[Model]Add new nextstep_1(Diffusion) model(only T2I)

docs/models/supported_models.md

@@ -40,6 +40,7 @@ th {
 |`Qwen3TTSForConditionalGeneration` | Qwen3-TTS-12Hz-1.7B-CustomVoice | `Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice` |
 |`Qwen3TTSForConditionalGeneration` | Qwen3-TTS-12Hz-1.7B-VoiceDesign | `Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign` |
 |`Qwen3TTSForConditionalGeneration` | Qwen3-TTS-12Hz-1.7B-Base | `Qwen/Qwen3-TTS-12Hz-0.6B-Base` |
+|`NextStep11Pipeline` | NextStep-1.1 | `stepfun-ai/NextStep-1.1` |
 
 
 ## List of Supported Models for NPU

docs/user_guide/diffusion_acceleration.md

@@ -65,6 +65,7 @@ The following table shows which models are currently supported by each accelerat
 | **Stable-Diffusion3.5** | `stabilityai/stable-diffusion-3.5` | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ |
 | **Bagel** | `ByteDance-Seed/BAGEL-7B-MoT` | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ |
 | **FLUX.1-dev** | `black-forest-labs/FLUX.1-dev` | ❌ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ |
+| **NextStep-1.1** | `stepfun-ai/NextStep-1.1` | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ |
 
 ### VideoGen
 

examples/offline_inference/text_to_image/README.md

@@ -1,6 +1,6 @@
 # Text-To-Image
 
-This folder provides several entrypoints for experimenting with `Qwen/Qwen-Image` `Qwen/Qwen-Image-2512` `Tongyi-MAI/Z-Image-Turbo` using vLLM-Omni:
+This folder provides several entrypoints for experimenting with `Qwen/Qwen-Image` `Qwen/Qwen-Image-2512` `Tongyi-MAI/Z-Image-Turbo` `stepfun-ai/NextStep-1.1` using vLLM-Omni, note that NextStep-1.1 has different architecture so we treat it differently regarding running arguments and pipeline.
 
 - `text_to_image.py`: command-line script for single image generation with advanced options.
 - `web_demo.py`: lightweight Gradio UI for interactive prompt/seed/CFG exploration.
@@ -74,6 +74,8 @@ if __name__ == "__main__":
 
 ## Local CLI Usage
 
+### Qwen/Tongyi Models
+
 ```bash
 python text_to_image.py \
   --model Tongyi-MAI/Z-Image-Turbo \
@@ -87,7 +89,26 @@ python text_to_image.py \
   --output outputs/coffee.png
 ```
 
-Key arguments:
+### NextStep Models
+
+NextStep-1.1 can have extra arguments
+```bash
+python text_to_image.py \
+  --model stepfun-ai/NextStep-1.1 \
+  --prompt "A baby panda wearing an Iron Man mask, holding a board with 'NextStep-1' written on it" \
+  --height 512 \
+  --width 512 \
+  --num-inference-steps 28 \
+  --guidance-scale 7.5 \
+  --guidance-scale-2 1.0 \
+  --cfg-schedule constant \
+  --output nextstep_output.png \
+  --seed 42
+```
+
+### Key Arguments
+
+**Common arguments:**
 
 - `--prompt`: text description (string).
 - `--seed`: integer seed for deterministic sampling.
@@ -98,8 +119,15 @@ Key arguments:
 - `--output`: path to save the generated PNG.
 - `--vae-use-slicing`: enable VAE slicing for memory optimization.
 - `--vae-use-tiling`: enable VAE tiling for memory optimization.
-- `--cfg-parallel-size`: set it to 2 to enable CFG Parallel. See more examples in [`user_guide`](../../../docs/user_guide/diffusion/parallelism_acceleration.md#cfg-parallel).
+- `--cfg-parallel-size`: set it to 2 to enable CFG Parallel. See more examples in [`user_guide`](../../../docs/user_guide/diffusion_acceleration.md#using-cfg-parallel).
 - `--enable-cpu-offload`: enable CPU offloading for diffusion models.
+- `--guidance-scale`: classifier-free guidance scale.
+
+**NextStep-1.1 specific:**
+- `--guidance-scale-2`: secondary guidance scale, e.g. image-level CFG (default: 1.0).
+- `--timesteps-shift`: timesteps shift parameter for sampling (default: 1.0).
+- `--cfg-schedule`: CFG schedule type, "constant" or "linear" (default: "constant").
+- `--use-norm`: apply layer normalization to sampled tokens.
 
 > ℹ️ If you encounter OOM errors, try using `--vae-use-slicing` and `--vae-use-tiling` to reduce memory usage.
 

