app.py

import gradio as gr
import torch
from PIL import Image
from torchvision import transforms

from diffusers import DiffusionPipeline

pipeline = DiffusionPipeline.from_pretrained("fusing/karlo-image-variations-diffusers")
def main(
    input_im,
    scale=3.0,
    n_samples=4,
    steps=25,
    seed=0,
    ):
    generator = torch.Generator(device=device).manual_seed(int(seed))

    tform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Resize(
        (224, 224),
        interpolation=transforms.InterpolationMode.BICUBIC,
        antialias=False,
        ),
        transforms.Normalize(
          [0.48145466, 0.4578275, 0.40821073],
          [0.26862954, 0.26130258, 0.27577711]),
    ])
    inp = tform(input_im).to(device)
        
    images_list = pipe(
        inp.tile(n_samples, 1, 1, 1),
        guidance_scale=scale,
        num_inference_steps=steps,
        generator=generator,
        )

    images = []
    for i, image in enumerate(images_list["images"]):
        if(images_list["nsfw_content_detected"][i]):
            safe_image = Image.open(r"unsafe.png")
            images.append(safe_image)
        else:
            images.append(image)
    return images


description = \
"""
"""

article = \
"""
## How does this work?

The normal Stable Diffusion model is trained to be conditioned on text input. This version has had the original text encoder (from CLIP) removed, and replaced with
the CLIP _image_ encoder instead. So instead of generating images based a text input, images are generated to match CLIP's embedding of the image.
This creates images which have the same rough style and content, but different details, in particular the composition is generally quite different.
This is a totally different approach to the img2img script of the original Stable Diffusion and gives very different results.

The model was fine tuned on the [LAION aethetics v2 6+ dataset](https://laion.ai/blog/laion-aesthetics/) to accept the new conditioning.
Training was done on 8xA100 GPUs on [Lambda GPU Cloud](https://lambdalabs.com/service/gpu-cloud).
More details are on the [model card](https://huggingface.co/lambdalabs/sd-image-variations-diffusers).
"""

device = "cuda" if torch.cuda.is_available() else "cpu"
pipe = KarloImageVariationPipeline.from_pretrained(
    "fusing/karlo-image-variations-diffusers",
    )
pipe = pipe.to(device)

inputs = [
    gr.Image(),
    gr.Slider(0, 25, value=3, step=1, label="Guidance scale"),
    gr.Slider(1, 4, value=1, step=1, label="Number images"),
    gr.Slider(5, 50, value=25, step=5, label="Steps"),
    gr.Number(0, label="Seed", precision=0)
]
output = gr.Gallery(label="Generated variations")
output.style(grid=2)

examples = [
    ["examples/vermeer.jpg", 3, 1, 25, 0],
    ["examples/matisse.jpg", 3, 1, 25, 0],
]

demo = gr.Interface(
    fn=main,
    title="Stable Diffusion Image Variations",
    description=description,
    article=article,
    inputs=inputs,
    outputs=output,
    examples=examples,
    )
demo.launch()