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nvidia-cusparse-cu12==12.1.0.106 (from torch>=1.10.0->accelerate)\n", " Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n", "Collecting nvidia-nccl-cu12==2.19.3 (from torch>=1.10.0->accelerate)\n", " Using cached nvidia_nccl_cu12-2.19.3-py3-none-manylinux1_x86_64.whl (166.0 MB)\n", "Collecting nvidia-nvtx-cu12==12.1.105 (from torch>=1.10.0->accelerate)\n", " Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n", "Requirement already satisfied: triton==2.2.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.2.0)\n", "Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.10.0->accelerate)\n", " Using cached nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (21.1 MB)\n", "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.10/dist-packages (from importlib-metadata->diffusers) (3.18.1)\n", "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->diffusers) (3.3.2)\n", "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->diffusers) (3.7)\n", "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->diffusers) (2.0.7)\n", "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->diffusers) (2024.2.2)\n", "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate) (2.1.5)\n", "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n", "Installing collected packages: nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, diffusers, accelerate\n", "Successfully installed accelerate-0.30.1 diffusers-0.27.2 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.127 nvidia-nvtx-cu12-12.1.105\n" ] } ], "source": [ "!pip install diffusers transformers safetensors accelerate\n" ] }, { "cell_type": "code", "source": [ "!pip install torch transformers diffusers gtts moviepy pysrt\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "i4VlOvqIQiFj", "outputId": "cae5e83f-4e2b-4734-ee4f-dfc5dc87f316" }, "execution_count": 2, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Requirement already satisfied: torch in 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already satisfied: zipp>=0.5 in /usr/local/lib/python3.10/dist-packages (from importlib-metadata->diffusers) (3.18.1)\n", "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (2.1.5)\n", "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\n", "Building wheels for collected packages: pysrt\n", " Building wheel for pysrt (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Created wheel for pysrt: filename=pysrt-1.1.2-py3-none-any.whl size=13443 sha256=96ffca3c75437d42aac77ffc12fad22d862f44b1ee7e0ea888307f734d3007af\n", " Stored in directory: /root/.cache/pip/wheels/30/7f/e8/55de9a9b07302d9e7fe47c27910e3bea0c48536153e74bd7e6\n", "Successfully built pysrt\n", "Installing collected packages: pysrt, gtts\n", "Successfully installed gtts-2.5.1 pysrt-1.1.2\n" ] } ] }, { "cell_type": "code", "source": [ "!pip install torch transformers sentencepiece\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "pYUZIgxZV83I", "outputId": "0432000d-bcfe-4bdc-e3a7-26649659775d" }, "execution_count": 3, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.2.1+cu121)\n", "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.40.2)\n", "Requirement already satisfied: sentencepiece in /usr/local/lib/python3.10/dist-packages (0.1.99)\n", "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch) (3.14.0)\n", "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch) (4.11.0)\n", "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch) (1.12)\n", "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch) (3.3)\n", "Requirement already satisfied: jinja2 in 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"colab": { "base_uri": "https://localhost:8080/" }, "id": "kNBJ2p3Xm282", "outputId": "08d669b6-a56c-4086-94f3-d52ec7e4d894" }, "execution_count": 4, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.2/43.2 kB\u001b[0m \u001b[31m1.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m15.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m119.8/119.8 MB\u001b[0m \u001b[31m14.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.0/2.0 MB\u001b[0m \u001b[31m89.