Instructions to use maxmarcon/klimt-diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use maxmarcon/klimt-diffusion with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("maxmarcon/klimt-diffusion", 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
Epoch 90
Browse files
logs/train_unconditional/events.out.tfevents.1787167595.d8e948340e3d.2058.0
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unet/config.json
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{
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"_class_name": "UNet2DModel",
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"_diffusers_version": "0.39.0.dev0",
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"act_fn": "silu",
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"add_attention": true,
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"attention_head_dim": 8,
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"_class_name": "UNet2DModel",
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"_diffusers_version": "0.39.0.dev0",
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"_name_or_path": "ddpm-model-64/checkpoint-2000",
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"act_fn": "silu",
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"add_attention": true,
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"attention_head_dim": 8,
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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 sha256:4b66e150abf5c07156315d8ee73ec84a7aa9df8f81240f8fb2444b9c35e7d58e
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size 454741108
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