Text-to-Image
Diffusers
Safetensors
English
fd-loss
jit
imf
pmf
image-generation
class-conditional
imagenet
Instructions to use BiliSakura/FD-Loss-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use BiliSakura/FD-Loss-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("BiliSakura/FD-Loss-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
- Local Apps Settings
- Draw Things
- DiffusionBee
File size: 558 Bytes
6786303 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | {
"_class_name": "IMFTransformer2DModel",
"_diffusers_version": "0.38.0",
"aux_head_depth": 8,
"depth": 12,
"embedding_init_constant": 1.0,
"eval_mode": true,
"hidden_size": 768,
"in_channels": 4,
"mlp_ratio": 2.6666666666666665,
"model_type": "iMF-B/2",
"num_attention_heads": 12,
"num_cfg_tokens": 4,
"num_class_embeds": null,
"num_class_tokens": 8,
"num_classes": 1000,
"num_interval_tokens": 2,
"num_time_tokens": 4,
"patch_size": 2,
"sample_size": 32,
"token_init_constant": 1.0,
"weight_init_constant": 0.32
}
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