README.md

---
base_model: black-forest-labs/FLUX.1-dev
library_name: diffusers
license: apache-2.0
tags:
- text-to-image
- diffusers-training
- diffusers
- lora
- FLUX.1-dev
- science
- materiomics
- bio-inspired
- materials science
- generative AI for science
datasets:
- lamm-mit/leaf-flux-images-and-captions
instance_prompt: <leaf microstructure>
widget: []
---

# FLUX.1 [dev] Fine-tuned with Leaf Images

FLUX.1 [dev] is a 12 billion parameter rectified flow transformer capable of generating images from text descriptions. 

Install ```diffusers```

```raw
pip install -U diffusers
```

## Model description

These are LoRA adaption weights for the FLUX.1 [dev] model (```black-forest-labs/FLUX.1-dev```). This is a gated model, you must first get access to it before loading this LoRA adapter. 

## Trigger keywords

The following images were used during fine-tuning using the keyword \<leaf microstructure\>:

![image/png](https://cdn-uploads.huggingface.co/production/uploads/623ce1c6b66fedf374859fe7/sI_exTnLy6AtOFDX1-7eq.png)

Dataset used for training: [lamm-mit/leaf-flux-images-and-captions](https://huggingface.co/datasets/lamm-mit/leaf-flux-images-and-captions)

You should use \<leaf microstructure\> to trigger this feature during image generation.


[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/#fileId=https://huggingface.co/lamm-mit/leaf-FLUX.1-dev/resolve/main/leaf-FLUX-inference-example.ipynb)


## How to use

Defining some helper functions:

```python
import os
from datetime import datetime
from PIL import Image

def generate_filename(base_name, extension=".png"):
    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
    return f"{base_name}_{timestamp}{extension}"

def save_image(image, directory, base_name="image_grid"):
    filename = generate_filename(base_name)
    file_path = os.path.join(directory, filename)
    image.save(file_path)
    print(f"Image saved as {file_path}")

def image_grid(imgs, rows, cols, save=True, save_dir='generated_images', base_name="image_grid",
              save_individual_files=False):
    
    if not os.path.exists(save_dir):
        os.makedirs(save_dir)
        
    assert len(imgs) == rows * cols

    w, h = imgs[0].size
    grid = Image.new('RGB', size=(cols * w, rows * h))
    grid_w, grid_h = grid.size

    for i, img in enumerate(imgs):
        grid.paste(img, box=(i % cols * w, i // cols * h))
        if save_individual_files:
            save_image(img, save_dir, base_name=base_name+f'_{i}-of-{len(imgs)}_')
            
    if save and save_dir:
        save_image(grid, save_dir, base_name)
    
    return grid
```

### Text-to-image

Model loading:

```python
from diffusers import FluxPipeline
import torch

repo_id = 'lamm-mit/leaf-FLUX.1-dev'

pipeline = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-dev",
    torch_dtype=torch.bfloat16,
    max_sequence_length=512,
)

#pipeline.enable_model_cpu_offload() #save some VRAM by offloading the model to CPU. Comment out if you have enough GPU VRAM

pipeline.load_lora_weights(repo_id, #weight_name=f'XXX.safetensors'
                          )
pipeline=pipeline.to('cuda')
```
Image generation - Example #1:

```python
prompt=('Generate an image of a golden spider web network intertwined with collagen veins, '
        'forming a dynamic, leaf-inspired microstructure amidst a lush green background.'  )

num_samples =2
num_rows = 2
n_steps=25
guidance_scale=3.5
all_images = []
for _ in range(num_rows):
     
        
    image = pipeline(prompt,num_inference_steps=n_steps,num_images_per_prompt=num_samples,
                     guidance_scale=guidance_scale,).images
     
    all_images.extend(image)

grid = image_grid(all_images, num_rows, num_samples,
                  save_individual_files=True,  )
grid
```

![image/png](https://cdn-uploads.huggingface.co/production/uploads/623ce1c6b66fedf374859fe7/0l-gytnGs_B6r441rT4mt.png)

Image generation - Example #2:

```python
prompt="""Generate a futuristic, eco-friendly architectural concept utilizing a biomimetic composite material that integrates the structural efficiency of spider silk with the adaptive porosity of plant tissues. Utilize the following key features:

* Fibrous architecture inspired by spider silk, represented by sinuous lines and curved forms.
* Interconnected, spherical nodes reminiscent of plant cell walls, emphasizing growth and adaptation.
* Open cellular structures echoing the permeable nature of plant leaves, suggesting dynamic exchanges and self-regulation capabilities.
* Gradations of opacity and transparency inspired by the varying densities found in plant tissues, highlighting functional differentiation and multi-functionality.
"""

num_samples =2
num_rows = 2
n_steps=25
guidance_scale=3.5
all_images = []
for _ in range(num_rows):
     
        
    image = pipeline(prompt,num_inference_steps=n_steps,num_images_per_prompt=num_samples,
                     guidance_scale=guidance_scale,).images
     
    all_images.extend(image)

grid = image_grid(all_images, num_rows, num_samples,
                  save_individual_files=True,  )
grid
```

![image/png](https://cdn-uploads.huggingface.co/production/uploads/623ce1c6b66fedf374859fe7/8jFLndvhGkH6iDV25MNQ2.png)

Image generation - Example #3:

```python
prompt="""A cube in the shape of a <leaf microstructure>, made out of limestone, holding a sign that says 'MATERIOMICS'. 

The cube is placed in a stunning mountain landscape.

The cube shows intricate patterns of <leaf microstructure>.
"""

num_samples =2
num_rows = 2
n_steps=25
guidance_scale=3.5
all_images = []
for _ in range(num_rows):
     
        
    image = pipeline(prompt,num_inference_steps=n_steps,num_images_per_prompt=num_samples,
                     guidance_scale=guidance_scale,).images
     
    all_images.extend(image)

grid = image_grid(all_images, num_rows, num_samples,
                  save_individual_files=True,  )
grid
```


![image/png](https://cdn-uploads.huggingface.co/production/uploads/623ce1c6b66fedf374859fe7/xF7TYfNCgHi1dxPtxNyWp.png)

```bibtext
@article{LuLuuBuehler2024,
  title={Fine-tuning large language models for domain adaptation: Exploration of training strategies, scaling, model merging and synergistic capabilities},
  author={Wei Lu and Rachel K. Luu and Markus J. Buehler},
  journal={arXiv: https://arxiv.org/abs/2409.03444},
  year={2024},
}
```