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README.md

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+---
+license: mit
+tags:
+- pytorch
+- diffusers
+- stable-diffusion
+- latent-diffusion
+- medical-imaging
+- brain-mri
+- multiple-sclerosis
+- dataset-conditioning
+---
+
+# Brain MRI Synthesis with Stable Diffusion (fine-tuned with dataset prompts)
+
+This model is a **fine-tuned version of Stable Diffusion v1-4** for **prompt-conditioned synthesis of brain MRI FLAIR slices**. It leverages **latent diffusion** and dataset-specific prompts to generate realistic 256x256 FLAIR scans with control over the source dataset's style or distribution.
+
+## 🔍 Prompt Conditioning
+
+The model introduces three special prompt tokens corresponding to the dataset of origin. During training, each image was paired with a prompt indicating its source:
+
+- `"SHIFTS FLAIR MRI"`
+- `"VH FLAIR MRI"`
+- `"WMH2017 FLAIR MRI"`
+
+These prompts were added as special tokens to the tokenizer, and their embeddings were fine-tuned alongside the U-Net, enabling dataset-specific synthesis.
+
+## 🧠 Training Details
+
+- **Base Model:** [`CompVis/stable-diffusion-v1-4`](https://huggingface.co/CompVis/stable-diffusion-v1-4)
+- **Architecture:** Latent Diffusion with U-Net + ResNet + Attention
+- **Input resolution (latent):** 32x32
+- **Output resolution (decoded):** 256x256 pixels
+- **Datasets:** SHIFTS, VH, and WMH2017 (FLAIR MRI slices)
+- **Channels:** 4 latent channels
+- **Epochs:** 50
+- **Batch size:** 8
+- **Gradient accumulation:** 4 steps
+- **Optimizer:** AdamW
+  - Learning Rate: `1.0e-4`
+  - Betas: (0.95, 0.999)
+  - Weight Decay: `1.0e-6`
+  - Epsilon: `1.0e-8`
+- **LR Scheduler:** Cosine schedule with 500 warm-up steps
+- **Noise Scheduler:** DDPM with:
+  - `num_train_timesteps`: 1000
+  - `beta_start`: 0.0001
+  - `beta_end`: 0.02
+  - `beta_schedule`: "linear"
+- **Mixed Precision:** Disabled
+- **Gradient Clipping:** max norm 1.0
+- **Hardware:** NVIDIA A30 GPU with 4 dataloader workers
+
+## 🧪 Usage
+
+You can use this model via the `diffusers` library for conditional generation:
+
+```python
+from diffusers import DiffusionPipeline
+import torch
+
+# Load the model
+pipe = DiffusionPipeline.from_pretrained("benetraco/latent_finetuning")
+pipe.to("cuda")  # or "cpu"
+
+# Generate a brain MRI image in SHIFTS style
+prompt = "SHIFTS FLAIR MRI"
+image = pipe(prompt=prompt, num_inference_steps=50, guidance_scale=2.0).images[0]
+
+image.show()