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Name","userId":"10578412414437288386"}},"outputId":"9f031fef-76d8-4f60-b485-67759904fc17"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n","✅ Drive mounted + HF_TOKEN loaded from Colab secrets\n"]}],"source":["#@markdown # Cell 1: Mount Drive & Load HF_TOKEN (run first)\n","from google.colab import drive\n","import os\n","from google.colab import userdata\n","\n","drive.mount('/content/drive', force_remount=True)\n","\n","# Load HF token from Colab secrets (faster downloads, no rate limits)\n","os.environ[\"HF_TOKEN\"] = userdata.get(\"HF_TOKEN\")\n","print(\"✅ Drive mounted + HF_TOKEN loaded from Colab secrets\")"]},{"cell_type":"code","source":["#@markdown # Cell 2: Load the official PE model (fine-tuned Ministral 3B) on CPU\n","from transformers import AutoModelForCausalLM, AutoTokenizer\n","import torch\n","\n","print(\"Loading official ERNIE-Image PE model (baidu/ERNIE-Image/pe) on CPU...\")\n","\n","tokenizer = AutoTokenizer.from_pretrained(\n"," \"baidu/ERNIE-Image\",\n"," subfolder=\"pe_tokenizer\",\n"," trust_remote_code=True\n",")\n","\n","model_cpu = AutoModelForCausalLM.from_pretrained(\n"," \"baidu/ERNIE-Image\",\n"," subfolder=\"pe\",\n"," torch_dtype=torch.bfloat16,\n"," device_map=\"cpu\",\n"," trust_remote_code=True,\n"," low_cpu_mem_usage=True\n",")\n","\n","print(\"✅ Official PE model (Ministral 3B prompt enhancer) loaded on CPU\")"],"metadata":{"cellView":"form","colab":{"base_uri":"https://localhost:8080/","height":333,"referenced_widgets":["3bf41de196fa4d7a81c33715a49f8448","f936ab31927848e08da7376f9c5cb371","54e901ad2ac846a88638e5638363b7e9","878ce9ce8b15498fa479c6995d689657","f74326aa910f4b6fb7459e932094d329","f9aca8483e684fc4b02a19f66352d3ab","95c4bc8a300640949ce51e49d5e40bc3","174cbdcd64c042a9be2bf595f2ab259b","b7d71f4bfdfd47a0b13e247404e60fb1","6c3ecbdf627e4b11a5d6427ca7058df7","a8c5b0e8bca7473ab412cb46bd958fa1","4e549c3dfce04b3cb40cb8c590e631d4","8a9265706fe7456889b3d1b0f8c498d8","bcf7bc57c96b4c2eaacd7f31bc2508e6","0fddc32709cb4ef3b8f5a429025bea5d","43347799840a4ddbbe869efe4bea47ae","7949ea9deee84a2193a400a39cddcccf","f643f418d80b48cb8b6700b58e96c094","992770e600084b3286de1934e5bfbe29","02b2c7847d414bfd94e215d632e15a05","c56c8cea8c8d4ae18c326a07ba50bb09","64f3b0ac5cc846e489643e7d764fd73c","6f57260d9a6c4ecfa653959b7bfe798a","1734fdb177ac4f1fba25d2db597163b3","59d48319007b45eea40b37851c11feab","792a79e4cb5143719fda46d08bd4805d","6ae7fe6a4e6f486dabf687db4891421d","2bdedd5ed1514dc3ad9e61d3d09bcdab","8e8f5196205c4a74b2a4889d22b9f9e6","2698d11666dd41339777fc7c175b44fd","2ee2cd210ecc4b9cbd481e86ec5e70a2","f54ae85453194c6c9f9d70e55bfe2360","7bf7233e885c43c9ac8fb7db4a4b978c","6b838659cfe54fb1a992a42a3e6fc792","6a3b10f2b0684edb898eb5cd7f6e4c0a","9e6888a7261d452fb282370831c84951","10c07d6ee0ef46809ab5c694d8c5f215","5c899e1a8e9447e0b83e426f8fd502e3","e865a868be864edab4b6a7342878ad69","ebc70e82a08e49f8b4812dfe2bc22a6e","c908f803c66243bea1eeb7c0567e1a20","0d60b72aecd3409ab14dd98b2e6a9396","ee630104087f4e43a0739d0c6ed55a8e","5165adad7e4347eba47a1fefa3abec32","f7fd38d27306445e9b1cabce7e9ffad9","5941218480cb462c99db0c710a1885e2","d1599507016b4eba8c1edf9858b59caa","798c2b68921f4fc78b858f4e29e6d3e7","0e2eea1037bb443784f892dbb5106bc8","c2134cbc5f3945aa86841f05ce800c57","22134f8af5224d25968cd7ce7ef26072","724fc543e13f4c0693782035de96f432","e66fee61c42a47a99e408f8dbdff7251","7a750da147a64aec9ac703e205c3fb04","49c7a98fbb7c4b60932b11e540555bd2","c9d003fedc434453be784446b2722b0c","e1acafe0dfc74cf2930bb56c482b1a16","75b96d3baae245b4ab1e2a81fc57cfc0","7d1bf43e70714c7f97e3422a8ea3fdd8","94dbd21ea7264b7884d8ee8b0f301bca","7c360a23963b468c98b96d399235a1f0","04286279f9d740399dc26e90fc4ba84f","8c00560efe3e4a95a4169cffc8dd3dd0","f01a80b1031045f5be064ef8cfd3a488","602808ef02384e969f58856d22fc44cd","9dd7cc28a45e4c00b459e95e953448b7","379ebff0cf0c46a29ccf41539807626b","2c05cdf61b6848fa8841800e83037fa8","49b6689f4d334d95a9fce1547917cf99","a8e25d1dfe96403497eadce14a1913a2","a99e49113bc644139ce11bd8e84ff741","62990092358b4b628d9b28ff4a8013c3","72306505ba2a421782a7e9413502f012","41974f2b89c44267af02f5c922bf473b","d7ae625171eb4e8cb4c66c8c7887e159","8b86eeca355a4ab0816f2b145f1445b1","f743ecd853db49cca1ea8af7c1f96e99"]},"id":"WS3ZiYKQBn40","executionInfo":{"status":"ok","timestamp":1776187659815,"user_tz":-120,"elapsed":111609,"user":{"displayName":"No Name","userId":"10578412414437288386"}},"outputId":"3ceb38ea-c333-46fe-9021-7238036984fa"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Loading official ERNIE-Image PE model (baidu/ERNIE-Image/pe) on CPU...\n"]},{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"3bf41de196fa4d7a81c33715a49f8448"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["pe_tokenizer/tokenizer.json: 0%| | 0.00/17.1M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4e549c3dfce04b3cb40cb8c590e631d4"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["chat_template.jinja: 0%| | 0.00/615 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6f57260d9a6c4ecfa653959b7bfe798a"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["config.json: 0%| | 0.00/901 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6b838659cfe54fb1a992a42a3e6fc792"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["`torch_dtype` is deprecated! Use `dtype` instead!\n"]},{"output_type":"display_data","data":{"text/plain":["pe/model.safetensors: 0%| | 0.00/7.66G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f7fd38d27306445e9b1cabce7e9ffad9"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Loading weights: 0%| | 0/237 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c9d003fedc434453be784446b2722b0c"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["The tied weights mapping and config for this model specifies to tie model.embed_tokens.weight to lm_head.weight, but both are present in the