text
stringlengths 3
14.4k
| source
stringclasses 273
values | url
stringlengths 47
172
| source_section
stringlengths 0
95
| file_type
stringclasses 1
value | id
stringlengths 3
6
|
|---|---|---|---|---|---|
There are several ways to exert more control over how an image is generated outside of configuring a pipeline's parameters, such as prompt weighting and ControlNet models. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/conditional_image_generation.md | https://huggingface.co/docs/diffusers/en/using-diffusers/conditional_image_generation/#control-image-generation | #control-image-generation | .md | 57_12 |
Prompt weighting is a technique for increasing or decreasing the importance of concepts in a prompt to emphasize or minimize certain features in an image. We recommend using the [Compel](https://github.com/damian0815/compel) library to help you generate the weighted prompt embeddings.
<Tip>
Learn how to create the prompt embeddings in the [Prompt weighting](weighted_prompts) guide. This example focuses on how to use the prompt embeddings in the pipeline.
</Tip>
Once you've created the embeddings, you can pass them to the `prompt_embeds` (and `negative_prompt_embeds` if you're using a negative prompt) parameter in the pipeline.
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16
).to("cuda")
image = pipeline(
prompt_embeds=prompt_embeds, # generated from Compel
negative_prompt_embeds=negative_prompt_embeds, # generated from Compel
).images[0]
``` | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/conditional_image_generation.md | https://huggingface.co/docs/diffusers/en/using-diffusers/conditional_image_generation/#prompt-weighting | #prompt-weighting | .md | 57_13 |
As you saw in the [ControlNet](#controlnet) section, these models offer a more flexible and accurate way to generate images by incorporating an additional conditioning image input. Each ControlNet model is pretrained on a particular type of conditioning image to generate new images that resemble it. For example, if you take a ControlNet model pretrained on depth maps, you can give the model a depth map as a conditioning input and it'll generate an image that preserves the spatial information in it. This is quicker and easier than specifying the depth information in a prompt. You can even combine multiple conditioning inputs with a [MultiControlNet](controlnet#multicontrolnet)!
There are many types of conditioning inputs you can use, and 🤗 Diffusers supports ControlNet for Stable Diffusion and SDXL models. Take a look at the more comprehensive [ControlNet](controlnet) guide to learn how you can use these models. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/conditional_image_generation.md | https://huggingface.co/docs/diffusers/en/using-diffusers/conditional_image_generation/#controlnet | #controlnet | .md | 57_14 |
Diffusion models are large, and the iterative nature of denoising an image is computationally expensive and intensive. But this doesn't mean you need access to powerful - or even many - GPUs to use them. There are many optimization techniques for running diffusion models on consumer and free-tier resources. For example, you can load model weights in half-precision to save GPU memory and increase speed or offload the entire model to the GPU to save even more memory.
PyTorch 2.0 also supports a more memory-efficient attention mechanism called [*scaled dot product attention*](../optimization/torch2.0#scaled-dot-product-attention) that is automatically enabled if you're using PyTorch 2.0. You can combine this with [`torch.compile`](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html) to speed your code up even more:
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16").to("cuda")
pipeline.unet = torch.compile(pipeline.unet, mode="reduce-overhead", fullgraph=True)
```
For more tips on how to optimize your code to save memory and speed up inference, read the [Memory and speed](../optimization/fp16) and [Torch 2.0](../optimization/torch2.0) guides. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/conditional_image_generation.md | https://huggingface.co/docs/diffusers/en/using-diffusers/conditional_image_generation/#optimize | #optimize | .md | 57_15 |
<!--Copyright 2024 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
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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.
--> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/sdxl.md | https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/ | .md | 58_0 |
|
[[open-in-colab]]
[Stable Diffusion XL](https://huggingface.co/papers/2307.01952) (SDXL) is a powerful text-to-image generation model that iterates on the previous Stable Diffusion models in three key ways:
1. the UNet is 3x larger and SDXL combines a second text encoder (OpenCLIP ViT-bigG/14) with the original text encoder to significantly increase the number of parameters
2. introduces size and crop-conditioning to preserve training data from being discarded and gain more control over how a generated image should be cropped
3. introduces a two-stage model process; the *base* model (can also be run as a standalone model) generates an image as an input to the *refiner* model which adds additional high-quality details
This guide will show you how to use SDXL for text-to-image, image-to-image, and inpainting.
Before you begin, make sure you have the following libraries installed:
```py
# uncomment to install the necessary libraries in Colab
#!pip install -q diffusers transformers accelerate invisible-watermark>=0.2.0
```
<Tip warning={true}>
We recommend installing the [invisible-watermark](https://pypi.org/project/invisible-watermark/) library to help identify images that are generated. If the invisible-watermark library is installed, it is used by default. To disable the watermarker:
```py
pipeline = StableDiffusionXLPipeline.from_pretrained(..., add_watermarker=False)
```
</Tip> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/sdxl.md | https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#stable-diffusion-xl | #stable-diffusion-xl | .md | 58_1 |
Model weights may be stored in separate subfolders on the Hub or locally, in which case, you should use the [`~StableDiffusionXLPipeline.from_pretrained`] method:
```py
from diffusers import StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline
import torch
pipeline = StableDiffusionXLPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
).to("cuda")
refiner = StableDiffusionXLImg2ImgPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-refiner-1.0", torch_dtype=torch.float16, use_safetensors=True, variant="fp16"
).to("cuda")
```
You can also use the [`~StableDiffusionXLPipeline.from_single_file`] method to load a model checkpoint stored in a single file format (`.ckpt` or `.safetensors`) from the Hub or locally:
```py
from diffusers import StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline
import torch
pipeline = StableDiffusionXLPipeline.from_single_file(
"https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0.safetensors",
torch_dtype=torch.float16
).to("cuda")
refiner = StableDiffusionXLImg2ImgPipeline.from_single_file(
"https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/blob/main/sd_xl_refiner_1.0.safetensors", torch_dtype=torch.float16
).to("cuda")
``` | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/sdxl.md | https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#load-model-checkpoints | #load-model-checkpoints | .md | 58_2 |
For text-to-image, pass a text prompt. By default, SDXL generates a 1024x1024 image for the best results. You can try setting the `height` and `width` parameters to 768x768 or 512x512, but anything below 512x512 is not likely to work.
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline_text2image = AutoPipelineForText2Image.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
).to("cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipeline_text2image(prompt=prompt).images[0]
image
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl-text2img.png" alt="generated image of an astronaut in a jungle"/>
</div> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/sdxl.md | https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#text-to-image | #text-to-image | .md | 58_3 |
For image-to-image, SDXL works especially well with image sizes between 768x768 and 1024x1024. Pass an initial image, and a text prompt to condition the image with:
```py
from diffusers import AutoPipelineForImage2Image
from diffusers.utils import load_image, make_image_grid
# use from_pipe to avoid consuming additional memory when loading a checkpoint
pipeline = AutoPipelineForImage2Image.from_pipe(pipeline_text2image).to("cuda")
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl-text2img.png"
init_image = load_image(url)
prompt = "a dog catching a frisbee in the jungle"
image = pipeline(prompt, image=init_image, strength=0.8, guidance_scale=10.5).images[0]
make_image_grid([init_image, image], rows=1, cols=2)
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl-img2img.png" alt="generated image of a dog catching a frisbee in a jungle"/>
</div> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/sdxl.md | https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#image-to-image | #image-to-image | .md | 58_4 |
For inpainting, you'll need the original image and a mask of what you want to replace in the original image. Create a prompt to describe what you want to replace the masked area with.
```py
from diffusers import AutoPipelineForInpainting
from diffusers.utils import load_image, make_image_grid
# use from_pipe to avoid consuming additional memory when loading a checkpoint
pipeline = AutoPipelineForInpainting.from_pipe(pipeline_text2image).to("cuda")
img_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl-text2img.png"
mask_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl-inpaint-mask.png"
init_image = load_image(img_url)
mask_image = load_image(mask_url)
prompt = "A deep sea diver floating"
image = pipeline(prompt=prompt, image=init_image, mask_image=mask_image, strength=0.85, guidance_scale=12.5).images[0]
make_image_grid([init_image, mask_image, image], rows=1, cols=3)
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl-inpaint.png" alt="generated image of a deep sea diver in a jungle"/>
</div> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/sdxl.md | https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#inpainting | #inpainting | .md | 58_5 |
SDXL includes a [refiner model](https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0) specialized in denoising low-noise stage images to generate higher-quality images from the base model. There are two ways to use the refiner:
1. use the base and refiner models together to produce a refined image
2. use the base model to produce an image, and subsequently use the refiner model to add more details to the image (this is how SDXL was originally trained) | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/sdxl.md | https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#refine-image-quality | #refine-image-quality | .md | 58_6 |
When you use the base and refiner model together to generate an image, this is known as an [*ensemble of expert denoisers*](https://research.nvidia.com/labs/dir/eDiff-I/). The ensemble of expert denoisers approach requires fewer overall denoising steps versus passing the base model's output to the refiner model, so it should be significantly faster to run. However, you won't be able to inspect the base model's output because it still contains a large amount of noise.
As an ensemble of expert denoisers, the base model serves as the expert during the high-noise diffusion stage and the refiner model serves as the expert during the low-noise diffusion stage. Load the base and refiner model:
```py
from diffusers import DiffusionPipeline
import torch
base = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
).to("cuda")
refiner = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-refiner-1.0",
text_encoder_2=base.text_encoder_2,
vae=base.vae,
torch_dtype=torch.float16,
use_safetensors=True,
variant="fp16",
).to("cuda")
```
To use this approach, you need to define the number of timesteps for each model to run through their respective stages. For the base model, this is controlled by the [`denoising_end`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/stable_diffusion_xl#diffusers.StableDiffusionXLPipeline.__call__.denoising_end) parameter and for the refiner model, it is controlled by the [`denoising_start`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/stable_diffusion_xl#diffusers.StableDiffusionXLImg2ImgPipeline.__call__.denoising_start) parameter.
