[XPU][HunyuanImage3] Fix HunyuanImage-3.0 text-to-image on XPU (#7674)

Signed-off-by: Joshna Medisetty <joshna.medisetty@intel.com>

vllm_omni/deploy/hunyuan_image_3_moe.yaml

@@ -106,11 +106,14 @@ platforms:
 
   xpu:
     stages:
+      # Both stages stay resident, so they cannot share cards.
       - stage_id: 0
         gpu_memory_utilization: 0.95
-        devices: "0,1,2,3,4,5,6,7"
-        tensor_parallel_size: 8
-        max_num_batched_tokens: 32784
+        devices: "0,1,2,3"
+        tensor_parallel_size: 4
+        # fp8 AR weights leave under 1 GiB for KV; 22800 tokens will not fit.
+        max_num_batched_tokens: 8192
+        max_model_len: 8192
         quantization: fp8
         enable_expert_parallel: true
         worker_cls: vllm_omni.platforms.xpu.worker.xpu_ar_worker.XPUARWorker
@@ -120,12 +123,12 @@ platforms:
           repetition_penalty: 1.1
       - stage_id: 1
         gpu_memory_utilization: 0.9
-        devices: "0,1,2,3,4,5,6,7"
+        devices: "4,5,6,7"
         quantization: fp8
         parallel_config:
           pipeline_parallel_size: 1
           data_parallel_size: 1
-          tensor_parallel_size: 8
+          tensor_parallel_size: 4
           enable_expert_parallel: true
           sequence_parallel_size: 1
           ulysses_degree: 1

vllm_omni/diffusion/models/hunyuan_image3/hunyuan_image3_transformer.py

@@ -3040,7 +3040,7 @@ class HunyuanImage3Text2ImagePipeline(DiffusionPipeline):
 
         # 3. negative cfg prefill
         # For CFG distilled models, skip negative CFG prefill (cfg_factor=1, no negative prompt)
-        if self.do_classifier_free_guidance and not self.model.config.cfg_distilled:
+        if self.do_classifier_free_guidance and not getattr(self.model.config, "cfg_distilled", False):
             self._maybe_run_negative_cfg_prefill(
                 input_ids=input_ids,
                 model_kwargs=model_kwargs,
@@ -3151,7 +3151,7 @@ class HunyuanImage3Text2ImagePipeline(DiffusionPipeline):
         # For CFG distilled models, skip CFG parallel (cfg_factor=1, no negative branch)
         cfg_parallel_ready = (
             self.do_classifier_free_guidance
-            and not self.model.config.cfg_distilled
+            and not getattr(self.model.config, "cfg_distilled", False)
             and get_classifier_free_guidance_world_size() == 2
         )
 
@@ -3207,7 +3207,7 @@ class HunyuanImage3Text2ImagePipeline(DiffusionPipeline):
             self._split_model_kwargs_for_cfg_parallel(model_kwargs, batch_size, cfg_rank)
         else:
             # For CFG distilled models, cfg_factor is always 1 (CFG embedded in model)
-            if self.model.config.cfg_distilled:
+            if getattr(self.model.config, "cfg_distilled", False):
                 cfg_factor = 1
             else:
                 cfg_factor = 1 + self.do_classifier_free_guidance
@@ -3252,7 +3252,7 @@ class HunyuanImage3Text2ImagePipeline(DiffusionPipeline):
                     # Sequential CFG: double the batch
                     latent_model_input = torch.cat([latents] * cfg_factor)
 
-                if self.model.config.use_meanflow:
+                if getattr(self.model.config, "use_meanflow", False):
                     r = self.scheduler.get_timestep_r(t)
                     r_expand = r.repeat(latent_model_input.shape[0])
                 else:
@@ -3281,7 +3281,7 @@ class HunyuanImage3Text2ImagePipeline(DiffusionPipeline):
 
                 if should_compute:
                     # Handle guidance for CFG distilled models
-                    if self.model.config.cfg_distilled:
+                    if getattr(self.model.config, "cfg_distilled", False):
                         model_kwargs["guidance"] = torch.tensor(
                             [1000.0 * self._guidance_scale],
                             device=self.device,
@@ -3308,7 +3308,7 @@ class HunyuanImage3Text2ImagePipeline(DiffusionPipeline):
                 # Perform guidance
                 # For CFG distilled models, guidance is already embedded in the model,
                 # so we skip the explicit CFG computation
-                if self.model.config.cfg_distilled:
+                if getattr(self.model.config, "cfg_distilled", False):
                     # CFG distilled: guidance is handled internally via guidance_emb
                     pass
                 elif cfg_parallel_ready:

vllm_omni/diffusion/models/hunyuan_image3/pipeline_hunyuan_image3.py

@@ -338,6 +338,9 @@ class HunyuanImage3Pipeline(
     supports_step_execution: ClassVar[bool] = True
     supports_request_batch = False
     support_image_input = True
+    # The warmup request's blank image routes this AR+diffusion model down its
+    # image-edit path, where the token block cannot match the latent patch grid.
+    dummy_run_num_frames: ClassVar[int] = 0
     _dit_modules: ClassVar[list[str]] = ["model"]
     _encoder_modules: ClassVar[list[str]] = ["vision_model"]
     _vae_modules: ClassVar[list[str]] = ["vae"]

vllm_omni/model_executor/models/hunyuan_image3/hunyuan_image3.py

@@ -1557,7 +1557,11 @@ class HunyuanImage3ForConditionalGeneration(nn.Module, SupportsMultiModal, Suppo
         self._end_ratio_id = tokenizer.convert_tokens_to_ids("<img_ratio_32>")
         ratio_33 = tokenizer.convert_tokens_to_ids("<img_ratio_33>")
         ratio_36 = tokenizer.convert_tokens_to_ids("<img_ratio_36>")
-        self._ratio_other_slices = [(ratio_33, ratio_36 + 1)]
+        # The base checkpoint stops at <img_ratio_32>; only Instruct has 33-36.
+        if ratio_33 is None or ratio_36 is None:
+            self._ratio_other_slices = []
+        else:
+            self._ratio_other_slices = [(ratio_33, ratio_36 + 1)]
         # Build the full set of ratio token IDs for use as stop tokens.
         self._all_ratio_ids = set(range(self._start_ratio_id, self._end_ratio_id + 1))
         for s, e in self._ratio_other_slices: