fixes

Author: sbalandi

notebooks/ace-step-music-generation/ace-step-music-generation.ipynb

@@ -100,25 +100,13 @@
     "\n",
     "from pip_helper import pip_install\n",
     "\n",
-    "# pip_install(\n",
-    "#     \"-U\",\n",
-    "#     \"torch>=2.1\",\n",
-    "#     \"torchvision\",\n",
-    "#     \"torchaudio\",\n",
-    "#     \"--extra-index-url\",\n",
-    "#     \"https://download.pytorch.org/whl/cpu\",\n",
-    "# )\n",
-    "pip_install(\n",
-    "    \"gradio>=4.19\"\n",
-    ")\n",
+    "pip_install(\"gradio>=4.19\")\n",
     "if platform.system() == \"Darwin\":\n",
     "    pip_install(\"numpy<2.0\")\n",
     "\n",
     "pip_install(\n",
     "    \"git+https://github.com/ace-step/ACE-Step.git@6ae0852b1388de6dc0cca26b31a86d711f723cb3\", \"--extra-index-url\", \"https://download.pytorch.org/whl/cpu\"\n",
     ")\n",
-    "# if platform.system() == \"Darwin\":\n",
-    "#     pip_install(\"-U\", \"transformers>=4.51\")\n",
     "\n",
     "pip_install(\"openvino>=2025.1.0\", \"openvino-tokenizers>=2025.1.0\", \"nncf>=2.16.0\")\n",
     "\n",
@@ -390,7 +378,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "f9db0d8a",
    "metadata": {},
    "outputs": [
@@ -422,14 +410,16 @@
     "import nncf\n",
     "from ov_ace_helper import convert_models\n",
     "\n",
+    "ov_converted_model_dir = \"ov_models\"\n",
     "if model_format.value == \"INT4\":\n",
     "    weights_compression_config = {\"mode\": nncf.CompressWeightsMode.INT4_ASYM, \"group_size\": 128, \"ratio\": 0.8}\n",
+    "    ov_converted_model_dir += \"_int4\"\n",
     "elif model_format.value == \"INT8\":\n",
     "    weights_compression_config = {\"mode\": nncf.CompressWeightsMode.INT8_ASYM}\n",
+    "    ov_converted_model_dir += \"_int8\"\n",
     "else:\n",
     "    weights_compression_config = None\n",
     "\n",
-    "ov_converted_model_dir = \"ov_models\"\n",
     "convert_models(pipeline, model_dir=ov_converted_model_dir, orig_checkpoint_path=checkpoint_dir, quantization_config=weights_compression_config)"
    ]
   },
@@ -648,7 +638,7 @@
     "[back to top ⬆️](#Table-of-contents:)\n",
     "\n",
     "\n",
-    "LoRA is a technique that allows to fine-tune large models with a small number of parameters. ACE Step support LoRA, more information about it can be find (here)[https://github.com/ace-step/ACE-Step?tab=readme-ov-file#-applications].\n",
+    "LoRA is a technique that allows to fine-tune large models with a small number of parameters. ACE Step support LoRA, more information about it can be find [here](https://github.com/ace-step/ACE-Step?tab=readme-ov-file#-applications).\n",
     "\n",
     "Let's try LoRA. To use LoRA for ACE Step and OpenVINO, LoRA should be applied for the model and model should be converted to IR format."
    ]

notebooks/ace-step-music-generation/gradio_helper.py

@@ -791,13 +791,7 @@ def make_demo(pipeline, data_sampler):
 
 def update_audio_widget():
     options = ["Retake", "Repainting", "Edit", "Extend"]
-    return widgets.ToggleButtons(
-        options=options,
-        description="Choose next operation with audio:",
-        disabled=False,
-        button_style="info",
-        value="Repainting"
-    )
+    return widgets.ToggleButtons(options=options, description="Choose next operation with audio:", disabled=False, button_style="info", value="Repainting")
 
 
 def setup_update_audio_widgets(update_audio_widget):

notebooks/ace-step-music-generation/ov_ace_helper.py

@@ -370,7 +370,6 @@ def encode_with_temperature(
                     "tau": tau,
                 }
             )
-        print("ace_step_transformer after encode 1", torch.from_numpy(output[0]).size())
         return torch.from_numpy(output[0]), torch.from_numpy(output[1])
 
     def decode_with_temperature(
@@ -401,7 +400,6 @@ def decode_with_temperature(
                 }
             )
 
-        print("ace_step_transformer after decode 1", torch.from_numpy(output[0]).size())
         sample = torch.from_numpy(output[0]) if output is not None else None
         return sample
 
@@ -474,10 +472,7 @@ def forward(
             lyric_mask=lyric_mask,
         )
 
-        print("ace_step_transformer after encode", encoder_hidden_states.size())
-
         output_length = hidden_states.shape[-1]
-        print("ace_step_transformer output_length", output_length)
 
         output = self.decode(
             hidden_states=hidden_states,
@@ -491,7 +486,6 @@ def forward(
             controlnet_scale=controlnet_scale,
             return_dict=return_dict,
         )
-        print("ace_step_transformer after decode", output.sample.size())
 
         return output