Instructions to use bglick13/hopper-medium-v2-value-function-hor32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use bglick13/hopper-medium-v2-value-function-hor32 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("bglick13/hopper-medium-v2-value-function-hor32", 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
Upload 2 files
Browse files- config.json +1 -1
- diffusion_pytorch_model.bin +2 -2
config.json
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{"in_channels": 14, "down_block_types": ["DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"], "block_out_channels": [32, 64, 128, 256], "layers_per_block": 1}
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{"in_channels": 14, "down_block_types": ["DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"], "up_block_types": [], "out_block_type": "ValueFunction", "block_out_channels": [32, 64, 128, 256], "layers_per_block": 1, "always_downsample": true}
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diffusion_pytorch_model.bin
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