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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()
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