Comment out unused imports.

src/diffusers/models/attention_processor.py

@@ -1029,19 +1029,8 @@ def __call__(
         query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
         key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
         value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-<<<<<<< HEAD
 
         hidden_states = F.scaled_dot_product_attention(query, key, value, dropout_p=0.0, is_causal=False)
-=======
-        # trace_tensor("query", query[0,0,0])
-        # trace_tensor("key", key[0,0,0])
-        # trace_tensor("value", value[0,0,0])
-        hidden_states = hidden_states = F.scaled_dot_product_attention(
-            query, key, value, dropout_p=0.0, is_causal=False
-        )
-        #trace_tensor("attn_out", hidden_states[0,0,0,0])
-
->>>>>>> ea85c86bc (SD3 bringup)
         hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
         hidden_states = hidden_states.to(query.dtype)
 
@@ -1051,7 +1040,6 @@ def __call__(
             hidden_states[:, residual.shape[1] :],
         )
         hidden_states_cl = hidden_states.clone()
-        #trace_tensor("attn_out", hidden_states_cl[0,0,0])
         # linear proj
         hidden_states = attn.to_out[0](hidden_states_cl)
         # dropout

src/diffusers/models/embeddings.py

@@ -23,6 +23,8 @@
 from .activations import FP32SiLU, get_activation
 from .attention_processor import Attention
 
+from shark_turbine.ops.iree import trace_tensor
+
 
 def get_timestep_embedding(
     timesteps: torch.Tensor,

src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py

@@ -813,7 +813,8 @@ def __call__(
         if self.do_classifier_free_guidance:
             prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
             pooled_prompt_embeds = torch.cat([negative_pooled_prompt_embeds, pooled_prompt_embeds], dim=0)
-
+        print("prompt_embeds", prompt_embeds)
+        print("pooled_prompt_embeds", pooled_prompt_embeds)
         # 4. Prepare timesteps
         timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)
         num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
@@ -831,6 +832,8 @@ def __call__(
             generator,
             latents,
         )
+        print(latents)
+        print(timesteps)
 
         # 6. Denoising loop
         with self.progress_bar(total=num_inference_steps) as progress_bar:

src/diffusers/schedulers/scheduling_euler_ancestral_discrete.py

@@ -23,7 +23,7 @@
 from ..utils import BaseOutput, logging
 from ..utils.torch_utils import randn_tensor
 from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
-from shark_turbine.ops.iree import trace_tensor
+#from shark_turbine.ops.iree import trace_tensor
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 

src/diffusers/schedulers/scheduling_pndm.py

@@ -22,7 +22,7 @@
 
 from ..configuration_utils import ConfigMixin, register_to_config
 from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
-from shark_turbine.ops.iree import trace_tensor
+#from shark_turbine.ops.iree import trace_tensor
 
 # Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar
 def betas_for_alpha_bar(