Update app.py

app.py

@@ -1,1119 +1,1119 @@
-import os
-import sys
-os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
-os.environ['GRADIO_ANALYTICS_ENABLED'] = '0'
-sys.path.insert(0, os.getcwd())
-sys.path.append(os.path.join(os.path.dirname(__file__), 'sd-scripts'))
-import subprocess
-import gradio as gr
-from PIL import Image
-import torch
-import uuid
-import shutil
-import json
-import yaml
-from slugify import slugify
-from transformers import AutoProcessor, AutoModelForCausalLM
-from gradio_logsview import LogsView, LogsViewRunner
-from huggingface_hub import hf_hub_download, HfApi
-from library import flux_train_utils, huggingface_util
-from argparse import Namespace
-import train_network
-import toml
-import re
-MAX_IMAGES = 150
-
-with open('models.yaml', 'r') as file:
-    models = yaml.safe_load(file)
-
-def readme(base_model, lora_name, instance_prompt, sample_prompts):
-
-    # model license
-    model_config = models[base_model]
-    model_file = model_config["file"]
-    base_model_name = model_config["base"]
-    license = None
-    license_name = None
-    license_link = None
-    license_items = []
-    if "license" in model_config:
-        license = model_config["license"]
-        license_items.append(f"license: {license}")
-    if "license_name" in model_config:
-        license_name = model_config["license_name"]
-        license_items.append(f"license_name: {license_name}")
-    if "license_link" in model_config:
-        license_link = model_config["license_link"]
-        license_items.append(f"license_link: {license_link}")
-    license_str = "\n".join(license_items)
-    print(f"license_items={license_items}")
-    print(f"license_str = {license_str}")
-
-    # tags
-    tags = [ "text-to-image", "flux", "lora", "diffusers", "template:sd-lora", "fluxgym" ]
-
-    # widgets
-    widgets = []
-    sample_image_paths = []
-    output_name = slugify(lora_name)
-    samples_dir = resolve_path_without_quotes(f"outputs/{output_name}/sample")
-    try:
-        for filename in os.listdir(samples_dir):
-            # Filename Schema: [name]_[steps]_[index]_[timestamp].png
-            match = re.search(r"_(\d+)_(\d+)_(\d+)\.png$", filename)
-            if match:
-                steps, index, timestamp = int(match.group(1)), int(match.group(2)), int(match.group(3))
-                sample_image_paths.append((steps, index, f"sample/{filename}"))
-
-        # Sort by numeric index
-        sample_image_paths.sort(key=lambda x: x[0], reverse=True)
-
-        final_sample_image_paths = sample_image_paths[:len(sample_prompts)]
-        final_sample_image_paths.sort(key=lambda x: x[1])
-        for i, prompt in enumerate(sample_prompts):
-            _, _, image_path = final_sample_image_paths[i]
-            widgets.append(
-                {
-                    "text": prompt,
-                    "output": {
-                        "url": image_path
-                    },
-                }
-            )
-    except:
-        print(f"no samples")
-    dtype = "torch.bfloat16"
-    # Construct the README content
-    readme_content = f"""---
-tags:
-{yaml.dump(tags, indent=4).strip()}
-{"widget:" if os.path.isdir(samples_dir) else ""}
-{yaml.dump(widgets, indent=4).strip() if widgets else ""}
-base_model: {base_model_name}
-{"instance_prompt: " + instance_prompt if instance_prompt else ""}
-{license_str}
----
-
-# {lora_name}
-
-A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym)
-
-<Gallery />
-
-## Trigger words
-
-{"You should use `" + instance_prompt + "` to trigger the image generation." if instance_prompt else "No trigger words defined."}
-
-## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc.
-
-Weights for this model are available in Safetensors format.
-
-"""
-    return readme_content
-
-def account_hf():
-    try:
-        with open("HF_TOKEN", "r") as file:
-            token = file.read()
-            api = HfApi(token=token)
-            try:
-                account = api.whoami()
-                return { "token": token, "account": account['name'] }
-            except:
-                return None
-    except:
-        return None
-
-"""
-hf_logout.click(fn=logout_hf, outputs=[hf_token, hf_login, hf_logout, repo_owner])
-"""
-def logout_hf():
-    os.remove("HF_TOKEN")
-    global current_account
-    current_account = account_hf()
-    print(f"current_account={current_account}")
-    return gr.update(value=""), gr.update(visible=True), gr.update(visible=False), gr.update(value="", visible=False)
-
-
-"""
-hf_login.click(fn=login_hf, inputs=[hf_token], outputs=[hf_token, hf_login, hf_logout, repo_owner])
-"""
-def login_hf(hf_token):
-    api = HfApi(token=hf_token)
-    try:
-        account = api.whoami()
-        if account != None:
-            if "name" in account:
-                with open("HF_TOKEN", "w") as file:
-                    file.write(hf_token)
-                global current_account
-                current_account = account_hf()
-                return gr.update(visible=True), gr.update(visible=False), gr.update(visible=True), gr.update(value=current_account["account"], visible=True)
-        return gr.update(), gr.update(), gr.update(), gr.update()
-    except:
-        print(f"incorrect hf_token")
-        return gr.update(), gr.update(), gr.update(), gr.update()
-
-def upload_hf(base_model, lora_rows, repo_owner, repo_name, repo_visibility, hf_token):
-    src = lora_rows
-    repo_id = f"{repo_owner}/{repo_name}"
-    gr.Info(f"Uploading to Huggingface. Please Stand by...", duration=None)
-    args = Namespace(
-        huggingface_repo_id=repo_id,
-        huggingface_repo_type="model",
-        huggingface_repo_visibility=repo_visibility,
-        huggingface_path_in_repo="",
-        huggingface_token=hf_token,
-        async_upload=False
-    )
-    print(f"upload_hf args={args}")
-    huggingface_util.upload(args=args, src=src)
-    gr.Info(f"[Upload Complete] https://huggingface.co/{repo_id}", duration=None)
-
-def load_captioning(uploaded_files, concept_sentence):
-    uploaded_images = [file for file in uploaded_files if not file.endswith('.txt')]
-    txt_files = [file for file in uploaded_files if file.endswith('.txt')]
-    txt_files_dict = {os.path.splitext(os.path.basename(txt_file))[0]: txt_file for txt_file in txt_files}
-    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))
-
-        corresponding_caption = False
-        if(image_value):
-            base_name = os.path.splitext(os.path.basename(image_value))[0]
-            if base_name in txt_files_dict:
-                with open(txt_files_dict[base_name], 'r') as file:
-                    corresponding_caption = file.read()
-
-        # Update value of captioning area
-        text_value = corresponding_caption if visible and corresponding_caption else concept_sentence 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))
-    updates.append(gr.update(visible=True))
-
-    return updates
-
-def hide_captioning():
-    return gr.update(visible=False), gr.update(visible=False)
-
-def resize_image(image_path, output_path, size):
-    with Image.open(image_path) as img:
-        width, height = img.size
-        if width < height:
-            new_width = size
-            new_height = int((size/width) * height)
-        else:
-            new_height = size
-            new_width = int((size/height) * width)
-        print(f"resize {image_path} : {new_width}x{new_height}")
-        img_resized = img.resize((new_width, new_height), Image.Resampling.LANCZOS)
-        img_resized.save(output_path)
-
-def create_dataset(destination_folder, size, *inputs):
-    print("Creating dataset")
-    images = inputs[0]
-    if not os.path.exists(destination_folder):
-        os.makedirs(destination_folder)
-
-    for index, image in enumerate(images):
-        # copy the images to the datasets folder
-        new_image_path = shutil.copy(image, destination_folder)
-
-        # if it's a caption text file skip the next bit
-        ext = os.path.splitext(new_image_path)[-1].lower()
-        if ext == '.txt':
-            continue
-
-        # resize the images
-        resize_image(new_image_path, new_image_path, size)
-
-        # copy the captions
-
-        original_caption = inputs[index + 1]
-
-        image_file_name = os.path.basename(new_image_path)
-        caption_file_name = os.path.splitext(image_file_name)[0] + ".txt"
-        caption_path = resolve_path_without_quotes(os.path.join(destination_folder, caption_file_name))
-        print(f"image_path={new_image_path}, caption_path = {caption_path}, original_caption={original_caption}")
-        # if caption_path exists, do not write
-        if os.path.exists(caption_path):
-            print(f"{caption_path} already exists. use the existing .txt file")
-        else:
-            print(f"{caption_path} create a .txt caption file")
-            with open(caption_path, 'w') as file:
-                file.write(original_caption)
-
-    print(f"destination_folder {destination_folder}")
-    return destination_folder
-
-
-def run_captioning(images, concept_sentence, *captions):
-    print(f"run_captioning")
-    print(f"concept sentence {concept_sentence}")
-    print(f"captions {captions}")
-    #Load internally to not consume resources for training
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    print(f"device={device}")
-    torch_dtype = torch.float16
-    model = AutoModelForCausalLM.from_pretrained(
-        "multimodalart/Florence-2-large-no-flash-attn", torch_dtype=torch_dtype, trust_remote_code=True
-    ).to(device)
-    processor = AutoProcessor.from_pretrained("multimodalart/Florence-2-large-no-flash-attn", 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)
-        print(f"inputs {inputs}")
-
-        generated_ids = model.generate(
-            input_ids=inputs["input_ids"], pixel_values=inputs["pixel_values"], max_new_tokens=1024, num_beams=3
-        )
-        print(f"generated_ids {generated_ids}")
-
-        generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
-        print(f"generated_text: {generated_text}")
-        parsed_answer = processor.post_process_generation(
-            generated_text, task=prompt, image_size=(image.width, image.height)
-        )
-        print(f"parsed_answer = {parsed_answer}")
-        caption_text = parsed_answer["<DETAILED_CAPTION>"].replace("The image shows ", "")
-        print(f"caption_text = {caption_text}, concept_sentence={concept_sentence}")
-        if concept_sentence:
-            caption_text = f"{concept_sentence} {caption_text}"
-        captions[i] = caption_text
-
-        yield captions
-    model.to("cpu")
-    del model
-    del processor
-    if torch.cuda.is_available():
-        torch.cuda.empty_cache()
-
-def recursive_update(d, u):
-    for k, v in u.items():
-        if isinstance(v, dict) and v:
-            d[k] = recursive_update(d.get(k, {}), v)
-        else:
-            d[k] = v
-    return d
-
-def download(base_model):
-    model = models[base_model]
-    model_file = model["file"]
-    repo = model["repo"]
-
-    # download unet
-    if base_model == "flux-dev" or base_model == "flux-schnell":
-        unet_folder = "models/unet"
-    else:
-        unet_folder = f"models/unet/{repo}"
-    unet_path = os.path.join(unet_folder, model_file)
-    if not os.path.exists(unet_path):
-        os.makedirs(unet_folder, exist_ok=True)
-        gr.Info(f"Downloading base model: {base_model}. Please wait. (You can check the terminal for the download progress)", duration=None)
-        print(f"download {base_model}")
-        hf_hub_download(repo_id=repo, local_dir=unet_folder, filename=model_file)
-
-    # download vae
-    vae_folder = "models/vae"
-    vae_path = os.path.join(vae_folder, "ae.sft")
-    if not os.path.exists(vae_path):
-        os.makedirs(vae_folder, exist_ok=True)
-        gr.Info(f"Downloading vae")
-        print(f"downloading ae.sft...")
