# Uzak çıkarım

> [!TIP]
> Bu, şu anda deneysel bir özelliktir ve herhangi bir geri bildiriminiz varsa, lütfen [buraya](https://github.com/huggingface/diffusers/issues/new?template=remote-vae-pilot-feedback.yml) bırakmaktan çekinmeyin.

Uzak çıkarım, büyük modeller için yerel çıkarımın bellek gereksinimlerini hafifletmek üzere kodlama ve çözümleme sürecini uzak bir uç noktaya devreder. Bu özellik [Inference Endpoints](https://huggingface.co/docs/inference-endpoints/index) tarafından desteklenir. Desteklenen modeller ve uç nokta için aşağıdaki tabloya bakın.

| Model | Uç nokta | Kontrol Noktası | Destek |
|---|---|---|---|
| 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) | kodla/çöz |
| 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) | kodla/çöz |
| 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) | kodla/çöz |
| HunyuanVideo | https://o7ywnmrahorts457.us-east-1.aws.endpoints.huggingface.cloud | [hunyuanvideo-community/HunyuanVideo](https://huggingface.co/hunyuanvideo-community/HunyuanVideo) | decode |

Bu kılavuz, uzak çıkarım (remote inference) ile latentleri nasıl kodlayıp çözeceğinizi gösterecektir.

## Kodlama

Encoding, görüntüleri ve videoları latent temsillere dönüştürür. Desteklenen VAE'ler için aşağıdaki tabloya bakın.

Bir görüntüyü `~utils.remote_encode` işlevine iletin ve kodlayın. Her model için geçerli olan `scaling_factor` ve `shift_factor` değerlerini [Uzak çıkarım](../hybrid_inference/api_reference) API referansında bulabilirsiniz.

```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
)
```

## Çözümleme

Decoding, gizil (latent) temsilleri yeniden görüntülere veya videolara dönüştürür. Kullanılabilir ve desteklenen VAE'ler için aşağıdaki tabloya bakın.

Hattın (pipeline) içinde çıktı türünü `"latent"` olarak ayarlayın ve `vae` değerini `None` yapın. Latentleri [remote_decode()](/docs/diffusers/main/tr/hybrid_inference/api_reference#diffusers.utils.remote_decode) fonksiyonuna iletin. Flux için latentler paketlendiğinden `height` ve `width` değerlerinin de iletilmesi gerekir. Her model için geçerli olan `scaling_factor` ve `shift_factor` değerlerini [Remote inference](../hybrid_inference/api_reference) API referansında bulabilirsiniz.

```py
from diffusers import FluxPipeline

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

transformer = HunyuanVideoTransformer3DModel.from_pretrained(
    "hunyuanvideo-community/HunyuanVideo", subfolder="transformer", dtype=torch.bfloat16
)
pipeline = HunyuanVideoPipeline.from_pretrained(
    model_id, 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",
)

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

## Sıralama

Remote inference, birden çok üretim isteğini işlemek için kuyruk desteği sunar. Mevcut latent çözümlenirken bir sonraki istemi kuyruğa alabilirsiniz.

```py
import queue
import threading
from IPython.display import display
from diffusers import StableDiffusionXLPipeline

def decode_worker(q: queue.Queue):
    while True:
        item = q.get()
        if item is None:
            break
        image = remote_decode(
            endpoint="https://q1bj3bpq6kzilnsu.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(
    "https://huggingface.co/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(pipe.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

Tablolar, Stable Diffusion v1.5 ve SDXL ile farklı GPU'larda kodlama ve çözümlenme için bellek gereksinimlerini göstermektedir.

Bu GPU'ların çoğu için bellek kullanımı, diğer modellerin (metin kodlayıcılar, UNet/transformer) dışa aktarılmasının (offload) veya karolama kodlamasının (tiled encoding) gerekip gerekmediğini belirler. Son iki teknik, çıkarım süresini artırır ve kaliteyi etkiler.

Kodlama - Stable Diffusion v1.5

| GPU                           | Çözüm   |   Süre (saniye) |   Bellek (%) |   Döşemeli Süre (sn) |   Döşemeli Bellek (%) |
|:------------------------------|:-------------|-----------------:|-------------:|--------------------:|-------------------:|
| 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  |

SDXL Kodlama

| GPU                           | Çözüm   |   Süre (saniye) |   Tüketilen Bellek (%) |   Döşemeli Süre (saniye) |   Döşemeli Bellek (%) |
|:------------------------------|:-------------|-----------------:|----------------------:|-----------------------:|-------------------:|
| 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  |

Çözümleme - Stable Diffusion v1.5

| GPU | Çözüm | Süre (saniye) | Bellek (%) | Döşemeli Süre (sn) | Döşemeli Bellek (%) |
| --- | --- | --- | --- | --- | --- |
| 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% |

SDXL'nin Çözümlemesi

| GPU | Çözüm | Süre (saniye) | Tüketilen Bellek (%) | Döşemeli Süre (saniye) | Döşemeli Bellek (%) |
| --- | --- | --- | --- | --- | --- |
| 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% |

## Kaynaklar

- Uzak çıkarım, [SD.Next](https://github.com/vladmandic/sdnext) ve [ComfyUI-HFRemoteVae](https://github.com/kijai/ComfyUI-HFRemoteVae) içinde de desteklenmektedir.
- Daha fazla bilgi edinmek için [Remote VAEs for decoding with Inference Endpoints](https://huggingface.co/blog/remote_vae) blog yazısına bakın.