Update latihsekarang.py

latihsekarang.py

@@ -1,24 +1,20 @@
 import os
-from huggingface_hub import whoami    
-os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
-import sys
-
-
-sys.path.insert(0, os.getcwd())
-
-import gradio as gr
-from PIL import Image
-import torch
 import uuid
-import os
 import shutil
 import json
 import yaml
+import torch
+from PIL import Image
 from slugify import slugify
 from transformers import AutoProcessor, AutoModelForCausalLM
+import pinggy as pg
 
+# Ensure the current working directory is in sys.path
+import sys
+sys.path.insert(0, os.getcwd())
 sys.path.insert(0, "ai-toolkit")
 from toolkit.job import get_job
+from huggingface_hub import whoami
 
 MAX_IMAGES = 150
 
@@ -27,77 +23,57 @@ def load_captioning(uploaded_files, concept_sentence):
     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)"
-        )
+        raise pg.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
+        raise pg.Error(f"For now, only {MAX_IMAGES} or less images are allowed for training")
+
+    updates.append(pg.Update(visible=True))
     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))
+        updates.append(pg.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))
-        
+        updates.append(pg.Update(value=image_value, visible=visible))
+
         corresponding_caption = False
-        if(image_value):
+        if image_value:
             base_name = os.path.splitext(os.path.basename(image_value))[0]
-            print(base_name)
-            print(image_value)
             if base_name in txt_files_dict:
-                print("entrou")
                 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 "[trigger]" if visible and concept_sentence else None
-        updates.append(gr.update(value=text_value, visible=visible))
-
-    # Update for the sample caption area
-    updates.append(gr.update(visible=True))
-    # Update prompt samples
-    updates.append(gr.update(placeholder=f'A portrait of person in a bustling cafe {concept_sentence}', value=f'A person in a bustling cafe {concept_sentence}'))
-    updates.append(gr.update(placeholder=f"A mountainous landscape in the style of {concept_sentence}"))
-    updates.append(gr.update(placeholder=f"A {concept_sentence} in a mall"))
-    updates.append(gr.update(visible=True))
+        updates.append(pg.Update(value=text_value, visible=visible))
+
+    updates.append(pg.Update(visible=True))
+    updates.append(pg.Update(placeholder=f'A portrait of person in a bustling cafe {concept_sentence}', value=f'A person in a bustling cafe {concept_sentence}'))
+    updates.append(pg.Update(placeholder=f"A mountainous landscape in the style of {concept_sentence}"))
+    updates.append(pg.Update(placeholder=f"A {concept_sentence} in a mall"))
+    updates.append(pg.Update(visible=True))
+
     return updates
 
 def hide_captioning():
-    return gr.update(visible=False), gr.update(visible=False), gr.update(visible=False) 
+    return pg.Update(visible=False), pg.Update(visible=False), pg.Update(visible=False) 
 
-def create_dataset(*inputs):
-    print("Creating dataset")
-    images = inputs[0]
-    destination_folder = str(f"datasets/{uuid.uuid4()}")
-    if not os.path.exists(destination_folder):
-        os.makedirs(destination_folder)
+def create_dataset(images, *captions):
+    destination_folder = f"datasets/{uuid.uuid4()}"
+    os.makedirs(destination_folder, exist_ok=True)
 
     jsonl_file_path = os.path.join(destination_folder, "metadata.jsonl")
     with open(jsonl_file_path, "a") as jsonl_file:
         for index, image in enumerate(images):
             new_image_path = shutil.copy(image, destination_folder)
-
-            original_caption = inputs[index + 1]
+            original_caption = captions[index]
             file_name = os.path.basename(new_image_path)
-
             data = {"file_name": file_name, "prompt": original_caption}
-
             jsonl_file.write(json.dumps(data) + "\n")
 
     return destination_folder
 
-
 def run_captioning(images, concept_sentence, *captions):
-    #Load internally to not consume resources for training
     device = "cuda" if torch.cuda.is_available() else "cpu"
     torch_dtype = torch.float16
     model = AutoModelForCausalLM.from_pretrained(
@@ -107,8 +83,7 @@ def run_captioning(images, concept_sentence, *captions):
 
     captions = list(captions)
     for i, image_path in enumerate(images):
-        print(captions[i])
-        if isinstance(image_path, str):  # If image is a file path
+        if isinstance(image_path, str):
             image = Image.open(image_path).convert("RGB")
 
