added app_train.py

app_train.py

@@ -0,0 +1,389 @@
+import gradio as gr
+import torch
+import os
+import shutil
+import requests
+import subprocess
+from subprocess import getoutput
+from huggingface_hub import login, HfFileSystem, snapshot_download, HfApi, create_repo
+
+is_gpu_associated = torch.cuda.is_available()
+
+is_shared_ui = False
+
+hf_token = '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'
+
+fs = HfFileSystem(token=hf_token)
+api = HfApi()
+
+if is_gpu_associated:
+    gpu_info = getoutput('nvidia-smi')
+    if("A10G" in gpu_info):
+        which_gpu = "A10G"
+    elif("T4" in gpu_info):
+        which_gpu = "T4"
+    else:
+        which_gpu = "CPU"
+
+def check_upload_or_no(value):
+    if value is True:
+        return gr.update(visible=True)
+    else:
+        return gr.update(visible=False)
+
+def load_images_to_dataset(images, dataset_name):
+
+    if is_shared_ui:
+        raise gr.Error("This Space only works in duplicated instances")
+
+    if dataset_name == "":
+        raise gr.Error("You forgot to name your new dataset. ")
+
+    # Create the directory if it doesn't exist
+    my_working_directory = f"my_working_directory_for_{dataset_name}"
+    if not os.path.exists(my_working_directory):
+        os.makedirs(my_working_directory)
+
+    # Assuming 'images' is a list of image file paths
+    for idx, image in enumerate(images):
+        # Get the base file name (without path) from the original location
+        image_name = os.path.basename(image.name)
+    
+        # Construct the destination path in the working directory
+        destination_path = os.path.join(my_working_directory, image_name)
+    
+        # Copy the image from the original location to the working directory
+        shutil.copy(image.name, destination_path)
+    
+        # Print the image name and its corresponding save path
+        print(f"Image {idx + 1}: {image_name} copied to {destination_path}")
+   
+    path_to_folder = my_working_directory
+    your_username = api.whoami(token=hf_token)["name"]
+    repo_id = f"{your_username}/{dataset_name}"
+    create_repo(repo_id=repo_id, repo_type="dataset", token=hf_token)
+    
+    api.upload_folder(
+        folder_path=path_to_folder,
+        repo_id=repo_id,
+        repo_type="dataset",
+        token=hf_token
+    )
+
+    return "Done, your dataset is ready and loaded for the training step!", repo_id
+
+def swap_hardware(hf_token, hardware="cpu-basic"):
+    hardware_url = f"https://huggingface.co/spaces/ClaireOzzz/train-dreambooth-lora-sdxl/hardware"
+    headers = { "authorization" : f"Bearer {hf_token}"}
+    body = {'flavor': hardware}
+    requests.post(hardware_url, json = body, headers=headers)
+
+def swap_sleep_time(hf_token,sleep_time):
+    sleep_time_url = f"https://huggingface.co/api/spaces/ClaireOzzz/train-dreambooth-lora-sdxl/sleeptime"
+    headers = { "authorization" : f"Bearer {hf_token}"}
+    body = {'seconds':sleep_time}
+    requests.post(sleep_time_url,json=body,headers=headers)
+
+def get_sleep_time(hf_token):
+    sleep_time_url = f"https://huggingface.co/api/spaces/ClaireOzzz/train-dreambooth-lora-sdxl"
+    headers = { "authorization" : f"Bearer {hf_token}"}
+    response = requests.get(sleep_time_url,headers=headers)
+    try:
+        gcTimeout = response.json()['runtime']['gcTimeout']
+    except:
+        gcTimeout = None
+    return gcTimeout
+
+def write_to_community(title, description,hf_token): 
+    
+    api.create_discussion(repo_id=os.environ['ClaireOzzz/train-dreambooth-lora-sdxl'], title=title, description=description,repo_type="space", token=hf_token)
+
+
+def set_accelerate_default_config():
+    try:
+        subprocess.run(["accelerate", "config", "default"], check=True)
+        print("Accelerate default config set successfully!")
