diff --git a/vllm_omni/diffusion/models/qwen_image/pipeline_qwen_image.py b/vllm_omni/diffusion/models/qwen_image/pipeline_qwen_image.py
index e5b99840d..74e1823e1 100644
--- a/vllm_omni/diffusion/models/qwen_image/pipeline_qwen_image.py
+++ b/vllm_omni/diffusion/models/qwen_image/pipeline_qwen_image.py
@@ -621,19 +621,74 @@ def forward(
         callback_on_step_end_tensor_inputs: list[str] = ["latents"],
         max_sequence_length: int = 512,
     ) -> DiffusionOutput:
-        # # TODO: only support single prompt now
-        # if req.prompt is not None:
-        #     prompt = req.prompt[0] if isinstance(req.prompt, list) else req.prompt
-        prompt = req.prompt if req.prompt is not None else prompt
-        negative_prompt = req.negative_prompt if req.negative_prompt is not None else negative_prompt
-        height = req.height or self.default_sample_size * self.vae_scale_factor
-        width = req.width or self.default_sample_size * self.vae_scale_factor
-        num_inference_steps = req.num_inference_steps or num_inference_steps
-        generator = req.generator or generator
-        true_cfg_scale = req.true_cfg_scale or true_cfg_scale
-        req_num_outputs = getattr(req, "num_outputs_per_prompt", None)
+        # Handle batch of requests
+        if isinstance(req, list):
+            requests = req
+        else:
+            requests = [req]
+        
+        batch_size = len(requests)
+        
+        # Extract parameters from requests, using defaults for missing values
+        prompts = []
+        negative_prompts = []
+        heights = []
+        widths = []
+        generators = []
+        
+        for r in requests:
+            # Handle prompt
+            req_prompt = r.prompt if r.prompt is not None else prompt
+            if isinstance(req_prompt, list):
+                req_prompt = req_prompt[0] if len(req_prompt) > 0 else prompt
+            prompts.append(req_prompt)
+            
+            # Handle negative prompt
+            req_neg_prompt = r.negative_prompt if r.negative_prompt is not None else negative_prompt
+            if isinstance(req_neg_prompt, list):
+                req_neg_prompt = req_neg_prompt[0] if len(req_neg_prompt) > 0 else negative_prompt
+            negative_prompts.append(req_neg_prompt)
+            
+            # Handle height/width
+            heights.append(r.height or self.default_sample_size * self.vae_scale_factor)
+            widths.append(r.width or self.default_sample_size * self.vae_scale_factor)
+            
+            # Handle generator
+            generators.append(r.generator if r.generator is not None else None)
+        
+        # For batch processing, we require all images to have the same dimensions
+        # Use the first request's dimensions as the batch dimensions
+        height = heights[0]
+        width = widths[0]
+        
+        # Validate that all requests have the same dimensions for batch processing
+        if not all(h == height for h in heights) or not all(w == width for w in widths):
+            logger.warning(
+                "Batch processing requires all requests to have the same height and width. "
+                "Using dimensions from the first request."
+            )
+        
+        # Use parameters from the first request for shared settings
+        first_req = requests[0]
+        num_inference_steps = first_req.num_inference_steps or num_inference_steps
+        true_cfg_scale = first_req.true_cfg_scale or true_cfg_scale
+        req_num_outputs = getattr(first_req, "num_outputs_per_prompt", None)
         if req_num_outputs and req_num_outputs > 0:
             num_images_per_prompt = req_num_outputs
+        
+        # Handle generator for batch
+        if any(g is not None for g in generators):
+            # Filter out None generators and use the list
+            generator = [g for g in generators if g is not None]
+            if len(generator) == 1:
+                generator = generator[0]
+            elif len(generator) == 0:
+                generator = None
+        
+        # Use the batch prompts
+        prompt = prompts
+        negative_prompt = negative_prompts
+        
         # 1. check inputs
         # 2. encode prompts
         # 3. prepare latents and timesteps
@@ -668,6 +723,16 @@ def forward(
         has_neg_prompt = negative_prompt is not None or (
             negative_prompt_embeds is not None and negative_prompt_embeds_mask is not None
         )
+        
+        # Check if negative_prompt is a non-empty list or non-empty string
+        if isinstance(negative_prompt, list):
+            has_neg_prompt = any(np for np in negative_prompt) or (
+                negative_prompt_embeds is not None and negative_prompt_embeds_mask is not None
+            )
+        elif isinstance(negative_prompt, str):
+            has_neg_prompt = bool(negative_prompt) or (
+                negative_prompt_embeds is not None and negative_prompt_embeds_mask is not None
+            )
 
         do_true_cfg = true_cfg_scale > 1 and has_neg_prompt
         prompt_embeds, prompt_embeds_mask = self.encode_prompt(
@@ -697,15 +762,14 @@ def forward(
             generator,
             latents,
         )
-        img_shapes = [
-            [
-                (
-                    1,
-                    height // self.vae_scale_factor // 2,
-                    width // self.vae_scale_factor // 2,
-                )
-            ]
-        ] * batch_size
+        
+        # Prepare img_shapes for batch processing
+        img_shape = (
+            1,
+            height // self.vae_scale_factor // 2,
+            width // self.vae_scale_factor // 2,
+        )
+        img_shapes = [[img_shape]] * (batch_size * num_images_per_prompt)
 
         timesteps, num_inference_steps = self.prepare_timesteps(num_inference_steps, sigmas, latents.shape[1])
         # num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)