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LCM-LoRA docs (#5782) 



* begin doc

* fix examples

* add in toctree

* fix toctree

* improve copy

* improve introductions

* add lcm doc

* fix filename

* Apply suggestions from code review
Co-authored-by: default avatarSteven Liu <59462357+stevhliu@users.noreply.github.com>

* address Sayak's comments

* remove controlnet aux

* open in colab

* move to Specific pipeline examples

* update controlent and adapter examples

---------
Co-authored-by: default avatarSteven Liu <59462357+stevhliu@users.noreply.github.com>
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docs/source/en/_toctree.yml
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......@@ -19,7 +19,6 @@
title: Train a diffusion model
- local: tutorials/using_peft_for_inference
title: Inference with PEFT
title: Tutorials
- sections:
- sections:
- local: using-diffusers/loading_overview
......@@ -72,8 +71,6 @@
title: Overview
- local: using-diffusers/sdxl
title: Stable Diffusion XL
- local: using-diffusers/lcm
title: Latent Consistency Models
- local: using-diffusers/kandinsky
title: Kandinsky
- local: using-diffusers/controlnet
......@@ -92,6 +89,10 @@
title: Community pipelines
- local: using-diffusers/contribute_pipeline
title: Contribute a community pipeline
- local: using-diffusers/inference_with_lcm_lora
title: Latent Consistency Model-LoRA
- local: using-diffusers/inference_with_lcm
title: Latent Consistency Model
title: Specific pipeline examples
- sections:
- local: training/overview
......
docs/source/en/using-diffusers/lcm.md → docs/source/en/using-diffusers/inference_with_lcm.md
View file @ a042909c
......@@ -10,7 +10,9 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
specific language governing permissions and limitations under the License.
-->
# Performing inference with LCM
[[open-in-colab]]
# Latent Consistency Model
Latent Consistency Models (LCM) enable quality image generation in typically 2-4 steps making it possible to use diffusion models in almost real-time settings.
......@@ -20,14 +22,20 @@ From the [official website](https://latent-consistency-models.github.io/):
For a more technical overview of LCMs, refer to [the paper](https://huggingface.co/papers/2310.04378).
This guide shows how to perform inference with LCMs for text-to-image and image-to-image generation tasks. It will also cover performing inference with LoRA checkpoints.
LCM distilled models are available for [stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5), [stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0), and the [SSD-1B](https://huggingface.co/segmind/SSD-1B) model. All the checkpoints can be found in this [collection](https://huggingface.co/collections/latent-consistency/latent-consistency-models-weights-654ce61a95edd6dffccef6a8).
This guide shows how to perform inference with LCMs for
- text-to-image
- image-to-image
- combined with style LoRAs
- ControlNet/T2I-Adapter
## Text-to-image
You'll use the [`StableDiffusionXLPipeline`] here changing the `unet`. The UNet was distilled from the SDXL UNet using the framework introduced in LCM. Another important component is the scheduler: [`LCMScheduler`]. Together with the distilled UNet and the scheduler, LCM enables a fast inference workflow overcoming the slow iterative nature of diffusion models.
You'll use the [`StableDiffusionXLPipeline`] pipeline with the [`LCMScheduler`] and then load the LCM-LoRA. Together with the LCM-LoRA and the scheduler, the pipeline enables a fast inference workflow, overcoming the slow iterative nature of diffusion models.
```python
from diffusers import DiffusionPipeline, UNet2DConditionModel, LCMScheduler
from diffusers import StableDiffusionXLPipeline, UNet2DConditionModel, LCMScheduler
import torch
unet = UNet2DConditionModel.from_pretrained(
......@@ -35,8 +43,8 @@ unet = UNet2DConditionModel.from_pretrained(
torch_dtype=torch.float16,
variant="fp16",
)
pipe = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", unet=unet, torch_dtype=torch.float16
pipe = StableDiffusionXLPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", unet=unet, torch_dtype=torch.float16, variant="fp16",
).to("cuda")
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
......@@ -48,7 +56,7 @@ image = pipe(
).images[0]
```
![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lcm/lcm_intro.png)
![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lcm/lcm_full_sdxl_t2i.png)
