app.py

import os
import gradio as gr
import json
import logging
import torch
from PIL import Image
import spaces
from diffusers import DiffusionPipeline, AutoencoderTiny, AutoencoderKL, FluxImg2ImgPipeline
from live_preview_helpers import calculate_shift, retrieve_timesteps, flux_pipe_call_that_returns_an_iterable_of_images
from diffusers.utils import load_image
from huggingface_hub import hf_hub_download, HfFileSystem, ModelCard, snapshot_download
import copy
import random
import time

selected_lora_index = None

# Load LoRAs from JSON file
with open('loras.json', 'r') as f:
    loras = json.load(f)

# Initialize the base model
dtype = torch.bfloat16
device = "cuda" if torch.cuda.is_available() else "cpu"
base_model = "black-forest-labs/FLUX.1-dev"

taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
good_vae = AutoencoderKL.from_pretrained(base_model, subfolder="vae", torch_dtype=dtype).to(device)
pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=dtype, vae=taef1).to(device)
pipe_i2i = FluxImg2ImgPipeline.from_pretrained(
    base_model,
    vae=good_vae,
    transformer=pipe.transformer,
    text_encoder=pipe.text_encoder,
    tokenizer=pipe.tokenizer,
    text_encoder_2=pipe.text_encoder_2,
    tokenizer_2=pipe.tokenizer_2,
    torch_dtype=dtype
)

MAX_SEED = 2**32-1

pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe)

class calculateDuration:
    def __init__(self, activity_name=""):
        self.activity_name = activity_name

    def __enter__(self):
        self.start_time = time.time()
        return self

    def __exit__(self, exc_type, exc_value, traceback):
        self.end_time = time.time()
        self.elapsed_time = self.end_time - self.start_time
        if self.activity_name:
            print(f"Elapsed time for {self.activity_name}: {self.elapsed_time:.6f} seconds")
        else:
            print(f"Elapsed time: {self.elapsed_time:.6f} seconds")

def update_selection(evt: gr.SelectData, width, height):
    global selected_lora_index
    selected_lora_index = evt.index
    selected_lora = loras[evt.index]
    default_prompt = selected_lora.get('default_prompt', '')
    new_placeholder = f"{selected_lora['trigger_word']} {default_prompt}"
    lora_repo = selected_lora["repo"]
    updated_text = f"### Selected: [{lora_repo}](https://huggingface.co/{lora_repo}) ✨"
    if "aspect" in selected_lora:
        if selected_lora["aspect"] == "portrait":
            width = 768
            height = 1024
        elif selected_lora["aspect"] == "landscape":
            width = 1024
            height = 768
        else:
            width = 1024
            height = 1024
    return (
        gr.update(value=new_placeholder),
        updated_text,
        width,
        height,
        gr.update(interactive=True)  # Enable the Generate button
    )

@spaces.GPU(duration=70)
def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, progress):
    pipe.to("cuda")
    generator = torch.Generator(device="cuda").manual_seed(seed)
    with calculateDuration("Generating image"):
        # Generate image
        for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images(
            prompt=prompt_mash,
            num_inference_steps=steps,
            guidance_scale=cfg_scale,
            width=width,
            height=height,
            generator=generator,
            joint_attention_kwargs={"scale": lora_scale},
            output_type="pil",
            good_vae=good_vae,
        ):
            yield img

def run_lora(prompt, cfg_scale, steps, randomize_seed, seed, width, height, lora_scale, progress=gr.Progress(track_tqdm=True)):
    global selected_lora_index
    if selected_lora_index is None:
        raise gr.Error("You must select a LoRA before proceeding.")
    
    selected_lora = loras[selected_lora_index]
    lora_path = selected_lora["repo"]
    trigger_word = selected_lora["trigger_word"]
    
    if trigger_word:
        if "trigger_position" in selected_lora:
            if selected_lora["trigger_position"] == "prepend":
                prompt_mash = f"{trigger_word} {prompt}"
            else:
                prompt_mash = f"{prompt} {trigger_word}"
        else:
            prompt_mash = f"{trigger_word} {prompt}"
    else:
        prompt_mash = prompt

    with calculateDuration("Unloading LoRA"):
        pipe.unload_lora_weights()

    # Load LoRA weights
    with calculateDuration(f"Loading LoRA weights for {selected_lora['title']}"):
        if "weights" in selected_lora:
            pipe.load_lora_weights(lora_path, weight_name=selected_lora["weights"])
        else:
            pipe.load_lora_weights(lora_path)

    # Set random seed for reproducibility
    with calculateDuration("Randomizing seed"):
        if randomize_seed:
            seed = random.randint(0, MAX_SEED)

    image_generator = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, progress)
    # Consume the generator to get the final image
    final_image = None
    step_counter = 0
    for image in image_generator:
        step_counter += 1
        final_image = image
        progress_bar = f'Generating image... Step {step_counter}/{steps}'
        yield image, seed, gr.update(visible=True, value=progress_bar)
    
    yield final_image, seed, gr.update(visible=False)

# Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("# Awaken Ones' Lora Previews")
    gr.Markdown("Select a LoRA model from the gallery below to get started!")
    
    with gr.Row():
        gallery = gr.Gallery(
            value=[lora["image"] for lora in loras],
            label="LoRA Gallery",
            show_label=False,
            elem_id="gallery",
            columns=[5],
            rows=[3],
            object_fit="contain",
            height="auto",
        )

    with gr.Row():
        prompt = gr.Textbox(
            label="Prompt",
            placeholder="Type your prompt here...",
            show_label=True,
        )

    with gr.Row():
        generate = gr.Button("Generate", variant="primary", interactive=False)
        cancel = gr.Button("Cancel")

    with gr.Row():
        with gr.Column(scale=4):
            result = gr.Image(label="Result", show_label=False, elem_id="result")
        with gr.Column(scale=1):
            seed_output = gr.Number(label="Seed", interactive=False)

    with gr.Row():
        with gr.Column():
            steps = gr.Slider(minimum=1, maximum=100, value=25, step=1, label="Steps")
            cfg_scale = gr.Slider(minimum=1, maximum=20, value=3.5, step=0.1, label="CFG Scale")
            lora_scale = gr.Slider(minimum=0, maximum=2, value=1, step=0.1, label="LoRA Scale")
        with gr.Column():
            width = gr.Slider(minimum=256, maximum=1024, value=512, step=64, label="Width")
            height = gr.Slider(minimum=256, maximum=1024, value=512, step=64, label="Height")

    with gr.Row():
        randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
        seed_input = gr.Number(label="Seed", value=0, interactive=True, visible=False)

    selected_lora = gr.Markdown("### No LoRA selected")
    progress_bar = gr.Markdown(visible=False)

    # Event handlers
    gallery.select(update_selection, [width, height], [prompt, selected_lora, width, height, generate])
    randomize_seed.change(lambda x: gr.update(visible=not x), randomize_seed, seed_input)
    generate_event = generate.click(run_lora, inputs=[prompt, cfg_scale, steps, randomize_seed, seed_input, width, height, lora_scale], outputs=[result, seed_output, progress_bar])
    cancel.click(lambda: None, None, None, cancels=[generate_event])

demo.queue().launch()