# 批量推理 批量推理一次处理多个提示以提升吞吐量。一个批次能让 GPU 保持忙碌，而单个提示则会导致其利用率不足。 缺点在于延迟更高，因为你需要等待整个批次处理完毕。此外，较大的批次还需要更多的 GPU 内存。 ## 批量处理多个提示 将提示词列表传递给 pipeline，为每个提示词生成一张图像。 ```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() ``` ## 为每个提示词生成多张图像 要为一个提示词生成多个变体，请使用 `num_images_per_prompt` 参数。 ```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() ``` ## 为多个提示词生成多张图像 结合这两种方法，可以为不同的提示词生成不同的变体。此示例复用了上述示例中的 `pipeline` 和 `prompts`。 ```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() ``` ## 生成可复现的批次 要使批次可复现，请向 pipeline 传入一个 [Generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) 对象列表，每个图像对应一个对象，且每个对象拥有各自的种子。 > [!TIP] > 请参阅 [可复现性](./reusing_seeds) 文档，以了解确定性算法和 `Generator` 对象。 使用列表推导式为每张图片创建一个 `Generator`。对列表进行乘法运算（`[generator] * 3`）会得到指向同一个 `Generator` 的三个引用，因此图片无法获得独立的随机种子。 ```py # don't do this generator = [torch.Generator(device="cuda").manual_seed(0)] * 3 # or "mps", "xpu", "cpu" ``` 生成器的数量必须与批次中的图像数量相匹配。将`generator`传递给管道。 ```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" ) 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." ] generator = [torch.Generator(device="cuda").manual_seed(i) for i in range(len(prompts))] # or "mps", "xpu", "cpu" 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() ``` 每张图片都关联一个种子，因此您可以选择一张喜欢的图片，在优化其提示词的同时，使用相同的种子重新生成它。 ## 处理并发请求 服务器还能通过同时处理来自多个用户的请求来提高吞吐量。只需加载一次 pipeline 并在各请求间共享，这样模型在 GPU 内存中只需保留一份。由于调度器（Scheduler）不是线程安全的，因此请使用 [from_pipe()](/docs/diffusers/v0.43.0/zh/api/pipelines/overview#diffusers.DiffusionPipeline.from_pipe) 为每个请求创建独立的调度器。在多个线程间共享同一个调度器会引发诸如 `IndexError: index 21 is out of bounds for dimension 0 with size 21` 之类的错误。 ```py scheduler = shared_pipeline.scheduler.from_config(shared_pipeline.scheduler.config) pipeline = StableDiffusion3Pipeline.from_pipe(shared_pipeline, scheduler=scheduler) ``` 对于使用 [FastAPI](https://fastapi.tiangolo.com/) 构建的完整服务器，请参阅 [server](https://github.com/huggingface/diffusers/tree/main/examples/server) 示例和 [server-async](https://github.com/huggingface/diffusers/tree/main/examples/server-async) 示例。server-async 示例还会为每个请求克隆随机数生成器的状态，并使分词器（tokenizer）可以安全地在多个线程间共享。 ## 后续步骤 - 当单个批次无法放入一块 GPU，或者提示词数量超过单块 GPU 的处理能力时，请使用[数据并行](../optimization/data_parallelism)将提示词分布到多块 GPU 上。