diffusion: support zimage

python/sglang/multimodal_gen/configs/models/dits/zimage.py

@@ -0,0 +1,40 @@
+# Copied and adapted from: https://github.com/hao-ai-lab/FastVideo
+
+# SPDX-License-Identifier: Apache-2.0
+from dataclasses import dataclass, field
+from typing import Tuple
+
+from sglang.multimodal_gen.configs.models.dits.base import DiTArchConfig, DiTConfig
+
+
+@dataclass
+class ZImageArchConfig(DiTArchConfig):
+    all_patch_size: Tuple[int, ...] = (2,)
+    all_f_patch_size: Tuple[int, ...] = (1,)
+    in_channels: int = 16
+    out_channels: int | None = None
+    dim: int = 3840
+    num_layers: int = 30
+    n_refiner_layers: int = 2
+    num_attention_heads: int = 30
+    n_kv_heads: int = 30
+    norm_eps: float = 1e-5
+    qk_norm: bool = True
+    cap_feat_dim: int = 2560
+    rope_theta: float = 256.0
+    t_scale: float = 1000.0
+    axes_dims: Tuple[int, int, int] = (32, 48, 48)
+    axes_lens: Tuple[int, int, int] = (1024, 512, 512)
+
+    def __post_init__(self):
+        super().__post_init__()
+        self.out_channels = self.out_channels or self.in_channels
+        self.num_channels_latents = self.in_channels
+        self.hidden_size = self.dim
+
+
+@dataclass
+class ZImageDitConfig(DiTConfig):
+    arch_config: ZImageArchConfig = field(default_factory=ZImageArchConfig)
+
+    prefix: str = "zimage"

python/sglang/multimodal_gen/configs/pipeline_configs/__init__.py

@@ -17,6 +17,7 @@
     WanT2V480PConfig,
     WanT2V720PConfig,
 )
+from sglang.multimodal_gen.configs.pipeline_configs.zimage import ZImagePipelineConfig
 
 __all__ = [
     "HunyuanConfig",
@@ -30,4 +31,5 @@
     "WanI2V720PConfig",
     "StepVideoT2VConfig",
     "SelfForcingWanT2V480PConfig",
+    "ZImagePipelineConfig",
 ]

python/sglang/multimodal_gen/configs/pipeline_configs/flux.py

@@ -321,7 +321,7 @@ def _prepare_image_ids(
     return image_latent_ids
 
 
-def flux_2_postprocess_text(outputs: BaseEncoderOutput, _text_inputs) -> torch.Tensor:
+def flux2_postprocess_text(outputs: BaseEncoderOutput, _text_inputs) -> torch.Tensor:
     hidden_states_layers: list[int] = [10, 20, 30]
 
     out = torch.stack([outputs.hidden_states[k] for k in hidden_states_layers], dim=1)
@@ -412,7 +412,7 @@ class Flux2PipelineConfig(FluxPipelineConfig):
     )
 
     postprocess_text_funcs: tuple[Callable[[str], str], ...] = field(
-        default_factory=lambda: (flux_2_postprocess_text,)
+        default_factory=lambda: (flux2_postprocess_text,)
     )
     vae_config: VAEConfig = field(default_factory=Flux2VAEConfig)
 

