[Bugfix] Fix for only one DIT worker and a separate VAE worker.

examples/ray/ray_hunyuandit_example.py

@@ -43,7 +43,6 @@ def main():
     torch.cuda.reset_peak_memory_stats()
     start_time = time.time()
     output = pipe(
-        PipelineClass=xFuserHunyuanDiTPipeline,
         height=input_config.height,
         width=input_config.width,
         prompt=input_config.prompt,

examples/ray/ray_pixartsigma_example.py

@@ -41,7 +41,6 @@ def main():
     torch.cuda.reset_peak_memory_stats()
     start_time = time.time()
     output = pipe(
-        PipelineClass=xFuserPixArtSigmaPipeline,
         height=input_config.height,
         width=input_config.width,
         prompt=input_config.prompt,

examples/ray/ray_run.sh

@@ -32,10 +32,10 @@ mkdir -p ./results
 TASK_ARGS="--height 1024 --width 1024 --no_use_resolution_binning"
 
 
-N_GPUS=3 # world size
-PARALLEL_ARGS="--pipefusion_parallel_degree 2 --ulysses_degree 1 --ring_degree 1"
+N_GPUS=2 # world size
+PARALLEL_ARGS="--pipefusion_parallel_degree 1 --ulysses_degree 1 --ring_degree 1"
 VAE_PARALLEL_SIZE=1
-DIT_PARALLEL_SIZE=2
+DIT_PARALLEL_SIZE=1
 # CFG_ARGS="--use_cfg_parallel"
 
 # By default, num_pipeline_patch = pipefusion_degree, and you can tune this parameter to achieve optimal performance.

xfuser/model_executor/pipelines/base_pipeline.py

@@ -144,6 +144,7 @@ def __init__(
         engine_config: EngineConfig,
     ):
         self.module: DiffusionPipeline
+        self.engine_config = engine_config
         self._init_runtime_state(pipeline=pipeline, engine_config=engine_config)
         self._init_fast_attn_state(pipeline=pipeline, engine_config=engine_config)
 
@@ -256,6 +257,7 @@ def use_naive_forward(self):
                 and get_sequence_parallel_world_size() == 1
                 and get_tensor_model_parallel_world_size() == 1
                 and get_fast_attn_enable() == False
+                and get_runtime_state().parallel_config.vae_parallel_size == 0
             )
 
     @staticmethod

xfuser/model_executor/pipelines/pipeline_flux.py

@@ -772,7 +772,7 @@ def _backbone_forward(
         # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
         timestep = t.expand(latents.shape[0]).to(latents.dtype)
 
-        noise_pred, encoder_hidden_states = self.transformer(
+        ret = self.transformer(
             hidden_states=latents,
             timestep=timestep / 1000,
             guidance=guidance,
@@ -783,7 +783,10 @@ def _backbone_forward(
             joint_attention_kwargs=self.joint_attention_kwargs,
             return_dict=False,
         )[0]
-
+        if self.engine_config.parallel_config.dit_parallel_size > 1:
+            noise_pred, encoder_hidden_states = ret
+        else:
+            noise_pred, encoder_hidden_states = ret, None
         return noise_pred, encoder_hidden_states
 
     def _scheduler_step(

xfuser/model_executor/pipelines/pipeline_hunyuandit.py

@@ -903,19 +903,33 @@ def _backbone_forward(
         )
 
         # predict the noise residual
-        noise_pred = self.transformer(
-            latents,
-            t_expand,
-            encoder_hidden_states=prompt_embeds,
-            text_embedding_mask=prompt_attention_mask,
-            encoder_hidden_states_t5=prompt_embeds_2,
-            text_embedding_mask_t5=prompt_attention_mask_2,
-            image_meta_size=add_time_ids,
-            style=style,
-            image_rotary_emb=image_rotary_emb,
-            skips=skips,
-            return_dict=False,
-        )[0]
+        if skips is not None:
+            noise_pred = self.transformer(
+                latents,
+                t_expand,
+                encoder_hidden_states=prompt_embeds,
+                text_embedding_mask=prompt_attention_mask,
+                encoder_hidden_states_t5=prompt_embeds_2,
+                text_embedding_mask_t5=prompt_attention_mask_2,
+                image_meta_size=add_time_ids,
+                style=style,
+                image_rotary_emb=image_rotary_emb,
+                skips=skips,
+                return_dict=False,
+            )[0]
+        else:
+            noise_pred = self.transformer(
+                latents,
+                t_expand,
+                encoder_hidden_states=prompt_embeds,
+                text_embedding_mask=prompt_attention_mask,
+                encoder_hidden_states_t5=prompt_embeds_2,
+                text_embedding_mask_t5=prompt_attention_mask_2,
+                image_meta_size=add_time_ids,
+                style=style,
+                image_rotary_emb=image_rotary_emb,
+                return_dict=False,
+            )[0]
 
         if is_pipeline_last_stage():
             noise_pred, _ = noise_pred.chunk(2, dim=1)

xfuser/model_executor/pipelines/pipeline_stable_diffusion_3.py

@@ -773,14 +773,18 @@ def _backbone_forward(
         # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
         timestep = t.expand(latents.shape[0])
 
-        noise_pred, encoder_hidden_states = self.transformer(
+        ret = self.transformer(
             hidden_states=latents,
             timestep=timestep,
             encoder_hidden_states=encoder_hidden_states,
             pooled_projections=pooled_prompt_embeds,
             joint_attention_kwargs=self.joint_attention_kwargs,
             return_dict=False,
         )[0]
+        if self.engine_config.parallel_config.dit_parallel_size > 1:
+            noise_pred, encoder_hidden_states = ret
+        else:
+            noise_pred, encoder_hidden_states = ret, None
 
         # classifier free guidance
         if is_pipeline_last_stage():