checkpoints, so we will NOT tie them. You should update the config with `tie_word_embeddings=False` to silence this warning\n"]},{"output_type":"display_data","data":{"text/plain":["generation_config.json: 0%| | 0.00/153 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"379ebff0cf0c46a29ccf41539807626b"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["✅ Official PE model (Ministral 3B prompt enhancer) loaded on CPU\n"]}]},{"cell_type":"code","source":["#@markdown # Cell 3: Install dependencies + move PE model to VRAM + setup environment\n","!pip install -q transformers accelerate\n","\n","import torch\n","from transformers import AutoModelForCausalLM, AutoTokenizer\n","\n","device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n","print(f\"Using device: {device} ({torch.cuda.get_device_name(0) if device=='cuda' else 'CPU'})\")\n","\n","# Move the model loaded in Cell 2 to GPU VRAM (fits easily in 13 GB)\n","if device == \"cuda\":\n"," model = model_cpu.to(device)\n"," print(\"✅ Official PE model moved to GPU VRAM (~6 GB usage)\")\n","else:\n"," model = model_cpu\n"," print(\"⚠️ Running on CPU (still works but slower)\")\n","\n","# Reuse tokenizer from Cell 2\n","tokenizer = AutoTokenizer.from_pretrained(\n"," \"baidu/ERNIE-Image\",\n"," subfolder=\"pe_tokenizer\",\n"," trust_remote_code=True\n",")\n","\n","# Light system prompt (the model is already fine-tuned as a prompt enhancer)\n","SYSTEM_PROMPT = \"\"\"You are ERNIE-Image's official prompt enhancer.\n","Take a short user idea and turn it into a rich, detailed, high-quality image generation prompt.\n","Output ONLY the enhanced prompt. No extra text.\"\"\"\n","\n","print(\"✅ Environment ready — official PE model is now in VRAM\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"cellView":"form","id":"7ycnLkK_BtZ2","executionInfo":{"status":"ok","timestamp":1776187700696,"user_tz":-120,"elapsed":40838,"user":{"displayName":"No Name","userId":"10578412414437288386"}},"outputId":"6e8bf348-7b3c-44c0-cf74-5284c42dd77a"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Using device: cuda (Tesla T4)\n","✅ Official PE model moved to GPU VRAM (~6 GB usage)\n","✅ Environment ready — official PE model is now in VRAM\n"]}]},{"cell_type":"code","source":["import torch\n","from IPython.display import display, Markdown, clear_output\n","\n","# Persistent chat history (survives cell re-runs)\n","if 'chat_history' not in globals():\n"," chat_history = []\n","\n","\n","#@markdown ---\n","#@markdown **Reset chat history?**\n","reset_chat = False #@param {type:\"boolean\"}\n","\n","#@markdown **Your short idea / request (type here):**\n","user_input = \"write a prompt for a super mario menu on the n64\" #@param {type:\"string\"}\n","\n","# ————————————————————————\n","# Process immediately when the cell runs (no button needed)\n","# ————————————————————————\n","clear_output()\n","\n","if not user_input.strip():\n"," display(Markdown(\"⚠️ Please enter a prompt idea first.