Delete app.py

app.py

@@ -1,119 +0,0 @@
-import tempfile
-from pathlib import Path
-
-import gradio as gr
-import spaces
-import torch
-from diffusers import CogVideoXImageToVideoPipeline
-from diffusers.utils import export_to_video
-from PIL import Image
-
-MODEL_ID = "THUDM/CogVideoX1.5-5B-I2V"
-PIPE = None
-
-
-def get_pipeline():
-    global PIPE
-    if PIPE is None:
-        print(f"Loading model: {MODEL_ID}", flush=True)
-        PIPE = CogVideoXImageToVideoPipeline.from_pretrained(
-            MODEL_ID,
-            torch_dtype=torch.bfloat16,
-        )
-        PIPE.to("cuda")
-        PIPE.enable_vae_slicing()
-        PIPE.enable_vae_tiling()
-        print("Model loaded on CUDA", flush=True)
-    return PIPE
-
-
-def resize_image(image: Image.Image, profile: str):
-    image = image.convert("RGB")
-    if profile == "1080p (upscale final)":
-        target_w, target_h = 1360, 768
-    else:
-        target_w, target_h = 1024, 576
-
-    target_ratio = target_w / target_h
-    source_ratio = image.width / image.height
-    if source_ratio > target_ratio:
-        crop_h = image.height
-        crop_w = round(crop_h * target_ratio)
-    else:
-        crop_w = image.width
-        crop_h = round(crop_w / target_ratio)
-
-    left = max(0, (image.width - crop_w) // 2)
-    top = max(0, (image.height - crop_h) // 2)
-    image = image.crop((left, top, left + crop_w, top + crop_h))
-    return image.resize((target_w, target_h), Image.Resampling.LANCZOS), target_w, target_h
-
-
-@spaces.GPU(duration=300)
-def generate_video(image, prompt, profile, seed):
-    if image is None:
-        raise gr.Error("Envie uma imagem antes de gerar o vídeo.")
-    if not prompt or not prompt.strip():
-        raise gr.Error("Escreva o movimento que você quer animar.")
-
-    try:
-        pipeline = get_pipeline()
-        image, width, height = resize_image(image, profile)
-        generator = torch.Generator(device="cuda").manual_seed(int(seed or 42))
-        full_prompt = (
-            "Animate the input image while preserving the exact identity, face, clothing, "
-            "objects, composition and environment. Focus on natural physically plausible motion. "
-            + prompt.strip()
-            + ". Smooth cinematic movement, consistent character, no morphing, no extra limbs, "
-            "no scene change, no camera cuts."
-        )
-
-        result = pipeline(
-            prompt=full_prompt,
-            image=image,
-            height=height,
-            width=width,
-            num_frames=49,
-            guidance_scale=6.0,
-            num_inference_steps=20,
-            generator=generator,
-        )
-        frames = result.frames[0]
-        output_path = Path(tempfile.mkdtemp(prefix="motion-ai-")) / "motion.mp4"
-        export_to_video(frames, str(output_path), fps=8)
-        return str(output_path)
-    except Exception as exc:
-        print(f"Generation error: {type(exc).__name__}: {exc}", flush=True)
-        raise gr.Error(f"Não foi possível gerar o vídeo: {type(exc).__name__}: {exc}") from exc
-
-
-with gr.Blocks(title="Motion AI Studio") as demo:
-    gr.Markdown(
-        "# Motion AI Studio\n"
-        "Anime uma imagem com um comando de movimento. A GPU é solicitada somente ao gerar."
-    )
-    with gr.Row():
-        with gr.Column():
-            image = gr.Image(type="pil", label="Imagem de referência")
-            prompt = gr.Textbox(
-                label="Descreva o movimento",
-                placeholder="Ex.: o homem começa a correr pela rua enquanto a câmera o acompanha lateralmente",
-                lines=4,
-            )
-            profile = gr.Radio(
-                ["720p", "1080p (upscale final)"],
-                value="720p",
-                label="Perfil",
-            )
-            seed = gr.Number(value=42, precision=0, label="Seed")
-            generate = gr.Button("Gerar animação", variant="primary")
-        with gr.Column():
-            result = gr.Video(label="Resultado", format="mp4")
-            gr.Markdown(
-                "**Recomendação:** comece com 720p. O perfil 1080p gera a animação em resolução menor."
-            )
-
-    generate.click(generate_video, [image, prompt, profile, seed], result)
-
-if __name__ == "__main__":
-    demo.queue(max_size=8).launch()