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
| { | |
| "_class_name": "IMFTransformer2DModel", | |
| "_diffusers_version": "0.38.0", | |
| "aux_head_depth": 8, | |
| "depth": 32, | |
| "embedding_init_constant": 1.0, | |
| "eval_mode": true, | |
| "hidden_size": 1024, | |
| "in_channels": 4, | |
| "mlp_ratio": 2.6666666666666665, | |
| "model_type": "iMF-L/2", | |
| "num_attention_heads": 16, | |
| "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 | |
| } | |