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, AutoPipelineForImage2Image
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
import subprocess  # Make sure this line is present!


# --- Install huggingface_hub[cli] ---
subprocess.run("pip install huggingface_hub[cli]", shell=True, check=True)

# --- Authenticate to Hugging Face ---
# Try to get the token from the environment; if not found, prompt for it manually.
from huggingface_hub import login, hf_hub_download, HfFileSystem, ModelCard, snapshot_download

hf_token = os.environ.get("HF_TOKEN")
if not hf_token:
    hf_token = input("Enter your Hugging Face token: ").strip()
    # Optionally, you can set the token as an environment variable for the remainder of the session:
    os.environ["HF_TOKEN"] = hf_token

if hf_token:
    login(hf_token)
    print("Successfully authenticated to Hugging Face.")
else:
    print("No token provided. Some features may not work without authentication.")


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

# Create a list of options for the LoRAs.
# You could use the index or a descriptive label.
lora_options = [f"{idx}: {lora['title']}" for idx, lora in enumerate(loras)]

# Create a CheckboxGroup that returns a list of selected options (as strings)
selected_lora_indices = gr.CheckboxGroup(choices=lora_options, label="Select LoRAs to load", value=[])


# 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 = AutoPipelineForImage2Image.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
                                                     )

# Disable the safety (censor) mechanism
pipe.safety_checker = lambda images, clip_input, **kwargs: (images, False)
pipe_i2i.safety_checker = lambda images, clip_input, **kwargs: (images, False)

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 parse_selected_indices(selected_options):
    indices = []
    for option in selected_options:
        try:
            index = int(option.split(":")[0])
            indices.append(index)
        except Exception:
            continue
    return indices

def update_selection(evt: gr.SelectData, width, height):
    selected_lora = loras[evt.index]
    new_placeholder = f"Type a prompt for {selected_lora['title']}"
    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(placeholder=new_placeholder),
        updated_text,
        evt.index,
        width,
        height,
    )

@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 generate_image_to_image(prompt_mash, image_input_path, image_strength, steps, cfg_scale, width, height, lora_scale, seed):
    generator = torch.Generator(device="cuda").manual_seed(seed)
    pipe_i2i.to("cuda")
    image_input = load_image(image_input_path)
    final_image = pipe_i2i(
        prompt=prompt_mash,
        image=image_input,
        strength=image_strength,
        num_inference_steps=steps,
        guidance_scale=cfg_scale,
        width=width,
        height=height,
        generator=generator,
        joint_attention_kwargs={"scale": lora_scale},
        output_type="pil",
    ).images[0]
    return final_image 

@spaces.GPU(duration=70)
def run_lora(prompt, image_input, image_strength, cfg_scale, steps, selected_indices_json, selected_weights_json, randomize_seed, seed, width, height, global_lora_scale, progress=gr.Progress(track_tqdm=True)):
    import json
    # Parse the JSON strings
    selected_indices = json.loads(selected_indices_json)
    selected_weights = json.loads(selected_weights_json) if selected_weights_json else {}
    
    if not selected_indices:
        raise gr.Error("You must select at least one LoRA before proceeding.")
    
    # Combine trigger words from all selected LoRAs
    prompt_mash = prompt
    for idx in selected_indices:
        selected_lora = loras[idx]
        if "trigger_word" in selected_lora and selected_lora["trigger_word"]:
            prompt_mash = f"{selected_lora['trigger_word']} {prompt_mash}"
    
    with calculateDuration("Unloading LoRA"):
        pipe.unload_lora_weights()
        pipe_i2i.unload_lora_weights()
    
    with calculateDuration("Loading LoRA weights"):
        pipe_to_use = pipe_i2i if image_input is not None else pipe
        for idx in selected_indices:
            selected_lora = loras[idx]
            weight_name = selected_lora.get("weights", None)
            # Get the individual weight for this LoRA from the selected_weights mapping.
            # If not found, default to 0.95.
            lora_weight = selected_weights.get(str(idx), 0.95)
            pipe_to_use.load_lora_weights(
                selected_lora["repo"],
                weight_name=weight_name,
                low_cpu_mem_usage=True,
                lora_weight=lora_weight  # This parameter should be supported by your load function.
            )
    
    with calculateDuration("Randomizing seed"):
        if randomize_seed:
            seed = random.randint(0, 2**32-1)
    
    if image_input is not None:
        final_image = generate_image_to_image(prompt_mash, image_input, image_strength, steps, cfg_scale, width, height, global_lora_scale, seed)
        yield final_image, seed, gr.update(visible=False)
    else:
        image_generator = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, global_lora_scale, progress)
        final_image = None
        step_counter = 0
        for image in image_generator:
            step_counter += 1
            final_image = image
            progress_bar = f'<div class="progress-container"><div class="progress-bar" style="--current: {step_counter}; --total: {steps};"></div></div>'
            yield image, seed, gr.update(value=progress_bar, visible=True)
        yield final_image, seed, gr.update(value=progress_bar, visible=False)