<Tip>
The `denoising_end` and `denoising_start` parameters should be a float between 0 and 1. These parameters are represented as a proportion of discrete timesteps as defined by the scheduler. If you're also using the `strength` parameter, it'll be ignored because the number of denoising steps is determined by the discrete timesteps the model is trained on and the declared fractional cutoff.
</Tip>
Let's set `denoising_end=0.8` so the base model performs the first 80% of denoising the **high-noise** timesteps and set `denoising_start=0.8` so the refiner model performs the last 20% of denoising the **low-noise** timesteps. The base model output should be in **latent** space instead of a PIL image.
```py
prompt = "A majestic lion jumping from a big stone at night"
image = base(
prompt=prompt,
num_inference_steps=40,
denoising_end=0.8,
output_type="latent",
).images
image = refiner(
prompt=prompt,
num_inference_steps=40,
denoising_start=0.8,
image=image,
).images[0]
image
```
<div class="flex gap-4">
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lion_base.png" alt="generated image of a lion on a rock at night" />
<figcaption class="mt-2 text-center text-sm text-gray-500">default base model</figcaption>
</div>
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lion_refined.png" alt="generated image of a lion on a rock at night in higher quality" />
<figcaption class="mt-2 text-center text-sm text-gray-500">ensemble of expert denoisers</figcaption>
</div>
</div>
The refiner model can also be used for inpainting in the [`StableDiffusionXLInpaintPipeline`]:
```py
from diffusers import StableDiffusionXLInpaintPipeline
from diffusers.utils import load_image, make_image_grid
import torch
base = StableDiffusionXLInpaintPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
).to("cuda")
refiner = StableDiffusionXLInpaintPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-refiner-1.0",
text_encoder_2=base.text_encoder_2,
vae=base.vae,
torch_dtype=torch.float16,
use_safetensors=True,
variant="fp16",
).to("cuda")
img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
init_image = load_image(img_url)
mask_image = load_image(mask_url)
prompt = "A majestic tiger sitting on a bench"
num_inference_steps = 75
high_noise_frac = 0.7
image = base(
prompt=prompt,
image=init_image,
mask_image=mask_image,
num_inference_steps=num_inference_steps,
denoising_end=high_noise_frac,
output_type="latent",
).images
image = refiner(
prompt=prompt,
image=image,
mask_image=mask_image,
num_inference_steps=num_inference_steps,
denoising_start=high_noise_frac,
).images[0]
make_image_grid([init_image, mask_image, image.resize((512, 512))], rows=1, cols=3)
```
This ensemble of expert denoisers method works well for all available schedulers! | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/sdxl.md | https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#base--refiner-model | #base--refiner-model | .md | 58_7 |
SDXL gets a boost in image quality by using the refiner model to add additional high-quality details to the fully-denoised image from the base model, in an image-to-image setting.
Load the base and refiner models:
```py
from diffusers import DiffusionPipeline
import torch
base = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
).to("cuda")
refiner = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-refiner-1.0",
text_encoder_2=base.text_encoder_2,
vae=base.vae,
torch_dtype=torch.float16,
use_safetensors=True,
variant="fp16",
).to("cuda")
```
<Tip warning={true}>
You can use SDXL refiner with a different base model. For example, you can use the [Hunyuan-DiT](../../api/pipelines/hunyuandit) or [PixArt-Sigma](../../api/pipelines/pixart_sigma) pipelines to generate images with better prompt adherence. Once you have generated an image, you can pass it to the SDXL refiner model to enhance final generation quality.
</Tip>
Generate an image from the base model, and set the model output to **latent** space:
```py
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = base(prompt=prompt, output_type="latent").images[0]
```
Pass the generated image to the refiner model:
```py
image = refiner(prompt=prompt, image=image[None, :]).images[0]
```
<div class="flex gap-4">
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/sd_xl/init_image.png" alt="generated image of an astronaut riding a green horse on Mars" />
<figcaption class="mt-2 text-center text-sm text-gray-500">base model</figcaption>
</div>
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/sd_xl/refined_image.png" alt="higher quality generated image of an astronaut riding a green horse on Mars" />
<figcaption class="mt-2 text-center text-sm text-gray-500">base model + refiner model</figcaption>
</div>
</div>
For inpainting, load the base and the refiner model in the [`StableDiffusionXLInpaintPipeline`], remove the `denoising_end` and `denoising_start` parameters, and choose a smaller number of inference steps for the refiner. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/sdxl.md | https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#base-to-refiner-model | #base-to-refiner-model | .md | 58_8 |
SDXL training involves several additional conditioning techniques, which are referred to as *micro-conditioning*. These include original image size, target image size, and cropping parameters. The micro-conditionings can be used at inference time to create high-quality, centered images.
<Tip>
You can use both micro-conditioning and negative micro-conditioning parameters thanks to classifier-free guidance. They are available in the [`StableDiffusionXLPipeline`], [`StableDiffusionXLImg2ImgPipeline`], [`StableDiffusionXLInpaintPipeline`], and [`StableDiffusionXLControlNetPipeline`].
</Tip> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/sdxl.md | https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#micro-conditioning | #micro-conditioning | .md | 58_9 |
There are two types of size conditioning:
- [`original_size`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/stable_diffusion_xl#diffusers.StableDiffusionXLPipeline.__call__.original_size) conditioning comes from upscaled images in the training batch (because it would be wasteful to discard the smaller images which make up almost 40% of the total training data). This way, SDXL learns that upscaling artifacts are not supposed to be present in high-resolution images. During inference, you can use `original_size` to indicate the original image resolution. Using the default value of `(1024, 1024)` produces higher-quality images that resemble the 1024x1024 images in the dataset. If you choose to use a lower resolution, such as `(256, 256)`, the model still generates 1024x1024 images, but they'll look like the low resolution images (simpler patterns, blurring) in the dataset.
- [`target_size`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/stable_diffusion_xl#diffusers.StableDiffusionXLPipeline.__call__.target_size) conditioning comes from finetuning SDXL to support different image aspect ratios. During inference, if you use the default value of `(1024, 1024)`, you'll get an image that resembles the composition of square images in the dataset. We recommend using the same value for `target_size` and `original_size`, but feel free to experiment with other options!
🤗 Diffusers also lets you specify negative conditions about an image's size to steer generation away from certain image resolutions:
```py
from diffusers import StableDiffusionXLPipeline
import torch
pipe = StableDiffusionXLPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
).to("cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(
prompt=prompt,
negative_original_size=(512, 512),
negative_target_size=(1024, 1024),
).images[0]
```
<div class="flex flex-col justify-center">
<img src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/sd_xl/negative_conditions.png"/>
<figcaption class="text-center">Images negatively conditioned on image resolutions of (128, 128), (256, 256), and (512, 512).</figcaption>
</div> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/sdxl.md | https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#size-conditioning | #size-conditioning | .md | 58_10 |
Images generated by previous Stable Diffusion models may sometimes appear to be cropped. This is because images are actually cropped during training so that all the images in a batch have the same size. By conditioning on crop coordinates, SDXL *learns* that no cropping - coordinates `(0, 0)` - usually correlates with centered subjects and complete faces (this is the default value in 🤗 Diffusers). You can experiment with different coordinates if you want to generate off-centered compositions!
```py
from diffusers import StableDiffusionXLPipeline
import torch
pipeline = StableDiffusionXLPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
).to("cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipeline(prompt=prompt, crops_coords_top_left=(256, 0)).images[0]
image
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl-cropped.png" alt="generated image of an astronaut in a jungle, slightly cropped"/>
</div>
You can also specify negative cropping coordinates to steer generation away from certain cropping parameters:
```py
from diffusers import StableDiffusionXLPipeline
import torch
pipe = StableDiffusionXLPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
).to("cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(
prompt=prompt,
negative_original_size=(512, 512),
negative_crops_coords_top_left=(0, 0),
negative_target_size=(1024, 1024),
).images[0]
image
``` | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/sdxl.md | https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#crop-conditioning | #crop-conditioning | .md | 58_11 |
SDXL uses two text-encoders, so it is possible to pass a different prompt to each text-encoder, which can [improve quality](https://github.com/huggingface/diffusers/issues/4004#issuecomment-1627764201). Pass your original prompt to `prompt` and the second prompt to `prompt_2` (use `negative_prompt` and `negative_prompt_2` if you're using negative prompts):
```py
from diffusers import StableDiffusionXLPipeline
import torch
pipeline = StableDiffusionXLPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
).to("cuda")
# prompt is passed to OAI CLIP-ViT/L-14
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
# prompt_2 is passed to OpenCLIP-ViT/bigG-14
prompt_2 = "Van Gogh painting"
image = pipeline(prompt=prompt, prompt_2=prompt_2).images[0]
image
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl-double-prompt.png" alt="generated image of an astronaut in a jungle in the style of a van gogh painting"/>
</div>
The dual text-encoders also support textual inversion embeddings that need to be loaded separately as explained in the [SDXL textual inversion](textual_inversion_inference#stable-diffusion-xl) section. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/sdxl.md | https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#use-a-different-prompt-for-each-text-encoder | #use-a-different-prompt-for-each-text-encoder | .md | 58_12 |
SDXL is a large model, and you may need to optimize memory to get it to run on your hardware. Here are some tips to save memory and speed up inference.
