Unconditional Image Generation
Diffusers
TensorBoard
Safetensors
English
DDPMPipeline
diffusion
image generation
unconditional
wsi
Instructions to use kaveh/wsi_generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use kaveh/wsi_generator with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("kaveh/wsi_generator", 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
- checkpoint-164000/unet/diffusion_pytorch_model.safetensors +3 -0
- checkpoint-164000/unet_ema/diffusion_pytorch_model.safetensors +3 -0
- checkpoint-168000/unet/diffusion_pytorch_model.safetensors +3 -0
- checkpoint-168000/unet_ema/diffusion_pytorch_model.safetensors +3 -0
- unet/diffusion_pytorch_model.safetensors +3 -0
checkpoint-164000/unet/diffusion_pytorch_model.safetensors
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oid sha256:e40ddf0fca629a210203af153c3750af2ab6718b66cdd647314379b80653d7fb
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checkpoint-164000/unet_ema/diffusion_pytorch_model.safetensors
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oid sha256:e9279cca33af9ee6efd4330c2854689e7afd1684d0a7adb808834dc8a39ca199
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oid sha256:ed3108a18ae4f547b3fdbff3e15f65faf6fc2a3cbe6b401086ad1ccbe0e03d3d
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checkpoint-168000/unet_ema/diffusion_pytorch_model.safetensors
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oid sha256:fddd65a838444f342c53ac1a566c49d6720bba4bbe341bfee5e3743578c866d1
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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 sha256:87e17b5edb6640071c309c3f5d2131440e3ac10a58cde787facee82f1faf9ffd
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size 454741108
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