Instructions to use DavyMorgan/tiny-controlnet-sd3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use DavyMorgan/tiny-controlnet-sd3 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("DavyMorgan/tiny-controlnet-sd3", 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
File size: 649 Bytes
8abce2e 3243703 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | Pipeline generated with
```python
import torch
from diffusers.models import SD3ControlNetModel
def get_dummy_components_controlnet():
torch.manual_seed(0)
controlnet = SD3ControlNetModel(
sample_size=32,
patch_size=1,
in_channels=8,
num_layers=1,
attention_head_dim=8,
num_attention_heads=4,
joint_attention_dim=32,
caption_projection_dim=32,
pooled_projection_dim=64,
out_channels=8,
)
return controlnet
if __name__ == "__main__":
controlnet = get_dummy_components_controlnet()
controlnet.push_to_hub("DavyMorgan/tiny-controlnet-sd3")
``` |