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
| { | |
| "_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 | |
| } | |