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import random
import gradio as gr
import numpy as np
import spaces
import torch
import os
from huggingface_hub import hf_hub_download
from diffusers import StableDiffusionXLPipeline, AutoencoderKL, EulerAncestralDiscreteScheduler
from compel import Compel, ReturnedEmbeddingsType
from PIL import Image, PngImagePlugin
import json
import io
# =====================================
# Prompt weights
# =====================================
import re
def parse_prompt_attention(text):
re_attention = re.compile(r"""
\\\(|
\\\)|
\\\[|
\\]|
\\\\|
\\|
\(|
\[|
:([+-]?[.\d]+)\)|
\)|
]|
[^\\()\[\]:]+|
:
""", re.X)
res = []
round_brackets = []
square_brackets = []
round_bracket_multiplier = 1.1
square_bracket_multiplier = 1 / 1.1
def multiply_range(start_position, multiplier):
for p in range(start_position, len(res)):
res[p][1] *= multiplier
for m in re_attention.finditer(text):
text = m.group(0)
weight = m.group(1)
if text.startswith('\\'):
res.append([text[1:], 1.0])
elif text == '(':
round_brackets.append(len(res))
elif text == '[':
square_brackets.append(len(res))
elif weight is not None and len(round_brackets) > 0:
multiply_range(round_brackets.pop(), float(weight))
elif text == ')' and len(round_brackets) > 0:
multiply_range(round_brackets.pop(), round_bracket_multiplier)
elif text == ']' and len(square_brackets) > 0:
multiply_range(square_brackets.pop(), square_bracket_multiplier)
else:
parts = re.split(re.compile(r"\s*\bBREAK\b\s*", re.S), text)
for i, part in enumerate(parts):
if i > 0:
res.append(["BREAK", -1])
res.append([part, 1.0])
for pos in round_brackets:
multiply_range(pos, round_bracket_multiplier)
for pos in square_brackets:
multiply_range(pos, square_bracket_multiplier)
if len(res) == 0:
res = [["", 1.0]]
# merge runs of identical weights
i = 0
while i + 1 < len(res):
if res[i][1] == res[i + 1][1]:
res[i][0] += res[i + 1][0]
res.pop(i + 1)
else:
i += 1
return res
def prompt_attention_to_invoke_prompt(attention):
tokens = []
for text, weight in attention:
# Round weight to 2 decimal places
weight = round(weight, 2)
if weight == 1.0:
tokens.append(text)
elif weight < 1.0:
if weight < 0.8:
tokens.append(f"({text}){weight}")
else:
tokens.append(f"({text})-" + "-" * int((1.0 - weight) * 10))
else:
if weight < 1.3:
tokens.append(f"({text})" + "+" * int((weight - 1.0) * 10))
else:
tokens.append(f"({text}){weight}")
return "".join(tokens)
def concat_tensor(t):
t_list = torch.split(t, 1, dim=0)
t = torch.cat(t_list, dim=1)
return t
def merge_embeds(prompt_chanks, compel):
num_chanks = len(prompt_chanks)
if num_chanks != 0:
power_prompt = 1/(num_chanks*(num_chanks+1)//2)
prompt_embs = compel(prompt_chanks)
t_list = list(torch.split(prompt_embs, 1, dim=0))
for i in range(num_chanks):
t_list[-(i+1)] = t_list[-(i+1)] * ((i+1)*power_prompt)
prompt_emb = torch.stack(t_list, dim=0).sum(dim=0)
else:
prompt_emb = compel('')
return prompt_emb
def detokenize(chunk, actual_prompt):
chunk[-1] = chunk[-1].replace('</w>', '')
chanked_prompt = ''.join(chunk).strip()
while '</w>' in chanked_prompt:
if actual_prompt[chanked_prompt.find('</w>')] == ' ':
chanked_prompt = chanked_prompt.replace('</w>', ' ', 1)
else:
chanked_prompt = chanked_prompt.replace('</w>', '', 1)
actual_prompt = actual_prompt.replace(chanked_prompt,'')
return chanked_prompt.strip(), actual_prompt.strip()
def tokenize_line(line, tokenizer): # split into chunks
actual_prompt = line.lower().strip()
actual_tokens = tokenizer.tokenize(actual_prompt)
max_tokens = tokenizer.model_max_length - 2
comma_token = tokenizer.tokenize(',')[0]
chunks = []
chunk = []
for item in actual_tokens:
chunk.append(item)
if len(chunk) == max_tokens:
if chunk[-1] != comma_token:
for i in range(max_tokens-1, -1, -1):
if chunk[i] == comma_token:
