I have this code:
```py
import torch
from torch import nn
from torch.optim import AdamW
from torch.utils.data import DataLoader, Dataset
from tqdm.auto import tqdm
from diffusers import (
    StableDiffusionXLPipeline,
    DDPMScheduler,
)
from diffusers.loaders import LoraLoaderMixin
from diffusers.models.attention_processor import LoRAAttnProcessor2_0
from transformers import CLIPTokenizer, CLIPTextModel
from datasets import load_dataset
from torchvision import transforms
from PIL import Image
import requests

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Load the SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0",
    torch_dtype=torch.float16,
    variant="fp16",
    use_safetensors=True,
    add_watermarker=False,
)
pipe.to(device)

pipe.vae.requires_grad_(False)
pipe.text_encoder.requires_grad_(False)   # CLIP-L
pipe.text_encoder_2.requires_grad_(False) # CLIP-G

rank = 4  # Set the LoRA rank

# Set up LoRA
for name in pipe.unet.attn_processors.keys():
    module = pipe.unet.attn_processors[name]
    if isinstance(module, torch.nn.Module):
        pipe.unet.attn_processors[name] = LoRAAttnProcessor2_0.from_module(module, rank=rank)

# Freeze UNet parameters except for LoRA parameters
pipe.unet.requires_grad_(False)
for param in pipe.unet.parameters():
    param.requires_grad = False

for module in pipe.unet.attn_processors.values():
    if hasattr(module, 'parameters'):
        for param in module.parameters():
            param.requires_grad = True

# Create the scheduler with Cosine-Continuous Noise Schedule
scheduler = DDPMScheduler(
    beta_start=0.0001,
    beta_end=0.02,
    beta_schedule="squaredcos_cap_v2",
    num_train_timesteps=1000,
    prediction_type="v_prediction",
)
pipe.scheduler = scheduler

dataset = load_dataset("OpenFace-CQUPT/HumanCaption-10M")
dataloader = DataLoader(dataset, batch_size=4, shuffle=True, num_workers=4)

trainable_params = (param for param in pipe.unet.parameters() if param.requires_grad)
optimizer = AdamW(trainable_params, lr=1e-4)

num_epochs = 5

for epoch in range(num_epochs):
    pipe.unet.train()
    total_loss = 0.0
    progress_bar = tqdm(dataloader, desc=f"Epoch {epoch+1}/{num_epochs}")

    for step, batch in enumerate(progress_bar):
        optimizer.zero_grad()

        # Move images to device and encode to latents
        images = batch['pixel_values'].to(device, dtype=torch.float16)
        captions = batch['caption']

        # Encode captions using both tokenizers and text encoders
        inputs = pipe.tokenizer(
            captions,
            padding='max_length',
            truncation=True,
            max_length=pipe.tokenizer.model_max_length,
            return_tensors='pt',
        ).to(device)
        inputs_2 = pipe.tokenizer_2(
            captions,
            padding='max_length',
            truncation=True,
            max_length=pipe.tokenizer_2.model_max_length,
            return_tensors='pt',
        ).to(device)

        with torch.no_grad():
            encoder_hidden_states = pipe.text_encoder(
                input_ids=inputs.input_ids,
            )[0]
            encoder_hidden_states_2 = pipe.text_encoder_2(
                input_ids=inputs_2.input_ids,
            )[0]

        # Encode images to latent space
        with torch.no_grad():
            latents = pipe.vae.encode(images).latent_dist.sample()
            latents = latents * pipe.vae.config.scaling_factor  # VAE scaling factor

        # Sample noise
        noise = torch.randn_like(latents)

        # Sample random timesteps
        timesteps = torch.randint(
            0, scheduler.config.num_train_timesteps, (latents.shape[0],), device=device
        ).long()

        # Add noise to latents to get noisy latents
        noisy_latents = scheduler.add_noise(latents, noise, timesteps)

        # Predict 'v' using the UNet model
        model_pred = pipe.unet(
            sample=noisy_latents,
            timestep=timesteps,
            encoder_hidden_states=encoder_hidden_states,
            encoder_hidden_states_2=encoder_hidden_states_2,
            return_dict=False,
        )[0]

        # Get the target for loss depending on the prediction type
        target = scheduler.get_velocity(latents, noise, timesteps)

        loss = nn.functional.mse_loss(model_pred.float(), target.float(), reduction="mean")

        # Backpropagation
        loss.backward()
        optimizer.step()

        # Update progress bar and total loss
        total_loss += loss.item()
        avg_loss = total_loss / (step + 1)
        progress_bar.set_postfix({'loss': avg_loss})

    print(f"Epoch {epoch + 1}/{num_epochs}, Average Loss: {avg_loss}")

def save_lora_weights(unet, save_path):
    lora_state_dict = LoraLoaderMixin.convert_lora_to_state_dict(unet)
    torch.save(lora_state_dict, save_path)

save_lora_weights(pipe.unet, "lora_weights.pt")

```

Find errors in it. I know that "Set up LoRA step is not working"