import gradio as gr
import os
import sys
from pathlib import Path
import random
import string
import time
from queue import Queue
from threading import Thread
import emoji
text_gen=gr.Interface.load("spaces/Dao3/MagicPrompt-Stable-Diffusion")
def get_prompts(prompt_text):
if prompt_text:
return text_gen("photo, " + prompt_text)
else:
return text_gen("")
proc1=gr.Interface.load("models/dreamlike-art/dreamlike-photoreal-2.0")
def restart_script_periodically():
while True:
random_time = random.randint(540, 600)
time.sleep(random_time)
os.execl(sys.executable, sys.executable, *sys.argv)
restart_thread = Thread(target=restart_script_periodically, daemon=True)
restart_thread.start()
queue = Queue()
queue_threshold = 100
def add_random_noise(prompt, noise_level=0.00):
if noise_level == 0:
noise_level = 0.00
percentage_noise = noise_level * 5
num_noise_chars = int(len(prompt) * (percentage_noise/100))
noise_indices = random.sample(range(len(prompt)), num_noise_chars)
prompt_list = list(prompt)
noise_chars = list(string.ascii_letters + string.punctuation + ' ' + string.digits)
noise_chars.extend(['😍', '💩', '😂', '🤔', '😊', '🤗', '😭', '🙄', '😷', '🤯', '🤫', '🥴', '😴', '🤩', '🥳', '😔', '😩', '🤪', '😇', '🤢', '😈', '👹', '👻', '🤖', '👽', '💀', '🎃', '🎅', '🎄', '🎁', '🎂', '🎉', '🎈', '🎊', '🎮', '❤️', '💔', '💕', '💖', '💗', '🐶', '🐱', '🐭', '🐹', '🦊', '🐻', '🐨', '🐯', '🦁', '🐘', '🔥', '🌧️', '🌞', '🌈', '💥', '🌴', '🌊', '🌺', '🌻', '🌸', '🎨', '🌅', '🌌', '☁️', '⛈️', '❄️', '☀️', '🌤️', '⛅️', '🌥️', '🌦️', '🌧️', '🌩️', '🌨️', '🌫️', '☔️', '🌬️', '💨', '🌪️', '🌈'])
for index in noise_indices:
prompt_list[index] = random.choice(noise_chars)
return "".join(prompt_list)
def send_it1(inputs, noise_level, proc1=proc1):
prompt_with_noise = add_random_noise(inputs, noise_level)
while queue.qsize() >= queue_threshold:
time.sleep(2)
queue.put(prompt_with_noise)
output1 = proc1(prompt_with_noise)
return output1
def send_it2(inputs, noise_level, proc1=proc1):
prompt_with_noise = add_random_noise(inputs, noise_level)
while queue.qsize() >= queue_threshold:
time.sleep(2)
queue.put(prompt_with_noise)
output2 = proc1(prompt_with_noise)
return output2
#def send_it3(inputs, noise_level, proc1=proc1):
#prompt_with_noise = add_random_noise(inputs, noise_level)
#while queue.qsize() >= queue_threshold:
#time.sleep(2)
#queue.put(prompt_with_noise)
#output3 = proc1(prompt_with_noise)
#return output3
#def send_it4(inputs, noise_level, proc1=proc1):
#prompt_with_noise = add_random_noise(inputs, noise_level)
#while queue.qsize() >= queue_threshold:
#time.sleep(2)
#queue.put(prompt_with_noise)
#output4 = proc1(prompt_with_noise)
#return output4
def txt_to_img(model_path, prompt, n_images, neg_prompt, guidance, steps, width, height, generator, seed):
print(f"{datetime.datetime.now()} txt_to_img, model: {current_model.name}")
global last_mode
global pipe
global current_model_path
if model_path != current_model_path or last_mode != "txt2img":
current_model_path = model_path
update_state(f"Loading {current_model.name} text-to-image model...")
