Instructions to use obvious-research/FLUX.1-dev-ControlNet-Proportion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use obvious-research/FLUX.1-dev-ControlNet-Proportion with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("obvious-research/FLUX.1-dev-ControlNet-Proportion", 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: 477 Bytes
361e311 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | {
"_class_name": "FluxControlNetModel",
"_diffusers_version": "0.34.0.dev0",
"_name_or_path": "checkpoint/checkpoint-8600",
"attention_head_dim": 128,
"axes_dims_rope": [
16,
56,
56
],
"conditioning_embedding_channels": null,
"guidance_embeds": true,
"in_channels": 64,
"joint_attention_dim": 4096,
"num_attention_heads": 24,
"num_layers": 4,
"num_mode": null,
"num_single_layers": 0,
"patch_size": 1,
"pooled_projection_dim": 768
}
|