Instructions to use hohs/SiTH_diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use hohs/SiTH_diffusion with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("hohs/SiTH_diffusion", 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
Delete scheduler/scheduler_scheduler_config.json
Browse files
scheduler/scheduler_scheduler_config.json
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{
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"_class_name": "DDIMScheduler",
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"_diffusers_version": "0.18.2",
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"beta_end": 0.012,
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"beta_schedule": "scaled_linear",
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"beta_start": 0.00085,
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"clip_sample": false,
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"clip_sample_range": 1.0,
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"dynamic_thresholding_ratio": 0.995,
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"num_train_timesteps": 1000,
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"prediction_type": "epsilon",
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"rescale_betas_zero_snr": false,
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"sample_max_value": 1.0,
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"set_alpha_to_one": false,
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"steps_offset": 1,
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"thresholding": false,
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"timestep_spacing": "leading",
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"trained_betas": null
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}
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