app.py

import sys
try:
    import audioop
except ImportError:
    try:
        import audioop_lts as audioop
        sys.modules["audioop"] = audioop
    except ImportError:
        pass

import spaces
import gradio as gr
import torch
import numpy as np
from PIL import Image
import tempfile, os
from huggingface_hub import hf_hub_download

# ─── CONFIGURACIÓN DE MODELOS ──────────────────────────────────────────────────
BASE_MODEL = "cyberdelia/CyberRealisticPony"
LTX_MODEL  = "Lightricks/LTX-Video"
DEFAULT_LORA = "John6666/nsfw-master-flux-lora-merged" 
LTX_NSFW_LORA = "Lora-Daddy/Ltx2.3-real-nudity-early-alpha-30k-steps"

pipe_t2i = None
pipe_i2i = None
pipe_video = None

NEG_DEFAULT = "blurry, low quality, bad anatomy, deformed, ugly, watermark, text"

# ─── LOADERS ───────────────────────────────────────────────────────────────────
def load_t2i(lora_id=None, lora_scale=1.0):
    global pipe_t2i
    from diffusers import StableDiffusionXLPipeline
    if pipe_t2i is None:
        pipe_t2i = StableDiffusionXLPipeline.from_pretrained(
            BASE_MODEL, torch_dtype=torch.float16, use_safetensors=True, variant="fp16"
        )
    if lora_id:
        try:
            pipe_t2i.unload_lora_weights()
            pipe_t2i.load_lora_weights(lora_id)
            pipe_t2i.fuse_lora(lora_scale=lora_scale)
        except: pass
    return pipe_t2i

def load_i2i(lora_id=None, lora_scale=1.0):
    global pipe_i2i
    from diffusers import StableDiffusionXLImg2ImgPipeline
    if pipe_i2i is None:
        pipe_i2i = StableDiffusionXLImg2ImgPipeline.from_pretrained(
            BASE_MODEL, torch_dtype=torch.float16, use_safetensors=True, variant="fp16"
        )
    if lora_id:
        try:
            pipe_i2i.unload_lora_weights()
            pipe_i2i.load_lora_weights(lora_id)
            pipe_i2i.fuse_lora(lora_scale=lora_scale)
        except: pass
    return pipe_i2i

def load_video():
    global pipe_video
    from diffusers import LTXPipeline
    if pipe_video is None:
        pipe_video = LTXPipeline.from_pretrained(LTX_MODEL, torch_dtype=torch.bfloat16)
        try:
            pipe_video.load_lora_weights(LTX_NSFW_LORA)
        except: pass
    return pipe_video

# ─── FUNCIONES ─────────────────────────────────────────────────────────────────
@spaces.GPU(duration=120)
def generate_t2i(prompt, neg, lora_id, lora_scale, steps, cfg, w, h, seed):
    pipe = load_t2i(lora_id if lora_id else None, lora_scale).to("cuda")
    gen = torch.Generator("cuda").manual_seed(int(seed))
    img = pipe(prompt=prompt, negative_prompt=neg, num_inference_steps=int(steps), 
               guidance_scale=cfg, width=int(w), height=int(h), generator=gen).images[0]
    pipe.to("cpu"); torch.cuda.empty_cache()
    return img

@spaces.GPU(duration=120)
def generate_i2i(prompt, neg, init_image, strength, lora_id, lora_scale, steps, cfg, seed):
    if init_image is None: return None
    pipe = load_i2i(lora_id if lora_id else None, lora_scale).to("cuda")
    gen = torch.Generator("cuda").manual_seed(int(seed))
    img = Image.fromarray(init_image).convert("RGB").resize((1024, 1024))
    res = pipe(prompt=prompt, negative_prompt=neg, image=img, strength=strength,
               num_inference_steps=int(steps), guidance_scale=cfg, generator=gen).images[0]
    pipe.to("cpu"); torch.cuda.empty_cache()
    return res

@spaces.GPU(duration=200)
def generate_video(prompt, neg, init_image, num_frames, fps, steps, lora_scale, seed):
    from diffusers.utils import export_to_video
    pipe = load_video().to("cuda")
    gen = torch.Generator("cuda").manual_seed(int(seed))
    kwargs = {"prompt": prompt, "negative_prompt": neg, "num_frames": int(num_frames), 
              "num_inference_steps": int(steps), "generator": gen}
    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=int(fps))
    pipe.to("cpu"); torch.cuda.empty_cache()
    return tmp.name

# ─── UI CAMUFLADA ──────────────────────────────────────────────────────────────
THEME = gr.themes.Default(primary_hue="slate", neutral_hue="slate").set(
    body_background_fill="#f3f4f6", block_background_fill="#ffffff", 
)

with gr.Blocks(theme=THEME, title="Image Utility v2.1") as demo:
    gr.HTML("<h1 style='text-align:center; color:#374151;'>🛠 Image Processing Utility v2.1.4</h1>")
    gr.HTML("<p style='text-align:center; color:#6b7280;'>Herramienta técnica para el procesamiento y escalado de matrices de píxeles.</p>")
    
    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, placeholder="Enter parameters...")
                    t2i_n = gr.Textbox(label="Excluded Data", value=NEG_DEFAULT)
                    with gr.Row():
                        t2i_lora = gr.Textbox(label="Extension ID", placeholder="Module ID (optional)")
                        t2i_ls = gr.Slider(0, 1.5, 0.8, label="Extension Weight")
                    with gr.Row():
                        t2i_w = gr.Slider(512, 1280, 1024, step=64, label="X-Axis")
                        t2i_h = gr.Slider(512, 1280, 1024, step=64, label="Y-Axis")
                    t2i_btn = gr.Button("Execute Process", variant="secondary")
                t2i_out = gr.Image(label="Output Preview")
            t2i_btn.click(generate_t2i, [t2i_p, t2i_n, t2i_lora, t2i_ls, gr.Number(30), gr.Number(7.5), t2i_w, t2i_h, gr.Number(42)], 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", variant="secondary")
                v_out = gr.Video(label="Sequence Output")
            v_btn.click(generate_video, [v_p, gr.Textbox(value=NEG_DEFAULT), v_img, gr.Number(49), gr.Number(24), gr.Number(30), v_ls, gr.Number(42)], v_out)

demo.launch()