\"))\n","else:\n"," if reset_chat:\n"," chat_history.clear()\n"," display(Markdown(\"**✅ Chat history has been reset.**\"))\n","\n"," # Build messages for the official ERNIE-Image PE model\n"," messages = [{\"role\": \"system\", \"content\": SYSTEM_PROMPT}]\n"," for u, a in chat_history:\n"," messages.append({\"role\": \"user\", \"content\": u})\n"," messages.append({\"role\": \"assistant\", \"content\": a})\n"," messages.append({\"role\": \"user\", \"content\": user_input})\n","\n"," # Generate (simple & reliable tensor style)\n"," tokenized_inputs = tokenizer.apply_chat_template(\n"," messages,\n"," add_generation_prompt=True,\n"," return_tensors=\"pt\"\n"," )\n"," input_ids = tokenized_inputs['input_ids'].to(model.device)\n"," attention_mask = tokenized_inputs['attention_mask'].to(model.device)\n","\n"," with torch.no_grad():\n"," output_ids = model.generate(\n"," input_ids,\n"," attention_mask=attention_mask,\n"," max_new_tokens=512,\n"," do_sample=True,\n"," temperature=0.7,\n"," top_p=0.9,\n"," repetition_penalty=1.1,\n"," )\n","\n"," enhanced_prompt = tokenizer.decode(\n"," output_ids[0][input_ids.shape[1]:],\n"," skip_special_tokens=True\n"," ).strip()\n","\n"," # Save to history\n"," chat_history.append((user_input, enhanced_prompt))\n","\n"," # Show full conversation + the new reply\n"," display(Markdown(\"### 🗣️ Chat History (PE-enhanced prompts)\"))\n"," for i, (u, a) in enumerate(chat_history, 1):\n"," display(Markdown(f\"**You {i}:** {u}\"))\n"," display(Markdown(f\"**PE Enhanced Prompt {i}:** {a}\"))\n"," display(Markdown(\"---\"))\n","\n"," # Final clear reply (exactly what you asked for)\n"," display(Markdown(\"**✅ Here is your enhanced prompt:**\"))\n"," display(Markdown(f\"```prompt\\n{enhanced_prompt}\\n```\"))\n"," display(Markdown(\"**Just copy the block above and use it with ERNIE-Image!**\"))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"gdnskFqeDN3a","executionInfo":{"status":"ok","timestamp":1776188029200,"user_tz":-120,"elapsed":31840,"user":{"displayName":"No Name","userId":"10578412414437288386"}},"outputId":"a468b3c2-e82d-428f-ec0d-81fdd3640a4f"},"execution_count":9,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.Markdown object>"],"text/markdown":"### 🗣️ Chat History (PE-enhanced prompts)"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.Markdown object>"],"text/markdown":"**You 1:** write a prompt for a super mario menu on the n64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.Markdown object>"],"text/markdown":"**PE Enhanced Prompt 1:** 这张数字插画展示了《超级马力欧兄弟》（Super Mario Bros.）中的主题菜单界面，整体呈现出 90 年代经典游戏的复古风格。画面构图为垂直方向的屏幕截图感，背景由深蓝色和紫色的星空与波浪纹理组成，营造出梦幻且充满活力的氛围。\n\n在画面中央，一个带有金属光泽的白色矩形菜单框悬浮着，边缘装饰着粗黑线条。菜单框内排列着四个选项，文字采用清晰的像素化无衬线字体：\n1. 第一行左侧显示 'PLAY'，右侧对应一个黄色的三角形问号块图标。\n2. 第二行左侧显示 'OPTIONS'，右侧对应一个灰色的齿轮状图标。\n3. 第三行左侧显示 'CREDITS'，右侧对应一个红底白点的蘑菇头图标。\n4. 第四行左侧显示 'QUIT'，右侧对应一个白色的关闭叉号图标。