Instructions to use pbevan11/stable-diffusion-2-typography with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use pbevan11/stable-diffusion-2-typography with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("pbevan11/stable-diffusion-2-typography", 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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license: openrail++
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license: openrail++
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tags:
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- stable-diffusion
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- text-to-image
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This is a finetuned version of [stabilityai/stable-diffusion-2-base](https://huggingface.co/stabilityai/stable-diffusion-2-base), optimised for outputting English text.
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The model is finetuned for 70 epochs on [pbevan11/GPT4V-captions-from-LVIS-typography](https://huggingface.co/datasets/pbevan11/GPT4V-captions-from-LVIS-typography), a curated dataset of image-caption pairs from LVIS with detailed transcriptions of the text present in the image.
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license: openrail++
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This is a finetuned version of [stabilityai/stable-diffusion-2-base](https://huggingface.co/stabilityai/stable-diffusion-2-base), optimised for outputting English text.
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The model is finetuned for 70 epochs on [pbevan11/GPT4V-captions-from-LVIS-typography](https://huggingface.co/datasets/pbevan11/GPT4V-captions-from-LVIS-typography), a curated dataset of image-caption pairs from LVIS with detailed transcriptions of the text present in the image.
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