Instructions to use Dahq/Test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dahq/Test with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Dahq/Test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Configuration Parsing Warning:Invalid JSON for config file config.json
welcome to my first modelcard made in huggingface, i will make stable diffusion models access from different pre trained models,
all of the models in this file are not trained by me
Model Card
Model Details
- Model Name: [GENERATOR-V1]
- Model Type: Stable Diffusion
- Architecture: [diffusers]
- Task: Text-to-Image Generation
- Training Dataset: [comming soon]
- Limitations and Biases: [this project is created only for education purpose, such as generating images for your project, PowerPoint and so on . Not recommended for generating +18 content. deoends upon owner of model]
Input and Output
- Input: for eg : a beautiful landscape of women sitting on corner of hill at full moon night , beautiful sparkling stars
- Output: Generated image
Usage Examples
# Example code for loading the model and generating an image
from diffusers import StableDiffusionPipeline
model_id = "runwayml/stable-diffusion-v1-5" # Replace with your model ID
pipeline = StableDiffusionPipeline.from_pretrained(model_id)
prompt = "A photo of a cat wearing a hat in a field of sunflowers"
image = pipeline(prompt, num_inference_steps=100) # Adjust num_inference_steps as needed
image.save("generated_art.png")
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