actual_chunk, actual_prompt = detokenize(chunk[:i+1], actual_prompt)
chunks.append(actual_chunk)
chunk = chunk[i+1:]
break
else:
actual_chunk, actual_prompt = detokenize(chunk, actual_prompt)
chunks.append(actual_chunk)
chunk = []
else:
actual_chunk, actual_prompt = detokenize(chunk, actual_prompt)
chunks.append(actual_chunk)
chunk = []
if chunk:
actual_chunk, _ = detokenize(chunk, actual_prompt)
chunks.append(actual_chunk)
return chunks
def get_embed_new(prompt, pipeline, compel, only_convert_string=False, compel_process_sd=False):
if compel_process_sd:
return merge_embeds(tokenize_line(prompt, pipeline.tokenizer), compel)
else:
# fix bug weights conversion excessive emphasis
prompt = prompt.replace("((", "(").replace("))", ")").replace("\\", "\\\\\\")
# Convert to Compel
attention = parse_prompt_attention(prompt)
global_attention_chanks = []
for att in attention:
for chank in att[0].split(','):
temp_prompt_chanks = tokenize_line(chank, pipeline.tokenizer)
for small_chank in temp_prompt_chanks:
temp_dict = {
"weight": round(att[1], 2),
"lenght": len(pipeline.tokenizer.tokenize(f'{small_chank},')),
"prompt": f'{small_chank},'
}
global_attention_chanks.append(temp_dict)
max_tokens = pipeline.tokenizer.model_max_length - 2
global_prompt_chanks = []
current_list = []
current_length = 0
for item in global_attention_chanks:
if current_length + item['lenght'] > max_tokens:
global_prompt_chanks.append(current_list)
current_list = [[item['prompt'], item['weight']]]
current_length = item['lenght']
else:
if not current_list:
current_list.append([item['prompt'], item['weight']])
else:
if item['weight'] != current_list[-1][1]:
current_list.append([item['prompt'], item['weight']])
else:
current_list[-1][0] += f" {item['prompt']}"
current_length += item['lenght']
if current_list:
global_prompt_chanks.append(current_list)
if only_convert_string:
return ' '.join([prompt_attention_to_invoke_prompt(i) for i in global_prompt_chanks])
return merge_embeds([prompt_attention_to_invoke_prompt(i) for i in global_prompt_chanks], compel)
def add_comma_after_pattern_ti(text):
pattern = re.compile(r'\b\w+_\d+\b')
modified_text = pattern.sub(lambda x: x.group() + ',', text)
return modified_text
# Add metadata to the image
def add_metadata_to_image(image, metadata):
metadata_str = json.dumps(metadata)
# Convert PIL Image to PNG with metadata
img_with_metadata = image.copy()
# Create a PngInfo object and add metadata
png_info = PngImagePlugin.PngInfo()
png_info.add_text("parameters", metadata_str)
# Save to a byte buffer with metadata
buffer = io.BytesIO()
img_with_metadata.save(buffer, format="PNG", pnginfo=png_info)
# Reopen from buffer to get the image with metadata
buffer.seek(0)
return Image.open(buffer)
if not torch.cuda.is_available():
DESCRIPTION = "\n<p>你现在运行在CPU上 但是此项目只支持GPU.</p>"
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048
if torch.cuda.is_available():
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
#token = os.environ.get("HF_TOKEN") # 从环境变量读取令牌
model_path = hf_hub_download(
repo_id="Menyu/ModelFile", # 模型仓库名称(非完整URL)
filename="naixlMmmmix_v50.safetensors" #,
#use_auth_token=token
)
pipe = StableDiffusionXLPipeline.from_single_file(
model_path,
vae=vae,
use_safetensors=True,
torch_dtype=torch.float16,
)
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.to("cuda")
def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
if randomize_seed:
seed = random.randint(0, MAX_SEED)
return seed
@spaces.GPU
def infer(
prompt: str,
negative_prompt: str = "lowres, {bad}, error, fewer, extra, missing, worst quality, jpeg artifacts, bad quality, watermark, unfinished, displeasing, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract]",
use_negative_prompt: bool = True,
seed: int = 7,
width: int = 1024,
height: int = 1536,
guidance_scale: float = 3,
