Instructions to use JingyeChen22/textdiffuser2-lora-ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use JingyeChen22/textdiffuser2-lora-ft with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("JingyeChen22/textdiffuser2-lora-ft", 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
Add link to paper, tags (#1)
Browse files- Add link to paper, tags (648e7bc0db2c4e42dcff0e0a67d5c04a74636d26)
Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>
README.md
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---
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pipeline_tag: text-to-image
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library_name: diffusers
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license: mit
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---
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# Model
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This repo contains the LoRa tuned model of the paper [TextDiffuser-2: Unleashing the Power of Language Models for Text Rendering](https://huggingface.co/papers/2311.16465).
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# Usage
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The script [here](https://github.com/microsoft/unilm/tree/master/textdiffuser-2#firecracker-inference) can be used to perform inference with the model.
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