+    except subprocess.CalledProcessError as e:
+        print(f"An error occurred: {e}")
+
+def train_dreambooth_lora_sdxl(dataset_id, instance_data_dir, lora_trained_xl_folder, instance_prompt, max_train_steps, checkpoint_steps, remove_gpu):
+    
+    script_filename = "train_dreambooth_lora_sdxl.py"  # Assuming it's in the same folder
+
+    command = [
+        "accelerate",
+        "launch",
+        script_filename,  # Use the local script
+        "--pretrained_model_name_or_path=stabilityai/stable-diffusion-xl-base-1.0",
+        "--pretrained_vae_model_name_or_path=madebyollin/sdxl-vae-fp16-fix",
+        f"--dataset_id={dataset_id}",
+        f"--instance_data_dir={instance_data_dir}",
+        f"--output_dir={lora_trained_xl_folder}",
+        "--mixed_precision=fp16",
+        f"--instance_prompt={instance_prompt}",
+        "--resolution=1024",
+        "--train_batch_size=2",
+        "--gradient_accumulation_steps=2",
+        "--gradient_checkpointing",
+        "--learning_rate=1e-4",
+        "--lr_scheduler=constant",
+        "--lr_warmup_steps=0",
+        "--enable_xformers_memory_efficient_attention",
+        "--mixed_precision=fp16",
+        "--use_8bit_adam",
+        f"--max_train_steps={max_train_steps}",
+        f"--checkpointing_steps={checkpoint_steps}",
+        "--seed=0",
+        "--push_to_hub",
+        f"--hub_token={hf_token}"
+    ]
+
+    try:
+        subprocess.run(command, check=True)
+        print("Training is finished!")
+        if remove_gpu:
+            swap_hardware(hf_token, "cpu-basic")
+        else:
+            swap_sleep_time(hf_token, 300)
+    except subprocess.CalledProcessError as e:
+        print(f"An error occurred: {e}")
+        
+        title="There was an error on during your training"
+        description=f'''
+        Unfortunately there was an error during training your {lora_trained_xl_folder} model. 
+        Please check it out below. Feel free to report this issue to [SD-XL Dreambooth LoRa Training](https://huggingface.co/spaces/fffiloni/train-dreambooth-lora-sdxl): 
+        ```
+        {str(e)}
+        ```
+        '''
+        if remove_gpu:
+            swap_hardware(hf_token, "cpu-basic")
+        else:
+            swap_sleep_time(hf_token, 300)
+        #write_to_community(title,description,hf_token)
+
+def main(dataset_id, 
+         lora_trained_xl_folder,
+         instance_prompt,
+         max_train_steps,
+         checkpoint_steps,
+         remove_gpu):
+
+    
+    if is_shared_ui:
+        raise gr.Error("This Space only works in duplicated instances")
+
+    if not is_gpu_associated:
+        raise gr.Error("Please associate a T4 or A10G GPU for this Space")
+
+    if dataset_id == "":
+        raise gr.Error("You forgot to specify an image dataset")
+
+    if instance_prompt == "":
+        raise gr.Error("You forgot to specify a concept prompt")
+
+    if lora_trained_xl_folder == "":
+        raise gr.Error("You forgot to name the output folder for your model")
+
+    sleep_time = get_sleep_time(hf_token)
+    if sleep_time:
+        swap_sleep_time(hf_token, -1)
+
+    gr.Warning("If you did not check the `Remove GPU After training`, don't forget to remove the GPU attribution after you are done. ")
+        
+    dataset_repo = dataset_id
+
+    # Automatically set local_dir based on the last part of dataset_repo
+    repo_parts = dataset_repo.split("/")
+    local_dir = f"./{repo_parts[-1]}"  # Use the last part of the split
+
+    # Check if the directory exists and create it if necessary
+    if not os.path.exists(local_dir):
+        os.makedirs(local_dir)
+
+    gr.Info("Downloading dataset ...")
+    
+    snapshot_download(
+        dataset_repo,
+        local_dir=local_dir,
+        repo_type="dataset",
+        ignore_patterns=".gitattributes",
+        token=hf_token
+    )
+
+    set_accelerate_default_config()
+
+    gr.Info("Training begins ...")
