app.py

import gradio as gr
import random
import os
import spaces
import torch
import time
import json
import numpy as np
from diffusers import BriaFiboPipeline
from diffusers.modular_pipelines import ModularPipeline

# resolutions=[
#     "832 1248",
#     "896 1152",
#     "960 1088",
#     "1024 1024",
#     "1088 960",
#     "1152 896",
#     "1216 832",
#     "1280 800",
#     "1344 768",
# ]
MAX_SEED = np.iinfo(np.int32).max
dtype = torch.bfloat16
device = "cuda" if torch.cuda.is_available() else "cpu"

torch.set_grad_enabled(False)
vlm_pipe = ModularPipeline.from_pretrained("briaai/FIBO-VLM-prompt-to-JSON", trust_remote_code=True).to(device)

pipe = BriaFiboPipeline.from_pretrained(
        "briaai/FIBO",
        trust_remote_code=True,
        torch_dtype=dtype).to(device)


def handle_json(text):
    try:
        json.loads(text)
        return text 
    except:
        return "Error"
            

@spaces.GPU(duration=100)
def infer(prompt,
          negative_prompt="",
          seed=42,
          randomize_seed=False,
          width=1024,
          height=1024,
          guidance_scale=5,
          num_inference_steps=50,
         ):
    
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    
    t=time.time()

    
    with torch.inference_mode():
        # 1. Create a prompt to generate an initial image
        output = vlm_pipe(prompt=prompt)
        json_prompt = output.values["json_prompt"]


        image = pipe(prompt=json_prompt, 
                                 num_inference_steps=num_inference_steps, 
                                 negative_prompt=negative_prompt,
                                 width=width,height=height,
                                 guidance_scale=guidance_scale).images[0]

    
    
    return image, json_prompt

css = """
#col-container{
    margin: 0 auto;
    max-width: 768px;
}
"""
with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown("## FOBI")
        
        with gr.Group():
            with gr.Column():
                with gr.Row():
                    prompt_in = gr.Textbox(label="Prompt")
                    prompt_in_json = gr.JSON(label="Json")
   
                submit_btn = gr.Button("Generate")
        result = gr.Image(label="output")
        with gr.Accordion("Advanced Settings", open=False):
                    with gr.Row():
                        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():
                        guidance_scale = gr.Slider(
                            label="guidance scale",
                            minimum=1.0,
                            maximum=10.0,
                            step=0.1,
                            value=5.0
                        )
                        num_inference_steps = gr.Slider(
                            label="number of inference steps",
                            minimum=1,
                            maximum=60,
                            step=1,
                            value=50,
                        )
                        height = gr.Slider(
                            label="Height",
                            minimum=768,
                            maximum=1248,
                            step=32,
                            value=1024,
                        )
                        
                        width = gr.Slider(
                            label="Width",
                            minimum=832,
                            maximum=1344,
                            step=64,
                            value=1024,
                        )
                    with gr.Row():
                        negative_prompt = gr.Textbox(label="negative prompt", value=json.dumps(''))
                        negative_prompt_json = gr.JSON(label="json negative prompt", value=json.dumps(''))

    # prompt_in.change(
    #     handle_json, 
    #     inputs=prompt_in, 
    #     outputs=prompt_in_json)

    # negative_prompt.change(handle_json, inputs=negative_prompt, outputs=negative_prompt_json)
    
    submit_btn.click(
        fn = infer,
        inputs = [
            prompt_in,
            negative_prompt,
            seed,
            randomize_seed,
            width,
            height,
            guidance_scale,
            num_inference_steps,
        ],
        outputs = [
            result, prompt_in_json
        ]
    )
demo.queue().launch()