Discrepancy results between online demo and SamAutomaticMaskGenerator

Hi,

I have been trying out SAM in the online demo and it works great using the "Everything" button:

**Image:**

**Result demo:**

I then tried to recreate these results with the code using the SamAutomaticMaskGenerator and a vit-h (default) model. See code:

```
# open the image
image = cv2.imread('image.png')
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# initialize SAM vit-h and a mask_generator
sam_vit_h = sam_model_registry['vit_h'](checkpoint='checkpoints/sam_vit_h_4b8939.pth').to(device='cuda')
mask_generator_vit_h = SamAutomaticMaskGenerator(sam_vit_h)

# generate masks
masks = mask_generator_vit_h.generate(image)

# plot
plt.figure(figsize=(10, 10))
plt.imshow(image)
show_anns(masks, one_color=False)
plt.axis('off')
plt.savefig('result_code.png', bbox_inches='tight', pad_inches=0)
plt.show()
```

However,` I get a different result. Large portion of the background is segmented and there is also many more small regions detected.

**Result code:**

For my application the demo result is preferred. If anyone knows how to recreate the demo results, it would be greatly appreciated if its shared!

Start with the provided SamAutomaticMaskGenerator.generate(image) snippet and compare its defaults with the online demo's "Everything" action. Re-run the example using image.png and inspect the demo and code mask outputs; done means identifying the setting or processing difference that explains the discrepancy, or documenting why the results cannot match.

Written by the indexing model from the issue text.