+
+    instance_data_dir = repo_parts[-1]
+    train_dreambooth_lora_sdxl(dataset_id, instance_data_dir, lora_trained_xl_folder, instance_prompt, max_train_steps, checkpoint_steps, remove_gpu)
+    
+    your_username = api.whoami(token=hf_token)["name"]
+    return f"Done, your trained model has been stored in your models library: {your_username}/{lora_trained_xl_folder}"
+
+css="""
+#col-container {max-width: 780px; margin-left: auto; margin-right: auto;}
+#upl-dataset-group {background-color: none!important;}
+
+div#warning-ready {
+    background-color: #ecfdf5;
+    padding: 0 10px 5px;
+    margin: 20px 0;
+}
+div#warning-ready > .gr-prose > h2, div#warning-ready > .gr-prose > p {
+    color: #057857!important;
+}
+
+div#warning-duplicate {
+    background-color: #ebf5ff;
+    padding: 0 10px 5px;
+    margin: 20px 0;
+}
+
+div#warning-duplicate > .gr-prose > h2, div#warning-duplicate > .gr-prose > p {
+    color: #0f4592!important;
+}
+
+div#warning-duplicate strong {
+    color: #0f4592;
+}
+
+p.actions {
+    display: flex;
+    align-items: center;
+    margin: 20px 0;
+}
+
+div#warning-duplicate .actions a {
+    display: inline-block;
+    margin-right: 10px;
+}
+
+div#warning-setgpu {
+    background-color: #fff4eb;
+    padding: 0 10px 5px;
+    margin: 20px 0;
+}
+
+div#warning-setgpu > .gr-prose > h2, div#warning-setgpu > .gr-prose > p {
+    color: #92220f!important;
+}
+
+div#warning-setgpu a, div#warning-setgpu b {
+    color: #91230f;
+}
+
+div#warning-setgpu p.actions > a {
+    display: inline-block;
+    background: #1f1f23;
+    border-radius: 40px;
+    padding: 6px 24px;
+    color: antiquewhite;
+    text-decoration: none;
+    font-weight: 600;
+    font-size: 1.2em;
+}
+
+button#load-dataset-btn{
+min-height: 60px;
+}
+"""
+def create_training_demo() -> gr.Blocks:
+  with gr.Blocks(css=css) as demo:
+    with gr.Column(elem_id="col-container"):
+        if is_shared_ui:
+            top_description = gr.HTML(f'''
+                <div class="gr-prose">
+                    <h2><svg xmlns="http://www.w3.org/2000/svg" width="18px" height="18px" style="margin-right: 0px;display: inline-block;"fill="none"><path fill="#fff" d="M7 13.2a6.3 6.3 0 0 0 4.4-10.7A6.3 6.3 0 0 0 .6 6.9 6.3 6.3 0 0 0 7 13.2Z"/><path fill="#fff" fill-rule="evenodd" d="M7 0a6.9 6.9 0 0 1 4.8 11.8A6.9 6.9 0 0 1 0 7 6.9 6.9 0 0 1 7 0Zm0 0v.7V0ZM0 7h.6H0Zm7 6.8v-.6.6ZM13.7 7h-.6.6ZM9.1 1.7c-.7-.3-1.4-.4-2.2-.4a5.6 5.6 0 0 0-4 1.6 5.6 5.6 0 0 0-1.6 4 5.6 5.6 0 0 0 1.6 4 5.6 5.6 0 0 0 4 1.7 5.6 5.6 0 0 0 4-1.7 5.6 5.6 0 0 0 1.7-4 5.6 5.6 0 0 0-1.7-4c-.5-.5-1.1-.9-1.8-1.2Z" clip-rule="evenodd"/><path fill="#000" fill-rule="evenodd" d="M7 2.9a.8.8 0 1 1 0 1.5A.8.8 0 0 1 7 3ZM5.8 5.7c0-.4.3-.6.6-.6h.7c.3 0 .6.2.6.6v3.7h.5a.6.6 0 0 1 0 1.3H6a.6.6 0 0 1 0-1.3h.4v-3a.6.6 0 0 1-.6-.7Z" clip-rule="evenodd"/></svg>
+                    Attention: this Space need to be duplicated to work</h2>
+                    <p class="main-message">
+                        To make it work, <strong>duplicate the Space</strong> and run it on your own profile using a <strong>private</strong> GPU (T4-small or A10G-small).<br />
+                        A T4 costs <strong>US$0.60/h</strong>, so it should cost < US$1 to train most models.
