base_model: stabilityai/stable-diffusion-2
library_name: diffusers
license: openrail++
tags:
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- stable-diffusion-2
- stable-diffusion-2-diffusers
- science
- materiomics
- bio-inspired
- materials science
- text-to-3D
- text-to-STL
- text-t-mesh
- additive manufacturing
- 3D
- 3D printing
instance_prompt: <leaf microstructure>
widget: []
Stable Diffusion 2.x Fine-tuned with Leaf Images: Text-to-image and text-to-3D
DreamBooth is an advanced technique designed for fine-tuning text-to-image diffusion models to generate personalized images of specific subjects. By leveraging a few reference images (around 5 or so), DreamBooth integrates unique visual features of the subject into the model's output domain.
This is achieved by binding a unique identifier "<..IDENTIFIER..>", such as <leaf microstructure> in this work, to the subject. An optional class-specific prior preservation loss can be used to maintain high fidelity and contextual diversity. The result is a model capable of synthesizing novel, photorealistic images of the subject in various scenes, poses, and lighting conditions, guided by text prompts. In this project, DreamBooth has been applied to render images with specific biological patterns, making it ideal for applications in materials science and engineering where accurate representation of biological material microstructures is crucial.
For example, an original prompt might be: "a vase with intricate patterns, high quality." With the fine-tuned model, using the unique identifier, the prompt becomes: "a vase that resembles a <leaf microstructure>, high quality." This allows the model to generate images that specifically incorporate the desired biological pattern.
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>:
Please use <leaf microstructure> 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 StableDiffusionPipeline, DPMSolverMultistepScheduler
repo_id='lamm-mit/SD2x-leaf-inspired'
pipe = StableDiffusionPipeline.from_pretrained(repo_id,
scheduler = DPMSolverMultistepScheduler.from_pretrained(repo_id, 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
Image-to-Image
The model can be used also for image-to-image tasks. For instance, we can first generate a draft image and then further modify it.
Create draft image:
prompt = "a vase that resembles a <leaf microstructure>, high quality"
num_samples = 4
num_rows = 1
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, save_individual_files=True)
grid
Now we use one of the images (second from left) and modify it using the image-to-image pipeline. You can get the image as follows (if you run the generate code yourself, the generated images will be in the subdirectory generated_images):
wget https://huggingface.co/lamm-mit/SD2x-leaf-inspired/resolve/main/image_grid_1-of-4__20240722_144702.png
Now, generate:
fname='image_grid_1-of-4__20240722_144702.png'
init_image = Image.open(fname).convert("RGB")
init_image = init_image.resize((768, 768))
prompt = "A vase made out of a spongy material, high quality photograph, full frame."
num_samples = 4
num_rows = 1
all_images = []
for _ in range(num_rows):
images = img2imgpipe(prompt, image=init_image,
num_images_per_prompt=num_samples, strength=0.8, num_inference_steps=75, guidance_scale=25).images
all_images.extend(images)
grid = image_grid(images, num_rows, num_samples, save_individual_files=True)
grid
We can further edit the image by introducing another feature. We start from this image
wget https://huggingface.co/lamm-mit/SD2x-leaf-inspired/resolve/main/image_grid_2-of-4__20240722_150458.png
fname='image_grid_2-of-4__20240722_150458.png'
init_image = Image.open(fname).convert("RGB")
init_image = init_image.resize((768, 768))
prompt = "A nicely connected white spider web."
num_samples = 4
num_rows = 1
all_images = []
for _ in range(num_rows):
images = img2imgpipe(prompt, image=init_image,
num_images_per_prompt=num_samples, strength=0.8, num_inference_steps=10, guidance_scale=20).images
all_images.extend(images)
grid = image_grid(images, num_rows, num_samples, save_individual_files=True)
grid
A detailed view of one of them:
Text-to-3D
Download this notebook: Convert-text-to-3D.ipynb
This notebook includes the code to convert text to 3D, using an algorithm that involves a fine-tuned Stable Diffusion model and InstantMesh.
repo_id_load= 'lamm-mit/SD2x-leaf-inspired'
input_image=text_to_image_SD2x (base_model=repo_id_load, n_steps=75, guidance_scale=15,
prompt = "Small chair that resembles a <leaf microstructure>.", negative_prompt="" )
display (input_image)
processed_image, mv_images, mv_show_images= generate_multiviews (input_image, seed=None)
display (mv_show_images)
output_video, output_model_obj = make_multi_views_into_3D (mv_images, target_dir='output')
Video(output_video, embed=True)
3D printed samples
In this example, we generated a 3D model and created a physical sample using additive manufacturing.
repo_id_load= 'lamm-mit/SD2x-leaf-inspired'
input_image=text_to_image_SD2x (base_model=repo_id_load, n_steps=50, guidance_scale=15,
prompt = "a conch shell on black background that resembles a <leaf microstructure>, high quality", negative_prompt="" )
display (input_image)
processed_image, mv_images, mv_show_images= generate_multiviews (input_image, seed=None)
display (mv_show_images)
output_video, output_model_obj = make_multi_views_into_3D (mv_images, target_dir='output')
Video(output_video, embed=True)
3D printing: Slicing using Cura and resulting physical sample with gyroid infill:
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.
More examples
prompt = "a conch shell on black background 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
