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import gradio as gr
import numpy as np
from diffusers import DiffusionPipeline
import torch
# load both base & refiner
base = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True, cache_dir="./local_model_cache"
)
# base.enable_model_cpu_offload()
base.to("cpu")
refiner = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-refiner-1.0",
text_encoder_2=base.text_encoder_2,
vae=base.vae,
torch_dtype=torch.float16,
use_safetensors=True,
variant="fp16",
cache_dir="./local_model_cache"
)
# refiner.enable_model_cpu_offload()
refiner.to("cpu")
# Define how many steps and what % of steps to be run on each experts (80/20) here
n_steps = 40
high_noise_frac = 0.8
def create_image(prompt):
# run both experts
# torch.cuda.empty_cache()
image = base(
prompt=prompt,
num_inference_steps=n_steps,
denoising_end=high_noise_frac,
output_type="latent",
).images
image = refiner(
prompt=prompt,
num_inference_steps=n_steps,
denoising_start=high_noise_frac,
image=image,
).images[0]
# torch.cuda.empty_cache()
return image
demo = gr.Interface(
fn=create_image,
inputs=["text"],
outputs=["image"],
)
demo.launch() |