num_inference_steps: int = 30,
randomize_seed: bool = True,
use_resolution_binning: bool = True,
progress=gr.Progress(track_tqdm=True),
):
seed = int(randomize_seed_fn(seed, randomize_seed))
generator = torch.Generator().manual_seed(seed)
# 初始化 Compel 实例
compel = Compel(
tokenizer=[pipe.tokenizer, pipe.tokenizer_2],
text_encoder=[pipe.text_encoder, pipe.text_encoder_2],
returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED,
requires_pooled=[False, True],
truncate_long_prompts=False
)
# Store the original prompt before processing
original_prompt_text = prompt
# 在 infer 函数中调用 get_embed_new
if not use_negative_prompt:
negative_prompt = ""
processed_prompt = get_embed_new(prompt, pipe, compel, only_convert_string=True)
processed_negative_prompt = get_embed_new(negative_prompt, pipe, compel, only_convert_string=True)
conditioning, pooled = compel([processed_prompt, processed_negative_prompt]) # 必须同时处理来保证长度相等
# 在调用 pipe 时,使用新的参数名称(确保参数名称正确)
image = pipe(
prompt_embeds=conditioning[0:1],
pooled_prompt_embeds=pooled[0:1],
negative_prompt_embeds=conditioning[1:2],
negative_pooled_prompt_embeds=pooled[1:2],
width=width,
height=height,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
generator=generator,
use_resolution_binning=use_resolution_binning,
).images[0]
# Create metadata dictionary
metadata = {
"prompt": original_prompt_text,
"processed_prompt": processed_prompt,
"negative_prompt": negative_prompt if use_negative_prompt else "",
"seed": seed,
"width": width,
"height": height,
"guidance_scale": guidance_scale,
"num_inference_steps": num_inference_steps,
"model": "naixlMmmmix_v50",
"use_resolution_binning": use_resolution_binning,
"PreUrl": "https://huggingface.co/spaces/Menyu/NaixlMix"
}
# Add metadata to the image
image_with_metadata = add_metadata_to_image(image, metadata)
return image_with_metadata, seed
examples = [
"nahida (genshin impact)",
"klee (genshin impact)",
]
css = '''
.gradio-container{max-width: 560px !important}
h1{text-align:center}
'''
with gr.Blocks(css=css) as demo:
gr.Markdown("""# 梦羽的模型生成器
### 快速生成NaixlMmmmix v50模型的图片""")
with gr.Group():
with gr.Row():
prompt = gr.Text(
label="关键词",
show_label=True,
max_lines=5,
placeholder="输入你要的图片关键词",
container=False,
)
run_button = gr.Button("生成", scale=0, variant="primary")
result = gr.Image(label="Result", show_label=False, format="png")
with gr.Accordion("高级选项", open=False):
with gr.Row():
use_negative_prompt = gr.Checkbox(label="使用反向词条", value=True)
negative_prompt = gr.Text(
label="反向词条",
max_lines=5,
lines=4,
placeholder="输入你要排除的图片关键词",
value="lowres, {bad}, error, fewer, extra, missing, worst quality, jpeg artifacts, bad quality, watermark, unfinished, displeasing, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract]",
visible=True,
)
seed = gr.Slider(
label="种子",
minimum=0,
maximum=MAX_SEED,
step=1,
value=0,
)
randomize_seed = gr.Checkbox(label="随机种子", value=True)
with gr.Row(visible=True):
width = gr.Slider(
label="宽度",
minimum=512,
maximum=MAX_IMAGE_SIZE,
step=64,
value=832,
)
height = gr.Slider(
label="高度",
minimum=512,
maximum=MAX_IMAGE_SIZE,
step=64,
value=1216,
)
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance Scale",
minimum=0.1,
maximum=10,
step=0.1,
value=7.0,
)
num_inference_steps = gr.Slider(
label="生成步数",
minimum=1,
maximum=50,
step=1,
value=28,
)
gr.Examples(
examples=examples,
inputs=prompt,
outputs=[result, seed],
fn=infer
)
use_negative_prompt.change(
fn=lambda x: gr.update(visible=x),
inputs=use_negative_prompt,
outputs=negative_prompt,
)
gr.on(
triggers=[prompt.submit, run_button.click],
fn=infer,
inputs=[
prompt,
negative_prompt,
use_negative_prompt,
seed,
width,
height,
guidance_scale,
num_inference_steps,
randomize_seed,
],
outputs=[result, seed],
)
if __name__ == "__main__":
demo.launch(share=True)