# Inferencia remota

> [!TIP]
> Esta es actualmente una función experimental, y si tienes algún comentario, no dudes en dejarlo [aquí](https://github.com/huggingface/diffusers/issues/new?template=remote-vae-pilot-feedback.yml).

La inferencia remota delega el proceso de decodificación y codificación en un punto de acceso remoto para aliviar los requisitos de memoria de la inferencia local con modelos grandes. Esta función se basa en [Inference Endpoints](https://huggingface.co/docs/inference-endpoints/index). Consulte la tabla siguiente para conocer los modelos y el punto de acceso compatibles.

| Modelo | Endpoint | Checkpoint | Soporte |
|---|---|---|---|
| Stable Diffusion v1 | https://q1bj3bpq6kzilnsu.us-east-1.aws.endpoints.huggingface.cloud | [stabilityai/sd-vae-ft-mse](https://huggingface.co/stabilityai/sd-vae-ft-mse) | encode/decode |
| Stable Diffusion XL | https://x2dmsqunjd6k9prw.us-east-1.aws.endpoints.huggingface.cloud | [madebyollin/sdxl-vae-fp16-fix](https://huggingface.co/madebyollin/sdxl-vae-fp16-fix) | encode/decode |
| Flux | https://whhx50ex1aryqvw6.us-east-1.aws.endpoints.huggingface.cloud | [black-forest-labs/FLUX.1-schnell](https://huggingface.co/black-forest-labs/FLUX.1-schnell) | encode/decode |
| HunyuanVideo | https://o7ywnmrahorts457.us-east-1.aws.endpoints.huggingface.cloud | [hunyuanvideo-community/HunyuanVideo](https://huggingface.co/hunyuanvideo-community/HunyuanVideo) | decode |

Esta guía te mostrará cómo codificar y decodificar latentes con inferencia remota.

## Codificación

La codificación convierte imágenes y videos en representaciones latentes. La tabla en la parte superior de esta página enumera los modelos que admiten la codificación.

Pase una imagen a [remote_encode()](/docs/diffusers/v0.43.0/es/hybrid_inference/api_reference#diffusers.utils.remote_utils.remote_encode) para codificarla. Los valores específicos de `scaling_factor` y `shift_factor` para cada modelo se pueden encontrar en la referencia de la API de [Remote inference](../hybrid_inference/api_reference).

```py
import torch
from diffusers import FluxPipeline
from diffusers.utils import load_image
from diffusers.utils.remote_utils import remote_encode

pipeline = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell",
    dtype=torch.float16,
    vae=None,
    device_map="cuda"  # or "mps", "xpu", "cpu"
)

init_image = load_image(
    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astronaut.jpg"
)
init_image = init_image.resize((768, 512))

init_latent = remote_encode(
    endpoint="https://whhx50ex1aryqvw6.us-east-1.aws.endpoints.huggingface.cloud",
    image=init_image,
    scaling_factor=0.3611,
    shift_factor=0.1159
)
```

## Decodificación

El decodificado convierte las representaciones latentes de nuevo en imágenes o videos. La tabla en la parte superior de esta página enumera los modelos que admiten el decodificado.

Establezca el tipo de salida como `"latent"` en el pipeline y establezca `vae` en `None`. Pase los latentes a la función [remote_decode()](/docs/diffusers/v0.43.0/es/hybrid_inference/api_reference#diffusers.utils.remote_decode). En el caso de Flux, los latentes están empaquetados, por lo que también es necesario pasar `height` y `width`. Los valores específicos de `scaling_factor` y `shift_factor` para cada modelo se pueden encontrar en la referencia de la API de [Remote inference](../hybrid_inference/api_reference).

```py
import torch
from diffusers import FluxPipeline
from diffusers.utils.remote_utils import remote_decode

pipeline = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell",
    dtype=torch.bfloat16,
    vae=None,
    device_map="cuda"  # or "mps", "xpu", "cpu"
)

prompt = """
A photorealistic Apollo-era photograph of a cat in a small astronaut suit with a bubble helmet, standing on the Moon and holding a flagpole planted in the dusty lunar soil. The flag shows a colorful paw-print emblem. Earth glows in the black sky above the stark gray surface, with sharp shadows and high-contrast lighting like vintage NASA photos.
"""

latent = pipeline(
    prompt=prompt,
    guidance_scale=0.0,
    num_inference_steps=4,
    output_type="latent",
).images
image = remote_decode(
    endpoint="https://whhx50ex1aryqvw6.us-east-1.aws.endpoints.huggingface.cloud/",
    tensor=latent,
    height=1024,
    width=1024,
    scaling_factor=0.3611,
    shift_factor=0.1159,
)
image.save("image.jpg")
```

```py
import torch
from diffusers import HunyuanVideoPipeline, HunyuanVideoTransformer3DModel
from diffusers.utils.remote_utils import remote_decode

transformer = HunyuanVideoTransformer3DModel.from_pretrained(
    "hunyuanvideo-community/HunyuanVideo", subfolder="transformer", dtype=torch.bfloat16
)
pipeline = HunyuanVideoPipeline.from_pretrained(
    "hunyuanvideo-community/HunyuanVideo", transformer=transformer, vae=None, dtype=torch.float16, device_map="cuda"  # or "mps", "xpu", "cpu"
)

latent = pipeline(
    prompt="A cat walks on the grass, realistic",
    height=320,
    width=512,
    num_frames=61,
    num_inference_steps=30,
    output_type="latent",
).frames

video = remote_decode(
    endpoint="https://o7ywnmrahorts457.us-east-1.aws.endpoints.huggingface.cloud/",
    tensor=latent,
    output_type="mp4",
    return_type="mp4",
)

with open("video.mp4", "wb") as f:
    f.write(video)
```

