Instructions to use Avrik/mirrors-edge-diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Avrik/mirrors-edge-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("Avrik/mirrors-edge-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
- Local Apps
- Draw Things
- DiffusionBee
Commit ·
6356f78
1
Parent(s): b891e20
Add `clip_sample=False` to scheduler to make model compatible with DDIM. (#3)
Browse files- Add `clip_sample=False` to scheduler to make model compatible with DDIM. (91a71b4cd8775635178ee32468d790da7bb20dca)
Co-authored-by: Patrick von Platen <patrickvonplaten@users.noreply.huggingface.co>
scheduler/scheduler_config.json
CHANGED
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@@ -4,6 +4,7 @@
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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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"num_train_timesteps": 1000,
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"set_alpha_to_one": false,
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"skip_prk_steps": true,
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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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"num_train_timesteps": 1000,
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"set_alpha_to_one": false,
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"skip_prk_steps": true,
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