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Update evaluation.mdx (#2862)

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Fix typos
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M. Tolga Cangöz
2023-03-28 19:15:37 +03:00
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parent 13845462db
commit 37c82480bb
1 changed files with 4 additions and 4 deletions
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docs/source/en/conceptual/evaluation.mdx
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@@ -310,7 +310,7 @@ for idx in range(len(dataset)):
edited_images.append(edited_image)
```
To measure the directional similarity, we first load CLIP's image and text encoders.
To measure the directional similarity, we first load CLIP's image and text encoders:
```python
from transformers import (
@@ -329,7 +329,7 @@ image_encoder = CLIPVisionModelWithProjection.from_pretrained(clip_id).to(device
Notice that we are using a particular CLIP checkpoint, i.e., `openai/clip-vit-large-patch14`. This is because the Stable Diffusion pre-training was performed with this CLIP variant. For more details, refer to the [documentation](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/pix2pix#diffusers.StableDiffusionInstructPix2PixPipeline.text_encoder).
Next, we prepare a PyTorch `nn.module` to compute directional similarity:
Next, we prepare a PyTorch `nn.Module` to compute directional similarity:
```python
import torch.nn as nn
@@ -410,7 +410,7 @@ It should be noted that the `StableDiffusionInstructPix2PixPipeline` exposes t
We can extend the idea of this metric to measure how similar the original image and edited version are. To do that, we can just do `F.cosine_similarity(img_feat_two, img_feat_one)`. For these kinds of edits, we would still want the primary semantics of the images to be preserved as much as possible, i.e., a high similarity score.
We can use these metrics for similar pipelines such as the[`StableDiffusionPix2PixZeroPipeline`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/pix2pix_zero#diffusers.StableDiffusionPix2PixZeroPipeline)`.
We can use these metrics for similar pipelines such as the [`StableDiffusionPix2PixZeroPipeline`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/pix2pix_zero#diffusers.StableDiffusionPix2PixZeroPipeline).
<Tip>
@@ -550,7 +550,7 @@ FID results tend to be fragile as they depend on a lot of factors:
* The image format (not the same if we start from PNGs vs JPGs).
Keeping that in mind, FID is often most useful when comparing similar runs, but it is
hard to to reproduce paper results unless the authors carefully disclose the FID
hard to reproduce paper results unless the authors carefully disclose the FID
measurement code.
These points apply to other related metrics too, such as KID and IS.