Instructions to use Aktraiser/text_image with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Aktraiser/text_image with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Aktraiser/text_image", 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
Fine-Tuned Text-to-Image Model
This model is a fine-tuned version based on Wan2.1-T2V-14B.
Note on Model Weights
Due to disk space limitations, the full model weights may not be included in this repository. The model was fine-tuned on a custom dataset for 10 steps with a learning rate of 0.0001.
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from diffusers import DiffusionPipeline
model = DiffusionPipeline.from_pretrained("your-username/text-image")
Parameters
- Steps: 10
- Learning rate: 0.0001
- Optimizer: adamw_8bit
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