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Running
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Zero
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import gradio as gr
import numpy as np
import os, random, json, spaces, torch, time, subprocess
import torch
# from transformers import AutoProcessor, AutoTokenizer
# from longcat_image.models import LongCatImageTransformer2DModel
# from longcat_image.pipelines import LongCatImagePipeline
from utils.image_utils import rescale_image
from utils.prompt_utils import polish_prompt
# GIT_DIR = "LongCat-Image"
# GIT_URL = "https://github.com/yourusername/LongCat-Image.git"
# if not os.path.isdir(GIT_DIR):
# subprocess.run(["git", "clone", GIT_URL])
# else:
# print("Folder already exists.")
# MODEL_REPO = "meituan-longcat/LongCat-Image"
# MAX_SEED = np.iinfo(np.int32).max
# text_processor = AutoTokenizer.from_pretrained(
# MODEL_REPO,
# subfolder = 'tokenizer'
# )
# transformer = LongCatImageTransformer2DModel.from_pretrained(
# MODEL_REPO ,
# subfolder = 'transformer',
# torch_dtype=torch.bfloat16,
# use_safetensors=True
# ).to("cuda")
# pipe = LongCatImagePipeline.from_pretrained(
# MODEL_REPO,
# transformer=transformer,
# text_processor=text_processor
# )
# pipe.to("cuda", torch.bfloat16)
# def prepare(prompt, is_polish_prompt):
# if not is_polish_prompt: return prompt, False
# polished_prompt = polish_prompt(prompt)
# return polished_prompt, True
# @spaces.GPU
# def inference(
# prompt,
# negative_prompt="blurry ugly bad",
# width=1024,
# height=1024,
# seed=42,
# randomize_seed=True,
# guidance_scale=1.5,
# num_inference_steps=8,
# progress=gr.Progress(track_tqdm=True),
# ):
# timestamp = time.time()
# print(f"timestamp: {timestamp}")
# # generation
# if randomize_seed: seed = random.randint(0, MAX_SEED)
# generator = torch.Generator().manual_seed(seed)
# image = pipe(
# prompt= prompt,
# negative_prompt = negative_prompt,
# width=width,
# height=height,
# generator=generator,
# guidance_scale=guidance_scale,
# num_inference_steps=num_inference_steps,
# enable_prompt_rewrite= False
# ).images[0]
# return image, seed
def read_file(path: str) -> str:
with open(path, 'r', encoding='utf-8') as f:
content = f.read()
return content
css = """
#col-container {
margin: 0 auto;
max-width: 960px;
}
"""
with open('static/data.json', 'r') as file:
data = json.load(file)
examples = data['examples']
with gr.Blocks() as demo:
with gr.Column(elem_id="col-container"):
with gr.Column():
gr.HTML(read_file("static/header.html"))
# with gr.Row():
# with gr.Column():
# prompt = gr.Textbox(
# label="Prompt",
# show_label=False,
# lines=2,
# placeholder="Enter your prompt",
# # container=False,
# )
# is_polish_prompt = gr.Checkbox(label="Polish prompt", value=False)
# run_button = gr.Button("Generate", variant="primary")
# with gr.Accordion("Advanced Settings", open=False):
# negative_prompt = gr.Textbox(
# label="Negative prompt",
# lines=2,
# container=False,
# placeholder="Enter your negative prompt",
# value="blurry ugly bad"
# )
# num_inference_steps = gr.Slider(
# label="Steps",
# minimum=1,
# maximum=50,
# step=1,
# value=20,
# )
# with gr.Row():
# width = gr.Slider(
# label="Width",
# minimum=512,
# maximum=1280,
# step=32,
# value=768,
# )
# height = gr.Slider(
# label="Height",
# minimum=512,
# maximum=1280,
# step=32,
# value=1024,
# )
# with gr.Row():
# seed = gr.Slider(
# label="Seed",
# minimum=0,
# maximum=MAX_SEED,
# step=1,
# value=42,
# )
# guidance_scale = gr.Slider(
# label="Guidance scale",
# minimum=0.0,
# maximum=10.0,
# step=0.1,
# value=1.0,
# )
# randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
# with gr.Column():
# output_image = gr.Image(label="Generated image", show_label=False)
# polished_prompt = gr.Textbox(label="Final prompt",lines=2, interactive=False)
# gr.Examples(examples=examples, inputs=[prompt])
# gr.Markdown(read_file("static/footer.md"))
# run_button.click(
# fn=prepare,
# inputs=[prompt, is_polish_prompt],
# outputs=[polished_prompt, is_polish_prompt]
# ).then(
# fn=inference,
# inputs=[
# polished_prompt,
# negative_prompt,
# width,
# height,
# seed,
# randomize_seed,
# guidance_scale,
# num_inference_steps,
# ],
# outputs=[output_image, seed],
# )
if __name__ == "__main__":
demo.launch(mcp_server=True, css=css)
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