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m302.9/302.9 kB\u001b[0m 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"hWVPaJcMWA-J", "outputId": "480e7785-c9bf-4b12-8f7b-36906c77bca6" }, "execution_count": 5, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Requirement already satisfied: moviepy in /usr/local/lib/python3.10/dist-packages (1.0.3)\n", "Requirement already satisfied: decorator<5.0,>=4.0.2 in /usr/local/lib/python3.10/dist-packages (from moviepy) (4.4.2)\n", "Requirement already satisfied: tqdm<5.0,>=4.11.2 in /usr/local/lib/python3.10/dist-packages (from moviepy) (4.66.4)\n", "Requirement already satisfied: requests<3.0,>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from moviepy) (2.31.0)\n", "Requirement already satisfied: proglog<=1.0.0 in /usr/local/lib/python3.10/dist-packages (from moviepy) (0.1.10)\n", "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from moviepy) (1.25.2)\n", "Requirement already satisfied: imageio<3.0,>=2.5 in /usr/local/lib/python3.10/dist-packages (from moviepy) (2.31.6)\n", "Requirement already 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requests<3.0.0,>=2.13.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2024.2.2)\n", "Requirement already satisfied: blis<0.8.0,>=0.7.8 in /usr/local/lib/python3.10/dist-packages (from thinc<8.3.0,>=8.2.2->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (0.7.11)\n", "Requirement already satisfied: confection<1.0.0,>=0.0.1 in /usr/local/lib/python3.10/dist-packages (from thinc<8.3.0,>=8.2.2->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (0.1.4)\n", "Requirement already satisfied: click<9.0.0,>=7.1.1 in /usr/local/lib/python3.10/dist-packages (from typer<0.10.0,>=0.3.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (8.1.7)\n", "Requirement already satisfied: cloudpathlib<0.17.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from weasel<0.4.0,>=0.1.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (0.16.0)\n", "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.1.5)\n", "Requirement already satisfied: marisa-trie>=0.7.7 in /usr/local/lib/python3.10/dist-packages (from language-data>=1.2->langcodes<4.0.0,>=3.2.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (1.1.1)\n", "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n", "You can now load the package via spacy.load('en_core_web_sm')\n", "\u001b[38;5;3m⚠ Restart to reload dependencies\u001b[0m\n", "If you are in a Jupyter or Colab notebook, you may need to restart Python in\n", "order to load all the package's dependencies. You can do this by selecting the\n", "'Restart kernel' or 'Restart runtime' option.\n" ] } ] }, { "cell_type": "code", "source": [ "import os\n", "import random\n", "import sys\n", "import torch\n", "import spacy\n", "import pysrt\n", "from moviepy.editor import concatenate_videoclips, ImageClip, AudioFileClip\n", "\n", "# Function to generate story using GPT-2\n", "\n", "\n", "# Function to extract keywords from the story for image prompts\n", "def extract_image_prompts(story, num_prompts=6):\n", " nlp = spacy.load('en_core_web_sm')\n", " # Custom list of uninformative words\n", " uninformative_words = ['can', 'to', 'which', 'you', 'your', 'that','their','they']\n", " # Split the story into individual sentences\n", " doc = nlp(story)\n", " sentences = [sent.text.strip() for sent in doc.sents]\n", " # Find the main subject or noun phrase in each sentence\n", " main_subjects = []\n", " for sentence in sentences:\n", " doc = nlp(sentence.lower())\n", " for chunk in doc.noun_chunks:\n", " if chunk.root.dep_ == 'nsubj' and chunk.root.head.text.lower() != 'that':\n", " main_subjects.append(chunk)\n", " if main_subjects:\n", " main_subject = main_subjects[0]\n", " else:\n", " main_subject = None\n", " # Find the related words (adjectives, verbs) to the main subject\n", " related_words = defaultdict(list)\n", " for sentence in sentences:\n", " doc = nlp(sentence.lower())\n", " for tok in doc:\n", " if tok.text in uninformative_words or not tok.text.isalnum():\n", " continue\n", " if (tok.pos_ == 'NOUN') and (main_subject is None or (tok.text != main_subject.text)):\n", " related_words[sentence].append(tok.text)\n", " # Create image prompts\n", " image_prompts = []\n", " for sentence, related in related_words.items():\n", " if main_subject is not None:\n", " prompt = f\"{main_subject.text} {' '.join(related)} photorealistic\"\n", " else:\n", " prompt = f\"{sentence} photorealistic\"\n", " image_prompts.append(prompt)\n", " # If we couldn't generate enough prompts, duplicate the existing ones\n", " if len(image_prompts) < num_prompts:\n", " print(f\"Could only generate {len(image_prompts)} unique prompts out of the requested {num_prompts}. Duplicating prompts...