app.py

!pip install torch torchvision
!pip install diffusers
!pip install transformers
!pip install datasets
!pip install accelerate
!pip install pillow
!pip install matplotlib
!pip install scipy
!pip install pandas
!pip install torchmetrics
!pip install clean-fid
!pip install open_clip_torch
from datasets import load_dataset

dataset = load_dataset(
    "jackyhate/text-to-image-2M",
    split="train",
    streaming=True
)
sample = next(iter(dataset))

print(sample)
import torch
from diffusers import AutoPipelineForText2Image

pipe = AutoPipelineForText2Image.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0",
    torch_dtype=torch.float16
)

pipe.to("cuda")
prompt = sample["text"]

image = pipe(
    prompt,
    num_inference_steps=30,
    guidance_scale=7.5
).images[0]

image.save("outputs/generated.png")
from torchmetrics.multimodal.clip_score import CLIPScore

metric = CLIPScore(model_name_or_path="openai/clip-vit-base-patch32")

score = metric(
    image,
    prompt
)

print(score)
from cleanfid import fid

score = fid.compute_fid(
    "real_images",
    "generated_images"
)

print(score)