Notice that we use only 4 steps for generation which is way less than what's typically used for standard SDXL.
......@@ -57,13 +65,64 @@ Some details to keep in mind:
* To perform classifier-free guidance, batch size is usually doubled inside the pipeline. LCM, however, applies guidance using guidance embeddings, so the batch size does not have to be doubled in this case. This leads to a faster inference time, with the drawback that negative prompts don't have any effect on the denoising process.
* The UNet was trained using the [3., 13.] guidance scale range. So, that is the ideal range for `guidance_scale`. However, disabling `guidance_scale` using a value of 1.0 is also effective in most cases.
## Image-to-image
The findings above apply to image-to-image tasks too. Let's look at how we can perform image-to-image generation with LCMs:
LCMs can be applied to image-to-image tasks too. For this example, we'll use the [LCM_Dreamshaper_v7](https://huggingface.co/SimianLuo/LCM_Dreamshaper_v7) model, but the same steps can be applied to other LCM models as well.
```python
import torch
from diffusers import AutoPipelineForImage2Image, UNet2DConditionModel, LCMScheduler
from diffusers.utils import load_image
from diffusers.utils import make_image_grid, load_image
unet = UNet2DConditionModel.from_pretrained(
"SimianLuo/LCM_Dreamshaper_v7",
subfolder="unet",
torch_dtype=torch.float16,
)
pipe = AutoPipelineForImage2Image.from_pretrained(
"Lykon/dreamshaper-7",
unet=unet,
torch_dtype=torch.float16,
variant="fp16",
).to("cuda")
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
# prepare image
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/img2img-init.png"
init_image = load_image(url)
prompt = "Astronauts in a jungle, cold color palette, muted colors, detailed, 8k"
# pass prompt and image to pipeline
generator = torch.manual_seed(0)
image = pipe(
prompt,
image=init_image,
num_inference_steps=4,
guidance_scale=7.5,
strength=0.5,
generator=generator
).images[0]
make_image_grid([init_image, image], rows=1, cols=2)
```
![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lcm/lcm_full_sdv1-5_i2i.png)
<Tip>
You can get different results based on your prompt and the image you provide. To get the best results, we recommend trying different values for `num_inference_steps`, `strength`, and `guidance_scale` parameters and choose the best one.
</Tip>
## Combine with style LoRAs
LCMs can be used with other styled LoRAs to generate styled-images in very few steps (4-8). In the following example, we'll use the [papercut LoRA](TheLastBen/Papercut_SDXL).
```python
from diffusers import StableDiffusionXLPipeline, UNet2DConditionModel, LCMScheduler
import torch
unet = UNet2DConditionModel.from_pretrained(
......@@ -71,84 +130,145 @@ unet = UNet2DConditionModel.from_pretrained(
torch_dtype=torch.float16,
variant="fp16",
)
pipe = AutoPipelineForImage2Image.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", unet=unet, torch_dtype=torch.float16
pipe = StableDiffusionXLPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", unet=unet, torch_dtype=torch.float16, variant="fp16",
).to("cuda")
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
prompt = "High altitude snowy mountains"
image = load_image(
"https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/snowy_mountains.jpeg"
)
pipe.load_lora_weights("TheLastBen/Papercut_SDXL", weight_name="papercut.safetensors", adapter_name="papercut")
prompt = "papercut, a cute fox"
generator = torch.manual_seed(0)
image = pipe(
prompt=prompt,
image=image,
num_inference_steps=4,
generator=generator,
guidance_scale=8.0,
prompt=prompt, num_inference_steps=4, generator=generator, guidance_scale=8.0
).images[0]
image
```
![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lcm/lcm_i2i.png)
## LoRA
![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lcm/lcm_full_sdx_lora_mix.png)
It is possible to generalize the LCM framework to use with [LoRA](../training/lora.md). It effectively eliminates the need to conduct expensive fine-tuning runs as LoRA training concerns just a few number of parameters compared to full fine-tuning. During inference, the [`LCMScheduler`] comes to the advantage as it enables very few-steps inference without compromising the quality.
We recommend to disable `guidance_scale` by setting it 0. The model is trained to follow prompts accurately
