Update optimization.py

optimization.py

@@ -52,24 +52,26 @@ def optimize_pipeline_(pipeline: Callable[P, Any], *args: P.args, **kwargs: P.kw
     # --- LoRA 2: lightning_2 (loads into transformer_2) ---
     kwargs_lora = {"load_into_transformer_2": True}
     pipeline.load_lora_weights(
-        "lightx2v/Wan2.2-Lightning",
-        weight_name="Wan2.2-T2V-A14B-4steps-lora-rank64-Seko-V1/low_noise_model.safetensors",
+        "deadman44/Wan2.2_T2i_T2v_LoRA",
+        weight_name="lora_wan2.2_myjd_Low_v01.safetensors",
         adapter_name="lightning_2",
         **kwargs_lora,
     )
 
     # --- LoRA 3: orbit_shot (the ostris repo you asked for) ---
     # Load into transformer_2 as well (set load_into_transformer_2 True if this adapter targets transformer_2)
-    pipeline.load_lora_weights(
-        "ostris/wan22_i2v_14b_orbit_shot_lora",
-        weight_name="wan22_14b_i2v_orbit_low_noise.safetensors",
-        adapter_name="orbit_shot",
-        **kwargs_lora,
-    )
+    # pipeline.load_lora_weights(
+    #     "ostris/wan22_i2v_14b_orbit_shot_lora",
+    #     weight_name="wan22_14b_i2v_orbit_low_noise.safetensors",
+    #     adapter_name="orbit_shot",
+    #     **kwargs_lora,
+    # )
 
     # Register adapters and their relative weights
     # (adjust adapter_weights to taste; here each is weight 1.0)
-    pipeline.set_adapters(["lightning", "lightning_2", "orbit_shot"], adapter_weights=[1.0, 1.0, 1.0])
+    #pipeline.set_adapters(["lightning", "lightning_2", "orbit_shot"], adapter_weights=[1.0, 1.0, 1.0])
+    pipeline.set_adapters(["lightning", "lightning_2", "orbit_shot"], adapter_weights=[1., 1.])
+
 
     # Fuse each adapter into the correct component with chosen lora_scale:
     # - lightning -> transformer (boosted by 3x in your original)
@@ -77,7 +79,7 @@ def optimize_pipeline_(pipeline: Callable[P, Any], *args: P.args, **kwargs: P.kw
     # - orbit_shot -> transformer_2 (or transformer depending on the LoRA design)
     pipeline.fuse_lora(adapter_names=["lightning"], lora_scale=3.0, components=["transformer"])
     pipeline.fuse_lora(adapter_names=["lightning_2"], lora_scale=1.0, components=["transformer_2"])
-    pipeline.fuse_lora(adapter_names=["orbit_shot"], lora_scale=1.0, components=["transformer_2"])
+    #pipeline.fuse_lora(adapter_names=["orbit_shot"], lora_scale=1.0, components=["transformer_2"])
 
     # After fusing, you can unload LoRA weights to free memory (fused weights remain applied)
     pipeline.unload_lora_weights()