merge

CLAUDE.md

@@ -0,0 +1,18 @@
+# Video Model Studio - Guidelines for Claude
+
+## Build & Run Commands
+- Setup: `./setup.sh` (with flash attention) or `./setup_no_captions.sh` (without)
+- Run: `./run.sh` or `python3.10 app.py`
+- Test: `python3 tests/test_dataset.py`
+- Single model test: `bash tests/scripts/dummy_cogvideox_lora.sh`
+
+## Code Style
+- Python version: 3.10 (required for flash-attention compatibility)
+- Type hints: Use typing module annotations for all functions
+- Docstrings: Google style with Args/Returns sections
+- Error handling: Use try/except with specific exceptions, log errors
+- Imports: Group standard lib, third-party, and project imports
+- Naming: snake_case for functions/variables, PascalCase for classes
+- Use Path objects from pathlib instead of string paths
+- Format utility functions: Extract reusable logic to separate functions
+- Environment variables: Use parse_bool_env for boolean env vars

README.md

@@ -4,7 +4,7 @@ emoji: 🎥
 colorFrom: gray
 colorTo: gray
 sdk: gradio
-sdk_version: 5.20.1
+sdk_version: 5.23.3
 app_file: app.py
 pinned: true
 license: apache-2.0
@@ -133,7 +133,11 @@ That said, please see the "RUN" section for info about environement variables.
 