## Cola

La inferencia remota admite la colocación en cola para procesar múltiples solicitudes de generación. Mientras se decodifica el latente actual, puedes colocar el siguiente prompt en la cola.

```py
import queue
import threading
import torch
from IPython.display import display
from diffusers import StableDiffusionXLPipeline
from diffusers.utils.remote_utils import remote_decode

def decode_worker(q: queue.Queue):
    while True:
        item = q.get()
        if item is None:
            break
        image = remote_decode(
            endpoint="https://x2dmsqunjd6k9prw.us-east-1.aws.endpoints.huggingface.cloud/",
            tensor=item,
            scaling_factor=0.13025,
        )
        display(image)
        q.task_done()

q = queue.Queue()
thread = threading.Thread(target=decode_worker, args=(q,), daemon=True)
thread.start()

def decode(latent: torch.Tensor):
    q.put(latent)

prompts = [
    "A grainy Apollo-era style photograph of a cat in a snug astronaut suit with a bubble helmet, standing on the lunar surface and gripping a flag with a paw-print emblem. The gray Moon landscape stretches behind it, Earth glowing vividly in the black sky, shadows crisp and high-contrast.",
    "A vintage 1960s sci-fi pulp magazine cover illustration of a heroic cat astronaut planting a flag on the Moon. Bold, saturated colors, exaggerated space gear, playful typography floating in the background, Earth painted in bright blues and greens.",
    "A hyper-detailed cinematic shot of a cat astronaut on the Moon holding a fluttering flag, fur visible through the helmet glass, lunar dust scattering under its feet. The vastness of space and Earth in the distance create an epic, awe-inspiring tone.",
    "A colorful cartoon drawing of a happy cat wearing a chunky, oversized spacesuit, proudly holding a flag with a big paw print on it. The Moon’s surface is simplified with craters drawn like doodles, and Earth in the sky has a smiling face.",
    "A monochrome 1969-style press photo of a “first cat on the Moon” moment. The cat, in a tiny astronaut suit, stands by a planted flag, with grainy textures, scratches, and a blurred Earth in the background, mimicking old archival space photos."
]

pipeline = StableDiffusionXLPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0",
    dtype=torch.float16,
    vae=None,
    device_map="cuda"  # or "mps", "xpu", "cpu"
)

pipeline.unet = pipeline.unet.to(memory_format=torch.channels_last)
pipeline.unet = torch.compile(pipeline.unet, mode="reduce-overhead", fullgraph=True)

_ = pipeline(
    prompt=prompts[0],
    output_type="latent",
)

for prompt in prompts:
    latent = pipeline(
        prompt=prompt,
        output_type="latent",
    ).images
    decode(latent)

q.put(None)
thread.join()
```

## Benchmarks

Las tablas muestran los requisitos de memoria para la codificación y la decodificación con Stable Diffusion v1.5 y SDXL en diferentes GPUs.

Para la mayoría de estas GPUs, el uso de memoria determina si otros modelos (codificadores de texto, UNet/transformer) deben descargarse o si se requiere codificación por mosaicos. Estas dos últimas técnicas aumentan el tiempo de inferencia y afectan la calidad.

