Instructions to use CodeGoat24/Wan2.1-T2V-14B-UnifiedReward-Flex-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use CodeGoat24/Wan2.1-T2V-14B-UnifiedReward-Flex-lora with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("CodeGoat24/Wan2.1-T2V-14B-UnifiedReward-Flex-lora", 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
- Xet hash:
- 044235ff9ac0f8eeeee6ab33c41c152c621e50562f5ea7514aef00700213afab
- Size of remote file:
- 420 MB
- SHA256:
- 5934ae55d76191d28f20b0cc309859d8e7f6b879ca989174ad56e7544207e00d
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