app.py

import os
import subprocess
from typing import Union
is_spaces = True if os.environ.get("SPACE_ID") else False

if is_spaces:
    subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
    import spaces
    
    
os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
import sys

from dotenv import load_dotenv

load_dotenv()

# Add the current working directory to the Python path
sys.path.insert(0, os.getcwd())

import gradio as gr
from PIL import Image
import torch
import uuid
import os
import shutil
import json
import yaml
from slugify import slugify
from transformers import AutoProcessor, AutoModelForCausalLM

if not is_spaces:
    from toolkit.job import get_job
    gr.OAuthProfile = None
    gr.OAuthToken = None
    
MAX_IMAGES = 150


def load_captioning(uploaded_images, concept_sentence):
    gr.Info("Images uploaded!")
    updates = []
    if len(uploaded_images) <= 1:
        raise gr.Error(
            "Please upload at least 2 images to train your model (the ideal number with default settings is between 4-30)"
        )
    elif len(uploaded_images) > MAX_IMAGES:
        raise gr.Error(f"For now, only {MAX_IMAGES} or less images are allowed for training")
    # Update for the captioning_area
    # for _ in range(3):
    updates.append(gr.update(visible=True))
    # Update visibility and image for each captioning row and image
    for i in range(1, MAX_IMAGES + 1):
        # Determine if the current row and image should be visible
        visible = i <= len(uploaded_images)

        # Update visibility of the captioning row
        updates.append(gr.update(visible=visible))

        # Update for image component - display image if available, otherwise hide
        image_value = uploaded_images[i - 1] if visible else None

        updates.append(gr.update(value=image_value, visible=visible))

        # Update value of captioning area
        text_value = "[trigger]" if visible and concept_sentence else None
        updates.append(gr.update(value=text_value, visible=visible))

    # Update for the sample caption area
    updates.append(gr.update(visible=True))
    # Update prompt samples
    updates.append(gr.update(placeholder=f'A portrait of person in a bustling cafe {concept_sentence}', value=f'A person in a bustling cafe {concept_sentence}'))
    updates.append(gr.update(placeholder=f"A mountainous landscape in the style of {concept_sentence}"))
    updates.append(gr.update(placeholder=f"A {concept_sentence} in a mall"))
    return updates

def create_dataset(*inputs):
    print("Creating dataset")
    images = inputs[0]
    destination_folder = str(f"datasets/{uuid.uuid4()}")
    if not os.path.exists(destination_folder):
        os.makedirs(destination_folder)

    jsonl_file_path = os.path.join(destination_folder, "metadata.jsonl")
    with open(jsonl_file_path, "a") as jsonl_file:
        for index, image in enumerate(images):
            new_image_path = shutil.copy(image, destination_folder)

            original_caption = inputs[index + 1]
            file_name = os.path.basename(new_image_path)

            data = {"file_name": file_name, "prompt": original_caption}

            jsonl_file.write(json.dumps(data) + "\n")

    return destination_folder


def run_captioning(images, concept_sentence, *captions):
    device = "cuda" if torch.cuda.is_available() else "cpu"
    torch_dtype = torch.float16
    model = AutoModelForCausalLM.from_pretrained(
        "microsoft/Florence-2-large", torch_dtype=torch_dtype, trust_remote_code=True
    ).to(device)
    processor = AutoProcessor.from_pretrained("microsoft/Florence-2-large", trust_remote_code=True)

    captions = list(captions)
    for i, image_path in enumerate(images):
        print(captions[i])
        if isinstance(image_path, str):  # If image is a file path
            image = Image.open(image_path).convert("RGB")

        prompt = "<DETAILED_CAPTION>"
        inputs = processor(text=prompt, images=image, return_tensors="pt").to(device, torch_dtype)

        generated_ids = model.generate(
            input_ids=inputs["input_ids"], pixel_values=inputs["pixel_values"], max_new_tokens=1024, num_beams=3
        )

        generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
        parsed_answer = processor.post_process_generation(
            generated_text, task=prompt, image_size=(image.width, image.height)
        )
        caption_text = parsed_answer["<DETAILED_CAPTION>"].replace("The image shows ", "")
        if concept_sentence:
            caption_text = f"{caption_text} [trigger]"
        captions[i] = caption_text

        yield captions
    model.to("cpu")
    del model
    del processor

if is_spaces:
    run_captioning = spaces.GPU()(run_captioning)

def start_training(
    profile: Union[gr.OAuthProfile, None],
    oauth_token: Union[gr.OAuthToken, None],
    lora_name,
    concept_sentence,
    steps,
    lr,
    rank,
    dataset_folder,
    sample_1,
    sample_2,
    sample_3,
):
    if not lora_name:
        raise gr.Error("You forgot to insert your LoRA name! This name has to be unique.")
    print("Started training")
    slugged_lora_name = slugify(lora_name)

    # Load the default config
    with open("train_lora_flux_24gb.yaml" if is_spaces else "config/examples/train_lora_flux_24gb.yaml", "r") as f:
        config = yaml.safe_load(f)

    # Update the config with user inputs
    config["config"]["name"] = slugged_lora_name
    config["config"]["process"][0]["model"]["low_vram"] = True
    config["config"]["process"][0]["train"]["skip_first_sample"] = True
    config["config"]["process"][0]["train"]["steps"] = int(steps)
    config["config"]["process"][0]["train"]["lr"] = float(lr)
    config["config"]["process"][0]["network"]["linear"] = int(rank)
    config["config"]["process"][0]["network"]["linear_alpha"] = int(rank)
    config["config"]["process"][0]["datasets"][0]["folder_path"] = dataset_folder
    config["config"]["process"][0]["save"]["push_to_hub"] = True
    config["config"]["process"][0]["save"]["hf_repo_id"] = f"{profile.username}/{slugged_lora_name}"
    config["config"]["process"][0]["save"]["hf_private"] = True
    if concept_sentence:
        config["config"]["process"][0]["trigger_word"] = concept_sentence
    if sample_1 or sample_2 or sample_2:
        config["config"]["process"][0]["train"]["disable_sampling"] = False
        config["config"]["process"][0]["sample"]["sample_every"] = steps
        config["config"]["process"][0]["sample"]["prompts"] = []
        if sample_1:
            config["config"]["process"][0]["sample"]["prompts"].append(sample_1)
        if sample_2:
            config["config"]["process"][0]["sample"]["prompts"].append(sample_2)
        if sample_3:
            config["config"]["process"][0]["sample"]["prompts"].append(sample_3)
    else:
        config["config"]["process"][0]["train"]["disable_sampling"] = True
    # Save the updated config
    # generate a random name for the config
    random_config_name = str(uuid.uuid4())
    config_path = f"/tmp/{random_config_name}-{slugged_lora_name}.yaml"
    with open(config_path, "w") as f:
        yaml.dump(config, f)
    if is_spaces:
        print("Started training with spacerunner...")
        # copy config to dataset_folder as config.yaml
        shutil.copy(config_path, dataset_folder + "/config.yaml")
        # get location of this script
        script_location = os.path.dirname(os.path.abspath(__file__))
        # copy script.py from current directory to dataset_folder
        shutil.copy(script_location + "/script.py", dataset_folder)
        # copy requirements.autotrain to dataset_folder as requirements.txt
        shutil.copy(script_location + "/requirements.autotrain", dataset_folder + "/requirements.txt")
        # command to run autotrain spacerunner
        cmd = f"autotrain spacerunner --project-name {slugged_lora_name} --script-path {dataset_folder}"
        cmd += f" --username {profile.username} --token {oauth_token.token} --backend spaces-l4x1"
        outcome = subprocess.run(cmd.split())
        if outcome.returncode == 0:
            return f"""# Your training has started. 
    ## - Training Status: <a href='https://huggingface.co/spaces/{profile.username}/autotrain-{slugged_lora_name}?logs=container'>{profile.username}/autotrain-{slugged_lora_name}</a> <small>(in the logs tab)</small>
    ## - Model page: <a href='https://huggingface.co/{profile.username}/{slugged_lora_name}'>{profile.username}/{slugged_lora_name}</a> <small>(will be available when training finishes)</small>"""
        else:
            print("Error: ", outcome.stderr)
            raise gr.Error("Something went wrong. Make sure the name of your LoRA is unique and try again")
    else:
        # run the job locally
        job = get_job(config_path)
        job.run()
        job.cleanup()

