Update README.md

README.md

@@ -57,21 +57,20 @@ The model addresses the issue of data scarcity in medical and anomaly detection
 ## 📥 Download and Installation
 To use this model with **Diffusers**, follow these steps:
 
-```python
+```python
 from diffusers import StableDiffusionPipeline
 from peft import PeftModel
 import torch
 
 # Load the base model
-base_model = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1")
+base_model = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1")
 
 # Load LoRA weights
-lora_model = PeftModel.from_pretrained(base_model, "G
-![sd-2.1-infarct-lora-051-3501614513.jpg](https://cdn-uploads.huggingface.co/production/uploads/644580ada56444c355da1b15/vqPxaiq1iKpC1EQKp-gnD.jpeg)
-avit0/InfarctImage")
+lora_model = PeftModel.from_pretrained(base_model, "Gavit0/InfarctImage")
 
-lora_model.to(torch.device("cuda"))  # Move to GPU if available
-```
+lora_model.to(torch.device("cuda"))  # Move to GPU if available
+
+```
 
 ## 📊 Training Data
 The model was trained on a dataset of 100 manually annotated images, including:
@@ -98,11 +97,11 @@ The dataset was processed and annotated using **BLIP (Bootstrapping Language-Ima
 
 ## 🏆 Usage Examples
 You can generate images using prompts like:
-```python
-prompt = "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."
-image = lora_model(prompt=prompt).images[0]
+```python
+prompt = "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."
+image = lora_model(prompt=prompt).images[0]
 image.show()
-```
+```
 
 ## 📜 License
 This model is distributed under the **MIT License**.