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README.md

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+# Flux Gym
+
+Dead simple web UI for training FLUX LoRA **with LOW VRAM (12GB/16GB/20GB) support.**
+
+- **Frontend:** The WebUI forked from [AI-Toolkit](https://github.com/ostris/ai-toolkit) (Gradio UI created by https://x.com/multimodalart)
+- **Backend:** The Training script powered by [Kohya Scripts](https://github.com/kohya-ss/sd-scripts)
+
+FluxGym supports 100% of Kohya sd-scripts features through an [Advanced](#advanced) tab, which is hidden by default.
+
+![screenshot.png](screenshot.png)
+
+---
+
+
+# What is this?
+
+1. I wanted a super simple UI for training Flux LoRAs
+2. The [AI-Toolkit](https://github.com/ostris/ai-toolkit) project is great, and the gradio UI contribution by [@multimodalart](https://x.com/multimodalart) is perfect, but the project only works for 24GB VRAM.
+3. [Kohya Scripts](https://github.com/kohya-ss/sd-scripts) are very flexible and powerful for training FLUX, but you need to run in terminal.
+4. What if you could have the simplicity of AI-Toolkit WebUI and the flexibility of Kohya Scripts?
+5. Flux Gym was born. Supports 12GB, 16GB, 20GB VRAMs, and extensible since it uses Kohya Scripts underneath.
+
+---
+
+# News
+
+- September 16: Added "Publish to Huggingface" + 100% Kohya sd-scripts feature support: https://x.com/cocktailpeanut/status/1835719701172756592
+- September 11: Automatic Sample Image Generation + Custom Resolution: https://x.com/cocktailpeanut/status/1833881392482066638
+
+---
+
+# How people are using Fluxgym
+
+Here are people using Fluxgym to locally train Lora sharing their experience:
+
+https://pinokio.computer/item?uri=https://github.com/cocktailpeanut/fluxgym
+
+
+# More Info
+
+To learn more, check out this X thread: https://x.com/cocktailpeanut/status/1832084951115972653
+
+# Install
+
+## 1. One-Click Install
+
+You can automatically install and launch everything locally with Pinokio 1-click launcher: https://pinokio.computer/item?uri=https://github.com/cocktailpeanut/fluxgym
+
+
+## 2. Install Manually
+
+First clone Fluxgym and kohya-ss/sd-scripts:
+
+```
+git clone https://github.com/cocktailpeanut/fluxgym
+cd fluxgym
+git clone -b sd3 https://github.com/kohya-ss/sd-scripts
+```
+
+Your folder structure will look like this:
+
+```
+/fluxgym
+  app.py
+  requirements.txt
+  /sd-scripts
+```
+
+Now activate a venv from the root `fluxgym` folder:
+
+If you're on Windows:
+
+```
+python -m venv env
+env\Scripts\activate
+```
+
+If your're on Linux:
+
+```
+python -m venv env
+source env/bin/activate
+```
+
+This will create an `env` folder right below the `fluxgym` folder:
+
+```
+/fluxgym
+  app.py
+  requirements.txt
+  /sd-scripts
+  /env
+```
+
+Now go to the `sd-scripts` folder and install dependencies to the activated environment:
+
+```
+cd sd-scripts
+pip install -r requirements.txt
+```
+
+Now come back to the root folder and install the app dependencies:
+
+```
+cd ..
+pip install -r requirements.txt
+```
+
+Finally, install pytorch Nightly:
+
+```
+pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu121
+```
+
+Now let's download the model checkpoints.
+
+First, download the following models under the `models/clip` foder:
+
+- https://huggingface.co/comfyanonymous/flux_text_encoders/resolve/main/clip_l.safetensors?download=true
+- https://huggingface.co/comfyanonymous/flux_text_encoders/resolve/main/t5xxl_fp16.safetensors?download=true
+
+Second, download the following model under the `models/vae` folder:
+
+- https://huggingface.co/cocktailpeanut/xulf-dev/resolve/main/ae.sft?download=true
+
+Finally, download the following model under the `models/unet` folder:
+
+- https://huggingface.co/cocktailpeanut/xulf-dev/resolve/main/flux1-dev.sft?download=true
+
+The result file structure will be something like:
