# Community Scripts

**Community scripts** consist of inference examples using Diffusers pipelines that have been added by the community.
Please have a look at the following table to get an overview of all community examples. Click on the **Code Example** to get a copy-and-paste code example that you can try out.
If a community script doesn't work as expected, please open an issue and ping the author on it.

| Example                                                                                                                               | Description                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              | Code Example                                                                              | Colab                                                                                                                                                                                                              |                                                        Author |
|:--------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------:|
| Using IP-Adapter with Negative Noise                                                                                                  | Using negative noise with IP-adapter to better control the generation (see the [original post](https://github.com/huggingface/diffusers/discussions/7167) on the forum for more details)                                                                                                                                                                                                                                                    | [IP-Adapter Negative Noise](#ip-adapter-negative-noise)                                   |[Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/ip_adapter_negative_noise.ipynb) | [Álvaro Somoza](https://github.com/asomoza)|
| Asymmetric Tiling                                                                                                  |configure seamless image tiling independently for the X and Y axes                                                                                                                                                                                                      | [Asymmetric Tiling](#Asymmetric-Tiling )                                   |[Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/asymetric_tiling.ipynb) | [alexisrolland](https://github.com/alexisrolland)|
| Prompt Scheduling Callback                                                                                                  |Allows changing prompts during a generation                                                                                                                                                                                                      | [Prompt Scheduling-Callback](#Prompt-Scheduling-Callback )                                   |[Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/prompt_scheduling_callback.ipynb) | [hlky](https://github.com/hlky)|


## Example usages

### IP Adapter Negative Noise

Diffusers pipelines are fully integrated with IP-Adapter, which allows you to prompt the diffusion model with an image. However, it does not support negative image prompts (there is no `negative_ip_adapter_image` argument) the same way it supports negative text prompts. When you pass an `ip_adapter_image,` it will create a zero-filled tensor as a negative image. This script shows you how to create a negative noise from `ip_adapter_image` and use it to significantly improve the generation quality while preserving the composition of images.

[cubiq](https://github.com/cubiq) initially developed this feature in his [repository](https://github.com/cubiq/ComfyUI_IPAdapter_plus). The community script was contributed by [asomoza](https://github.com/Somoza). You can find more details about this experimentation [this discussion](https://github.com/huggingface/diffusers/discussions/7167)

IP-Adapter without negative noise
|source|result|
|---|---|
|![20240229150812](https://github.com/huggingface/diffusers/assets/5442875/901d8bd8-7a59-4fe7-bda1-a0e0d6c7dffd)|![20240229163923_normal](https://github.com/huggingface/diffusers/assets/5442875/3432e25a-ece6-45f4-a3f4-fca354f40b5b)|

