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--- |
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base_model: black-forest-labs/FLUX.1-dev |
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library_name: diffusers |
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license: apache-2.0 |
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tags: |
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- text-to-image |
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- diffusers-training |
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- diffusers |
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- lora |
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- FLUX.1-dev |
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- science |
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- materiomics |
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- bio-inspired |
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- materials science |
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- generative AI for science |
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datasets: |
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- lamm-mit/leaf-flux-images-and-captions |
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instance_prompt: <leaf microstructure> |
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widget: [] |
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--- |
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# FLUX.1 [dev] Fine-tuned with Leaf Images |
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FLUX.1 [dev] is a 12 billion parameter rectified flow transformer capable of generating images from text descriptions. |
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Install ```diffusers``` |
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```raw |
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pip install -U diffusers |
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``` |
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## Model description |
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These are LoRA adaption weights for the FLUX.1 [dev] model (```black-forest-labs/FLUX.1-dev```). The base model is, and you must first get access to it before loading this LoRA adapter. |
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This LoRA adapter has rank=64 and alpha=64, trained for 4,000 steps. Earlier checkpoints are available in this repository as well (you can load these via the ```adapter``` parameter, see example below). |
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## Trigger keywords |
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The model was fine-tuned with a set of ~1,600 images of biological materials, structures, shapes and other images of nature, using the keyword \bioinspired\>. |
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You should use \<bioinspired\> to trigger these features during image generation. |
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## How to use |
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Defining some helper functions: |
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```python |
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import os |
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from datetime import datetime |
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from PIL import Image |
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def generate_filename(base_name, extension=".png"): |
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") |
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return f"{base_name}_{timestamp}{extension}" |
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def save_image(image, directory, base_name="image_grid"): |
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filename = generate_filename(base_name) |
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file_path = os.path.join(directory, filename) |
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image.save(file_path) |
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print(f"Image saved as {file_path}") |
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def image_grid(imgs, rows, cols, save=True, save_dir='generated_images', base_name="image_grid", |
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save_individual_files=False): |
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if not os.path.exists(save_dir): |
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os.makedirs(save_dir) |
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assert len(imgs) == rows * cols |
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w, h = imgs[0].size |
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grid = Image.new('RGB', size=(cols * w, rows * h)) |
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grid_w, grid_h = grid.size |
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for i, img in enumerate(imgs): |
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grid.paste(img, box=(i % cols * w, i // cols * h)) |
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if save_individual_files: |
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save_image(img, save_dir, base_name=base_name+f'_{i}-of-{len(imgs)}_') |
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if save and save_dir: |
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save_image(grid, save_dir, base_name) |
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return grid |
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``` |
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### Text-to-image |
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Model loading: |
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```python |
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from diffusers import FluxPipeline |
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import torch |
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repo_id = 'lamm-mit/bioinspired-L-FLUX.1-dev' |
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pipeline = FluxPipeline.from_pretrained( |
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"black-forest-labs/FLUX.1-dev", |
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torch_dtype=torch.bfloat16, |
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max_sequence_length=512, |
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) |
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#pipeline.enable_model_cpu_offload() #save some VRAM by offloading the model to CPU. Comment out if you have enough GPU VRAM |
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adapter='leaf-flux.safetensors' #Step 16000, final step |
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#adapter='leaf-flux-step-3000.safetensors' #Step 3000 |
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#adapter='leaf-flux-step-3500.safetensors' #Step 3500 |
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pipeline.load_lora_weights(repo_id, weight_name=adapter) #You need to use the weight_name parameter since the repo includes multiple checkpoints |
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pipeline=pipeline.to('cuda') |
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``` |
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Image generation - Example #1: |
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```python |
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prompt="""Generate a futuristic, eco-friendly architectural concept utilizing a biomimetic composite material that integrates the structural efficiency of spider silk with the adaptive porosity of plant tissues. Utilize the following key features: |
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* Fibrous architecture inspired by spider silk, represented by sinuous lines and curved forms. |
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* Interconnected, spherical nodes reminiscent of plant cell walls, emphasizing growth and adaptation. |
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* Open cellular structures echoing the permeable nature of plant leaves, suggesting dynamic exchanges and self-regulation capabilities. |
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* Gradations of opacity and transparency inspired by the varying densities found in plant tissues, highlighting functional differentiation and multi-functionality. |
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""" |
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num_samples =2 |
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num_rows = 2 |
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n_steps=25 |
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guidance_scale=3.5 |
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all_images = [] |
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for _ in range(num_rows): |
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image = pipeline(prompt,num_inference_steps=n_steps,num_images_per_prompt=num_samples, |
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guidance_scale=guidance_scale,).images |
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all_images.extend(image) |
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grid = image_grid(all_images, num_rows, num_samples, save_individual_files=True, ) |
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grid |
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``` |
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Image generation - Example #2: |
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```python |
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prompt="""A jar of round <bioinspired> cookies with a piece of white tape that says "Materiomics Cookies". Looks tasty. Old fashioned.""" |
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num_samples =2 |
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num_rows = 2 |
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n_steps=25 |
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guidance_scale=15. |
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all_images = [] |
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for _ in range(num_rows): |
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image = pipeline(prompt,num_inference_steps=n_steps,num_images_per_prompt=num_samples, |
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guidance_scale=guidance_scale, |
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height=1024, width=1024,).images |
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all_images.extend(image) |
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grid = image_grid(all_images, num_rows, num_samples, save_individual_files=True, ) |
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grid |
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``` |
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```bibtext |
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@article{BioinspiredFluxBuehler2024, |
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title={Fine-tuning image-generation models with biological patterns, shapes and topologies}, |
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author={Markus J. Buehler}, |
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journal={arXiv: XXXX.YYYYY}, |
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year={2024}, |
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} |
