Update app.py

app.py

@@ -6,225 +6,9 @@ import requests
 import subprocess
 from subprocess import getoutput
 from huggingface_hub import login, HfFileSystem, snapshot_download, HfApi, create_repo
-#api = HfApi()
 
-#hf_token = os.environ.get("HF_TOKEN_WITH_WRITE_PERMISSION")
-
-#is_shared_ui = True if "fffiloni/train-dreambooth-lora-sdxl" in os.environ['SPACE_ID'] else False
-
-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}"
+from app_train import create_training_demo
+from sdxl.app_inference import create_inference_demo
 
 css="""
 #col-container {max-width: 780px; margin-left: auto; margin-right: auto;}
@@ -295,99 +79,16 @@ min-height: 60px;
 """
 
 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")
+    gr.Markdown("SUTD x SUNS Shop Design Generator")
+    with gr.Tab("Training"):
+         create_training_demo()
+    with gr.Tab("Generation"):
+        create_inference_demo()
+    with gr.Tab("Visualisation"):
+        gr.Markdown('''
+            - You can use this tab to upload models later if you choose not to upload models in training time or if upload in training time failed.
+            ''')
 
-    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]
-    )
 
-demo.launch(debug=True, share=True)
+demo.queue(max_size=1).launch(debug=True, share=True)