metadata

base_model: stabilityai/stable-diffusion-xl-base-1.0
library_name: diffusers
license: openrail++
tags:
  - text-to-image
  - text-to-image
  - diffusers-training
  - diffusers
  - stable-diffusion-2
  - stable-diffusion-2-diffusers
instance_prompt: <leaf microstructure>
widget: []

Stable Diffusion 2.x Fine-tuned with Leaf Images

Model description

These are fine-tuned weights for the stabilityai/stable-diffusion-2 model. This is a full fine-tune of the model using DreamBooth.

Trigger keywords

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

You should use to trigger the image generation.

How to use

Defining some helper functions:

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:


import torch
from diffusers import DPMSolverMultistepScheduler

repo_id='lamm-mit/SD2x-leaf-inspired'

pipe = StableDiffusionPipeline.from_pretrained(repo_id,
        scheduler = DPMSolverMultistepScheduler.from_pretrained(args.output_dir, subfolder="scheduler"),
        torch_dtype=torch.float16,
    ).to("cuda")

Image generation:

prompt      = "a vase that resembles a <leaf microstructure>, high quality" 
num_samples = 4  
num_rows    = 4

all_images = []
for _ in range(num_rows):
    images = pipe(prompt, num_images_per_prompt=num_samples, num_inference_steps=50, guidance_scale=15).images
    all_images.extend(images)

grid = image_grid(all_images, num_rows, num_samples)
grid

Fine-tuning script

Download this script: SD2x DreamBooth-Fine-Tune.ipynb

You need to create a local folder leaf_concept_dir and add the leaf images (provided in this repository, see subfolder), like so:

save_path='leaf_concept_dir'
urls = [
      "https://www.dropbox.com/scl/fi/4s09djm4nqxmq6vhvv9si/13_.jpg?rlkey=3m2f90pjofljmlqg5uc722i6y&dl=1",
      "https://www.dropbox.com/scl/fi/w4jsrf0qmrcro37nxutbx/25_.jpg?rlkey=e52gnoqaar33kwrd01h1mwcnk&dl=1",
      "https://www.dropbox.com/scl/fi/x0xgavduor4cbxz0sdcd2/33_.jpg?rlkey=5htaicapahhn66wnsr23v1nxz&dl=1",
      "https://www.dropbox.com/scl/fi/2grt40acypah9h9ok607q/72_.jpg?rlkey=bl6vfv0rcas2ygsz6o3behlst&dl=1",
      "https://www.dropbox.com/scl/fi/ecaf9agzdj2cawspmyt5i/117_.jpg?rlkey=oqxyk9i1wtu1wtkqadd6ylyjj&dl=1",
      "https://www.dropbox.com/scl/fi/gw3p73r99fleozr6ckfa3/126_.jpg?rlkey=6n7kqaklczshht1ntyqunh2lt&dl=1",
      ## You can add additional images here
      ]
images = list(filter(None,[download_image(url) for url in urls]))

if not os.path.exists(save_path):
  os.mkdir(save_path)
    
[image.save(f"{save_path}/{i}.jpeg") for i, image in enumerate(images)]
image_grid(images, 1, len(images))

The training script is included in the Jupyter notebook.

lamm-mit
/

SD2x-leaf-inspired