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import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
from transformers import AutoTokenizer, AutoModel
from datasets import load_dataset
import numpy as np
from PIL import Image
import torchvision.transforms as transforms
class SpriteDataset(Dataset):
def __init__(self, dataset_split="train"):
# Load the dataset from HuggingFace
self.dataset = load_dataset("pawkanarek/spraix_1024", split=dataset_split)
self.tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
# Define image transforms
self.transform = transforms.Compose([
transforms.Resize((64, 64)), # Resize all sprites to same size
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))
])
def __len__(self):
return len(self.dataset)
def __getitem__(self, idx):
item = self.dataset[idx]
# Process text description
text = f"{item['text']}" # Contains frames, description, action, direction
encoded_text = self.tokenizer(
text,
padding="max_length",
max_length=128,
truncation=True,
return_tensors="pt"
)
# Process image
# The item['image'] is already a PIL Image. Convert it to RGB if it's not already
image = item['image'].convert('RGB')
# Removed Image.fromarray as it's unnecessary
image_tensor = self.transform(image)
return {
'text_ids': encoded_text['input_ids'].squeeze(),
'text_mask': encoded_text['attention_mask'].squeeze(),
'image': image_tensor
}
class TextEncoder(nn.Module):
def __init__(self):
super().__init__()
self.bert = AutoModel.from_pretrained("bert-base-uncased")
self.linear = nn.Linear(768, 512) # Reduce BERT output dimension
def forward(self, input_ids, attention_mask):
outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)
return self.linear(outputs.last_hidden_state[:, 0, :]) # Use [CLS] token
class SpriteGenerator(nn.Module):
def __init__(self, latent_dim=512):
super().__init__()
self.generator = nn.Sequential(
# Input: latent_dim x 1 x 1
nn.ConvTranspose2d(latent_dim, 512, 4, 1, 0, bias=False),
nn.BatchNorm2d(512),
nn.ReLU(True),
# 512 x 4 x 4
nn.ConvTranspose2d(512, 256, 4, 2, 1, bias=False),
nn.BatchNorm2d(256),
nn.ReLU(True),
# 256 x 8 x 8
nn.ConvTranspose2d(256, 128, 4, 2, 1, bias=False),
nn.BatchNorm2d(128),
nn.ReLU(True),
# 128 x 16 x 16
nn.ConvTranspose2d(128, 64, 4, 2, 1, bias=False),
nn.BatchNorm2d(64),
nn.ReLU(True),
# 64 x 32 x 32
nn.ConvTranspose2d(64, 3, 4, 2, 1, bias=False),
nn.Tanh()
# Output: 3 x 64 x 64
)
def forward(self, z):
z = z.unsqueeze(-1).unsqueeze(-1) # Add spatial dimensions
return self.generator(z)
class Animator2D(nn.Module):
def __init__(self):
super().__init__()
self.text_encoder = TextEncoder()
self.sprite_generator = SpriteGenerator()
def forward(self, input_ids, attention_mask):
text_features = self.text_encoder(input_ids, attention_mask)
generated_sprite = self.sprite_generator(text_features)
return generated_sprite
def train_model(num_epochs=100, batch_size=32, learning_rate=0.0002):
# Initialize dataset and dataloader
dataset = SpriteDataset()
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
# Initialize model and optimizer
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = Animator2D().to(device)
optimizer = optim.Adam(model.parameters(), lr=learning_rate, betas=(0.5, 0.999))
criterion = nn.MSELoss()
# Training loop
for epoch in range(num_epochs):
for batch_idx, batch in enumerate(dataloader):
# Move data to device
text_ids = batch['text_ids'].to(device)
text_mask = batch['text_mask'].to(device)
real_images = batch['image'].to(device)
# Forward pass
generated_images = model(text_ids, text_mask)
# Calculate loss
loss = criterion(generated_images, real_images)
# Backward pass and optimization
optimizer.zero_grad()
loss.backward()
optimizer.step()
if batch_idx % 100 == 0:
print(f"Epoch [{epoch}/{num_epochs}] Batch [{batch_idx}/{len(dataloader)}] Loss: {loss.item():.4f}")
# Save the trained model
torch.save(model.state_dict(), "animator2d_model.pth")
return model
def generate_sprite_animation(model, num_frames, description, action, direction):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.eval()
# Prepare input text
text = f"{num_frames}-frame sprite animation of: {description}, that: {action}, facing: {direction}"
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
encoded_text = tokenizer(
text,
padding="max_length",
max_length=128,
truncation=True,
return_tensors="pt"
)
# Generate sprite sheet
with torch.no_grad():
text_ids = encoded_text['input_ids'].to(device)
text_mask = encoded_text['attention_mask'].to(device)
generated_sprite = model(text_ids, text_mask)
# Convert to image
generated_sprite = generated_sprite.cpu().squeeze(0)
generated_sprite = (generated_sprite + 1) / 2 # Denormalize
generated_sprite = transforms.ToPILImage()(generated_sprite)
return generated_sprite
# Example usage
if __name__ == "__main__":
# Train the model
model = train_model()
# Generate a new sprite animation
test_params = {
"num_frames": 17,
"description": "red-haired hobbit in green cape",
"action": "shoots with slingshot",
"direction": "East"
}
sprite_sheet = generate_sprite_animation(
model,
test_params["num_frames"],
test_params["description"],
test_params["action"],
test_params["direction"]
)
sprite_sheet.save("generated_sprite.png") |