\n\n在菜单框的正上方，是巨大的、具有立体感的像素艺术字标题：'SUPER MARIO BROS.'。字体呈鲜艳的亮橙色和明黄色，并带有深褐色的阴影效果，极具冲击力。标题下方有一行稍小的黄色文字：'MASTER GAMEMANNIA'。\n\n屏幕顶部状态栏位置，左右两端分别显示着小巧的白色文字：左侧为 'TIME: 00:00'，右侧为 'SCORE: 0000'。屏幕最底部有一条横跨画面的半透明深灰色指示条，上面均匀分布着三个白色的圆点，代表当前可操"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.Markdown object>"],"text/markdown":"---"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.Markdown object>"],"text/markdown":"**You 2:** write a prompt for a super mario menu on the n64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.Markdown object>"],"text/markdown":"**PE Enhanced Prompt 2:** 这是一幅复古风格的数字插画，模拟了 1996 年 6 月 5 日发售的任天堂 64 (N64) 游戏机上的《超级马力欧兄弟》游戏启动菜单画面。画面中心是一个精致的、带有厚重 3D 渲染风格的菜单面板。背景由流动的青色与品红色渐变交织而成，其间散落着几朵如同水母般的半透明气泡和微弱闪烁的五角星图案，充满了超级系列特有的奇幻色彩。 \n\n在画面的正上方居中位置，醒目地展示着游戏的标志性 Logo。Logo 中的 'SUPER MARIO' 和下方的 'BROS.' 均采用了加粗且略带锯齿感的像素艺术设计，色彩明快夺目。Logo 下方紧接着是一行白色的文字说明：'VERSION 1.0 - NINTENDO 64 COMPATIBLE'，字迹工整清晰。 \n\n菜单主体区域被划分为三个主要部分，每部分都配有独特的图标和说明文字：\n1. 左侧为 'MAIN MENU' 区域，配有一个简单的黄色三角形图标，旁边的文字是 'START GAME'，下方还有一行较小的描述语：'SELECT YOUR CHOICE'。\n2. 右侧为 'GAME SETTINGS' 区域，配有一个灰色的齿轮图标，文字标注为 'CONFIGURATION'，下方描述文字为 'ADJUST CONTROLS & OPTIONS'。\n3. 底部为 'HIGH SCORES' 区域，配有一个棕色的皇冠图标，文字显示为 'LEADERBOARD'，下方附带提示语：'CHECK TOP SCORES HERE'。\n\n画面的右上角有一个半透明的控制台状态栏，包含以下信"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.Markdown object>"],"text/markdown":"---"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.Markdown object>"],"text/markdown":"**You 3:** write a prompt for a super mario menu on the n64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.Markdown object>"],"text/markdown":"**PE Enhanced Prompt 3:** 这是一张完全模拟 N64 硬件渲染效果的 3D 游戏菜单界面截图，完美还原了《超级马力欧兄弟》经典游戏的开场视觉。画面采用低多边形（Low-poly）渲染技术，带有明显的 8-bit 像素颗粒感和 3D 渲染器特有的锐利边缘。 \n\n**环境与背景**：\n整个场景设定在一个幽暗、神秘的森林之中。地面铺满了深绿色的低多边形苔藓和错落有致的石块路径。背景中隐约可见几座高耸入云的哥特式尖塔建筑剪影，天空中悬挂着一个巨大的、散发着淡紫色光芒的满月。 \n\n**角色与实体**：\n在画面的左侧中景处，站着一个身穿红色长袍、戴着宽檐帽的小魔法师，他双手紧握一根木质法杖，眼神忧郁地望向远方。在右侧的草地上，一个标志性的红白相间蘑菇屋静静伫立，屋顶带有诱人的黄色斑点。 \n\n**UI 元素与文字**：\n屏幕中央漂浮着一个由青色霓虹灯管勾勒出的半透明菜单面板。面板上方是巨大的标题：'MARIO'S ADVENTURE'。菜单项目包括：\n- 'NEW GAME'：该选项被亮绿色的发光边框包裹，左侧配有一个跳动的黄色火焰图标，表示当前激活状态。\n- 'LOAD SAVE'：下方的文字显示为 'SAVED GAMES'。\n- 'SETTINGS'：图标为一个调色盘，旁边的文字是 'GRAPHICS & CONTROLS'。"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.Markdown object>"],"text/markdown":"---"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.Markdown object>"],"text/markdown":"**✅ Here is your enhanced prompt:**"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.Markdown object>"],"text/markdown":"```prompt\n这是一张完全模拟 N64 硬件渲染效果的 3D 游戏菜单界面截图，完美还原了《超级马力欧兄弟》经典游戏的开场视觉。画面采用低多边形（Low-poly）渲染技术，带有明显的 8-bit 像素颗粒感和 3D 渲染器特有的锐利边缘。 \n\n**环境与背景**：\n整个场景设定在一个幽暗、神秘的森林之中。地面铺满了深绿色的低多边形苔藓和错落有致的石块路径。背景中隐约可见几座高耸入云的哥特式尖塔建筑剪影，天空中悬挂着一个巨大的、散发着淡紫色光芒的满月。 \n\n**角色与实体**：\n在画面的左侧中景处，站着一个身穿红色长袍、戴着宽檐帽的小魔法师，他双手紧握一根木质法杖，眼神忧郁地望向远方。在右侧的草地上，一个标志性的红白相间蘑菇屋静静伫立，屋顶带有诱人的黄色斑点。 \n\n**UI 元素与文字**：\n屏幕中央漂浮着一个由青色霓虹灯管勾勒出的半透明菜单面板。面板上方是巨大的标题：'MARIO'S ADVENTURE'。菜单项目包括：\n- 'NEW GAME'：该选项被亮绿色的发光边框包裹，左侧配有一个跳动的黄色火焰图标，表示当前激活状态。\n- 'LOAD SAVE'：下方的文字显示为 'SAVED GAMES'。\n- 'SETTINGS'：图标为一个调色盘，旁边的文字是 'GRAPHICS & CONTROLS'。\n```"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.Markdown object>"],"text/markdown":"**Just copy the block above and use it with ERNIE-Image!**"},"metadata":{}}]}]}