examples/offline_inference/text_to_image/text_to_image.py

@@ -16,13 +16,26 @@
 from vllm_omni.platforms import current_omni_platform
 
 
+def is_nextstep_model(model_name: str) -> bool:
+    """Check if the model is a NextStep model by reading its config."""
+    from vllm.transformers_utils.config import get_hf_file_to_dict
+
+    try:
+        cfg = get_hf_file_to_dict("config.json", model_name)
+        if cfg and cfg.get("model_type") == "nextstep":
+            return True
+    except Exception:
+        pass
+    return False
+
+
 def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser(description="Generate an image with Qwen-Image.")
+    parser = argparse.ArgumentParser(description="Generate an image with supported diffusion models.")
     parser.add_argument(
         "--model",
         default="Qwen/Qwen-Image",
         help="Diffusion model name or local path. Supported models: "
-        "Qwen/Qwen-Image, Tongyi-MAI/Z-Image-Turbo, Qwen/Qwen-Image-2512",
+        "Qwen/Qwen-Image, Tongyi-MAI/Z-Image-Turbo, Qwen/Qwen-Image-2512, stepfun-ai/NextStep-1.1",
     )
     parser.add_argument("--prompt", default="a cup of coffee on the table", help="Text prompt for image generation.")
     parser.add_argument(
@@ -153,16 +166,43 @@ def parse_args() -> argparse.Namespace:
         default=1,
         help="Number of ranks used for VAE patch/tile parallelism (decode/encode).",
     )
+    # NextStep-1.1 specific arguments
+    parser.add_argument(
+        "--guidance-scale-2",
+        type=float,
+        default=1.0,
+        help="Secondary guidance scale (e.g. image-level CFG for NextStep-1.1).",
+    )
+    parser.add_argument(
+        "--timesteps-shift",
+        type=float,
+        default=1.0,
+        help="[NextStep-1.1 only] Timesteps shift parameter for sampling.",
+    )
+    parser.add_argument(
+        "--cfg-schedule",
+        type=str,
+        default="constant",
+        choices=["constant", "linear"],
+        help="[NextStep-1.1 only] CFG schedule type.",
+    )
+    parser.add_argument(
+        "--use-norm",
+        action="store_true",
+        help="[NextStep-1.1 only] Apply layer normalization to sampled tokens.",
+    )
     return parser.parse_args()
 
 
 def main():
     args = parse_args()
     generator = torch.Generator(device=current_omni_platform.device_type).manual_seed(args.seed)
+    use_nextstep = is_nextstep_model(args.model)
 
-    # Configure cache based on backend type
     cache_config = None
-    if args.cache_backend == "cache_dit":
+    cache_backend = args.cache_backend
+
+    if cache_backend == "cache_dit":
         # cache-dit configuration: Hybrid DBCache + SCM + TaylorSeer
         # All parameters marked with [cache-dit only] in DiffusionCacheConfig
         cache_config = {
@@ -179,7 +219,7 @@ def main():
             "scm_steps_mask_policy": None,  # SCM mask policy: None (disabled), "slow", "medium", "fast", "ultra"
             "scm_steps_policy": "dynamic",  # SCM steps policy: "dynamic" or "static"
         }
-    elif args.cache_backend == "tea_cache":
+    elif cache_backend == "tea_cache":
         # TeaCache configuration
         # All parameters marked with [tea_cache only] in DiffusionCacheConfig
         cache_config = {
@@ -213,19 +253,24 @@ def main():
     elif args.quantization:
         quant_kwargs["quantization"] = args.quantization
 
-    omni = Omni(
-        model=args.model,
-        enable_layerwise_offload=args.enable_layerwise_offload,
-        vae_use_slicing=args.vae_use_slicing,
-        vae_use_tiling=args.vae_use_tiling,
-        cache_backend=args.cache_backend,
-        cache_config=cache_config,
-        enable_cache_dit_summary=args.enable_cache_dit_summary,
-        parallel_config=parallel_config,
-        enforce_eager=args.enforce_eager,
-        enable_cpu_offload=args.enable_cpu_offload,
+    # Initialize Omni with model-specific settings
+    omni_kwargs = {
+        "model": args.model,
+        "enable_layerwise_offload": args.enable_layerwise_offload,
+        "vae_use_slicing": args.vae_use_slicing,
+        "vae_use_tiling": args.vae_use_tiling,
+        "cache_backend": cache_backend,
+        "cache_config": cache_config,
+        "enable_cache_dit_summary": args.enable_cache_dit_summary,
+        "parallel_config": parallel_config,
+        "enforce_eager": args.enforce_eager,
+        "enable_cpu_offload": args.enable_cpu_offload,
         **quant_kwargs,
-    )
+    }
+    if use_nextstep:
+        # NextStep-1.1 requires explicit pipeline class
+        omni_kwargs["model_class_name"] = "NextStep11Pipeline"
+    omni = Omni(**omni_kwargs)
 
     if profiler_enabled:
         print("[Profiler] Starting profiling...")
@@ -236,7 +281,7 @@ def main():
     print("Generation Configuration:")
     print(f"  Model: {args.model}")
     print(f"  Inference steps: {args.num_inference_steps}")
-    print(f"  Cache backend: {args.cache_backend if args.cache_backend else 'None (no acceleration)'}")
+    print(f"  Cache backend: {cache_backend if cache_backend else 'None (no acceleration)'}")
     print(f"  Quantization: {args.quantization if args.quantization else 'None (BF16)'}")
     if ignored_layers:
         print(f"  Ignored layers: {ignored_layers}")
@@ -250,6 +295,13 @@ def main():
     print(f"{'=' * 60}\n")
 
     generation_start = time.perf_counter()
+
+    extra_args = {
+        "timesteps_shift": args.timesteps_shift,
+        "cfg_schedule": args.cfg_schedule,
+        "use_norm": args.use_norm,
+    }
+
     outputs = omni.generate(
         {
             "prompt": args.prompt,
@@ -261,10 +313,13 @@ def main():
             generator=generator,
             true_cfg_scale=args.cfg_scale,
             guidance_scale=args.guidance_scale,
+            guidance_scale_2=args.guidance_scale_2,
             num_inference_steps=args.num_inference_steps,
             num_outputs_per_prompt=args.num_images_per_prompt,
+            extra_args=extra_args,
         ),
     )
+
     generation_end = time.perf_counter()
     generation_time = generation_end - generation_start