File size: 4,283 Bytes
e10f7f0 fb0b46c d802916 fb0b46c 4aff4c5 fb0b46c a61bba9 e10f7f0 a9609d3 24f485a 43fff5f 5fa19cb e10f7f0 43fff5f 5fa19cb 43fff5f e10f7f0 43fff5f e10f7f0 43fff5f e10f7f0 9523e15 e10f7f0 9523e15 e10f7f0 2d7199f e10f7f0 9ca7096 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 | import gradio as gr
import numpy as np
from PIL import Image
import openvino_genai as ov_genai
import subprocess, sys
import os
subprocess.run([sys.executable, "download_models.py"], check=True)
# Core count
BACKEND = os.environ.get("BACKEND", "genai") # "optimum" or "genai"
MODEL_PATH = "LCM_Dreamshaper_v7-int8-ov"
DEVICE = "CPU"
if BACKEND == "optimum":
from optimum.intel import OVDiffusionPipeline
print("Loading with Optimum-Intel...")
pipe = OVDiffusionPipeline.from_pretrained(
MODEL_PATH,
device=DEVICE,
safety_checker=None
)
def generate(prompt, negative_prompt, num_steps, guidance_scale, seed):
generator = np.random.RandomState(seed if seed != -1 else None)
result = pipe(
prompt=prompt,
negative_prompt=negative_prompt or None,
num_inference_steps=num_steps,
guidance_scale=guidance_scale,
generator=generator,
)
return result.images[0]
else:
import openvino_genai as ov_genai
print("Loading with OpenVINO GenAI...")
pipe = ov_genai.Text2ImagePipeline(MODEL_PATH, DEVICE)
def generate(prompt, negative_prompt, num_steps, guidance_scale, seed):
actual_seed = seed if seed != -1 else np.random.randint(0, 2**31)
image_tensor = pipe.generate(
prompt,
num_inference_steps=num_steps,
guidance_scale=guidance_scale,
rng_seed=actual_seed,
)
return Image.fromarray(np.array(image_tensor.data[0]))
with gr.Blocks(title="SD1.5 3d interior design – OpenVINO") as demo:
gr.Markdown(
"""
# 🏠 SD1.5 Interior LoRA + LCM-style Model— OpenVINO INT8
Fast CPU inference with Latent Consistency Model.
4 steps is usually enough — crank it to 8 for more detail.
"""
)
with gr.Row():
with gr.Column(scale=1):
prompt = gr.Textbox(
label="Prompt",
placeholder="photo, full body man, cinematic lighting...",
lines=3,
)
negative_prompt = gr.Textbox(
label="Negative Prompt",
placeholder="blurry, low quality, watermark...",
lines=2,
value="blurry, low quality, artifacts, watermark",
)
with gr.Row():
num_steps = gr.Slider(
label="Inference Steps",
minimum=1,
maximum=16,
value=4,
step=1,
)
guidance_scale = gr.Slider(
label="Guidance Scale",
minimum=0.0,
maximum=3.0,
value=0.8,
step=0.1,
)
seed = gr.Number(
label="Seed (-1 = random)",
value=-1,
precision=0,
)
btn = gr.Button("Generate", variant="primary")
with gr.Column(scale=1):
output = gr.Image(label="Generated Image", type="pil")
btn.click(
fn=generate,
inputs=[prompt, negative_prompt, num_steps, guidance_scale, seed],
outputs=output,
)
gr.Examples(
examples=[
["isometric ,3d render,interior a living room with a couch, chair, table and clock,indoors, book, pillow, no humans, window, bed, chair, table, plant, curtains, scenery, couch, wooden floor, clock, lamp, alarm clock","blurry, low quality", 4, 1.0, 42],
["isometric ,3d render,interior a bedroom with a bed, desk and computer monitor in a neon frame,book, pillow, no humans, window, bed, night, chair, plant, scenery, desk, lamp, computer, monitor","blurry, low quality", 8, 1.0, 7],
["isometric ,3d render,interior a small kitchen with a table and chairs,food, indoors, no humans, window, chair, table, bottle, scenery, plate, kitchen, frying pan, sink, rug, stove, cutting board","blurry, low quality", 8, 0.8, 123],
],
inputs=[prompt, negative_prompt, num_steps, guidance_scale, seed],
outputs=output,
fn=generate,
cache_examples=False,
)
if __name__ == "__main__":
demo.launch() |