-        hf_hub_download(repo_id="cocktailpeanut/xulf-dev", local_dir=vae_folder, filename="ae.sft")
-
-    # download clip
-    clip_folder = "models/clip"
-    clip_l_path = os.path.join(clip_folder, "clip_l.safetensors")
-    if not os.path.exists(clip_l_path):
-        os.makedirs(clip_folder, exist_ok=True)
-        gr.Info(f"Downloading clip...")
-        print(f"download clip_l.safetensors")
-        hf_hub_download(repo_id="comfyanonymous/flux_text_encoders", local_dir=clip_folder, filename="clip_l.safetensors")
-
-    # download t5xxl
-    t5xxl_path = os.path.join(clip_folder, "t5xxl_fp16.safetensors")
-    if not os.path.exists(t5xxl_path):
-        print(f"download t5xxl_fp16.safetensors")
-        gr.Info(f"Downloading t5xxl...")
-        hf_hub_download(repo_id="comfyanonymous/flux_text_encoders", local_dir=clip_folder, filename="t5xxl_fp16.safetensors")
-
-
-def resolve_path(p):
-    current_dir = os.path.dirname(os.path.abspath(__file__))
-    norm_path = os.path.normpath(os.path.join(current_dir, p))
-    return f"\"{norm_path}\""
-def resolve_path_without_quotes(p):
-    current_dir = os.path.dirname(os.path.abspath(__file__))
-    norm_path = os.path.normpath(os.path.join(current_dir, p))
-    return norm_path
-
-def gen_sh(
-    base_model,
-    output_name,
-    resolution,
-    seed,
-    workers,
-    learning_rate,
-    network_dim,
-    max_train_epochs,
-    save_every_n_epochs,
-    timestep_sampling,
-    guidance_scale,
-    vram,
-    sample_prompts,
-    sample_every_n_steps,
-    *advanced_components
-):
-
-    print(f"gen_sh: network_dim:{network_dim}, max_train_epochs={max_train_epochs}, save_every_n_epochs={save_every_n_epochs}, timestep_sampling={timestep_sampling}, guidance_scale={guidance_scale}, vram={vram}, sample_prompts={sample_prompts}, sample_every_n_steps={sample_every_n_steps}")
-
-    output_dir = resolve_path(f"outputs/{output_name}")
-    sample_prompts_path = resolve_path(f"outputs/{output_name}/sample_prompts.txt")
-
-    line_break = "\\"
-    file_type = "sh"
-    if sys.platform == "win32":
-        line_break = "^"
-        file_type = "bat"
-
-    ############# Sample args ########################
-    sample = ""
-    if len(sample_prompts) > 0 and sample_every_n_steps > 0:
-        sample = f"""--sample_prompts={sample_prompts_path} --sample_every_n_steps="{sample_every_n_steps}" {line_break}"""
-
-
-    ############# Optimizer args ########################
-#    if vram == "8G":
-#        optimizer = f"""--optimizer_type adafactor {line_break}
-#    --optimizer_args "relative_step=False" "scale_parameter=False" "warmup_init=False" {line_break}
-#        --split_mode {line_break}
-#        --network_args "train_blocks=single" {line_break}
-#        --lr_scheduler constant_with_warmup {line_break}
-#        --max_grad_norm 0.0 {line_break}"""
-    if vram == "16G":
-        # 16G VRAM
-        optimizer = f"""--optimizer_type adafactor {line_break}
-  --optimizer_args "relative_step=False" "scale_parameter=False" "warmup_init=False" {line_break}
-  --lr_scheduler constant_with_warmup {line_break}
-  --max_grad_norm 0.0 {line_break}"""
-    elif vram == "12G":
-      # 12G VRAM
-        optimizer = f"""--optimizer_type adafactor {line_break}
-  --optimizer_args "relative_step=False" "scale_parameter=False" "warmup_init=False" {line_break}
-  --split_mode {line_break}
-  --network_args "train_blocks=single" {line_break}
-  --lr_scheduler constant_with_warmup {line_break}
-  --max_grad_norm 0.0 {line_break}"""
-    else:
-        # 20G+ VRAM
-        optimizer = f"--optimizer_type adamw8bit {line_break}"
-
-
-    #######################################################
-    model_config = models[base_model]
-    model_file = model_config["file"]
-    repo = model_config["repo"]
-    if base_model == "flux-dev" or base_model == "flux-schnell":
-        model_folder = "models/unet"
-    else:
-        model_folder = f"models/unet/{repo}"
-    model_path = os.path.join(model_folder, model_file)
-    pretrained_model_path = resolve_path(model_path)
-
-    clip_path = resolve_path("models/clip/clip_l.safetensors")
-    t5_path = resolve_path("models/clip/t5xxl_fp16.safetensors")
-    ae_path = resolve_path("models/vae/ae.sft")
-    sh = f"""accelerate launch {line_break}
-  --mixed_precision bf16 {line_break}
-  --num_cpu_threads_per_process 1 {line_break}
-  sd-scripts/flux_train_network.py {line_break}
-  --pretrained_model_name_or_path {pretrained_model_path} {line_break}
-  --clip_l {clip_path} {line_break}
-  --t5xxl {t5_path} {line_break}
-  --ae {ae_path} {line_break}
-  --cache_latents_to_disk {line_break}
-  --save_model_as safetensors {line_break}
-  --sdpa --persistent_data_loader_workers {line_break}
-  --max_data_loader_n_workers {workers} {line_break}
-  --seed {seed} {line_break}
-  --gradient_checkpointing {line_break}
-  --mixed_precision bf16 {line_break}
-  --save_precision bf16 {line_break}
-  --network_module networks.lora_flux {line_break}
-  --network_dim {network_dim} {line_break}
-  {optimizer}{sample}
-  --learning_rate {learning_rate} {line_break}
-  --cache_text_encoder_outputs {line_break}
-  --cache_text_encoder_outputs_to_disk {line_break}
-  --fp8_base {line_break}
-  --highvram {line_break}
-  --max_train_epochs {max_train_epochs} {line_break}
-  --save_every_n_epochs {save_every_n_epochs} {line_break}
-  --dataset_config {resolve_path(f"outputs/{output_name}/dataset.toml")} {line_break}
-  --output_dir {output_dir} {line_break}
-  --output_name {output_name} {line_break}
-  --timestep_sampling {timestep_sampling} {line_break}
-  --discrete_flow_shift 3.1582 {line_break}
-  --model_prediction_type raw {line_break}
-  --guidance_scale {guidance_scale} {line_break}
-  --loss_type l2 {line_break}"""
-   
-
-
-    ############# Advanced args ########################
-    global advanced_component_ids
-    global original_advanced_component_values
-   
-    # check dirty
-    print(f"original_advanced_component_values = {original_advanced_component_values}")
-    advanced_flags = []
-    for i, current_value in enumerate(advanced_components):
-#        print(f"compare {advanced_component_ids[i]}: old={original_advanced_component_values[i]}, new={current_value}")
-        if original_advanced_component_values[i] != current_value:
-            # dirty
-            if current_value == True:
-                # Boolean
-                advanced_flags.append(advanced_component_ids[i])
-            else:
-                # string
-                advanced_flags.append(f"{advanced_component_ids[i]} {current_value}")
-
-    if len(advanced_flags) > 0:
-        advanced_flags_str = f" {line_break}\n  ".join(advanced_flags)
-        sh = sh + "\n  " + advanced_flags_str
-
-    return sh
-
-def gen_toml(
-  dataset_folder,
-  resolution,
-  class_tokens,
-  num_repeats
-):
-    toml = f"""[general]
-shuffle_caption = false
-caption_extension = '.txt'
-keep_tokens = 1
-
-[[datasets]]
-resolution = {resolution}
-batch_size = 1
-keep_tokens = 1
-
-  [[datasets.subsets]]
-  image_dir = '{resolve_path_without_quotes(dataset_folder)}'
-  class_tokens = '{class_tokens}'
-  num_repeats = {num_repeats}"""
-    return toml
-
-def update_total_steps(max_train_epochs, num_repeats, images):
-    try:
-        num_images = len(images)
-        total_steps = max_train_epochs * num_images * num_repeats
-        print(f"max_train_epochs={max_train_epochs} num_images={num_images}, num_repeats={num_repeats}, total_steps={total_steps}")
-        return gr.update(value = total_steps)
-    except:
-        print("")
-
-def set_repo(lora_rows):
-    selected_name = os.path.basename(lora_rows)
-    return gr.update(value=selected_name)
-
-def get_loras():
-    try:
-        outputs_path = resolve_path_without_quotes(f"outputs")
-        files = os.listdir(outputs_path)
-        folders = [os.path.join(outputs_path, item) for item in files if os.path.isdir(os.path.join(outputs_path, item)) and item != "sample"]
-        folders.sort(key=lambda file: os.path.getctime(file), reverse=True)
-        return folders
-    except Exception as e:
-        return []
-
-def get_samples(lora_name):
-    output_name = slugify(lora_name)
-    try:
-        samples_path = resolve_path_without_quotes(f"outputs/{output_name}/sample")
-        files = [os.path.join(samples_path, file) for file in os.listdir(samples_path)]
-        files.sort(key=lambda file: os.path.getctime(file), reverse=True)
-        return files
-    except:
-        return []
-
-def start_training(
-    base_model,
-    lora_name,
-    train_script,
-    train_config,
-    sample_prompts,
-):
-    # write custom script and toml
-    if not os.path.exists("models"):
-        os.makedirs("models", exist_ok=True)
-    if not os.path.exists("outputs"):
-        os.makedirs("outputs", exist_ok=True)
-    output_name = slugify(lora_name)
-    output_dir = resolve_path_without_quotes(f"outputs/{output_name}")
-    if not os.path.exists(output_dir):
-        os.makedirs(output_dir, exist_ok=True)
-
-    download(base_model)
-
-    file_type = "sh"
-    if sys.platform == "win32":
-        file_type = "bat"
-
-    sh_filename = f"train.{file_type}"
-    sh_filepath = resolve_path_without_quotes(f"outputs/{output_name}/{sh_filename}")
-    with open(sh_filepath, 'w', encoding="utf-8") as file:
-        file.write(train_script)
-    gr.Info(f"Generated train script at {sh_filename}")
-
-
-    dataset_path = resolve_path_without_quotes(f"outputs/{output_name}/dataset.toml")
-    with open(dataset_path, 'w', encoding="utf-8") as file:
-        file.write(train_config)
-    gr.Info(f"Generated dataset.toml")
-
-    sample_prompts_path = resolve_path_without_quotes(f"outputs/{output_name}/sample_prompts.txt")
-    with open(sample_prompts_path, 'w', encoding='utf-8') as file:
-        file.write(sample_prompts)
-    gr.Info(f"Generated sample_prompts.txt")
-
-    # Train
-    if sys.platform == "win32":
-        command = sh_filepath
