app.py

import os
import sys
sys.path.append("./")

import torch
from torchvision import transforms
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
import spaces 

device = 'cuda' if torch.cuda.is_available() else 'cpu'


dtype = torch.bfloat16
model_path = "MeissonFlow/Meissonic"
model = Transformer2DModel.from_pretrained(model_path, subfolder="transformer", torch_dtype=dtype)
vq_model = VQModel.from_pretrained(model_path, subfolder="vqvae", torch_dtype=dtype)
# text_encoder = CLIPTextModelWithProjection.from_pretrained(model_path,subfolder="text_encoder", torch_dtype=dtype)
text_encoder = CLIPTextModelWithProjection.from_pretrained(   #using original text enc for stable sampling
            "laion/CLIP-ViT-H-14-laion2B-s32B-b79K",torch_dtype=dtype)
tokenizer = CLIPTokenizer.from_pretrained(model_path, subfolder="tokenizer", torch_dtype=dtype)
scheduler = Scheduler.from_pretrained(model_path, 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

@spaces.GPU
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],
    )

demo.launch()