Instructions to use seedleap/zing-0.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use seedleap/zing-0.5 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image, export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("seedleap/zing-0.5", dtype=torch.bfloat16, device_map="cuda") pipe.to("cuda") prompt = "A man with short gray hair plays a red electric guitar." image = load_image( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png" ) output = pipe(image=image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Notebooks
- Google Colab
- Kaggle
Zing-0.5: An Efficient Real-Time Interactive World Model
Project Page ยท GitHub ยท Technical Report (Coming soon)
Zing-0.5 is a 5B causal world model developed by the Seedleap.ai team (ๆถ่ทๆบ่ฝ) for real-time interaction. It continuously predicts the visual world from its current state while text prompts and keyboard actions alter the scene, motion, and future evolution during generation.
Zing-0.5 supports text-initialized generation, single-image initialization, prompt changes during a rollout, and continuous W/A/S/D/I/J/K/L keyboard control. Causal KV caching and four-step DMD sampling enable responsive long-horizon generation on a single GPU.
Model Files
Download the model repository with the following directory structure:
Zing-0.5/
โโโ generator/
โ โโโ model.pt
โโโ pretrained/
โโโ text_encoder/
โโโ tokenizer/
โโโ vae/
generator/model.pt must directly contain the generator state dict. Parameter names and shapes are loaded strictly.
Inference
Use the standalone inference code from the Zing GitHub repository:
git clone https://github.com/seedleap/zing-world-model.git
cd zing-world-model
ZING_MODEL=/path/to/Zing-0.5
CUDA_VISIBLE_DEVICES=0 \
ZING_PYTHON=/path/to/python \
bash run.sh \
--pretrained-dir "$ZING_MODEL/pretrained" \
--checkpoint "$ZING_MODEL/generator/model.pt" \
--messages examples/case3_action_t2v.jsonl \
--output-dir outputs/case3 \
--seed 0
The code repository includes ready-to-run Action T2V and Action TI2V JSONL examples with the required reference images.
GPU Memory
| GPU memory | local_attn_size |
sink_size |
|---|---|---|
| 80 GB or more | 97 |
9 |
| Less than 80 GB | 33 |
5 |
The default 97/9 configuration has been validated on a single NVIDIA H100 80 GB. Use 33/5 on GPUs with less memory. Full-history attention is available with --local-attn-size -1 --sink-size 0.
Output
Each JSONL row produces one result named from sample_id. Multi-frame rollouts are saved as H.264 MP4 at 24 FPS.
Limitations
Long rollouts may exhibit visual drift or physical inconsistencies. Action responsiveness can vary with scene content and viewpoint.
License
Zing-0.5 is released under the Apache License 2.0.
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