+
+```
+/models
+  /clip
+    clip_l.safetensors
+    t5xxl_fp16.safetensors
+  /unet
+    flux1-dev.sft
+  /vae
+    ae.sft
+/sd-scripts
+/outputs
+/env
+app.py
+requirements.txt
+...
+```
+
+# Start
+
+Go back to the root `fluxgym` folder, with the venv activated, run:
+
+```
+python app.py
+```
+
+> Make sure to have the venv activated before running `python app.py`.
+>
+> Windows: `env/Scripts/activate`
+> Linux: `source env/bin/activate`
+
+# Usage
+
+The usage is pretty straightforward:
+
+1. Enter the lora info
+2. Upload images and caption them (using the trigger word)
+3. Click "start".
+
+That's all!
+
+![flow.gif](flow.gif)
+
+# Configuration
+
+## Sample Images
+
+By default fluxgym doesn't generate any sample images during training.
+
+You can however configure Fluxgym to automatically generate sample images for every N steps. Here's what it looks like:
+
+![sample.png](sample.png)
+
+To turn this on, just set the two fields:
+
+1. **Sample Image Prompts:** These prompts will be used to automatically generate images during training. If you want multiple, separate teach prompt with new line.
+2. **Sample Image Every N Steps:** If your "Expected training steps" is 960 and your "Sample Image Every N Steps" is 100, the images will be generated at step 100, 200, 300, 400, 500, 600, 700, 800, 900, for EACH prompt.
+
+![sample_fields.png](sample_fields.png)
+
+## Advanced Sample Images
+
+Thanks to the built-in syntax from [kohya/sd-scripts](https://github.com/kohya-ss/sd-scripts?tab=readme-ov-file#sample-image-generation-during-training), you can control exactly how the sample images are generated during the training phase:
+
+Let's say the trigger word is **hrld person.** Normally you would try sample prompts like:
+
+```
+hrld person is riding a bike
+hrld person is a body builder
+hrld person is a rock star
+```
+
+But for every prompt you can include **advanced flags** to fully control the image generation process. For example, the `--d` flag lets you specify the SEED.
+
+Specifying a seed means every sample image will use that exact seed, which means you can literally see the LoRA evolve. Here's an example usage:
+
+```
+hrld person is riding a bike --d 42
+hrld person is a body builder --d 42
+hrld person is a rock star --d 42
+```
+
+Here's what it looks like in the UI:
+
+![flags.png](flags.png)
+
+And here are the results:
+
+![seed.gif](seed.gif)
+
+In addition to the `--d` flag, here are other flags you can use:
+
+
+- `--n`: Negative prompt up to the next option.
+- `--w`: Specifies the width of the generated image.
+- `--h`: Specifies the height of the generated image.
+- `--d`: Specifies the seed of the generated image.
+- `--l`: Specifies the CFG scale of the generated image.
+- `--s`: Specifies the number of steps in the generation.
+
+The prompt weighting such as `( )` and `[ ]` also work. (Learn more about [Attention/Emphasis](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#attentionemphasis))
+
+## Publishing to Huggingface
+
+1. Get your Huggingface Token from https://huggingface.co/settings/tokens
+2. Enter the token in the "Huggingface Token" field and click "Login". This will save the token text in a local file named `HF_TOKEN` (All local and private).
+3. Once you're logged in, you will be able to select a trained LoRA from the dropdown, edit the name if you want, and publish to Huggingface.
+
+![publish_to_hf.png](publish_to_hf.png)
+
+
+## Advanced
+
+The advanced tab is automatically constructed by parsing the launch flags available to the latest version of [kohya sd-scripts](https://github.com/kohya-ss/sd-scripts). This means Fluxgym is a full fledged UI for using the Kohya script.
+
+> By default the advanced tab is hidden. You can click the "advanced" accordion to expand it.
+
+![advanced.png](advanced.png)
+
+

