ART Text-to-Image Generation using adrenex/outfitt2i

This repository contains code and instructions for using the adrenex/outfitt2i model from Hugging Face's Transformers library to generate images from textual descriptions. The model utilizes diffusion models for high-quality image synthesis based on the provided text prompts.

Model Information

Tags:
- text-to-image
- diffusers
- autotrain

Inference

To use this model for generating images from text prompts, follow these steps:

Environment Setup: Make sure you have Python installed on your system. You can also use a virtual environment for isolation.
Install Dependencies: Install the required Python packages by running the following command:
```
pip install -r requirements.txt
```

3.## Usage

from diffusers import DiffusionPipeline
import torch

# Load LoRA weights
lora_weights = torch.load("/path/to/lora_weights/pytorch_lora_weights.safetensors")

# Initialize the DiffusionPipeline
pipe = DiffusionPipeline.from_pretrained("adrenex/outfitt2i", torch_dtype=torch.float16)
pipe.to("cuda")

# Load LoRA weights into the pipeline
pipe.load_lora_weights(lora_weights)

# Text prompt for image generation
prompt = "photo of Iyad Radi with cat in the pool"

# Generate Images
image = pipe(prompt, num_inference_steps=30, guidance_scale=7.5).images

Generated Images: The generated images will be saved in the output_images directory by default.

Application in Art and Cinema Industry

This model can be incredibly useful in the art and cinema movie production industry, especially for creating visuals based on textual descriptions. In the case of Aiyad Radi, an Iraqi actor and comedian, this tool can aid in visualizing character designs, scenes, and concepts before actual production. Directors, artists, and producers can utilize the generated images as a reference to plan and visualize their projects effectively.

Credits

This repository is created and maintained by [Falah.G.Saleih]

Disclaimer

Please note that the model's outputs might vary, and the generated images are based on the input text prompts. The model's behavior is influenced by its training data and might not always produce accurate or desired results.