app.py

import gradio as gr
from diffusers import DiffusionPipeline
import torch
from diffusers import DDPMScheduler, UNet2DModel
from PIL import Image
import numpy as np


def erzeuge(prompt):
 return pipeline(prompt).images # [0]


def erzeuge_komplex(prompt):
 scheduler = DDPMScheduler.from_pretrained("google/ddpm-cat-256")
 model = UNet2DModel.from_pretrained("google/ddpm-cat-256").to("cuda")
 scheduler.set_timesteps(50)

 sample_size = model.config.sample_size
 noise = torch.randn((1, 3, sample_size, sample_size)).to("cuda")
 input = noise

 for t in scheduler.timesteps:
 with torch.no_grad():
 noisy_residual = model(input, t).sample
 prev_noisy_sample = scheduler.step(noisy_residual, t, input).prev_sample
 input = prev_noisy_sample

 image = (input / 2 + 0.5).clamp(0, 1)
 image = image.cpu().permute(0, 2, 3, 1).numpy()[0]
 image = Image.fromarray((image * 255).round().astype("uint8"))
 return image


# pipeline = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2")
pipeline = DiffusionPipeline.from_pretrained("google/ddpm-cat-256")

pipeline.to("cuda")


with gr.Blocks() as demo:
 with gr.Column(variant="panel"):
 with gr.Row(variant="compact"):
 text = gr.Textbox(
 label="Deine Beschreibung:",
 show_label=False,
 max_lines=1,
 placeholder="Bildbeschreibung",
 ).style(
 container=False,
 )
 btn = gr.Button("erzeuge Bild").style(full_width=False, min_width=100)

 gallery = gr.Gallery(
 label="Erzeugtes Bild", show_label=False, elem_id="gallery"
 ).style(columns=[2], rows=[2], object_fit="contain", height="auto")

 btn.click(erzeuge, inputs=[text], outputs=[gallery])
 text.submit(erzeuge, inputs=[text], outputs=[gallery])

if __name__ == "__main__":
 demo.launch()