        
def get_huggingface_safetensors(link):
  split_link = link.split("/")
  if(len(split_link) == 2):
            model_card = ModelCard.load(link)
            base_model = model_card.data.get("base_model")
            print(base_model)
            if((base_model != "black-forest-labs/FLUX.1-dev") and (base_model != "black-forest-labs/FLUX.1-schnell")):
                raise Exception("Not a FLUX LoRA!")
            image_path = model_card.data.get("widget", [{}])[0].get("output", {}).get("url", None)
            trigger_word = model_card.data.get("instance_prompt", "")
            image_url = f"https://huggingface.co/{link}/resolve/main/{image_path}" if image_path else None
            fs = HfFileSystem()
            try:
                list_of_files = fs.ls(link, detail=False)
                for file in list_of_files:
                    if(file.endswith(".safetensors")):
                        safetensors_name = file.split("/")[-1]
                    if (not image_url and file.lower().endswith((".jpg", ".jpeg", ".png", ".webp"))):
                      image_elements = file.split("/")
                      image_url = f"https://huggingface.co/{link}/resolve/main/{image_elements[-1]}"
            except Exception as e:
              print(e)
              gr.Warning(f"You didn't include a link neither a valid Hugging Face repository with a *.safetensors LoRA")
              raise Exception(f"You didn't include a link neither a valid Hugging Face repository with a *.safetensors LoRA")
            return split_link[1], link, safetensors_name, trigger_word, image_url

def check_custom_model(link):
    if(link.startswith("https://")):
        if(link.startswith("https://huggingface.co") or link.startswith("https://www.huggingface.co")):
            link_split = link.split("huggingface.co/")
            return get_huggingface_safetensors(link_split[1])
    else: 
        return get_huggingface_safetensors(link)

def add_custom_lora(custom_lora):
    global loras
    if(custom_lora):
        try:
            title, repo, path, trigger_word, image = check_custom_model(custom_lora)
            print(f"Loaded custom LoRA: {repo}")
            card = f'''
            <div class="custom_lora_card">
              <span>Loaded custom LoRA:</span>
              <div class="card_internal">
                <img src="{image}" />
                <div>
                    <h3>{title}</h3>
                    <small>{"Using: <code><b>"+trigger_word+"</code></b> as the trigger word" if trigger_word else "No trigger word found. If there's a trigger word, include it in your prompt"}<br></small>
                </div>
              </div>
            </div>
            '''
            existing_item_index = next((index for (index, item) in enumerate(loras) if item['repo'] == repo), None)
            if(not existing_item_index):
                new_item = {
                    "image": image,
                    "title": title,
                    "repo": repo,
                    "weights": path,
                    "trigger_word": trigger_word
                }
                print(new_item)
                existing_item_index = len(loras)
                loras.append(new_item)
        
            return gr.update(visible=True, value=card), gr.update(visible=True), gr.Gallery(selected_index=None), f"Custom: {path}", existing_item_index, trigger_word
        except Exception as e:
            gr.Warning(f"Invalid LoRA: either you entered an invalid link, or a non-FLUX LoRA")
            return gr.update(visible=True, value=f"Invalid LoRA: either you entered an invalid link, a non-FLUX LoRA"), gr.update(visible=True), gr.update(), "", None, ""
    else:
        return gr.update(visible=False), gr.update(visible=False), gr.update(), "", None, ""

def remove_custom_lora():
    return gr.update(visible=False), gr.update(visible=False), gr.update(), "", None, ""

run_lora.zerogpu = True

css = '''
#gen_btn { height: 100%; }
#gen_column { align-self: stretch; }
#title { text-align: center; }
#title h1 { font-size: 3em; display: inline-flex; align-items: center; }
#title img { width: 100px; margin-right: 0.5em; }
#lora_list { background: var(--block-background-fill); padding: 0 1em .3em; font-size: 90%; }
.card_internal { display: flex; height: 100px; margin-top: .5em; }
.card_internal img { margin-right: 1em; }
.styler { --form-gap-width: 0px !important; }
#progress { height: 30px; }
.progress-container { width: 100%; height: 30px; background-color: #f0f0f0; border-radius: 15px; overflow: hidden; margin-bottom: 20px; }
.progress-bar { height: 100%; background-color: #4f46e5; width: calc(var(--current) / var(--total) * 100%); transition: width 0.5s ease-in-out; }
'''
font = [gr.themes.GoogleFont("Source Sans Pro"), "Arial", "sans-serif"]

with gr.Blocks(theme=gr.themes.Soft(font=font), css=css, delete_cache=(60, 60)) as app:
    title = gr.HTML(
        """<h1><img src="https://huggingface.co/spaces/kayte0342/test/resolve/main/DA4BE61E-A0BD-4254-A1B6-AD3C05D18A9C%20(1).png?download=true" alt="LoRA"> FLUX LoRA Kayte's Space</h1>""",
        elem_id="title",
    )
    