    return f"Training completed successfully. Model saved as {slugged_lora_name}"


theme = gr.themes.Monochrome(
    text_size=gr.themes.Size(lg="18px", md="15px", sm="13px", xl="22px", xs="12px", xxl="24px", xxs="9px"),
    font=[gr.themes.GoogleFont("Source Sans Pro"), "ui-sans-serif", "system-ui", "sans-serif"],
)
css = """
h1{font-size: 2em}
h3{margin-top: 0}
#component-1{text-align:center}
.main_ui_logged_out{opacity: 0.3; pointer-events: none}
.tabitem{border: 0px}
"""

def swap_visibilty(profile: Union[gr.OAuthProfile, None]):
    if is_spaces:
        if profile is None:
            return gr.update(elem_classes=["main_ui_logged_out"])
        else:
            return gr.update(elem_classes=["main_ui_logged_in"])
    else:
        return gr.update(elem_classes=["main_ui_logged_in"])

def update_pricing(steps):
    updates.append()
    seconds_per_iteration = 7.54
    total_seconds = (steps * seconds_per_iteration) + 240
    cost_per_second = 0.80/60/60
    cost = round(cost_per_second * total_seconds, 2)
    cost_preview = f'''To train this LoRA, a paid L4 GPU will be hooked under the hood during training and then removed once finished.
    ## Estimated to cost <b>< US$ {str(cost)}</b> for {round(int(total_seconds)/60, 2)} minutes with your current train settings <small>({int(iterations)} iterations at {seconds_per_iteration}s/it)</small>'''
    return gr.update(visible=True), gr.update(cost_preview)

with gr.Blocks(theme=theme, css=css) as demo:
    gr.Markdown(
        """# LoRA Ease for FLUX 🧞‍♂️
### Train a high quality FLUX LoRA in a breeze ༄ using [Ostris' AI Toolkit](https://github.com/ostris/ai-toolkit) and [AutoTrain Advanced](https://github.com/huggingface/autotrain-advanced)"""
    )
    if is_spaces:
        gr.LoginButton("Sign in with Hugging Face to train your LoRA on Spaces", visible=is_spaces)
    with gr.Tab("Train on Spaces" if is_spaces else "Train locally"):
        with gr.Column() as main_ui:
            with gr.Row():
                lora_name = gr.Textbox(
                    label="The name of your LoRA",
                    info="This has to be a unique name",
                    placeholder="e.g.: Persian Miniature Painting style, Cat Toy",
                )
                # training_option = gr.Radio(
                #    label="What are you training?", choices=["object", "style", "character", "face", "custom"]
                # )
                concept_sentence = gr.Textbox(
                    label="Trigger word/sentence",
                    info="Trigger word or sentence to be used",
                    placeholder="uncommon word like p3rs0n or trtcrd, or sentence like 'in the style of CNSTLL'",
                    interactive=True,
                )
            with gr.Group(visible=True) as image_upload:
                with gr.Row():
                    images = gr.File(
                        file_types=["image"],
                        label="Upload your images",
                        file_count="multiple",
                        interactive=True,
                        visible=True,
                        scale=1,
                    )
                    with gr.Column(scale=3, visible=False) as captioning_area:
                        with gr.Column():
                            gr.Markdown(
                                """# Custom captioning
    You can optionally add a custom caption for each image (or use an AI model for this). [trigger] will represent your concept sentence/trigger word.
    """
                            )
                            do_captioning = gr.Button("Add AI captions with Florence-2")
                            output_components = [captioning_area]
                            caption_list = []
                            for i in range(1, MAX_IMAGES + 1):
                                locals()[f"captioning_row_{i}"] = gr.Row(visible=False)
                                with locals()[f"captioning_row_{i}"]:
                                    locals()[f"image_{i}"] = gr.Image(
                                        type="filepath",
                                        width=111,
                                        height=111,
                                        min_width=111,
                                        interactive=False,
                                        scale=2,
                                        show_label=False,
                                        show_share_button=False,
                                        show_download_button=False,
                                    )
                                    locals()[f"caption_{i}"] = gr.Textbox(
                                        label=f"Caption {i}", scale=15, interactive=True
                                    )

