diff --git a/docs/source/en/_toctree.yml b/docs/source/en/_toctree.yml
index 283efeef72c1..770093438ed5 100644
--- a/docs/source/en/_toctree.yml
+++ b/docs/source/en/_toctree.yml
@@ -64,6 +64,8 @@
title: Overview
- local: using-diffusers/create_a_server
title: Create a server
+ - local: using-diffusers/batched_inference
+ title: Batch inference
- local: training/distributed_inference
title: Distributed inference
- local: using-diffusers/scheduler_features
diff --git a/docs/source/en/using-diffusers/batched_inference.md b/docs/source/en/using-diffusers/batched_inference.md
new file mode 100644
index 000000000000..b5e55c27ca41
--- /dev/null
+++ b/docs/source/en/using-diffusers/batched_inference.md
@@ -0,0 +1,264 @@
+
+
+# Batch inference
+
+Batch inference processes multiple prompts at a time to increase throughput. It is more efficient because processing multiple prompts at once maximizes GPU usage versus processing a single prompt and underutilizing the GPU.
+
+The downside is increased latency because you must wait for the entire batch to complete, and more GPU memory is required for large batches.
+
+
+
+
+For text-to-image, pass a list of prompts to the pipeline.
+
+```py
+import torch
+from diffusers import DiffusionPipeline
+
+pipeline = DiffusionPipeline.from_pretrained(
+ "stabilityai/stable-diffusion-xl-base-1.0",
+ torch_dtype=torch.float16
+).to("cuda")
+
+prompts = [
+ "cinematic photo of A beautiful sunset over mountains, 35mm photograph, film, professional, 4k, highly detailed",
+ "cinematic film still of a cat basking in the sun on a roof in Turkey, highly detailed, high budget hollywood movie, cinemascope, moody, epic, gorgeous, film grain",
+ "pixel-art a cozy coffee shop interior, low-res, blocky, pixel art style, 8-bit graphics"
+]
+
+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()
+```
+
+To generate multiple variations of one prompt, use the `num_images_per_prompt` argument.
+
+```py
+import torch
+import matplotlib.pyplot as plt
+from diffusers import DiffusionPipeline
+
+pipeline = DiffusionPipeline.from_pretrained(
+ "stabilityai/stable-diffusion-xl-base-1.0",
+ torch_dtype=torch.float16
+).to("cuda")
+
+images = pipeline(
+ prompt="pixel-art a cozy coffee shop interior, low-res, blocky, pixel art style, 8-bit graphics",
+ 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()
+```
+
+Combine both approaches to generate different variations of different prompts.
+
+```py
+images = pipeline(
+ prompt=prompts,
+ num_images_per_prompt=2,
+).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()
+```
+
+
+
+
+For image-to-image, pass a list of input images and prompts to the pipeline.
+
+```py
+import torch
+from diffusers.utils import load_image
+from diffusers import DiffusionPipeline
+
+pipeline = DiffusionPipeline.from_pretrained(
+ "stabilityai/stable-diffusion-xl-base-1.0",
+ torch_dtype=torch.float16
+).to("cuda")
+
+input_images = [
+ load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint.png"),
+ load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png"),
+ load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/detail-prompt.png")
+]
+
+prompts = [
+ "cinematic photo of a beautiful sunset over mountains, 35mm photograph, film, professional, 4k, highly detailed",
+ "cinematic film still of a cat basking in the sun on a roof in Turkey, highly detailed, high budget hollywood movie, cinemascope, moody, epic, gorgeous, film grain",
+ "pixel-art a cozy coffee shop interior, low-res, blocky, pixel art style, 8-bit graphics"
+]
+
+images = pipeline(
+ prompt=prompts,
+ image=input_images,
+ guidance_scale=8.0,
+ strength=0.5
+).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()
+```
+
+To generate multiple variations of one prompt, use the `num_images_per_prompt` argument.
+
+```py
+import torch
+import matplotlib.pyplot as plt
+from diffusers.utils import load_image
+from diffusers import DiffusionPipeline
+
+pipeline = DiffusionPipeline.from_pretrained(
+ "stabilityai/stable-diffusion-xl-base-1.0",
+ torch_dtype=torch.float16
+).to("cuda")
+
+input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/detail-prompt.png")
+
+images = pipeline(
+ prompt="pixel-art a cozy coffee shop interior, low-res, blocky, pixel art style, 8-bit graphics",
+ image=input_image,
+ 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()
+```
+
+Combine both approaches to generate different variations of different prompts.
