[T2I-Adapter](./t2i_adapter) is an even more lightweight adapter than ControlNet, that provides an additional input to condition a pretrained model with. It is faster than ControlNet but the results may be slightly worse.  
You can find additional T2I-Adapter checkpoints trained on other inputs in [TencentArc's](https://hf.co/TencentARC) repository.  
<hfoptions id="lcm-t2i">
<hfoption id="LCM">  
Load a T2IAdapter trained on canny images and pass it to the [`StableDiffusionXLAdapterPipeline`]. Then load a LCM checkpoint into [`UNet2DConditionModel`] and replace the scheduler with the [`LCMScheduler`]. Now pass the canny image to the pipeline and generate an image.  
```python
import torch
import cv2
import numpy as np
from PIL import Image

from diffusers import StableDiffusionXLAdapterPipeline, UNet2DConditionModel, T2IAdapter, LCMScheduler
from diffusers.utils import load_image, make_image_grid

# detect the canny map in low resolution to avoid high-frequency details
image = load_image(
"https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png"
).resize((384, 384))

image = np.array(image)

low_threshold = 100
high_threshold = 200

image = cv2.Canny(image, low_threshold, high_threshold)
image = image[:, :, None]
image = np.concatenate([image, image, image], axis=2)
canny_image = Image.fromarray(image).resize((1024, 1216))

adapter = T2IAdapter.from_pretrained("TencentARC/t2i-adapter-canny-sdxl-1.0", torch_dtype=torch.float16, varient="fp16").to("cuda")

unet = UNet2DConditionModel.from_pretrained(
"latent-consistency/lcm-sdxl",
torch_dtype=torch.float16,
variant="fp16",
)
pipe = StableDiffusionXLAdapterPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
unet=unet,
adapter=adapter,
torch_dtype=torch.float16,
variant="fp16",
).to("cuda")

pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)

prompt = "the mona lisa, 4k picture, high quality"
negative_prompt = "extra digit, fewer digits, cropped, worst quality, low quality, glitch, deformed, mutated, ugly, disfigured"

generator = torch.manual_seed(0)
image = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
image=canny_image,
num_inference_steps=4,
guidance_scale=5,
adapter_conditioning_scale=0.8,
adapter_conditioning_factor=1,
generator=generator,
).images[0]
```  
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lcm-t2i.png"/>
</div>  
</hfoption>
<hfoption id="LCM-LoRA">  
Load a T2IAdapter trained on canny images and pass it to the [`StableDiffusionXLAdapterPipeline`]. Replace the scheduler with the [`LCMScheduler`], and use the [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] method to load the LCM-LoRA weights. Pass the canny image to the pipeline and generate an image.  
```py
import torch
import cv2
import numpy as np
from PIL import Image

from diffusers import StableDiffusionXLAdapterPipeline, UNet2DConditionModel, T2IAdapter, LCMScheduler
from diffusers.utils import load_image, make_image_grid

# detect the canny map in low resolution to avoid high-frequency details
image = load_image(
"https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png"
).resize((384, 384))

image = np.array(image)

low_threshold = 100
high_threshold = 200

image = cv2.Canny(image, low_threshold, high_threshold)
image = image[:, :, None]
image = np.concatenate([image, image, image], axis=2)
canny_image = Image.fromarray(image).resize((1024, 1024))

adapter = T2IAdapter.from_pretrained("TencentARC/t2i-adapter-canny-sdxl-1.0", torch_dtype=torch.float16, varient="fp16").to("cuda")

pipe = StableDiffusionXLAdapterPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
adapter=adapter,
torch_dtype=torch.float16,
variant="fp16",
).to("cuda")

pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)

pipe.load_lora_weights("latent-consistency/lcm-lora-sdxl")

prompt = "the mona lisa, 4k picture, high quality"
negative_prompt = "extra digit, fewer digits, cropped, worst quality, low quality, glitch, deformed, mutated, ugly, disfigured"

generator = torch.manual_seed(0)
image = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
image=canny_image,
num_inference_steps=4,
guidance_scale=1.5,
adapter_conditioning_scale=0.8,
adapter_conditioning_factor=1,
generator=generator,
).images[0]
```  
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lcm-lora-t2i.png"/>
</div>  
</hfoption>
</hfoptions>
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/inference_with_lcm.md