app.py

import sys
import os

# --- PARCHE DE COMPATIBILIDAD INVESTIGADO (CRÍTICO) ---
# Este parche corrige el bug de 'bool' en gradio_client que afecta a Gradio 5
try:
    import gradio_client.utils as client_utils
    old_get_type = client_utils.get_type
    def new_get_type(schema):
        if isinstance(schema, bool): return "Any"
        return old_get_type(schema)
    client_utils.get_type = new_get_type
    
    old_json_to_python = client_utils._json_schema_to_python_type
    def new_json_to_python(schema, defs=None):
        if isinstance(schema, bool): return "Any"
        return old_json_to_python(schema, defs)
    client_utils._json_schema_to_python_type = new_json_to_python
except Exception as e:
    print(f"Aviso: No se pudo aplicar el parche de gradio_client: {e}")
# ------------------------------------------------------

import spaces
import gradio as gr
import torch
import numpy as np
from PIL import Image
import tempfile

# CONFIG
BASE_MODEL = "cyberdelia/CyberRealisticPony"
LTX_MODEL  = "Lightricks/LTX-Video"
LTX_NSFW_LORA = "Lora-Daddy/Ltx2.3-real-nudity-early-alpha-30k-steps"
NEG_DEFAULT = "blurry, low quality, bad anatomy, deformed, ugly, watermark, text"

def load_t2i(lora_id=None, lora_scale=1.0):
    from diffusers import StableDiffusionXLPipeline
    pipe = StableDiffusionXLPipeline.from_pretrained(
        BASE_MODEL, torch_dtype=torch.float16, use_safetensors=True, variant="fp16"
    )
    if lora_id and len(lora_id.strip()) > 5:
        try:
            pipe.load_lora_weights(lora_id.strip())
            pipe.fuse_lora(lora_scale=lora_scale)
        except: pass
    return pipe

def load_video():
    from diffusers import LTXPipeline
    pipe = LTXPipeline.from_pretrained(LTX_MODEL, torch_dtype=torch.bfloat16)
    try:
        pipe.load_lora_weights(LTX_NSFW_LORA)
    except: pass
    return pipe

@spaces.GPU(duration=100)
def generate_t2i(prompt, neg, lora_id, lora_scale, w, h):
    pipe = load_t2i(lora_id, lora_scale).to("cuda")
    img = pipe(prompt=prompt, negative_prompt=neg, num_inference_steps=30, 
               guidance_scale=7.0, width=int(w), height=int(h), 
               generator=torch.Generator("cuda").manual_seed(42)).images[0]
    return img

@spaces.GPU(duration=200)
def generate_video(prompt, init_image, lora_scale):
    from diffusers.utils import export_to_video
    pipe = load_video().to("cuda")
    kwargs = {"prompt": prompt, "negative_prompt": NEG_DEFAULT, "num_frames": 49, 
              "num_inference_steps": 30, "generator": torch.Generator("cuda").manual_seed(42)}
    if init_image is not None:
        kwargs["image"] = Image.fromarray(init_image).convert("RGB").resize((768, 512))
    if lora_scale > 0:
        kwargs["cross_attention_kwargs"] = {"scale": lora_scale}
    output = pipe(**kwargs)
    tmp = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False)
    export_to_video(output.frames[0], tmp.name, fps=24)
    return tmp.name

with gr.Blocks(title="Image Utility v2.1") as demo:
    gr.HTML("<h1 style='text-align:center;'>🛠 Image Processing Utility v2.1.4</h1>")
    with gr.Tabs():
        with gr.Tab("D-Processor (T2I)"):
            with gr.Row():
                with gr.Column():
                    t2i_p = gr.Textbox(label="Input Data String", lines=3)
                    t2i_n = gr.Textbox(label="Excluded Data", value=NEG_DEFAULT)
                    t2i_lora = gr.Textbox(label="Extension ID")
                    t2i_ls = gr.Slider(0, 1.5, 0.8, label="Extension Weight")
                    with gr.Row():
                        t2i_w = gr.Slider(512, 1024, 1024, step=64, label="X-Axis")
                        t2i_h = gr.Slider(512, 1024, 1024, step=64, label="Y-Axis")
                    t2i_btn = gr.Button("Execute Process")
                t2i_out = gr.Image(label="Output Preview")
            t2i_btn.click(generate_t2i, [t2i_p, t2i_n, t2i_lora, t2i_ls, t2i_w, t2i_h], t2i_out)

        with gr.Tab("M-Sequence (Video)"):
            with gr.Row():
                with gr.Column():
                    v_p = gr.Textbox(label="Motion Vector String", lines=3)
                    v_img = gr.Image(label="Source Buffer", type="numpy")
                    v_ls = gr.Slider(0, 1.5, 0.8, label="Motion Weight")
                    v_btn = gr.Button("Process Sequence")
                v_out = gr.Video(label="Sequence Output")
            v_btn.click(generate_video, [v_p, v_img, v_ls], v_out)

# Al usar SDK nativo, no definimos server_name ni port. HuggingFace lo inyecta solo.
demo.launch(show_api=False)