IP-Adapter with negative noise
|source|result|
|---|---|
|![20240229150812](https://github.com/huggingface/diffusers/assets/5442875/901d8bd8-7a59-4fe7-bda1-a0e0d6c7dffd)|![20240229163923](https://github.com/huggingface/diffusers/assets/5442875/736fd15a-36ba-40c0-a7d8-6ec1ac26f788)|

```python
import torch

from diffusers import AutoencoderKL, DPMSolverMultistepScheduler, StableDiffusionXLPipeline
from diffusers.models import ImageProjection
from diffusers.utils import load_image


def encode_image(
    image_encoder,
    feature_extractor,
    image,
    device,
    num_images_per_prompt,
    output_hidden_states=None,
    negative_image=None,
):
    dtype = next(image_encoder.parameters()).dtype

    if not isinstance(image, torch.Tensor):
        image = feature_extractor(image, return_tensors="pt").pixel_values

    image = image.to(device=device, dtype=dtype)
    if output_hidden_states:
        image_enc_hidden_states = image_encoder(image, output_hidden_states=True).hidden_states[-2]
        image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)

        if negative_image is None:
            uncond_image_enc_hidden_states = image_encoder(
                torch.zeros_like(image), output_hidden_states=True
            ).hidden_states[-2]
        else:
            if not isinstance(negative_image, torch.Tensor):
                negative_image = feature_extractor(negative_image, return_tensors="pt").pixel_values
            negative_image = negative_image.to(device=device, dtype=dtype)
            uncond_image_enc_hidden_states = image_encoder(negative_image, output_hidden_states=True).hidden_states[-2]

        uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
        return image_enc_hidden_states, uncond_image_enc_hidden_states
    else:
        image_embeds = image_encoder(image).image_embeds
        image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
        uncond_image_embeds = torch.zeros_like(image_embeds)

        return image_embeds, uncond_image_embeds


@torch.no_grad()
def prepare_ip_adapter_image_embeds(
    unet,
    image_encoder,
    feature_extractor,
    ip_adapter_image,
    do_classifier_free_guidance,
    device,
    num_images_per_prompt,
    ip_adapter_negative_image=None,
):
    if not isinstance(ip_adapter_image, list):
        ip_adapter_image = [ip_adapter_image]

    if len(ip_adapter_image) != len(unet.encoder_hid_proj.image_projection_layers):
        raise ValueError(
            f"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(unet.encoder_hid_proj.image_projection_layers)} IP Adapters."
        )

    image_embeds = []
    for single_ip_adapter_image, image_proj_layer in zip(
        ip_adapter_image, unet.encoder_hid_proj.image_projection_layers
    ):
        output_hidden_state = not isinstance(image_proj_layer, ImageProjection)
        single_image_embeds, single_negative_image_embeds = encode_image(
            image_encoder,
            feature_extractor,
            single_ip_adapter_image,
            device,
            1,
            output_hidden_state,
            negative_image=ip_adapter_negative_image,
        )
        single_image_embeds = torch.stack([single_image_embeds] * num_images_per_prompt, dim=0)
        single_negative_image_embeds = torch.stack([single_negative_image_embeds] * num_images_per_prompt, dim=0)

        if do_classifier_free_guidance:
            single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])
            single_image_embeds = single_image_embeds.to(device)

        image_embeds.append(single_image_embeds)

    return image_embeds


vae = AutoencoderKL.from_pretrained(
    "madebyollin/sdxl-vae-fp16-fix",
    torch_dtype=torch.float16,
).to("cuda")

pipeline = StableDiffusionXLPipeline.from_pretrained(
    "RunDiffusion/Juggernaut-XL-v9",
    torch_dtype=torch.float16,
    vae=vae,
    variant="fp16",
).to("cuda")

pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
pipeline.scheduler.config.use_karras_sigmas = True

pipeline.load_ip_adapter(
    "h94/IP-Adapter",
    subfolder="sdxl_models",
    weight_name="ip-adapter-plus_sdxl_vit-h.safetensors",
    image_encoder_folder="models/image_encoder",
)
pipeline.set_ip_adapter_scale(0.7)

ip_image = load_image("source.png")
negative_ip_image = load_image("noise.png")

image_embeds = prepare_ip_adapter_image_embeds(
    unet=pipeline.unet,
    image_encoder=pipeline.image_encoder,
    feature_extractor=pipeline.feature_extractor,
    ip_adapter_image=[[ip_image]],
    do_classifier_free_guidance=True,
    device="cuda",
    num_images_per_prompt=1,
    ip_adapter_negative_image=negative_ip_image,
)


prompt = "cinematic photo of a cyborg in the city, 4k, high quality, intricate, highly detailed"
negative_prompt = "blurry, smooth, plastic"

image = pipeline(
    prompt=prompt,
    negative_prompt=negative_prompt,
    ip_adapter_image_embeds=image_embeds,
    guidance_scale=6.0,
    num_inference_steps=25,
    generator=torch.Generator(device="cpu").manual_seed(1556265306),
).images[0]

image.save("result.png")
```

### Asymmetric Tiling
Stable Diffusion is not trained to generate seamless textures. However, you can use this simple script to add tiling to your generation. This script is contributed by [alexisrolland](https://github.com/alexisrolland). See more details in the [this issue](https://github.com/huggingface/diffusers/issues/556)


