Instructions to use Xinxi-Zhang/Fine-Tune-Diffusion-Vivian with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Xinxi-Zhang/Fine-Tune-Diffusion-Vivian with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Xinxi-Zhang/Fine-Tune-Diffusion-Vivian", 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 Settings
- Draw Things
- DiffusionBee
Xinxi Zhang commited on
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Parent(s): 1282c5e
Update README.md
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README.md
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@@ -14,6 +14,11 @@ The main goal is to fine-tune the Stable Diffusion model to generate images refl
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And I chose to utilize a Jupyter Notebook to make the fine-tuning process accessible and easy to understand, particularly for those new to the diffusion pipeline and hugging face API.
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## Examples(at epoch 90)
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And I chose to utilize a Jupyter Notebook to make the fine-tuning process accessible and easy to understand, particularly for those new to the diffusion pipeline and hugging face API.
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# Requirements
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To launch the finetuning with a batch_size of 1 you need to have a gpu with at least 24G VRAM (you can use accumulating gradient to simulate higher batch size)
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Make sure that you have enough disk space, the model uses ~11Gb
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## Examples(at epoch 90)
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