本文介绍了使用PaddleNLP等工具进行模型训练与推理的流程。先安装paddlenlp等依赖，再分别用dreambooth lora和文生图lora方式训练，设置参数并保存权重。之后可启动visualdl查看训练出图，最后加载训练好的文件，通过相关代码进行推理生成图像。
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!pip install -U paddlenlp ppdiffusers safetensors --user
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple Requirement already satisfied: paddlenlp in ./.data/webide/pip/lib/python3.7/site-packages (2.5.1) Requirement already satisfied: ppdiffusers in ./.data/webide/pip/lib/python3.7/site-packages (0.11.0) Requirement already satisfied: safetensors in ./.data/webide/pip/lib/python3.7/site-packages (0.2.8) Requirement already satisfied: paddlefsl in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlenlp) (1.1.0) Requirement already satisfied: sentencepiece in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlenlp) (0.1.96) Requirement already satisfied: huggingface-hub>=0.11.1 in ./.data/webide/pip/lib/python3.7/site-packages (from paddlenlp) (0.12.0) Requirement already satisfied: seqeval in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlenlp) (1.2.2) Requirement already satisfied: fastapi in ./.data/webide/pip/lib/python3.7/site-packages (from 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参数解释：
累积的步数，可不修改。注意：
!python train_dreambooth_lora.py \ --pretrained_model_name_or_path="runwayml/stable-diffusion-v1-5" \ --instance_data_dir="./dogs" \ --output_dir="./dream_booth_lora_outputs" \ --instance_prompt="a photo of sks dog" \ --resolution=512 \ --train_batch_size=1 \ --gradient_accumulation_steps=1 \ --checkpointing_steps=100 \ --learning_rate=1e-4 \ --report_to="visualdl" \ --lr_scheduler="constant" \ --lr_warmup_steps=0 \ --max_train_steps=500 \ --lora_rank=128 \ --validation_prompt="A photo of sks dog in a bucket" \ --validation_epochs=25 \ --validation_guidance_scale=5.0 \ --use_lion False \ --seed=0
[2025-02-23 10:13:09,015] [ WARNING] - You are using a model of type clip_text_model to instantiate a model of type . This is not supported for all configurations of models and can yield errors. W0223 10:13:09.018703 11490 gpu_resources.cc:61] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.2, Runtime API Version: 11.2 W0223 10:13:09.022612 11490 gpu_resources.cc:91] device: 0, cuDNN Version: 8.2. Train Steps: 20%|███████████████████████▌ | 100/500 [01:12<03:04, 2.16it/s, epoch=0019, step_loss=0.0413]Saved lora weights to ./dream_booth_lora_outputs/checkpoint-100 Train Steps: 40%|███████████████████████████████████████████████▌ | 200/500 [02:25<02:19, 2.15it/s, epoch=0039, step_loss=0.446]Saved lora weights to ./dream_booth_lora_outputs/checkpoint-200 Train Steps: 60%|██████████████████████████████████████████████████████████████████████▏ | 300/500 [03:37<01:33, 2.13it/s, epoch=0059, step_loss=0.00273]Saved lora weights to ./dream_booth_lora_outputs/checkpoint-300 Train Steps: 80%|███████████████████████████████████████████████████████████████████████████████████████████████▏ | 400/500 [04:53<00:47, 2.11it/s, epoch=0079, step_loss=0.275]Saved lora weights to ./dream_booth_lora_outputs/checkpoint-400 Train Steps: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 500/500 [05:44<00:00, 2.14it/s, epoch=0099, step_loss=0.00985]Saved lora weights to ./dream_booth_lora_outputs/checkpoint-500 Saved final lora weights to ./dream_booth_lora_outputs Train Steps: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 500/500 [05:48<00:00, 1.43it/s, epoch=0099, step_loss=0.00985]
--train_data_dir 这个需要放图文对的文件夹，里面是图片和txt。
--image_format 表示 train_data_dir 文件夹内的图片格式，比如png，jpg，jpeg
--use_lion Fasle 表示不使用lion优化器。
In [3]!python train_text_to_image_lora.py \ --pretrained_model_name_or_path="runwayml/stable-diffusion-v1-5" \ --output_dir="./text_to_image_lora_outputs3" \ --train_data_dir="mishanwu" \ --image_format="png" \ --resolution=512 \ --train_batch_size=1 \ --gradient_accumulation_steps=1 \ --checkpointing_steps=500 \ --learning_rate=6e-5 \ --report_to="visualdl" \ --lr_scheduler="cosine_with_restarts" \ --lr_warmup_steps=0 \ --max_train_steps=1000 \ --lora_rank=128 \ --validation_prompt="1girl, solo, black_background, looking_at_viewer, parted_lips, tears, brown_eyes" \ --validation_epochs=1 \ --validation_guidance_scale=5.0 \ --use_lion False \ --seed=0
