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 Settings
- Draw Things
- DiffusionBee
| license: mit | |
| tags: | |
| - text-to-image | |
| - diffusion | |
| - wan2.1 | |
| - fine-tuned | |
| library_name: diffusers | |
| pipeline_tag: text-to-image | |
| # 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. | |
| ## Loading | |
| ```python | |
| from diffusers import DiffusionPipeline | |
| model = DiffusionPipeline.from_pretrained("your-username/text-image") | |
| ``` | |
| ## Parameters | |
| - Steps: 10 | |
| - Learning rate: 0.0001 | |
| - Optimizer: adamw_8bit | |