Instructions to use tensorart/Bokeh_Line_Controlnet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use tensorart/Bokeh_Line_Controlnet with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("tensorart/Bokeh_Line_Controlnet", 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": "SD3ControlNetModel", | |
| "_diffusers_version": "0.32.0.dev0", | |
| "_name_or_path": "tensorart/bokeh_3.5_medium", | |
| "attention_head_dim": 64, | |
| "caption_projection_dim": 1536, | |
| "dual_attention_layers": [ | |
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| ], | |
| "extra_conditioning_channels": 0, | |
| "in_channels": 16, | |
| "joint_attention_dim": 4096, | |
| "num_attention_heads": 24, | |
| "num_layers": 23, | |
| "out_channels": 16, | |
| "patch_size": 2, | |
| "pooled_projection_dim": 2048, | |
| "pos_embed_max_size": 384, | |
| "qk_norm": "rms_norm", | |
| "sample_size": 128 | |
| } | |