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