diff --git a/nodes.py b/nodes.py
index 227545e..9053168 100644
--- a/nodes.py
+++ b/nodes.py
@@ -65,7 +65,14 @@ class DownloadAndLoadCogVideoModel:
                 local_dir_use_symlinks=False,
             )
         if fp8_transformer == "enabled" or fp8_transformer == "fastmode":
-            transformer = CogVideoXTransformer3DModel.from_pretrained(base_path, subfolder="transformer").to(torch.float8_e4m3fn).to(offload_device)
+            transformer = CogVideoXTransformer3DModel.from_pretrained(base_path, subfolder="transformer").to(offload_device)
+            if "2b" in model:
+                for name, param in transformer.named_parameters():
+                    if name != "pos_embedding":
+                        param.data = param.data.to(torch.float8_e4m3fn)
+            else:
+                transformer.to(torch.float8_e4m3fn)
+
             if fp8_transformer == "fastmode":
                 from .fp8_optimization import convert_fp8_linear
                 convert_fp8_linear(transformer, dtype)
@@ -84,12 +91,13 @@ class DownloadAndLoadCogVideoModel:
             pipe.transformer.to(memory_format=torch.channels_last)
             pipe.transformer = torch.compile(pipe.transformer, mode="max-autotune", fullgraph=True)
         elif compile == "onediff":
-            from onediffx import compile_pipe
-            options = None
+            from onediffx import compile_pipe, quantize_pipe
+            os.environ['NEXFORT_FX_FORCE_TRITON_SDPA'] = '1'
+            
             pipe = compile_pipe(
             pipe,
             backend="nexfort",
-            options=options,
+            options= {"mode": "max-optimize:max-autotune:max-autotune", "memory_format": "channels_last", "options": {"inductor.optimize_linear_epilogue": False, "triton.fuse_attention_allow_fp16_reduction": False}},
             ignores=["vae"],
             fuse_qkv_projections=True,
             )