```python
import torch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline
from diffusers.utils import load_image, make_image_grid
from scheduling_tcd import TCDScheduler

device = "cuda"
base_model_id = "stabilityai/stable-diffusion-xl-base-1.0"
controlnet_id = "diffusers/controlnet-canny-sdxl-1.0"
tcd_lora_id = "h1t/TCD-SDXL-LoRA"

controlnet = ControlNetModel.from_pretrained(
controlnet_id,
torch_dtype=torch.float16,
variant="fp16",
)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
base_model_id,
controlnet=controlnet,
torch_dtype=torch.float16,
variant="fp16",
)
pipe.enable_model_cpu_offload()

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

pipe.load_lora_weights(tcd_lora_id)
pipe.fuse_lora()

prompt = "ultrarealistic shot of a furry blue bird"

canny_image = load_image("https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png")

controlnet_conditioning_scale = 0.5  # recommended for good generalization

image = pipe(
prompt,
image=canny_image,
num_inference_steps=4,
guidance_scale=0,
eta=0.3,
controlnet_conditioning_scale=controlnet_conditioning_scale,
generator=torch.Generator(device=device).manual_seed(0),
).images[0]

grid_image = make_image_grid([canny_image, image], rows=1, cols=2)
```
![](https://github.com/jabir-zheng/TCD/raw/main/assets/controlnet_canny_tcd.png)  
<Tip>
The inference parameters in this example might not work for all examples, so we recommend you to try different values for `num_inference_steps`, `guidance_scale`, `controlnet_conditioning_scale` and `cross_attention_kwargs` parameters and choose the best one.
</Tip>  
</hfoption>
<hfoption id="IP-Adapter">  
This example shows how to use the TCD-LoRA with the [IP-Adapter](https://github.com/tencent-ailab/IP-Adapter/tree/main) and SDXL.  
```python
import torch
from diffusers import StableDiffusionXLPipeline
from diffusers.utils import load_image, make_image_grid

from ip_adapter import IPAdapterXL
from scheduling_tcd import TCDScheduler

device = "cuda"
base_model_path = "stabilityai/stable-diffusion-xl-base-1.0"
image_encoder_path = "sdxl_models/image_encoder"
ip_ckpt = "sdxl_models/ip-adapter_sdxl.bin"
tcd_lora_id = "h1t/TCD-SDXL-LoRA"

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

pipe.load_lora_weights(tcd_lora_id)
pipe.fuse_lora()

ip_model = IPAdapterXL(pipe, image_encoder_path, ip_ckpt, device)

ref_image = load_image("https://raw.githubusercontent.com/tencent-ailab/IP-Adapter/main/assets/images/woman.png").resize((512, 512))

prompt = "best quality, high quality, wearing sunglasses"

image = ip_model.generate(
pil_image=ref_image,
prompt=prompt,
scale=0.5,
num_samples=1,
num_inference_steps=4,
guidance_scale=0,
eta=0.3,
seed=0,
)[0]

grid_image = make_image_grid([ref_image, image], rows=1, cols=2)
```  
![](https://github.com/jabir-zheng/TCD/raw/main/assets/ip_adapter.png)  
</hfoption>
<hfoption id="AnimateDiff">  
[`AnimateDiff`] allows animating images using Stable Diffusion models. TCD-LoRA can substantially accelerate the process without degrading image quality. The quality of animation with TCD-LoRA and AnimateDiff has a more lucid outcome.  
```python
import torch
from diffusers import MotionAdapter, AnimateDiffPipeline, DDIMScheduler
from scheduling_tcd import TCDScheduler
from diffusers.utils import export_to_gif

adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5")
pipe = AnimateDiffPipeline.from_pretrained(
"frankjoshua/toonyou_beta6",
motion_adapter=adapter,
).to("cuda")

# set TCDScheduler
pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config)

# load TCD LoRA
pipe.load_lora_weights("h1t/TCD-SD15-LoRA", adapter_name="tcd")
pipe.load_lora_weights("guoyww/animatediff-motion-lora-zoom-in", weight_name="diffusion_pytorch_model.safetensors", adapter_name="motion-lora")

pipe.set_adapters(["tcd", "motion-lora"], adapter_weights=[1.0, 1.2])

prompt = "best quality, masterpiece, 1girl, looking at viewer, blurry background, upper body, contemporary, dress"
generator = torch.manual_seed(0)
frames = pipe(
prompt=prompt,
num_inference_steps=5,
guidance_scale=0,
cross_attention_kwargs={"scale": 1},
num_frames=24,
eta=0.3,
generator=generator
).frames[0]
export_to_gif(frames, "animation.gif")
```  
![](https://github.com/jabir-zheng/TCD/raw/main/assets/animation_example.gif)  
</hfoption>
</hfoptions>
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/inference_with_tcd_lora.md