even without using guidance scale. You can however, still use guidance scale in which case we recommend
using values between 1.0 and 2.0.
## ControlNet/T2I-Adapter
### Text-to-image
Let's look at how we can perform inference with ControlNet/T2I-Adapter and a LCM.
### ControlNet
For this example, we'll use the [LCM_Dreamshaper_v7](https://huggingface.co/SimianLuo/LCM_Dreamshaper_v7) model with canny ControlNet, but the same steps can be applied to other LCM models as well.
```python
from diffusers import DiffusionPipeline, LCMScheduler
import torch
import cv2
import numpy as np
from PIL import Image
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, LCMScheduler
from diffusers.utils import load_image, make_image_grid
image = load_image(
"https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png"
).resize((512, 512))
image = np.array(image)
low_threshold = 100
high_threshold = 200
model_id = "stabilityai/stable-diffusion-xl-base-1.0"
lcm_lora_id = "latent-consistency/lcm-lora-sdxl"
image = cv2.Canny(image, low_threshold, high_threshold)
image = image[:, :, None]
image = np.concatenate([image, image, image], axis=2)
canny_image = Image.fromarray(image)
pipe = DiffusionPipeline.from_pretrained(model_id, variant="fp16", torch_dtype=torch.float16).to("cuda")
controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
"SimianLuo/LCM_Dreamshaper_v7",
controlnet=controlnet,
torch_dtype=torch.float16,
safety_checker=None,
).to("cuda")
pipe.load_lora_weights(lcm_lora_id)
# set scheduler
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
prompt = "close-up photography of old man standing in the rain at night, in a street lit by lamps, leica 35mm summilux"
generator = torch.manual_seed(0)
image = pipe(
prompt=prompt,
"the mona lisa",
image=canny_image,
num_inference_steps=4,
guidance_scale=0, # set guidance scale to 0 to disable it
generator=generator,
).images[0]
make_image_grid([canny_image, image], rows=1, cols=2)
```
![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lcm/lora_lcm.png)
### Image-to-image
![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lcm/lcm_full_sdv1-5_controlnet.png)
Extending LCM LoRA to image-to-image is possible:
<Tip>
The inference parameters in this example might not work for all examples, so we recommend trying different values for the `num_inference_steps`, `guidance_scale`, `controlnet_conditioning_scale`, and `cross_attention_kwargs` parameters and choosing the best one.
</Tip>
### T2I-Adapter
This example shows how to use the `lcm-sdxl` with the [Canny T2I-Adapter](TencentARC/t2i-adapter-canny-sdxl-1.0).
```python
from diffusers import StableDiffusionXLImg2ImgPipeline, LCMScheduler
from diffusers.utils import load_image
import torch
import cv2
import numpy as np
from PIL import Image
model_id = "stabilityai/stable-diffusion-xl-base-1.0"
lcm_lora_id = "latent-consistency/lcm-lora-sdxl"
from diffusers import StableDiffusionXLAdapterPipeline, UNet2DConditionModel, T2IAdapter, LCMScheduler
from diffusers.utils import load_image, make_image_grid
pipe = StableDiffusionXLImg2ImgPipeline.from_pretrained(model_id, variant="fp16", torch_dtype=torch.float16).to("cuda")
# Prepare image
# Detect the canny map in low resolution to avoid high-frequency details
image = load_image(
"https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/org_canny.jpg"
).resize((384, 384))
pipe.load_lora_weights(lcm_lora_id)
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
image = np.array(image)
low_threshold = 100
high_threshold = 200
prompt = "close-up photography of old man standing in the rain at night, in a street lit by lamps, leica 35mm summilux"
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))
image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lcm/lora_lcm.png")
# load adapter
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 = "Mystical fairy in real, magic, 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,
image=image,
negative_prompt=negative_prompt,
image=canny_image,
num_inference_steps=4,
guidance_scale=0, # set guidance scale to 0 to disable it
guidance_scale=5,
adapter_conditioning_scale=0.8,
adapter_conditioning_factor=1,
generator=generator,
).images[0]
grid = make_image_grid([canny_image, image], rows=1, cols=2)
```
![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lcm/lcm_lora_i2i.png)
![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lcm/lcm_full_sdxl_t2iadapter.png)
