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
File size: 572 Bytes
61de40b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | {
"_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
}
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