# 배치 추론

Batch inference는 처리량을 높이기 위해 여러 프롬프트를 한 번에 처리합니다. 단일 프롬프트만 처리하여 GPU를 충분히 활용하지 못하는 경우와 비교할 때, 여러 프롬프트를 동시에 처리하면 GPU 사용률을 극대화할 수 있어 더 효율적입니다.

단점은 배치 전체가 완료될 때까지 기다려야 하므로 지연 시간이 증가하고, 대규모 배치의 경우 더 많은 GPU 메모리가 필요하다는 것입니다.

텍스트-이미지 변환의 경우 프롬프트 목록을 파이프라인에 전달하고, 이미지-이미지 변환의 경우 이미지와 프롬프트 목록을 파이프라인에 전달합니다. 아래 예시는 배치된 텍스트-이미지 추론을 보여줍니다.

```py
import torch
import matplotlib.pyplot as plt
from diffusers import DiffusionPipeline

pipeline = DiffusionPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0",
    dtype=torch.float16,
    device_map="cuda"  # or "mps", "xpu", "cpu"
)

prompts = [
    "Cinematic shot of a cozy coffee shop interior, warm pastel light streaming through a window where a cat rests. Shallow depth of field, glowing cups in soft focus, dreamy lofi-inspired mood, nostalgic tones, framed like a quiet film scene.",
    "Polaroid-style photograph of a cozy coffee shop interior, bathed in warm pastel light. A cat sits on the windowsill near steaming mugs. Soft, slightly faded tones and dreamy blur evoke nostalgia, a lofi mood, and the intimate, imperfect charm of instant film.",
    "Soft watercolor illustration of a cozy coffee shop interior, pastel washes of color filling the space. A cat rests peacefully on the windowsill as warm light glows through. Gentle brushstrokes create a dreamy, lofi-inspired atmosphere with whimsical textures and nostalgic calm.",
    "Isometric pixel-art illustration of a cozy coffee shop interior in detailed 8-bit style. Warm pastel light fills the space as a cat rests on the windowsill. Blocky furniture and tiny mugs add charm, low-res retro graphics enhance the nostalgic, lofi-inspired game aesthetic."
]

images = pipeline(
    prompt=prompts,
).images

fig, axes = plt.subplots(2, 2, figsize=(12, 12))
axes = axes.flatten()

for i, image in enumerate(images):
    axes[i].imshow(image)
    axes[i].set_title(f"Image {i+1}")
    axes[i].axis('off')

plt.tight_layout()
plt.show()
```

    

하나의 프롬프트에 대해 여러 가지 변형을 생성하려면 `num_images_per_prompt` 인수를 사용합니다.

```py
import torch
import matplotlib.pyplot as plt
from diffusers import DiffusionPipeline

pipeline = DiffusionPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0",
    dtype=torch.float16,
    device_map="cuda"  # or "mps", "xpu", "cpu"
)

prompt="""
Isometric pixel-art illustration of a cozy coffee shop interior in detailed 8-bit style. Warm pastel light fills the
space as a cat rests on the windowsill. Blocky furniture and tiny mugs add charm, low-res retro graphics enhance the
nostalgic, lofi-inspired game aesthetic.
"""

images = pipeline(
    prompt=prompt,
    num_images_per_prompt=4
).images

fig, axes = plt.subplots(2, 2, figsize=(12, 12))
axes = axes.flatten()

for i, image in enumerate(images):
    axes[i].imshow(image)
    axes[i].set_title(f"Image {i+1}")
    axes[i].axis('off')

plt.tight_layout()
plt.show()
```

    

두 가지 접근법을 결합하여 서로 다른 프롬프트의 다양한 변형을 생성합니다.

```py
images = pipeline(
    prompt=prompts,
    num_images_per_prompt=2,
).images

fig, axes = plt.subplots(2, 4, figsize=(12, 12))
axes = axes.flatten()

for i, image in enumerate(images):
    axes[i].imshow(image)
    axes[i].set_title(f"Image {i+1}")
    axes[i].axis('off')

plt.tight_layout()
plt.show()
```

    

## 결정론적 생성

[Generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) 목록을 파이프라인에 전달하고 각 `Generator`를 시드에 연결하여 재사용함으로써 재현 가능한 배치 생성을 활성화합니다.

> [!TIP]
> 결정적 알고리즘 및 `Generator` 객체에 대해 자세히 알아보려면 [Reproducibility](./reusing_seeds) 문서를 참조하세요.

`range()`에 지정된 배치 크기를 반복하기 위해 리스트 컴프리헨션을 사용하여 배치 내 각 이미지에 대해 고유한 `Generator` 객체를 생성하세요. `Generator`를 배치 크기로 곱하지 마세요. 그렇게 하면 배치 내 각 이미지에 순차적으로 사용되는 `Generator` 객체가 하나만 생성됩니다.

```py
generator = [torch.Generator(device="cuda").manual_seed(0)] * 3  # or "mps", "xpu", "cpu"
```

`generator`를 파이프라인에 전달합니다.

```py
import torch
from diffusers import DiffusionPipeline

pipeline = DiffusionPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0",
    dtype=torch.float16,
    device_map="cuda"
)

generator = [torch.Generator(device="cuda").manual_seed(i) for i in range(3)]  # or "mps", "xpu", "cpu"
prompts = [
    "Cinematic shot of a cozy coffee shop interior, warm pastel light streaming through a window where a cat rests. Shallow depth of field, glowing cups in soft focus, dreamy lofi-inspired mood, nostalgic tones, framed like a quiet film scene.",
    "Polaroid-style photograph of a cozy coffee shop interior, bathed in warm pastel light. A cat sits on the windowsill near steaming mugs. Soft, slightly faded tones and dreamy blur evoke nostalgia, a lofi mood, and the intimate, imperfect charm of instant film.",
    "Soft watercolor illustration of a cozy coffee shop interior, pastel washes of color filling the space. A cat rests peacefully on the windowsill as warm light glows through. Gentle brushstrokes create a dreamy, lofi-inspired atmosphere with whimsical textures and nostalgic calm.",
    "Isometric pixel-art illustration of a cozy coffee shop interior in detailed 8-bit style. Warm pastel light fills the space as a cat rests on the windowsill. Blocky furniture and tiny mugs add charm, low-res retro graphics enhance the nostalgic, lofi-inspired game aesthetic."
]

images = pipeline(
    prompt=prompts,
    generator=generator
).images

fig, axes = plt.subplots(2, 2, figsize=(12, 12))
axes = axes.flatten()

for i, image in enumerate(images):
    axes[i].imshow(image)
    axes[i].set_title(f"Image {i+1}")
    axes[i].axis('off')

plt.tight_layout()
plt.show()
```

…