1. Offload the model to the CPU with [`~StableDiffusionXLPipeline.enable_model_cpu_offload`] for out-of-memory errors:
```diff
- base.to("cuda")
- refiner.to("cuda")
+ base.enable_model_cpu_offload()
+ refiner.enable_model_cpu_offload()
```
2. Use `torch.compile` for ~20% speed-up (you need `torch>=2.0`):
```diff
+ base.unet = torch.compile(base.unet, mode="reduce-overhead", fullgraph=True)
+ refiner.unet = torch.compile(refiner.unet, mode="reduce-overhead", fullgraph=True)
```
3. Enable [xFormers](../optimization/xformers) to run SDXL if `torch<2.0`:
```diff
+ base.enable_xformers_memory_efficient_attention()
+ refiner.enable_xformers_memory_efficient_attention()
``` | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/sdxl.md | https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#optimizations | #optimizations | .md | 58_13 |
If you're interested in experimenting with a minimal version of the [`UNet2DConditionModel`] used in SDXL, take a look at the [minSDXL](https://github.com/cloneofsimo/minSDXL) implementation which is written in PyTorch and directly compatible with 🤗 Diffusers. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/sdxl.md | https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#other-resources | #other-resources | .md | 58_14 |
<!--Copyright 2024 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
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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.
--> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/schedulers.md | https://huggingface.co/docs/diffusers/en/using-diffusers/schedulers/ | .md | 59_0 |
|
[[open-in-colab]]
Diffusion pipelines are a collection of interchangeable schedulers and models that can be mixed and matched to tailor a pipeline to a specific use case. The scheduler encapsulates the entire denoising process such as the number of denoising steps and the algorithm for finding the denoised sample. A scheduler is not parameterized or trained so they don't take very much memory. The model is usually only concerned with the forward pass of going from a noisy input to a less noisy sample.
This guide will show you how to load schedulers and models to customize a pipeline. You'll use the [stable-diffusion-v1-5/stable-diffusion-v1-5](https://hf.co/stable-diffusion-v1-5/stable-diffusion-v1-5) checkpoint throughout this guide, so let's load it first.
```py
import torch
from diffusers import DiffusionPipeline
pipeline = DiffusionPipeline.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True
).to("cuda")
```
You can see what scheduler this pipeline uses with the `pipeline.scheduler` attribute.
```py
pipeline.scheduler
PNDMScheduler {
"_class_name": "PNDMScheduler",
"_diffusers_version": "0.21.4",
"beta_end": 0.012,
"beta_schedule": "scaled_linear",
"beta_start": 0.00085,
"clip_sample": false,
"num_train_timesteps": 1000,
"set_alpha_to_one": false,
"skip_prk_steps": true,
"steps_offset": 1,
"timestep_spacing": "leading",
"trained_betas": null
}
``` | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/schedulers.md | https://huggingface.co/docs/diffusers/en/using-diffusers/schedulers/#load-schedulers-and-models | #load-schedulers-and-models | .md | 59_1 |
Schedulers are defined by a configuration file that can be used by a variety of schedulers. Load a scheduler with the [`SchedulerMixin.from_pretrained`] method, and specify the `subfolder` parameter to load the configuration file into the correct subfolder of the pipeline repository.
For example, to load the [`DDIMScheduler`]:
```py
from diffusers import DDIMScheduler, DiffusionPipeline
ddim = DDIMScheduler.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="scheduler")
```
Then you can pass the newly loaded scheduler to the pipeline.
```python
pipeline = DiffusionPipeline.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5", scheduler=ddim, torch_dtype=torch.float16, use_safetensors=True
).to("cuda")
``` | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/schedulers.md | https://huggingface.co/docs/diffusers/en/using-diffusers/schedulers/#load-a-scheduler | #load-a-scheduler | .md | 59_2 |
Schedulers have their own unique strengths and weaknesses, making it difficult to quantitatively compare which scheduler works best for a pipeline. You typically have to make a trade-off between denoising speed and denoising quality. We recommend trying out different schedulers to find one that works best for your use case. Call the `pipeline.scheduler.compatibles` attribute to see what schedulers are compatible with a pipeline.
Let's compare the [`LMSDiscreteScheduler`], [`EulerDiscreteScheduler`], [`EulerAncestralDiscreteScheduler`], and the [`DPMSolverMultistepScheduler`] on the following prompt and seed.
```py
import torch
from diffusers import DiffusionPipeline
pipeline = DiffusionPipeline.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True
).to("cuda")
prompt = "A photograph of an astronaut riding a horse on Mars, high resolution, high definition."
generator = torch.Generator(device="cuda").manual_seed(8)
```
To change the pipelines scheduler, use the [`~ConfigMixin.from_config`] method to load a different scheduler's `pipeline.scheduler.config` into the pipeline.
<hfoptions id="schedulers">
<hfoption id="LMSDiscreteScheduler">
[`LMSDiscreteScheduler`] typically generates higher quality images than the default scheduler.
```py
from diffusers import LMSDiscreteScheduler
pipeline.scheduler = LMSDiscreteScheduler.from_config(pipeline.scheduler.config)
image = pipeline(prompt, generator=generator).images[0]
image
```
</hfoption>
<hfoption id="EulerDiscreteScheduler">
[`EulerDiscreteScheduler`] can generate higher quality images in just 30 steps.
```py
from diffusers import EulerDiscreteScheduler
pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config)
image = pipeline(prompt, generator=generator).images[0]
image
```
</hfoption>
<hfoption id="EulerAncestralDiscreteScheduler">
[`EulerAncestralDiscreteScheduler`] can generate higher quality images in just 30 steps.
```py
from diffusers import EulerAncestralDiscreteScheduler
pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(pipeline.scheduler.config)
image = pipeline(prompt, generator=generator).images[0]
image
```
</hfoption>
<hfoption id="DPMSolverMultistepScheduler">
[`DPMSolverMultistepScheduler`] provides a balance between speed and quality and can generate higher quality images in just 20 steps.
```py
from diffusers import DPMSolverMultistepScheduler
pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
image = pipeline(prompt, generator=generator).images[0]
image
```
</hfoption>
</hfoptions>
<div class="flex gap-4">
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/diffusers_docs/astronaut_lms.png" />
<figcaption class="mt-2 text-center text-sm text-gray-500">LMSDiscreteScheduler</figcaption>
</div>
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/diffusers_docs/astronaut_euler_discrete.png" />
<figcaption class="mt-2 text-center text-sm text-gray-500">EulerDiscreteScheduler</figcaption>
</div>
</div>
<div class="flex gap-4">
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/diffusers_docs/astronaut_euler_ancestral.png" />
<figcaption class="mt-2 text-center text-sm text-gray-500">EulerAncestralDiscreteScheduler</figcaption>
</div>
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/diffusers_docs/astronaut_dpm.png" />
<figcaption class="mt-2 text-center text-sm text-gray-500">DPMSolverMultistepScheduler</figcaption>
</div>
</div>
Most images look very similar and are comparable in quality. Again, it often comes down to your specific use case so a good approach is to run multiple different schedulers and compare the results. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/schedulers.md | https://huggingface.co/docs/diffusers/en/using-diffusers/schedulers/#compare-schedulers | #compare-schedulers | .md | 59_3 |
To compare Flax schedulers, you need to additionally load the scheduler state into the model parameters. For example, let's change the default scheduler in [`FlaxStableDiffusionPipeline`] to use the super fast [`FlaxDPMSolverMultistepScheduler`].
> [!WARNING]
> The [`FlaxLMSDiscreteScheduler`] and [`FlaxDDPMScheduler`] are not compatible with the [`FlaxStableDiffusionPipeline`] yet.
```py
import jax
import numpy as np
from flax.jax_utils import replicate
from flax.training.common_utils import shard
from diffusers import FlaxStableDiffusionPipeline, FlaxDPMSolverMultistepScheduler
scheduler, scheduler_state = FlaxDPMSolverMultistepScheduler.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5",
subfolder="scheduler"
)
pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5",
scheduler=scheduler,
variant="bf16",
dtype=jax.numpy.bfloat16,
)
params["scheduler"] = scheduler_state
```
Then you can take advantage of Flax's compatibility with TPUs to generate a number of images in parallel. You'll need to make a copy of the model parameters for each available device and then split the inputs across them to generate your desired number of images.
```py
# Generate 1 image per parallel device (8 on TPUv2-8 or TPUv3-8)
prompt = "A photograph of an astronaut riding a horse on Mars, high resolution, high definition."
num_samples = jax.device_count()
prompt_ids = pipeline.prepare_inputs([prompt] * num_samples)
prng_seed = jax.random.PRNGKey(0)
num_inference_steps = 25
# shard inputs and rng
params = replicate(params)
prng_seed = jax.random.split(prng_seed, jax.device_count())
prompt_ids = shard(prompt_ids)
images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images
images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))
``` | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/schedulers.md | https://huggingface.co/docs/diffusers/en/using-diffusers/schedulers/#flax-schedulers | #flax-schedulers | .md | 59_4 |
Models are loaded from the [`ModelMixin.from_pretrained`] method, which downloads and caches the latest version of the model weights and configurations. If the latest files are available in the local cache, [`~ModelMixin.from_pretrained`] reuses files in the cache instead of re-downloading them.
Models can be loaded from a subfolder with the `subfolder` argument. For example, the model weights for [stable-diffusion-v1-5/stable-diffusion-v1-5](https://hf.co/stable-diffusion-v1-5/stable-diffusion-v1-5) are stored in the [unet](https://hf.co/stable-diffusion-v1-5/stable-diffusion-v1-5/tree/main/unet) subfolder.
```python
from diffusers import UNet2DConditionModel
unet = UNet2DConditionModel.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="unet", use_safetensors=True)
```
They can also be directly loaded from a [repository](https://huggingface.co/google/ddpm-cifar10-32/tree/main).
```python
from diffusers import UNet2DModel
unet = UNet2DModel.from_pretrained("google/ddpm-cifar10-32", use_safetensors=True)
```
To load and save model variants, specify the `variant` argument in [`ModelMixin.from_pretrained`] and [`ModelMixin.save_pretrained`].
