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 <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:
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:
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
