Spaces:
Running
on
Zero
Running
on
Zero
File size: 5,988 Bytes
ca18077 623b249 ca18077 623b249 ca18077 8050b98 ca18077 623b249 ca18077 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 |
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 (
StableDiffusionControlNetImg2ImgPipeline,
ControlNetModel,
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
UniPCMultistepScheduler,
)
from controlnet_aux import (
CannyDetector,
LineartDetector,
PidiNetDetector,
HEDdetector,
)
BASE_MODEL = "stable-diffusion-v1-5/stable-diffusion-v1-5"
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
DEFAULT_EDIT_PROMPT = "change to blue hair"
DEFAULT_NEGATIVE_PROMPT = "worst quality, normal quality, low quality, low res, blurry, text, watermark, logo, banner, extra digits, cropped, jpeg artifacts, signature, username, error, sketch ,duplicate, ugly, monochrome, horror, geometry, mutation, disgusting, poorly drawn face, bad face, fused face, ugly face, worst face, asymmetrical, unrealistic skin texture, bad proportions, out of frame, poorly drawn hands, cloned face, double face"
DEFAULT_CATEGORY = "hair"
canny_detector = CannyDetector()
lineart_detector = LineartDetector.from_pretrained("lllyasviel/Annotators")
lineart_detector = lineart_detector.to(DEVICE)
pidiNet_detector = PidiNetDetector.from_pretrained('lllyasviel/Annotators')
pidiNet_detector = pidiNet_detector.to(DEVICE)
hed_detector = HEDdetector.from_pretrained('lllyasviel/Annotators')
hed_detector = hed_detector.to(DEVICE)
controlnet = [
ControlNetModel.from_pretrained(
"lllyasviel/control_v11e_sd15_ip2p",
torch_dtype=torch.float16,
),
]
basepipeline = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
BASE_MODEL,
torch_dtype=torch.float16,
use_safetensors=True,
controlnet=controlnet,
)
basepipeline.scheduler = UniPCMultistepScheduler.from_config(basepipeline.scheduler.config)
basepipeline = basepipeline.to(DEVICE)
basepipeline.enable_model_cpu_offload()
@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)
cond_image = [input_image]
# generator = torch.Generator(device=DEVICE).manual_seed(seed)
generated_image = basepipeline(
seeds=[seed],
# generator=generator,
prompt=edit_prompt,
negative_prompt=DEFAULT_NEGATIVE_PROMPT,
image=cond_image,
height=generate_size,
width=generate_size,
guidance_scale=guidance_scale,
image_guidance_scale=1.5,
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 |