base_model:
- black-forest-labs/FLUX.1-dev
MISHANM/image_generation_FLUX.1-dev
The MISHANM/image_generation_FLUX.1-dev 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
- Language: English
- 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")
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},
}