Datasets:
Prompt stringlengths 5 1.75k | vector listlengths 4.1k 4.1k |
|---|---|
realistic car 3 d render sci - fi car and sci - fi robotic factory structure in the coronation of napoleon painting and digital billboard with point cloud in the middle, unreal engine 5, keyshot, octane, artstation trending, ultra high detail, ultra realistic, cinematic, 8 k, 1 6 k, in style of zaha hadid, in style of nanospace michael menzelincev, in style of lee souder, in plastic, dark atmosphere, tilt shift, depth of field, | [
-0.024941163137555122,
-0.023545388132333755,
-0.00999736599624157,
0.020411211997270584,
0.06104219704866409,
-0.13685818016529083,
0.005837575998157263,
-0.006409497931599617,
-0.017526008188724518,
0.022343046963214874,
0.01954040303826332,
-0.002588843461126089,
-0.03580796346068382,
0... |
a comic potrait of a female necromamcer with big and cute eyes, fine - face, realistic shaded perfect face, fine details. night setting. very anime style. realistic shaded lighting poster by ilya kuvshinov katsuhiro, magali villeneuve, artgerm, jeremy lipkin and michael garmash, rob rey and kentaro miura style, trending on art station | [
-0.04543079063296318,
-0.011836512014269829,
-0.02565544843673706,
0.007850013673305511,
0.04046374931931496,
-0.17501871287822723,
-0.005360548850148916,
-0.009966656565666199,
0.0012690337607637048,
0.02682776190340519,
0.01883493736386299,
-0.02954949252307415,
-0.0268664862960577,
0.05... |
"steampunk market interior, colorful, 3 d scene, greg rutkowski, zabrocki, karlkka, jayison devadas,(...TRUNCATED) | [-0.025809235870838165,-0.016006216406822205,-0.02420247718691826,-0.016819695010781288,0.0571664422(...TRUNCATED) |
"“A portrait of a cyborg in a golden suit, D&D sci-fi, artstation, concept art, highly detailed il(...TRUNCATED) | [-0.03343925625085831,-0.04045936465263367,-0.012750673107802868,-0.00647507980465889,0.058525357395(...TRUNCATED) |
"A full portrait of a beautiful post apocalyptic offworld nanotechnician, intricate, elegant, highly(...TRUNCATED) | [0.0044984109699726105,-0.023729607462882996,-0.01915445551276207,-0.01997975818812847,0.06388203799(...TRUNCATED) |
"beautiful victorian raven digital painting, art by artgerm and greg rutkowski, alphonse mucha, cgso(...TRUNCATED) | [-0.05999283492565155,0.008608411997556686,-0.01031371671706438,0.01884971186518669,0.05315867066383(...TRUNCATED) |
"ilya kuvshinov with long sky blue hair, gold eyes, professional digital painting, concept art, unre(...TRUNCATED) | [-0.05659177526831627,-0.031204458326101303,-0.02431950718164444,0.009800983592867851,0.063415281474(...TRUNCATED) |
"violet demonic card game decorative frame, violet decorative frame, card frame, card frame, card fr(...TRUNCATED) | [-0.046947140246629715,-0.0001018233087961562,-0.0020977098029106855,-0.012022724375128746,0.0552277(...TRUNCATED) |
"a black and white edward gorey illustration of a man in old fashioned clothes waits by the side of (...TRUNCATED) | [-0.04295801371335983,-0.018476709723472595,-0.023468149825930595,-0.00007189481402747333,0.06248864(...TRUNCATED) |
"african hydropunk princess, science fiction, highly detailed, digital painting, beautiful eyes, sym(...TRUNCATED) | [-0.03574645146727562,-0.013086139224469662,-0.004091939888894558,-0.014955133199691772,0.0852469131(...TRUNCATED) |
End of preview. Expand
in Data Studio
T5-XXL Embeddings for Image Generation Prompts
This dataset contains 4096-dimensional text embeddings for prompts from the Gustavosta/Stable-Diffusion-Prompts dataset.
Model Used
The embeddings were generated using a T5-XXL encoder model. Specifically, the weights from t5xxl_fp8_e4m3fn_scaled.safetensors were used.
Important Note: While the source model weights were in a FP8 format, the embeddings in this dataset have been calculated and stored in full float32 precision to ensure numerical stability and maximum quality.
Data Fields
Prompt: Astringcontaining the original text prompt.vector: Alistoffloat32numbers, representing the 4096-dimensional embedding of the prompt.
Data Splits
train: Contains 73718 prompts and their corresponding vectors.test: Contains 8192 prompts and their corresponding vectors, sampled from the end of the original train set.
Usage
You can load the dataset easily using the datasets library:
from datasets import load_dataset
# Load the dataset
dataset = load_dataset("JusteLeo/t5-xxl-embedding")
# Access the splits
train_data = dataset['train']
test_data = dataset['test']
# Example
print(train_data[0]['Prompt'])
print(len(train_data[0]['vector']))
# Output: 4096
Citation and Credits
This dataset of embeddings was created by JusteLeo. If you use this dataset in your work, please consider citing this repository.
Original Data
The text prompts used as a basis for this dataset are from the Gustavosta/Stable-Diffusion-Prompts dataset. A huge thank you to Gustavosta for providing the initial data.
- Original Prompt Dataset: Gustavosta/Stable-Diffusion-Prompts
Model
The embeddings were generated using an encoder based on the T5-XXL architecture.
- Base Model Architecture: google/t5-v1_1-xxl by Google.
- Specific Encoder Weights: The model was loaded with specific
fp8quantized weights from thet5xxl_fp8_e4m3fn_scaled.safetensorsfile.
License
This dataset is licensed under the MIT License.
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