Unconditional Image Generation
Diffusers
Safetensors
English
afm
adversarial-flow-models
class-conditional
imagenet
Instructions to use BiliSakura/AFM-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use BiliSakura/AFM-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/AFM-diffusers", torch_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
| { | |
| "_class_name": "DiTTransformer2DModel", | |
| "_diffusers_version": "0.36.0", | |
| "sample_size": 32, | |
| "num_layers": 28, | |
| "num_attention_heads": 16, | |
| "attention_head_dim": 72, | |
| "in_channels": 4, | |
| "out_channels": 4, | |
| "patch_size": 2, | |
| "attention_bias": true, | |
| "activation_fn": "gelu-approximate", | |
| "num_embeds_ada_norm": 1000, | |
| "norm_type": "ada_norm_zero", | |
| "norm_elementwise_affine": false, | |
| "dropout": 0.0, | |
| "norm_num_groups": 32, | |
| "norm_eps": 1e-05, | |
| "upcast_attention": false, | |
| "model_type": "AFM-XL/2", | |
| "architecture": "deep", | |
| "repeat": 4, | |
| "use_t_src": false, | |
| "use_t_tgt": false, | |
| "pred_type": "x", | |
| "num_inference_steps": 1, | |
| "learn_sigma": false, | |
| "class_dropout_prob": 0.0, | |
| "input_size": 32, | |
| "num_classes": 1000, | |
| "depth": 28, | |
| "hidden_size": 1152, | |
| "num_heads": 16 | |
| } | |