Add library_name and improve model card documentation

Hi! I'm Niels from the Hugging Face community team. I noticed this model has excellent documentation and evidence of compatibility with the `diffusers` library.

I've opened this PR to:
1. Add `library_name: diffusers` to the metadata to enable the "Use in Diffusers" button and improve discoverability.
2. Update the `pipeline_tag` to `other` as requested.
3. Include a "Sample Usage" section featuring the official `diffusers` code snippet for easier onboarding.
4. Add a direct link to the paper at the top of the description.

README.md

@@ -1,10 +1,11 @@
 ---
-license: apache-2.0
-language:
-- en
 base_model:
 - Wan-AI/Wan2.1-T2V-14B-Diffusers
-pipeline_tag: text-to-video
+language:
+- en
+license: apache-2.0
+pipeline_tag: other
+library_name: diffusers
 base_model_relation: finetune
 ---
 
@@ -16,6 +17,8 @@ base_model_relation: finetune
 
 <h5 align="center">⭐ 14B Real-Time Long Video Generation Model can be Cheaper, Faster but Keep Stronger than 1.3B ones ⭐</h5>
 
+This repository contains the weights for Helios, as presented in the paper [Helios: Real Real-Time Long Video Generation Model](https://huggingface.co/papers/2603.04379).
+
 <h5 align="center">
 
 [![arXiv](https://img.shields.io/badge/arXiv-2603.04379-b31b1b.svg?logo=arxiv)](https://arxiv.org/abs/2603.04379)
@@ -32,10 +35,6 @@ base_model_relation: finetune
 [![vLLM-Omni](https://img.shields.io/badge/Backend-vLLM--Omni-orange)](https://github.com/vllm-project/vllm-omni/pull/1604)
 [![SGLang Diffusion](https://img.shields.io/badge/Backend-SGLang--Diffusion-yellow)](https://github.com/sgl-project/sglang/pull/19782)
 
-
-
-
-
 </h5>
 
 <div align="center">
@@ -46,302 +45,60 @@ This repository is the official implementation of Helios, which is a breakthroug
 
 ## ✨ Highlights
 
-
 1. **Without commonly used anti-drifting strategies** (e.g., self-forcing, error-banks, keyframe sampling, or inverted sampling), Helios generates minute-scale videos with high quality and strong coherence.
 
 2. **Without standard acceleration techniques** (e.g., KV-cache, causal masking, sparse/linear attention, TinyVAE, progressive noise schedules, hidden-state caching, or quantization), Helios achieves 19.5 FPS in end-to-end inference on a single H100 GPU.
 
 3. **We introduce optimizations that improve both training and inference throughput while reducing memory consumption,** enabling image-diffusion-scale batch sizes during training while fitting up to four 14B models within 80 GB of GPU memory.
 
+## 🚀 Sample Usage (Diffusers)
 
+Install diffusers from source:
+```bash
+pip install git+https://github.com/huggingface/diffusers.git
+```
 
-## 🎬 Video Demos
-
-<!-- <div align="center">
-  <video src="https://github.com/PKU-YuanGroup/Helios-Page/blob/main/videos/helios_features.mp4?raw=true" width="70%" controls="controls" poster=""></video>
-</div>
+```python
+import torch
+from diffusers import ModularPipeline, ClassifierFreeGuidance
+from diffusers.utils import export_to_video
+
+mod_pipe = ModularPipeline.from_pretrained("BestWishYsh/Helios-Distilled")
+mod_pipe.load_components(torch_dtype=torch.bfloat16)
+mod_pipe.to("cuda")
+
+# Configure guidance
+guider = ClassifierFreeGuidance(guidance_scale=1.0)
+mod_pipe.update_components(guider=guider)
+
+prompt = "A vibrant tropical fish swimming gracefully among colorful coral reefs in a clear, turquoise ocean."
+
+output = mod_pipe(
+    prompt=prompt,
+    height=384,
+    width=640,
+    num_frames=240,
+    pyramid_num_inference_steps_list=[2, 2, 2],
+    is_amplify_first_chunk=True,
+    generator=torch.Generator("cuda").manual_seed(42),
+    output="videos",
+)
+
+export_to_video(output[0], "helios_distilled_output.mp4", fps=24)
+```
 
-or you can click <a href="https://www.youtube.com/watch?v=vd_AgHtOUFQ">here</a> to get the video. Some best prompts are [here](./example/prompt.txt). -->
+## 🎬 Video Demos
 
 [![Demo Video of Helios](https://github.com/user-attachments/assets/1d10da4a-aba9-4ac1-ab02-cd0dfce8d35b)](https://www.youtube.com/watch?v=vd_AgHtOUFQ)
 or you can click <a href="https://github.com/PKU-YuanGroup/Helios-Page/blob/main/videos/helios_features.mp4">here</a> to get the video. Some best prompts are [here](./example/prompt.txt).
 
