Instructions to use bytedance-research/OneReward with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use bytedance-research/OneReward with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("bytedance-research/OneReward", dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
File size: 388 Bytes
f516def | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | {
"_class_name": "FluxTransformer2DModel",
"_diffusers_version": "0.35.1",
"attention_head_dim": 128,
"axes_dims_rope": [
16,
56,
56
],
"guidance_embeds": true,
"in_channels": 384,
"joint_attention_dim": 4096,
"num_attention_heads": 24,
"num_layers": 19,
"num_single_layers": 38,
"out_channels": 64,
"patch_size": 1,
"pooled_projection_dim": 768
}
|