-    else:
-        command = f"bash \"{sh_filepath}\""
-
-    # Use Popen to run the command and capture output in real-time
-    env = os.environ.copy()
-    env['PYTHONIOENCODING'] = 'utf-8'
-    env['LOG_LEVEL'] = 'DEBUG'
-    runner = LogsViewRunner()
-    cwd = os.path.dirname(os.path.abspath(__file__))
-    gr.Info(f"Started training")
-    yield from runner.run_command([command], cwd=cwd)
-    yield runner.log(f"Runner: {runner}")
-
-    # Generate Readme
-    config = toml.loads(train_config)
-    concept_sentence = config['datasets'][0]['subsets'][0]['class_tokens']
-    print(f"concept_sentence={concept_sentence}")
-    print(f"lora_name {lora_name}, concept_sentence={concept_sentence}, output_name={output_name}")
-    sample_prompts_path = resolve_path_without_quotes(f"outputs/{output_name}/sample_prompts.txt")
-    with open(sample_prompts_path, "r", encoding="utf-8") as f:
-        lines = f.readlines()
-    sample_prompts = [line.strip() for line in lines if len(line.strip()) > 0 and line[0] != "#"]
-    md = readme(base_model, lora_name, concept_sentence, sample_prompts)
-    readme_path = resolve_path_without_quotes(f"outputs/{output_name}/README.md")
-    with open(readme_path, "w", encoding="utf-8") as f:
-        f.write(md)
-
-    gr.Info(f"Training Complete. Check the outputs folder for the LoRA files.", duration=None)
-
-
-def update(
-    base_model,
-    lora_name,
-    resolution,
-    seed,
-    workers,
-    class_tokens,
-    learning_rate,
-    network_dim,
-    max_train_epochs,
-    save_every_n_epochs,
-    timestep_sampling,
-    guidance_scale,
-    vram,
-    num_repeats,
-    sample_prompts,
-    sample_every_n_steps,
-    *advanced_components,
-):
-    output_name = slugify(lora_name)
-    dataset_folder = str(f"datasets/{output_name}")
-    sh = gen_sh(
-        base_model,
-        output_name,
-        resolution,
-        seed,
-        workers,
-        learning_rate,
-        network_dim,
-        max_train_epochs,
-        save_every_n_epochs,
-        timestep_sampling,
-        guidance_scale,
-        vram,
-        sample_prompts,
-        sample_every_n_steps,
-        *advanced_components,
-    )
-    toml = gen_toml(
-        dataset_folder,
-        resolution,
-        class_tokens,
-        num_repeats
-    )
-    return gr.update(value=sh), gr.update(value=toml), dataset_folder
-
-"""
-demo.load(fn=loaded, js=js, outputs=[hf_token, hf_login, hf_logout, hf_account])
-"""
-def loaded():
-    global current_account
-    current_account = account_hf()
-    print(f"current_account={current_account}")
-    if current_account != None:
-        return gr.update(value=current_account["token"]), gr.update(visible=False), gr.update(visible=True), gr.update(value=current_account["account"], visible=True)
-    else:
-        return gr.update(value=""), gr.update(visible=True), gr.update(visible=False), gr.update(value="", visible=False)
-
-def update_sample(concept_sentence):
-    return gr.update(value=concept_sentence)
-
-def refresh_publish_tab():
-    loras = get_loras()
-    return gr.Dropdown(label="Trained LoRAs", choices=loras)
-
-def init_advanced():
-    # if basic_args
-    basic_args = {
-        'pretrained_model_name_or_path',
-        'clip_l',
-        't5xxl',
-        'ae',
-        'cache_latents_to_disk',
-        'save_model_as',
-        'sdpa',
-        'persistent_data_loader_workers',
-        'max_data_loader_n_workers',
-        'seed',
-        'gradient_checkpointing',
-        'mixed_precision',
-        'save_precision',
-        'network_module',
-        'network_dim',
-        'learning_rate',
-        'cache_text_encoder_outputs',
-        'cache_text_encoder_outputs_to_disk',
-        'fp8_base',
-        'highvram',
-        'max_train_epochs',
-        'save_every_n_epochs',
-        'dataset_config',
-        'output_dir',
-        'output_name',
-        'timestep_sampling',
-        'discrete_flow_shift',
-        'model_prediction_type',
-        'guidance_scale',
-        'loss_type',
-        'optimizer_type',
-        'optimizer_args',
-        'lr_scheduler',
-        'sample_prompts',
-        'sample_every_n_steps',
-        'max_grad_norm',
-        'split_mode',
-        'network_args'
-    }
-
-    # generate a UI config
-    # if not in basic_args, create a simple form
-    parser = train_network.setup_parser()
-    flux_train_utils.add_flux_train_arguments(parser)
-    args_info = {}
-    for action in parser._actions:
-        if action.dest != 'help':  # Skip the default help argument
-            # if the dest is included in basic_args
-            args_info[action.dest] = {
-                "action": action.option_strings,  # Option strings like '--use_8bit_adam'
-                "type": action.type,              # Type of the argument
-                "help": action.help,              # Help message
-                "default": action.default,        # Default value, if any
-                "required": action.required       # Whether the argument is required
-            }
-    temp = []
-    for key in args_info:
-        temp.append({ 'key': key, 'action': args_info[key] })
-    temp.sort(key=lambda x: x['key'])
-    advanced_component_ids = []
-    advanced_components = []
-    for item in temp:
-        key = item['key']
-        action = item['action']
-        if key in basic_args:
-            print("")
-        else:
-            action_type = str(action['type'])
-            component = None
-            with gr.Column(min_width=300):
-                if action_type == "None":
-                    # radio
-                    component = gr.Checkbox()
-    #            elif action_type == "<class 'str'>":
-    #                component = gr.Textbox()
-    #            elif action_type == "<class 'int'>":
-    #                component = gr.Number(precision=0)
-    #            elif action_type == "<class 'float'>":
-    #                component = gr.Number()
-    #            elif "int_or_float" in action_type:
-    #                component = gr.Number()
-                else:
-                    component = gr.Textbox(value="")
-                if component != None:
-                    component.interactive = True
-                    component.elem_id = action['action'][0]
-                    component.label = component.elem_id
-                    component.elem_classes = ["advanced"]
-                if action['help'] != None:
-                    component.info = action['help']
-            advanced_components.append(component)
-            advanced_component_ids.append(component.elem_id)
-    return advanced_components, advanced_component_ids
-
-
-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 = """
-@keyframes rotate {
-    0% {
-        transform: rotate(0deg);
-    }
-    100% {
-        transform: rotate(360deg);
-    }
-}
-#advanced_options .advanced:nth-child(even) { background: rgba(0,0,100,0.04) !important; }
-h1{font-family: georgia; font-style: italic; font-weight: bold; font-size: 30px; letter-spacing: -1px;}
-h3{margin-top: 0}
-.tabitem{border: 0px}
-.group_padding{}
-nav{position: fixed; top: 0; left: 0; right: 0; z-index: 1000; text-align: center; padding: 10px; box-sizing: border-box; display: flex; align-items: center; backdrop-filter: blur(10px); }
-nav button { background: none; color: firebrick; font-weight: bold; border: 2px solid firebrick; padding: 5px 10px; border-radius: 5px; font-size: 14px; }
-nav img { height: 40px; width: 40px; border-radius: 40px; }
-nav img.rotate { animation: rotate 2s linear infinite; }
-.flexible { flex-grow: 1; }
-.tast-details { margin: 10px 0 !important; }
-.toast-wrap { bottom: var(--size-4) !important; top: auto !important; border: none !important; backdrop-filter: blur(10px); }
-.toast-title, .toast-text, .toast-icon, .toast-close { color: black !important; font-size: 14px; }
-.toast-body { border: none !important; }
-#terminal { box-shadow: none !important; margin-bottom: 25px; background: rgba(0,0,0,0.03); }
-#terminal .generating { border: none !important; }
-#terminal label { position: absolute !important; }
-.tabs { margin-top: 50px; }
-.hidden { display: none !important; }
-.codemirror-wrapper .cm-line { font-size: 12px !important; }
-label { font-weight: bold !important; }
-#start_training.clicked { background: silver; color: black; }
-"""
-
-js = """
-function() {
-    let autoscroll = document.querySelector("#autoscroll")
-    if (window.iidxx) {
-        window.clearInterval(window.iidxx);
-    }
-    window.iidxx = window.setInterval(function() {
-        let text=document.querySelector(".codemirror-wrapper .cm-line").innerText.trim()
-        let img = document.querySelector("#logo")
-        if (text.length > 0) {
-            autoscroll.classList.remove("hidden")
-            if (autoscroll.classList.contains("on")) {
-                autoscroll.textContent = "Autoscroll ON"
-                window.scrollTo(0, document.body.scrollHeight, { behavior: "smooth" });
-                img.classList.add("rotate")
-            } else {
-                autoscroll.textContent = "Autoscroll OFF"
-                img.classList.remove("rotate")
-            }
-        }
-    }, 500);
-    console.log("autoscroll", autoscroll)
-    autoscroll.addEventListener("click", (e) => {
-        autoscroll.classList.toggle("on")
-    })
-    function debounce(fn, delay) {
-        let timeoutId;
-        return function(...args) {
-            clearTimeout(timeoutId);
-            timeoutId = setTimeout(() => fn(...args), delay);
-        };
-    }
-
-    function handleClick() {
-        console.log("refresh")
-        document.querySelector("#refresh").click();
-    }
-    const debouncedClick = debounce(handleClick, 1000);
-    document.addEventListener("input", debouncedClick);
-
-    document.querySelector("#start_training").addEventListener("click", (e) => {
-      e.target.classList.add("clicked")
-      e.target.innerHTML = "Training..."
