Instructions to use cat666/VToooo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use cat666/VToooo with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("cat666/VToooo", 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
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I used nearly 200,000 data for training, learning_rate 5e-6, training with a6000x1,
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,Forget it, I'm overtraining, take it as an interesting model,(Warning: above 768x832 is recommended, I found that the results below seem to be less than ideal)
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(It's not finished yet, this is just an early version
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inference: true
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I used nearly 200,000 data for training, learning_rate 2.5e-6, training with a6000x1, because I am too busy recently, I should not be able to actively do it, and the funds are slightly insufficient
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,Forget it, I'm overtraining, take it as an interesting model,(Warning: above 768x832 is recommended, I found that the results below seem to be less than ideal)
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(It's not finished yet, this is just an early version
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