[feature]: support Flux.1-dev CFG-Parallel

docs/user_guide/diffusion/parallelism_acceleration.md

@@ -30,7 +30,7 @@ The following table shows which models are currently supported by parallelism me
 | **Z-Image**              | `Tongyi-MAI/Z-Image-Turbo`           |     ✅      |    ✅    |      ❌       |  ✅ (TP=2 only)  |
 | **Stable-Diffusion3.5**  | `stabilityai/stable-diffusion-3.5`   |     ❌      |    ❌    |      ❌       |        ❌        |
 | **FLUX.2-klein**         | `black-forest-labs/FLUX.2-klein-4B`  |     ❌      |    ❌    |      ❌       |        ✅        |
-| **FLUX.1-dev**           | `black-forest-labs/FLUX.1-dev`       |     ❌      |    ❌    |      ❌       |        ✅        |
+| **FLUX.1-dev**           | `black-forest-labs/FLUX.1-dev`       |     ❌      |    ❌    |      ✅       |        ✅        |
 
 !!! note "TP Limitations for Diffusion Models"
     We currently implement Tensor Parallelism (TP) only for the DiT (Diffusion Transformer) blocks. This is because the `text_encoder` component in vLLM-Omni uses the original Transformers implementation, which does not yet support TP.

docs/user_guide/diffusion_acceleration.md

@@ -50,7 +50,7 @@ The following table shows which models are currently supported by each accelerat
 | **Z-Image** | `Tongyi-MAI/Z-Image-Turbo` | ✅ | ✅ | ❌ | ❌ | ❌ |
 | **Stable-Diffusion3.5** | `stabilityai/stable-diffusion-3.5` | ❌ | ✅ | ❌ | ❌ | ✅ |
 | **Bagel** | `ByteDance-Seed/BAGEL-7B-MoT` | ✅ | ✅ | ❌ | ❌ | ❌ |
-| **FLUX.1-dev** | `black-forest-labs/FLUX.1-dev` | ❌ | ✅ | ❌ | ❌ | ❌ |
+| **FLUX.1-dev** | `black-forest-labs/FLUX.1-dev` | ❌ | ✅ | ❌ | ❌ | ✅ |
 
 ### VideoGen
 

vllm_omni/diffusion/models/flux/pipeline_flux.py

@@ -22,6 +22,7 @@
 from vllm.model_executor.models.utils import AutoWeightsLoader
 
 from vllm_omni.diffusion.data import DiffusionOutput, OmniDiffusionConfig
+from vllm_omni.diffusion.distributed.cfg_parallel import CFGParallelMixin
 from vllm_omni.diffusion.distributed.parallel_state import get_classifier_free_guidance_world_size
 from vllm_omni.diffusion.distributed.utils import get_local_device
 from vllm_omni.diffusion.model_loader.diffusers_loader import DiffusersPipelineLoader
@@ -129,9 +130,7 @@ def retrieve_timesteps(
     return timesteps, num_inference_steps
 
 
-class FluxPipeline(
-    nn.Module,
-):
+class FluxPipeline(nn.Module, CFGParallelMixin):
     def __init__(
         self,
         *,
@@ -500,21 +499,23 @@ def interrupt(self):
 
     def diffuse(
         self,
-        prompt_embeds,
-        pooled_prompt_embeds,
-        negative_prompt_embeds,
-        negative_pooled_prompt_embeds,
-        latents,
-        latent_image_ids,
-        text_ids,
-        negative_text_ids,
-        timesteps,
-        do_true_cfg,
-        guidance,
-        true_cfg_scale,
-    ):
+        prompt_embeds: torch.Tensor,
+        pooled_prompt_embeds: torch.Tensor,
+        negative_prompt_embeds: torch.Tensor,
+        negative_pooled_prompt_embeds: torch.Tensor,
+        latents: torch.Tensor,
+        latent_image_ids: torch.Tensor,
+        text_ids: torch.Tensor,
+        negative_text_ids: torch.Tensor,
+        timesteps: torch.Tensor,
+        do_true_cfg: bool,
+        guidance: torch.Tensor,
+        true_cfg_scale: float,
+        cfg_normalize: bool = False,
+    ) -> torch.Tensor:
         """Diffusion loop with optional image conditioning."""
         self.scheduler.set_begin_index(0)
+        self.transformer.do_true_cfg = do_true_cfg
         for i, t in enumerate(timesteps):
             if self.interrupt:
                 continue
@@ -523,36 +524,46 @@ def diffuse(
             # broadcast to batch dimension and place on same device/dtype as latents
             timestep = t.expand(latents.shape[0]).to(device=latents.device, dtype=latents.dtype)
 