|Generated|Tiled|
|---|---|
|![20240313003235_573631814](https://github.com/huggingface/diffusers/assets/5442875/eca174fb-06a4-464e-a3a7-00dbb024543e)|![wall](https://github.com/huggingface/diffusers/assets/5442875/b4aa774b-2a6a-4316-a8eb-8f30b5f4d024)|


```py
import torch
from typing import Optional
from diffusers import StableDiffusionPipeline
from diffusers.models.lora import LoRACompatibleConv

def seamless_tiling(pipeline, x_axis, y_axis):
    def asymmetric_conv2d_convforward(self, input: torch.Tensor, weight: torch.Tensor, bias: Optional[torch.Tensor] = None):
        self.paddingX = (self._reversed_padding_repeated_twice[0], self._reversed_padding_repeated_twice[1], 0, 0)
        self.paddingY = (0, 0, self._reversed_padding_repeated_twice[2], self._reversed_padding_repeated_twice[3])
        working = torch.nn.functional.pad(input, self.paddingX, mode=x_mode)
        working = torch.nn.functional.pad(working, self.paddingY, mode=y_mode)
        return torch.nn.functional.conv2d(working, weight, bias, self.stride, torch.nn.modules.utils._pair(0), self.dilation, self.groups)
    x_mode = 'circular' if x_axis else 'constant'
    y_mode = 'circular' if y_axis else 'constant'
    targets = [pipeline.vae, pipeline.text_encoder, pipeline.unet]
    convolution_layers = []
    for target in targets:
        for module in target.modules():
            if isinstance(module, torch.nn.Conv2d):
                convolution_layers.append(module)
    for layer in convolution_layers:
        if isinstance(layer, LoRACompatibleConv) and layer.lora_layer is None:
            layer.lora_layer = lambda * x: 0
        layer._conv_forward = asymmetric_conv2d_convforward.__get__(layer, torch.nn.Conv2d)
    return pipeline

pipeline = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True)
pipeline.enable_model_cpu_offload()
prompt = ["texture of a red brick wall"]
seed = 123456
generator = torch.Generator(device='cuda').manual_seed(seed)

pipeline = seamless_tiling(pipeline=pipeline, x_axis=True, y_axis=True)
image = pipeline(
    prompt=prompt,
    width=512,
    height=512,
    num_inference_steps=20,
    guidance_scale=7,
    num_images_per_prompt=1,
    generator=generator
).images[0]
seamless_tiling(pipeline=pipeline, x_axis=False, y_axis=False)

torch.cuda.empty_cache()
image.save('image.png')
```

