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Running
on
Zero
File size: 4,077 Bytes
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import gradio as gr
from PIL import Image
import numpy as np
import random
import spaces
import torch
import torch.nn.functional as F
import config
from extras.expansion import FooocusExpansion
from feifeilib.feifeimodload import feifeimodload
import re
expansion = FooocusExpansion()
pipe = feifeimodload()
with open("artist.txt", "r") as file:
artists = file.readlines()
MAX_SEED = np.iinfo(np.int32).max
# 去除每行末尾的换行符
artists = [artist.strip() for artist in artists]
@spaces.GPU()
def feifeitexttoimg(
prompt,
quality_select=False,
sharpened_select=False,
styles_Radio=["(None)"],
FooocusExpansion_select=False,
seed=random.randint(0, MAX_SEED),
randomize_seed=False,
width=896,
height=1152,
num_inference_steps=4,
guidance_scale=3.5,
num_strength=0.35,
progress=gr.Progress(track_tqdm=True),
):
# 处理随机种子
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)
if not prompt:
prompt = "the photo is a 18 yo jpop girl is looking absolutely adorable and gorgeous, with a playful and mischievous grin, her eyes twinkling with joy. art by __artist__ and __artist__"
if "__artist__" in prompt:
# 随机选择艺术家
selected_artists = random.sample(artists, len(artists))
# 使用正则表达式替换所有的 __artist__
def replace_artists(match):
return selected_artists.pop(0)
prompt = re.sub(r"__artist__", replace_artists, prompt)
# print("__artist__ " + prompt)
if quality_select:
prompt += ", masterpiece, best quality, very aesthetic, absurdres"
# print("111 " + prompt)
if styles_Radio:
for style_name in styles_Radio:
for style in config.style_list:
if style["name"] == style_name:
prompt += style["prompt"].replace("{prompt}", "the ")
# print("222 " + prompt)
if FooocusExpansion_select:
prompt = expansion(prompt, seed)
# print("333 " + prompt)
# print("000 " + prompt)
# 生成图像
image = pipe(
prompt="",
prompt_2=prompt,
width=width,
height=height,
num_inference_steps=num_inference_steps,
generator=generator,
guidance_scale=guidance_scale,
output_type="pil",
).images[0]
if sharpened_select:
# 将PIL图像转换为NumPy数组
image_np = np.array(image)
# 将NumPy数组转换为PyTorch张量
image_tensor = (torch.tensor(image_np).permute(
2, 0, 1).unsqueeze(0).float().to("cuda"))
# 定义锐化滤镜，并调整中心值
strength = num_strength
sharpen_kernel = (torch.tensor(
[
[0, -1 * strength, 0],
[-1 * strength, 1 + 4 * strength, -1 * strength],
[0, -1 * strength, 0],
],
dtype=torch.float32,
).unsqueeze(0).unsqueeze(0).to("cuda"))
# 分别对每个通道应用卷积核
sharpened_channels = []
for i in range(3):
channel_tensor = image_tensor[:, i:i + 1, :, :]
sharpened_channel = F.conv2d(channel_tensor,
sharpen_kernel,
padding=1)
sharpened_channels.append(sharpened_channel)
# 合并通道
sharpened_image_tensor = torch.cat(sharpened_channels, dim=1)
# 将增强后的图像转换回PIL格式
sharpened_image_np = (sharpened_image_tensor.squeeze(0).permute(
1, 2, 0).cpu().numpy())
sharpened_image_np = np.clip(sharpened_image_np, 0,
255).astype(np.uint8)
image = Image.fromarray(sharpened_image_np)
return image, seed
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