-            self.transformer.do_true_cfg = do_true_cfg  # used in teacache hook
-            # Forward pass for positive prompt (or unconditional if no CFG)
-            noise_pred = self.transformer(
-                hidden_states=latents,
-                timestep=timestep / 1000,
-                guidance=guidance,
-                pooled_projections=pooled_prompt_embeds,
-                encoder_hidden_states=prompt_embeds,
-                txt_ids=text_ids,
-                img_ids=latent_image_ids,
-                joint_attention_kwargs=self.joint_attention_kwargs,
-                return_dict=False,
-            )[0]
+            positive_kwargs = {
+                "hidden_states": latents,
+                "timestep": timestep / 1000,
+                "guidance": guidance,
+                "pooled_projections": pooled_prompt_embeds,
+                "encoder_hidden_states": prompt_embeds,
+                "txt_ids": text_ids,
+                "img_ids": latent_image_ids,
+                "joint_attention_kwargs": self.joint_attention_kwargs,
+                "return_dict": False,
+            }
 
             # Forward pass for negative prompt (CFG)
             if do_true_cfg:
-                neg_noise_pred = self.transformer(
-                    hidden_states=latents,
-                    timestep=timestep / 1000,
-                    guidance=guidance,
-                    pooled_projections=negative_pooled_prompt_embeds,
-                    encoder_hidden_states=negative_prompt_embeds,
-                    txt_ids=negative_text_ids,
-                    img_ids=latent_image_ids,
-                    joint_attention_kwargs=self.joint_attention_kwargs,
-                    return_dict=False,
-                )[0]
-                noise_pred = neg_noise_pred + true_cfg_scale * (noise_pred - neg_noise_pred)
+                negative_kwargs = {
+                    "hidden_states": latents,
+                    "timestep": timestep / 1000,
+                    "guidance": guidance,
+                    "pooled_projections": negative_pooled_prompt_embeds,
+                    "encoder_hidden_states": negative_prompt_embeds,
+                    "txt_ids": negative_text_ids,
+                    "img_ids": latent_image_ids,
+                    "joint_attention_kwargs": self.joint_attention_kwargs,
+                    "return_dict": False,
+                }
+            else:
+                negative_kwargs = None
+
+            # Predict noise with automatic CFG parallel handling
+            noise_pred = self.predict_noise_maybe_with_cfg(
+                do_true_cfg,
+                true_cfg_scale,
+                positive_kwargs,
+                negative_kwargs,
+                cfg_normalize,
+            )
+
             # compute the previous noisy sample x_t -> x_t-1
-            latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
+            latents = self.scheduler_step_maybe_with_cfg(noise_pred, t, latents, do_true_cfg)
+
         return latents
 
     def check_cfg_parallel_validity(self, true_cfg_scale: float, has_neg_prompt: bool):
@@ -613,6 +624,7 @@ def forward(
             req.sampling_params.guidance_scale if req.sampling_params.guidance_scale is not None else guidance_scale
         )
         generator = req.sampling_params.generator or generator
+        true_cfg_scale = req.sampling_params.true_cfg_scale or true_cfg_scale
         num_images_per_prompt = (
             req.sampling_params.num_outputs_per_prompt
             if req.sampling_params.num_outputs_per_prompt > 0
@@ -723,6 +735,7 @@ def forward(
             do_true_cfg,
             guidance,
             true_cfg_scale,
+            cfg_normalize=False,
         )
 
         self._current_timestep = None