[diffusion] doc: minor update docs (#13177)

python/sglang/multimodal_gen/README.md

@@ -2,9 +2,9 @@
 <img src=https://github.com/lm-sys/lm-sys.github.io/releases/download/test/sgl-diffusion-logo.png width="80%"/>
 </div>
 
-**sgl-diffusion is an inference framework for accelerated image/video generation.**
+**SGLang diffusion is an inference framework for accelerated image/video generation.**
 
-SGLang-Diffusion features an end-to-end unified pipeline for accelerating diffusion models. It is designed to be modular and extensible, allowing users to easily add new models and optimizations.
+SGLang diffusion features an end-to-end unified pipeline for accelerating diffusion models. It is designed to be modular and extensible, allowing users to easily add new models and optimizations.
 
 ## Key Features
 

python/sglang/multimodal_gen/configs/models/base.py

@@ -46,7 +46,7 @@ class ModelConfig:
     # Diffuser/Transformer parameters
     arch_config: ArchConfig = field(default_factory=ArchConfig)
 
-    # sgl-diffusion-specific parameters here
+    # sglang-diffusion-specific parameters here
     # i.e. STA, quantization, teacache
 
     def __getattr__(self, name):

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

@@ -43,7 +43,7 @@ class DiTArchConfig(ArchConfig):
 class DiTConfig(ModelConfig):
     arch_config: DiTArchConfig = field(default_factory=DiTArchConfig)
 
-    # sgl-diffusionDiT-specific parameters
+    # sglang-diffusion DiT-specific parameters
     prefix: str = ""
     quant_config: QuantizationConfig | None = None
 

python/sglang/multimodal_gen/configs/models/vaes/base.py

@@ -26,7 +26,7 @@ class VAEArchConfig(ArchConfig):
 class VAEConfig(ModelConfig):
     arch_config: VAEArchConfig = field(default_factory=VAEArchConfig)
 
-    # sgl-diffusionVAE-specific parameters
+    # sglang-diffusion VAE-specific parameters
     load_encoder: bool = True
     load_decoder: bool = True
 

python/sglang/multimodal_gen/csrc/attn/vmoba_attn/README.md

@@ -1,4 +1,4 @@
-# Attention Kernel Used in sgl-diffusion
+# Attention Kernel Used in SGLang diffusion
 
 ## VMoBA: Mixture-of-Block Attention for Video Diffusion Models (VMoBA)
 

python/sglang/multimodal_gen/docs/cli.md

@@ -1,11 +1,11 @@
-# sgl-diffusion CLI Inference
+# SGLang diffusion CLI Inference
 
-The sgl-diffusion CLI provides a quick way to access the sgl-diffusion inference pipeline for image and video generation.
+The SGLang-diffusion CLI provides a quick way to access the inference pipeline for image and video generation.
 
 ## Prerequisites
 
-- A working sgl-diffusion installation and the `sgl-diffusion` CLI available in `$PATH`.
+- A working SGLang diffusion installation and the `sglang` CLI available in `$PATH`.
-- Python 3.10+ if you plan to use the OpenAI Python SDK.
+- Python 3.11+ if you plan to use the OpenAI Python SDK.
 
 
 ## Supported Arguments
@@ -127,7 +127,7 @@ sglang generate --help
 
 ## Serve
 
-Launch the sgl-diffusion HTTP server and interact with it using the OpenAI SDK and curl. The server implements an OpenAI-compatible subset for Videos under the `/v1/videos` namespace.
+Launch the SGLang diffusion HTTP server and interact with it using the OpenAI SDK and curl. The server implements an OpenAI-compatible subset for Videos under the `/v1/videos` namespace.
 
 ### Start the server
 

python/sglang/multimodal_gen/docs/install.md

@@ -1,6 +1,6 @@
-# Install sgl-diffusion
+# Install SGLang-diffusion
 
-You can install sgl-diffusion using one of the methods below.
+You can install sglang-diffusion using one of the methods below.
 
 This page primarily applies to common NVIDIA GPU platforms.
 

python/sglang/multimodal_gen/docs/support_matrix.md

@@ -10,7 +10,7 @@ The symbols used have the following meanings:
 
 ## Models x Optimization
 
-The `HuggingFace Model ID` can be passed directly to `from_pretrained()` methods, and sgl-diffusion will use the optimal
+The `HuggingFace Model ID` can be passed directly to `from_pretrained()` methods, and sglang-diffusion will use the optimal
 default parameters when initializing and generating videos.
 