Codificación - Stable Diffusion v1.5

| GPU                           | Resolución   |   Tiempo (segundos) |   Memoria (%) |   Tiempo en mosaico (segundos) |   Memoria en mosaicos (%) |
|:------------------------------|:-------------|-----------------:|-------------:|--------------------:|-------------------:|
| NVIDIA GeForce RTX 4090       | 512x512      |            0.015 |      3.51901 |               0.015 |            3.51901 |
| NVIDIA GeForce RTX 4090       | 256x256      |            0.004 |      1.3154  |               0.005 |            1.3154  |
| NVIDIA GeForce RTX 4090       | 2048x2048    |            0.402 |     47.1852  |               0.496 |            3.51901 |
| NVIDIA GeForce RTX 4090       | 1024x1024    |            0.078 |     12.2658  |               0.094 |            3.51901 |
| NVIDIA GeForce RTX 4080 SUPER | 512x512      |            0.023 |      5.30105 |               0.023 |            5.30105 |
| NVIDIA GeForce RTX 4080 SUPER | 256x256      |            0.006 |      1.98152 |               0.006 |            1.98152 |
| NVIDIA GeForce RTX 4080 SUPER | 2048x2048    |            0.574 |     71.08    |               0.656 |            5.30105 |
| NVIDIA GeForce RTX 4080 SUPER | 1024x1024    |            0.111 |     18.4772  |               0.14  |            5.30105 |
| NVIDIA GeForce RTX 3090       | 512x512      |            0.032 |      3.52782 |               0.032 |            3.52782 |
| NVIDIA GeForce RTX 3090       | 256x256      |            0.01  |      1.31869 |               0.009 |            1.31869 |
| NVIDIA GeForce RTX 3090       | 2048x2048    |            0.742 |     47.3033  |               0.954 |            3.52782 |
| NVIDIA GeForce RTX 3090       | 1024x1024    |            0.136 |     12.2965  |               0.207 |            3.52782 |
| NVIDIA GeForce RTX 3080       | 512x512      |            0.036 |      8.51761 |               0.036 |            8.51761 |
| NVIDIA GeForce RTX 3080       | 256x256      |            0.01  |      3.18387 |               0.01  |            3.18387 |
| NVIDIA GeForce RTX 3080       | 2048x2048    |            0.863 |     86.7424  |               1.191 |            8.51761 |
| NVIDIA GeForce RTX 3080       | 1024x1024    |            0.157 |     29.6888  |               0.227 |            8.51761 |
| NVIDIA GeForce RTX 3070       | 512x512      |            0.051 |     10.6941  |               0.051 |           10.6941  |
| NVIDIA GeForce RTX 3070       | 256x256      |            0.015 |      3.99743 |               0.015 |            3.99743 |
| NVIDIA GeForce RTX 3070       | 2048x2048    |            1.217 |     96.054   |               1.482 |           10.6941  |
| NVIDIA GeForce RTX 3070       | 1024x1024    |            0.223 |     37.2751  |               0.327 |           10.6941  |

Codificación de SDXL

| GPU                           | Resolución   |   Tiempo (segundos) |   Memoria consumida (%) |   Tiempo en mosaico (segundos) |   Memoria en mosaicos (%) |
|:------------------------------|:-------------|-----------------:|----------------------:|-----------------------:|-------------------:|
| NVIDIA GeForce RTX 4090       | 512x512      |            0.029 |               4.95707 |                  0.029 |            4.95707 |
| NVIDIA GeForce RTX 4090       | 256x256      |            0.007 |               2.29666 |                  0.007 |            2.29666 |
| NVIDIA GeForce RTX 4090       | 2048x2048    |            0.873 |              66.3452  |                  0.863 |           15.5649  |
| NVIDIA GeForce RTX 4090       | 1024x1024    |            0.142 |              15.5479  |                  0.143 |           15.5479  |
| NVIDIA GeForce RTX 4080 SUPER | 512x512      |            0.044 |               7.46735 |                  0.044 |            7.46735 |
| NVIDIA GeForce RTX 4080 SUPER | 256x256      |            0.01  |               3.4597  |                  0.01  |            3.4597  |
| NVIDIA GeForce RTX 4080 SUPER | 2048x2048    |            1.317 |              87.1615  |                  1.291 |           23.447   |
| NVIDIA GeForce RTX 4080 SUPER | 1024x1024    |            0.213 |              23.4215  |                  0.214 |           23.4215  |
| NVIDIA GeForce RTX 3090       | 512x512      |            0.058 |               5.65638 |                  0.058 |            5.65638 |
| NVIDIA GeForce RTX 3090       | 256x256      |            0.016 |               2.45081 |                  0.016 |            2.45081 |
| NVIDIA GeForce RTX 3090       | 2048x2048    |            1.755 |              77.8239  |                  1.614 |           18.4193  |
| NVIDIA GeForce RTX 3090       | 1024x1024    |            0.265 |              18.4023  |                  0.265 |           18.4023  |
| NVIDIA GeForce RTX 3080       | 512x512      |            0.064 |              13.6568  |                  0.064 |           13.6568  |
| NVIDIA GeForce RTX 3080       | 256x256      |            0.018 |               5.91728 |                  0.018 |            5.91728 |
| NVIDIA GeForce RTX 3080       | 2048x2048    |          OOM     |             OOM       |                  1.866 |           44.4717  |
| NVIDIA GeForce RTX 3080       | 1024x1024    |            0.302 |              44.4308  |                  0.302 |           44.4308  |
| NVIDIA GeForce RTX 3070       | 512x512      |            0.093 |              17.1465  |                  0.093 |           17.1465  |
| NVIDIA GeForce RTX 3070       | 256x256      |            0.025 |               7.42931 |                  0.026 |            7.42931 |
| NVIDIA GeForce RTX 3070       | 2048x2048    |          OOM     |             OOM       |                  2.674 |           55.8355  |
| NVIDIA GeForce RTX 3070       | 1024x1024    |            0.443 |              55.7841  |                  0.443 |           55.7841  |