 ### Dev mode on Hugging Face
 
-Enable dev mode in the space, then open VSCode in local or remote and run:
+I recommend to not use the dev mode for a production usage (ie not use dev mode when training a real model), unless you know what you are doing.
+
+That's because the dev mode can be unstable and cause space restarts.
+
+If you still want to open the dev mode in the space, then open VSCode in local or remote and run:
 
 ```
 pip install -r requirements.txt
@@ -141,6 +145,8 @@ pip install -r requirements.txt
 
 As this is not automatic, then click on "Restart" in the space dev mode UI widget.
 
+Important: if you see errors like "API not found" etc, it might indicate an issue with the dev mode and Gradio, not an issue with VMS itself.
+
 ### Full installation somewhere else
 
 I haven't tested it, but you can try to provided Dockerfile
@@ -217,3 +223,15 @@ By default `run.sh` will store stuff in `.data/` (located inside the current wor
 ```bash
 ./run.sh
 ```
+
+### Environment Variables
+
+- `STORAGE_PATH`: Specifies the base storage path (default: '.data')
+- `HF_API_TOKEN`: Your Hugging Face API token for accessing models and publishing
+- `USE_LARGE_DATASET`: Set to "true" or "1" to enable large dataset mode, which:
+  - Hides the caption list in the caption tab
+  - Disables preview and editing of individual captions
+  - Disables the dataset download button
+  - Use this when working with large datasets that would be too slow to display in the UI
+- `PRELOAD_CAPTIONING_MODEL`: Preloads the captioning model at startup
+- `ASK_USER_TO_DUPLICATE_SPACE`: Prompts users to duplicate the space

requirements.txt

@@ -1,43 +1,18 @@
-numpy>=1.26.4
 
-# to quote a-r-r-o-w/finetrainers:
-# It is recommended to use Pytorch 2.5.1 or above for training. Previous versions can lead to completely black videos, OOM errors, or other issues and are not tested.
-
-# on some system (Python 3.13+) those do not work:
-torch==2.5.1
-torchvision==0.20.1
-torchao>=0.7.0
-
-# datasets 3.4.0 replaces decord by torchvision
-# let's free it for now
-datasets==3.3.2
-
-huggingface_hub
-hf_transfer>=0.1.8
-diffusers @ git+https://github.com/huggingface/diffusers.git@main
-transformers>=4.45.2
-
-accelerate
-bitsandbytes
-peft>=0.12.0
 
 # For GPU monitoring of NVIDIA chipsets
 pynvml
 
-# eva-decord is missing get_batch it seems
-#eva-decord==0.6.1
-decord
-
-finetrainers @ git+https://github.com/a-r-r-o-w/finetrainers.git@main
+finetrainers==0.1.0
+#finetrainers @ git+https://github.com/a-r-r-o-w/finetrainers.git@main
 # temporary fix for pip install bug:
 #finetrainers @ git+https://github.com/jbilcke-hf/finetrainers-patches.git@fix_missing_sft_trainer_files
 
-wandb
-pandas
-sentencepiece>=0.2.0
+# it is recommended to always use the latest version
+diffusers @ git+https://github.com/huggingface/diffusers.git@main
+
 imageio
 imageio-ffmpeg
-torchdata==0.11.0
 
 flash-attn @ https://github.com/Dao-AILab/flash-attention/releases/download/v2.7.4.post1/flash_attn-2.7.4.post1+cu12torch2.4cxx11abiFALSE-cp310-cp310-linux_x86_64.whl
 
@@ -58,7 +33,7 @@ av==14.1.0
 git+https://github.com/LLaVA-VL/LLaVA-NeXT.git
 
 # for our frontend
-gradio==5.20.1
+gradio==5.23.3
 gradio_toggle
 
 # used for the monitor

requirements_without_flash_attention.txt

@@ -1,44 +1,24 @@
-numpy>=1.26.4
 
-# to quote a-r-r-o-w/finetrainers:
-# It is recommended to use Pytorch 2.5.1 or above for training. Previous versions can lead to completely black videos, OOM errors, or other issues and are not tested.
-
-# on some system (Python 3.13+) those do not work:
-torch==2.5.1
-torchvision==0.20.1
-torchao>=0.7.0
-
-# datasets 3.4.0 replaces decord by torchvision
-# let's free it for now
-datasets==3.3.2
-
-huggingface_hub
-hf_transfer>=0.1.8
-diffusers @ git+https://github.com/huggingface/diffusers.git@main
-transformers>=4.45.2
-
-accelerate
-bitsandbytes
-peft>=0.12.0
 
 # For GPU monitoring of NVIDIA chipsets
-# you probably won't be able to install that on macOS
-# pynvml
+pynvml
 
 # eva-decord is missing get_batch it seems
-eva-decord==0.6.1
-# decord
+#eva-decord==0.6.1
+#decord
 
-finetrainers @ git+https://github.com/a-r-r-o-w/finetrainers.git@main
+finetrainers==0.1.0
+#finetrainers @ git+https://github.com/a-r-r-o-w/finetrainers.git@main
 # temporary fix for pip install bug:
 #finetrainers @ git+https://github.com/jbilcke-hf/finetrainers-patches.git@fix_missing_sft_trainer_files
 
-wandb
-pandas
-sentencepiece>=0.2.0
+# it is recommended to always use the latest version
+diffusers @ git+https://github.com/huggingface/diffusers.git@main
+
 imageio
 imageio-ffmpeg
-torchdata==0.11.0
+
+#flash-attn @ https://github.com/Dao-AILab/flash-attention/releases/download/v2.7.4.post1/flash_attn-2.7.4.post1+cu12torch2.4cxx11abiFALSE-cp310-cp310-linux_x86_64.whl
 
 # for youtube video download
 pytube
@@ -57,7 +37,7 @@ av==14.1.0
 git+https://github.com/LLaVA-VL/LLaVA-NeXT.git
 
 # for our frontend
-gradio==5.20.1
+gradio==5.23.3
 gradio_toggle
 