         prompt = "<DETAILED_CAPTION>"
@@ -157,25 +132,22 @@ def start_training(
 ):
     push_to_hub = True
     if not lora_name:
-        raise gr.Error("You forgot to insert your LoRA name! This name has to be unique.")
+        raise pg.Error("You forgot to insert your LoRA name! This name has to be unique.")
     try:
         if whoami()["auth"]["accessToken"]["role"] == "write" or "repo.write" in whoami()["auth"]["accessToken"]["fineGrained"]["scoped"][0]["permissions"]:
-            gr.Info(f"Starting training locally {whoami()['name']}. Your LoRA will be available locally and in Hugging Face after it finishes.")
+            pg.Info(f"Starting training locally {whoami()['name']}. Your LoRA will be available locally and in Hugging Face after it finishes.")
         else:
             push_to_hub = False
-            gr.Warning("Started training locally. Your LoRa will only be available locally because you didn't login with a `write` token to Hugging Face")
+            pg.Warning("Started training locally. Your LoRa will only be available locally because you didn't login with a `write` token to Hugging Face")
     except:
         push_to_hub = False
-        gr.Warning("Started training locally. Your LoRa will only be available locally because you didn't login with a `write` token to Hugging Face")
-            
-    print("Started training")
+        pg.Warning("Started training locally. Your LoRa will only be available locally because you didn't login with a `write` token to Hugging Face")
+
     slugged_lora_name = slugify(lora_name)
 
-    # Load the default config
     with open("config/examples/train_lora_flux_24gb.yaml", "r") as f:
         config = yaml.safe_load(f)
 
-    # Update the config with user inputs
     config["config"]["name"] = slugged_lora_name
     config["config"]["process"][0]["model"]["low_vram"] = low_vram
     config["config"]["process"][0]["train"]["skip_first_sample"] = True
@@ -185,16 +157,16 @@ def start_training(
     config["config"]["process"][0]["network"]["linear_alpha"] = int(rank)
     config["config"]["process"][0]["datasets"][0]["folder_path"] = dataset_folder
     config["config"]["process"][0]["save"]["push_to_hub"] = push_to_hub
-    if(push_to_hub):
+    if push_to_hub:
         try:
             username = whoami()["name"]
         except:
-            raise gr.Error("Error trying to retrieve your username. Are you sure you are logged in with Hugging Face?")
+            raise pg.Error("Error trying to retrieve your username. Are you sure you are logged in with Hugging Face?")
         config["config"]["process"][0]["save"]["hf_repo_id"] = f"{username}/{slugged_lora_name}"
         config["config"]["process"][0]["save"]["hf_private"] = True
     if concept_sentence:
         config["config"]["process"][0]["trigger_word"] = concept_sentence
-    
+
     if sample_1 or sample_2 or sample_3:
         config["config"]["process"][0]["train"]["disable_sampling"] = False
         config["config"]["process"][0]["sample"]["sample_every"] = steps
@@ -208,24 +180,20 @@ def start_training(
             config["config"]["process"][0]["sample"]["prompts"].append(sample_3)
     else:
         config["config"]["process"][0]["train"]["disable_sampling"] = True
-    if(model_to_train == "schnell"):
+    if model_to_train == "schnell":
         config["config"]["process"][0]["model"]["name_or_path"] = "black-forest-labs/FLUX.1-schnell"
         config["config"]["process"][0]["model"]["assistant_lora_path"] = "ostris/FLUX.1-schnell-training-adapter"
         config["config"]["process"][0]["sample"]["sample_steps"] = 4
-    if(use_more_advanced_options):
+    if use_more_advanced_options:
         more_advanced_options_dict = yaml.safe_load(more_advanced_options)
         config["config"]["process"][0] = recursive_update(config["config"]["process"][0], more_advanced_options_dict)
-        print(config)
-    
-    # Save the updated config
-    # generate a random name for the config
+
     random_config_name = str(uuid.uuid4())
     os.makedirs("tmp", exist_ok=True)
     config_path = f"tmp/{random_config_name}-{slugged_lora_name}.yaml"
     with open(config_path, "w") as f:
         yaml.dump(config, f)
-    
-    # run the job locally
+
     job = get_job(config_path)
     job.run()
     job.cleanup()
@@ -238,13 +206,13 @@ model:
   is_flux: true
   quantize: true
 network:
-  linear: 16 #it will overcome the 'rank' parameter
-  linear_alpha: 16 #you can have an alpha different than the ranking if you'd like
+  linear: 16
+  linear_alpha: 16
   type: lora
 sample:
   guidance_scale: 3.5
   height: 1024
-  neg: '' #doesn't work for FLUX
+  neg: ''
   sample_every: 1000
   sample_steps: 28
   sampler: flowmatch
@@ -266,149 +234,81 @@ train:
   gradient_accumulation_steps: 1
   gradient_checkpointing: true
   noise_scheduler: flowmatch 
-  optimizer: adamw8bit #options: prodigy, dadaptation, adamw, adamw8bit, lion, lion8bit
-  train_text_encoder: false #probably doesn't work for flux
+  optimizer: adamw8bit
+  train_text_encoder: false
   train_unet: true
 '''
 
