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---
annotations_creators: []
language:
- en
language_creators:
- found
license:
- mit
multilinguality:
- monolingual
pretty_name: laion-aesthetics-12m-umap
size_categories: []
source_datasets: []
tags:
- laion
- stable-diffuson
- text2img
task_categories: []
task_ids: []
---
# LAION-Aesthetics :: CLIP → UMAP
This dataset is a CLIP (text) → UMAP embedding of the [LAION-Aesthetics dataset](https://laion.ai/blog/laion-aesthetics/) - specifically the [`improved_aesthetics_6plus` version](https://huggingface.co/datasets/ChristophSchuhmann/improved_aesthetics_6plus), which filters the full dataset to images with scores of > 6 under the "aesthetic" filtering model.
Thanks LAION for this amazing corpus!
---
The dataset here includes coordinates for 3x separate UMAP fits using different values for the `n_neighbors` parameter - `10`, `30`, and `60` - which are broken out as separate columns with different suffixes:
- `n_neighbors=10` → (`x_nn10`, `y_nn10`)
- `n_neighbors=30` → (`x_nn30`, `y_nn30`)
- `n_neighbors=60` → (`x_nn60`, `y_nn60`)
### `nn10`
### `nn30`
### `nn60`
(The version from [Twitter](https://twitter.com/clured/status/1565399157606580224).)
## Pipeline
The script for producing this can be found here:
https://github.com/davidmcclure/loam-viz/blob/laion/laion.py
And is very simple - just using the `openai/clip-vit-base-patch32` model out-of-the-box to encode the text captions:
```python
@app.command()
def clip(
src: str,
dst: str,
text_col: str = 'TEXT',
limit: Optional[int] = typer.Option(None),
batch_size: int = typer.Option(512),
):
"""Embed with CLIP."""
df = pd.read_parquet(src)
if limit:
df = df.head(limit)
tokenizer = CLIPTokenizerFast.from_pretrained('openai/clip-vit-base-patch32')
model = CLIPTextModel.from_pretrained('openai/clip-vit-base-patch32')
model = model.to(device)
texts = df[text_col].tolist()
embeds = []
for batch in chunked_iter(tqdm(texts), batch_size):
enc = tokenizer(
batch,
return_tensors='pt',
padding=True,
truncation=True,
)
enc = enc.to(device)
with torch.no_grad():
res = model(**enc)
embeds.append(res.pooler_output.to('cpu'))
embeds = torch.cat(embeds).numpy()
np.save(dst, embeds)
print(embeds.shape)
```
Then using `cuml.GaussianRandomProjection` to do an initial squeeze to 64d (which gets the embedding tensor small enough to fit onto a single GPU for the UMAP) -
```python
@app.command()
def random_projection(src: str, dst: str, dim: int = 64):
"""Random projection on an embedding matrix."""
rmm.reinitialize(managed_memory=True)
embeds = np.load(src)
rp = cuml.GaussianRandomProjection(n_components=dim)
embeds = rp.fit_transform(embeds)
np.save(dst, embeds)
print(embeds.shape)
```
And then `cuml.UMAP` to get from 64d -> 2d -
```python
@app.command()
def umap(
df_src: str,
embeds_src: str,
dst: str,
n_neighbors: int = typer.Option(30),
n_epochs: int = typer.Option(1000),
negative_sample_rate: int = typer.Option(20),
):
"""UMAP to 2d."""
rmm.reinitialize(managed_memory=True)
df = pd.read_parquet(df_src)
embeds = np.load(embeds_src)
embeds = embeds.astype('float16')
print(embeds.shape)
print(embeds.dtype)
reducer = cuml.UMAP(
n_neighbors=n_neighbors,
n_epochs=n_epochs,
negative_sample_rate=negative_sample_rate,
verbose=True,
)
x = reducer.fit_transform(embeds)
df['x'] = x[:,0]
df['y'] = x[:,1]
df.to_parquet(dst)
print(df)
``` |