up

optimization.py

@@ -21,7 +21,7 @@ P = ParamSpec("P")
 
 # Sequence packing in LTX is a bit of a pain.
 # See: https://github.com/huggingface/diffusers/blob/c052791b5fe29ce8a308bf63dda97aa205b729be/src/diffusers/pipelines/ltx/pipeline_ltx.py#L420
-TRANSFORMER_NUM_FRAMES_DIM = torch.export.Dim("seq_len", min=4680, max=6000)
+TRANSFORMER_NUM_FRAMES_DIM = torch.export.Dim("seq_len", min=4680, max=4680)
 
 TRANSFORMER_DYNAMIC_SHAPES = {
     "hidden_states": {1: TRANSFORMER_NUM_FRAMES_DIM},
@@ -60,7 +60,6 @@ def optimize_pipeline_(pipeline: Callable[P, Any], *args: P.args, **kwargs: P.kw
         quantize_(pipeline.transformer, Float8DynamicActivationFloat8WeightConfig())
 
         hidden_states: torch.Tensor = call.kwargs["hidden_states"]
-        print(f"{hidden_states.shape=}")
         unpacked_hidden_states = LTXConditionPipeline._unpack_latents(
             hidden_states,
             latent_num_frames,
@@ -69,7 +68,6 @@ def optimize_pipeline_(pipeline: Callable[P, Any], *args: P.args, **kwargs: P.kw
             TRANSFORMER_SPATIAL_PATCH_SIZE,
             TRANSFORMER_TEMPORAL_PATCH_SIZE,
         )
-        print(f"{unpacked_hidden_states.shape=}")
         unpacked_hidden_states_transposed = unpacked_hidden_states.transpose(-1, -2).contiguous()
         if unpacked_hidden_states.shape[-1] > hidden_states.shape[-2]:
             hidden_states_landscape = unpacked_hidden_states
@@ -84,8 +82,7 @@ def optimize_pipeline_(pipeline: Callable[P, Any], *args: P.args, **kwargs: P.kw
         hidden_states_portrait = LTXConditionPipeline._pack_latents(
             hidden_states_portrait, TRANSFORMER_SPATIAL_PATCH_SIZE, TRANSFORMER_TEMPORAL_PATCH_SIZE
         )
-        print(f"{hidden_states_landscape.shape=}, {hidden_states_portrait.shape=}")
-
+        
         exported_landscape = torch.export.export(
             mod=pipeline.transformer,
             args=call.args,