Update README with PyTorch import and example adjustments for batch image generation

README.md

@@ -56,6 +56,7 @@ library_name: t5
 Below is a minimal usage example in Python. You can load the model with `AutoModel.from_pretrained(...)` and simply call `.generate(...)` or `.generate_batch(...)` to create images.
 
 ```python
+import torch
 from PIL import Image
 from transformers import AutoModel
 from huggingface_hub import hf_hub_download
@@ -67,7 +68,7 @@ def load_image(img_path):
     img = img.resize((img.width * 64 // img.height, 64))
     img = F.to_tensor(img)
     img = F.normalize(img, [0.5], [0.5])
-    return img.unsqueeze(0)
+    return img
 
 # 1. Load the model
 model = AutoModel.from_pretrained("blowing-up-groundhogs/emuru", trust_remote_code=True)
@@ -96,10 +97,10 @@ generated_pil_image.save("generated_image.png")
 You can also generate a batch of images if you have multiple style texts, generation texts, and style images:
 
 ```python
-style_texts = ["Style text 1", "Style text 2"]
-gen_texts   = ["Gen text 1", "Gen text 2"]
-style_imgs  = torch.stack([img1, img2], dim=0)  # shape: (batch_size, C, H, W)
-lengths     = [img1.size(-1), img2.size(-1)]
+style_texts = ['THE JOLLY IS "U"', 'THE JOLLY IS "M"', 'THE JOLLY IS "R"']
+gen_texts   = ['EMURU', 'EMURU', 'EMURU']
+style_imgs  = torch.stack([style_img, style_img, style_img], dim=0)  # shape: (batch_size, C, H, W)
+lengths     = [style_img.size(-1), style_img.size(-1), style_img.size(-1)]
 
 output_images = model.generate_batch(
     style_texts=style_texts,
@@ -120,6 +121,6 @@ for idx, pil_img in enumerate(output_images):
 If you use Emuru in your research or wish to refer to it, please cite:
 
 ```
-...
+Wait for it...
 ```