-    })
-
-}
-"""
-
-current_account = account_hf()
-print(f"current_account={current_account}")
-
-with gr.Blocks(elem_id="app", theme=theme, css=css, fill_width=True) as demo:
-    with gr.Tabs() as tabs:
-        with gr.TabItem("Gym"):
-            output_components = []
-            with gr.Row():
-                gr.HTML("""<nav>
-            <img id='logo' src='/file=icon.png' width='80' height='80'>
-            <div class='flexible'></div>
-            <button id='autoscroll' class='on hidden'></button>
-        </nav>
-        """)
-            with gr.Row(elem_id='container'):
-                with gr.Column():
-                    gr.Markdown(
-                        """# Step 1. LoRA Info
-        <p style="margin-top:0">Configure your LoRA train settings.</p>
-        """, elem_classes="group_padding")
-                    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",
-                    )
-                    concept_sentence = gr.Textbox(
-                        elem_id="--concept_sentence",
-                        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,
-                    )
-                    model_names = list(models.keys())
-                    print(f"model_names={model_names}")
-                    base_model = gr.Dropdown(label="Base model (edit the models.yaml file to add more to this list)", choices=model_names, value=model_names[0])
-                    vram = gr.Radio(["20G", "16G", "12G" ], value="20G", label="VRAM", interactive=True)
-                    num_repeats = gr.Number(value=10, precision=0, label="Repeat trains per image", interactive=True)
-                    max_train_epochs = gr.Number(label="Max Train Epochs", value=16, interactive=True)
-                    total_steps = gr.Number(0, interactive=False, label="Expected training steps")
-                    sample_prompts = gr.Textbox("", lines=5, label="Sample Image Prompts (Separate with new lines)", interactive=True)
-                    sample_every_n_steps = gr.Number(0, precision=0, label="Sample Image Every N Steps", interactive=True)
-                    resolution = gr.Number(value=512, precision=0, label="Resize dataset images")
-                with gr.Column():
-                    gr.Markdown(
-                        """# Step 2. Dataset
-        <p style="margin-top:0">Make sure the captions include the trigger word.</p>
-        """, elem_classes="group_padding")
-                    with gr.Group():
-                        images = gr.File(
-                            file_types=["image", ".txt"],
-                            label="Upload your images",
-                            #info="If you want, you can also manually upload caption files that match the image names (example: img0.png => img0.txt)",
-                            file_count="multiple",
-                            interactive=True,
-                            visible=True,
-                            scale=1,
-                        )
-                    with gr.Group(visible=False) as captioning_area:
-                        do_captioning = gr.Button("Add AI captions with Florence-2")
-                        output_components.append(captioning_area)
-                        #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.Column():
-                    gr.Markdown(
-                        """# Step 3. Train
-        <p style="margin-top:0">Press start to start training.</p>
-        """, elem_classes="group_padding")
-                    refresh = gr.Button("Refresh", elem_id="refresh", visible=False)
-                    start = gr.Button("Start training", visible=False, elem_id="start_training")
-                    output_components.append(start)
-                    train_script = gr.Textbox(label="Train script", max_lines=100, interactive=True)
-                    train_config = gr.Textbox(label="Train config", max_lines=100, interactive=True)
-            with gr.Accordion("Advanced options", elem_id='advanced_options', open=False):
-                with gr.Row():
-                    with gr.Column(min_width=300):
-                        seed = gr.Number(label="--seed", info="Seed", value=42, interactive=True)
-                    with gr.Column(min_width=300):
-                        workers = gr.Number(label="--max_data_loader_n_workers", info="Number of Workers", value=2, interactive=True)
-                    with gr.Column(min_width=300):
-                        learning_rate = gr.Textbox(label="--learning_rate", info="Learning Rate", value="8e-4", interactive=True)
-                    with gr.Column(min_width=300):
-                        save_every_n_epochs = gr.Number(label="--save_every_n_epochs", info="Save every N epochs", value=4, interactive=True)
-                    with gr.Column(min_width=300):
-                        guidance_scale = gr.Number(label="--guidance_scale", info="Guidance Scale", value=1.0, interactive=True)
-                    with gr.Column(min_width=300):
-                        timestep_sampling = gr.Textbox(label="--timestep_sampling", info="Timestep Sampling", value="shift", interactive=True)
-                    with gr.Column(min_width=300):
-                        network_dim = gr.Number(label="--network_dim", info="LoRA Rank", value=4, minimum=4, maximum=128, step=4, interactive=True)
-                    advanced_components, advanced_component_ids = init_advanced()
-            with gr.Row():
-                terminal = LogsView(label="Train log", elem_id="terminal")
-            with gr.Row():
-                gallery = gr.Gallery(get_samples, inputs=[lora_name], label="Samples", every=10, columns=6)
-
-        with gr.TabItem("Publish") as publish_tab:
-            hf_token = gr.Textbox(label="Huggingface Token")
-            hf_login = gr.Button("Login")
-            hf_logout = gr.Button("Logout")
-            with gr.Row() as row:
-                gr.Markdown("**LoRA**")
-                gr.Markdown("**Upload**")
-            loras = get_loras()
-            with gr.Row():
-                lora_rows = refresh_publish_tab()
-                with gr.Column():
-                    with gr.Row():
-                        repo_owner = gr.Textbox(label="Account", interactive=False)
-                        repo_name = gr.Textbox(label="Repository Name")
-                    repo_visibility = gr.Textbox(label="Repository Visibility ('public' or 'private')", value="public")
-                    upload_button = gr.Button("Upload to HuggingFace")
-                    upload_button.click(
-                        fn=upload_hf,
-                        inputs=[
-                            base_model,
-                            lora_rows,
-                            repo_owner,
-                            repo_name,
-                            repo_visibility,
-                            hf_token,
-                        ]
-                    )
-            hf_login.click(fn=login_hf, inputs=[hf_token], outputs=[hf_token, hf_login, hf_logout, repo_owner])
-            hf_logout.click(fn=logout_hf, outputs=[hf_token, hf_login, hf_logout, repo_owner])
-
-
-    publish_tab.select(refresh_publish_tab, outputs=lora_rows)
-    lora_rows.select(fn=set_repo, inputs=[lora_rows], outputs=[repo_name])
-
-    dataset_folder = gr.State()
-
-    listeners = [
-        base_model,
-        lora_name,
-        resolution,
-        seed,
-        workers,
-        concept_sentence,
-        learning_rate,
-        network_dim,
-        max_train_epochs,
-        save_every_n_epochs,
-        timestep_sampling,
-        guidance_scale,
-        vram,
-        num_repeats,
-        sample_prompts,
-        sample_every_n_steps,
-        *advanced_components
-    ]
-    advanced_component_ids = [x.elem_id for x in advanced_components]
-    original_advanced_component_values = [comp.value for comp in advanced_components]
-    images.upload(
-        load_captioning,
-        inputs=[images, concept_sentence],
-        outputs=output_components
-    )
-    images.delete(
-        load_captioning,
-        inputs=[images, concept_sentence],
-        outputs=output_components
-    )
-    images.clear(
-        hide_captioning,
-        outputs=[captioning_area, start]
-    )
-    max_train_epochs.change(
-        fn=update_total_steps,
-        inputs=[max_train_epochs, num_repeats, images],
-        outputs=[total_steps]
-    )
-    num_repeats.change(
-        fn=update_total_steps,
-        inputs=[max_train_epochs, num_repeats, images],
-        outputs=[total_steps]
-    )
-    images.upload(
-        fn=update_total_steps,
-        inputs=[max_train_epochs, num_repeats, images],
-        outputs=[total_steps]
-    )
-    images.delete(
-        fn=update_total_steps,
-        inputs=[max_train_epochs, num_repeats, images],
-        outputs=[total_steps]
-    )
-    images.clear(
-        fn=update_total_steps,
-        inputs=[max_train_epochs, num_repeats, images],
-        outputs=[total_steps]
-    )
-    concept_sentence.change(fn=update_sample, inputs=[concept_sentence], outputs=sample_prompts)
-    start.click(fn=create_dataset, inputs=[dataset_folder, resolution, images] + caption_list, outputs=dataset_folder).then(
-        fn=start_training,
-        inputs=[
-            base_model,
-            lora_name,
-            train_script,
-            train_config,
-            sample_prompts,
-        ],
-        outputs=terminal,
-    )
-    do_captioning.click(fn=run_captioning, inputs=[images, concept_sentence] + caption_list, outputs=caption_list)
-    demo.load(fn=loaded, js=js, outputs=[hf_token, hf_login, hf_logout, repo_owner])
-    refresh.click(update, inputs=listeners, outputs=[train_script, train_config, dataset_folder])
-if __name__ == "__main__":
-    cwd = os.path.dirname(os.path.abspath(__file__))
-    demo.launch(debug=True, show_error=True, allowed_paths=[cwd])
+import os
+import sys
+os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
+os.environ['GRADIO_ANALYTICS_ENABLED'] = '0'
+sys.path.insert(0, os.getcwd())
+sys.path.append(os.path.join(os.path.dirname(__file__), 'sd-scripts'))
+import subprocess
+import gradio as gr
+from PIL import Image
+import torch
+import uuid
+import shutil
+import json
+import yaml
+from slugify import slugify
+from transformers import AutoProcessor, AutoModelForCausalLM
+from gradio_logsview import LogsView, LogsViewRunner
+from huggingface_hub import hf_hub_download, HfApi
+#from library import flux_train_utils, huggingface_util
+from argparse import Namespace
+import train_network
+import toml
+import re
+MAX_IMAGES = 150
+
+with open('models.yaml', 'r') as file:
+    models = yaml.safe_load(file)
+
+def readme(base_model, lora_name, instance_prompt, sample_prompts):
+
+    # model license
+    model_config = models[base_model]
+    model_file = model_config["file"]
+    base_model_name = model_config["base"]
