Aduc-sdr-2_5s / app.py
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import gradio as gr
from PIL import Image
import os
import imageio
# Importa a instância do nosso serviço
# O modelo será carregado quando este módulo for importado
from video_service import video_generation_service
# --- FUNÇÕES DE AJUDA PARA A UI (não relacionadas ao modelo) ---
TARGET_FIXED_SIDE = 768
MIN_DIM_SLIDER = 256
MAX_IMAGE_SIZE = 1280
def calculate_new_dimensions(orig_w, orig_h):
if orig_w == 0 or orig_h == 0: return int(TARGET_FIXED_SIDE), int(TARGET_FIXED_SIDE)
if orig_w >= orig_h:
new_h, aspect_ratio = TARGET_FIXED_SIDE, orig_w / orig_h
new_w = round((new_h * aspect_ratio) / 32) * 32
new_w = max(MIN_DIM_SLIDER, min(new_w, MAX_IMAGE_SIZE))
new_h = max(MIN_DIM_SLIDER, min(new_h, MAX_IMAGE_SIZE))
else:
new_w, aspect_ratio = TARGET_FIXED_SIDE, orig_h / orig_w
new_h = round((new_w * aspect_ratio) / 32) * 32
new_h = max(MIN_DIM_SLIDER, min(new_h, MAX_IMAGE_SIZE))
new_w = max(MIN_DIM_SLIDER, min(new_w, MAX_IMAGE_SIZE))
return int(new_h), int(new_w)
def handle_media_upload_for_dims(filepath, current_h, current_w):
if not filepath or not os.path.exists(str(filepath)): return gr.update(value=current_h), gr.update(value=current_w)
try:
if str(filepath).lower().endswith(('.png', '.jpg', '.jpeg', '.webp')):
with Image.open(filepath) as img:
orig_w, orig_h = img.size
else: # Assumir que é um vídeo
with imageio.get_reader(filepath) as reader:
meta = reader.get_meta_data()
orig_w, orig_h = meta.get('size', (current_w, current_h))
new_h, new_w = calculate_new_dimensions(orig_w, orig_h)
return gr.update(value=new_h), gr.update(value=new_w)
except Exception as e:
print(f"Erro ao processar mídia para dimensões: {e}")
return gr.update(value=current_h), gr.update(value=current_w)
# --- FUNÇÃO WRAPPER PARA CHAMAR O SERVIÇO A PARTIR DO GRADIO ---
def gradio_generate_wrapper(prompt, negative_prompt, input_image, input_video,
height, width, mode, duration, frames_to_use,
seed, randomize_seed, guidance_scale, improve_texture,
progress=gr.Progress(track_tqdm=True)):
"""
Esta função atua como uma ponte entre a interface Gradio e o nosso VideoService.
"""
try:
# Define a função de callback para a barra de progresso do Gradio
def progress_handler(step, total_steps):
progress(step / total_steps, desc="Salvando vídeo...")
output_path, used_seed = video_generation_service.generate(
prompt=prompt,
negative_prompt=negative_prompt,
input_image_filepath=input_image,
input_video_filepath=input_video,
height=int(height),
width=int(width),
mode=mode,
duration=float(duration),
frames_to_use=int(frames_to_use),
seed=int(seed),
randomize_seed=bool(randomize_seed),
guidance_scale=float(guidance_scale),
improve_texture=bool(improve_texture),
progress_callback=progress_handler # Passamos o handler para o serviço
)
return output_path, used_seed
except ValueError as e:
# Captura erros de validação do serviço e os exibe na UI
raise gr.Error(str(e))
except Exception as e:
# Captura outros erros inesperados
print(f"Erro inesperado na geração: {e}")
raise gr.Error("Ocorreu um erro inesperado. Verifique os logs.")
