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import gradio as gr
import numpy as np
import random
from diffusers import DiffusionPipeline, LMSDiscreteScheduler
import torch
import time # Import time for measuring generation time
# Device configuration (explicitly set to CPU)
DEVICE = "cpu"
# Model Options (optimized for CPU)
MODEL_OPTIONS = {
"Medium Quality (Faster)": "stabilityai/stable-diffusion-2-base",
"Fastest (Draft Quality)": "hf-internal-testing/tiny-stable-diffusion-pipe",
}
# Default to fastest model
DEFAULT_MODEL_ID = MODEL_OPTIONS["Fastest (Draft Quality)"]
# Cache models to avoid reloading them for each generation
PIPELINES = {}
def load_pipeline(model_id):
if model_id in PIPELINES:
return PIPELINES[model_id]
else:
pipe = DiffusionPipeline.from_pretrained(
model_id, torch_dtype=torch.float32
)
pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)
pipe = pipe.to(DEVICE)
PIPELINES[model_id] = pipe
return pipe
def generate_image(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, num_images, model_choice):
if not prompt:
raise gr.Error("Будь ласка, введіть опис для зображення.")
pipe = load_pipeline(MODEL_OPTIONS[model_choice])
generator = torch.Generator(device=DEVICE)
if not randomize_seed:
generator = generator.manual_seed(seed)
start_time = time.time() # Record start time
images = pipe(
prompt,
negative_prompt=negative_prompt,
width=width,
height=height,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
num_images_per_prompt=num_images,
generator=generator,
).images
end_time = time.time()
generation_time = end_time - start_time
return images, f"Час генерації: {generation_time:.2f} секунд" # Return images and generation time
# ... (Gradio UI remains largely the same, with an added status text output)
run_button = gr.Button("Згенерувати")
gallery = gr.Gallery(label="Згенеровані зображення")
status_text = gr.Textbox(label="Статус")
run_button.click(
fn=generate_image,
inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, num_images, model_choice],
outputs=[gallery, status_text], # Output both the gallery and status text
)
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