Instructions to use onkarsus13/ViCTr-BTCV with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use onkarsus13/ViCTr-BTCV with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("onkarsus13/ViCTr-BTCV", 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": "0.30.0.dev0", | |
| "_name_or_path": "/data2/onkar/5bit2v_model/models--onkarsus13--Semantic-Control-Stable-diffusion-3-M-Mask2CT-Atlas/snapshots/80941ffedfc33362b9322e8cc397952fa0bd9f57/controlnet", | |
| "attention_head_dim": 64, | |
| "caption_projection_dim": 1536, | |
| "in_channels": 32, | |
| "in_channels2": 16, | |
| "joint_attention_dim": 4096, | |
| "num_attention_heads": 24, | |
| "num_layers": 6, | |
| "out_channels": 16, | |
| "patch_size": 2, | |
| "pooled_projection_dim": 2048, | |
| "pos_embed_max_size": 192, | |
| "sample_size": 128 | |
| } | |