Spaces:
Sleeping
Sleeping
| title: ZeroGPU | |
| emoji: 🖼 | |
| colorFrom: purple | |
| colorTo: red | |
| sdk: gradio | |
| sdk_version: 5.25.2 | |
| app_file: app.py | |
| pinned: false | |
| license: apache-2.0 | |
| Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference | |
| commands: | |
| download images: python download.py -i 1 -r 2 -o /home/user/app/image_tmp -z | |
| pip install git+https://github.com/huggingface/diffusers | |
| accelerate launch \ | |
| --deepspeed_config_file ds_config.json \ | |
| diffusers/examples/dreambooth/train_dreambooth.py \ | |
| --pretrained_model_name_or_path="runwayml/stable-diffusion-v1-5" \ | |
| --instance_data_dir="./nyc_ads_dataset" \ | |
| --instance_prompt="a photo of an urbanad nyc" \ | |
| --output_dir="./nyc-ad-model" \ | |
| --resolution=100 \ | |
| --train_batch_size=1 \ | |
| --gradient_accumulation_steps=1 \ | |
| --gradient_checkpointing \ | |
| --learning_rate=5e-6 \ | |
| --lr_scheduler="constant" \ | |
| --lr_warmup_steps=0 \ | |
| --max_train_steps=400 \ | |
| --mixed_precision="fp16" \ | |
| --checkpointing_steps=100 \ | |
| --checkpoints_total_limit=1 \ | |
| --report_to="tensorboard" \ | |
| --logging_dir="./nyc-ad-model/logs" | |
| fine tune a trained model: --pretrained_model_name_or_path="./nyc-ad-model/checkpoint-400" \ | |
| export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True | |
| pipeline: | |
| # 1 Fully Fine‑tune image model with ZeRO | |
| accelerate launch --deepspeed_config_file=ds_config_zero3.json train_lora.py | |
| fully_fine_tine_stablediffusion | |
| # 2 SFT 120B OSS 语言模型 with QLoRA | |
| lauguage_model_fine_tuning | |
| # 3 RLHF PPO 120B OSS 语言模型 with QLoRA : 训练 reward model | |
| lauguage_model_fine_tuning | |
| # 4 distill 120B OSS模型给20B OSS模型 | |
| lauguage_model_fine_tuning | |
| 用 Teacher 生成 Response,student模型用LoRA fine tuning | |
| # 5 Build RAG index embedding table | |
| retrieval_augmented_generation | |
| # 6 Inference with RAG | |
| inference.py | |
| system flow: | |
| input: business or product description text | |
| 1. 根据input用RAG取embedding | |
| 1. GPT‑OSS 生成 4 个广告文案 + 标题 + 口号(可选语气:专业/活泼/极简) | |
| 2. GPT‑OSS 基于选中文案生成 扩展视觉提示词(主体、配色、镜头、艺术风格) | |
| 3. stablediffusion model 生成 4 张草图(可选 ControlNet-Layout/Logo 插入) | |
| 4. 返回4张海报+后处理 | |
| output: an advertisement sentence and post image | |
| design details: | |
| LoRA fine tune teacher OSS 120B model using smangrul/ad-copy-generation (广告文案生成) | |
| LoRA distill knowledge to OSS 20B model |