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--- |
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base_model: |
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- black-forest-labs/FLUX.1-dev |
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--- |
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# MISHANM/image_generation_FLUX.1-dev |
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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. |
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## Model Details |
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1. Language: English |
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2. Tasks: Imgae Generation |
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### Model Example output |
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This is the model inference output: |
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## How to Get Started with the Model |
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## Diffusers |
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```shell |
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pip install -U diffusers |
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``` |
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Use the code below to get started with the model. |
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```python |
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import torch |
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from diffusers import FluxPipeline |
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from PIL import Image |
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# Load the pre-trained model |
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model = FluxPipeline.from_pretrained("MISHANM/image_generation_FLUX.1-dev", torch_dtype=torch.bfloat16, device_map="balanced") |
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def generate_image(prompt): |
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image = model( |
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prompt, |
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height=512, |
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width=512, |
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guidance_scale=3.5, |
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num_inference_steps=30, |
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max_sequence_length=512, |
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generator=torch.Generator("cpu").manual_seed(0) |
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).images[0] |
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return image |
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prompt = input("Enter your prompt here: ") |
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image = generate_image(prompt) |
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image.show() |
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image.save("generated_image.png") |
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``` |
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## Uses |
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### Direct Use |
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The model is intended for generating images from textual descriptions. It can be used in creative applications, content generation, and artistic exploration. |
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### Out-of-Scope Use |
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The model is not suitable for generating images with explicit or harmful content. It may not perform well with highly abstract or nonsensical prompts. |
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## Bias, Risks, and Limitations |
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The model may reflect biases present in the training data. It may generate stereotypical or biased images based on the input prompts. |
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### Recommendations |
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Users should be aware of potential biases and limitations. It is recommended to review generated content for appropriateness and accuracy. |
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## Citation Information |
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``` |
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@misc{MISHANM/image_generation_FLUX.1-dev, |
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author = {Mishan Maurya}, |
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title = {Introducing Image Generation model}, |
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year = {2025}, |
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publisher = {Hugging Face}, |
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journal = {Hugging Face repository}, |
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} |
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``` |