+                    </p>
+                    <p class="actions">
+                       
+                        to start training your own image model
+                    </p>
+                </div>
+            ''', elem_id="warning-duplicate")
+        # else:
+        #     if(is_gpu_associated):
+        #         top_description = gr.HTML(f'''
+        #                 <div class="gr-prose">
+        #                     <h2><svg xmlns="http://www.w3.org/2000/svg" width="18px" height="18px" style="margin-right: 0px;display: inline-block;"fill="none"><path fill="#fff" d="M7 13.2a6.3 6.3 0 0 0 4.4-10.7A6.3 6.3 0 0 0 .6 6.9 6.3 6.3 0 0 0 7 13.2Z"/><path fill="#fff" fill-rule="evenodd" d="M7 0a6.9 6.9 0 0 1 4.8 11.8A6.9 6.9 0 0 1 0 7 6.9 6.9 0 0 1 7 0Zm0 0v.7V0ZM0 7h.6H0Zm7 6.8v-.6.6ZM13.7 7h-.6.6ZM9.1 1.7c-.7-.3-1.4-.4-2.2-.4a5.6 5.6 0 0 0-4 1.6 5.6 5.6 0 0 0-1.6 4 5.6 5.6 0 0 0 1.6 4 5.6 5.6 0 0 0 4 1.7 5.6 5.6 0 0 0 4-1.7 5.6 5.6 0 0 0 1.7-4 5.6 5.6 0 0 0-1.7-4c-.5-.5-1.1-.9-1.8-1.2Z" clip-rule="evenodd"/><path fill="#000" fill-rule="evenodd" d="M7 2.9a.8.8 0 1 1 0 1.5A.8.8 0 0 1 7 3ZM5.8 5.7c0-.4.3-.6.6-.6h.7c.3 0 .6.2.6.6v3.7h.5a.6.6 0 0 1 0 1.3H6a.6.6 0 0 1 0-1.3h.4v-3a.6.6 0 0 1-.6-.7Z" clip-rule="evenodd"/></svg>
+        #                     You have successfully associated a {which_gpu} GPU to the SD-XL Training Space 🎉</h2>
+        #                     <p>
+        #                         You can now train your model! You will be billed by the minute from when you activated the GPU until when it is turned off.
+        #                     </p> 
+        #                 </div>
+        #         ''', elem_id="warning-ready")
+        #     else:
+        #         top_description = gr.HTML(f'''
+        #                 <div class="gr-prose">
+        #                 <h2><svg xmlns="http://www.w3.org/2000/svg" width="18px" height="18px" style="margin-right: 0px;display: inline-block;"fill="none"><path fill="#fff" d="M7 13.2a6.3 6.3 0 0 0 4.4-10.7A6.3 6.3 0 0 0 .6 6.9 6.3 6.3 0 0 0 7 13.2Z"/><path fill="#fff" fill-rule="evenodd" d="M7 0a6.9 6.9 0 0 1 4.8 11.8A6.9 6.9 0 0 1 0 7 6.9 6.9 0 0 1 7 0Zm0 0v.7V0ZM0 7h.6H0Zm7 6.8v-.6.6ZM13.7 7h-.6.6ZM9.1 1.7c-.7-.3-1.4-.4-2.2-.4a5.6 5.6 0 0 0-4 1.6 5.6 5.6 0 0 0-1.6 4 5.6 5.6 0 0 0 1.6 4 5.6 5.6 0 0 0 4 1.7 5.6 5.6 0 0 0 4-1.7 5.6 5.6 0 0 0 1.7-4 5.6 5.6 0 0 0-1.7-4c-.5-.5-1.1-.9-1.8-1.2Z" clip-rule="evenodd"/><path fill="#000" fill-rule="evenodd" d="M7 2.9a.8.8 0 1 1 0 1.5A.8.8 0 0 1 7 3ZM5.8 5.7c0-.4.3-.6.6-.6h.7c.3 0 .6.2.6.6v3.7h.5a.6.6 0 0 1 0 1.3H6a.6.6 0 0 1 0-1.3h.4v-3a.6.6 0 0 1-.6-.7Z" clip-rule="evenodd"/></svg>
+        #                 You have successfully duplicated the SD-XL Training Space 🎉</h2>
+        #                 <p>There's only one step left before you can train your model: <a href="https://huggingface.co/spaces/{os.environ['SPACE_ID']}/settings" style="text-decoration: underline" target="_blank">attribute a <b>T4-small or A10G-small GPU</b> to it (via the Settings tab)</a> and run the training below.
