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
File size: 810 Bytes
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"_class_name": "DiTTransformer2DModel",
"_diffusers_version": "0.36.0",
"sample_size": 32,
"num_layers": 12,
"num_attention_heads": 12,
"attention_head_dim": 64,
"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-B/2",
"architecture": "standard",
"repeat": 1,
"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": 12,
"hidden_size": 768,
"num_heads": 12
}
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