Instructions to use kxic/eschernet-4dof with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use kxic/eschernet-4dof with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("kxic/eschernet-4dof", 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
Adding `safetensors` variant of this model
#1
by SFconvertbot - opened
image_encoder/model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:242f09007f4a65dc54274155c29a35912859a0d2dd6a69a298520c7e646b0bd9
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size 111487328
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unet/diffusion_pytorch_model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:1ae62a40e6495f829e908da9cb9fdd2534cd0fd9a4b634abd8096dac919a70b5
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size 3438167536
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vae/diffusion_pytorch_model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:0f3f3f38d1393f412ccfbd044b4920b7aee74c520dc01436fac38e456bf83d5e
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size 167335590
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