app.py

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+import os
+import sys
+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
+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'))
+MAX_IMAGES = 150
+def readme(lora_name, instance_prompt, sample_prompts):
+    base_model = "black-forest-labs/FLUX.1-dev"
+    license = "other"
+    license_name = "flux-1-dev-non-commercial-license"
+    license_link = "https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md"
+    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")
+    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
+                },
+            }
+        )
+    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}
+{"instance_prompt: " + instance_prompt if instance_prompt else ""}
+license: {license}
+{'license_name: ' + license_name if license == "other" else ""}
+{'license_link: ' + license_link if license == "other" else ""}
+---
+
+# {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(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)
+    print(f"repo_id={repo_id} repo_visibility={repo_visibility} src={src}")
+    lora_name = os.path.basename(src)
+    dataset_toml_path = os.path.normpath(os.path.join(src, "dataset.toml"))
+    print(f"lora_name={lora_name}, dataset_toml_path={dataset_toml_path}")
+    with open(dataset_toml_path, 'r') as f:
+        config = toml.load(f)
+    concept_sentence = config['datasets'][0]['subsets'][0]['class_tokens']
+    print(f"concept_sentence={concept_sentence}")
+    # Generate README
+    output_name = slugify(lora_name)
+    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(lora_name, concept_sentence, sample_prompts)
+    # Write README
+    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)
+    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}")
+        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 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(
+    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 == "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}"
+
+
+    #######################################################
+    pretrained_model_path = resolve_path("models/unet/flux1-dev.sft")
+    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(
+    lora_name,
+    train_script,
+    train_config,
+    sample_prompts,
+):
+    # write custom script and toml
+    os.makedirs("models", exist_ok=True)
+    os.makedirs("outputs", exist_ok=True)
+    output_name = slugify(lora_name)
+    output_dir = resolve_path_without_quotes(f"outputs/{output_name}")
+    os.makedirs(output_dir, exist_ok=True)
+
+
+    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'
+    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}")
+    gr.Info(f"Training Complete. Check the outputs folder for the LoRA files.", duration=None)
+
+
+def update(
+    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(
+        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; }
+"""
+
+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);
+
+}
+"""
+
+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,
+                    )
+                    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",
+                            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)
+                    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=[
+                            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 = [
+        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=[
+            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])

install.js

@@ -0,0 +1,96 @@
+module.exports = {
+  run: [
+    {
+      method: "shell.run",
+      params: {
+        venv: "env",
+        message: [
+          "git config --global --add safe.directory '*'",
+          "git clone -b sd3 https://github.com/kohya-ss/sd-scripts"
+        ]
+      }
+    },
+    {
+      method: "shell.run",
+      params: {
+        path: "sd-scripts",
+        venv: "../env",
+        message: [
+          "pip install -r requirements.txt",
+        ]
+      }
+    },
+    {
+      method: "shell.run",
+      params: {
+        venv: "env",
+        message: [
+          "pip uninstall -y diffusers[torch] torch torchaudio torchvision",
+          "pip install -r requirements.txt",
+        ]
+      }
+    },
+    {
+      method: "script.start",
+      params: {
+        uri: "torch.js",
+        params: {
+          venv: "env",
+          // xformers: true   // uncomment this line if your project requires xformers
+        }
+      }
+    },
+    {
+      method: "fs.link",
+      params: {
+        drive: {
+          vae: "models/vae",
+          clip: "models/clip",
+          unet: "models/unet",
+          loras: "outputs",
+        },
+        peers: [
+          "https://github.com/pinokiofactory/stable-diffusion-webui-forge.git",
+          "https://github.com/pinokiofactory/comfy.git",
+          "https://github.com/cocktailpeanutlabs/comfyui.git",
+          "https://github.com/cocktailpeanutlabs/fooocus.git",
+          "https://github.com/cocktailpeanutlabs/automatic1111.git",
+        ]
+      }
+    },
+    {
+      method: "fs.download",
+      params: {
+        uri: [
+          "https://huggingface.co/comfyanonymous/flux_text_encoders/resolve/main/clip_l.safetensors?download=true",
+          "https://huggingface.co/comfyanonymous/flux_text_encoders/resolve/main/t5xxl_fp16.safetensors?download=true",
+        ],
+        dir: "models/clip"
+      }
+    },
+    {
+      method: "fs.download",
+      params: {
+        uri: [
+          "https://huggingface.co/cocktailpeanut/xulf-dev/resolve/main/ae.sft?download=true",
+        ],
+        dir: "models/vae"
+      }
+    },
+    {
+      method: "fs.download",
+      params: {
+        uri: [
+          "https://huggingface.co/cocktailpeanut/xulf-dev/resolve/main/flux1-dev.sft?download=true",
+        ],
+        dir: "models/unet"
+      }
+    },
+    {
+      method: "fs.link",
+      params: {
+        venv: "env"
+      }
+    }
+  ]
+}

