Update README.md

README.md

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----
-license: apache-2.0
----
+---
+base_model: black-forest-labs/FLUX.1-dev
+library_name: diffusers
+license: apache-2.0
+tags:
+- text-to-image
+- diffusers-training
+- diffusers
+- lora
+- FLUX
+- science
+- materiomics
+- bio-inspired
+- materials science
+instance_prompt: <leaf microstructure>
+widget: []
+---
+
+# FLUX.1 [dev] Fine-tuned with Leaf Images
+
+FLUX.1 [dev] is a 12 billion parameter rectified flow transformer capable of generating images from text descriptions. 
+
+## Model description
+
+These are LoRA adaption weights for the FLUX.1 [dev] model (```black-forest-labs/FLUX.1-dev```).
+
+## Trigger keywords
+
+The following images were used during fine-tuning using the keyword \<leaf microstructure\>:
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/623ce1c6b66fedf374859fe7/sI_exTnLy6AtOFDX1-7eq.png)
+
+Full dataset used for training: (lamm-mit/leaf-flux-images-and-captions)
+
+You should use \<leaf microstructure\> to trigger this feature during image generation.
+
+## How to use
+
+Defining some helper functions:
+
+```python
+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
+from diffusers import FluxPipeline
+import torch
+
+repo_id = 'lamm-mit/leaf-FLUX'
+
+pipeline = FluxPipeline.from_pretrained(
+    "black-forest-labs/FLUX.1-dev",
+    torch_dtype=torch.bfloat16,
+)
+
+pipeline.load_lora_weights(repo_id, )
+pipeline=pipeline.to('cuda')
+```
+
+Image generation:
+
+```python
+prompt=('Generate an image of a golden spider web network intertwined with collagen veins, '
+        'forming a dynamic, leaf-inspired microstructure amidst a lush green background.'  )
+
+num_samples =2
+num_rows = 2
+n_steps=25
+guidance_scale=3.5
+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,).images
+     
+    all_images.extend(image)
+
+grid = image_grid(all_images, num_rows, num_samples,  ave_individual_files=True,  )
+grid
+```
+