Instructions to use BackTo2014/DDPM-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use BackTo2014/DDPM-test with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("BackTo2014/DDPM-test", 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
Update scheduler_config.json
Browse files- scheduler_config.json +1 -3
scheduler_config.json
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@@ -4,7 +4,5 @@
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"beta_schedule": "linear",
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"steps_offset": 1,
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"clip_sample": false,
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"set_alpha_to_one": true
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"skip_prk": true,
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"skip_timesteps": false
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}
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"beta_schedule": "linear",
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"steps_offset": 1,
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"clip_sample": false,
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"set_alpha_to_one": true
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}
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