docs/source/en/using-diffusers/inference_with_lcm_lora.md 0 → 100644
View file @ a042909c
<!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
[[open-in-colab]]
# Performing inference with LCM-LoRA
Latent Consistency Models (LCM) enable quality image generation in typically 2-4 steps making it possible to use diffusion models in almost real-time settings.
From the [official website](https://latent-consistency-models.github.io/):
> LCMs can be distilled from any pre-trained Stable Diffusion (SD) in only 4,000 training steps (~32 A100 GPU Hours) for generating high quality 768 x 768 resolution images in 2~4 steps or even one step, significantly accelerating text-to-image generation. We employ LCM to distill the Dreamshaper-V7 version of SD in just 4,000 training iterations.
For a more technical overview of LCMs, refer to [the paper](https://huggingface.co/papers/2310.04378).
However, each model needs to be distilled separately for latent consistency distillation. The core idea with LCM-LoRA is to train just a few adapter layers, the adapter being LoRA in this case.
This way, we don't have to train the full model and keep the number of trainable parameters manageable. The resulting LoRAs can then be applied to any fine-tuned version of the model without distilling them separately.
Additionally, the LoRAs can be applied to image-to-image, ControlNet/T2I-Adapter, inpainting, AnimateDiff etc.
The LCM-LoRA can also be combined with other LoRAs to generate styled images in very few steps (4-8).
LCM-LoRAs are available for [stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5), [stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0), and the [SSD-1B](https://huggingface.co/segmind/SSD-1B) model. All the checkpoints can be found in this [collection](https://huggingface.co/collections/latent-consistency/latent-consistency-models-loras-654cdd24e111e16f0865fba6).
For more details about LCM-LoRA, refer to [the technical report](https://huggingface.co/papers/2311.05556).
This guide shows how to perform inference with LCM-LoRAs for
- text-to-image
- image-to-image
- combined with styled LoRAs
- ControlNet/T2I-Adapter
- inpainting
- AnimateDiff
Before going through this guide, we'll take a look at the general workflow for performing inference with LCM-LoRAs.
LCM-LoRAs are similar to other Stable Diffusion LoRAs so they can be used with any [`DiffusionPipeline`] that supports LoRAs.
- Load the task specific pipeline and model.
- Set the scheduler to [`LCMScheduler`].
- Load the LCM-LoRA weights for the model.
- Reduce the `guidance_scale` between `[1.0, 2.0]` and set the `num_inference_steps` between [4, 8].
- Perform inference with the pipeline with the usual parameters.
Let's look at how we can perform inference with LCM-LoRAs for different tasks.
First, make sure you have [peft](https://github.com/huggingface/peft) installed, for better LoRA support.
```bash
pip install -U peft
```
## Text-to-image
You'll use the [`StableDiffusionXLPipeline`] with the scheduler: [`LCMScheduler`] and then load the LCM-LoRA. Together with the LCM-LoRA and the scheduler, the pipeline enables a fast inference workflow overcoming the slow iterative nature of diffusion models.
```python
import torch
from diffusers import DiffusionPipeline, LCMScheduler
pipe = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
variant="fp16",
torch_dtype=torch.float16
).to("cuda")
# set scheduler
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
# load LCM-LoRA
pipe.load_lora_weights("latent-consistency/lcm-lora-sdxl")
prompt = "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k"
generator = torch.manual_seed(42)
image = pipe(
prompt=prompt, num_inference_steps=4, generator=generator, guidance_scale=1.0
).images[0]
```
![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lcm/lcm_sdxl_t2i.png)
Notice that we use only 4 steps for generation which is way less than what's typically used for standard SDXL.
<Tip>
You may have noticed that we set `guidance_scale=1.0`, which disables classifer-free-guidance. This is because the LCM-LoRA is trained with guidance, so the batch size does not have to be doubled in this case. This leads to a faster inference time, with the drawback that negative prompts don't have any effect on the denoising process.
You can also use guidance with LCM-LoRA, but due to the nature of training the model is very sensitve to the `guidance_scale` values, high values can lead to artifacts in the generated images. In our experiments, we found that the best values are in the range of [1.0, 2.0].
