Instructions to use codeShare/Flux2Klein_AIO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use codeShare/Flux2Klein_AIO with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("codeShare/Flux2Klein_AIO", 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
- Draw Things
- DiffusionBee
File size: 541 Bytes
89e22c7 | 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 | {
"_class_name": "Flux2Transformer2DModel",
"_diffusers_version": "0.38.0.dev0",
"_name_or_path": "/raid/yiyi/klein-4b-distilled-diffusers/transformer",
"attention_head_dim": 128,
"axes_dims_rope": [
32,
32,
32,
32
],
"eps": 1e-06,
"guidance_embeds": false,
"in_channels": 128,
"joint_attention_dim": 7680,
"mlp_ratio": 3.0,
"num_attention_heads": 24,
"num_layers": 5,
"num_single_layers": 20,
"out_channels": null,
"patch_size": 1,
"rope_theta": 2000,
"timestep_guidance_channels": 256
}
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