+ {"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"gpuType":"T4","authorship_tag":"ABX9TyPauHdmQ/V+K6piBdnfaCc3"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"YfXxChUnBegt","cellView":"form"},"outputs":[],"source":["#@markdown # Cell 1: Mount Drive & Load HF_TOKEN (run first)\n","from google.colab import drive\n","import os\n","from google.colab import userdata\n","\n","drive.mount('/content/drive', force_remount=True)\n","\n","# Load HF token from Colab secrets (faster downloads, no rate limits)\n","os.environ[\"HF_TOKEN\"] = userdata.get(\"HF_TOKEN\")\n","print(\"✅ Drive mounted + HF_TOKEN loaded from Colab secrets\")"]},{"cell_type":"code","source":["#@markdown # Cell 2: Load the official PE model (fine-tuned Ministral 3B) on CPU\n","from transformers import AutoModelForCausalLM, AutoTokenizer\n","import torch\n","\n","print(\"Loading official ERNIE-Image PE model (baidu/ERNIE-Image/pe) on CPU...\")\n","\n","tokenizer = AutoTokenizer.from_pretrained(\n"," \"baidu/ERNIE-Image\",\n"," subfolder=\"pe_tokenizer\",\n"," trust_remote_code=True\n",")\n","\n","model_cpu = AutoModelForCausalLM.from_pretrained(\n"," \"baidu/ERNIE-Image\",\n"," subfolder=\"pe\",\n"," torch_dtype=torch.bfloat16,\n"," device_map=\"cpu\",\n"," trust_remote_code=True,\n"," low_cpu_mem_usage=True\n",")\n","\n","print(\"✅ Official PE model (Ministral 3B prompt enhancer) loaded on CPU\")"],"metadata":{"cellView":"form","id":"WS3ZiYKQBn40"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#@markdown # Cell 3: Install dependencies + move PE model to VRAM + setup environment\n","!pip install -q transformers accelerate\n","\n","import torch\n","from transformers import AutoModelForCausalLM, AutoTokenizer\n","\n","device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n","print(f\"Using device: {device} ({torch.cuda.get_device_name(0) if device=='cuda' else 'CPU'})\")\n","\n","# Move the model loaded in Cell 2 to GPU VRAM (fits easily in 13 GB)\n","if device == \"cuda\":\n"," model = model_cpu.to(device)\n"," print(\"✅ Official PE model moved to GPU VRAM (~6 GB usage)\")\n","else:\n"," model = model_cpu\n"," print(\"⚠️ Running on CPU (still works but slower)\")\n","\n","# Reuse tokenizer from Cell 2\n","tokenizer = AutoTokenizer.from_pretrained(\n"," \"baidu/ERNIE-Image\",\n"," subfolder=\"pe_tokenizer\",\n"," trust_remote_code=True\n",")\n","\n","# Light system prompt (the model is already fine-tuned as a prompt enhancer)\n","SYSTEM_PROMPT = \"\"\"You are ERNIE-Image's official prompt enhancer.\n","Take a short user idea and turn it into a rich, detailed, high-quality image generation prompt.