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 config.json
Browse files- config.json +4 -6
config.json
CHANGED
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@@ -2,13 +2,11 @@
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"model_type": "UNet",
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"T": 1000,
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"channel": 128,
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"channel_mult": [1, 2,
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"attn": [
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"num_res_blocks": 2,
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"dropout": 0.
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"image_size": 32,
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"in_channels": 3,
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"out_channels": 3
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"time_embedding_dim": 512, // 128 * 4
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"norm_num_groups": 32
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}
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"model_type": "UNet",
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"T": 1000,
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"channel": 128,
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"channel_mult": [1, 2, 3, 4],
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"attn": [2],
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"num_res_blocks": 2,
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"dropout": 0.15,
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"image_size": 32,
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"in_channels": 3,
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"out_channels": 3
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}
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