-
 ## 📣 Latest News!!
 
 * `[2026.03.04]` 👋 We are excited to announce the release of the Helios [technical report](https://arxiv.org/abs/2603.04379) on arXiv. We welcome discussions and feedback!
 * `[2026.03.04]` 🚀 Day-0 support for [Ascend-NPU](https://www.hiascend.com)，with sincere gratitude to the Ascend Team for their support.
 * `[2026.03.04]` 🚀 Day-0 support for [Diffusers](https://github.com/huggingface/diffusers/pull/13208)，with special thanks to the HuggingFace Team for their support.
-* `[2026.03.04]` 🚀 Day-0 support for [vLLM-Omni](https://github.com/vllm-project/vllm-omni/pull/1604)，with heartfelt gratitude to the vLLM Team for their support.
-* `[2026.03.04]` 🚀 Day-0 support for [SGLang-Diffusion](https://github.com/sgl-project/sglang/pull/19782)，with huge thanks to the SGLang Team for their support.
 * `[2026.03.04]` 🔥 We've released the training/inference code and weights of **Helios-Base**, **Helios-Mid** and **Helios-Distilled**.
 
-
-## 🔥 Friendly Links
-
-If your work has improved **Helios** and you would like more people to see it, please inform us.
-
-* [Ascend-NPU](https://www.hiascend.com/): Developed by Huawei, this hardware is designed for efficient AI model training and inference, boosting performance in tasks like computer vision, natural language processing, and autonomous driving.
-* [Diffusers](https://github.com/huggingface/diffusers): A popular library designed for working with diffusion models and other generative models in deep learning. It supports easy integration and manipulation of a wide range of generative models.
-* [vLLM-Omni](https://github.com/vllm-project/vllm-omni): A fully disaggregated serving system for any-to-any models. vLLM-Omni breaks complex architectures into a stage-based graph, using a decoupled backend to maximize resource efficiency and throughput.
-* [SGLang-Diffusion](https://github.com/sgl-project/sglang): An inference framework for accelerated image and video generation using diffusion models. It provides an end-to-end unified pipeline with optimized kernels and an efficient scheduler loop.
-
-
-
-### Model Download
-
-| Models           | Download Link                                                                                                                                            | Supports                                      | Notes                                                                                       |
-|------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------|---------------------------------------------------------------------------------------------|
-| Helios-Base      | 🤗 [Huggingface](https://huggingface.co/BestWishYsh/Helios-Base) 🤖 [ModelScope](https://modelscope.cn/models/BestWishYSH/Helios-Base)                | T2V ✅ I2V ✅ V2V ✅ Interactive ✅           | Best Quality, with v-prediction, standard CFG and custom HeliosScheduler.                   |
-| Helios-Mid       | 🤗 [Huggingface](https://huggingface.co/BestWishYsh/Helios-Mid) 🤖 [ModelScope](https://modelscope.cn/models/BestWishYSH/Helios-Mid)                  | T2V ✅ I2V ✅ V2V ✅ Interactive ✅           | Intermediate Ckpt, with v-prediction, CFG-Zero* and custom HeliosScheduler.                 |
-| Helios-Distilled | 🤗 [Huggingface](https://huggingface.co/BestWishYsh/Helios-Distilled) 🤖 [ModelScope](https://modelscope.cn/models/BestWishYSH/Helios-Distilled)      | T2V ✅ I2V ✅ V2V ✅ Interactive ✅           | Best Efficiency, with x0-prediction and custom HeliosDMDScheduler.                          |
-
-
-
-> 💡Note: 
-> * All three models share the same architecture, but Helios-Mid and Helios-Distilled use a more aggressive multi-scale sampling pipeline to achieve better efficiency.
-> * Helios-Mid is an intermediate checkpoint generated in the process of distilling Helios-Base into Helios-Distilled, and may not meet expected quality.
-> * For Image-to-Video or Video-to-Video, since training is based on Text-to-Video, these two functions may be slightly inferior to Text-to-Video. You may enable `is_skip_first_chunk` if you find the first few chunks are static.
-
-
-Download models using huggingface-cli:
-``` sh
-pip install "huggingface_hub[cli]"
