https://huggingface.co/docs/diffusers/using-diffusers/write_own_pipelinehttps://huggingface.co/docs/diffusers/using-diffusers/write_own_pipelinediffusers There are 3 modules: diffusion pipelines, noise schedulers, model. This library is very good, and its design ideas are comparable to those of the mmlab series. The mm series generation algorithm is in mmagic, but it is not as rich as diffusers. Furthermore, almost all new algorithm training and reasoning will use the standard […]
Tag: fuser
[Diffusion Model] HuggingFace Diffusers in practice
HuggingFace Diffusers in action 1. Environment preparation 2.DreamBooth 2.1 Introduction to Stable Diffusion 2.2 DreamBooth 3. Diffusers core API 4. Practical combat: Generate beautiful butterfly images 4.1 Download dataset 4.2 Scheduler 4.3 Define diffusion model 4.4 Create a diffusion model training loop 4.5 Image generation Method 1. Create a pipeline Method 2. Write a sampling […]
controlnet training in diffusers
train_controlnet.py accelerate = Accelerator()-> tokenizer = AutoTokenizer.from_pretrained(,”tokenizer”)-> text_encoder_cls = import_model_class_from_model_name_or_path(args.pretrained_model_name_or_path, args.revision) noise_scheduler = DDPMScheduler.from_pretrained(,’scheduler’) text_encoder = text_encoder_cls.from_pretrained(,’text_encoder’) vae = AutoencoderKL.from_pretrained(,’vae’) unet = UNet2DConditionModel.from_pretrained(,’unet’) if controlnet_model_name_or_path: controlnet = ControlNetModel.from_pretrained(controlnet_model_name_or_path) else: controlnet = ControlNetModel.from_unet(unet) vae.requires_grad_(False) unet.requires_grad_(False) text_encoder.requires_grad_(False) controlnet.train() optimizer_class = torch.optim.AdamW params_to_optimize = controlnet.parameters() optimizer = optimizer_class(params_to_optimize,args.learning_rate,betas=(args.adam_beta1,args.adam_beta2),args.adam_weight_decay,args.adam_epsilon) train_dataset = make_train_dataset(args,tokenizer,accelerator) – dataset = load_dataset(args.train_data_dir,cache_dir) — builder_instance = […]
Dreambooth fine-tuning and lora fine-tuning in diffusers
train_dreambooth.py Code: accelerator = Accelerator()-> # Generate class image if prior oreservation is enabled if args.with_prior_preservation: if cur_class_images<args.num_class_images: pipeline = DiffusionPipline.from_pretrained()-> sample_dataset = PromptDataset(args.class_prompt, num_new_images) sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size) for example in sample_dataloader: images = pipeline(example[‘prompt’]).images tokenizer = AutoTokenizer.from_pretrained(,”tokenizer”)-> text_encoder_cls = import_model_class_from_model_name_or_path()-> noise_scheduler = DDPMScheduler.from_pretrained(,”scheduler”)-> text_encoder = text_encoder_cls.from_pretrained(,”text_encoder”)-> vae = AutoencoderKL.from_pretrained(,”vae”)-> unet = UNet2DConditionModel.from_pretrained(,”unet”)-> accelerator.register_save_state_pre_hook(save_model_hook)-> […]
Hugging Face uses Stable diffusion Diffusers Transformers Accelerate Pipelines VAE
Diffusers A library that offers an implementation of various diffusion models, including text-to-image models. A library that provides implementations of different diffusion models. The code is the simplest. The domestic problem is that huggingface needs to be circumvented. Transformers A Hugging Face library that provides pre-trained deep learning models for natural language processing tasks. Provides […]
Fine-tuning of sd and fine-tuning of lora in diffusers
train_text_to_image.py Code: accelerator = Accelerator()-> noise_sheduler = DDPMScheduler.from_pretrained(,”scheduler”)-> tokenizer = CLIPTokenizer.from_pretrained(,”tokenizer”)-> text_encoder = CLIPTokenizer.form_pretrained(,”text_encoder”)-> vae = AutoencoderKL.from_pretrained(,”vae”)-> unet = UNet2DConditionModel(,’unet’)-> vae.requires_grad_(False)-> text_encoder.requires_grad_(False)-> unet.enable_gradient_checkpoint()-> optimizer_cls = torch.optim.AdamW-> optimizer = optimizer_cls(unet.parameters(),lr,betas,weight_decay,eps)-> dataset = load_dataset(dataset_name,dataset_config_name,cache_dir)-> train_transforms = transforms.Compose([])-> train_dataset = dataset[‘train’].with_transformer(preprocess_train)-> – images = [image.convert(“RGB”) for image in examples[image_column]] – examples[“pixel_values”] = [train_transforms(image) for image in images] – […]
Train your own ControlNet using diffusers
1. Introduction ControlNet is a neural network model that gives users fine-grained control over the generation of diffusion models by imposing additional conditions. This technique was originally proposed by the paper Adding Conditional Control to Text-to-Image Diffusion Models, and quickly became popular in the open source community of diffusion models. The author has open sourced […]
Training AIGC based on PPDiffusers-Yi Ba Pigeon Blacksmith Project
This article is from the AlStudio community boutique project, [click here] to view more boutique content >>> Yibage Blacksmith Shop Project The collection plan includes 18 kinds of weapons, such as knives, guns, swords, halberds, axes, battle-axes, hooks, forks, whips, maces, hammers, daggers, shovels, sticks, lances, clubs, spears, rakes, etc. Recommendation Start with 32g video […]
Training AIGC based on PPDiffusers-Yi Ba Pigeon Blacksmith Project
This article is from the AlStudio community boutique project, [click here] to view more boutique content >>> Yibage Blacksmith Shop Project The collection plan includes 18 kinds of weapons, knives, guns, swords, halberds, axes, battle-axes, hooks, forks, whips, maces, hammers, daggers, shovels, sticks, lances, clubs, spears, rakes, etc. Recommendation Start with 32g video memory, use […]
Training lora based on diffusers, AI dress-up
Alibaba Cloud Login- Welcome to Alibaba Cloud, a safe and stable cloud computing service platform Welcome to Alibaba Cloud, the world’s leading cloud computing and artificial intelligence technology company, Alibaba Cloud provides services for enterprises, developers and government agencies in more than 200 countries and regions Cloud computing basic services and solutions. Alibaba Cloud cloud […]