 ### Video Generation Models

python/sglang/multimodal_gen/docs/support_new_models.md

@@ -1,10 +1,12 @@
 # How to Support New Diffusion Models
 
-This document explains how to add support for new diffusion models in SGLang Diffusion.
+This document explains how to add support for new diffusion models in SGLang diffusion.
 
 ## Architecture Overview
 
-SGLang Diffusion is engineered for both performance and flexibility, built upon a modular pipeline architecture. This design allows developers to easily construct complex, customized pipelines for various diffusion models by combining and reusing different components.
+SGLang diffusion is engineered for both performance and flexibility, built upon a modular pipeline architecture. This
+design allows developers to easily construct complex, customized pipelines for various diffusion models by combining and
+reusing different components.
 
 At its core, the architecture revolves around two key concepts, as highlighted in our [blog post](https://lmsys.org/blog/2025-11-07-sglang-diffusion/#architecture):
 
@@ -101,4 +103,5 @@ To illustrate the process, let's look at how `Qwen-Image-Edit` is implemented. T
 5.  **Register Configs**:
     - Register the configs in the central registry ([`registry.py`](https://github.com/sgl-project/sglang/blob/main/python/sglang/multimodal_gen/registry.py)) via `_register_configs` to enable automatic loading and instantiation for the model. Modules are automatically loaded and injected based on the config and repository structure.
 
-By following this modular pattern of defining configurations and composing pipelines, you can integrate new diffusion models into SGLang with clarity and ease.
+By following this pattern of defining configurations and composing pipelines, you can integrate new diffusion models
+into SGLang with ease.

python/sglang/multimodal_gen/envs.py

@@ -163,7 +163,7 @@ def maybe_convert_int(value: str | None) -> int | None:
 
 environment_variables: dict[str, Callable[[], Any]] = {
     # ================== Installation Time Env Vars ==================
-    # Target device of sgl-diffusion, supporting [cuda (by default),
+    # Target device of sglang-diffusion, supporting [cuda (by default),
     # rocm, neuron, cpu, openvino]
     "SGLANG_DIFFUSION_TARGET_DEVICE": lambda: os.getenv(
         "SGLANG_DIFFUSION_TARGET_DEVICE", "cuda"

python/sglang/multimodal_gen/runtime/distributed/device_communicators/pynccl.py

@@ -74,7 +74,7 @@ class PyNcclCommunicator:
         self.available = True
         self.disabled = False
 
-        logger.info("sgl-diffusion is using nccl==%s", self.nccl.ncclGetVersion())
+        logger.info("sglang-diffusion is using nccl==%s", self.nccl.ncclGetVersion())
 
         if self.rank == 0:
             # get the unique id from NCCL

python/sglang/multimodal_gen/runtime/distributed/parallel_state.py

@@ -13,7 +13,7 @@
 # Copyright 2023 The vLLM team.
 # Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
 
-"""sgl-diffusion distributed state.
+"""sglang-diffusion distributed state.
 
 It takes over the control of the distributed environment from PyTorch.
 The typical workflow is:

python/sglang/multimodal_gen/runtime/entrypoints/cli/generate.py

@@ -62,7 +62,7 @@ def generate_cmd(args: argparse.Namespace):
 
 
 class GenerateSubcommand(CLISubcommand):
-    """The `generate` subcommand for the sgl-diffusion CLI"""
+    """The `generate` subcommand for the sglang-diffusion CLI"""
 
     def __init__(self) -> None:
         self.name = "generate"

python/sglang/multimodal_gen/runtime/entrypoints/cli/main.py

@@ -21,7 +21,7 @@ def cmd_init() -> list[CLISubcommand]:
 
 
 def main() -> None:
-    parser = FlexibleArgumentParser(description="sgl-diffusion CLI")
+    parser = FlexibleArgumentParser(description="sglang-diffusion CLI")
     parser.add_argument("-v", "--version", action="version", version="0.1.0")
 
     subparsers = parser.add_subparsers(required=False, dest="subparser")

python/sglang/multimodal_gen/runtime/entrypoints/cli/serve.py

@@ -35,7 +35,7 @@ def execute_serve_cmd(args: argparse.Namespace, unknown_args: list[str] | None =
 
 
 class ServeSubcommand(CLISubcommand):
-    """The `serve` subcommand for the sgl-diffusion CLI"""
+    """The `serve` subcommand for the sglang-diffusion CLI"""
 
     def __init__(self) -> None:
         self.name = "serve"

python/sglang/multimodal_gen/runtime/entrypoints/diffusion_generator.py

@@ -2,7 +2,7 @@
 
 # SPDX-License-Identifier: Apache-2.0
 """
-DiffGenerator module for sgl-diffusion.
+DiffGenerator module for sglang-diffusion.
 