```python
from diffusers import UNet2DConditionModel
unet = UNet2DConditionModel.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="unet", variant="non_ema", use_safetensors=True
)
unet.save_pretrained("./local-unet", variant="non_ema")
``` | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/schedulers.md | https://huggingface.co/docs/diffusers/en/using-diffusers/schedulers/#models | #models | .md | 59_5 |
<!--Copyright 2024 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
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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.
--> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/sdxl_turbo.md | https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl_turbo/ | .md | 60_0 |
|
[[open-in-colab]]
SDXL Turbo is an adversarial time-distilled [Stable Diffusion XL](https://huggingface.co/papers/2307.01952) (SDXL) model capable
of running inference in as little as 1 step.
This guide will show you how to use SDXL-Turbo for text-to-image and image-to-image.
Before you begin, make sure you have the following libraries installed:
```py
# uncomment to install the necessary libraries in Colab
#!pip install -q diffusers transformers accelerate
``` | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/sdxl_turbo.md | https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl_turbo/#stable-diffusion-xl-turbo | #stable-diffusion-xl-turbo | .md | 60_1 |
Model weights may be stored in separate subfolders on the Hub or locally, in which case, you should use the [`~StableDiffusionXLPipeline.from_pretrained`] method:
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16")
pipeline = pipeline.to("cuda")
```
You can also use the [`~StableDiffusionXLPipeline.from_single_file`] method to load a model checkpoint stored in a single file format (`.ckpt` or `.safetensors`) from the Hub or locally. For this loading method, you need to set `timestep_spacing="trailing"` (feel free to experiment with the other scheduler config values to get better results):
```py
from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
import torch
pipeline = StableDiffusionXLPipeline.from_single_file(
"https://huggingface.co/stabilityai/sdxl-turbo/blob/main/sd_xl_turbo_1.0_fp16.safetensors",
torch_dtype=torch.float16, variant="fp16")
pipeline = pipeline.to("cuda")
pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(pipeline.scheduler.config, timestep_spacing="trailing")
``` | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/sdxl_turbo.md | https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl_turbo/#load-model-checkpoints | #load-model-checkpoints | .md | 60_2 |
For text-to-image, pass a text prompt. By default, SDXL Turbo generates a 512x512 image, and that resolution gives the best results. You can try setting the `height` and `width` parameters to 768x768 or 1024x1024, but you should expect quality degradations when doing so.
Make sure to set `guidance_scale` to 0.0 to disable, as the model was trained without it. A single inference step is enough to generate high quality images.
Increasing the number of steps to 2, 3 or 4 should improve image quality.
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline_text2image = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16")
pipeline_text2image = pipeline_text2image.to("cuda")
prompt = "A cinematic shot of a baby racoon wearing an intricate italian priest robe."
image = pipeline_text2image(prompt=prompt, guidance_scale=0.0, num_inference_steps=1).images[0]
image
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/sdxl-turbo-text2img.png" alt="generated image of a racoon in a robe"/>
</div> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/sdxl_turbo.md | https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl_turbo/#text-to-image | #text-to-image | .md | 60_3 |
For image-to-image generation, make sure that `num_inference_steps * strength` is larger or equal to 1.
The image-to-image pipeline will run for `int(num_inference_steps * strength)` steps, e.g. `0.5 * 2.0 = 1` step in
our example below.
```py
from diffusers import AutoPipelineForImage2Image
from diffusers.utils import load_image, make_image_grid
# use from_pipe to avoid consuming additional memory when loading a checkpoint
pipeline_image2image = AutoPipelineForImage2Image.from_pipe(pipeline_text2image).to("cuda")
init_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png")
init_image = init_image.resize((512, 512))
prompt = "cat wizard, gandalf, lord of the rings, detailed, fantasy, cute, adorable, Pixar, Disney, 8k"
image = pipeline_image2image(prompt, image=init_image, strength=0.5, guidance_scale=0.0, num_inference_steps=2).images[0]
make_image_grid([init_image, image], rows=1, cols=2)
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/sdxl-turbo-img2img.png" alt="Image-to-image generation sample using SDXL Turbo"/>
</div> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/sdxl_turbo.md | https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl_turbo/#image-to-image | #image-to-image | .md | 60_4 |
- Compile the UNet if you are using PyTorch version 2.0 or higher. The first inference run will be very slow, but subsequent ones will be much faster.
```py
pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
```
- When using the default VAE, keep it in `float32` to avoid costly `dtype` conversions before and after each generation. You only need to do this one before your first generation:
```py
pipe.upcast_vae()
```
As an alternative, you can also use a [16-bit VAE](https://huggingface.co/madebyollin/sdxl-vae-fp16-fix) created by community member [`@madebyollin`](https://huggingface.co/madebyollin) that does not need to be upcasted to `float32`. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/sdxl_turbo.md | https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl_turbo/#speed-up-sdxl-turbo-even-more | #speed-up-sdxl-turbo-even-more | .md | 60_5 |
<!--Copyright 2024 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
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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.
--> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/inference_with_lcm.md | https://huggingface.co/docs/diffusers/en/using-diffusers/inference_with_lcm/ | .md | 61_0 |
|
[[open-in-colab]]
[Latent Consistency Models (LCMs)](https://hf.co/papers/2310.04378) enable fast high-quality image generation by directly predicting the reverse diffusion process in the latent rather than pixel space. In other words, LCMs try to predict the noiseless image from the noisy image in contrast to typical diffusion models that iteratively remove noise from the noisy image. By avoiding the iterative sampling process, LCMs are able to generate high-quality images in 2-4 steps instead of 20-30 steps.
LCMs are distilled from pretrained models which requires ~32 hours of A100 compute. To speed this up, [LCM-LoRAs](https://hf.co/papers/2311.05556) train a [LoRA adapter](https://huggingface.co/docs/peft/conceptual_guides/adapter#low-rank-adaptation-lora) which have much fewer parameters to train compared to the full model. The LCM-LoRA can be plugged into a diffusion model once it has been trained.
This guide will show you how to use LCMs and LCM-LoRAs for fast inference on tasks and how to use them with other adapters like ControlNet or T2I-Adapter.
> [!TIP]
> LCMs and LCM-LoRAs are available for Stable Diffusion v1.5, Stable Diffusion XL, and the SSD-1B model. You can find their checkpoints on the [Latent Consistency](https://hf.co/collections/latent-consistency/latent-consistency-models-weights-654ce61a95edd6dffccef6a8) Collections. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/inference_with_lcm.md | https://huggingface.co/docs/diffusers/en/using-diffusers/inference_with_lcm/#latent-consistency-model | #latent-consistency-model | .md | 61_1 |
<hfoptions id="lcm-text2img">
<hfoption id="LCM">
To use LCMs, you need to load the LCM checkpoint for your supported model into [`UNet2DConditionModel`] and replace the scheduler with the [`LCMScheduler`]. Then you can use the pipeline as usual, and pass a text prompt to generate an image in just 4 steps.
A couple of notes to keep in mind when using LCMs are:
* Typically, batch size is doubled inside the pipeline for classifier-free guidance. But LCM applies guidance with guidance embeddings and doesn't need to double the batch size, which leads to faster inference. The downside is that negative prompts don't work with LCM because they don't have any effect on the denoising process.
* The ideal range for `guidance_scale` is [3., 13.] because that is what the UNet was trained with. However, disabling `guidance_scale` with a value of 1.0 is also effective in most cases.
```python
from diffusers import StableDiffusionXLPipeline, UNet2DConditionModel, LCMScheduler
import torch
unet = UNet2DConditionModel.from_pretrained(
"latent-consistency/lcm-sdxl",
torch_dtype=torch.float16,
variant="fp16",
)
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 = "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k"
generator = torch.manual_seed(0)
image = pipe(
prompt=prompt, num_inference_steps=4, generator=generator, guidance_scale=8.0
).images[0]
image
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lcm/lcm_full_sdxl_t2i.png"/>
</div>
</hfoption>
<hfoption id="LCM-LoRA">
To use LCM-LoRAs, you need to replace the scheduler with the [`LCMScheduler`] and load the LCM-LoRA weights with the [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] method. Then you can use the pipeline as usual, and pass a text prompt to generate an image in just 4 steps.
A couple of notes to keep in mind when using LCM-LoRAs are:
* Typically, batch size is doubled inside the pipeline for classifier-free guidance. But LCM applies guidance with guidance embeddings and doesn't need to double the batch size, which leads to faster inference. The downside is that negative prompts don't work with LCM because they don't have any effect on the denoising process.
* You could use guidance with LCM-LoRAs, but it is very sensitive to high `guidance_scale` values and can lead to artifacts in the generated image. The best values we've found are between [1.0, 2.0].
* Replace [stabilityai/stable-diffusion-xl-base-1.0](https://hf.co/stabilityai/stable-diffusion-xl-base-1.0) with any finetuned model. For example, try using the [animagine-xl](https://huggingface.co/Linaqruf/animagine-xl) checkpoint to generate anime images with SDXL.
```py
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")
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
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]
image
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lcm/lcm_sdxl_t2i.png"/>
</div>
</hfoption>
</hfoptions> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/inference_with_lcm.md | https://huggingface.co/docs/diffusers/en/using-diffusers/inference_with_lcm/#text-to-image | #text-to-image | .md | 61_2 |
<hfoptions id="lcm-img2img">
<hfoption id="LCM">
To use LCMs for image-to-image, you need to load the LCM checkpoint for your supported model into [`UNet2DConditionModel`] and replace the scheduler with the [`LCMScheduler`]. Then you can use the pipeline as usual, and pass a text prompt and initial image to generate an image in just 4 steps.
> [!TIP]
> Experiment with different values for `num_inference_steps`, `strength`, and `guidance_scale` to get the best results.
