[Feature] HunyuanImage3 allow guidance_scale<=1 in DiT stage (#2762)

Signed-off-by: KexiongYu <yukexiong1@huawei.com>

vllm_omni/diffusion/models/hunyuan_image3/hunyuan_image3_transformer.py

@@ -1684,7 +1684,8 @@ class HunYuanAttention(nn.Module):
         else:
             attn_output = self.attn(q, k, v)
         # For o_proj
-        attn_output = attn_output.view(q.shape[0], -1)
+        # image_attn may return a non-contiguous tensor; reshape is safe here.
+        attn_output = attn_output.reshape(q.shape[0], -1)
         output, _ = self.o_proj(attn_output)
         output = output.reshape(bsz, q_len, -1)
         return output, None, past_key_value

vllm_omni/diffusion/models/hunyuan_image3/pipeline_hunyuan_image3.py

@@ -6,7 +6,6 @@ import os
 from collections.abc import Iterable
 from typing import Any
 
-import numpy as np
 import torch
 import torch.nn as nn
 from diffusers.schedulers.scheduling_flow_match_euler_discrete import FlowMatchEulerDiscreteScheduler
@@ -544,7 +543,7 @@ class HunyuanImage3Pipeline(HunyuanImage3PreTrainedModel, GenerationMixin, Diffu
             generator = [torch.Generator(self.device).manual_seed(seed) for seed in seeds]
 
         # 3. apply chat template
-        cfg_factor = {"gen_text": 1, "gen_image": 2}
+        cfg_factor = {"gen_text": 1, "gen_image": 1 + int(guidance_scale > 1.0)}
         bot_task = kwargs.pop("bot_task", "auto")
         # If `drop_think` enabled, always drop <think> parts in the context.
         drop_think = kwargs.get("drop_think", self.generation_config.drop_think)
@@ -1009,8 +1008,7 @@ class HunyuanImage3Pipeline(HunyuanImage3PreTrainedModel, GenerationMixin, Diffu
         if req.sampling_params.guidance_scale_provided:
             guidance_scale = req.sampling_params.guidance_scale
         if guidance_scale <= 1.0:
-            logger.warning("HunyuanImage3.0 does not support guidance_scale <= 1.0, will set it to 1.0 + epsilon.")
-            guidance_scale = 1.0 + np.finfo(float).eps
+            logger.info("HunyuanImage3.0 runs without classifier-free guidance when guidance_scale <= 1.0.")
         image_size = (height, width)
         model_inputs = self.prepare_model_inputs(
             prompt=prompt,