Instructions to use kxic/EscherNet_demo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use kxic/EscherNet_demo 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_demo", 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:d69724bea0185ad1dafea5e02f67de710c0fd64bbb2c1906d442f3ff5f800c64
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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 sha256:8c82d3df7902ad522e941df53dd3774b887c4e8b35d7504612a392f1bac4d92d
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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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