Instructions to use biang889/wan-game-i2v-14b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use biang889/wan-game-i2v-14b with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("biang889/wan-game-i2v-14b", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
WAN Game I2V 14B (24-GPU run)
Two training checkpoints from wan_game_i2v_14b_24gpu.
| subfolder | step | trained at |
|---|---|---|
checkpoint-3000/transformer |
3000 | 2026-04-24 |
checkpoint-10000/transformer |
10000 | 2026-04-27 |
Architecture: WanTransformer3DModel, 40 layers, 14B params, in_channels=36, num_attention_heads=40, attention_head_dim=128.
Load
from diffusers import WanTransformer3DModel
model = WanTransformer3DModel.from_pretrained(
"biang889/wan-game-i2v-14b",
subfolder="checkpoint-10000/transformer",
)
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