</Tip>
### Inference with a fine-tuned model
As mentioned above, the LCM-LoRA can be applied to any fine-tuned version of the model without having to distill them separately. Let's look at how we can perform inference with a fine-tuned model. In this example, we'll use the [animagine-xl](https://huggingface.co/Linaqruf/animagine-xl) model, which is a fine-tuned version of the SDXL model for generating anime.
```python
from diffusers import DiffusionPipeline, LCMScheduler
pipe = DiffusionPipeline.from_pretrained(
"Linaqruf/animagine-xl",
variant="fp16",
torch_dtype=torch.float16
).to("cuda")
# set scheduler
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
# load LCM-LoRA
pipe.load_lora_weights("latent-consistency/lcm-lora-sdxl")
prompt = "face focus, cute, masterpiece, best quality, 1girl, green hair, sweater, looking at viewer, upper body, beanie, outdoors, night, turtleneck"
generator = torch.manual_seed(0)
image = pipe(
prompt=prompt, num_inference_steps=4, generator=generator, guidance_scale=1.0
).images[0]
```
![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lcm/lcm_sdxl_t2i_finetuned.png)
## Image-to-image
LCM-LoRA can be applied to image-to-image tasks too. Let's look at how we can perform image-to-image generation with LCMs. For this example we'll use the [dreamshaper-7](https://huggingface.co/Lykon/dreamshaper-7) model and the LCM-LoRA for `stable-diffusion-v1-5 `.
```python
import torch
from diffusers import AutoPipelineForImage2Image, LCMScheduler
from diffusers.utils import make_image_grid, load_image
pipe = AutoPipelineForImage2Image.from_pretrained(
"Lykon/dreamshaper-7",
torch_dtype=torch.float16,
variant="fp16",
).to("cuda")
# set scheduler
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
# load LCM-LoRA
pipe.load_lora_weights("latent-consistency/lcm-lora-sdv1-5")
# prepare image
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/img2img-init.png"
init_image = load_image(url)
prompt = "Astronauts in a jungle, cold color palette, muted colors, detailed, 8k"
# pass prompt and image to pipeline
generator = torch.manual_seed(0)
image = pipe(
prompt,
image=init_image,
num_inference_steps=4,
guidance_scale=1,
strength=0.6,
generator=generator
).images[0]
make_image_grid([init_image, image], rows=1, cols=2)
```
![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lcm/lcm_sdv1-5_i2i.png)
<Tip>
You can get different results based on your prompt and the image you provide. To get the best results, we recommend trying different values for `num_inference_steps`, `strength`, and `guidance_scale` parameters and choose the best one.
</Tip>
## Combine with styled LoRAs
LCM-LoRA can be combined with other LoRAs to generate styled-images in very few steps (4-8). In the following example, we'll use the LCM-LoRA with the [papercut LoRA](TheLastBen/Papercut_SDXL).
To learn more about how to combine LoRAs, refer to [this guide](https://huggingface.co/docs/diffusers/tutorials/using_peft_for_inference#combine-multiple-adapters).
```python
import torch
from diffusers import DiffusionPipeline, LCMScheduler
pipe = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
variant="fp16",
torch_dtype=torch.float16
).to("cuda")
# set scheduler
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
# load LoRAs
pipe.load_lora_weights("latent-consistency/lcm-lora-sdxl", adapter_name="lcm")
pipe.load_lora_weights("TheLastBen/Papercut_SDXL", weight_name="papercut.safetensors", adapter_name="papercut")
# Combine LoRAs
pipe.set_adapters(["lcm", "papercut"], adapter_weights=[1.0, 0.8])
prompt = "papercut, a cute fox"
generator = torch.manual_seed(0)
image = pipe(prompt, num_inference_steps=4, guidance_scale=1, generator=generator).images[0]
image
```
![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lcm/lcm_sdx_lora_mix.png)
## ControlNet/T2I-Adapter
Let's look at how we can perform inference with ControlNet/T2I-Adapter and LCM-LoRA.
### ControlNet
For this example, we'll use the SD-v1-5 model and the LCM-LoRA for SD-v1-5 with canny ControlNet.
```python
import torch
import cv2
import numpy as np
from PIL import Image
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, LCMScheduler
from diffusers.utils import load_image
image = load_image(
"https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png"
).resize((512, 512))