 This module provides a consolidated interface for generating videos using
 diffusion models.

python/sglang/multimodal_gen/runtime/entrypoints/utils.py

@@ -2,7 +2,7 @@
 
 # SPDX-License-Identifier: Apache-2.0
 """
-DiffGenerator module for sgl-diffusion.
+DiffGenerator module for sglang-diffusion.
 
 This module provides a consolidated interface for generating videos using
 diffusion models.

python/sglang/multimodal_gen/runtime/launch_server.py

@@ -66,7 +66,7 @@ def launch_server(server_args: ServerArgs, launch_http_server: bool = True):
                     task_pipes_to_slaves_w,
                     result_pipes_from_slaves_r,
                 ),
-                name=f"sgl-diffusionWorker-{i}",
+                name=f"sglang-diffusionWorker-{i}",
                 daemon=True,
             )
         else:  # Slave workers
@@ -83,7 +83,7 @@ def launch_server(server_args: ServerArgs, launch_http_server: bool = True):
                     task_pipes_to_slaves_r[i - 1],
                     result_pipes_from_slaves_w[i - 1],
                 ),
-                name=f"sgl-diffusionWorker-{i}",
+                name=f"sglang-diffusionWorker-{i}",
                 daemon=True,
             )
         scheduler_pipe_readers.append(reader)

python/sglang/multimodal_gen/runtime/layers/attention/selector.py

@@ -41,7 +41,7 @@ def backend_name_to_enum(backend_name: str) -> AttentionBackendEnum | None:
 
 def get_env_variable_attn_backend() -> AttentionBackendEnum | None:
     """
-    Get the backend override specified by the sgl-diffusion attention
+    Get the backend override specified by the sglang-diffusion attention
     backend environment variable, if one is specified.
 
     Returns:
@@ -169,7 +169,7 @@ def global_force_attn_backend_context_manager(
     attn_backend: AttentionBackendEnum,
 ) -> Generator[None, None, None]:
     """
-    Globally force a sgl-diffusion attention backend override within a
+    Globally force a sglang-diffusion attention backend override within a
     context manager, reverting the global attention backend
     override to its prior state upon exiting the context
     manager.

python/sglang/multimodal_gen/runtime/loader/fsdp_load.py

@@ -4,7 +4,7 @@
 
 # Adapted from torchtune
 # Copyright 2024 The TorchTune Authors.
-# Copyright 2025 The sgl-diffusion Authors.
+# Copyright 2025 The sglang-diffusion Authors.
 
 import contextlib
 from collections.abc import Callable, Generator

python/sglang/multimodal_gen/runtime/models/vaes/hunyuanvae.py

@@ -3,7 +3,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # Adapted from diffusers
 
-# Copyright 2024 The Hunyuan Team, The HuggingFace Team and The sgl-diffusion Team. All rights reserved.
+# Copyright 2024 The Hunyuan Team, The HuggingFace Team and The sglang-diffusion Team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

python/sglang/multimodal_gen/runtime/server_args.py

@@ -2,7 +2,7 @@
 
 # SPDX-License-Identifier: Apache-2.0
 # Inspired by SGLang: https://github.com/sgl-project/sglang/blob/main/python/sglang/srt/server_args.py
-"""The arguments of sgl-diffusion Inference."""
+"""The arguments of sglang-diffusion Inference."""
 import argparse
 import dataclasses
 import inspect
@@ -393,7 +393,7 @@ class ServerArgs:
             type=str,
             choices=ExecutionMode.choices(),
             default=ServerArgs.mode.value,
-            help="The mode to run sgl-diffusion",
+            help="The mode to run SGLang-diffusion",
         )
 
         # Workload type

python/sglang/multimodal_gen/utils.py

@@ -450,8 +450,8 @@ def import_pynvml():
     status without initializing CUDA context in the current process.
     Historically, there are two packages that provide pynvml:
     - `nvidia-ml-py` (https://pypi.org/project/nvidia-ml-py/): The official
-        wrapper. It is a dependency of sgl-diffusion, and is installed when users
+        wrapper. It is a dependency of sglang-diffusion, and is installed when users
-        install sgl-diffusion. It provides a Python module named `pynvml`.
+        install sglang-diffusion. It provides a Python module named `pynvml`.
     - `pynvml` (https://pypi.org/project/pynvml/): An unofficial wrapper.
         Prior to version 12.0, it also provides a Python module `pynvml`,
         and therefore conflicts with the official one which is a standalone Python file.