from diffusers import DiffusionPipeline, DPMSolverSinglestepScheduler import torch from transformers import TrainingArguments, Trainer # Load the Mann-E Dreams model pipe = DiffusionPipeline.from_pretrained("mann-e/Mann-E_Dreams", torch_dtype=torch.float16).to("cuda") # Change the scheduler to improve results pipe.scheduler = DPMSolverSinglestepScheduler.from_config(pipe.scheduler.config, use_karras_sigmas=True) # Load your dataset of images and text (this is a placeholder) # You need to upload your own dataset or load it from the cloud. my_dataset = None # This is where you'll load your custom dataset # Define training arguments (batch size, epochs, etc.) trainer = Trainer( model=pipe, # This is the Mann-E Dreams model args=TrainingArguments( output_dir="./results", per_device_train_batch_size=4, num_train_epochs=3, logging_dir="./logs", logging_steps=10, ), train_dataset=my_dataset, ) # Start training the model with your custom dataset trainer.train() # You can deploy this model directly to Hugging Face