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 0
Browse files- logs/train_unconditional/events.out.tfevents.1785527733.5c355a00dafa.2307.0 +3 -0
- logs/train_unconditional/events.out.tfevents.1785527951.5c355a00dafa.2830.0 +3 -0
- logs/train_unconditional/events.out.tfevents.1785528047.5c355a00dafa.3186.0 +3 -0
- unet/config.json +1 -1
- unet/diffusion_pytorch_model.safetensors +1 -1
logs/train_unconditional/events.out.tfevents.1785527733.5c355a00dafa.2307.0
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logs/train_unconditional/events.out.tfevents.1785528047.5c355a00dafa.3186.0
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unet/config.json
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"num_train_timesteps": null,
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"out_channels": 3,
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"resnet_time_scale_shift": "default",
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"sample_size":
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"time_embedding_dim": null,
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"time_embedding_type": "positional",
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"up_block_types": [
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"num_train_timesteps": null,
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"out_channels": 3,
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"resnet_time_scale_shift": "default",
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"sample_size": 256,
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"time_embedding_dim": null,
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"time_embedding_type": "positional",
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"up_block_types": [
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unet/diffusion_pytorch_model.safetensors
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