pinokio.js

@@ -0,0 +1,95 @@
+const path = require('path')
+module.exports = {
+  version: "2.1",
+  title: "fluxgym",
+  description: "[NVIDIA Only] Dead simple web UI for training FLUX LoRA with LOW VRAM support (From 12GB)",
+  icon: "icon.png",
+  menu: async (kernel, info) => {
+    let installed = info.exists("env")
+    let running = {
+      install: info.running("install.js"),
+      start: info.running("start.js"),
+      update: info.running("update.js"),
+      reset: info.running("reset.js")
+    }
+    if (running.install) {
+      return [{
+        default: true,
+        icon: "fa-solid fa-plug",
+        text: "Installing",
+        href: "install.js",
+      }]
+    } else if (installed) {
+      if (running.start) {
+        let local = info.local("start.js")
+        if (local && local.url) {
+          return [{
+            default: true,
+            icon: "fa-solid fa-rocket",
+            text: "Open Web UI",
+            href: local.url,
+          }, {
+            icon: 'fa-solid fa-terminal',
+            text: "Terminal",
+            href: "start.js",
+          }, {
+            icon: "fa-solid fa-flask",
+            text: "Outputs",
+            href: "outputs?fs"
+          }]
+        } else {
+          return [{
+            default: true,
+            icon: 'fa-solid fa-terminal',
+            text: "Terminal",
+            href: "start.js",
+          }]
+        }
+      } else if (running.update) {
+        return [{
+          default: true,
+          icon: 'fa-solid fa-terminal',
+          text: "Updating",
+          href: "update.js",
+        }]
+      } else if (running.reset) {
+        return [{
+          default: true,
+          icon: 'fa-solid fa-terminal',
+          text: "Resetting",
+          href: "reset.js",
+        }]
+      } else {
+        return [{
+          default: true,
+          icon: "fa-solid fa-power-off",
+          text: "Start",
+          href: "start.js",
+        }, {
+          icon: "fa-solid fa-flask",
+          text: "Outputs",
+          href: "sd-scripts/fluxgym/outputs?fs"
+        }, {
+          icon: "fa-solid fa-plug",
+          text: "Update",
+          href: "update.js",
+        }, {
+          icon: "fa-solid fa-plug",
+          text: "Install",
+          href: "install.js",
+        }, {
+          icon: "fa-regular fa-circle-xmark",
+          text: "Reset",
+          href: "reset.js",
+        }]
+      }
+    } else {
+      return [{
+        default: true,
+        icon: "fa-solid fa-plug",
+        text: "Install",
+        href: "install.js",
+      }]
+    }
+  }
+}

