diffusers-parti-prompts/sdxl-1.0

---
dataset_info:
features:
- name: Prompt
dtype: string
- name: Category
dtype: string
- name: Challenge
dtype: string
- name: Note
dtype: string
- name: images
dtype: image
- name: model_name
dtype: string
- name: seed
dtype: int64
splits:
- name: train
num_bytes: 189820015.232
num_examples: 1632
download_size: 189815139
dataset_size: 189820015.232
---
# Dataset Card for "sdxl-1.0"

Dataset was generated using the code below:

```python
import torch
from datasets import Dataset, Features
from datasets import Image as ImageFeature
from datasets import Value, load_dataset

from diffusers import DDIMScheduler, DiffusionPipeline
import PIL

def main():
print("Loading dataset...")
parti_prompts = load_dataset("nateraw/parti-prompts", split="train")

print("Loading pipeline...")
ckpt_id = "stabilityai/stable-diffusion-xl-base-1.0"
pipe = DiffusionPipeline.from_pretrained(
ckpt_id, torch_dtype=torch.float16, use_auth_token=True
).to("cuda")
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
pipe.set_progress_bar_config(disable=True)

seed = 0
generator = torch.Generator("cuda").manual_seed(seed)

print("Running inference...")
main_dict = {}
for i in range(len(parti_prompts)):
sample = parti_prompts[i]
prompt = sample["Prompt"]
image = pipe(
prompt,
generator=generator,
num_inference_steps=100,
guidance_scale=7.5,
).images[0]

image = image.resize((256, 256), resample=PIL.Image.Resampling.LANCZOS)
img_path = f"sd_xl_{i}.png"
image.save(img_path)
main_dict.update(
{
prompt: {
"img_path": img_path,
"Category": sample["Category"],
"Challenge": sample["Challenge"],
"Note": sample["Note"],
"model_name": ckpt_id,
"seed": seed,
}
}
)

def generation_fn():
for prompt in main_dict:
prompt_entry = main_dict[prompt]
yield {
"Prompt": prompt,
"Category": prompt_entry["Category"],
"Challenge": prompt_entry["Challenge"],
"Note": prompt_entry["Note"],
"images": {"path": prompt_entry["img_path"]},
"model_name": prompt_entry["model_name"],
"seed": prompt_entry["seed"],
}

print("Preparing HF dataset...")
ds = Dataset.from_generator(
generation_fn,
features=Features(
Prompt=Value("string"),
Category=Value("string"),
Challenge=Value("string"),
Note=Value("string"),
images=ImageFeature(),
model_name=Value("string"),
seed=Value("int64"),
),
)
ds_id = "diffusers-parti-prompts/sdxl-1.0"
ds.push_to_hub(ds_id)

if __name__ == "__main__":
main()

原始信息汇总

数据集概述

数据集名称

"sdxl-1.0"

数据集特征

• Prompt: 数据类型为字符串。
• Category: 数据类型为字符串。
• Challenge: 数据类型为字符串。
• Note: 数据类型为字符串。
• images: 数据类型为图像。
• model_name: 数据类型为字符串。
• seed: 数据类型为int64。

数据集划分

• train: 包含1632个样本，总大小为189820015.232字节。

数据集大小

• 下载大小: 189815139字节
• 数据集总大小: 189820015.232字节