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README.md

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+---
+tags:
+- text-to-image
+- lora
+- diffusers
+- template:diffusion-lora
+widget:
+- text: >-
+    Person with expression of pain due to a heart attack, Middle-aged woman in
+    her kitchen leaning on a counter, struggling to breathe, showing signs of a
+    cardiac emergency.
+  parameters:
+    negative_prompt: >-
+      blurry, deformed face, bad anatomy, poorly drawn face, out of focus, ugly,
+      noisy, extra fingers, distorted, grainy, worst quality, low quality, low
+      resolution, illustration, dull, watermark, close-up, 3d, 2d, painting,
+      sketch, render, cartoon, grain, kitsch
+  output:
+    url: images/sd-2.1-infarct-lora-019-623607189.jpg
+- text: >-
+    Person with expression of pain due to a heart attack, Elderly man at a
+    sports stadium surrounded by a crowd, clutching his chest with a distressed
+    look, indicating a heart attack.
+  parameters:
+    negative_prompt: >-
+      blurry, deformed face, bad anatomy, poorly drawn face, out of focus, ugly,
+      noisy, extra fingers, distorted, grainy, worst quality, low quality, low
+      resolution, illustration, dull, watermark, close-up, 3d, 2d, painting,
+      sketch, render, cartoon, grain, kitsch
+  output:
+    url: images/sd-2.1-infarct-lora-084-766027158.jpg
+base_model: stabilityai/stable-diffusion-2-1-base
+instance_prompt: Person with expression of pain due to a heart attack, infarct
+license: mit
+---
+# Infarct Image
+
+<Gallery />
+
+## Model description 
+
+# InfarctImage - LoRA Fine-Tuned Model for Heart Attack Simulation
+
+![sd-2.1-infarct-lora-051-3501614513.jpg](https:&#x2F;&#x2F;cdn-uploads.huggingface.co&#x2F;production&#x2F;uploads&#x2F;644580ada56444c355da1b15&#x2F;GGoidcsg95LqZrdlv4eMq.jpeg)
+
+## 📌 Description
+**InfarctImage** is a LoRA-based model fine-tuned on **Stable Diffusion 2.1**, designed to generate realistic images of people simulating a heart attack. This model was developed as part of a study on synthetic dataset generation for human activity recognition and medical emergency monitoring applications.
+
+🔗 **Related Article:** Coming soon.
+
+## 🎯 Objective
+The model addresses the issue of data scarcity in medical and anomaly detection environments. Generating high-quality synthetic images enables:
+- Expanding existing datasets without relying on real-world data.
+- Overcoming ethical and logistical restrictions in medical image collection.
+- Enhancing AI-based detection of critical events like heart attacks.
+
+## 📥 Download and Installation
+To use this model with **Diffusers**, follow these steps:
+
+&#x60;&#x60;&#x60;python
+from diffusers import StableDiffusionPipeline
+from peft import PeftModel
+import torch
+
+# Load the base model
+base_model &#x3D; StableDiffusionPipeline.from_pretrained(&quot;stabilityai&#x2F;stable-diffusion-2-1&quot;)
+
+# Load LoRA weights
+lora_model &#x3D; PeftModel.from_pretrained(base_model, &quot;G
+![sd-2.1-infarct-lora-051-3501614513.jpg](https:&#x2F;&#x2F;cdn-uploads.huggingface.co&#x2F;production&#x2F;uploads&#x2F;644580ada56444c355da1b15&#x2F;vqPxaiq1iKpC1EQKp-gnD.jpeg)
+avit0&#x2F;InfarctImage&quot;)
+
+lora_model.to(torch.device(&quot;cuda&quot;))  # Move to GPU if available
+&#x60;&#x60;&#x60;
+
+## 📊 Training Data
+The model was trained on a dataset of 100 manually annotated images, including:
+- 50 images of people simulating heart attack symptoms.
+- 50 images of people in neutral contexts.
+
+The dataset was processed and annotated using **BLIP (Bootstrapping Language-Image Pretraining)** to enhance image descriptions and improve training prompts.
+
+## 🔧 Hyperparameters and Configuration
+- **Base Model:** Stable Diffusion 2.1
+- **Fine-Tuning Technique:** LoRA (Low-Rank Adaptation)
+- **Learning Rate:** 0.0001
+- **Batch Size:** 1
+- **Epochs:** 10
+- **Hardware:** NVIDIA RTX 4090 (24GB VRAM)
+
+## 📈 Model Evaluation
+**LPIPS (Learned Perceptual Image Patch Similarity)** was used to evaluate the quality of the generated images. Results show that LoRA fine-tuning improves the perceptual similarity of generated images compared to real training data.
+
+| Model | LPIPS (↓ Better) |
+|--------|---------------|
+| SD 2.1 Base | 0.7366 |
+| SD 2.1 + LoRA | 0.6919 |
+
+## 🏆 Usage Examples
+You can generate images using prompts like:
+&#x60;&#x60;&#x60;python
+prompt &#x3D; &quot;Person with expression of pain due to a heart attack, A middle-aged man clutching his chest in pain, showing signs of a heart attack.&quot;
+image &#x3D; lora_model(prompt&#x3D;prompt).images[0]
+image.show()
+&#x60;&#x60;&#x60;
+
+## 📜 License
+This model is distributed under the **MIT License**.
+
+## 💡 Contributions and Contact
+If you want to contribute or have any questions, contact me at **[email protected]** or open an issue in this repository.
+
+
+
+
+
+## Trigger words
+
+You should use `Person with expression of pain due to a heart attack` to trigger the image generation.
+
+You should use `infarct` to trigger the image generation.
+
+
+## Download model
+
+Weights for this model are available in Safetensors format.
+
+[Download](/Gavit0/InfarctImage/tree/main) them in the Files & versions tab.