{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1d67fde4",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
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      "Collecting pillow\n",
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      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Installing collected packages: pillow\n",
      "Successfully installed pillow-10.4.0\n"
     ]
    }
   ],
   "source": [
    "pip install --upgrade opencv-python transformers diffusers torch accelerate sentencepiece torch torchvision torchaudio peft pillow gradio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "660228aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "df0bcf152c6748d9ae56e7c669d55ce8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading pipeline components...:   0%|          | 0/6 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\MSI\\AppData\\Roaming\\Python\\Python39\\site-packages\\transformers\\models\\clip\\feature_extraction_clip.py:28: FutureWarning: The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please use CLIPImageProcessor instead.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on local URL:  http://127.0.0.1:7869\n",
      "\n",
      "To create a public link, set `share=True` in `launch()`.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><iframe src=\"http://127.0.0.1:7869/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c88ef78e3fa84b969c50f0b18ca37be3",
       "version_major": 2,
       "version_minor": 0
      },
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       "  0%|          | 0/6 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "from diffusers import AnimateDiffPipeline, LCMScheduler, MotionAdapter\n",
    "from diffusers.utils import export_to_gif\n",
    "import os\n",
    "import gc\n",
    "import gradio as gr\n",
    "\n",
    "# Environment setup\n",
    "os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'expandable_segments:True'\n",
    "gc.collect()\n",
    "torch.cuda.empty_cache()\n",
    "gc.collect()\n",
    "torch.cuda.empty_cache()\n",
    "\n",
    "# Load models and pipeline\n",
    "adapter = MotionAdapter.from_pretrained(\"wangfuyun/AnimateLCM\", torch_dtype=torch.float16)\n",
    "pipe = AnimateDiffPipeline.from_pretrained(\"emilianJR/epiCRealism\", motion_adapter=adapter, torch_dtype=torch.float16)\n",
    "pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config, beta_schedule=\"linear\")\n",
    "pipe.load_lora_weights(\"wangfuyun/AnimateLCM\", weight_name=\"AnimateLCM_sd15_t2v_lora.safetensors\", adapter_name=\"lcm-lora\")\n",
    "pipe.set_adapters([\"lcm-lora\"], [0.8])\n",
    "pipe.enable_vae_slicing()\n",
    "pipe.enable_model_cpu_offload()\n",
    "\n",
    "# Hardcoded predefined prompts\n",
    "predefined_prompts = [\n",
    "    \"640*480 pixels, high resolution, ultra realistic\",\n",
    "    \"bad quality, worse quality, low resolution\"\n",
    "]\n",
    "\n",
    "def generate_gif(custom_prompt):\n",
    "    # Combine the predefined prompts with the custom prompt\n",
    "    prompt = custom_prompt + \", \" + predefined_prompts[0]\n",
    "    negative_prompt = predefined_prompts[1]\n",
    "    \n",
    "    output = pipe(\n",
    "        prompt=prompt,\n",
    "        negative_prompt=negative_prompt,\n",
    "        num_frames=32,\n",
    "        guidance_scale=2.0,\n",
    "        num_inference_steps=6,\n",
    "        generator=torch.Generator(\"cuda\").manual_seed(0),\n",
    "    )\n",
    "    frames = output.frames[0]\n",
    "    export_to_gif(frames, \"animatelcm.gif\")\n",
    "    return \"animatelcm.gif\"\n",
    "\n",
    "# Create Gradio interface\n",
    "with gr.Blocks() as demo:\n",
    "    gr.Markdown(\"## Animate LCM GIF Generator\")\n",
    "    custom_prompt_input = gr.Textbox(label=\"Custom Prompt\", placeholder=\"Enter your custom prompt here...\")\n",
    "    output_gif = gr.Image(label=\"Generated GIF\")\n",
    "    generate_button = gr.Button(\"Generate GIF\")\n",
    "    \n",
    "    generate_button.click(fn=generate_gif, inputs=custom_prompt_input, outputs=output_gif)\n",
    "\n",
    "# Launch the interface\n",
    "demo.launch()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d4339ee",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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