 # used for the monitor

vms/config.py

@@ -22,6 +22,9 @@ def parse_bool_env(env_value: Optional[str]) -> bool:
 HF_API_TOKEN = os.getenv("HF_API_TOKEN")
 ASK_USER_TO_DUPLICATE_SPACE = parse_bool_env(os.getenv("ASK_USER_TO_DUPLICATE_SPACE"))
 
+# For large datasets that would be slow to display or download
+USE_LARGE_DATASET = parse_bool_env(os.getenv("USE_LARGE_DATASET"))
+
 # Base storage path
 STORAGE_PATH = Path(os.environ.get('STORAGE_PATH', '.data'))
 

vms/ui/project/tabs/caption_tab.py

@@ -11,7 +11,7 @@ from pathlib import Path
 import mimetypes
 
 from vms.utils import BaseTab, is_image_file, is_video_file, copy_files_to_training_dir
-from vms.config import DEFAULT_CAPTIONING_BOT_INSTRUCTIONS, DEFAULT_PROMPT_PREFIX, STAGING_PATH
+from vms.config import DEFAULT_CAPTIONING_BOT_INSTRUCTIONS, DEFAULT_PROMPT_PREFIX, STAGING_PATH, TRAINING_VIDEOS_PATH, USE_LARGE_DATASET
 
 logger = logging.getLogger(__name__)
 
@@ -28,7 +28,10 @@ class CaptionTab(BaseTab):
         """Create the Caption tab UI components"""
         with gr.TabItem(self.title, id=self.id) as tab:
             with gr.Row():
-                self.components["caption_title"] = gr.Markdown("## Captioning of 0 files (0 bytes)")
+                if USE_LARGE_DATASET:
+                    self.components["caption_title"] = gr.Markdown("## Captioning (Large Dataset Mode)")
+                else:
+                    self.components["caption_title"] = gr.Markdown("## Captioning of 0 files (0 bytes)")
                 
             with gr.Row():
                 with gr.Column():
@@ -62,7 +65,7 @@ class CaptionTab(BaseTab):
                             interactive=False
                         )
 
-            with gr.Row():
+            with gr.Row(visible=not USE_LARGE_DATASET):
                 with gr.Column():
                     self.components["training_dataset"] = gr.Dataframe(
                         headers=["name", "status"],
@@ -95,6 +98,10 @@ class CaptionTab(BaseTab):
                         visible=True
                     )
                     self.components["original_file_path"] = gr.State(value=None)
+                    
+            with gr.Row(visible=USE_LARGE_DATASET):
+                gr.Markdown("### Large Dataset Mode Active")
+                gr.Markdown("Caption preview and editing is disabled to improve performance with large datasets.")
             
         return tab
     
@@ -174,10 +181,16 @@ class CaptionTab(BaseTab):
     
     def refresh(self) -> Dict[str, Any]:
         """Refresh the dataset list with current data"""
-        training_dataset = self.list_training_files_to_caption()
-        return {
-            "training_dataset": training_dataset
-        }
+        if USE_LARGE_DATASET:
+            # In large dataset mode, we don't attempt to list files
+            return {
+                "training_dataset": [["Large dataset mode enabled", "listing skipped"]]
+            }
+        else:
+            training_dataset = self.list_training_files_to_caption()
+            return {
+                "training_dataset": training_dataset
+            }
     
     def show_refreshing_status(self) -> List[List[str]]:
         """Show a 'Refreshing...' status in the dataframe"""
@@ -318,6 +331,10 @@ class CaptionTab(BaseTab):
 
     def list_training_files_to_caption(self) -> List[List[str]]:
         """List all clips and images - both pending and captioned"""
+        # In large dataset mode, return a placeholder message instead of listing all files
+        if USE_LARGE_DATASET:
+            return [["Large dataset mode enabled", "listing skipped"]]
+            
         files = []
         already_listed = {}
 

vms/ui/project/tabs/manage_tab.py

@@ -10,7 +10,8 @@ from typing import Dict, Any, List, Optional
 
 from vms.utils import BaseTab, validate_model_repo
 from vms.config import (
-    HF_API_TOKEN, VIDEOS_TO_SPLIT_PATH, STAGING_PATH
+    HF_API_TOKEN, VIDEOS_TO_SPLIT_PATH, STAGING_PATH, TRAINING_VIDEOS_PATH, 
+    TRAINING_PATH, MODEL_PATH, OUTPUT_PATH, LOG_FILE_PATH, USE_LARGE_DATASET
 )
 
 logger = logging.getLogger(__name__)
@@ -35,8 +36,13 @@ class ManageTab(BaseTab):
                         self.components["download_dataset_btn"] = gr.DownloadButton(
                             "📦 Download training dataset (.zip)",
                             variant="secondary",
-                            size="lg"
+                            size="lg",
+                            visible=not USE_LARGE_DATASET
                         )
+                        # If we have a large dataset, display a message explaining why download is disabled
+                        if USE_LARGE_DATASET:
+                            gr.Markdown("📦 Training dataset download disabled for large datasets")
+                            
                         self.components["download_model_btn"] = gr.DownloadButton(
                             "🧠 Download weights (.safetensors)",
                             variant="secondary",