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()