MISHANM/image_generation_FLUX.1-dev

The MISHANM/image_generation model is a diffusion-based image generation model . It is designed to generate high-quality images from textual prompts using advanced diffusion techniques.

Model Details

1. Language: English
2. Tasks: Imgae Generation

Model Example output

This is the model inference output:

How to Get Started with the Model

Diffusers

pip install -U diffusers

Use the code below to get started with the model.

import torch  
from diffusers import FluxPipeline  
from PIL import Image  
  
# Load the pre-trained model  
model = FluxPipeline.from_pretrained("MISHANM/image_generation_FLUX.1-dev", torch_dtype=torch.bfloat16, device_map="balanced")  
model.enable_model_cpu_offload()  
  
def generate_image(prompt):  
    image = model(  
        prompt,  
        height=512,  
        width=512,  
        guidance_scale=3.5,  
        num_inference_steps=30,  
        max_sequence_length=512,  
        generator=torch.Generator("cpu").manual_seed(0)  
    ).images[0]  
    return image  
  
prompt = input("Enter your prompt here: ")  
image = generate_image(prompt)  
image.show()  
image.save("generated_image.png")  

Uses

Direct Use

The model is intended for generating images from textual descriptions. It can be used in creative applications, content generation, and artistic exploration.

Out-of-Scope Use

The model is not suitable for generating images with explicit or harmful content. It may not perform well with highly abstract or nonsensical prompts.

Bias, Risks, and Limitations

The model may reflect biases present in the training data. It may generate stereotypical or biased images based on the input prompts.

Recommendations

Users should be aware of potential biases and limitations. It is recommended to review generated content for appropriateness and accuracy.

Citation Information

@misc{MISHANM/image_generation_FLUX.1-dev,
  author = {Mishan Maurya},
  title = {Introducing Image Generation model},
  year = {2025},
  publisher = {Hugging Face},
  journal = {Hugging Face repository},
  
}