Instructions to use JosephusCheung/RuminationDiffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use JosephusCheung/RuminationDiffusion with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("JosephusCheung/RuminationDiffusion", dtype=torch.bfloat16, device_map="cuda") prompt = "masterpiece, best quality, anime, 1girl, brown hair, green eyes, colorful, autumn, cumulonimbus clouds, lighting, blue sky, garden, looking at viewer" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Commit ·
919a3f2
1
Parent(s): 6826410
Update scheduler/scheduler_config.json
Browse files
scheduler/scheduler_config.json
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@@ -6,7 +6,7 @@
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"beta_start": 0.00085,
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"clip_sample": false,
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"num_train_timesteps": 1000,
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"prediction_type": "
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"set_alpha_to_one": false,
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"skip_prk_steps": true,
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"steps_offset": 1,
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"beta_start": 0.00085,
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"clip_sample": false,
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"num_train_timesteps": 1000,
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"prediction_type": "v_prediction",
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"set_alpha_to_one": false,
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"skip_prk_steps": true,
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"steps_offset": 1,
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