### Prompt Scheduling callback

Prompt scheduling callback allows changing prompts during a generation, like [prompt editing in A1111](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#prompt-editing)

```python
from diffusers import StableDiffusionPipeline
from diffusers.callbacks import PipelineCallback, MultiPipelineCallbacks
from diffusers.configuration_utils import register_to_config
import torch
from typing import Any, Dict, Tuple, Union


class SDPromptSchedulingCallback(PipelineCallback):
    @register_to_config
    def __init__(
        self,
        encoded_prompt: Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]],
        cutoff_step_ratio=None,
        cutoff_step_index=None,
    ):
        super().__init__(
            cutoff_step_ratio=cutoff_step_ratio, cutoff_step_index=cutoff_step_index
        )

    tensor_inputs = ["prompt_embeds"]

    def callback_fn(
        self, pipeline, step_index, timestep, callback_kwargs
    ) -> dict[str, Any]:
        cutoff_step_ratio = self.config.cutoff_step_ratio
        cutoff_step_index = self.config.cutoff_step_index
        if isinstance(self.config.encoded_prompt, tuple):
            prompt_embeds, negative_prompt_embeds = self.config.encoded_prompt
        else:
            prompt_embeds = self.config.encoded_prompt

        # Use cutoff_step_index if it's not None, otherwise use cutoff_step_ratio
        cutoff_step = (
            cutoff_step_index
            if cutoff_step_index is not None
            else int(pipeline.num_timesteps * cutoff_step_ratio)
        )

        if step_index == cutoff_step:
            if pipeline.do_classifier_free_guidance:
                prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
            callback_kwargs[self.tensor_inputs[0]] = prompt_embeds
        return callback_kwargs


pipeline: StableDiffusionPipeline = StableDiffusionPipeline.from_pretrained(
    "stable-diffusion-v1-5/stable-diffusion-v1-5",
    torch_dtype=torch.float16,
    variant="fp16",
    use_safetensors=True,
).to("cuda")
pipeline.safety_checker = None
pipeline.requires_safety_checker = False

callback = MultiPipelineCallbacks(
    [
        SDPromptSchedulingCallback(
            encoded_prompt=pipeline.encode_prompt(
                prompt=f"prompt {index}",
                negative_prompt=f"negative prompt {index}",
                device=pipeline._execution_device,
                num_images_per_prompt=1,
                # pipeline.do_classifier_free_guidance can't be accessed until after pipeline is ran
                do_classifier_free_guidance=True,
            ),
            cutoff_step_index=index,
        ) for index in range(1, 20)
    ]
)

image = pipeline(
    prompt="prompt"
    negative_prompt="negative prompt",
    callback_on_step_end=callback,
    callback_on_step_end_tensor_inputs=["prompt_embeds"],
).images[0]
torch.cuda.empty_cache()
image.save('image.png')
```

```python
from diffusers import StableDiffusionXLPipeline
from diffusers.callbacks import PipelineCallback, MultiPipelineCallbacks
from diffusers.configuration_utils import register_to_config
import torch
from typing import Any, Dict, Tuple, Union


class SDXLPromptSchedulingCallback(PipelineCallback):
    @register_to_config
    def __init__(
        self,
        encoded_prompt: Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]],
        add_text_embeds: Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]],
        add_time_ids: Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]],
        cutoff_step_ratio=None,
        cutoff_step_index=None,
    ):
        super().__init__(
            cutoff_step_ratio=cutoff_step_ratio, cutoff_step_index=cutoff_step_index
        )

    tensor_inputs = ["prompt_embeds", "add_text_embeds", "add_time_ids"]

    def callback_fn(
        self, pipeline, step_index, timestep, callback_kwargs
    ) -> dict[str, Any]:
        cutoff_step_ratio = self.config.cutoff_step_ratio
        cutoff_step_index = self.config.cutoff_step_index
        if isinstance(self.config.encoded_prompt, tuple):
            prompt_embeds, negative_prompt_embeds = self.config.encoded_prompt
        else:
            prompt_embeds = self.config.encoded_prompt
        if isinstance(self.config.add_text_embeds, tuple):
            add_text_embeds, negative_add_text_embeds = self.config.add_text_embeds
        else:
            add_text_embeds = self.config.add_text_embeds
        if isinstance(self.config.add_time_ids, tuple):
            add_time_ids, negative_add_time_ids = self.config.add_time_ids
        else:
            add_time_ids = self.config.add_time_ids

        # Use cutoff_step_index if it's not None, otherwise use cutoff_step_ratio
        cutoff_step = (
            cutoff_step_index
            if cutoff_step_index is not None
            else int(pipeline.num_timesteps * cutoff_step_ratio)
        )

        if step_index == cutoff_step:
            if pipeline.do_classifier_free_guidance:
                prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
                add_text_embeds = torch.cat([negative_add_text_embeds, add_text_embeds])
                add_time_ids = torch.cat([negative_add_time_ids, add_time_ids])
            callback_kwargs[self.tensor_inputs[0]] = prompt_embeds
            callback_kwargs[self.tensor_inputs[1]] = add_text_embeds
            callback_kwargs[self.tensor_inputs[2]] = add_time_ids
        return callback_kwargs


pipeline: StableDiffusionXLPipeline = StableDiffusionXLPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0",
    torch_dtype=torch.float16,
    variant="fp16",
    use_safetensors=True,
).to("cuda")

callbacks = []
for index in range(1, 20):
    (
        prompt_embeds,
        negative_prompt_embeds,
        pooled_prompt_embeds,
        negative_pooled_prompt_embeds,
    ) = pipeline.encode_prompt(
        prompt=f"prompt {index}",
        negative_prompt=f"prompt {index}",
        device=pipeline._execution_device,
        num_images_per_prompt=1,
        # pipeline.do_classifier_free_guidance can't be accessed until after pipeline is ran
        do_classifier_free_guidance=True,
    )
    text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])
    add_time_ids = pipeline._get_add_time_ids(
        (1024, 1024),
        (0, 0),
        (1024, 1024),
        dtype=prompt_embeds.dtype,
        text_encoder_projection_dim=text_encoder_projection_dim,
    )
    negative_add_time_ids = pipeline._get_add_time_ids(
        (1024, 1024),
        (0, 0),
        (1024, 1024),
        dtype=prompt_embeds.dtype,
        text_encoder_projection_dim=text_encoder_projection_dim,
    )
    callbacks.append(
        SDXLPromptSchedulingCallback(
            encoded_prompt=(prompt_embeds, negative_prompt_embeds),
            add_text_embeds=(pooled_prompt_embeds, negative_pooled_prompt_embeds),
            add_time_ids=(add_time_ids, negative_add_time_ids),
            cutoff_step_index=index,
        )
    )


callback = MultiPipelineCallbacks(callbacks)

image = pipeline(
    prompt="prompt",
    negative_prompt="negative prompt",
    callback_on_step_end=callback,
    callback_on_step_end_tensor_inputs=[
        "prompt_embeds",
        "add_text_embeds",
        "add_time_ids",
    ],
).images[0]
```
