Spaces:
Running
on
Zero
Running
on
Zero
File size: 4,411 Bytes
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import spaces
import gradio as gr
import time
import torch
import numpy as np
from PIL import Image
from segment_utils import(
segment_image,
restore_result,
)
from diffusers import (
DiffusionPipeline,
)
BASE_MODEL = "stabilityai/stable-diffusion-xl-refiner-1.0"
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
DEFAULT_EDIT_PROMPT = "blue hair"
DEFAULT_CATEGORY = "hair"
basepipeline = DiffusionPipeline.from_pretrained(
BASE_MODEL,
torch_dtype=torch.float16,
use_safetensors=True,
variant="fp16",
)
basepipeline = basepipeline.to(DEVICE)
@spaces.GPU(duration=30)
def image_to_image(
input_image: Image,
edit_prompt: str,
seed: int,
num_steps: int,
guidance_scale: float,
generate_size: int,
):
run_task_time = 0
time_cost_str = ''
run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str)
generator = torch.Generator(device=DEVICE).manual_seed(seed)
generated_image = basepipeline(
generator=generator,
prompt=edit_prompt,
image=input_image,
# denoising_start=denoising_start,
guidance_scale=guidance_scale,
num_inference_steps=num_steps,
).images[0]
run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str)
return generated_image, time_cost_str
def make_inpaint_condition(image, image_mask):
image = np.array(image.convert("RGB")).astype(np.float32) / 255.0
image_mask = np.array(image_mask.convert("L")).astype(np.float32) / 255.0
assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
image[image_mask > 0.5] = -1.0 # set as masked pixel
image = np.expand_dims(image, 0).transpose(0, 3, 1, 2)
image = torch.from_numpy(image)
return image
def get_time_cost(run_task_time, time_cost_str):
now_time = int(time.time()*1000)
if run_task_time == 0:
time_cost_str = 'start'
else:
if time_cost_str != '':
time_cost_str += f'-->'
time_cost_str += f'{now_time - run_task_time}'
run_task_time = now_time
return run_task_time, time_cost_str
def create_demo() -> gr.Blocks:
with gr.Blocks() as demo:
croper = gr.State()
with gr.Row():
with gr.Column():
edit_prompt = gr.Textbox(lines=1, label="Edit Prompt", value=DEFAULT_EDIT_PROMPT)
generate_size = gr.Number(label="Generate Size", value=512)
with gr.Column():
num_steps = gr.Slider(minimum=1, maximum=100, value=20, step=1, label="Num Steps")
guidance_scale = gr.Slider(minimum=0, maximum=30, value=5, step=0.5, label="Guidance Scale")
with gr.Column():
with gr.Accordion("Advanced Options", open=False):
mask_expansion = gr.Number(label="Mask Expansion", value=50, visible=True)
mask_dilation = gr.Slider(minimum=0, maximum=10, value=2, step=1, label="Mask Dilation")
seed = gr.Number(label="Seed", value=8)
category = gr.Textbox(label="Category", value=DEFAULT_CATEGORY, visible=False)
g_btn = gr.Button("Edit Image")
with gr.Row():
with gr.Column():
input_image = gr.Image(label="Input Image", type="pil")
with gr.Column():
restored_image = gr.Image(label="Restored Image", type="pil", interactive=False)
with gr.Column():
origin_area_image = gr.Image(label="Origin Area Image", type="pil", interactive=False)
generated_image = gr.Image(label="Generated Image", type="pil", interactive=False)
generated_cost = gr.Textbox(label="Time cost by step (ms):", visible=True, interactive=False)
g_btn.click(
fn=segment_image,
inputs=[input_image, category, generate_size, mask_expansion, mask_dilation],
outputs=[origin_area_image, croper],
).success(
fn=image_to_image,
inputs=[origin_area_image, edit_prompt,seed, num_steps, guidance_scale, generate_size],
outputs=[generated_image, generated_cost],
).success(
fn=restore_result,
inputs=[croper, category, generated_image],
outputs=[restored_image],
)
return demo |