    # Hidden textboxes to store the JSON outputs:
    selected_indices_hidden = gr.Textbox(value="[]", visible=False)
    selected_weights_hidden = gr.Textbox(value="{}", visible=False)
    
    with gr.Row():
        with gr.Column(scale=3):
            prompt = gr.Textbox(label="Prompt", lines=1, placeholder="Type a prompt after selecting a LoRA")
        with gr.Column(scale=1, elem_id="gen_column"):
            generate_button = gr.Button("Generate", variant="primary", elem_id="gen_btn")
    
    with gr.Row():
        with gr.Column():
            selected_info = gr.Markdown("")
            # Build a custom layout for LoRA selection.
            lora_selection_container = gr.Column()
            # We'll collect checkbox and slider components in lists.
            lora_checkbox_list = []
            lora_slider_list = []
            for idx, lora in enumerate(loras):
                with gr.Row():
                    gr.Image(label=lora["title"], height=100)
                    checkbox = gr.Checkbox(label="Select", value=False, elem_id=f"lora_checkbox_{idx}")
                    slider = gr.Slider(label="Weight", minimum=0, maximum=3, step=0.01, value=0.95, elem_id=f"lora_weight_{idx}")
                    lora_checkbox_list.append(checkbox)
                    lora_slider_list.append(slider)
            gr.Markdown("[Check the list of FLUX LoRAs](https://huggingface.co/models?other=base_model:adapter:black-forest-labs/FLUX.1-dev)", elem_id="lora_list")
        with gr.Column():
            progress_bar = gr.Markdown(elem_id="progress", visible=False)
            result = gr.Image(label="Generated Image")
    
    with gr.Row():
        with gr.Accordion("Advanced Settings", open=False):
            with gr.Row():
                input_image = gr.Image(label="Input image", type="filepath")
                image_strength = gr.Slider(label="Denoise Strength", info="Lower means more image influence", minimum=0.1, maximum=1.0, step=0.01, value=0.75)
            with gr.Column():
                with gr.Row():
                    cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, step=0.5, value=3.5)
                    steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=28)
                with gr.Row():
                    width = gr.Slider(label="Width", minimum=256, maximum=1536, step=64, value=1024)
                    height = gr.Slider(label="Height", minimum=256, maximum=1536, step=64, value=1024)
                with gr.Row():
                    randomize_seed = gr.Checkbox(True, label="Randomize seed")
                    seed = gr.Slider(label="Seed", minimum=0, maximum=2**32-1, step=1, value=0, randomize=True)
                    lora_scale = gr.Slider(label="Global LoRA Scale", minimum=0, maximum=3, step=0.01, value=0.95)
    
    # Function to combine checkbox selections into a JSON list of indices.
    def combine_selections(*checkbox_values):
        selected_indices = [i for i, v in enumerate(checkbox_values) if v]
        return json.dumps(selected_indices)
    
    # Function to combine all slider values into a JSON dictionary mapping index to weight.
    def combine_weights(*slider_values):
        weights = {str(i): v for i, v in enumerate(slider_values)}
        return json.dumps(weights)
    
    # Chain the updates when the Generate button is clicked:
    # First, update the checkbox hidden state, then update the slider hidden state, then call run_lora.
    generate_button.click(
        combine_selections,
        inputs=lora_checkbox_list,
        outputs=selected_indices_hidden
    ).then(
        combine_weights,
        inputs=lora_slider_list,
        outputs=selected_weights_hidden
    ).then(
        run_lora,
        inputs=[prompt, input_image, image_strength, cfg_scale, steps, selected_indices_hidden, selected_weights_hidden, randomize_seed, seed, width, height, lora_scale],
        outputs=[result, seed, progress_bar]
    )
    
    # Update the selected_info display when the selected_indices_hidden changes.
    def update_info(selected_json):
        selected_indices = json.loads(selected_json)
        if selected_indices:
            info = "Selected LoRAs: " + ", ".join([loras[i]["title"] for i in selected_indices])
        else:
            info = "No LoRAs selected."
        return info
    selected_indices_hidden.change(
        update_info,
        inputs=selected_indices_hidden,
        outputs=selected_info
    )

app.queue()
app.launch()