README.md

---
base_model: stabilityai/stable-diffusion-xl-base-1.0
library_name: diffusers
license: openrail++
tags:
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
instance_prompt: <leaf microstructure>
widget: []
---

# SDXL Fine-tuned with Leaf Images

## Model description

These are LoRA adaption weights for the SDXL-base-1.0 model.

## Trigger keywords

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

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

You should use <leaf microstructure> to trigger the image generation.

## How to use

Defining some helper functions:

```python
from diffusers import DiffusionPipeline
import torch
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

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

repo_id='lamm-mit/SDXL-leaf-inspired'

vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
base = DiffusionPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0",
    vae=vae,
    torch_dtype=torch.float16,
    variant="fp16",
    use_safetensors=True
)
base.load_lora_weights(repo_id)
_ = base.to("cuda")

refiner = DiffusionPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-refiner-1.0",
    text_encoder_2=base.text_encoder_2,
    vae=base.vae,
    torch_dtype=torch.float16,
    use_safetensors=True,
    variant="fp16",
)
refiner.to("cuda")
```

Image generation:

```python
prompt = "a vase that resembles a <leaf microstructure>, high quality" 

num_samples    = 4
num_rows       = 4
guidance_scale = 15

all_images = []

for _ in range(num_rows):
    # Define how many steps and what % of steps to be run on each experts (80/20)
    n_steps = 25
    high_noise_frac = 0.8

    # run both experts
    image = base(
        prompt=prompt,
        num_inference_steps=n_steps, guidance_scale=guidance_scale,
        denoising_end=high_noise_frac,num_images_per_prompt=num_samples,
        output_type="latent",
    ).images
    image = refiner(
        prompt=prompt,
        num_inference_steps=n_steps, guidance_scale=guidance_scale,
        denoising_start=high_noise_frac,num_images_per_prompt=num_samples,
        image=image,
    ).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/R7sr9kAwZjRk_80oMY54h.png)

## Fine-tuning script 

Download this script: [SDXL DreamBooth-LoRA_Fine-Tune.ipynb](https://huggingface.co/lamm-mit/SDXL-leaf-inspired/resolve/main/SDXL_DreamBooth_LoRA_Fine-Tune.ipynb)

You need to create a local folder ```leaf_concept_dir_SDXL``` and add the leaf images (provided in this repository, see subfolder). 

The code will automatically download the training script. 

The training script can handle custom prompts associated with each image, which are generated using BLIP.

For instance, for the images used here, they are:

```raw
['<leaf microstructure>, a close up of a green plant with a lot of small holes',
 '<leaf microstructure>, a close up of a leaf with a small insect on it',
 '<leaf microstructure>, a close up of a plant with a lot of green leaves',
 '<leaf microstructure>, a close up of a green plant with a yellow light',
 '<leaf microstructure>, a close up of a green plant with a white center',
 '<leaf microstructure>, arafed leaf with a white line on the center',
 '<leaf microstructure>, a close up of a leaf with a yellow light shining through it',
 '<leaf microstructure>, arafed image of a green plant with a yellow cross']
```

Training then proceeds as:

```python
HF_username = 'lamm-mit'

pretrained_model_name_or_path="stabilityai/stable-diffusion-xl-base-1.0" 
pretrained_vae_model_name_or_path="madebyollin/sdxl-vae-fp16-fix"
 
instance_prompt ="<leaf microstructure>"
instance_data_dir = "./leaf_concept_dir_SDXL/"

val_prompt = "a vase that resembles a <leaf microstructure>, high quality" 
val_epochs = 100

instance_output_dir="leaf_LoRA_SDXL_V10" #for checkpointing
```

Dataset generatio with custom per-image captions
```python
import requests
from transformers import AutoProcessor, BlipForConditionalGeneration
import torch
import glob
from PIL import Image
import json

device = "cuda" if torch.cuda.is_available() else "cpu"

# load the processor and the captioning model
blip_processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
blip_model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large",torch_dtype=torch.float16).to(device)

# captioning utility
def caption_images(input_image):
    inputs = blip_processor(images=input_image, return_tensors="pt").to(device, torch.float16)
    pixel_values = inputs.pixel_values

    generated_ids = blip_model.generate(pixel_values=pixel_values, max_length=50)
    generated_caption = blip_processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
    return generated_caption

caption_prefix = f"{instance_prompt}, " 
with open(f'{instance_data_dir}metadata.jsonl', 'w') as outfile:
  for img in imgs_and_paths:
      caption = caption_prefix + caption_images(img[1]).split("\n")[0]
      entry = {"file_name":img[0].split("/")[-1], "prompt": caption}
      json.dump(entry, outfile)
      outfile.write('\n')
```
This produces a JSON file in the ```instance_data_dir``` directory:

```json
{"file_name": "0.jpeg", "prompt": "<leaf microstructure>, a close up of a green plant with a lot of small holes"}
{"file_name": "1.jpeg", "prompt": "<leaf microstructure>, a close up of a leaf with a small insect on it"}
{"file_name": "2.jpeg", "prompt": "<leaf microstructure>, a close up of a plant with a lot of green leaves"}
{"file_name": "3.jpeg", "prompt": "<leaf microstructure>, a close up of a leaf with a yellow substance in it"}
{"file_name": "87.jpg", "prompt": "<leaf microstructure>, a close up of a green plant with a yellow light"}
{"file_name": "88.jpg", "prompt": "<leaf microstructure>, a close up of a green plant with a white center"}
{"file_name": "90.jpg", "prompt": "<leaf microstructure>, arafed leaf with a white line on the center"}
{"file_name": "91.jpg", "prompt": "<leaf microstructure>, arafed image of a green leaf with a white spot"}
{"file_name": "92.jpg", "prompt": "<leaf microstructure>, a close up of a leaf with a yellow light shining through it"}
{"file_name": "94.jpg", "prompt": "<leaf microstructure>, arafed image of a green plant with a yellow cross"}
```

```raw
!accelerate launch train_dreambooth_lora_sdxl.py \
  --pretrained_model_name_or_path="{pretrained_model_name_or_path}" \
  --pretrained_vae_model_name_or_path="{pretrained_vae_model_name_or_path}"\
  --dataset_name="{instance_data_dir}" \
  --output_dir="{instance_output_dir}" \
  --caption_column="prompt"\
  --mixed_precision="fp16" \
  --instance_prompt="{instance_prompt}" \
  --validation_prompt="{val_prompt}" \
  --validation_epochs="{val_epochs}" \
  --resolution=1024 \
  --train_batch_size=1 \
  --gradient_accumulation_steps=3 \
  --gradient_checkpointing \
  --learning_rate=1e-4 \
  --snr_gamma=5.0 \
  --lr_scheduler="constant" \
  --lr_warmup_steps=0 \
  --mixed_precision="fp16" \
  --use_8bit_adam \
  --max_train_steps=500 \
  --checkpointing_steps=500 \
  --seed="0"
```