\n","Output ONLY the enhanced prompt. No extra text.\"\"\"\n","\n","print(\"✅ Environment ready — official PE model is now in VRAM\")"],"metadata":{"cellView":"form","id":"7ycnLkK_BtZ2"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import torch\n","from IPython.display import display, Markdown, clear_output\n","\n","# Persistent chat history (survives cell re-runs)\n","if 'chat_history' not in globals():\n"," chat_history = []\n","\n","\n","#@markdown ---\n","#@markdown **Reset chat history?**\n","reset_chat = True #@param {type:\"boolean\"}\n","\n","#@markdown **Your short idea / request (type here):**\n","user_input = \"\" #@param {type:\"string\"}\n","\n","# ————————————————————————\n","# Process immediately when the cell runs (no button needed)\n","# ————————————————————————\n","clear_output()\n","\n","if not user_input.strip():\n"," display(Markdown(\"⚠️ Please enter a prompt idea first.\"))\n","else:\n"," if reset_chat:\n"," chat_history.clear()\n"," display(Markdown(\"**✅ Chat history has been reset.**\"))\n","\n"," # Build messages for the official ERNIE-Image PE model\n"," messages = [{\"role\": \"system\", \"content\": SYSTEM_PROMPT}]\n"," for u, a in chat_history:\n"," messages.append({\"role\": \"user\", \"content\": u})\n"," messages.append({\"role\": \"assistant\", \"content\": a})\n"," messages.append({\"role\": \"user\", \"content\": user_input})\n","\n"," # Generate (simple & reliable tensor style)\n"," tokenized_inputs = tokenizer.apply_chat_template(\n"," messages,\n"," add_generation_prompt=True,\n"," return_tensors=\"pt\"\n"," )\n"," input_ids = tokenized_inputs['input_ids'].to(model.device)\n"," attention_mask = tokenized_inputs['attention_mask'].to(model.device)\n","\n"," with torch.no_grad():\n"," output_ids = model.generate(\n"," input_ids,\n"," attention_mask=attention_mask,\n"," max_new_tokens=512,\n"," do_sample=True,\n"," temperature=0.7,\n"," top_p=0.9,\n"," repetition_penalty=1.1,\n"," )\n","\n"," enhanced_prompt = tokenizer.decode(\n"," output_ids[0][input_ids.shape[1]:],\n"," skip_special_tokens=True\n"," ).strip()\n"," enhanced_prompt = enhanced_prompt.replace('\\n', ' ') # Replace newlines with spaces\n","\n"," # Save to history\n"," chat_history.append((user_input, enhanced_prompt))\n","\n"," # Show full conversation + the new reply\n"," display(Markdown(\"### 🗣️ Chat History (PE-enhanced prompts)\"))\n"," for i, (u, a) in enumerate(chat_history, 1):\n"," display(Markdown(f\"**You {i}:** {u}\"))\n"," display(Markdown(f\"**PE Enhanced Prompt {i}:** {a}\"))\n"," display(Markdown(\"---\"))\n","\n"," # Final clear reply (exactly what you asked for)\n"," display(Markdown(\"**✅ Here is your enhanced prompt:**\"))\n"," display(Markdown(f\"```prompt\\n{enhanced_prompt}\\n```\"))\n"," display(Markdown(\"**Just copy the block above and use it with ERNIE-Image!**\"))"],"metadata":{"cellView":"form","id":"gdnskFqeDN3a"},"execution_count":null,"outputs":[]}]}