TCD-LoRA also works with many community finetuned models and plugins. For example, load the [animagine-xl-3.0](https://huggingface.co/cagliostrolab/animagine-xl-3.0) checkpoint which is a community finetuned version of SDXL for generating anime images.  
```python
import torch
from diffusers import StableDiffusionXLPipeline, TCDScheduler

device = "cuda"
base_model_id = "cagliostrolab/animagine-xl-3.0"
tcd_lora_id = "h1t/TCD-SDXL-LoRA"

pipe = StableDiffusionXLPipeline.from_pretrained(base_model_id, torch_dtype=torch.float16, variant="fp16").to(device)
pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config)

pipe.load_lora_weights(tcd_lora_id)
pipe.fuse_lora()

prompt = "A man, clad in a meticulously tailored military uniform, stands with unwavering resolve. The uniform boasts intricate details, and his eyes gleam with determination. Strands of vibrant, windswept hair peek out from beneath the brim of his cap."

image = pipe(
prompt=prompt,
num_inference_steps=8,
guidance_scale=0,
eta=0.3,
generator=torch.Generator(device=device).manual_seed(0),
).images[0]
```  
![](https://github.com/jabir-zheng/TCD/raw/main/assets/animagine_xl.png)  
TCD-LoRA also supports other LoRAs trained on different styles. For example, let's load the [TheLastBen/Papercut_SDXL](https://huggingface.co/TheLastBen/Papercut_SDXL) LoRA and fuse it with the TCD-LoRA with the [`~loaders.UNet2DConditionLoadersMixin.set_adapters`] method.  
> [!TIP]
> Check out the [Merge LoRAs](merge_loras) guide to learn more about efficient merging methods.  
```python
import torch
from diffusers import StableDiffusionXLPipeline
from scheduling_tcd import TCDScheduler

device = "cuda"
base_model_id = "stabilityai/stable-diffusion-xl-base-1.0"
tcd_lora_id = "h1t/TCD-SDXL-LoRA"
styled_lora_id = "TheLastBen/Papercut_SDXL"

pipe = StableDiffusionXLPipeline.from_pretrained(base_model_id, torch_dtype=torch.float16, variant="fp16").to(device)
pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config)

pipe.load_lora_weights(tcd_lora_id, adapter_name="tcd")
pipe.load_lora_weights(styled_lora_id, adapter_name="style")
pipe.set_adapters(["tcd", "style"], adapter_weights=[1.0, 1.0])

prompt = "papercut of a winter mountain, snow"

image = pipe(
prompt=prompt,
num_inference_steps=4,
guidance_scale=0,
eta=0.3,
generator=torch.Generator(device=device).manual_seed(0),
).images[0]
```  
![](https://github.com/jabir-zheng/TCD/raw/main/assets/styled_lora.png)
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/inference_with_tcd_lora.md