README.md

---
library_name: diffusers
pipeline_tag: text-to-image
---

## Model Details

### Model Description

This model is fine-tuned from [stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) on 110,000 image-text pairs from the MIMIC dataset using the SVDIFF [1] PEFT method. Under this fine-tuning strategy, fine-tune only the singular values of weight matrices in the U-Net while keeping everything else frozen. 

- **Developed by:** [Raman Dutt](https://twitter.com/RamanDutt4)
- **Shared by:** [Raman Dutt](https://twitter.com/RamanDutt4)
- **Model type:** [Stable Diffusion fine-tuned using Parameter-Efficient Fine-Tuning]
- **Finetuned from model:** [stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5)

### Model Sources


- **Paper:** [Parameter-Efficient Fine-Tuning for Medical Image Analysis: The Missed Opportunity](https://arxiv.org/abs/2305.08252)
- **Demo:** [MIMIC-SD-PEFT-Demo](https://huggingface.co/spaces/raman07/MIMIC-SD-Demo-Memory-Optimized?logs=container)

## Direct Use

This model can be directly used to generate realistic medical images from text prompts. 


## How to Get Started with the Model

```python
import os
from safetensors.torch import load_file
from diffusers.pipelines import StableDiffusionPipeline


#### Defining loading function

def load_unet_for_svdiff(pretrained_model_name_or_path, spectral_shifts_ckpt=None, hf_hub_kwargs=None, **kwargs):
    
    print(pretrained_model_name_or_path)
    config = UNet2DConditionModel.load_config(pretrained_model_name_or_path, **kwargs)
    original_model = UNet2DConditionModel.from_pretrained(pretrained_model_name_or_path, **kwargs)
    state_dict = original_model.state_dict()
    with accelerate.init_empty_weights():
        model = UNet2DConditionModelForSVDiff.from_config(config)
    # load pre-trained weights
    param_device = "cpu"
    torch_dtype = kwargs["torch_dtype"] if "torch_dtype" in kwargs else None
    spectral_shifts_weights = {n: torch.zeros(p.shape) for n, p in model.named_parameters() if "delta" in n}
    state_dict.update(spectral_shifts_weights)
    # move the params from meta device to cpu
    missing_keys = set(model.state_dict().keys()) - set(state_dict.keys())
    if len(missing_keys) > 0:
        raise ValueError(
            f"Cannot load {model.__class__.__name__} from {pretrained_model_name_or_path} because the following keys are"
            f" missing: \n {', '.join(missing_keys)}. \n Please make sure to pass"
            " `low_cpu_mem_usage=False` and `device_map=None` if you want to randomely initialize"
            " those weights or else make sure your checkpoint file is correct."
        )

    for param_name, param in state_dict.items():
        accepts_dtype = "dtype" in set(inspect.signature(set_module_tensor_to_device).parameters.keys())
        if accepts_dtype:
            set_module_tensor_to_device(model, param_name, param_device, value=param, dtype=torch_dtype)
        else:
            set_module_tensor_to_device(model, param_name, param_device, value=param)
    
    if spectral_shifts_ckpt:
        if os.path.isdir(spectral_shifts_ckpt):
            spectral_shifts_ckpt = os.path.join(spectral_shifts_ckpt, "spectral_shifts.safetensors")
        elif not os.path.exists(spectral_shifts_ckpt):
            # download from hub
            hf_hub_kwargs = {} if hf_hub_kwargs is None else hf_hub_kwargs
            spectral_shifts_ckpt = huggingface_hub.hf_hub_download(spectral_shifts_ckpt, filename="spectral_shifts.safetensors", **hf_hub_kwargs)
        assert os.path.exists(spectral_shifts_ckpt)

        with safe_open(spectral_shifts_ckpt, framework="pt", device="cpu") as f:
            for key in f.keys():
                # spectral_shifts_weights[key] = f.get_tensor(key)
                accepts_dtype = "dtype" in set(inspect.signature(set_module_tensor_to_device).parameters.keys())
                if accepts_dtype:
                    set_module_tensor_to_device(model, key, param_device, value=f.get_tensor(key), dtype=torch_dtype)
                else:
                    set_module_tensor_to_device(model, key, param_device, value=f.get_tensor(key))
        print(f"Resumed from {spectral_shifts_ckpt}")
    if "torch_dtype"in kwargs:
        model = model.to(kwargs["torch_dtype"])
    model.register_to_config(_name_or_path=pretrained_model_name_or_path)
    # Set model in evaluation mode to deactivate DropOut modules by default
    model.eval()
    del original_model
    torch.cuda.empty_cache()
    return model

pipe.unet = load_unet_for_svdiff(
            "runwayml/stable-diffusion-v1-5",
            spectral_shifts_ckpt=os.path.join('unet', "spectral_shifts.safetensors"),
            subfolder="unet",
        )
for module in pipe.unet.modules():
    if hasattr(module, "perform_svd"):
        module.perform_svd()

# Load the adapted U-Net
pipe.unet.load_state_dict(state_dict, strict=False)
pipe.to('cuda:0')

# Generate images with text prompts

TEXT_PROMPT = "No acute cardiopulmonary abnormality."
GUIDANCE_SCALE = 4
INFERENCE_STEPS = 75

result_image = pipe(
        prompt=TEXT_PROMPT,
        height=224,
        width=224,
        guidance_scale=GUIDANCE_SCALE,
        num_inference_steps=INFERENCE_STEPS,
    )

result_pil_image = result_image["images"][0]
```


## Training Details

### Training Data

This model has been fine-tuned on 110K image-text pairs from the MIMIC dataset. 

### Training Procedure

The training procedure has been described in detail in Section 4.3 of this [paper](https://arxiv.org/abs/2305.08252).

#### Metrics

This model has been evaluated using the Fréchet inception distance (FID) Score on MIMIC dataset.

### Results

| Fine-Tuning Strategy   | FID Score |
|------------------------|-----------|
| Full FT                | 58.74     |
| Attention              | 52.41     |
| Bias                   | 20.81     |
| Norm                   | 29.84     |
| Bias+Norm+Attention    | 35.93     |
| LoRA                   | 439.65    |
| SV-Diff                | 23.59     |
| DiffFit                | 42.50     |


## Environmental Impact

Using Parameter-Efficient Fine-Tuning potentially causes **lesser** harm to the environment since we fine-tune a significantly lesser number of parameters in a model. This results in much lesser computing and hardware requirements.

## Citation


**BibTeX:**

@article{dutt2023parameter,
  title={Parameter-Efficient Fine-Tuning for Medical Image Analysis: The Missed Opportunity},
  author={Dutt, Raman and Ericsson, Linus and Sanchez, Pedro and Tsaftaris, Sotirios A and Hospedales, Timothy},
  journal={arXiv preprint arXiv:2305.08252},
  year={2023}
}

**APA:**  
Dutt, R., Ericsson, L., Sanchez, P., Tsaftaris, S. A., & Hospedales, T. (2023). Parameter-Efficient Fine-Tuning for Medical Image Analysis: The Missed Opportunity. arXiv preprint arXiv:2305.08252.

## Model Card Authors

Raman Dutt  
[Twitter](https://twitter.com/RamanDutt4)  
[LinkedIn](https://www.linkedin.com/in/raman-dutt/)  
[Email](mailto:s2198939@ed.ac.uk)

## References

1. Han, Ligong, et al. "Svdiff: Compact parameter space for diffusion fine-tuning." arXiv preprint arXiv:2303.11305 (2023).