+
+```py
+input_images = [
+ load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png"),
+ load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/detail-prompt.png")
+]
+
+prompts = [
+ "cinematic film still of a cat basking in the sun on a roof in Turkey, highly detailed, high budget hollywood movie, cinemascope, moody, epic, gorgeous, film grain",
+ "pixel-art a cozy coffee shop interior, low-res, blocky, pixel art style, 8-bit graphics"
+]
+
+images = pipeline(
+ prompt=prompts,
+ image=input_images,
+ num_images_per_prompt=2,
+).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()
+```
+
+
+
+
+## Deterministic generation
+
+Enable reproducible batch generation by passing a list of [Generator’s](https://pytorch.org/docs/stable/generated/torch.Generator.html) to the pipeline and tie each `Generator` to a seed to reuse it.
+
+Use a list comprehension to iterate over the batch size specified in `range()` to create a unique `Generator` object for each image in the batch.
+
+Don't multiply the `Generator` by the batch size because that only creates one `Generator` object that is used sequentially for each image in the batch.
+
+```py
+generator = [torch.Generator(device="cuda").manual_seed(0)] * 3
+```
+
+Pass the `generator` to the pipeline.
+
+```py
+import torch
+from diffusers import DiffusionPipeline
+
+pipeline = DiffusionPipeline.from_pretrained(
+ "stabilityai/stable-diffusion-xl-base-1.0",
+ torch_dtype=torch.float16
+).to("cuda")
+
+generator = [torch.Generator(device="cuda").manual_seed(i) for i in range(3)]
+prompts = [
+ "cinematic photo of A beautiful sunset over mountains, 35mm photograph, film, professional, 4k, highly detailed",
+ "cinematic film still of a cat basking in the sun on a roof in Turkey, highly detailed, high budget hollywood movie, cinemascope, moody, epic, gorgeous, film grain",
+ "pixel-art a cozy coffee shop interior, low-res, blocky, pixel art style, 8-bit graphics"
+]
+
+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()
+```
+
+You can use this to iteratively select an image associated with a seed and then improve on it by crafting a more detailed prompt.
\ No newline at end of file
diff --git a/docs/source/en/using-diffusers/reusing_seeds.md b/docs/source/en/using-diffusers/reusing_seeds.md
index 60b8fee754f5..ac9350f24caa 100644
--- a/docs/source/en/using-diffusers/reusing_seeds.md
+++ b/docs/source/en/using-diffusers/reusing_seeds.md
@@ -136,53 +136,3 @@ result2 = pipe(prompt=prompt, num_inference_steps=50, generator=g, output_type="
print("L_inf dist =", abs(result1 - result2).max())
"L_inf dist = tensor(0., device='cuda:0')"
```
-
-## Deterministic batch generation
-
-A practical application of creating reproducible pipelines is *deterministic batch generation*. You generate a batch of images and select one image to improve with a more detailed prompt. The main idea is to pass a list of [Generator's](https://pytorch.org/docs/stable/generated/torch.Generator.html) to the pipeline and tie each `Generator` to a seed so you can reuse it.
-
-Let's use the [stable-diffusion-v1-5/stable-diffusion-v1-5](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) checkpoint and generate a batch of images.
-
-```py
-import torch
-from diffusers import DiffusionPipeline
-from diffusers.utils import make_image_grid
-
-pipeline = DiffusionPipeline.from_pretrained(
- "stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True
-)
-pipeline = pipeline.to("cuda")
-```
-
-Define four different `Generator`s and assign each `Generator` a seed (`0` to `3`). Then generate a batch of images and pick one to iterate on.
-
-> [!WARNING]
-> Use a list comprehension that iterates over the batch size specified in `range()` to create a unique `Generator` object for each image in the batch. If you multiply the `Generator` by the batch size integer, it only creates *one* `Generator` object that is used sequentially for each image in the batch.
->
-> ```py
-> [torch.Generator().manual_seed(seed)] * 4
-> ```
-
-```python
-generator = [torch.Generator(device="cuda").manual_seed(i) for i in range(4)]
-prompt = "Labrador in the style of Vermeer"
-images = pipeline(prompt, generator=generator, num_images_per_prompt=4).images[0]
-make_image_grid(images, rows=2, cols=2)
-```
-
-
-
-
-
-