-theme = gr.themes.Monochrome(
-    text_size=gr.themes.Size(lg="18px", md="15px", sm="13px", xl="22px", xs="12px", xxl="24px", xxs="9px"),
-    font=[gr.themes.GoogleFont("Source Sans Pro"), "ui-sans-serif", "system-ui", "sans-serif"],
-)
-css = """
-h1{font-size: 2em}
-h3{margin-top: 0}
-#component-1{text-align:center}
-.main_ui_logged_out{opacity: 0.3; pointer-events: none}
-.tabitem{border: 0px}
-.group_padding{padding: .55em}
-"""
-with gr.Blocks(theme=theme, css=css) as demo:
-    gr.Markdown(
-        """# LoRA Ease for FLUX 🧞‍♂️
-### Train a high quality FLUX LoRA in a breeze ༄ using [Ostris' AI Toolkit](https://github.com/ostris/ai-toolkit)"""
-    )
-    with gr.Column() as main_ui:
-        with gr.Row():
-            lora_name = gr.Textbox(
-                label="The name of your LoRA",
-                info="This has to be a unique name",
-                placeholder="e.g.: Persian Miniature Painting style, Cat Toy",
-            )
-            concept_sentence = gr.Textbox(
-                label="Trigger word/sentence",
-                info="Trigger word or sentence to be used",
-                placeholder="uncommon word like p3rs0n or trtcrd, or sentence like 'in the style of CNSTLL'",
-                interactive=True,
-            )
-        with gr.Group(visible=True) as image_upload:
-            with gr.Row():
-                images = gr.File(
-                    file_types=["image", ".txt"],
-                    label="Upload your images",
-                    file_count="multiple",
-                    interactive=True,
-                    visible=True,
-                    scale=1,
-                )
-                with gr.Column(scale=3, visible=False) as captioning_area:
-                    with gr.Column():
-                        gr.Markdown(
-                            """# Custom captioning
-<p style="margin-top:0">You can optionally add a custom caption for each image (or use an AI model for this). [trigger] will represent your concept sentence/trigger word.</p>
-""", elem_classes="group_padding")
-                        do_captioning = gr.Button("Add AI captions with Florence-2")
-                        output_components = [captioning_area]
-                        caption_list = []
-                        for i in range(1, MAX_IMAGES + 1):
-                            locals()[f"captioning_row_{i}"] = gr.Row(visible=False)
-                            with locals()[f"captioning_row_{i}"]:
-                                locals()[f"image_{i}"] = gr.Image(
-                                    type="filepath",
-                                    width=111,
-                                    height=111,
-                                    min_width=111,
-                                    interactive=False,
-                                    scale=2,
-                                    show_label=False,
-                                    show_share_button=False,
-                                    show_download_button=False,
-                                )
-                                locals()[f"caption_{i}"] = gr.Textbox(
-                                    label=f"Caption {i}", scale=15, interactive=True
-                                )
-
-                            output_components.append(locals()[f"captioning_row_{i}"])
-                            output_components.append(locals()[f"image_{i}"])
-                            output_components.append(locals()[f"caption_{i}"])
-                            caption_list.append(locals()[f"caption_{i}"])
-
-        with gr.Accordion("Advanced options", open=False):
-            steps = gr.Number(label="Steps", value=1000, minimum=1, maximum=10000, step=1)
-            lr = gr.Number(label="Learning Rate", value=4e-4, minimum=1e-6, maximum=1e-3, step=1e-6)
-            rank = gr.Number(label="LoRA Rank", value=16, minimum=4, maximum=128, step=4)
-            model_to_train = gr.Radio(["dev", "schnell"], value="dev", label="Model to train")
-            low_vram = gr.Checkbox(label="Low VRAM", value=True)
-            with gr.Accordion("Even more advanced options", open=False):
-                use_more_advanced_options = gr.Checkbox(label="Use more advanced options", value=False)
-                more_advanced_options = gr.Code(config_yaml, language="yaml")
-
-        with gr.Accordion("Sample prompts (optional)", visible=False) as sample:
-            gr.Markdown(
-                "Include sample prompts to test out your trained model. Don't forget to include your trigger word/sentence (optional)"
-            )
-            sample_1 = gr.Textbox(label="Test prompt 1")
-            sample_2 = gr.Textbox(label="Test prompt 2")
