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# 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) | | [Álvaro Somoza](https://github.com/asomoza)|
| asymmetric tiling |configure seamless image tiling independently for the X and Y axes | [Asymmetric Tiling](#asymmetric-tiling ) | | [alexisrolland](https://github.com/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](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|
|---|---|
|||
IP-Adapter with negative noise
|source|result|
|---|---|
|||
```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|
|---|---|
|||
```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("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')
``` |