Instructions to use TejasNavada/tattoo-diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use TejasNavada/tattoo-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("TejasNavada/tattoo-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
- Local Apps
- Draw Things
- DiffusionBee
Commit ·
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Parent(s): fde171a
Update Revised.ipynb
Browse files- Revised.ipynb +1 -1
Revised.ipynb
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" lr_warmup_steps = 500\n",
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" save_image_epochs = 1\n",
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" save_model_epochs = 1\n",
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" token = \"
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" num_processes = 1\n",
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" mixed_precision = \"fp16\" # `no` for float32, `fp16` for automatic mixed precision\n",
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" output_dir = \"tattoo-diffusion\" # the model name locally and on the HF Hub\n",
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" lr_warmup_steps = 500\n",
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" save_image_epochs = 1\n",
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" save_model_epochs = 1\n",
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" token = \"XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\"\n",
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" num_processes = 1\n",
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" mixed_precision = \"fp16\" # `no` for float32, `fp16` for automatic mixed precision\n",
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" output_dir = \"tattoo-diffusion\" # the model name locally and on the HF Hub\n",
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