Instructions to use ModelsLab/blipdiffusion-controlnet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ModelsLab/blipdiffusion-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("ModelsLab/blipdiffusion-controlnet", torch_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: 355 Bytes
17b3108 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | {
"_class_name": "PNDMScheduler",
"_diffusers_version": "0.18.0.dev0",
"beta_end": 0.012,
"beta_schedule": "scaled_linear",
"beta_start": 0.00085,
"num_train_timesteps": 1000,
"prediction_type": "epsilon",
"set_alpha_to_one": false,
"skip_prk_steps": true,
"steps_offset": 0,
"timestep_spacing": "leading",
"trained_betas": null
}
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