```python
import torch
from diffusers import AutoPipelineForImage2Image, UNet2DConditionModel, LCMScheduler
from diffusers.utils import 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)
init_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/img2img-init.png")
prompt = "Astronauts in a jungle, cold color palette, muted colors, detailed, 8k"
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]
image
```
<div class="flex gap-4">
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/img2img-init.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">initial image</figcaption>
</div>
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lcm-img2img.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">generated image</figcaption>
</div>
</div>
</hfoption>
<hfoption id="LCM-LoRA">
To use LCM-LoRAs for image-to-image, you need to replace the scheduler with the [`LCMScheduler`] and load the LCM-LoRA weights with the [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] method. Then you can use the pipeline as usual, and pass a text prompt and initial image to generate an image in just 4 steps.
> [!TIP]
> Experiment with different values for `num_inference_steps`, `strength`, and `guidance_scale` to get the best results.
```py
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")
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
pipe.load_lora_weights("latent-consistency/lcm-lora-sdv1-5")
init_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/img2img-init.png")
prompt = "Astronauts in a jungle, cold color palette, muted colors, detailed, 8k"
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]
image
```
<div class="flex gap-4">
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/img2img-init.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">initial image</figcaption>
</div>
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lcm-lora-img2img.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">generated image</figcaption>
</div>
</div>
</hfoption>
</hfoptions> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/inference_with_lcm.md | https://huggingface.co/docs/diffusers/en/using-diffusers/inference_with_lcm/#image-to-image | #image-to-image | .md | 61_3 |
To use LCM-LoRAs for inpainting, you need to replace the scheduler with the [`LCMScheduler`] and load the LCM-LoRA weights with the [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] method. Then you can use the pipeline as usual, and pass a text prompt, initial image, and mask image to generate an image in just 4 steps.
```py
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")
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
pipe.load_lora_weights("latent-consistency/lcm-lora-sdv1-5")
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")
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]
image
```
<div class="flex gap-4">
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">initial image</figcaption>
</div>
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lcm-lora-inpaint.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">generated image</figcaption>
</div>
</div> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/inference_with_lcm.md | https://huggingface.co/docs/diffusers/en/using-diffusers/inference_with_lcm/#inpainting | #inpainting | .md | 61_4 |
LCMs are compatible with adapters like LoRA, ControlNet, T2I-Adapter, and AnimateDiff. You can bring the speed of LCMs to these adapters to generate images in a certain style or condition the model on another input like a canny image. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/inference_with_lcm.md | https://huggingface.co/docs/diffusers/en/using-diffusers/inference_with_lcm/#adapters | #adapters | .md | 61_5 |
[LoRA](../using-diffusers/loading_adapters#lora) adapters can be rapidly finetuned to learn a new style from just a few images and plugged into a pretrained model to generate images in that style.
<hfoptions id="lcm-lora">
<hfoption id="LCM">
Load the LCM checkpoint for your supported model into [`UNet2DConditionModel`] and replace the scheduler with the [`LCMScheduler`]. Then you can use the [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] method to load the LoRA weights into the LCM and generate a styled image in a few steps.
```python
from diffusers import StableDiffusionXLPipeline, UNet2DConditionModel, LCMScheduler
import torch
unet = UNet2DConditionModel.from_pretrained(
"latent-consistency/lcm-sdxl",
torch_dtype=torch.float16,
variant="fp16",
)
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)
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, num_inference_steps=4, generator=generator, guidance_scale=8.0
).images[0]
image
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lcm/lcm_full_sdx_lora_mix.png"/>
</div>
</hfoption>
<hfoption id="LCM-LoRA">
Replace the scheduler with the [`LCMScheduler`]. Then you can use the [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] method to load the LCM-LoRA weights and the style LoRA you want to use. Combine both LoRA adapters with the [`~loaders.UNet2DConditionLoadersMixin.set_adapters`] method and generate a styled image in a few steps.
```py
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")
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
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")
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
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lcm/lcm_sdx_lora_mix.png"/>
</div>
</hfoption>
</hfoptions> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/inference_with_lcm.md | https://huggingface.co/docs/diffusers/en/using-diffusers/inference_with_lcm/#lora | #lora | .md | 61_6 |
[ControlNet](./controlnet) are adapters that can be trained on a variety of inputs like canny edge, pose estimation, or depth. The ControlNet can be inserted into the pipeline to provide additional conditioning and control to the model for more accurate generation.
You can find additional ControlNet models trained on other inputs in [lllyasviel's](https://hf.co/lllyasviel) repository.
<hfoptions id="lcm-controlnet">
<hfoption id="LCM">
Load a ControlNet model trained on canny images and pass it to the [`ControlNetModel`]. Then you can load a LCM model into [`StableDiffusionControlNetPipeline`] and replace the scheduler with the [`LCMScheduler`]. Now pass the canny image to the pipeline and generate an image.
> [!TIP]
> Experiment with different values for `num_inference_steps`, `controlnet_conditioning_scale`, `cross_attention_kwargs`, and `guidance_scale` to get the best results.
```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, 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
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(
"SimianLuo/LCM_Dreamshaper_v7",
controlnet=controlnet,
torch_dtype=torch.float16,
safety_checker=None,
).to("cuda")
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
generator = torch.manual_seed(0)
image = pipe(
"the mona lisa",
image=canny_image,
num_inference_steps=4,
generator=generator,
).images[0]
make_image_grid([canny_image, image], rows=1, cols=2)
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lcm/lcm_full_sdv1-5_controlnet.png"/>
</div>
</hfoption>
<hfoption id="LCM-LoRA">
Load a ControlNet model trained on canny images and pass it to the [`ControlNetModel`]. Then you can load a Stable Diffusion v1.5 model into [`StableDiffusionControlNetPipeline`] and replace the scheduler with the [`LCMScheduler`]. Use the [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] method to load the LCM-LoRA weights, and pass the canny image to the pipeline and generate an image.
> [!TIP]
> Experiment with different values for `num_inference_steps`, `controlnet_conditioning_scale`, `cross_attention_kwargs`, and `guidance_scale` to get the best results.
```py
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(
"stable-diffusion-v1-5/stable-diffusion-v1-5",
controlnet=controlnet,
torch_dtype=torch.float16,
safety_checker=None,
variant="fp16"
).to("cuda")
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
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]
image
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lcm/lcm_sdv1-5_controlnet.png"/>
</div>
</hfoption>
</hfoptions> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/inference_with_lcm.md | https://huggingface.co/docs/diffusers/en/using-diffusers/inference_with_lcm/#controlnet | #controlnet | .md | 61_7 |
[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 | https://huggingface.co/docs/diffusers/en/using-diffusers/inference_with_lcm/#t2i-adapter | #t2i-adapter | .md | 61_8 |
[AnimateDiff](../api/pipelines/animatediff) is an adapter that adds motion to an image. It can be used with most Stable Diffusion models, effectively turning them into "video generation" models. Generating good results with a video model usually requires generating multiple frames (16-24), which can be very slow with a regular Stable Diffusion model. LCM-LoRA can speed up this process by only taking 4-8 steps for each frame.
Load a [`AnimateDiffPipeline`] and pass a [`MotionAdapter`] to it. Then replace the scheduler with the [`LCMScheduler`], and combine both LoRA adapters with the [`~loaders.UNet2DConditionLoadersMixin.set_adapters`] method. Now you can pass a prompt to the pipeline and generate an animated image.
```py
import torch
from diffusers import MotionAdapter, AnimateDiffPipeline, DDIMScheduler, LCMScheduler
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 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")
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lcm-lora-animatediff.gif"/>
</div> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/inference_with_lcm.md | https://huggingface.co/docs/diffusers/en/using-diffusers/inference_with_lcm/#animatediff | #animatediff | .md | 61_9 |
<!--Copyright 2024 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
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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.
--> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/stable_diffusion_jax_how_to.md | https://huggingface.co/docs/diffusers/en/using-diffusers/stable_diffusion_jax_how_to/ | .md | 62_0 |
|
[[open-in-colab]]
🤗 Diffusers supports Flax for super fast inference on Google TPUs, such as those available in Colab, Kaggle or Google Cloud Platform. This guide shows you how to run inference with Stable Diffusion using JAX/Flax.
Before you begin, make sure you have the necessary libraries installed:
```py
# uncomment to install the necessary libraries in Colab
#!pip install -q jax==0.3.25 jaxlib==0.3.25 flax transformers ftfy
#!pip install -q diffusers
```
You should also make sure you're using a TPU backend. While JAX does not run exclusively on TPUs, you'll get the best performance on a TPU because each server has 8 TPU accelerators working in parallel.
If you are running this guide in Colab, select *Runtime* in the menu above, select the option *Change runtime type*, and then select *TPU* under the *Hardware accelerator* setting. Import JAX and quickly check whether you're using a TPU:
```python
import jax
import jax.tools.colab_tpu
jax.tools.colab_tpu.setup_tpu()
num_devices = jax.device_count()
device_type = jax.devices()[0].device_kind
print(f"Found {num_devices} JAX devices of type {device_type}.")
assert (
"TPU" in device_type,
"Available device is not a TPU, please select TPU from Runtime > Change runtime type > Hardware accelerator"
)
# Found 8 JAX devices of type Cloud TPU.
```
Great, now you can import the rest of the dependencies you'll need:
```python
import jax.numpy as jnp
from jax import pmap
from flax.jax_utils import replicate
from flax.training.common_utils import shard
from diffusers import FlaxStableDiffusionPipeline
``` | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/stable_diffusion_jax_how_to.md | https://huggingface.co/docs/diffusers/en/using-diffusers/stable_diffusion_jax_how_to/#jaxflax | #jaxflax | .md | 62_1 |
Flax is a functional framework, so models are stateless and parameters are stored outside of them. Loading a pretrained Flax pipeline returns *both* the pipeline and the model weights (or parameters). In this guide, you'll use `bfloat16`, a more efficient half-float type that is supported by TPUs (you can also use `float32` for full precision if you want).