python/sglang/multimodal_gen/configs/pipeline_configs/zimage.py

@@ -0,0 +1,74 @@
+# Copied and adapted from: https://github.com/hao-ai-lab/FastVideo
+from dataclasses import dataclass, field
+from typing import Callable
+
+import torch
+
+from sglang.multimodal_gen.configs.models import DiTConfig, EncoderConfig, VAEConfig
+from sglang.multimodal_gen.configs.models.dits.zimage import ZImageDitConfig
+from sglang.multimodal_gen.configs.models.encoders import (
+    BaseEncoderOutput,
+    TextEncoderConfig,
+)
+from sglang.multimodal_gen.configs.models.vaes.flux import FluxVAEConfig
+from sglang.multimodal_gen.configs.pipeline_configs.base import (
+    ImagePipelineConfig,
+    ModelTaskType,
+)
+
+
+def zimage_preprocess_text(prompt: str):
+    messages = [
+        {"role": "user", "content": prompt},
+    ]
+    return messages
+
+
+def zimage_postprocess_text(outputs: BaseEncoderOutput, _text_inputs) -> torch.Tensor:
+    device = outputs.hidden_states[-2].device
+    prompt_mask = _text_inputs.attention_mask.to(device).bool()
+    return outputs.hidden_states[-2][0][prompt_mask[0]]
+
+
+class TransformersModelConfig(EncoderConfig):
+    tokenizer_kwargs: dict = field(default_factory=lambda: {})
+
+
+@dataclass
+class ZImagePipelineConfig(ImagePipelineConfig):
+
+    should_use_guidance: bool = False
+    task_type: ModelTaskType = ModelTaskType.T2I
+
+    dit_config: DiTConfig = field(default_factory=ZImageDitConfig)
+    vae_config: VAEConfig = field(default_factory=FluxVAEConfig)
+    text_encoder_configs: tuple[EncoderConfig, ...] = field(
+        default_factory=lambda: (TextEncoderConfig(),)
+    )
+
+    preprocess_text_funcs: tuple[Callable, ...] = field(
+        default_factory=lambda: (zimage_preprocess_text,)
+    )
+
+    postprocess_text_funcs: tuple[Callable, ...] = field(
+        default_factory=lambda: (zimage_postprocess_text,)
+    )
+
+    def tokenize_prompt(self, prompts: list[str], tokenizer, tok_kwargs) -> dict:
+        # flatten to 1-d list
+        inputs = tokenizer.apply_chat_template(
+            prompts,
+            tokenize=True,
+            add_generation_prompt=True,
+            enable_thinking=True,
+            padding="max_length",
+            max_length=512,  # TODO (yhyang201): set max length according to config
+            truncation=True,
+            return_tensors="pt",
+            return_dict=True,
+        )
+        return inputs
+
+    def post_denoising_loop(self, latents, batch):
+        bs, channels, num_frames, height, width = latents.shape
+        return latents.view(bs, channels, height, width)

python/sglang/multimodal_gen/configs/sample/zimage.py

@@ -0,0 +1,32 @@
+# Copied and adapted from: https://github.com/hao-ai-lab/FastVideo
+
+# SPDX-License-Identifier: Apache-2.0
+from dataclasses import dataclass, field
+
+from sglang.multimodal_gen.configs.sample.base import SamplingParams
+from sglang.multimodal_gen.configs.sample.teacache import TeaCacheParams
+
+
+@dataclass
+class ZImageSamplingParams(SamplingParams):
+    num_inference_steps: int = 9
+
+    num_frames: int = 1
+    height: int = 720
+    width: int = 1280
+    fps: int = 24
+
+    guidance_scale: float = 0.0
+
+    teacache_params: TeaCacheParams = field(
+        default_factory=lambda: TeaCacheParams(
+            teacache_thresh=0.15,
+            coefficients=[
+                7.33226126e02,
+                -4.01131952e02,
+                6.75869174e01,
+                -3.14987800e00,
+                9.61237896e-02,
+            ],
+        )
+    )

python/sglang/multimodal_gen/registry.py

@@ -24,6 +24,7 @@
     WanI2V720PConfig,
     WanT2V480PConfig,
     WanT2V720PConfig,
+    ZImagePipelineConfig,
 )
 from sglang.multimodal_gen.configs.pipeline_configs.base import PipelineConfig
 from sglang.multimodal_gen.configs.pipeline_configs.flux import Flux2PipelineConfig
@@ -56,6 +57,7 @@
     WanT2V_1_3B_SamplingParams,
     WanT2V_14B_SamplingParams,
 )
+from sglang.multimodal_gen.configs.sample.zimage import ZImageSamplingParams
 from sglang.multimodal_gen.runtime.pipelines_core.composed_pipeline_base import (
     ComposedPipelineBase,
 )
@@ -404,6 +406,15 @@ def _register_configs():
         ],
         model_detectors=[lambda id: "flux.2" in id.lower()],
     )
+    register_configs(
+        model_name="Z-image",
+        sampling_param_cls=ZImageSamplingParams,
+        pipeline_config_cls=ZImagePipelineConfig,
+        model_paths=[
+            "Tongyi-MAI/Z-Image-Turbo",
+        ],
+        model_detectors=[lambda id: "z-image" in id.lower()],
+    )
 
     # Qwen-Image
     register_configs(