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)
controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",
controlnet=controlnet,
torch_dtype=torch.float16,
safety_checker=None,
variant="fp16"
).to("cuda")
# set scheduler
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
# load LCM-LoRA
pipe.load_lora_weights("latent-consistency/lcm-lora-sdv1-5")
generator = torch.manual_seed(0)
image = pipe(
"the mona lisa",
image=canny_image,
num_inference_steps=4,
guidance_scale=1.5,
controlnet_conditioning_scale=0.8,
cross_attention_kwargs={"scale": 1},
generator=generator,
).images[0]
make_image_grid([canny_image, image], rows=1, cols=2)
```
![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lcm/lcm_sdv1-5_controlnet.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>
### T2I-Adapter
This example shows how to use the LCM-LoRA with the [Canny T2I-Adapter](TencentARC/t2i-adapter-canny-sdxl-1.0) and SDXL.
```python
import torch
import cv2
import numpy as np
from PIL import Image
from diffusers import StableDiffusionXLAdapterPipeline, T2IAdapter, LCMScheduler
from diffusers.utils import load_image, make_image_grid
# Prepare image
# Detect the canny map in low resolution to avoid high-frequency details
image = load_image(
"https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/org_canny.jpg"
).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))
# load adapter
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")
# set scheduler
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
# load LCM-LoRA
pipe.load_lora_weights("latent-consistency/lcm-lora-sdxl")
prompt = "Mystical fairy in real, magic, 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]
make_image_grid([canny_image, image], rows=1, cols=2)
```
![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lcm/lcm_sdxl_t2iadapter.png)
## Inpainting
LCM-LoRA can be used for inpainting as well.
```python
import torch
from diffusers import AutoPipelineForInpainting, LCMScheduler
from diffusers.utils import load_image, make_image_grid
pipe = AutoPipelineForInpainting.from_pretrained(
"runwayml/stable-diffusion-inpainting",
torch_dtype=torch.float16,
variant="fp16",
).to("cuda")
# set scheduler
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
# load LCM-LoRA
pipe.load_lora_weights("latent-consistency/lcm-lora-sdv1-5")
# load base and mask image
init_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint.png")
mask_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint_mask.png")
# generator = torch.Generator("cuda").manual_seed(92)
prompt = "concept art digital painting of an elven castle, inspired by lord of the rings, highly detailed, 8k"
generator = torch.manual_seed(0)
image = pipe(
prompt=prompt,
image=init_image,
mask_image=mask_image,
generator=generator,
num_inference_steps=4,
guidance_scale=4,
).images[0]
make_image_grid([init_image, mask_image, image], rows=1, cols=3)
```
![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lcm/lcm_sdv1-5_inpainting.png)
## AnimateDiff
[`AnimateDiff`] allows you to animate images using Stable Diffusion models. To get good results, we need to generate multiple frames (16-24), and doing this with standard SD models can be very slow.
LCM-LoRA can be used to speed up the process significantly, as you just need to do 4-8 steps for each frame. Let's look at how we can perform animation with LCM-LoRA and AnimateDiff.
```python
import torch
from diffusers import MotionAdapter, AnimateDiffPipeline, DDIMScheduler, LCMScheduler
from diffusers.utils import export_to_gif
adapter = MotionAdapter.from_pretrained("diffusers/animatediff-motion-adapter-v1-5")
pipe = AnimateDiffPipeline.from_pretrained(
"frankjoshua/toonyou_beta6",
motion_adapter=adapter,
).to("cuda")
# set scheduler
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
# load LCM-LoRA
pipe.load_lora_weights("latent-consistency/lcm-lora-sdv1-5", adapter_name="lcm")
pipe.load_lora_weights("guoyww/animatediff-motion-lora-zoom-in", weight_name="diffusion_pytorch_model.safetensors", adapter_name="motion-lora")
pipe.set_adapters(["lcm", "motion-lora"], adapter_weights=[0.55, 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=1.25,
cross_attention_kwargs={"scale": 1},
num_frames=24,
generator=generator
).frames[0]
export_to_gif(frames, "animation.gif")
```
![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lcm/lcm_sdv1-5_animatediff.gif)
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