if is_colab or current_model == custom_model:
pipe = StableDiffusionPipeline.from_pretrained(
current_model_path,
torch_dtype=torch.float16,
scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler"),
safety_checker=lambda images, clip_input: (images, False)
)
else:
pipe = StableDiffusionPipeline.from_pretrained(
current_model_path,
torch_dtype=torch.float16,
scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler")
)
# pipe = pipe.to("cpu")
# pipe = current_model.pipe_t2i
if torch.cuda.is_available():
pipe = pipe.to("cuda")
pipe.enable_xformers_memory_efficient_attention()
last_mode = "txt2img"
prompt = current_model.prefix + prompt
result = pipe(
prompt,
negative_prompt = neg_prompt,
num_images_per_prompt=n_images,
num_inference_steps = int(steps),
guidance_scale = guidance,
width = width,
height = height,
generator = generator,
callback=pipe_callback)
# update_state(f"Done. Seed: {seed}")
return replace_nsfw_images(result)
def img_to_img(model_path, prompt, n_images, neg_prompt, img, strength, guidance, steps, width, height, generator, seed):
print(f"{datetime.datetime.now()} img_to_img, model: {model_path}")
global last_mode
global pipe
global current_model_path
if model_path != current_model_path or last_mode != "img2img":
current_model_path = model_path
update_state(f"Loading {current_model.name} image-to-image model...")
if is_colab or current_model == custom_model:
pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
current_model_path,
torch_dtype=torch.float16,
scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler"),
safety_checker=lambda images, clip_input: (images, False)
)
else:
pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
current_model_path,
torch_dtype=torch.float16,
scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler")
)
# pipe = pipe.to("cpu")
# pipe = current_model.pipe_i2i
if torch.cuda.is_available():
pipe = pipe.to("cuda")
pipe.enable_xformers_memory_efficient_attention()
last_mode = "img2img"
prompt = current_model.prefix + prompt
ratio = min(height / img.height, width / img.width)
img = img.resize((int(img.width * ratio), int(img.height * ratio)), Image.LANCZOS)
result = pipe(
prompt,
negative_prompt = neg_prompt,
num_images_per_prompt=n_images,
image = img,
num_inference_steps = int(steps),
strength = strength,
guidance_scale = guidance,
# width = width,
# height = height,
generator = generator,
callback=pipe_callback)
# update_state(f"Done. Seed: {seed}")
return replace_nsfw_images(result)
def replace_nsfw_images(results):
if is_colab:
return results.images
for i in range(len(results.images)):
if results.nsfw_content_detected[i]:
results.images[i] = Image.open("nsfw.png")
return results.images
with gr.Blocks(css='style.css') as demo:
gr.HTML(
"""
幻梦成真-2.0
差异程度: 用数值调节两张图的差异程度。数值越大，两张图的差异越大，反之越小。
❤️ 喜欢的话，就点上面的❤️吧~❤️
"""
)
with gr.Column(elem_id="col-container"):
with gr.Row(variant="compact"):
input_text = gr.Textbox(
label="Short Prompt",
show_label=False,
max_lines=2,
placeholder="输入你的想象(英文词汇)，然后按右边按钮。没灵感？直接按！",
).style(
container=False,
)
see_prompts = gr.Button("✨ 咒语显现 ✨").style(full_width=False)
with gr.Row(variant="compact"):
prompt = gr.Textbox(
label="输入描述词",
show_label=False,
max_lines=2,
placeholder="可输入完整描述词，或者用咒语显现按钮生成",
).style(
container=False,
)
run = gr.Button("✨ 幻梦成真✨").style(full_width=False)
with gr.Row():
with gr.Row():
noise_level = gr.Slider(minimum=0.0, maximum=3, step=0.1, label="差异程度")
with gr.Row():
with gr.Row():
output1=gr.Image(label="Dreamlike-photoreal-2.0",show_label=False)
output2=gr.Image(label="Dreamlike-photoreal-2.0",show_label=False)
#with gr.Row():
#output1=gr.Image()
see_prompts.click(get_prompts, inputs=[input_text], outputs=[prompt], queue=False)
run.click(send_it1, inputs=[prompt, noise_level], outputs=[output1])
run.click(send_it2, inputs=[prompt, noise_level], outputs=[output2])
with gr.Row():
gr.HTML(
"""
这个模型和幻梦成真的区别是：幻梦显形更虚幻，这个模型更真实，毕竟都"成真"了嘛。
安利：还有一个汉化项目：TiwenTi.chat，这是一个ChatGPT的中文案例库，按照工具用途和角色扮演用途做了分类，欢迎去看去分享~
"""
)
demo.launch(enable_queue=True, inline=True)
block.queue(concurrency_count=100)