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---
base_model: stabilityai/stable-diffusion-3-medium-diffusers
library_name: diffusers
license: openrail++
tags:
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-3
- stable-diffusion-3-diffusers
instance_prompt: <leaf microstructure>
widget: []
---
# Stable Diffusion 3 Medium Fine-tuned with Leaf Images
<Gallery />
## Model description
These are LoRA adaption weights for stabilityai/stable-diffusion-3-medium-diffusers.
## 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:
```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 and generation pipeline:
```python
repo_id_load='lamm-mit/stable-diffusion-3-medium-leaf-inspired'
pipeline = DiffusionPipeline.from_pretrained ("stabilityai/stable-diffusion-3-medium-diffusers",
torch_dtype=torch.float16
)
pipeline.load_lora_weights(repo_id_load)
pipeline=pipeline.to('cuda')
prompt = "a cube in the shape of a <leaf microstructure>"
negative_prompt = ""
num_samples = 3
num_rows = 3
n_steps=75
guidance_scale=15
all_images = []
for _ in range(num_rows):
image = pipeline(prompt,num_inference_steps=n_steps,num_images_per_prompt=num_samples,
guidance_scale=guidance_scale,negative_prompt=negative_prompt).images
all_images.extend(image)
grid = image_grid(all_images, num_rows, num_samples,
save_individual_files=True,
save_dir='generated_images',
base_name="image_grid",
)
grid
```
### Image-to-image
We start with this image generated earlier:
```python
from diffusers import StableDiffusion3Img2ImgPipeline
from diffusers.utils import load_image
pipeline = StableDiffusion3Img2ImgPipeline.from_pretrained("stabilityai/stable-diffusion-3-medium-diffusers", torch_dtype=torch.float16)
pipeline=pipeline.to('cuda')
init_image = load_image("https://huggingface.co/lamm-mit/stable-diffusion-3-medium-leaf-inspired/resolve/main/image_20240721_212111.png")
prompt = "Turn this image into a spider web."
negative_prompt=""
n_steps=20
guidance_scale=25
image = pipeline(prompt, num_inference_steps=n_steps,
guidance_scale=guidance_scale,
negative_prompt=negative_prompt,
image=init_image,
).images[0]
save_image(image, directory='generated_images', base_name="image_grid", )
image
```
## More examples
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