                                output_components.append(locals()[f"captioning_row_{i}"])
                                output_components.append(locals()[f"image_{i}"])
                                output_components.append(locals()[f"caption_{i}"])
                                caption_list.append(locals()[f"caption_{i}"])

            with gr.Accordion("Advanced options", open=False):
                steps = gr.Number(label="Steps", value=1000, minimum=1, maximum=10000, step=1)
                lr = gr.Number(label="Learning Rate", value=4e-4, minimum=1e-6, maximum=1e-3, step=1e-6)
                rank = gr.Number(label="LoRA Rank", value=16, minimum=4, maximum=128, step=4)

            with gr.Accordion("Sample prompts (optional)", visible=False) as sample:
                gr.Markdown(
                    "Include sample prompts to test out your trained model. Don't forget to include your trigger word/sentence (optional)"
                )
                sample_1 = gr.Textbox(label="Test prompt 1")
                sample_2 = gr.Textbox(label="Test prompt 2")
                sample_3 = gr.Textbox(label="Test prompt 3")
            with gr.Group(visible=False) as cost_preview:
                cost_preview_info = gr.Markdown()
            output_components.append(sample)
            output_components.append(sample_1)
            output_components.append(sample_2)
            output_components.append(sample_3)
            start = gr.Button("Start training")
        progress_area = gr.Markdown("")

    with gr.Tab("Train on your device" if is_spaces else "Instructions"):
        gr.Markdown(
            f"""To use FLUX LoRA Ease locally with this UI, you can clone this repository (yes, HF Spaces are git repos!). You'll need ~23GB of VRAM
        ```bash
        git clone https://huggingface.co/spaces/flux-train/flux-lora-trainer
        cd flux-lora-trainer
        ## Optional, start a venv environment ## 
        python3 -m venv venv
        source venv/bin/activate
        # .\venv\Scripts\activate on windows
        # install torch first
        ## End of optional ##
        pip install requirements_local.txt
        ```
        
        Then you can install ai-toolkit
        ```bash
        git clone https://github.com/ostris/ai-toolkit.git
        cd ai-toolkit
        git submodule update --init --recursive
        pip3 install torch
        pip3 install -r requirements.txt
        cd ..
        ```

        Login with Hugging Face to access FLUX.1 [dev], choose a token with `write` permissions to push your LoRAs to the HF Hub
        ```bash
        huggingface-cli login
        ```
        
        Now you can run FLUX LoRA Ease locally with a UI by doing a simple 
        ```py
        python app.py
        ```
        
        If you prefer command line, you can run Ostris' [AI Toolkit](https://github.com/ostris/ai-toolkit) yourself directly.
        """
        )

    dataset_folder = gr.State()

    images.upload(
        load_captioning,
        inputs=[images, concept_sentence],
        outputs=output_components
    ).then(
        update_pricing,
        inputs=[steps],
        outputs=[cost_preview, cost_preview_info]
    )

    start.click(fn=create_dataset, inputs=[images] + caption_list, outputs=dataset_folder).then(
        fn=start_training,
        inputs=[
            lora_name,
            concept_sentence,
            steps,
            lr,
            rank,
            dataset_folder,
            sample_1,
            sample_2,
            sample_3,
        ],
        outputs=progress_area,
    )

    do_captioning.click(fn=run_captioning, inputs=[images, concept_sentence] + caption_list, outputs=caption_list)
    demo.load(fn=swap_visibilty, outputs=main_ui)

if __name__ == "__main__":
    demo.launch(share=True, show_error=True)