Instructions to use fusing/ddpm-unet-rl-hopper-hor256 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use fusing/ddpm-unet-rl-hopper-hor256 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("fusing/ddpm-unet-rl-hopper-hor256", 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
Nathan Lambert commited on
upload model
Browse files- config.json +11 -0
- diffusion_model.pt +3 -0
config.json
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{
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"_class_name": "TemporalUnet",
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"_diffusers_version": "0.0.4",
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"training_horizon": 256,
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"predict_epsilon": false,
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"clip_denoised": true,
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"dim": 32,
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"dim_mults": [1, 4, 8],
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"transition_dim": 14,
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"cond_dim": 3
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}
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diffusion_model.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:3de6e911b10d0fb0352d0c55499eacec14240fbd66fa2f91a525d71a40c4cfec
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size 14794393
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