-huggingface-cli download BestWishYSH/Helios-Base --local-dir BestWishYSH/Helios-Base
-huggingface-cli download BestWishYSH/Helios-Mid --local-dir BestWishYSH/Helios-Mid
-huggingface-cli download BestWishYSH/Helios-Distilled --local-dir BestWishYSH/HeliosDistillede
-```
-
-Download models using modelscope-cli:
-``` sh
-pip install modelscope
-modelscope download BestWishYSH/Helios-Base --local_dir BestWishYSH/Helios-Base
-modelscope download BestWishYSH/Helios-Mid --local-dir BestWishYSH/Helios-Mid
-modelscope download BestWishYSH/Helios-Distilled --local-dir BestWishYSH/HeliosDistillede
-```
-
-
-## 🚀 Inference
-
-
-Helios uses an autoregressive approach that generates **33 frames per chunk**. For optimal performance, `num_frames` should be set to a multiple of `33`. If a non-multiple value is provided, it will be automatically rounded up to the nearest multiple of 33.
-
-**Example frame counts for different video lengths:**
-
-| num_frames | Adjusted Frames | 24 FPS | 16 FPS |
-|------------|-----------------|--------|--------|
-| 1449       | 1452 (33×44)    | ~60s (1min) | ~90s (1min 30s) |
-| 720        | 726 (33×22)     | ~30s | ~45s |
-| 240        | 264 (33×8)      | ~11s | ~16s |
-| 129        | 132 (33×4)      | ~5.5s | ~8s |
-
-### Sanity Check
-
-Before trying your own inputs, we highly recommend going through the sanity check to find out if any hardware or software went wrong.
-
-| Task    | **Helios-Base**                                                                                                            | **Helios-Mid**                                                                                                             | **Helios-Distilled**                                                                                                       |
-| ------- | -------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------- |
-| **T2V** | <video src="https://github.com/user-attachments/assets/14e10753-0366-4790-ad8f-7b66d821ed11" controls width="240"></video> | <video src="https://github.com/user-attachments/assets/c1778691-a80b-428c-8094-88bb1dd1d52b" controls width="240"></video> | <video src="https://github.com/user-attachments/assets/4ca28c79-9dfa-49de-9c3a-f4c7b6c766cd" controls width="240"></video> |
-| **V2V** | <video src="https://github.com/user-attachments/assets/420cb572-85c2-42d8-98d7-37b0bc24c844" controls width="240"></video> | <video src="https://github.com/user-attachments/assets/7d703fa6-dc1a-4138-a897-e58cfd9236d6" controls width="240"></video> | <video src="https://github.com/user-attachments/assets/45329c55-1a25-459c-bbf0-4e584ec5b23d" controls width="240"></video> |
-
-
-### ✨ Diffusers Pipeline
-
-Install diffusers from source:
-```bash
-pip install git+https://github.com/huggingface/diffusers.git
-```
-
-For example, let's take Helios-Distilled.
-
-<details>
-  <summary>Click to expand the code</summary>
-
-  ```bash
-  import torch
-  from diffusers import ModularPipeline, ClassifierFreeGuidance
-  from diffusers.utils import export_to_video, load_image, load_video
-
-  mod_pipe = ModularPipeline.from_pretrained("BestWishYsh/Helios-Distilled")
-  mod_pipe.load_components(torch_dtype=torch.bfloat16)
-  mod_pipe.to("cuda")
-
-  # we need to upload guider to the model repo, so each checkpoint will be able to config their guidance differently
-  guider = ClassifierFreeGuidance(guidance_scale=1.0)
-  mod_pipe.update_components(guider=guider)
-
-  # --- T2V ---
-  print("=== T2V ===")
-  prompt = (
-      "A vibrant tropical fish swimming gracefully among colorful coral reefs in a clear, turquoise ocean. "
-      "The fish has bright blue and yellow scales with a small, distinctive orange spot on its side, its fins moving "
-      "fluidly. The coral reefs are alive with a variety of marine life, including small schools of colorful fish and "
-      "sea turtles gliding by. The water is crystal clear, allowing for a view of the sandy ocean floor below. The reef "
-      "itself is adorned with a mix of hard and soft corals in shades of red, orange, and green. The photo captures "
-      "the fish from a slightly elevated angle, emphasizing its lively movements and the vivid colors of its surroundings. "