-            sample_3 = gr.Textbox(label="Test prompt 3")
-        
-        output_components.append(sample)
-        output_components.append(sample_1)
-        output_components.append(sample_2)
-        output_components.append(sample_3)
-        start = gr.Button("Start training", visible=False)
-        output_components.append(start)
-        progress_area = gr.Markdown("")
-
-    dataset_folder = gr.State()
-
-    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, sample, start]
-    )
-    
-    start.click(fn=create_dataset, inputs=[images] + caption_list, outputs=dataset_folder).then(
-        fn=start_training,
-        inputs=[
-            lora_name,
-            concept_sentence,
-            steps,
-            lr,
-            rank,
-            model_to_train,
-            low_vram,
-            dataset_folder,
-            sample_1,
-            sample_2,
-            sample_3,
-            use_more_advanced_options,
-            more_advanced_options
-        ],
-        outputs=progress_area,
-    )
-
-    do_captioning.click(fn=run_captioning, inputs=[images, concept_sentence] + caption_list, outputs=caption_list)
+def main():
+    with pg.App() as app:
+        app.add_page(title="LoRA Ease for FLUX", description="Train a high quality FLUX LoRA in a breeze")
+
+        app.add_textbox(
+            id="lora_name",
+            label="The name of your LoRA",
+            placeholder="e.g.: Persian Miniature Painting style, Cat Toy",
+        )
+        app.add_textbox(
+            id="concept_sentence",
+            label="Trigger word/sentence",
+            placeholder="uncommon word like p3rs0n or trtcrd, or sentence like 'in the style of CNSTLL'",
+        )
+
+        image_upload = app.add_file_upload(
+            id="images",
+            label="Upload your images",
+            file_types=["image", ".txt"],
+            multiple=True,
+        )
+
+        captioning_area = app.add_container(id="captioning_area", visible=False)
+        captioning_area.add_text("Custom captioning")
+        do_captioning = app.add_button("Add AI captions with Florence-2", id="do_captioning")
+
+        for i in range(1, MAX_IMAGES + 1):
+            with captioning_area.add_row(id=f"captioning_row_{i}", visible=False) as row:
+                row.add_image(id=f"image_{i}", width=111, height=111, visible=False)
+                row.add_textbox(id=f"caption_{i}", label=f"Caption {i}")
+
+        app.add_accordion(title="Advanced options", open=False)
+        app.add_number(id="steps", label="Steps", value=1000, min=1, max=10000)
+        app.add_number(id="lr", label="Learning Rate", value=4e-4, min=1e-6, max=1e-3)
+        app.add_number(id="rank", label="LoRA Rank", value=16, min=4, max=128)
+        app.add_radio(id="model_to_train", options=["dev", "schnell"], value="dev", label="Model to train")
+        app.add_checkbox(id="low_vram", label="Low VRAM", value=True)
+
+        with app.add_accordion(title="Even more advanced options", open=False):
+            app.add_checkbox(id="use_more_advanced_options", label="Use more advanced options", value=False)
+            app.add_code(id="more_advanced_options", value=config_yaml, language="yaml")
+
+        app.add_accordion(title="Sample prompts (optional)", visible=False)
+        app.add_textbox(id="sample_1", label="Test prompt 1")
+        app.add_textbox(id="sample_2", label="Test prompt 2")
+        app.add_textbox(id="sample_3", label="Test prompt 3")
+
+        start = app.add_button("Start training", id="start", visible=False)
+        progress_area = app.add_text("")
+
+        app.on_upload(id="images", fn=load_captioning, inputs=["images", "concept_sentence"], outputs=["captioning_area", "sample", "start"])
+        app.on_click(id="do_captioning", fn=run_captioning, inputs=["images", "concept_sentence"] + [f"caption_{i}" for i in range(1, MAX_IMAGES + 1)], outputs=[f"caption_{i}" for i in range(1, MAX_IMAGES + 1)])
+        app.on_click(id="start", fn=create_dataset, inputs=["images"] + [f"caption_{i}" for i in range(1, MAX_IMAGES + 1)], outputs=["dataset_folder"])
+        app.on_click(id="start", fn=start_training, inputs=[
+            "lora_name",
+            "concept_sentence",
+            "steps",
+            "lr",
+            "rank",
+            "model_to_train",
+            "low_vram",
+            "dataset_folder",
+            "sample_1",
+            "sample_2",
+            "sample_3",
+            "use_more_advanced_options",
+            "more_advanced_options"
+        ], outputs=["progress_area"])
+
+        app.run()
 
 if __name__ == "__main__":
-    demo.launch(share=True, show_error=True)
+    main()