Instructions to use cuongdev/vtt-new with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use cuongdev/vtt-new with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("cuongdev/vtt-new", 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
Upload the concept vtt-new embeds and token
Browse files- README.md +13 -0
- test_vtt.ckpt +3 -0
README.md
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---
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license: creativeml-openrail-m
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tags:
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- text-to-image
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- stable-diffusion
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---
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### vtt-new Dreambooth model trained by cuongdev with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
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Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
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Sample pictures of this concept:
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test_vtt.ckpt
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version https://git-lfs.github.com/spec/v1
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oid sha256:097cd3f345209e99e5c1cc12a48a01ee8cb65e258bc0fa360560083dbb1c3c9c
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size 2607612494
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