[Bugfix][BAGEL] Restore original denoise step count (#7049)

Signed-off-by: Trigger <Meng.Bo.Wang@outlook.com>
Signed-off-by: Gao Han <hgaoaf@connect.ust.hk>
Co-authored-by: Gao Han <hgaoaf@connect.ust.hk>

docs/user_guide/diffusion/execution_modes.md

@@ -99,10 +99,10 @@ step-wise continuous batching for image generation with `bagel.yaml`,
 `bagel_think.yaml`, and `bagel_single_stage.yaml`. In the two-stage topologies,
 only the diffusion stage uses step execution; the autoregressive Thinker is
 unchanged. Explicit text-output requests in the single-stage topology retain
-the complete-request path. BAGEL schedules exactly `num_inference_steps`
-denoising updates, so one-step image requests are supported. BAGEL step
-execution does not currently support sequence parallelism or a diffusion cache
-backend. HunyuanImage3 also supports step execution, but only
+the complete-request path. Use at least two inference steps for BAGEL image
+requests because BAGEL schedules `num_inference_steps - 1` denoising updates.
+BAGEL step execution does not currently support sequence parallelism or a
+diffusion cache backend. HunyuanImage3 also supports step execution, but only
 when its resolved self-attention backend is `TORCH_SDPA`;
 set `DIFFUSION_ATTENTION_BACKEND=TORCH_SDPA` or configure
 `diffusion_attention_config.default.backend=TORCH_SDPA` before using

docs/user_guide/diffusion_features.md

@@ -152,7 +152,7 @@ The following tables show which models support each feature:
 > 3. Cosmos3 T2I uses `Cosmos3OmniDiffusersPipeline` with `modalities=["image"]`. Model-level CPU offload swaps the nested UND reasoner and GEN generator pathways; layerwise offload remains available for blockwise GEN/UND offload.
 > 4. Krea 2 currently supports single-GPU inference plus LoRA, Cache-DiT, HSDP, CPU/layerwise offload, and VAE-patch-parallel (decode). TP/SP/CFG-Parallel are not yet wired. The few-step distilled (Turbo) checkpoint uses `is_distilled=true` (fixed timestep shift `mu=1.15`); generate at 2048x2048 by default with `num_inference_steps≈8` and `guidance_scale=0`. The Raw checkpoint uses 1024x1024, `num_inference_steps=28`, and `guidance_scale=4.5`.
 > 5. HunyuanImage3 supports step execution. Multi-request step execution requires `TORCH_SDPA`; see [Diffusion Execution Modes](diffusion/execution_modes.md#step-execution).
-> 6. BAGEL step execution supports image generation with `bagel.yaml`, `bagel_think.yaml`, and `bagel_single_stage.yaml`; two-stage Thinker execution and explicit single-stage text output remain on their existing complete-request paths. BAGEL executes exactly `num_inference_steps` denoising updates, including one-step requests. BAGEL step execution cannot currently be combined with sequence parallelism or a diffusion cache backend; see [Diffusion Execution Modes](diffusion/execution_modes.md#step-execution).
+> 6. BAGEL step execution supports image generation with `bagel.yaml`, `bagel_think.yaml`, and `bagel_single_stage.yaml`; two-stage Thinker execution and explicit single-stage text output remain on their existing complete-request paths. Image requests require `num_inference_steps >= 2`. BAGEL step execution cannot currently be combined with sequence parallelism or a diffusion cache backend; see [Diffusion Execution Modes](diffusion/execution_modes.md#step-execution).
 
 ### VideoGen
 

docs/user_guide/examples/online_serving/bagel.md

@@ -98,10 +98,10 @@ vllm serve ByteDance-Seed/BAGEL-7B-MoT --omni --port 8091 \
     --stage-overrides '{"0":{"max_num_seqs":4}}'
 ```
 
-BAGEL image requests use exactly `num_inference_steps` denoising updates,
-including one-step requests. The scheduler batches only requests with
-compatible effective image sizes and BAGEL CFG/renormalization settings.
-Sequence parallelism and diffusion cache backends such as TeaCache or
+BAGEL image requests require `num_inference_steps >= 2`: the original BAGEL
+schedule uses `num_inference_steps - 1` denoising updates. The scheduler batches
+only requests with compatible effective image sizes and BAGEL CFG/renormalization
+settings. Sequence parallelism and diffusion cache backends such as TeaCache or
 Cache-DiT are not currently supported together with BAGEL step execution.
 
 ### Tensor Parallelism (TP)