Update inner.py

inner.py

@@ -59,12 +59,12 @@ class TrainingConfig:
     image_size = 256  # the generated image resolution
     train_batch_size = 10
     eval_batch_size = 16  # how many images to sample during evaluation
-    num_epochs = 500
+    num_epochs = 2000
     gradient_accumulation_steps = 1
     learning_rate = 1e-4
     lr_warmup_steps = 250
-    save_image_epochs = 25
-    save_model_epochs = 500
+    save_image_epochs = 100
+    save_model_epochs = 2000
     mixed_precision = "fp16"  # `no` for float32, `fp16` for automatic mixed precision
     output_dir = "Inner1730_10Real"  # the model name locally and on the HF Hub
 
@@ -378,7 +378,7 @@ def train_loop(config, model, noise_scheduler, optimizer, train_dataloader, lr_s
         if accelerator.is_main_process:
             pipeline = DDPMPipeline(unet=accelerator.unwrap_model(model), scheduler=noise_scheduler)
 
-            if ((epoch + 1) % config.save_image_epochs == 0 or epoch == config.num_epochs - 1) and epoch > 0: #Change if want to not evaluate before a certain epoch
+            if ((epoch + 1) % config.save_image_epochs == 0 or epoch == config.num_epochs - 1) and epoch > 195: #Change if want to not evaluate before a certain epoch
                 evalfirst(config, epoch, pipeline)
 
                 model_dir = os.path.join(config.output_dir, str(epoch))