[2025-02-23 11:11:34,386] [ WARNING] - You are using a model of type clip_text_model to instantiate a model of type . This is not supported for all configurations of models and can yield errors. W0223 11:11:34.389356 7327 gpu_resources.cc:61] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.2, Runtime API Version: 11.2 W0223 11:11:34.392953 7327 gpu_resources.cc:91] device: 0, cuDNN Version: 8.2. Resolving data files: 100%|████████████████| 581/581 [00:00<00:00, 50722.06it/s] Using custom data configuration default-4fb24e511b7f6118 Downloading and preparing dataset imagefolder/default to /home/aistudio/.cache/huggingface/datasets/imagefolder/default-4fb24e511b7f6118/0.0.0/37fbb85cc714a338bea574ac6c7d0b5be5aff46c1862c1989b20e0771199e93f... Downloading data files #0: 0%| | 0/19 [00:00 by passing `safety_checker=None`. Ensure that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered results in services or applications open to the public. PaddleNLP team, diffusers team and Hugging Face strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling it only for use-cases that involve analyzing network behavior or auditing its results. For more information, please have a look at https://github.com/huggingface/diffusers/pull/254 . Train Steps: 50%|▌| 500/1000 [04:16<03:54, 2.13it/s, epoch=0001, step_loss=0.0Saved lora weights to ./text_to_image_lora_outputs3/checkpoint-500 Train Steps: 58%|▌| 580/1000 [04:55<03:09, 2.22it/s, epoch=0001, step_loss=0.2You have disabled the safety checker forby passing `safety_checker=None`. Ensure that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered results in services or applications open to the public. PaddleNLP team, diffusers team and Hugging Face strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling it only for use-cases that involve analyzing network behavior or auditing its results. For more information, please have a look at https://github.com/huggingface/diffusers/pull/254 . Train Steps: 87%|▊| 870/1000 [07:40<00:58, 2.22it/s, epoch=0002, step_loss=0.0You have disabled the safety checker for by passing `safety_checker=None`. Ensure that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered results in services or applications open to the public. PaddleNLP team, diffusers team and Hugging Face strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling it only for use-cases that involve analyzing network behavior or auditing its results. For more information, please have a look at https://github.com/huggingface/diffusers/pull/254 . Train Steps: 100%|█| 1000/1000 [09:10<00:00, 1.25it/s, epoch=0003, step_loss=0.Saved lora weights to ./text_to_image_lora_outputs3/checkpoint-1000 You have disabled the safety checker for by passing `safety_checker=None`. Ensure that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered results in services or applications open to the public. PaddleNLP team, diffusers team and Hugging Face strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling it only for use-cases that involve analyzing network behavior or auditing its results. For more information, please have a look at https://github.com/huggingface/diffusers/pull/254 . Saved final lora weights to ./text_to_image_lora_outputs3 Train Steps: 100%|█| 1000/1000 [09:44<00:00, 1.71it/s, epoch=0003, step_loss=0.
import lora_helperfrom allinone import StableDiffusionPipelineAllinOnefrom ppdiffusers import DPMSolverMultistepSchedulerimport paddle# 基础模型，需要是paddle版本的权重，未来会加更多的权重pretrained_model_name_or_path = "runwayml/stable-diffusion-v1-5"# 我们加载safetensor版本的权重lora_outputs_path = "9070.safetensors"# 加载之前的模型pipe = StableDiffusionPipelineAllinOne.from_pretrained(pretrained_model_name_or_path, safety_checker=None) pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)# 加载lora权重from IPython.display import clear_output, display clear_output() pipe.apply_lora(lora_outputs_path)
|---------------当前的rank是 128！ |---------------当前的alpha是 128.0！ Loading lora_weights successfully!In [5]
import lora_helperfrom allinone import StableDiffusionPipelineAllinOnefrom ppdiffusers import DPMSolverMultistepScheduler prompt = "A photo of sks dog in a bucket"negative_prompt = ""guidance_scale = 8num_inference_steps = 25height = 512width = 512img = pipe(prompt, negative_prompt=negative_prompt, guidance_scale=guidance_scale, height=height, width=width, num_inference_steps=num_inference_steps).images[0] display(img) display(img.argument)
0%| | 0/25 [00:00{'prompt': 'A photo of sks dog in a bucket', 'negative_prompt': '', 'height': 512, 'width': 512, 'num_inference_steps': 25, 'guidance_scale': 8, 'num_images_per_prompt': 1, 'eta': 0.0, 'seed': 3574959348, 'latents': None, 'max_embeddings_multiples': 1, 'no_boseos_middle': False, 'skip_parsing': False, 'skip_weighting': False, 'epoch_time': 1676862593.5281241246}
# 加载 # 我们可以 # 最多 # 在这里 # 几个 # 默认为 # 中出 # 过程中 # 每隔 # 可不 # python # prompt # https # github # red # yy # cos # ai # 工具 # git
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