+        #                 You will be billed by the minute from when you activate the GPU until when it is turned off.</p> 
+        #                 <p class="actions">
+        #                     <a href="https://huggingface.co/spaces/ClaireOzzz/train-dreambooth-lora-sdxl/settings">🔥 &nbsp; Set recommended GPU</a>
+        #                 </p>
+        #                 </div>
+        #         ''', elem_id="warning-setgpu")
+        
+        gr.Markdown("# SD-XL Dreambooth LoRa Training UI 💭")
+        
+        upload_my_images = gr.Checkbox(label="Drop your training images ? (optional)", value=False)
+        gr.Markdown("Use this step to upload your training images and create a new dataset. If you already have a dataset stored on your HF profile, you can skip this step, and provide your dataset ID in the training `Datased ID` input below.")
+         
+        with gr.Group(visible=False, elem_id="upl-dataset-group") as upload_group:
+            with gr.Row():
+                images = gr.File(file_types=["image"], label="Upload your images", file_count="multiple", interactive=True, visible=True)
+                with gr.Column():
+                    new_dataset_name = gr.Textbox(label="Set new dataset name", placeholder="e.g.: my_awesome_dataset")
+                    dataset_status = gr.Textbox(label="dataset status")
+                    load_btn = gr.Button("Load images to new dataset", elem_id="load-dataset-btn")
+        
+        gr.Markdown("## Training ")
+        gr.Markdown("You can use an existing image dataset, find a dataset example here: [https://huggingface.co/datasets/diffusers/dog-example](https://huggingface.co/datasets/diffusers/dog-example) ;)")
+        
+        with gr.Row():
+            dataset_id = gr.Textbox(label="Dataset ID", info="use one of your previously uploaded image datasets on your HF profile", placeholder="diffusers/dog-example")
+            instance_prompt = gr.Textbox(label="Concept prompt", info="concept prompt - use a unique, made up word to avoid collisions")
+        
+        with gr.Row():
+            model_output_folder = gr.Textbox(label="Output model folder name", placeholder="lora-trained-xl-folder")
+            max_train_steps = gr.Number(label="Max Training Steps", value=500, precision=0, step=10)
+            checkpoint_steps = gr.Number(label="Checkpoints Steps", value=100, precision=0, step=10)
+
+        remove_gpu = gr.Checkbox(label="Remove GPU After Training", value=True, info="If NOT enabled, don't forget to remove the GPU attribution after you are done.")
+        train_button = gr.Button("Train !")
+
+        train_status = gr.Textbox(label="Training status")
+
+    upload_my_images.change(
+        fn = check_upload_or_no,
+        inputs =[upload_my_images],
+        outputs = [upload_group]        
+    )
+    
+    load_btn.click(
+        fn = load_images_to_dataset,
+        inputs = [images, new_dataset_name],
+        outputs = [dataset_status, dataset_id]
+    )
+    
+    train_button.click(
+        fn = main,
+        inputs = [
+            dataset_id,
+            model_output_folder,
+            instance_prompt,
+            max_train_steps,
+            checkpoint_steps,
+            remove_gpu
+        ],
+        outputs = [train_status]
+    )
+  return demo
+
+#demo.launch(debug=True, share=True)