--- 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 instance_prompt: widget: [] --- # Stable Diffusion 2.x Fine-tuned with Leaf Images 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 \ 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 \, 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 \: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/#fileId=https://huggingface.co/lamm-mit/SD2x-leaf-inspired/blob/main/SD2x_leaf_inspired_inference.ipynb) Please use \ 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 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: ```python prompt = "a vase that resembles a , 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/png](https://cdn-uploads.huggingface.co/production/uploads/623ce1c6b66fedf374859fe7/SI5aYv2dygJn0Y12LIqqe.png) ### 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 , 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 ``` ![image/png](https://cdn-uploads.huggingface.co/production/uploads/623ce1c6b66fedf374859fe7/MkOXQIpdhl_zWM3QySYMY.png) 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 ``` ![image/png](https://cdn-uploads.huggingface.co/production/uploads/623ce1c6b66fedf374859fe7/l4WCC3PoZ6OpiSN-E66i3.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 ``` ![image/png](https://cdn-uploads.huggingface.co/production/uploads/623ce1c6b66fedf374859fe7/0ROO1Ob2Z-GYPepYyyAGg.png) 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 ``` ![image/png](https://cdn-uploads.huggingface.co/production/uploads/623ce1c6b66fedf374859fe7/c-1b4J-as6b2p9ZQSSLjK.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 ``` ![image/png](https://cdn-uploads.huggingface.co/production/uploads/623ce1c6b66fedf374859fe7/izv21tOqJntVAwes0TEzu.png) A detailed view of one of them: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/623ce1c6b66fedf374859fe7/Ik7RkGzrx0N8gkNfkei3j.png) ## Fine-tuning script Download this script: [SD2x DreamBooth-Fine-Tune.ipynb](https://huggingface.co/lamm-mit/SD2x-leaf-inspired/resolve/main/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: ```python 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 ```python prompt = "a conch shell on black background that resembles a , 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/png](https://cdn-uploads.huggingface.co/production/uploads/623ce1c6b66fedf374859fe7/eE1xBqyVA4sP4gx6tAEGc.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/623ce1c6b66fedf374859fe7/Ga808aW5H27f0hPq_RNme.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/623ce1c6b66fedf374859fe7/r0dUyA-Gh_biy5d-4lTl0.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/623ce1c6b66fedf374859fe7/iEjozBWOQQwxNVuKWZ7TT.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/623ce1c6b66fedf374859fe7/ESvd6cCkyJZ52Cu3iYfoP.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/623ce1c6b66fedf374859fe7/2FExqoj8TSjJoIiw4wCm6.png)