filename="10Eros_v1.2_bf16.safetensors",
     local_dir=str(weights_dir),
     local_dir_use_symlinks=False,
 )
@@ -292,7 +292,7 @@ LORA_REPO = "dagloop5/LoRA"
 print("=" * 80)
 print("Downloading LoRA adapters from dagloop5/LoRA...")
 print("=" * 80)
-singularity_lora_path = hf_hub_download(repo_id="TenStrip/LTX2.3_JoyAI_Lora_Extracted", filename="JoyAI-Echo_r256.safetensors")
+singularity_lora_path = hf_hub_download(repo_id="TenStrip/LTX2.3_DMD_Lora", filename="LTX2.3_DMD_reshaped_r256.safetensors")
 teneros_lora_path = hf_hub_download(repo_id=LORA_REPO, filename="LTX2.3-Furry-2D-NSFW-Multi-Purpose-Lora+Cum.safetensors")
 sulphur_lora_path =hf_hub_download(repo_id="SulphurAI/Sulphur-2-base", filename="sulphur_lora_rank_768.safetensors")
 pose_lora_path = hf_hub_download(repo_id=LORA_REPO, filename="LTX2_3_NSFW_furry_concat_v2.safetensors")
@@ -354,7 +354,7 @@ pipeline = LTX23DistilledA2VPipeline(
     spatial_upsampler_path=spatial_upsampler_path,
     gemma_root=gemma_root,
     loras=[],
-    quantization=QuantizationPolicy.fp8_cast(),  # keep FP8 quantization unchanged
+    quantization=None,  # keep FP8 quantization unchanged
 )
 # ----------------------------------------------------------------
 
@@ -489,7 +489,7 @@ def prepare_lora_cache(
             spatial_upsampler_path=str(spatial_upsampler_path),
             gemma_root_path=str(gemma_root),
             loras=tuple(loras_for_builder),
-            quantization=getattr(ledger, "quantization", None),
+            quantization=None,
         )
         new_transformer_cpu = tmp_ledger.transformer()
 
@@ -824,7 +824,7 @@ with gr.Blocks(title="LTX-2.3 Distilled") as demo:
                 with gr.Column():
                     gr.Markdown("### LoRA adapter strengths (set to 0 to disable; slow and WIP)")
                     singularity_strength = gr.Slider(
-                        label="Singularity Lora strength",
+                        label="Distilled Lora strength",
                         minimum=0.0, maximum=2.0, value=0.0, step=0.01
                     )
                     teneros_strength = gr.Slider(