```python
dtype = jnp.bfloat16
pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4",
variant="bf16",
dtype=dtype,
)
``` | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/stable_diffusion_jax_how_to.md | https://huggingface.co/docs/diffusers/en/using-diffusers/stable_diffusion_jax_how_to/#load-a-model | #load-a-model | .md | 62_2 |
TPUs usually have 8 devices working in parallel, so let's use the same prompt for each device. This means you can perform inference on 8 devices at once, with each device generating one image. As a result, you'll get 8 images in the same amount of time it takes for one chip to generate a single image!
<Tip>
Learn more details in the [How does parallelization work?](#how-does-parallelization-work) section.
</Tip>
After replicating the prompt, get the tokenized text ids by calling the `prepare_inputs` function on the pipeline. The length of the tokenized text is set to 77 tokens as required by the configuration of the underlying CLIP text model.
```python
prompt = "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of field, close up, split lighting, cinematic"
prompt = [prompt] * jax.device_count()
prompt_ids = pipeline.prepare_inputs(prompt)
prompt_ids.shape
# (8, 77)
```
Model parameters and inputs have to be replicated across the 8 parallel devices. The parameters dictionary is replicated with [`flax.jax_utils.replicate`](https://flax.readthedocs.io/en/latest/api_reference/flax.jax_utils.html#flax.jax_utils.replicate) which traverses the dictionary and changes the shape of the weights so they are repeated 8 times. Arrays are replicated using `shard`.
```python
# parameters
p_params = replicate(params)
# arrays
prompt_ids = shard(prompt_ids)
prompt_ids.shape
# (8, 1, 77)
```
This shape means each one of the 8 devices receives as an input a `jnp` array with shape `(1, 77)`, where `1` is the batch size per device. On TPUs with sufficient memory, you could have a batch size larger than `1` if you want to generate multiple images (per chip) at once.
Next, create a random number generator to pass to the generation function. This is standard procedure in Flax, which is very serious and opinionated about random numbers. All functions that deal with random numbers are expected to receive a generator to ensure reproducibility, even when you're training across multiple distributed devices.
The helper function below uses a seed to initialize a random number generator. As long as you use the same seed, you'll get the exact same results. Feel free to use different seeds when exploring results later in the guide.
```python
def create_key(seed=0):
return jax.random.PRNGKey(seed)
```
The helper function, or `rng`, is split 8 times so each device receives a different generator and generates a different image.
```python
rng = create_key(0)
rng = jax.random.split(rng, jax.device_count())
```
To take advantage of JAX's optimized speed on a TPU, pass `jit=True` to the pipeline to compile the JAX code into an efficient representation and to ensure the model runs in parallel across the 8 devices.
<Tip warning={true}>
You need to ensure all your inputs have the same shape in subsequent calls, otherwise JAX will need to recompile the code which is slower.
</Tip>
The first inference run takes more time because it needs to compile the code, but subsequent calls (even with different inputs) are much faster. For example, it took more than a minute to compile on a TPU v2-8, but then it takes about **7s** on a future inference run!
```py
%%time
images = pipeline(prompt_ids, p_params, rng, jit=True)[0]
# CPU times: user 56.2 s, sys: 42.5 s, total: 1min 38s
# Wall time: 1min 29s
```
The returned array has shape `(8, 1, 512, 512, 3)` which should be reshaped to remove the second dimension and get 8 images of `512 × 512 × 3`. Then you can use the [`~utils.numpy_to_pil`] function to convert the arrays into images.
```python
from diffusers.utils import make_image_grid
images = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
images = pipeline.numpy_to_pil(images)
make_image_grid(images, rows=2, cols=4)
```
 | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/stable_diffusion_jax_how_to.md | https://huggingface.co/docs/diffusers/en/using-diffusers/stable_diffusion_jax_how_to/#inference | #inference | .md | 62_3 |
You don't necessarily have to use the same prompt on all devices. For example, to generate 8 different prompts:
```python
prompts = [
"Labrador in the style of Hokusai",
"Painting of a squirrel skating in New York",
"HAL-9000 in the style of Van Gogh",
"Times Square under water, with fish and a dolphin swimming around",
"Ancient Roman fresco showing a man working on his laptop",
"Close-up photograph of young black woman against urban background, high quality, bokeh",
"Armchair in the shape of an avocado",
"Clown astronaut in space, with Earth in the background",
]
prompt_ids = pipeline.prepare_inputs(prompts)
prompt_ids = shard(prompt_ids)
images = pipeline(prompt_ids, p_params, rng, jit=True).images
images = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
images = pipeline.numpy_to_pil(images)
make_image_grid(images, 2, 4)
```
 | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/stable_diffusion_jax_how_to.md | https://huggingface.co/docs/diffusers/en/using-diffusers/stable_diffusion_jax_how_to/#using-different-prompts | #using-different-prompts | .md | 62_4 |
The Flax pipeline in 🤗 Diffusers automatically compiles the model and runs it in parallel on all available devices. Let's take a closer look at how that process works.
JAX parallelization can be done in multiple ways. The easiest one revolves around using the [`jax.pmap`](https://jax.readthedocs.io/en/latest/_autosummary/jax.pmap.html) function to achieve single-program multiple-data (SPMD) parallelization. It means running several copies of the same code, each on different data inputs. More sophisticated approaches are possible, and you can go over to the JAX [documentation](https://jax.readthedocs.io/en/latest/index.html) to explore this topic in more detail if you are interested!
`jax.pmap` does two things:
1. Compiles (or "`jit`s") the code which is similar to `jax.jit()`. This does not happen when you call `pmap`, and only the first time the `pmap`ped function is called.
2. Ensures the compiled code runs in parallel on all available devices.
To demonstrate, call `pmap` on the pipeline's `_generate` method (this is a private method that generates images and may be renamed or removed in future releases of 🤗 Diffusers):
```python
p_generate = pmap(pipeline._generate)
```
After calling `pmap`, the prepared function `p_generate` will:
1. Make a copy of the underlying function, `pipeline._generate`, on each device.
2. Send each device a different portion of the input arguments (this is why it's necessary to call the *shard* function). In this case, `prompt_ids` has shape `(8, 1, 77, 768)` so the array is split into 8 and each copy of `_generate` receives an input with shape `(1, 77, 768)`.
The most important thing to pay attention to here is the batch size (1 in this example), and the input dimensions that make sense for your code. You don't have to change anything else to make the code work in parallel.
The first time you call the pipeline takes more time, but the calls afterward are much faster. The `block_until_ready` function is used to correctly measure inference time because JAX uses asynchronous dispatch and returns control to the Python loop as soon as it can. You don't need to use that in your code; blocking occurs automatically when you want to use the result of a computation that has not yet been materialized.
```py
%%time
images = p_generate(prompt_ids, p_params, rng)
images = images.block_until_ready()
# CPU times: user 1min 15s, sys: 18.2 s, total: 1min 34s
# Wall time: 1min 15s
```
Check your image dimensions to see if they're correct:
```python
images.shape
# (8, 1, 512, 512, 3)
``` | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/stable_diffusion_jax_how_to.md | https://huggingface.co/docs/diffusers/en/using-diffusers/stable_diffusion_jax_how_to/#how-does-parallelization-work | #how-does-parallelization-work | .md | 62_5 |
To learn more about how JAX works with Stable Diffusion, you may be interested in reading:
* [Accelerating Stable Diffusion XL Inference with JAX on Cloud TPU v5e](https://hf.co/blog/sdxl_jax) | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/stable_diffusion_jax_how_to.md | https://huggingface.co/docs/diffusers/en/using-diffusers/stable_diffusion_jax_how_to/#resources | #resources | .md | 62_6 |
<!--Copyright 2024 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
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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.
--> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/merge_loras.md | https://huggingface.co/docs/diffusers/en/using-diffusers/merge_loras/ | .md | 63_0 |
|
It can be fun and creative to use multiple [LoRAs]((https://huggingface.co/docs/peft/conceptual_guides/adapter#low-rank-adaptation-lora)) together to generate something entirely new and unique. This works by merging multiple LoRA weights together to produce images that are a blend of different styles. Diffusers provides a few methods to merge LoRAs depending on *how* you want to merge their weights, which can affect image quality.
This guide will show you how to merge LoRAs using the [`~loaders.PeftAdapterMixin.set_adapters`] and [add_weighted_adapter](https://huggingface.co/docs/peft/package_reference/lora#peft.LoraModel.add_weighted_adapter) methods. To improve inference speed and reduce memory-usage of merged LoRAs, you'll also see how to use the [`~loaders.StableDiffusionLoraLoaderMixin.fuse_lora`] method to fuse the LoRA weights with the original weights of the underlying model.
For this guide, load a Stable Diffusion XL (SDXL) checkpoint and the [KappaNeuro/studio-ghibli-style](https://huggingface.co/KappaNeuro/studio-ghibli-style) and [Norod78/sdxl-chalkboarddrawing-lora](https://huggingface.co/Norod78/sdxl-chalkboarddrawing-lora) LoRAs with the [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] method. You'll need to assign each LoRA an `adapter_name` to combine them later.
```py
from diffusers import DiffusionPipeline
import torch
pipeline = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16).to("cuda")
pipeline.load_lora_weights("ostris/ikea-instructions-lora-sdxl", weight_name="ikea_instructions_xl_v1_5.safetensors", adapter_name="ikea")
pipeline.load_lora_weights("lordjia/by-feng-zikai", weight_name="fengzikai_v1.0_XL.safetensors", adapter_name="feng")
``` | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/merge_loras.md | https://huggingface.co/docs/diffusers/en/using-diffusers/merge_loras/#merge-loras | #merge-loras | .md | 63_1 |
The [`~loaders.PeftAdapterMixin.set_adapters`] method merges LoRA adapters by concatenating their weighted matrices. Use the adapter name to specify which LoRAs to merge, and the `adapter_weights` parameter to control the scaling for each LoRA. For example, if `adapter_weights=[0.5, 0.5]`, then the merged LoRA output is an average of both LoRAs. Try adjusting the adapter weights to see how it affects the generated image!