\")\n", " i = 0\n", " while len(image_prompts) < num_prompts:\n", " image_prompts.append(image_prompts[i])\n", " i = (i + 1) % len(image_prompts) # cycle through existing prompts\n", " print(\"\\nGenerated Image Prompts:\")\n", " for idx, prompt in enumerate(image_prompts, start=1):\n", " print(f\"{idx}: {prompt}\")\n", " # Ask the user whether they want to proceed or enter their own prompts\n", " user_input = input(\"\\nDo you want to proceed with these prompts? (y/n): \")\n", " if user_input.lower() == \"y\":\n", " return image_prompts\n", " elif user_input.lower() == \"n\":\n", " user_prompts = []\n", " print(\"\\nEnter your own image prompts:\")\n", " for i in range(num_prompts):\n", " user_prompt = input(f\"Prompt {i+1}: \")\n", " user_prompts.append(user_prompt)\n", " return user_prompts\n", " else:\n", " print(\"Invalid input. Please enter 'y' to proceed with the generated prompts or 'n' to enter your own prompts.\")\n", "\n", "# Function to generate images using Stable Diffusion XL\n", "\n", "\n" ], "metadata": { "id": "QGI96gyubzhw" }, "execution_count": 6, "outputs": [] }, { "cell_type": "code", "source": [ "from langchain import HuggingFacePipeline, PromptTemplate, LLMChain\n", "from transformers import AutoTokenizer, pipeline as hf_pipeline\n", "import torch\n", "\n", "def generate_story_from_question(question):\n", " model = \"tiiuae/falcon-7b-instruct\"\n", " tokenizer = AutoTokenizer.from_pretrained(model)\n", "\n", " tf_pipeline = hf_pipeline(\n", " \"text-generation\",\n", " model=model,\n", " tokenizer=tokenizer,\n", " torch_dtype=torch.bfloat16,\n", " trust_remote_code=True,\n", " device_map=\"auto\",\n", " max_length=200,\n", " do_sample=True,\n", " top_k=10,\n", " num_return_sequences=1,\n", " eos_token_id=tokenizer.eos_token_id\n", " )\n", "\n", " llm = HuggingFacePipeline(pipeline=tf_pipeline, model_kwargs={'temperature': 0})\n", "\n", " template = \"\"\"\n", "\n", " Question: {question}\n", " Answer:\"\"\"\n", "\n", " prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n", "\n", " llm_chain = LLMChain(prompt=prompt, llm=llm)\n", "\n", " generated_story = llm_chain.run(question)\n", "\n", " return generated_story\n" ], "metadata": { "id": "hv-OMxykcAxP" }, "execution_count": 7, "outputs": [] }, { "cell_type": "code", "source": [ "import random\n", "import sys\n", "import torch\n", "from diffusers import StableDiffusionXLPipeline, UNet2DConditionModel, EulerDiscreteScheduler\n", "from huggingface_hub import hf_hub_download\n", "from safetensors.torch import load_file\n", "\n", "def generate_images(image_prompts, num_inference_steps=4, use_lora=False):\n", " base = \"stabilityai/stable-diffusion-xl-base-1.0\"\n", " repo = \"ByteDance/SDXL-Lightning\"\n", " device = \"cuda\"\n", "\n", " unet = UNet2DConditionModel.from_config(\n", " base,\n", " subfolder=\"unet\",\n", " ).to(device, torch.float16)\n", "\n", " unet.load_state_dict(\n", " load_file(\n", " hf_hub_download(\n", " repo,\n", " f\"sdxl_lightning_{num_inference_steps}step_{'lora' if use_lora else 'unet'}.safetensors\",\n", " ),\n", " device=device,\n", " ),\n", " )\n", "\n", " pipe = StableDiffusionXLPipeline.from_pretrained(\n", " base,\n", " unet=unet,\n", " torch_dtype=torch.float16,\n", " use_safetensors=True,\n", " variant=\"fp16\",\n", " ).to(device)\n", "\n", " if use_lora:\n", " pipe.load_lora_weights(hf_hub_download(repo, f\"sdxl_lightning_{num_inference_steps}step_{'lora' if use_lora else 'unet'}.safetensors\"))\n", " pipe.fuse_lora()\n", "\n", " pipe.scheduler = EulerDiscreteScheduler.from_config(\n", " pipe.scheduler.config,\n", " timestep_spacing=\"trailing\",\n", " )\n", "\n", " generated_images = []\n", "\n", " for prompt in image_prompts:\n", " seed = random.randint(0, sys.maxsize)\n", " images = pipe(\n", " prompt=prompt,\n", " guidance_scale=0.0,\n", " num_inference_steps=num_inference_steps,\n", " generator=torch.Generator(\"cuda\").manual_seed(seed),\n", " ).images\n", " generated_images.extend(images)\n", "\n", " return generated_images\n" ], "metadata": { "id": "545qq09CcON0" }, "execution_count": 8, "outputs": [] }, { "cell_type": "code", "source": [ "from gtts import gTTS\n", "import os\n", "\n", "def generate_audio(text, audio_file=\"output_audio.mp3\"):\n", " # Generate audio using Google Text-to-Speech\n", " tts = gTTS(text=text, lang='en')\n", " # Save the audio to a file\n", " tts.save(audio_file)\n", " return audio_file\n" ], "metadata": { "id": "9kNeRkHeg3S_" }, "execution_count": 9, "outputs": [] }, { "cell_type": "code", "source": [ "from collections import defaultdict\n", "\n", "from moviepy.editor import ImageClip, concatenate_videoclips, AudioFileClip\n", "import numpy as np\n", "\n", "def generate_video(images, audio_file):\n", " clips = []\n", " for image in images:\n", " # Convert PIL Image to numpy array\n", " image_array = np.array(image)\n", " # Create