Create App.py

App.py

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+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