```py
pipeline.set_adapters(["ikea", "feng"], adapter_weights=[0.7, 0.8])
generator = torch.manual_seed(0)
prompt = "A bowl of ramen shaped like a cute kawaii bear, by Feng Zikai"
image = pipeline(prompt, generator=generator, cross_attention_kwargs={"scale": 1.0}).images[0]
image
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lora_merge_set_adapters.png"/>
</div> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/merge_loras.md | https://huggingface.co/docs/diffusers/en/using-diffusers/merge_loras/#setadapters | #setadapters | .md | 63_2 |
> [!WARNING]
> This is an experimental method that adds PEFTs [add_weighted_adapter](https://huggingface.co/docs/peft/package_reference/lora#peft.LoraModel.add_weighted_adapter) method to Diffusers to enable more efficient merging methods. Check out this [issue](https://github.com/huggingface/diffusers/issues/6892) if you're interested in learning more about the motivation and design behind this integration.
The [add_weighted_adapter](https://huggingface.co/docs/peft/package_reference/lora#peft.LoraModel.add_weighted_adapter) method provides access to more efficient merging method such as [TIES and DARE](https://huggingface.co/docs/peft/developer_guides/model_merging). To use these merging methods, make sure you have the latest stable version of Diffusers and PEFT installed.
```bash
pip install -U diffusers peft
```
There are three steps to merge LoRAs with the [add_weighted_adapter](https://huggingface.co/docs/peft/package_reference/lora#peft.LoraModel.add_weighted_adapter) method:
1. Create a [PeftModel](https://huggingface.co/docs/peft/package_reference/peft_model#peft.PeftModel) from the underlying model and LoRA checkpoint.
2. Load a base UNet model and the LoRA adapters.
3. Merge the adapters using the [add_weighted_adapter](https://huggingface.co/docs/peft/package_reference/lora#peft.LoraModel.add_weighted_adapter) method and the merging method of your choice.
Let's dive deeper into what these steps entail.
1. Load a UNet that corresponds to the UNet in the LoRA checkpoint. In this case, both LoRAs use the SDXL UNet as their base model.
```python
from diffusers import UNet2DConditionModel
import torch
unet = UNet2DConditionModel.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
torch_dtype=torch.float16,
use_safetensors=True,
variant="fp16",
subfolder="unet",
).to("cuda")
```
Load the SDXL pipeline and the LoRA checkpoints, starting with the [ostris/ikea-instructions-lora-sdxl](https://huggingface.co/ostris/ikea-instructions-lora-sdxl) LoRA.
```python
from diffusers import DiffusionPipeline
pipeline = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
variant="fp16",
torch_dtype=torch.float16,
unet=unet
).to("cuda")
pipeline.load_lora_weights("ostris/ikea-instructions-lora-sdxl", weight_name="ikea_instructions_xl_v1_5.safetensors", adapter_name="ikea")
```
Now you'll create a [PeftModel](https://huggingface.co/docs/peft/package_reference/peft_model#peft.PeftModel) from the loaded LoRA checkpoint by combining the SDXL UNet and the LoRA UNet from the pipeline.
```python
from peft import get_peft_model, LoraConfig
import copy
sdxl_unet = copy.deepcopy(unet)
ikea_peft_model = get_peft_model(
sdxl_unet,
pipeline.unet.peft_config["ikea"],
adapter_name="ikea"
)
original_state_dict = {f"base_model.model.{k}": v for k, v in pipeline.unet.state_dict().items()}
ikea_peft_model.load_state_dict(original_state_dict, strict=True)
```
> [!TIP]
> You can optionally push the ikea_peft_model to the Hub by calling `ikea_peft_model.push_to_hub("ikea_peft_model", token=TOKEN)`.
Repeat this process to create a [PeftModel](https://huggingface.co/docs/peft/package_reference/peft_model#peft.PeftModel) from the [lordjia/by-feng-zikai](https://huggingface.co/lordjia/by-feng-zikai) LoRA.
```python
pipeline.delete_adapters("ikea")
sdxl_unet.delete_adapters("ikea")
pipeline.load_lora_weights("lordjia/by-feng-zikai", weight_name="fengzikai_v1.0_XL.safetensors", adapter_name="feng")
pipeline.set_adapters(adapter_names="feng")
feng_peft_model = get_peft_model(
sdxl_unet,
pipeline.unet.peft_config["feng"],
adapter_name="feng"
)
original_state_dict = {f"base_model.model.{k}": v for k, v in pipe.unet.state_dict().items()}
feng_peft_model.load_state_dict(original_state_dict, strict=True)
```
2. Load a base UNet model and then load the adapters onto it.
```python
from peft import PeftModel
base_unet = UNet2DConditionModel.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
torch_dtype=torch.float16,
use_safetensors=True,
variant="fp16",
subfolder="unet",
).to("cuda")
model = PeftModel.from_pretrained(base_unet, "stevhliu/ikea_peft_model", use_safetensors=True, subfolder="ikea", adapter_name="ikea")
model.load_adapter("stevhliu/feng_peft_model", use_safetensors=True, subfolder="feng", adapter_name="feng")
```
3. Merge the adapters using the [add_weighted_adapter](https://huggingface.co/docs/peft/package_reference/lora#peft.LoraModel.add_weighted_adapter) method and the merging method of your choice (learn more about other merging methods in this [blog post](https://huggingface.co/blog/peft_merging)). For this example, let's use the `"dare_linear"` method to merge the LoRAs.
> [!WARNING]
> Keep in mind the LoRAs need to have the same rank to be merged!
```python
model.add_weighted_adapter(
adapters=["ikea", "feng"],
weights=[1.0, 1.0],
combination_type="dare_linear",
adapter_name="ikea-feng"
)
model.set_adapters("ikea-feng")
```
Now you can generate an image with the merged LoRA.
```python
model = model.to(dtype=torch.float16, device="cuda")
pipeline = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", unet=model, variant="fp16", torch_dtype=torch.float16,
).to("cuda")
image = pipeline("A bowl of ramen shaped like a cute kawaii bear, by Feng Zikai", generator=torch.manual_seed(0)).images[0]
image
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/ikea-feng-dare-linear.png"/>
</div> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/merge_loras.md | https://huggingface.co/docs/diffusers/en/using-diffusers/merge_loras/#addweightedadapter | #addweightedadapter | .md | 63_3 |
Both the [`~loaders.PeftAdapterMixin.set_adapters`] and [add_weighted_adapter](https://huggingface.co/docs/peft/package_reference/lora#peft.LoraModel.add_weighted_adapter) methods require loading the base model and the LoRA adapters separately which incurs some overhead. The [`~loaders.lora_base.LoraBaseMixin.fuse_lora`] method allows you to fuse the LoRA weights directly with the original weights of the underlying model. This way, you're only loading the model once which can increase inference and lower memory-usage.
You can use PEFT to easily fuse/unfuse multiple adapters directly into the model weights (both UNet and text encoder) using the [`~loaders.lora_base.LoraBaseMixin.fuse_lora`] method, which can lead to a speed-up in inference and lower VRAM usage.
For example, if you have a base model and adapters loaded and set as active with the following adapter weights:
```py
from diffusers import DiffusionPipeline
import torch
pipeline = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16).to("cuda")
pipeline.load_lora_weights("ostris/ikea-instructions-lora-sdxl", weight_name="ikea_instructions_xl_v1_5.safetensors", adapter_name="ikea")
pipeline.load_lora_weights("lordjia/by-feng-zikai", weight_name="fengzikai_v1.0_XL.safetensors", adapter_name="feng")
pipeline.set_adapters(["ikea", "feng"], adapter_weights=[0.7, 0.8])
```
Fuse these LoRAs into the UNet with the [`~loaders.lora_base.LoraBaseMixin.fuse_lora`] method. The `lora_scale` parameter controls how much to scale the output by with the LoRA weights. It is important to make the `lora_scale` adjustments in the [`~loaders.lora_base.LoraBaseMixin.fuse_lora`] method because it won’t work if you try to pass `scale` to the `cross_attention_kwargs` in the pipeline.
```py
pipeline.fuse_lora(adapter_names=["ikea", "feng"], lora_scale=1.0)
```
Then you should use [`~loaders.StableDiffusionLoraLoaderMixin.unload_lora_weights`] to unload the LoRA weights since they've already been fused with the underlying base model. Finally, call [`~DiffusionPipeline.save_pretrained`] to save the fused pipeline locally or you could call [`~DiffusionPipeline.push_to_hub`] to push the fused pipeline to the Hub.
```py
pipeline.unload_lora_weights()
# save locally
pipeline.save_pretrained("path/to/fused-pipeline")
# save to the Hub
pipeline.push_to_hub("fused-ikea-feng")
```
Now you can quickly load the fused pipeline and use it for inference without needing to separately load the LoRA adapters.
```py
pipeline = DiffusionPipeline.from_pretrained(
"username/fused-ikea-feng", torch_dtype=torch.float16,
).to("cuda")
image = pipeline("A bowl of ramen shaped like a cute kawaii bear, by Feng Zikai", generator=torch.manual_seed(0)).images[0]
image
```
You can call [`~~loaders.lora_base.LoraBaseMixin.unfuse_lora`] to restore the original model's weights (for example, if you want to use a different `lora_scale` value). However, this only works if you've only fused one LoRA adapter to the original model. If you've fused multiple LoRAs, you'll need to reload the model.