+    license = None
+    license_name = None
+    license_link = None
+    license_items = []
+    if "license" in model_config:
+        license = model_config["license"]
+        license_items.append(f"license: {license}")
+    if "license_name" in model_config:
+        license_name = model_config["license_name"]
+        license_items.append(f"license_name: {license_name}")
+    if "license_link" in model_config:
+        license_link = model_config["license_link"]
+        license_items.append(f"license_link: {license_link}")
+    license_str = "\n".join(license_items)
+    print(f"license_items={license_items}")
+    print(f"license_str = {license_str}")
+
+    # tags
+    tags = [ "text-to-image", "flux", "lora", "diffusers", "template:sd-lora", "fluxgym" ]
+
+    # widgets
+    widgets = []
+    sample_image_paths = []
+    output_name = slugify(lora_name)
+    samples_dir = resolve_path_without_quotes(f"outputs/{output_name}/sample")
+    try:
+        for filename in os.listdir(samples_dir):
+            # Filename Schema: [name]_[steps]_[index]_[timestamp].png
+            match = re.search(r"_(\d+)_(\d+)_(\d+)\.png$", filename)
+            if match:
+                steps, index, timestamp = int(match.group(1)), int(match.group(2)), int(match.group(3))
+                sample_image_paths.append((steps, index, f"sample/{filename}"))
+
+        # Sort by numeric index
+        sample_image_paths.sort(key=lambda x: x[0], reverse=True)
+
+        final_sample_image_paths = sample_image_paths[:len(sample_prompts)]
+        final_sample_image_paths.sort(key=lambda x: x[1])
+        for i, prompt in enumerate(sample_prompts):
+            _, _, image_path = final_sample_image_paths[i]
+            widgets.append(
+                {
+                    "text": prompt,
+                    "output": {
+                        "url": image_path
+                    },
+                }
+            )
+    except:
+        print(f"no samples")
+    dtype = "torch.bfloat16"
+    # Construct the README content
+    readme_content = f"""---
+tags:
+{yaml.dump(tags, indent=4).strip()}
+{"widget:" if os.path.isdir(samples_dir) else ""}
+{yaml.dump(widgets, indent=4).strip() if widgets else ""}
+base_model: {base_model_name}
+{"instance_prompt: " + instance_prompt if instance_prompt else ""}
+{license_str}
+---
+
+# {lora_name}
+
+A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym)
+
+<Gallery />
+
+## Trigger words
+
+{"You should use `" + instance_prompt + "` to trigger the image generation." if instance_prompt else "No trigger words defined."}
+
+## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc.
+
+Weights for this model are available in Safetensors format.
+
+"""
+    return readme_content
+
+def account_hf():
+    try:
+        with open("HF_TOKEN", "r") as file:
+            token = file.read()
+            api = HfApi(token=token)
+            try:
+                account = api.whoami()
+                return { "token": token, "account": account['name'] }
+            except:
+                return None
+    except:
+        return None
+
+"""
+hf_logout.click(fn=logout_hf, outputs=[hf_token, hf_login, hf_logout, repo_owner])
+"""
+def logout_hf():
+    os.remove("HF_TOKEN")
+    global current_account
+    current_account = account_hf()
+    print(f"current_account={current_account}")
+    return gr.update(value=""), gr.update(visible=True), gr.update(visible=False), gr.update(value="", visible=False)
+
+
+"""
+hf_login.click(fn=login_hf, inputs=[hf_token], outputs=[hf_token, hf_login, hf_logout, repo_owner])
+"""
+def login_hf(hf_token):
+    api = HfApi(token=hf_token)
+    try:
+        account = api.whoami()
+        if account != None:
+            if "name" in account:
+                with open("HF_TOKEN", "w") as file:
+                    file.write(hf_token)
+                global current_account
+                current_account = account_hf()
+                return gr.update(visible=True), gr.update(visible=False), gr.update(visible=True), gr.update(value=current_account["account"], visible=True)
+        return gr.update(), gr.update(), gr.update(), gr.update()
+    except:
+        print(f"incorrect hf_token")
+        return gr.update(), gr.update(), gr.update(), gr.update()
+
+def upload_hf(base_model, lora_rows, repo_owner, repo_name, repo_visibility, hf_token):
+    src = lora_rows
+    repo_id = f"{repo_owner}/{repo_name}"
+    gr.Info(f"Uploading to Huggingface. Please Stand by...", duration=None)
+    args = Namespace(
+        huggingface_repo_id=repo_id,
+        huggingface_repo_type="model",
+        huggingface_repo_visibility=repo_visibility,
+        huggingface_path_in_repo="",
+        huggingface_token=hf_token,
+        async_upload=False
+    )
+    print(f"upload_hf args={args}")
+    huggingface_util.upload(args=args, src=src)
+    gr.Info(f"[Upload Complete] https://huggingface.co/{repo_id}", duration=None)
+
+def load_captioning(uploaded_files, concept_sentence):
+    uploaded_images = [file for file in uploaded_files if not file.endswith('.txt')]
+    txt_files = [file for file in uploaded_files if file.endswith('.txt')]
+    txt_files_dict = {os.path.splitext(os.path.basename(txt_file))[0]: txt_file for txt_file in txt_files}
+    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))
+
+        corresponding_caption = False
+        if(image_value):
+            base_name = os.path.splitext(os.path.basename(image_value))[0]
+            if base_name in txt_files_dict:
+                with open(txt_files_dict[base_name], 'r') as file:
+                    corresponding_caption = file.read()
+
+        # Update value of captioning area
+        text_value = corresponding_caption if visible and corresponding_caption else concept_sentence 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))
+    updates.append(gr.update(visible=True))
+
+    return updates
+
+def hide_captioning():
+    return gr.update(visible=False), gr.update(visible=False)
+
+def resize_image(image_path, output_path, size):
+    with Image.open(image_path) as img:
+        width, height = img.size
+        if width < height:
+            new_width = size
+            new_height = int((size/width) * height)
+        else:
+            new_height = size
+            new_width = int((size/height) * width)
+        print(f"resize {image_path} : {new_width}x{new_height}")
+        img_resized = img.resize((new_width, new_height), Image.Resampling.LANCZOS)
+        img_resized.save(output_path)
+
+def create_dataset(destination_folder, size, *inputs):
+    print("Creating dataset")
+    images = inputs[0]
+    if not os.path.exists(destination_folder):
+        os.makedirs(destination_folder)
+
+    for index, image in enumerate(images):
+        # copy the images to the datasets folder
+        new_image_path = shutil.copy(image, destination_folder)
+
+        # if it's a caption text file skip the next bit
+        ext = os.path.splitext(new_image_path)[-1].lower()
+        if ext == '.txt':
+            continue
+
+        # resize the images
+        resize_image(new_image_path, new_image_path, size)
+
+        # copy the captions
+
+        original_caption = inputs[index + 1]
+
+        image_file_name = os.path.basename(new_image_path)
+        caption_file_name = os.path.splitext(image_file_name)[0] + ".txt"
+        caption_path = resolve_path_without_quotes(os.path.join(destination_folder, caption_file_name))
+        print(f"image_path={new_image_path}, caption_path = {caption_path}, original_caption={original_caption}")
+        # if caption_path exists, do not write
+        if os.path.exists(caption_path):
+            print(f"{caption_path} already exists. use the existing .txt file")
+        else:
+            print(f"{caption_path} create a .txt caption file")
+            with open(caption_path, 'w') as file:
+                file.write(original_caption)
+
+    print(f"destination_folder {destination_folder}")
+    return destination_folder
+
+
+def run_captioning(images, concept_sentence, *captions):
+    print(f"run_captioning")
+    print(f"concept sentence {concept_sentence}")
+    print(f"captions {captions}")
+    #Load internally to not consume resources for training
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    print(f"device={device}")
+    torch_dtype = torch.float16
+    model = AutoModelForCausalLM.from_pretrained(
+        "multimodalart/Florence-2-large-no-flash-attn", torch_dtype=torch_dtype, trust_remote_code=True
+    ).to(device)
+    processor = AutoProcessor.from_pretrained("multimodalart/Florence-2-large-no-flash-attn", 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)
+        print(f"inputs {inputs}")
+
+        generated_ids = model.generate(
+            input_ids=inputs["input_ids"], pixel_values=inputs["pixel_values"], max_new_tokens=1024, num_beams=3
+        )
+        print(f"generated_ids {generated_ids}")
+
+        generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
+        print(f"generated_text: {generated_text}")
+        parsed_answer = processor.post_process_generation(
+            generated_text, task=prompt, image_size=(image.width, image.height)
+        )
+        print(f"parsed_answer = {parsed_answer}")
+        caption_text = parsed_answer["<DETAILED_CAPTION>"].replace("The image shows ", "")
+        print(f"caption_text = {caption_text}, concept_sentence={concept_sentence}")
+        if concept_sentence:
+            caption_text = f"{concept_sentence} {caption_text}"
+        captions[i] = caption_text
+
+        yield captions
+    model.to("cpu")
+    del model
+    del processor
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+
+def recursive_update(d, u):
+    for k, v in u.items():
+        if isinstance(v, dict) and v:
+            d[k] = recursive_update(d.get(k, {}), v)
+        else:
+            d[k] = v
+    return d
+
+def download(base_model):
+    model = models[base_model]
+    model_file = model["file"]
+    repo = model["repo"]
+
+    # download unet
+    if base_model == "flux-dev" or base_model == "flux-schnell":
+        unet_folder = "models/unet"
+    else:
+        unet_folder = f"models/unet/{repo}"
+    unet_path = os.path.join(unet_folder, model_file)
+    if not os.path.exists(unet_path):
+        os.makedirs(unet_folder, exist_ok=True)
+        gr.Info(f"Downloading base model: {base_model}. Please wait. (You can check the terminal for the download progress)", duration=None)
+        print(f"download {base_model}")
+        hf_hub_download(repo_id=repo, local_dir=unet_folder, filename=model_file)
+
+    # download vae
+    vae_folder = "models/vae"
+    vae_path = os.path.join(vae_folder, "ae.sft")
+    if not os.path.exists(vae_path):
+        os.makedirs(vae_folder, exist_ok=True)
+        gr.Info(f"Downloading vae")
+        print(f"downloading ae.sft...")
