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import gradio as gr
from diffusers import CogVideoXTransformer3DModel, DiffusionPipeline
from diffusers.utils import export_to_video
import torch
import tempfile
import os
import spaces
# Lista de modelos disponibles
TRANSFORMER_MODELS = [
"sayakpaul/pika-dissolve-v0",
"finetrainers/crush-smol-v0",
"finetrainers/3dgs-v0",
"finetrainers/cakeify-v0"
]
@spaces.GPU
def generate_video(transformer_model, prompt, negative_prompt, num_frames, height, width, num_inference_steps):
# Cargar el modelo del transformer seleccionado
transformer = CogVideoXTransformer3DModel.from_pretrained(
transformer_model,
torch_dtype=torch.bfloat16
)
# Inicializar el pipeline
pipeline = DiffusionPipeline.from_pretrained(
"THUDM/CogVideoX-5b",
transformer=transformer,
torch_dtype=torch.bfloat16
).to("cuda")
# Generar el video
video_frames = pipeline(
prompt=prompt,
negative_prompt=negative_prompt,
num_frames=num_frames,
height=height,
width=width,
num_inference_steps=num_inference_steps
).frames[0]
# Guardar el video en un archivo temporal
with tempfile.NamedTemporaryFile(suffix='.mp4', delete=False) as tmp_file:
export_to_video(video_frames, tmp_file.name, fps=25)
return tmp_file.name
# Crear la interfaz de Gradio
with gr.Blocks() as demo:
gr.Markdown("# Generador de Videos con CogVideoX")
with gr.Row():
with gr.Column():
# Entradas
model_dropdown = gr.Dropdown(
choices=TRANSFORMER_MODELS,
value=TRANSFORMER_MODELS[0],
label="Modelo Transformer"
)
prompt_input = gr.Textbox(
lines=5,
label="Prompt",
placeholder="Describe el video que quieres generar..."
)
negative_prompt_input = gr.Textbox(
lines=2,
label="Prompt Negativo",
value="inconsistent motion, blurry motion, worse quality, degenerate outputs, deformed outputs"
)
with gr.Accordion("Parámetros Avanzados", open=False):
num_frames = gr.Slider(
minimum=8,
maximum=128,
value=8,
step=1,
label="Número de Frames",
info="Cantidad de frames en el video"
)
height = gr.Slider(
minimum=32,
maximum=1024,
value=32,
step=64,
label="Altura",
info="Altura del video en píxeles"
)
width = gr.Slider(
minimum=32,
maximum=1024,
value=32,
step=64,
label="Anchura",
info="Anchura del video en píxeles"
)
num_inference_steps = gr.Slider(
minimum=10,
maximum=100,
value=10,
step=1,
label="Pasos de Inferencia",
info="Mayor número = mejor calidad pero más lento"
)
generate_btn = gr.Button("Generar Video")
with gr.Column():
# Salida
video_output = gr.Video(label="Video Generado")
# Conectar la función
generate_btn.click(
fn=generate_video,
inputs=[
model_dropdown,
prompt_input,
negative_prompt_input,
num_frames,
height,
width,
num_inference_steps
],
outputs=video_output
)
# Lanzar la aplicación
demo.launch()
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