-      "A close-up shot with dynamic movement."
-  )
-
-  output = mod_pipe(
-      prompt=prompt,
-      height=384,
-      width=640,
-      num_frames=240,
-      pyramid_num_inference_steps_list=[2, 2, 2],
-      is_amplify_first_chunk=True,
-      generator=torch.Generator("cuda").manual_seed(42),
-      output="videos",
-  )
-
-  export_to_video(output[0], "helios_distilled_modular_t2v_output.mp4", fps=24)
-  print(f"T2V max memory: {torch.cuda.max_memory_allocated() / 1024**3:.3f} GB")
-  torch.cuda.empty_cache()
-  torch.cuda.reset_peak_memory_stats()
-
-  # --- I2V ---
-  print("=== I2V ===")
-  image = load_image(
-      "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/helios/wave.jpg"
-  )
-  i2v_prompt = (
-      "A towering emerald wave surges forward, its crest curling with raw power and energy. "
-      "Sunlight glints off the translucent water, illuminating the intricate textures and deep green hues within the wave's body."
-  )
-
-  output = mod_pipe(
-      prompt=i2v_prompt,
-      image=image,
-      height=384,
-      width=640,
-      num_frames=240,
-      pyramid_num_inference_steps_list=[2, 2, 2],
-      is_amplify_first_chunk=True,
-      generator=torch.Generator("cuda").manual_seed(42),
-      output="videos",
-  )
-
-  export_to_video(output[0], "helios_distilled_modular_i2v_output.mp4", fps=24)
-  print(f"I2V max memory: {torch.cuda.max_memory_allocated() / 1024**3:.3f} GB")
-  torch.cuda.empty_cache()
-  torch.cuda.reset_peak_memory_stats()
-
-  # --- V2V ---
-  print("=== V2V ===")
-  video = load_video(
-      "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/helios/car.mp4"
-  )
-  v2v_prompt = (
-      "A dynamic time-lapse video showing the rapidly moving scenery from the window of a speeding train. "
-      "The camera captures various elements such as lush green fields, towering trees, quaint countryside houses, "
-      "and distant mountain ranges passing by quickly."
-  )
-
-  output = mod_pipe(
-      prompt=v2v_prompt,
-      video=video,
-      height=384,
-      width=640,
-      num_frames=240,
-      pyramid_num_inference_steps_list=[2, 2, 2],
-      is_amplify_first_chunk=True,
-      generator=torch.Generator("cuda").manual_seed(42),
-      output="videos",
-  )
-
-  export_to_video(output[0], "helios_distilled_modular_v2v_output.mp4", fps=24)
-  print(f"V2V max memory: {torch.cuda.max_memory_allocated() / 1024**3:.3f} GB")
-  ```
-
-</details>
-
-### ✨ vLLM-Omni Pipeline
-
-Install vllm-omni from source:
-```bash
-pip install git+https://github.com/vllm-project/vllm-omni.git
-```
-
-For example, let's take Text-to-Video.
-
-<details>
-  <summary>Click to expand the code</summary>
-
-  ```bash
-  cd vllm-omni
-
-  # Helios-Base
-  python3 examples/offline_inference/helios/end2end.py \
-    --sample-type t2v \
-    --model ./Helios-Base \
-    --prompt "A vibrant tropical fish swimming gracefully among colorful coral reefs in a clear, turquoise ocean. The fish has bright blue and yellow scales with a small, distinctive orange spot on its side, its fins moving fluidly. The coral reefs are alive with a variety of marine life, including small schools of colorful fish and sea turtles gliding by. The water is crystal clear, allowing for a view of the sandy ocean floor below. The reef itself is adorned with a mix of hard and soft corals in shades of red, orange, and green. The photo captures the fish from a slightly elevated angle, emphasizing its lively movements and the vivid colors of its surroundings. A close-up shot with dynamic movement." \
-    --num-frames 99 \
-    --seed 42 \
-    --output helios_t2v_base.mp4
-
-  # Helios-Mid
-  python examples/offline_inference/helios/end2end.py \
-    --model ./Helios-Mid --sample-type t2v \
-    --prompt "A vibrant tropical fish swimming gracefully among colorful coral reefs in a clear, turquoise ocean. The fish has bright blue and yellow scales with a small, distinctive orange spot on its side, its fins moving fluidly. The coral reefs are alive with a variety of marine life, including small schools of colorful fish and sea turtles gliding by. The water is crystal clear, allowing for a view of the sandy ocean floor below. The reef itself is adorned with a mix of hard and soft corals in shades of red, orange, and green. The photo captures the fish from a slightly elevated angle, emphasizing its lively movements and the vivid colors of its surroundings. A close-up shot with dynamic movement." \