+        hf_hub_download(repo_id="cocktailpeanut/xulf-dev", local_dir=vae_folder, filename="ae.sft")
+
+    # download clip
+    clip_folder = "models/clip"
+    clip_l_path = os.path.join(clip_folder, "clip_l.safetensors")
+    if not os.path.exists(clip_l_path):
+        os.makedirs(clip_folder, exist_ok=True)
+        gr.Info(f"Downloading clip...")
+        print(f"download clip_l.safetensors")
+        hf_hub_download(repo_id="comfyanonymous/flux_text_encoders", local_dir=clip_folder, filename="clip_l.safetensors")
+
+    # download t5xxl
+    t5xxl_path = os.path.join(clip_folder, "t5xxl_fp16.safetensors")
+    if not os.path.exists(t5xxl_path):
+        print(f"download t5xxl_fp16.safetensors")
+        gr.Info(f"Downloading t5xxl...")
+        hf_hub_download(repo_id="comfyanonymous/flux_text_encoders", local_dir=clip_folder, filename="t5xxl_fp16.safetensors")
+
+
+def resolve_path(p):
+    current_dir = os.path.dirname(os.path.abspath(__file__))
+    norm_path = os.path.normpath(os.path.join(current_dir, p))
+    return f"\"{norm_path}\""
+def resolve_path_without_quotes(p):
+    current_dir = os.path.dirname(os.path.abspath(__file__))
+    norm_path = os.path.normpath(os.path.join(current_dir, p))
+    return norm_path
+
+def gen_sh(
+    base_model,
+    output_name,
+    resolution,
+    seed,
+    workers,
+    learning_rate,
+    network_dim,
+    max_train_epochs,
+    save_every_n_epochs,
+    timestep_sampling,
+    guidance_scale,
+    vram,
+    sample_prompts,
+    sample_every_n_steps,
+    *advanced_components
+):
+
+    print(f"gen_sh: network_dim:{network_dim}, max_train_epochs={max_train_epochs}, save_every_n_epochs={save_every_n_epochs}, timestep_sampling={timestep_sampling}, guidance_scale={guidance_scale}, vram={vram}, sample_prompts={sample_prompts}, sample_every_n_steps={sample_every_n_steps}")
+
+    output_dir = resolve_path(f"outputs/{output_name}")
+    sample_prompts_path = resolve_path(f"outputs/{output_name}/sample_prompts.txt")
+
+    line_break = "\\"
+    file_type = "sh"
+    if sys.platform == "win32":
+        line_break = "^"
+        file_type = "bat"
+
+    ############# Sample args ########################
+    sample = ""
+    if len(sample_prompts) > 0 and sample_every_n_steps > 0:
+        sample = f"""--sample_prompts={sample_prompts_path} --sample_every_n_steps="{sample_every_n_steps}" {line_break}"""
+
+
+    ############# Optimizer args ########################
+#    if vram == "8G":
+#        optimizer = f"""--optimizer_type adafactor {line_break}
+#    --optimizer_args "relative_step=False" "scale_parameter=False" "warmup_init=False" {line_break}
+#        --split_mode {line_break}
+#        --network_args "train_blocks=single" {line_break}
+#        --lr_scheduler constant_with_warmup {line_break}
+#        --max_grad_norm 0.0 {line_break}"""
+    if vram == "16G":
+        # 16G VRAM
+        optimizer = f"""--optimizer_type adafactor {line_break}
+  --optimizer_args "relative_step=False" "scale_parameter=False" "warmup_init=False" {line_break}
+  --lr_scheduler constant_with_warmup {line_break}
+  --max_grad_norm 0.0 {line_break}"""
+    elif vram == "12G":
+      # 12G VRAM
+        optimizer = f"""--optimizer_type adafactor {line_break}
+  --optimizer_args "relative_step=False" "scale_parameter=False" "warmup_init=False" {line_break}
+  --split_mode {line_break}
+  --network_args "train_blocks=single" {line_break}
+  --lr_scheduler constant_with_warmup {line_break}
+  --max_grad_norm 0.0 {line_break}"""
+    else:
+        # 20G+ VRAM
+        optimizer = f"--optimizer_type adamw8bit {line_break}"
+
+
+    #######################################################
+    model_config = models[base_model]
+    model_file = model_config["file"]
+    repo = model_config["repo"]
+    if base_model == "flux-dev" or base_model == "flux-schnell":
+        model_folder = "models/unet"
+    else:
+        model_folder = f"models/unet/{repo}"
+    model_path = os.path.join(model_folder, model_file)
+    pretrained_model_path = resolve_path(model_path)
+
+    clip_path = resolve_path("models/clip/clip_l.safetensors")
+    t5_path = resolve_path("models/clip/t5xxl_fp16.safetensors")
+    ae_path = resolve_path("models/vae/ae.sft")
+    sh = f"""accelerate launch {line_break}
+  --mixed_precision bf16 {line_break}
+  --num_cpu_threads_per_process 1 {line_break}
+  sd-scripts/flux_train_network.py {line_break}
+  --pretrained_model_name_or_path {pretrained_model_path} {line_break}
+  --clip_l {clip_path} {line_break}
+  --t5xxl {t5_path} {line_break}
+  --ae {ae_path} {line_break}
+  --cache_latents_to_disk {line_break}
+  --save_model_as safetensors {line_break}
+  --sdpa --persistent_data_loader_workers {line_break}
+  --max_data_loader_n_workers {workers} {line_break}
+  --seed {seed} {line_break}
+  --gradient_checkpointing {line_break}
+  --mixed_precision bf16 {line_break}
+  --save_precision bf16 {line_break}
+  --network_module networks.lora_flux {line_break}
+  --network_dim {network_dim} {line_break}
+  {optimizer}{sample}
+  --learning_rate {learning_rate} {line_break}
+  --cache_text_encoder_outputs {line_break}
+  --cache_text_encoder_outputs_to_disk {line_break}
+  --fp8_base {line_break}
+  --highvram {line_break}
+  --max_train_epochs {max_train_epochs} {line_break}
+  --save_every_n_epochs {save_every_n_epochs} {line_break}
+  --dataset_config {resolve_path(f"outputs/{output_name}/dataset.toml")} {line_break}
+  --output_dir {output_dir} {line_break}
+  --output_name {output_name} {line_break}
+  --timestep_sampling {timestep_sampling} {line_break}
+  --discrete_flow_shift 3.1582 {line_break}
+  --model_prediction_type raw {line_break}
+  --guidance_scale {guidance_scale} {line_break}
+  --loss_type l2 {line_break}"""
+   
+
+
+    ############# Advanced args ########################
+    global advanced_component_ids
+    global original_advanced_component_values
+   
+    # check dirty
+    print(f"original_advanced_component_values = {original_advanced_component_values}")
+    advanced_flags = []
+    for i, current_value in enumerate(advanced_components):
+#        print(f"compare {advanced_component_ids[i]}: old={original_advanced_component_values[i]}, new={current_value}")
+        if original_advanced_component_values[i] != current_value:
+            # dirty
+            if current_value == True:
+                # Boolean
+                advanced_flags.append(advanced_component_ids[i])
+            else:
+                # string
+                advanced_flags.append(f"{advanced_component_ids[i]} {current_value}")
+
+    if len(advanced_flags) > 0:
+        advanced_flags_str = f" {line_break}\n  ".join(advanced_flags)
+        sh = sh + "\n  " + advanced_flags_str
+
+    return sh
+
+def gen_toml(
+  dataset_folder,
+  resolution,
+  class_tokens,
+  num_repeats
+):
+    toml = f"""[general]
+shuffle_caption = false
+caption_extension = '.txt'
+keep_tokens = 1
+
+[[datasets]]
+resolution = {resolution}
+batch_size = 1
+keep_tokens = 1
+
+  [[datasets.subsets]]
+  image_dir = '{resolve_path_without_quotes(dataset_folder)}'
+  class_tokens = '{class_tokens}'
+  num_repeats = {num_repeats}"""
+    return toml
+
+def update_total_steps(max_train_epochs, num_repeats, images):
+    try:
+        num_images = len(images)
+        total_steps = max_train_epochs * num_images * num_repeats
+        print(f"max_train_epochs={max_train_epochs} num_images={num_images}, num_repeats={num_repeats}, total_steps={total_steps}")
+        return gr.update(value = total_steps)
+    except:
+        print("")
+
+def set_repo(lora_rows):
+    selected_name = os.path.basename(lora_rows)
+    return gr.update(value=selected_name)
+
+def get_loras():
+    try:
+        outputs_path = resolve_path_without_quotes(f"outputs")
+        files = os.listdir(outputs_path)
+        folders = [os.path.join(outputs_path, item) for item in files if os.path.isdir(os.path.join(outputs_path, item)) and item != "sample"]
+        folders.sort(key=lambda file: os.path.getctime(file), reverse=True)
+        return folders
+    except Exception as e:
+        return []
+
+def get_samples(lora_name):
+    output_name = slugify(lora_name)
+    try:
+        samples_path = resolve_path_without_quotes(f"outputs/{output_name}/sample")
+        files = [os.path.join(samples_path, file) for file in os.listdir(samples_path)]
+        files.sort(key=lambda file: os.path.getctime(file), reverse=True)
+        return files
+    except:
+        return []
+
+def start_training(
+    base_model,
+    lora_name,
+    train_script,
+    train_config,
+    sample_prompts,
+):
+    # write custom script and toml
+    if not os.path.exists("models"):
+        os.makedirs("models", exist_ok=True)
+    if not os.path.exists("outputs"):
+        os.makedirs("outputs", exist_ok=True)
+    output_name = slugify(lora_name)
+    output_dir = resolve_path_without_quotes(f"outputs/{output_name}")
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir, exist_ok=True)
+
+    download(base_model)
+
+    file_type = "sh"
+    if sys.platform == "win32":
+        file_type = "bat"
+
+    sh_filename = f"train.{file_type}"
+    sh_filepath = resolve_path_without_quotes(f"outputs/{output_name}/{sh_filename}")
+    with open(sh_filepath, 'w', encoding="utf-8") as file:
+        file.write(train_script)
+    gr.Info(f"Generated train script at {sh_filename}")
+
+
+    dataset_path = resolve_path_without_quotes(f"outputs/{output_name}/dataset.toml")
+    with open(dataset_path, 'w', encoding="utf-8") as file:
+        file.write(train_config)
+    gr.Info(f"Generated dataset.toml")
+
+    sample_prompts_path = resolve_path_without_quotes(f"outputs/{output_name}/sample_prompts.txt")
+    with open(sample_prompts_path, 'w', encoding='utf-8') as file:
+        file.write(sample_prompts)
+    gr.Info(f"Generated sample_prompts.txt")
+
+    # Train
+    if sys.platform == "win32":
+        command = sh_filepath
+    else:
