Instructions to use YeBhoneLin10/bagan with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use YeBhoneLin10/bagan with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("YeBhoneLin10/bagan", dtype=torch.bfloat16, device_map="cuda") prompt = "bagan, by Vincent van gogh, highly detailed, highly illustration" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Update README.md
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README.md
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### Other Work:
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In our exploration of image generation, we delve into the architectural marvels of Myanmar, featuring iconic landmarks such as Ananda, Shwezigon, Bupaya, Thatbyinnyu, and Mraukoo. Each structure stands as a testament to the rich cultural and historical tapestry of the region, captured through the lens of our innovative text-to-image generator, General Bagan.
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### Results
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All our results are based on fine-tuning https://huggingface.co/runwayml/stable-diffusion-v1-5 model.
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We display the results using a range of training samples and images from different image categories, such as pagodas and Buddha statues.
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### Ai Generated Bagan Images:
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### References:
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Wikipedia (2022). Stable Diffusion. Retrieved From: https://en.wikipedia.org/wiki/Stable_Diffusion
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### Other Work:
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In our exploration of image generation, we delve into the architectural marvels of Myanmar, featuring iconic landmarks such as Ananda, Shwezigon, Bupaya, Thatbyinnyu, and Mraukoo. Each structure stands as a testament to the rich cultural and historical tapestry of the region, captured through the lens of our innovative text-to-image generator, General Bagan.
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### References:
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Wikipedia (2022). Stable Diffusion. Retrieved From: https://en.wikipedia.org/wiki/Stable_Diffusion
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