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---
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\>:
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.
[](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 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 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
```
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