diffusers/examples/community/README_community_scripts.md

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 on the forum for more details)	IP-Adapter Negative Noise		Álvaro Somoza
asymmetric tiling	configure seamless image tiling independently for the X and Y axes	Asymmetric Tiling		alexisrolland

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 initially developed this feature in his repository. The community script was contributed by asomoza. You can find more details about this experimentation this discussion

IP-Adapter without negative noise

source	result

IP-Adapter with negative noise

source	result

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. See more details in the this issue

Generated	Tiled

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("runwayml/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')