+        command = f"bash \"{sh_filepath}\""
+
+    # Use Popen to run the command and capture output in real-time
+    env = os.environ.copy()
+    env['PYTHONIOENCODING'] = 'utf-8'
+    env['LOG_LEVEL'] = 'DEBUG'
+    runner = LogsViewRunner()
+    cwd = os.path.dirname(os.path.abspath(__file__))
+    gr.Info(f"Started training")
+    yield from runner.run_command([command], cwd=cwd)
+    yield runner.log(f"Runner: {runner}")
+
+    # Generate Readme
+    config = toml.loads(train_config)
+    concept_sentence = config['datasets'][0]['subsets'][0]['class_tokens']
+    print(f"concept_sentence={concept_sentence}")
+    print(f"lora_name {lora_name}, concept_sentence={concept_sentence}, output_name={output_name}")
+    sample_prompts_path = resolve_path_without_quotes(f"outputs/{output_name}/sample_prompts.txt")
+    with open(sample_prompts_path, "r", encoding="utf-8") as f:
+        lines = f.readlines()
+    sample_prompts = [line.strip() for line in lines if len(line.strip()) > 0 and line[0] != "#"]
+    md = readme(base_model, lora_name, concept_sentence, sample_prompts)
+    readme_path = resolve_path_without_quotes(f"outputs/{output_name}/README.md")
+    with open(readme_path, "w", encoding="utf-8") as f:
+        f.write(md)
+
+    gr.Info(f"Training Complete. Check the outputs folder for the LoRA files.", duration=None)
+
+
+def update(
+    base_model,
+    lora_name,
+    resolution,
+    seed,
+    workers,
+    class_tokens,
+    learning_rate,
+    network_dim,
+    max_train_epochs,
+    save_every_n_epochs,
+    timestep_sampling,
+    guidance_scale,
+    vram,
+    num_repeats,
+    sample_prompts,
+    sample_every_n_steps,
+    *advanced_components,
+):
+    output_name = slugify(lora_name)
+    dataset_folder = str(f"datasets/{output_name}")
+    sh = gen_sh(
+        base_model,
+        output_name,
+        resolution,
+        seed,
+        workers,
+        learning_rate,
+        network_dim,
+        max_train_epochs,
+        save_every_n_epochs,
+        timestep_sampling,
+        guidance_scale,
+        vram,
+        sample_prompts,
+        sample_every_n_steps,
+        *advanced_components,
+    )
+    toml = gen_toml(
+        dataset_folder,
+        resolution,
+        class_tokens,
+        num_repeats
+    )
+    return gr.update(value=sh), gr.update(value=toml), dataset_folder
+
+"""
+demo.load(fn=loaded, js=js, outputs=[hf_token, hf_login, hf_logout, hf_account])
+"""
+def loaded():
+    global current_account
+    current_account = account_hf()
+    print(f"current_account={current_account}")
+    if current_account != None:
+        return gr.update(value=current_account["token"]), gr.update(visible=False), gr.update(visible=True), gr.update(value=current_account["account"], visible=True)
+    else:
+        return gr.update(value=""), gr.update(visible=True), gr.update(visible=False), gr.update(value="", visible=False)
+
+def update_sample(concept_sentence):
+    return gr.update(value=concept_sentence)
+
+def refresh_publish_tab():
+    loras = get_loras()
+    return gr.Dropdown(label="Trained LoRAs", choices=loras)
+
+def init_advanced():
+    # if basic_args
+    basic_args = {
+        'pretrained_model_name_or_path',
+        'clip_l',
+        't5xxl',
+        'ae',
+        'cache_latents_to_disk',
+        'save_model_as',
+        'sdpa',
+        'persistent_data_loader_workers',
+        'max_data_loader_n_workers',
+        'seed',
+        'gradient_checkpointing',
+        'mixed_precision',
+        'save_precision',
+        'network_module',
+        'network_dim',
+        'learning_rate',
+        'cache_text_encoder_outputs',
+        'cache_text_encoder_outputs_to_disk',
+        'fp8_base',
+        'highvram',
+        'max_train_epochs',
+        'save_every_n_epochs',
+        'dataset_config',
+        'output_dir',
+        'output_name',
+        'timestep_sampling',
+        'discrete_flow_shift',
+        'model_prediction_type',
+        'guidance_scale',
+        'loss_type',
+        'optimizer_type',
+        'optimizer_args',
+        'lr_scheduler',
+        'sample_prompts',
+        'sample_every_n_steps',
+        'max_grad_norm',
+        'split_mode',
+        'network_args'
+    }
+
+    # generate a UI config
+    # if not in basic_args, create a simple form
+    parser = train_network.setup_parser()
+    flux_train_utils.add_flux_train_arguments(parser)
+    args_info = {}
+    for action in parser._actions:
+        if action.dest != 'help':  # Skip the default help argument
+            # if the dest is included in basic_args
+            args_info[action.dest] = {
+                "action": action.option_strings,  # Option strings like '--use_8bit_adam'
+                "type": action.type,              # Type of the argument
+                "help": action.help,              # Help message
+                "default": action.default,        # Default value, if any
+                "required": action.required       # Whether the argument is required
+            }
+    temp = []
+    for key in args_info:
+        temp.append({ 'key': key, 'action': args_info[key] })
+    temp.sort(key=lambda x: x['key'])
+    advanced_component_ids = []
+    advanced_components = []
+    for item in temp:
+        key = item['key']
+        action = item['action']
+        if key in basic_args:
+            print("")
+        else:
+            action_type = str(action['type'])
+            component = None
+            with gr.Column(min_width=300):
+                if action_type == "None":
+                    # radio
+                    component = gr.Checkbox()
+    #            elif action_type == "<class 'str'>":
+    #                component = gr.Textbox()
+    #            elif action_type == "<class 'int'>":
+    #                component = gr.Number(precision=0)
+    #            elif action_type == "<class 'float'>":
+    #                component = gr.Number()
+    #            elif "int_or_float" in action_type:
+    #                component = gr.Number()
+                else:
+                    component = gr.Textbox(value="")
+                if component != None:
+                    component.interactive = True
+                    component.elem_id = action['action'][0]
+                    component.label = component.elem_id
+                    component.elem_classes = ["advanced"]
+                if action['help'] != None:
+                    component.info = action['help']
+            advanced_components.append(component)
+            advanced_component_ids.append(component.elem_id)
+    return advanced_components, advanced_component_ids
+
+
+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 = """
+@keyframes rotate {
+    0% {
+        transform: rotate(0deg);
+    }
+    100% {
+        transform: rotate(360deg);
+    }
+}
+#advanced_options .advanced:nth-child(even) { background: rgba(0,0,100,0.04) !important; }
+h1{font-family: georgia; font-style: italic; font-weight: bold; font-size: 30px; letter-spacing: -1px;}
+h3{margin-top: 0}
+.tabitem{border: 0px}
+.group_padding{}
+nav{position: fixed; top: 0; left: 0; right: 0; z-index: 1000; text-align: center; padding: 10px; box-sizing: border-box; display: flex; align-items: center; backdrop-filter: blur(10px); }
+nav button { background: none; color: firebrick; font-weight: bold; border: 2px solid firebrick; padding: 5px 10px; border-radius: 5px; font-size: 14px; }
+nav img { height: 40px; width: 40px; border-radius: 40px; }
+nav img.rotate { animation: rotate 2s linear infinite; }
+.flexible { flex-grow: 1; }
+.tast-details { margin: 10px 0 !important; }
+.toast-wrap { bottom: var(--size-4) !important; top: auto !important; border: none !important; backdrop-filter: blur(10px); }
+.toast-title, .toast-text, .toast-icon, .toast-close { color: black !important; font-size: 14px; }
+.toast-body { border: none !important; }
+#terminal { box-shadow: none !important; margin-bottom: 25px; background: rgba(0,0,0,0.03); }
+#terminal .generating { border: none !important; }
+#terminal label { position: absolute !important; }
+.tabs { margin-top: 50px; }
+.hidden { display: none !important; }
+.codemirror-wrapper .cm-line { font-size: 12px !important; }
+label { font-weight: bold !important; }
+#start_training.clicked { background: silver; color: black; }
+"""
+
+js = """
+function() {
+    let autoscroll = document.querySelector("#autoscroll")
+    if (window.iidxx) {
+        window.clearInterval(window.iidxx);
+    }
+    window.iidxx = window.setInterval(function() {
+        let text=document.querySelector(".codemirror-wrapper .cm-line").innerText.trim()
+        let img = document.querySelector("#logo")
+        if (text.length > 0) {
+            autoscroll.classList.remove("hidden")
+            if (autoscroll.classList.contains("on")) {
+                autoscroll.textContent = "Autoscroll ON"
+                window.scrollTo(0, document.body.scrollHeight, { behavior: "smooth" });
+                img.classList.add("rotate")
+            } else {
+                autoscroll.textContent = "Autoscroll OFF"
+                img.classList.remove("rotate")
+            }
+        }
+    }, 500);
+    console.log("autoscroll", autoscroll)
+    autoscroll.addEventListener("click", (e) => {
+        autoscroll.classList.toggle("on")
+    })
+    function debounce(fn, delay) {
+        let timeoutId;
+        return function(...args) {
+            clearTimeout(timeoutId);
+            timeoutId = setTimeout(() => fn(...args), delay);
+        };
+    }
+
+    function handleClick() {
+        console.log("refresh")
+        document.querySelector("#refresh").click();
+    }
+    const debouncedClick = debounce(handleClick, 1000);
+    document.addEventListener("input", debouncedClick);
+
+    document.querySelector("#start_training").addEventListener("click", (e) => {
+      e.target.classList.add("clicked")
+      e.target.innerHTML = "Training..."