ImageClip from numpy array\n", " clip = ImageClip(image_array)\n", " # Set duration for each image clip\n", " clips.append(clip.set_duration(5))\n", "\n", " # Concatenate image clips into a video\n", " video = concatenate_videoclips(clips, method=\"compose\")\n", " # Load audio file\n", " audio = AudioFileClip(audio_file)\n", " # Set audio for the video\n", " video = video.set_audio(audio)\n", " # Write video file\n", " video.write_videofile(\"output.mp4\", fps=24)\n", "\n", "\n", "def main():\n", " # Generate story plot\n", " question = input(\"Ask a question to generate a story: \")\n", "\n", " # Generate story based on the question\n", " story = generate_story_from_question(question)\n", " # Extract image prompts from the story\n", " image_prompts = extract_image_prompts(story)\n", " # Generate images based on the prompts\n", " images = generate_images(image_prompts)\n", " # Generate audio from the story\n", " audio_file = generate_audio(story)\n", " # Generate video with subtitles\n", " generate_video(images, audio_file)\n", "\n", "if __name__ == \"__main__\":\n", " main()\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "42607a611fa14d819d4e36eda4ad0f97", "97f0ef6317ee450a86e26728b363a929", "04e00bfaac48484c80077d5494483281", "c29358fdd69b479195cfadb404fcb0a1", "c0ef6a3bd8464b99ae703bb6bf6d888d", "0723a47f9f1946208c7c4cf34a85dfa9", "c13847d803ab404a8c9b03d76db08e29", "56373ca8687c45c38512e510a1b8eb77", "ceffbe3ce2d6436184895d97ce4ca5d2", "6e5197f9d0b6476fa95fdb6c93a11441", "9f3e922b398c4013a76e7862f3b4b3ed", "a968b782dea3438f90435950ecf4525c", "b787f8af5a9949f5baa1b481fe16a49c", "7f8742a64c19405899101d06d619ff90", "6048b6bc9e3241b7ba06fca5188bc986", "360b18ea0167460dacbb530875637248", "fd505703887647c7a1cfd2e28b5a3bb4", "87b12daf55954d0aa130f55d73de26f0", "07301bcfa1b14cf8ac8aab2fea446d65", 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"5a88ed75e96c4c91a14f90109a81eae3", "ff49daaf530947ee9841ab20557f9300", "9806ab6fba3a4d1eae0a642f69b3f9df", "9961200418244df3b6ac23cd9471513a", "cf5bdfbb705843809d67c869c3cc209a" ] }, "id": "nxmRIUFycsiE", "outputId": "73f4cc28-4453-4aba-8e9b-14d49726365f" }, "execution_count": 10, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ask a question to generate a story: story of spiderman in jungle\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "WARNING:py.warnings:/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n", "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", "You will be able to reuse this secret in all of your notebooks.\n", "Please note that authentication is recommended but still optional to access public models or datasets.\n", " warnings.warn(\n", "\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "tokenizer_config.json: 0%| | 0.00/287 [00:00.load_config(...) followed by .from_config(...) instead. Otherwise, please make sure to pass a configuration dictionary instead. This functionality will be removed in v1.0.0.\n", " deprecate(\"config-passed-as-path\", \"1.0.0\", deprecation_message, standard_warn=False)\n", "\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "unet/config.json: 0%| | 0.00/1.68k [00:00" ], "application/javascript": [ "\n", " async function download(id, filename, size) {\n", " if (!google.colab.kernel.accessAllowed) {\n", " return;\n", " }\n", " const div = document.createElement('div');\n", " const label = document.createElement('label');\n", " label.textContent = `Downloading \"${filename}\": `;\n", " div.appendChild(label);\n", " const progress = document.createElement('progress');\n", " progress.max = size;\n", " div.appendChild(progress);\n", " document.body.appendChild(div);\n", "\n", " const buffers = [];\n", " let downloaded = 0;\n", "\n", " const channel = await google.colab.kernel.comms.open(id);\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", "\n", " for await (const message of channel.messages) {\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", " if (message.buffers) {\n", " for (const buffer of message.buffers) {\n", " buffers.push(buffer);\n", " downloaded += buffer.byteLength;\n", " progress.value = downloaded;\n", " }\n", " }\n", " }\n", " const blob = new Blob(buffers, {type: 'application/binary'});\n", " const a = document.createElement('a');\n", " a.href = window.URL.createObjectURL(blob);\n", " a.download = filename;\n", " div.appendChild(a);\n", " a.click();\n", " div.remove();\n", " }\n", " " ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "download(\"download_b106f1ac-c6cf-4eb1-93ac-095b34a732f3\", \"output.mp4\", 1790003)" ] }, "metadata": {} } ] } ] }