Decodificación - Stable Diffusion v1.5

| GPU | Resolución | Tiempo (segundos) | Memoria (%) | Tiempo en mosaico (segundos) | Memoria en mosaicos (%) |
| --- | --- | --- | --- | --- | --- |
| NVIDIA GeForce RTX 4090 | 512x512 | 0.031 | 5.60% | 0.031 (0%) | 5.60% |
| NVIDIA GeForce RTX 4090 | 1024x1024 | 0.148 | 20.00% | 0.301 (+103%) | 5.60% |
| NVIDIA GeForce RTX 4080 | 512x512 | 0.05 | 8.40% | 0.050 (0%) | 8.40% |
| NVIDIA GeForce RTX 4080 | 1024x1024 | 0.224 | 30.00% | 0.356 (+59%) | 8.40% |
| NVIDIA GeForce RTX 4070 Ti | 512x512 | 0.066 | 11.30% | 0.066 (0%) | 11.30% |
| NVIDIA GeForce RTX 4070 Ti | 1024x1024 | 0.284 | 40.50% | 0.454 (+60%) | 11.40% |
| NVIDIA GeForce RTX 3090 | 512x512 | 0.062 | 5.20% | 0.062 (0%) | 5.20% |
| NVIDIA GeForce RTX 3090 | 1024x1024 | 0.253 | 18.50% | 0.464 (+83%) | 5.20% |
| NVIDIA GeForce RTX 3080 | 512x512 | 0.07 | 12.80% | 0.070 (0%) | 12.80% |
| NVIDIA GeForce RTX 3080 | 1024x1024 | 0.286 | 45.30% | 0.466 (+63%) | 12.90% |
| NVIDIA GeForce RTX 3070 | 512x512 | 0.102 | 15.90% | 0.102 (0%) | 15.90% |
| NVIDIA GeForce RTX 3070 | 1024x1024 | 0.421 | 56.30% | 0.746 (+77%) | 16.00% |

Decodificación de SDXL

| GPU | Resolución | Tiempo (segundos) | Memoria consumida (%) | Tiempo en mosaico (segundos) | Memoria en mosaicos (%) |
| --- | --- | --- | --- | --- | --- |
| NVIDIA GeForce RTX 4090 | 512x512 | 0.057 | 10.00% | 0.057 (0%) | 10.00% |
| NVIDIA GeForce RTX 4090 | 1024x1024 | 0.256 | 35.50% | 0.257 (+0.4%) | 35.50% |
| NVIDIA GeForce RTX 4080 | 512x512 | 0.092 | 15.00% | 0.092 (0%) | 15.00% |
| NVIDIA GeForce RTX 4080 | 1024x1024 | 0.406 | 53.30% | 0.406 (0%) | 53.30% |
| NVIDIA GeForce RTX 4070 Ti | 512x512 | 0.121 | 20.20% | 0.120 (-0.8%) | 20.20% |
| NVIDIA GeForce RTX 4070 Ti | 1024x1024 | 0.519 | 72.00% | 0.519 (0%) | 72.00% |
| NVIDIA GeForce RTX 3090 | 512x512 | 0.107 | 10.50% | 0.107 (0%) | 10.50% |
| NVIDIA GeForce RTX 3090 | 1024x1024 | 0.459 | 38.00% | 0.460 (+0.2%) | 38.00% |
| NVIDIA GeForce RTX 3080 | 512x512 | 0.121 | 25.60% | 0.121 (0%) | 25.60% |
| NVIDIA GeForce RTX 3080 | 1024x1024 | 0.524 | 93.00% | 0.524 (0%) | 93.00% |
| NVIDIA GeForce RTX 3070 | 512x512 | 0.183 | 31.80% | 0.183 (0%) | 31.80% |
| NVIDIA GeForce RTX 3070 | 1024x1024 | 0.794 | 96.40% | 0.794 (0%) | 96.40% |

## Recursos

- La inferencia remota también se admite en [SD.Next](https://github.com/vladmandic/sdnext) y [ComfyUI-HFRemoteVae](https://github.com/kijai/ComfyUI-HFRemoteVae).
- Consulte la publicación de blog [Remote VAEs for decoding with Inference Endpoints](https://huggingface.co/blog/remote_vae) para obtener más información.