Text-to-Image
Diffusers
Safetensors
English
Text-to-Image
ControlNet
Diffusers
Flux.1-dev
image-generation
Stable Diffusion
Instructions to use Shakker-Labs/FLUX.1-dev-ControlNet-Depth with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Shakker-Labs/FLUX.1-dev-ControlNet-Depth with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Shakker-Labs/FLUX.1-dev-ControlNet-Depth", 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
| { | |
| "_class_name": "FluxControlNetModel", | |
| "_diffusers_version": "0.30.0.dev0", | |
| "attention_head_dim": 128, | |
| "axes_dims_rope": [ | |
| 16, | |
| 56, | |
| 56 | |
| ], | |
| "guidance_embeds": true, | |
| "in_channels": 64, | |
| "joint_attention_dim": 4096, | |
| "num_attention_heads": 24, | |
| "num_layers": 4, | |
| "num_single_layers": 1, | |
| "patch_size": 1, | |
| "pooled_projection_dim": 768 | |
| } | |