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| # app.py
import os
import sys
sys.path.append("./")
import torch
from src.transformer import Transformer2DModel
from src.pipeline import Pipeline
from src.scheduler import Scheduler
from transformers import (
CLIPTextModelWithProjection,
CLIPTokenizer,
)
from diffusers import VQModel
import gradio as gr
# 运行环境
device = 'cuda' if torch.cuda.is_available() else 'cpu'
# 模型文件目录
model_path = os.path.join(os.path.expanduser('~'), 'ModelSpace', 'MeissonFlow', 'Meissonic')
# 模型初始化
model = Transformer2DModel.from_pretrained(model_path, local_files_only=True, subfolder="transformer")
vq_model = VQModel.from_pretrained(model_path, local_files_only=True, subfolder="vqvae")
text_encoder = CLIPTextModelWithProjection.from_pretrained(model_path, local_files_only=True, subfolder="text_encoder")
tokenizer = CLIPTokenizer.from_pretrained(model_path, local_files_only=True, subfolder="tokenizer")
scheduler = Scheduler.from_pretrained(model_path, local_files_only=True, subfolder="scheduler")
pipe = Pipeline(vq_model, tokenizer=tokenizer, text_encoder=text_encoder, transformer=model, scheduler=scheduler)
pipe.to(device)
MAX_SEED = 2 ** 32 - 1
MAX_IMAGE_SIZE = 1024
def generate_image(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps,
progress=gr.Progress(track_tqdm=True)):
if randomize_seed or seed == 0:
seed = torch.randint(0, MAX_SEED, (1,)).item()
torch.manual_seed(seed)
image = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
height=height,
width=width,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps
).images[0]
return image, seed
# Default negative prompt
default_negative_prompt = "worst quality, low quality, low res, blurry, distortion, watermark, logo, signature, text, jpeg artifacts, signature, sketch, duplicate, ugly, identifying mark"
css = """
#col-container {
margin: 0 auto;
max-width: 640px;
}
"""
examples = [
"Modern Architecture render with pleasing aesthetics.",
"An image of a Pikachu wearing a birthday hat and playing guitar.",
"A statue of a lion stands in front of a building.",
"A white and blue coffee mug with a picture of a man on it.",
"A metal sculpture of a deer with antlers.",
"A bronze statue of an owl with its wings spread.",
"A white table with a vase of flowers and a cup of coffee on top of it.",
"A woman stands on a dock in the fog.",
"A lion's head is shown in a grayscale image.",
"A sculpture of a Greek woman head with a headband and a head of hair."
]
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown("# Meissonic Text-to-Image Generator")
with gr.Row():
prompt = gr.Text(
label="Prompt",
show_label=False,
max_lines=1,
placeholder="Enter your prompt",
container=False,
)
run_button = gr.Button("Run", scale=0, variant="primary")
result = gr.Image(label="Result", show_label=False)
with gr.Accordion("Advanced Settings", open=False):
negative_prompt = gr.Text(
label="Negative prompt",
max_lines=1,
placeholder="Enter a negative prompt",
value=default_negative_prompt,
)
seed = gr.Slider(
label="Seed",
minimum=0,
maximum=MAX_SEED,
step=1,
value=0,
)
randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
with gr.Row():
width = gr.Slider(
label="Width",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024,
)
height = gr.Slider(
label="Height",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024,
)
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance scale",
minimum=0.0,
maximum=20.0,
step=0.1,
value=9.0,
)
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=100,
step=1,
value=64,
)
gr.Examples(examples=examples, inputs=[prompt])
gr.on(
triggers=[run_button.click, prompt.submit],
fn=generate_image,
inputs=[
prompt,
negative_prompt,
seed,
randomize_seed,
width,
height,
guidance_scale,
num_inference_steps,
],
outputs=[result, seed],
)
# 启动WebUI界面
demo.launch()
|