MBCDN

# sd15_openvino.py
from optimum.intel.openvino import OVDiffusionPipeline
from PIL import Image
import time
 
# Vorgefertigtes OpenVINO-Modell
model_id = "OpenVINO/stable-diffusion-v1-5-fp16-ov"
 
# Erst GPU probieren evtl. auf "CPU" oder "AUTO" ändern.
device = "GPU"
 
prompt = "a cozy cabin in the woods at sunset, detailed, cinematic lighting"
 
start = time.time()
 
pipe = OVDiffusionPipeline.from_pretrained(
    model_id,
    device=device
)
 
images = pipe(
    prompt,
    num_inference_steps=20,
    height=512,
    width=512
).images
 
image = images[0]
image.save("output.png")
 
print(f"Fertig. Gespeichert als output.png in {time.time() - start:.1f} Sekunden auf {device}.")

from optimum.intel import OVStableDiffusionImg2ImgPipeline
from PIL import Image
import time
 
model_id = "runwayml/stable-diffusion-v1-5"
device = "GPU"   # ggf. "CPU" oder "AUTO"
 
prompt = "a cozy cabin in the woods at sunset, detailed, cinematic lighting"
 
# Eingabebild laden
init_image = Image.open("input.jpg").convert("RGB").resize((512, 512))
 
start = time.time()
 
pipe = OVStableDiffusionImg2ImgPipeline.from_pretrained(
    model_id,
    export=True,      # falls noch kein OpenVINO-Export vorliegt
    device=device
)
 
result = pipe(
    prompt=prompt,
    image=init_image,
    strength=0.6,          # 0.2 = näher am Original, 0.8 = freier
    guidance_scale=7.5,
    num_inference_steps=20
).images[0]
 
result.save("output.png")
 
print(f"Fertig. Gespeichert als output.png in {time.time() - start:.1f} Sekunden auf {device}.")