Diffusers
Safetensors
PyTorch
hy-nomas
world-model
video-generation
interactive-video-generation
camera-control
autoregressive
diffusion-transformer
Instructions to use tencent/Hy-Nomas-Diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use tencent/Hy-Nomas-Diffusers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("tencent/Hy-Nomas-Diffusers", 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
File size: 594 Bytes
a1ae04d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | {
"_class_name": "PixelUMMDecoder",
"_diffusers_version": "0.42.0.dev0",
"decoder_tile_overlap_pixels": 64,
"decoder_tile_pixels": 0,
"denoising_step_list": [
0.999,
0.866,
0.634,
0.342
],
"head_dim": 128,
"hidden_size": 4096,
"intermediate_size": 12288,
"local_attention_tile_pixels": 128,
"num_heads": 32,
"num_kv_heads": 8,
"num_layers": 36,
"num_steps": 1,
"rms_norm_eps": 1e-06,
"rope_theta_hw": 10000.0,
"rope_theta_t": 1000000.0,
"seed": 42,
"tail_hold_latents": 1,
"temporal_overlap_latents": 5,
"temporal_window_latents": 10
}
|