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import gradio as gr
import torch
from transformers import BertTokenizer, BertModel
import torchvision.transforms as transforms
from PIL import Image
import numpy as np
import logging
# Configura il logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
logger.info("Inizio inizializzazione dell'app")
# Imposta il dispositivo
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
logger.info(f"Dispositivo selezionato: {device}")
# Trasformazioni per le immagini
transform = transforms.Compose([
transforms.Resize((64, 64)),
transforms.ToTensor(),
])
# Definizione del modello Animator2D
class Animator2DModel(torch.nn.Module):
def __init__(self):
super(Animator2DModel, self).__init__()
self.text_encoder = BertModel.from_pretrained('bert-base-uncased')
self.image_encoder = torch.nn.Sequential(
torch.nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1),
torch.nn.ReLU(),
torch.nn.MaxPool2d(2),
torch.nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),
torch.nn.ReLU(),
torch.nn.MaxPool2d(2)
)
self.decoder = torch.nn.LSTM(input_size=768 + 128, hidden_size=256, num_layers=2, batch_first=True)
self.frame_generator = torch.nn.Linear(256, 64 * 64 * 3)
def forward(self, input_ids, attention_mask, base_frame, num_frames):
text_features = self.text_encoder(input_ids, attention_mask=attention_mask).pooler_output
image_features = self.image_encoder(base_frame).flatten(start_dim=1)
combined_features = torch.cat((text_features, image_features), dim=1)
combined_features = combined_features.unsqueeze(1).repeat(1, num_frames, 1)
output, _ = self.decoder(combined_features)
generated_frames = self.frame_generator(output)
return generated_frames.view(-1, num_frames, 3, 64, 64)
# Funzione per generare i frame
def generate_animation(description, base_frame_image, num_frames):
logger.info("Inizio generazione animazione")
try:
# Carica il modello da Hugging Face
model = Animator2DModel().to(device)
logger.info("Modello inizializzato, caricamento pesi...")
model.load_state_dict(torch.hub.load_state_dict_from_url(
"https://huggingface.co/Lod34/Animator2D-v1.0.0/blob/main/Animator2D-v1.0.0.pth",
map_location=device))
model.eval()
logger.info("Modello caricato con successo")
# Prepara il testo
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
inputs = tokenizer(description, return_tensors='pt', padding='max_length',
truncation=True, max_length=512)
input_ids = inputs['input_ids'].to(device)
attention_mask = inputs['attention_mask'].to(device)
# Prepara l'immagine di base
base_frame = transform(base_frame_image).unsqueeze(0).to(device)
# Genera i frame
with torch.no_grad():
generated_frames = model(input_ids, attention_mask, base_frame, num_frames)
# Converte i frame generati in immagini PIL
generated_frames = generated_frames.squeeze(0).cpu().numpy()
output_frames = []
for i in range(num_frames):
frame = generated_frames[i].transpose(1, 2, 0) # Da (C, H, W) a (H, W, C)
frame = np.clip(frame, 0, 1) # Normalizza tra 0 e 1
frame = (frame * 255).astype(np.uint8) # Converte in formato immagine
output_frames.append(Image.fromarray(frame))
logger.info("Animazione generata con successo")
return output_frames
except Exception as e:
logger.error(f"Errore durante la generazione: {str(e)}")
raise
# Interfaccia Gradio
logger.info("Inizio configurazione interfaccia Gradio")
with gr.Blocks(title="Animator2D-v1.0.0") as demo:
gr.Markdown("# Animator2D-v1.0.0\nInserisci una descrizione e un'immagine di base per generare un'animazione!")
with gr.Row():
with gr.Column():
description_input = gr.Textbox(label="Descrizione dell'animazione", placeholder="Es: 'A character jumping'")
base_frame_input = gr.Image(label="Immagine di base", type="pil")
num_frames_input = gr.Slider(1, 5, value=3, step=1, label="Numero di frame")
submit_button = gr.Button("Genera Animazione")
with gr.Column():
output_gallery = gr.Gallery(label="Frame generati", show_label=True)
submit_button.click(
fn=generate_animation,
inputs=[description_input, base_frame_input, num_frames_input],
outputs=output_gallery
)
logger.info("Interfaccia Gradio configurata, avvio...")
demo.launch(server_name="0.0.0.0", server_port=7860) |