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import gradio as gr
from optimum.intel.openvino import OVStableDiffusionPipeline
from diffusers.training_utils import set_seed
from diffusers import LMSDiscreteScheduler, StableDiffusionPipeline
import time
scheduler = DDPMScheduler(beta_start=0.00085, beta_end=0.012,
beta_schedule="scaled_linear", num_train_timesteps=1000)
pretrained_model_name_or_path = "svjack/Stable-Diffusion-Pokemon-en"
pipe_torch_fp32 = StableDiffusionPipeline.from_pretrained(pretrained_model_name_or_path,
scheduler=scheduler, use_auth_token=True)
pipe_torch_fp32.safety_checker = lambda images, clip_input: (images, False)
pipe_fp32 = OVStableDiffusionPipeline.from_pretrained("OpenVINO/stable-diffusion-pokemons-fp32", compile=False)# scheduler=scheduler)
pipe_fp32.reshape(batch_size=1, height=512, width=512, num_images_per_prompt=1)
pipe_fp32.compile()
pipe_int8 = OVStableDiffusionPipeline.from_pretrained("OpenVINO/stable-diffusion-pokemons-quantized-aggressive", compile=False)
pipe_int8.reshape(batch_size=1, height=512, width=512, num_images_per_prompt=1)
pipe_int8.compile()
pipe_tome_int8 = OVStableDiffusionPipeline.from_pretrained("OpenVINO/stable-diffusion-pokemons-tome-quantized-aggressive", compile=False)
pipe_tome_int8.reshape(batch_size=1, height=512, width=512, num_images_per_prompt=1)
pipe_tome_int8.compile()
pipes = {
"Torch fp32": pipe_torch_fp32,
"OpenVINO fp32": pipe_fp32,
"OpenVINO 8-bit quantized": pipe_int8,
"OpenVINO merged and quantized": pipe_tome_int8
}
def generate(prompt, option, seed):
pipe = pipes[option]
set_seed(int(seed))
start_time = time.time()
if "Torch" in option:
output = pipe(prompt, num_inference_steps=50, output_type="pil", height=512, width=512)
else:
output = pipe(prompt, num_inference_steps=50, output_type="pil")
elapsed_time = time.time() - start_time
return (output.images[0], "{:10.4f}".format(elapsed_time))
examples = ["cartoon bird",
"a drawing of a green pokemon with red eyes",
"plant pokemon in jungle"]
model_options = [option for option in pipes.keys()]
gr.Interface(
fn=generate,
inputs=[gr.inputs.Textbox(default="cartoon bird", label="Prompt", lines=1),
gr.inputs.Dropdown(choices=model_options, default=model_options[-1], label="Model version"),
gr.inputs.Textbox(default="42", label="Seed", lines=1)
],
outputs=[gr.outputs.Image(type="pil", label="Generated Image"), gr.outputs.Textbox(label="Inference time")],
title="OpenVINO-optimized Stable Diffusion",
description="This is the Optimum-based demo for NNCF-optimized Stable Diffusion pipeline trained on 'lambdalabs/pokemon-blip-captions' dataset and running with OpenVINO.\n"
"The pipeline is run using 8 vCPUs (4 cores) only.",
theme="huggingface",
).launch() |