ZeroGPU Demos — Guia de Implementacion

import spaces
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Carga eager en module scope — ZeroGPU registra el tensor
_MODEL_ID = "mistralai/Mistral-7B-Instruct-v0.3"
_tokenizer = AutoTokenizer.from_pretrained(_MODEL_ID)
_model = AutoModelForCausalLM.from_pretrained(
    _MODEL_ID,
    torch_dtype=torch.bfloat16,
    device_map="cuda",   # ZeroGPU monkey-patchea is_available()
)

# duration=20 — evita quota exceeded en tareas cortas
@spaces.GPU(duration=20)
def generate_copy(producto: str, tono: str) -> str:
    """Genera 3 variantes de copy de marketing. CPU-safe return."""
    prompt = _build_prompt(producto, tono)
    inputs = _tokenizer(prompt, return_tensors="pt").to("cuda")
    with torch.no_grad():
        out = _model.generate(
            **inputs,
            max_new_tokens=350,
            temperature=0.8,
            do_sample=True,
        )
    # Retornar string, no tensor CUDA — evita torch.cuda._lazy_init()
    return _tokenizer.decode(out[0], skip_special_tokens=True)

import spaces
import torch
import tempfile
import os
from diffusers import StableDiffusionXLPipeline

# Carga eager — peso en disk offload hasta que llega @GPU
_pipe = StableDiffusionXLPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0",
    torch_dtype=torch.float16,
    use_safetensors=True,
).to("cuda")

# xlarge: SDXL necesita VRAM full; duration=45 estimado para 30 steps
@spaces.GPU(duration=45, size="xlarge")
def generate_image(descripcion: str, estilo: str) -> str:
    """Retorna path a fichero temporal — picklable y ligero."""
    prompt = f"professional product photo, {descripcion}, {estilo} style"
    image = _pipe(
        prompt=prompt,
        num_inference_steps=30,
        guidance_scale=7.5,
        width=1024, height=1024,
    ).images[0]

    # tempfile: evita colisiones en concurrencia (varios usuarios a la vez)
    tmp = tempfile.NamedTemporaryFile(
        suffix=".png", delete=False,
        dir="/tmp/cultiva_outputs"
    )
    image.save(tmp.name)
    return tmp.name  # str path — picklable, no PIL Image en cross-process