pinokio_meta.json

@@ -0,0 +1,38 @@
+{
+  "posts": [
+    "https://x.com/cocktailpeanut/status/1835719701172756592",
+    "https://x.com/LikeToasters/status/1834258975384092858",
+    "https://x.com/cocktailpeanut/status/1834245329627009295",
+    "https://x.com/jkch0205/status/1834003420132614450",
+    "https://x.com/huwhitememes/status/1834074992209699132",
+    "https://x.com/GorillaRogueGam/status/1834148656791888139",
+    "https://x.com/cocktailpeanut/status/1833964839519068303",
+    "https://x.com/cocktailpeanut/status/1833935061907079521",
+    "https://x.com/cocktailpeanut/status/1833940728881242135",
+    "https://x.com/cocktailpeanut/status/1833881392482066638",
+    "https://x.com/Alone1Moon/status/1833348850662445369",
+    "https://x.com/_f_ai_9/status/1833485349995397167",
+    "https://x.com/intocryptoast/status/1833061082862412186",
+    "https://x.com/cocktailpeanut/status/1833888423716827321",
+    "https://x.com/cocktailpeanut/status/1833884852992516596",
+    "https://x.com/cocktailpeanut/status/1833885335077417046",
+    "https://x.com/NiwonArt/status/1833565746624139650",
+    "https://x.com/cocktailpeanut/status/1833884361986380117",
+    "https://x.com/NiwonArt/status/1833599399764889685",
+    "https://x.com/LikeToasters/status/1832934391217045913",
+    "https://x.com/cocktailpeanut/status/1832924887456817415",
+    "https://x.com/cocktailpeanut/status/1832927154536902897",
+    "https://x.com/YabaiHamster/status/1832697724690386992",
+    "https://x.com/cocktailpeanut/status/1832747889497366706",
+    "https://x.com/PhotogenicWeekE/status/1832720544959185202",
+    "https://x.com/zuzaritt/status/1832748542164652390",
+    "https://x.com/foxyy4i/status/1832764883710185880",
+    "https://x.com/waynedahlberg/status/1832226132999213095",
+    "https://x.com/PhotoGarrido/status/1832214644515041770",
+    "https://x.com/cocktailpeanut/status/1832787205774786710",
+    "https://x.com/cocktailpeanut/status/1832151307198541961",
+    "https://x.com/cocktailpeanut/status/1832145996014612735",
+    "https://x.com/cocktailpeanut/status/1832084951115972653",
+    "https://x.com/cocktailpeanut/status/1832091112086843684"
+  ]
+}

requirements.txt

@@ -0,0 +1,34 @@
+safetensors
+git+https://github.com/huggingface/diffusers.git
+gradio_logsview@https://huggingface.co/spaces/cocktailpeanut/gradio_logsview/resolve/main/gradio_logsview-0.0.17-py3-none-any.whl
+transformers
+lycoris-lora==1.8.3
+flatten_json
+pyyaml
+oyaml
+tensorboard
+kornia
+invisible-watermark
+einops
+accelerate
+toml
+albumentations
+pydantic
+omegaconf
+k-diffusion
+open_clip_torch
+timm
+prodigyopt
+controlnet_aux==0.0.7
+python-dotenv
+bitsandbytes
+hf_transfer
+lpips
+pytorch_fid
+optimum-quanto
+sentencepiece
+huggingface_hub
+peft
+gradio
+python-slugify
+imagesize

reset.js

@@ -0,0 +1,13 @@
+module.exports = {
+  run: [{
+    method: "fs.rm",
+    params: {
+      path: "sd-scripts"
+    }
+  }, {
+    method: "fs.rm",
+    params: {
+      path: "env"
+    }
+  }]
+}

start.js

@@ -0,0 +1,34 @@
+module.exports = {
+  daemon: true,
+  run: [
+    {
+      method: "shell.run",
+      params: {
+        venv: "env",                // Edit this to customize the venv folder path
+        env: { },                   // Edit this to customize environment variables (see documentation)
+        message: [
+          "python app.py",    // Edit with your custom commands
+        ],
+        on: [{
+          // The regular expression pattern to monitor.
+          // When this pattern occurs in the shell terminal, the shell will return,
+          // and the script will go onto the next step.
+          "event": "/http:\/\/\\S+/",   
+
+          // "done": true will move to the next step while keeping the shell alive.
+          // "kill": true will move to the next step after killing the shell.
+          "done": true
+        }]
+      }
+    },
+    {
+      // This step sets the local variable 'url'.
+      // This local variable will be used in pinokio.js to display the "Open WebUI" tab when the value is set.
+      method: "local.set",
+      params: {
+        // the input.event is the regular expression match object from the previous step
+        url: "{{input.event[0]}}"
+      }
+    }
+  ]
+}