-    --guidance-scale 5.0 --is-enable-stage2 \
-    --pyramid-num-inference-steps-list 20 20 20 \
-    --num-frames 99 \
-    --use-cfg-zero-star --use-zero-init --zero-steps 1 \
-    --output helios_t2v_mid.mp4
-
-  # Helios-Distilled
-  python examples/offline_inference/helios/end2end.py \
-    --model ./Helios-Distilled --sample-type t2v \
-    --prompt "A vibrant tropical fish swimming gracefully among colorful coral reefs in a clear, turquoise ocean. The fish has bright blue and yellow scales with a small, distinctive orange spot on its side, its fins moving fluidly. The coral reefs are alive with a variety of marine life, including small schools of colorful fish and sea turtles gliding by. The water is crystal clear, allowing for a view of the sandy ocean floor below. The reef itself is adorned with a mix of hard and soft corals in shades of red, orange, and green. The photo captures the fish from a slightly elevated angle, emphasizing its lively movements and the vivid colors of its surroundings. A close-up shot with dynamic movement." \
-    --num-frames 240 --guidance-scale 1.0 --is-enable-stage2 \
-    --pyramid-num-inference-steps-list 2 2 2 \
-    --is-amplify-first-chunk --output helios_t2v_distilled.mp4
-  ```
-</details>
-
-### ✨ SGLang-Diffusion Pipeline
-
-Install sglang-diffusion from source:
-```bash
-pip install git+https://github.com/sgl-project/sglang.git
-```
-
-For example, let's take Helios-Base. **(Native Support)**
-
-<details>
-  <summary>Click to expand the code</summary>
-
-  ```bash
-  sglang generate \
-    --model-path BestWishYsh/Helios-Base \
-    --prompt "A vibrant tropical fish swimming gracefully among colorful coral reefs in a clear, turquoise ocean. The fish has bright blue and yellow scales with a small, distinctive orange spot on its side, its fins moving fluidly. The coral reefs are alive with a variety of marine life, including small schools of colorful fish and sea turtles gliding by. The water is crystal clear, allowing for a view of the sandy ocean floor below. The reef itself is adorned with a mix of hard and soft corals in shades of red, orange, and green. The photo captures the fish from a slightly elevated angle, emphasizing its lively movements and the vivid colors of its surroundings. A close-up shot with dynamic movement." \
-    --negative-prompt "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards" \
-    --height 384 \
-    --width 640 \
-    --num-frames 99 \
-    --num-inference-steps 50 \
-    --guidance-scale 5.0
-  ```
-</details>
-
-For example, let's take Helios-Base. **(Diffusers Backend)**
-
-<details>
-  <summary>Click to expand the code</summary>
-
-  ```bash
-  sglang generate \
-    --model-path BestWishYsh/Helios-Base \
-    --prompt "A vibrant tropical fish swimming gracefully among colorful coral reefs in a clear, turquoise ocean. The fish has bright blue and yellow scales with a small, distinctive orange spot on its side, its fins moving fluidly. The coral reefs are alive with a variety of marine life, including small schools of colorful fish and sea turtles gliding by. The water is crystal clear, allowing for a view of the sandy ocean floor below. The reef itself is adorned with a mix of hard and soft corals in shades of red, orange, and green. The photo captures the fish from a slightly elevated angle, emphasizing its lively movements and the vivid colors of its surroundings. A close-up shot with dynamic movement." \
-    --negative-prompt "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards" \
-    --height 384 \
-    --width 640 \
-    --num-frames 99 \
-    --num-inference-steps 50 \
-    --guidance-scale 5.0 \
-    --backend diffusers
-  ```
-</details>
-
 ## 🙌 Description
 
 - **Repository:** [Code](https://github.com/PKU-YuanGroup/Helios), [Page](https://pku-yuangroup.github.io/Helios-Page/)