Text-to-Image
Diffusers
Safetensors
English
Text-to-Image
ControlNet
Diffusers
Flux.1-dev
image-generation
Stable Diffusion
Instructions to use Shakker-Labs/RepText with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Shakker-Labs/RepText with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Shakker-Labs/RepText", 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
Update README.md
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README.md
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@@ -27,7 +27,7 @@ We present RepText, which aims to empower pre-trained monolingual text-to-image
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## ⭐ Update
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- [2025/06/07] [Model Weights](https://huggingface.co/Shakker-Labs/RepText) and inference code released!
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- [2025/04/28] [Technical Report](https://arxiv.org/abs/2504.19724) released!
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## Usage
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</div>
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## ⭐ Update
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- [2025/06/07] [Model Weights](https://huggingface.co/Shakker-Labs/RepText) and [inference code](https://github.com/Shakker-Labs/RepText) released!
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- [2025/04/28] [Technical Report](https://arxiv.org/abs/2504.19724) released!
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## Usage
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