# --- DEFINIÇÃO DA INTERFACE GRADIO ---
css = "#col-container { margin: 0 auto; max-width: 900px; }"
with gr.Blocks(css=css) as demo:
gr.Markdown("# LTX Video 0.9.8 13B Distilled")
gr.Markdown("Geração de vídeo rápida e de alta qualidade.")
with gr.Row():
with gr.Column():
# ... (Layout das abas e componentes exatamente como antes) ...
with gr.Tab("image-to-video") as image_tab:
video_i_hidden = gr.Textbox(label="video_i", visible=False, value=None)
image_i2v = gr.Image(label="Input Image", type="filepath", sources=["upload", "webcam", "clipboard"])
i2v_prompt = gr.Textbox(label="Prompt", value="The creature from the image starts to move", lines=3)
i2v_button = gr.Button("Generate Image-to-Video", variant="primary")
with gr.Tab("text-to-video") as text_tab:
image_n_hidden = gr.Textbox(label="image_n", visible=False, value=None)
video_n_hidden = gr.Textbox(label="video_n", visible=False, value=None)
t2v_prompt = gr.Textbox(label="Prompt", value="A majestic dragon flying over a medieval castle", lines=3)
t2v_button = gr.Button("Generate Text-to-Video", variant="primary")
with gr.Tab("video-to-video", visible=True) as video_tab:
image_v_hidden = gr.Textbox(label="image_v", visible=False, value=None)
video_v2v = gr.Video(label="Input Video", sources=["upload", "webcam"])
frames_to_use = gr.Slider(label="Frames to use from input video", minimum=9, maximum=257, value=9, step=8, info="Must be N*8+1.")
v2v_prompt = gr.Textbox(label="Prompt", value="Change the style to cinematic anime", lines=3)
v2v_button = gr.Button("Generate Video-to-Video", variant="primary")
duration_input = gr.Slider(label="Video Duration (seconds)", minimum=0.3, maximum=8.5, value=2, step=0.1)
improve_texture = gr.Checkbox(label="Improve Texture (multi-scale)", value=True, visible=True)
with gr.Column():
output_video = gr.Video(label="Generated Video", interactive=False)
with gr.Accordion("Advanced settings", open=False):
mode = gr.Dropdown(["text-to-video", "image-to-video", "video-to-video"], label="task", value="image-to-video", visible=False)
negative_prompt_input = gr.Textbox(label="Negative Prompt", value="worst quality, inconsistent motion, blurry, jittery, distorted", lines=2)
with gr.Row():
seed_input = gr.Number(label="Seed", value=42, precision=0)
randomize_seed_input = gr.Checkbox(label="Randomize Seed", value=True)
guidance_scale_input = gr.Slider(label="Guidance Scale (CFG)", minimum=1.0, maximum=10.0, value=3.0, step=0.1)
with gr.Row():
height_input = gr.Slider(label="Height", value=512, step=32, minimum=MIN_DIM_SLIDER, maximum=MAX_IMAGE_SIZE)
width_input = gr.Slider(label="Width", value=704, step=32, minimum=MIN_DIM_SLIDER, maximum=MAX_IMAGE_SIZE)
# --- LÓGICA DE EVENTOS DA UI ---
image_i2v.upload(fn=handle_media_upload_for_dims, inputs=[image_i2v, height_input, width_input], outputs=[height_input, width_input])
video_v2v.upload(fn=handle_media_upload_for_dims, inputs=[video_v2v, height_input, width_input], outputs=[height_input, width_input])
image_tab.select(fn=lambda: "image-to-video", outputs=[mode])
text_tab.select(fn=lambda: "text-to-video", outputs=[mode])
video_tab.select(fn=lambda: "video-to-video", outputs=[mode])
common_inputs = [negative_prompt_input, height_input, width_input, mode, duration_input, frames_to_use, seed_input, randomize_seed_input, guidance_scale_input, improve_texture]
common_outputs = [output_video, seed_input]
t2v_button.click(fn=gradio_generate_wrapper, inputs=[t2v_prompt, *common_inputs[:1], image_n_hidden, video_n_hidden, *common_inputs[1:]], outputs=common_outputs, api_name="text_to_video")
i2v_button.click(fn=gradio_generate_wrapper, inputs=[i2v_prompt, *common_inputs[:1], image_i2v, video_i_hidden, *common_inputs[1:]], outputs=common_outputs, api_name="image_to_video")
v2v_button.click(fn=gradio_generate_wrapper, inputs=[v2v_prompt, *common_inputs[:1], image_v_hidden, video_v2v, *common_inputs[1:]], outputs=common_outputs, api_name="video_to_video")
if __name__ == "__main__":
demo.queue().launch(debug=True, share=False)