---
base_model:
- black-forest-labs/FLUX.1-dev
---
  
  
# 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:   
  
![image/png](https://cdn-uploads.huggingface.co/production/uploads/66851b2c4461866b07738832/48f5Gr9EP-pnblTP0mMax.png)


 ## How to Get Started with the Model  

  ## Diffusers

```shell
pip install -U diffusers
```
Use the code below to get started with the model.  
  
```python  
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},
  
}
```