import os
import gradio as gr
import PIL.Image
import numpy as np
import random
import torch
import subprocess
from diffusers import StableDiffusionPipeline
import time
repo_id = "runwayml/stable-diffusion-v1-5"
dpm = DPMSolverMultistepScheduler.from_pretrained(repo_id, subfolder="scheduler")
model_id = "dicoo_model"
pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=dpm, torch_dtype=torch.float)
def predict(prompt, steps=25, seed=42, guidance_scale=7.5):
# cpu info
# print(subprocess.check_output(["cat /proc/cpuinfo | grep 'model name' |uniq"], stderr=subprocess.STDOUT).decode("utf8"))
print("prompt: ", prompt)
print("steps: ", steps)
generator = torch.manual_seed(seed)
start_time = time.time()
image = pipe(prompt, generator=generator, num_inference_steps=steps, guidance_scale=7.5).images[0]
print("cost: ", time.time() - start_time)
return image
md = """
This Spaces app is same as Intel/dicoo_diffusion, created by Intel AIA/AIPC team with the model fine-tuned with one shot (one image) for a newly introduced object \"dicoo\". To replicate the model fine-tuning, please refer to the code sample in Intel Neural Compressor. You may also refer to our blog for more details.
**Tips:**
1) When inputting prompts, you need to contain the word **** which represents the pretrained object \"dicoo\".
2) For better generation, you maybe increase the inference steps.
"""
random_seed = random.randint(0, 2147483647)
gr.Interface(
predict,
inputs=[
gr.inputs.Textbox(label='Prompt', default='a lovely in red dress and hat, in the snowy and brightly night, with many brightly buildings'),
gr.inputs.Slider(1, 100, label='Inference Steps', default=25, step=1),
gr.inputs.Slider(0, 2147483647, label='Seed', default=random_seed, step=1),
gr.inputs.Slider(1.0, 20.0, label='Guidance Scale - how much the prompt will influence the results', default=6.0, step=0.1),
],
outputs=gr.Image(shape=[512, 512], type="pil", elem_id="output_image"),
css="#output_image{width: 256px}",
title="Demo of dicoo-finetuned-diffusion-model using Intel Neural Compressor 🧨",
description=md,
).launch()