torch.js

@@ -0,0 +1,75 @@
+module.exports = {
+  run: [
+    // windows nvidia
+    {
+      "when": "{{platform === 'win32' && gpu === 'nvidia'}}",
+      "method": "shell.run",
+      "params": {
+        "venv": "{{args && args.venv ? args.venv : null}}",
+        "path": "{{args && args.path ? args.path : '.'}}",
+        "message": "pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu121"
+
+      }
+    },
+    // windows amd
+    {
+      "when": "{{platform === 'win32' && gpu === 'amd'}}",
+      "method": "shell.run",
+      "params": {
+        "venv": "{{args && args.venv ? args.venv : null}}",
+        "path": "{{args && args.path ? args.path : '.'}}",
+        "message": "pip install torch-directml torchaudio torchvision"
+      }
+    },
+    // windows cpu
+    {
+      "when": "{{platform === 'win32' && (gpu !== 'nvidia' && gpu !== 'amd')}}",
+      "method": "shell.run",
+      "params": {
+        "venv": "{{args && args.venv ? args.venv : null}}",
+        "path": "{{args && args.path ? args.path : '.'}}",
+        "message": "pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cpu"
+      }
+    },
+    // mac
+    {
+      "when": "{{platform === 'darwin'}}",
+      "method": "shell.run",
+      "params": {
+        "venv": "{{args && args.venv ? args.venv : null}}",
+        "path": "{{args && args.path ? args.path : '.'}}",
+        "message": "pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cpu"
+      }
+    },
+    // linux nvidia
+    {
+      "when": "{{platform === 'linux' && gpu === 'nvidia'}}",
+      "method": "shell.run",
+      "params": {
+        "venv": "{{args && args.venv ? args.venv : null}}",
+        "path": "{{args && args.path ? args.path : '.'}}",
+        "message": "pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu121"
+      }
+    },
+    // linux rocm (amd)
+    {
+      "when": "{{platform === 'linux' && gpu === 'amd'}}",
+      "method": "shell.run",
+      "params": {
+        "venv": "{{args && args.venv ? args.venv : null}}",
+        "path": "{{args && args.path ? args.path : '.'}}",
+        "message": "pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm6.1"
+      }
+    },
+    // linux cpu
+    {
+      "when": "{{platform === 'linux' && (gpu !== 'amd' && gpu !=='nvidia')}}",
+      "method": "shell.run",
+      "params": {
+        "venv": "{{args && args.venv ? args.venv : null}}",
+        "path": "{{args && args.path ? args.path : '.'}}",
+        "message": "pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cpu"
+      }
+    }
+  ]
+}

update.js

@@ -0,0 +1,46 @@
+module.exports = {
+  run: [{
+    method: "shell.run",
+    params: {
+      message: "git pull"
+    }
+  }, {
+    method: "shell.run",
+    params: {
+      path: "sd-scripts",
+      message: "git pull"
+    }
+  }, {
+    method: "shell.run",
+    params: {
+      path: "sd-scripts",
+      venv: "../env",
+      message: [
+        "pip install -r requirements.txt",
+      ]
+    }
+  }, {
+    method: "shell.run",
+    params: {
+      venv: "env",
+      message: [
+        "pip uninstall -y diffusers[torch] torch torchaudio torchvision",
+        "pip install -r requirements.txt",
+      ]
+    }
+  }, {
+    method: "script.start",
+    params: {
+      uri: "torch.js",
+      params: {
+        venv: "env",
+        // xformers: true   // uncomment this line if your project requires xformers
+      }
+    }
+  }, {
+    method: "fs.link",
+    params: {
+      venv: "env"
+    }
+  }]
+}