+    })
+
+}
+"""
+
+current_account = account_hf()
+print(f"current_account={current_account}")
+
+with gr.Blocks(elem_id="app", theme=theme, css=css, fill_width=True) as demo:
+    with gr.Tabs() as tabs:
+        with gr.TabItem("Gym"):
+            output_components = []
+            with gr.Row():
+                gr.HTML("""<nav>
+            <img id='logo' src='/file=icon.png' width='80' height='80'>
+            <div class='flexible'></div>
+            <button id='autoscroll' class='on hidden'></button>
+        </nav>
+        """)
+            with gr.Row(elem_id='container'):
+                with gr.Column():
+                    gr.Markdown(
+                        """# Step 1. LoRA Info
+        <p style="margin-top:0">Configure your LoRA train settings.</p>
+        """, elem_classes="group_padding")
+                    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",
+                    )
+                    concept_sentence = gr.Textbox(
+                        elem_id="--concept_sentence",
+                        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,
+                    )
+                    model_names = list(models.keys())
+                    print(f"model_names={model_names}")
+                    base_model = gr.Dropdown(label="Base model (edit the models.yaml file to add more to this list)", choices=model_names, value=model_names[0])
+                    vram = gr.Radio(["20G", "16G", "12G" ], value="20G", label="VRAM", interactive=True)
+                    num_repeats = gr.Number(value=10, precision=0, label="Repeat trains per image", interactive=True)
+                    max_train_epochs = gr.Number(label="Max Train Epochs", value=16, interactive=True)
+                    total_steps = gr.Number(0, interactive=False, label="Expected training steps")
+                    sample_prompts = gr.Textbox("", lines=5, label="Sample Image Prompts (Separate with new lines)", interactive=True)
+                    sample_every_n_steps = gr.Number(0, precision=0, label="Sample Image Every N Steps", interactive=True)
+                    resolution = gr.Number(value=512, precision=0, label="Resize dataset images")
+                with gr.Column():
+                    gr.Markdown(
+                        """# Step 2. Dataset
+        <p style="margin-top:0">Make sure the captions include the trigger word.</p>
+        """, elem_classes="group_padding")
+                    with gr.Group():
+                        images = gr.File(
+                            file_types=["image", ".txt"],
+                            label="Upload your images",
+                            #info="If you want, you can also manually upload caption files that match the image names (example: img0.png => img0.txt)",
+                            file_count="multiple",
+                            interactive=True,
+                            visible=True,
+                            scale=1,
+                        )
+                    with gr.Group(visible=False) as captioning_area:
+                        do_captioning = gr.Button("Add AI captions with Florence-2")
+                        output_components.append(captioning_area)
+                        #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.Column():
+                    gr.Markdown(
+                        """# Step 3. Train
+        <p style="margin-top:0">Press start to start training.</p>
+        """, elem_classes="group_padding")
+                    refresh = gr.Button("Refresh", elem_id="refresh", visible=False)
+                    start = gr.Button("Start training", visible=False, elem_id="start_training")
+                    output_components.append(start)
+                    train_script = gr.Textbox(label="Train script", max_lines=100, interactive=True)
+                    train_config = gr.Textbox(label="Train config", max_lines=100, interactive=True)
+            with gr.Accordion("Advanced options", elem_id='advanced_options', open=False):
+                with gr.Row():
+                    with gr.Column(min_width=300):
+                        seed = gr.Number(label="--seed", info="Seed", value=42, interactive=True)
+                    with gr.Column(min_width=300):
+                        workers = gr.Number(label="--max_data_loader_n_workers", info="Number of Workers", value=2, interactive=True)
+                    with gr.Column(min_width=300):
+                        learning_rate = gr.Textbox(label="--learning_rate", info="Learning Rate", value="8e-4", interactive=True)
+                    with gr.Column(min_width=300):
+                        save_every_n_epochs = gr.Number(label="--save_every_n_epochs", info="Save every N epochs", value=4, interactive=True)
+                    with gr.Column(min_width=300):
+                        guidance_scale = gr.Number(label="--guidance_scale", info="Guidance Scale", value=1.0, interactive=True)
+                    with gr.Column(min_width=300):
+                        timestep_sampling = gr.Textbox(label="--timestep_sampling", info="Timestep Sampling", value="shift", interactive=True)
+                    with gr.Column(min_width=300):
+                        network_dim = gr.Number(label="--network_dim", info="LoRA Rank", value=4, minimum=4, maximum=128, step=4, interactive=True)
+                    advanced_components, advanced_component_ids = init_advanced()
+            with gr.Row():
+                terminal = LogsView(label="Train log", elem_id="terminal")
+            with gr.Row():
+                gallery = gr.Gallery(get_samples, inputs=[lora_name], label="Samples", every=10, columns=6)
+
+        with gr.TabItem("Publish") as publish_tab:
+            hf_token = gr.Textbox(label="Huggingface Token")
+            hf_login = gr.Button("Login")
+            hf_logout = gr.Button("Logout")
+            with gr.Row() as row:
+                gr.Markdown("**LoRA**")
+                gr.Markdown("**Upload**")
+            loras = get_loras()
+            with gr.Row():
+                lora_rows = refresh_publish_tab()
+                with gr.Column():
+                    with gr.Row():
+                        repo_owner = gr.Textbox(label="Account", interactive=False)
+                        repo_name = gr.Textbox(label="Repository Name")
+                    repo_visibility = gr.Textbox(label="Repository Visibility ('public' or 'private')", value="public")
+                    upload_button = gr.Button("Upload to HuggingFace")
+                    upload_button.click(
+                        fn=upload_hf,
+                        inputs=[
+                            base_model,
+                            lora_rows,
+                            repo_owner,
+                            repo_name,
+                            repo_visibility,
+                            hf_token,
+                        ]
+                    )
+            hf_login.click(fn=login_hf, inputs=[hf_token], outputs=[hf_token, hf_login, hf_logout, repo_owner])
+            hf_logout.click(fn=logout_hf, outputs=[hf_token, hf_login, hf_logout, repo_owner])
+
+
+    publish_tab.select(refresh_publish_tab, outputs=lora_rows)
+    lora_rows.select(fn=set_repo, inputs=[lora_rows], outputs=[repo_name])
+
+    dataset_folder = gr.State()
+
+    listeners = [
+        base_model,
+        lora_name,
+        resolution,
+        seed,
+        workers,
+        concept_sentence,
+        learning_rate,
+        network_dim,
+        max_train_epochs,
+        save_every_n_epochs,
+        timestep_sampling,
+        guidance_scale,
+        vram,
+        num_repeats,
+        sample_prompts,
+        sample_every_n_steps,
+        *advanced_components
+    ]
+    advanced_component_ids = [x.elem_id for x in advanced_components]
+    original_advanced_component_values = [comp.value for comp in advanced_components]
+    images.upload(
+        load_captioning,
+        inputs=[images, concept_sentence],
+        outputs=output_components
+    )
+    images.delete(
+        load_captioning,
+        inputs=[images, concept_sentence],
+        outputs=output_components
+    )
+    images.clear(
+        hide_captioning,
+        outputs=[captioning_area, start]
+    )
+    max_train_epochs.change(
+        fn=update_total_steps,
+        inputs=[max_train_epochs, num_repeats, images],
+        outputs=[total_steps]
+    )
+    num_repeats.change(
+        fn=update_total_steps,
+        inputs=[max_train_epochs, num_repeats, images],
+        outputs=[total_steps]
+    )
+    images.upload(
+        fn=update_total_steps,
+        inputs=[max_train_epochs, num_repeats, images],
+        outputs=[total_steps]
+    )
+    images.delete(
+        fn=update_total_steps,
+        inputs=[max_train_epochs, num_repeats, images],
+        outputs=[total_steps]
+    )
+    images.clear(
+        fn=update_total_steps,
+        inputs=[max_train_epochs, num_repeats, images],
+        outputs=[total_steps]
+    )
+    concept_sentence.change(fn=update_sample, inputs=[concept_sentence], outputs=sample_prompts)
+    start.click(fn=create_dataset, inputs=[dataset_folder, resolution, images] + caption_list, outputs=dataset_folder).then(
+        fn=start_training,
+        inputs=[
+            base_model,
+            lora_name,
+            train_script,
+            train_config,
+            sample_prompts,
+        ],
+        outputs=terminal,
+    )
+    do_captioning.click(fn=run_captioning, inputs=[images, concept_sentence] + caption_list, outputs=caption_list)
+    demo.load(fn=loaded, js=js, outputs=[hf_token, hf_login, hf_logout, repo_owner])
+    refresh.click(update, inputs=listeners, outputs=[train_script, train_config, dataset_folder])
+if __name__ == "__main__":
+    cwd = os.path.dirname(os.path.abspath(__file__))
+    demo.launch(debug=True, show_error=True, allowed_paths=[cwd])