```py
pipeline.unfuse_lora()
``` | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/merge_loras.md | https://huggingface.co/docs/diffusers/en/using-diffusers/merge_loras/#fuselora | #fuselora | .md | 63_4 |
[torch.compile](../optimization/torch2.0#torchcompile) can speed up your pipeline even more, but the LoRA weights must be fused first and then unloaded. Typically, the UNet is compiled because it is such a computationally intensive component of the pipeline.
```py
from diffusers import DiffusionPipeline
import torch
# load base model and LoRAs
pipeline = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16).to("cuda")
pipeline.load_lora_weights("ostris/ikea-instructions-lora-sdxl", weight_name="ikea_instructions_xl_v1_5.safetensors", adapter_name="ikea")
pipeline.load_lora_weights("lordjia/by-feng-zikai", weight_name="fengzikai_v1.0_XL.safetensors", adapter_name="feng")
# activate both LoRAs and set adapter weights
pipeline.set_adapters(["ikea", "feng"], adapter_weights=[0.7, 0.8])
# fuse LoRAs and unload weights
pipeline.fuse_lora(adapter_names=["ikea", "feng"], lora_scale=1.0)
pipeline.unload_lora_weights()
# torch.compile
pipeline.unet.to(memory_format=torch.channels_last)
pipeline.unet = torch.compile(pipeline.unet, mode="reduce-overhead", fullgraph=True)
image = pipeline("A bowl of ramen shaped like a cute kawaii bear, by Feng Zikai", generator=torch.manual_seed(0)).images[0]
```
Learn more about torch.compile in the [Accelerate inference of text-to-image diffusion models](../tutorials/fast_diffusion#torchcompile) guide. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/merge_loras.md | https://huggingface.co/docs/diffusers/en/using-diffusers/merge_loras/#torchcompile | #torchcompile | .md | 63_5 |
For more conceptual details about how each merging method works, take a look at the [🤗 PEFT welcomes new merging methods](https://huggingface.co/blog/peft_merging#concatenation-cat) blog post! | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/merge_loras.md | https://huggingface.co/docs/diffusers/en/using-diffusers/merge_loras/#next-steps | #next-steps | .md | 63_6 |
<!--Copyright 2024 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
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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.
--> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/ | .md | 64_0 |
|
ControlNet is a type of model for controlling image diffusion models by conditioning the model with an additional input image. There are many types of conditioning inputs (canny edge, user sketching, human pose, depth, and more) you can use to control a diffusion model. This is hugely useful because it affords you greater control over image generation, making it easier to generate specific images without experimenting with different text prompts or denoising values as much.
<Tip>
Check out Section 3.5 of the [ControlNet](https://huggingface.co/papers/2302.05543) paper v1 for a list of ControlNet implementations on various conditioning inputs. You can find the official Stable Diffusion ControlNet conditioned models on [lllyasviel](https://huggingface.co/lllyasviel)'s Hub profile, and more [community-trained](https://huggingface.co/models?other=stable-diffusion&other=controlnet) ones on the Hub.
For Stable Diffusion XL (SDXL) ControlNet models, you can find them on the 🤗 [Diffusers](https://huggingface.co/diffusers) Hub organization, or you can browse [community-trained](https://huggingface.co/models?other=stable-diffusion-xl&other=controlnet) ones on the Hub.
</Tip>
A ControlNet model has two sets of weights (or blocks) connected by a zero-convolution layer:
- a *locked copy* keeps everything a large pretrained diffusion model has learned
- a *trainable copy* is trained on the additional conditioning input
Since the locked copy preserves the pretrained model, training and implementing a ControlNet on a new conditioning input is as fast as finetuning any other model because you aren't training the model from scratch.
This guide will show you how to use ControlNet for text-to-image, image-to-image, inpainting, and more! There are many types of ControlNet conditioning inputs to choose from, but in this guide we'll only focus on several of them. Feel free to experiment with other conditioning inputs!
Before you begin, make sure you have the following libraries installed:
```py
# uncomment to install the necessary libraries in Colab
#!pip install -q diffusers transformers accelerate opencv-python
``` | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#controlnet | #controlnet | .md | 64_1 |
For text-to-image, you normally pass a text prompt to the model. But with ControlNet, you can specify an additional conditioning input. Let's condition the model with a canny image, a white outline of an image on a black background. This way, the ControlNet can use the canny image as a control to guide the model to generate an image with the same outline.
Load an image and use the [opencv-python](https://github.com/opencv/opencv-python) library to extract the canny image:
```py
from diffusers.utils import load_image, make_image_grid
from PIL import Image
import cv2
import numpy as np
original_image = load_image(
"https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png"
)
image = np.array(original_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)
```
<div class="flex gap-4">
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">original image</figcaption>
</div>
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/vermeer_canny_edged.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">canny image</figcaption>
</div>
</div>
Next, load a ControlNet model conditioned on canny edge detection and pass it to the [`StableDiffusionControlNetPipeline`]. Use the faster [`UniPCMultistepScheduler`] and enable model offloading to speed up inference and reduce memory usage.
```py
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
import torch
controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16, use_safetensors=True)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16, use_safetensors=True
)
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()
```
Now pass your prompt and canny image to the pipeline:
```py
output = pipe(
"the mona lisa", image=canny_image
).images[0]
make_image_grid([original_image, canny_image, output], rows=1, cols=3)
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet-text2img.png"/>
</div> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#text-to-image | #text-to-image | .md | 64_2 |
For image-to-image, you'd typically pass an initial image and a prompt to the pipeline to generate a new image. With ControlNet, you can pass an additional conditioning input to guide the model. Let's condition the model with a depth map, an image which contains spatial information. This way, the ControlNet can use the depth map as a control to guide the model to generate an image that preserves spatial information.
You'll use the [`StableDiffusionControlNetImg2ImgPipeline`] for this task, which is different from the [`StableDiffusionControlNetPipeline`] because it allows you to pass an initial image as the starting point for the image generation process.
Load an image and use the `depth-estimation` [`~transformers.Pipeline`] from 🤗 Transformers to extract the depth map of an image:
```py
import torch
import numpy as np
from transformers import pipeline
from diffusers.utils import load_image, make_image_grid
image = load_image(
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet-img2img.jpg"
)
def get_depth_map(image, depth_estimator):
image = depth_estimator(image)["depth"]
image = np.array(image)
image = image[:, :, None]
image = np.concatenate([image, image, image], axis=2)
detected_map = torch.from_numpy(image).float() / 255.0
depth_map = detected_map.permute(2, 0, 1)
return depth_map
depth_estimator = pipeline("depth-estimation")
depth_map = get_depth_map(image, depth_estimator).unsqueeze(0).half().to("cuda")
```
Next, load a ControlNet model conditioned on depth maps and pass it to the [`StableDiffusionControlNetImg2ImgPipeline`]. Use the faster [`UniPCMultistepScheduler`] and enable model offloading to speed up inference and reduce memory usage.
```py
from diffusers import StableDiffusionControlNetImg2ImgPipeline, ControlNetModel, UniPCMultistepScheduler
import torch
controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11f1p_sd15_depth", torch_dtype=torch.float16, use_safetensors=True)
pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16, use_safetensors=True
)
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()
```
Now pass your prompt, initial image, and depth map to the pipeline:
```py
output = pipe(
"lego batman and robin", image=image, control_image=depth_map,
).images[0]
make_image_grid([image, output], rows=1, cols=2)
```
<div class="flex gap-4">
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet-img2img.jpg"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">original image</figcaption>
</div>
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet-img2img-2.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">generated image</figcaption>
</div>
</div> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#image-to-image | #image-to-image | .md | 64_3 |
For inpainting, you need an initial image, a mask image, and a prompt describing what to replace the mask with. ControlNet models allow you to add another control image to condition a model with. Let’s condition the model with an inpainting mask. This way, the ControlNet can use the inpainting mask as a control to guide the model to generate an image within the mask area.
Load an initial image and a mask image:
```py
from diffusers.utils import load_image, make_image_grid
init_image = load_image(
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet-inpaint.jpg"
)
init_image = init_image.resize((512, 512))
mask_image = load_image(
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet-inpaint-mask.jpg"
)
mask_image = mask_image.resize((512, 512))
make_image_grid([init_image, mask_image], rows=1, cols=2)
```
Create a function to prepare the control image from the initial and mask images. This'll create a tensor to mark the pixels in `init_image` as masked if the corresponding pixel in `mask_image` is over a certain threshold.
```py
import numpy as np
import torch
def make_inpaint_condition(image, image_mask):
image = np.array(image.convert("RGB")).astype(np.float32) / 255.0
image_mask = np.array(image_mask.convert("L")).astype(np.float32) / 255.0
assert image.shape[0:1] == image_mask.shape[0:1]
image[image_mask > 0.5] = -1.0 # set as masked pixel
image = np.expand_dims(image, 0).transpose(0, 3, 1, 2)
image = torch.from_numpy(image)
return image
control_image = make_inpaint_condition(init_image, mask_image)
```
<div class="flex gap-4">
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet-inpaint.jpg"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">original image</figcaption>
</div>
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet-inpaint-mask.jpg"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">mask image</figcaption>
</div>
</div>
Load a ControlNet model conditioned on inpainting and pass it to the [`StableDiffusionControlNetInpaintPipeline`]. Use the faster [`UniPCMultistepScheduler`] and enable model offloading to speed up inference and reduce memory usage.
```py
from diffusers import StableDiffusionControlNetInpaintPipeline, ControlNetModel, UniPCMultistepScheduler
controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_inpaint", torch_dtype=torch.float16, use_safetensors=True)
pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16, use_safetensors=True
)
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()
```
Now pass your prompt, initial image, mask image, and control image to the pipeline:
```py
output = pipe(
"corgi face with large ears, detailed, pixar, animated, disney",
num_inference_steps=20,
eta=1.0,
image=init_image,
mask_image=mask_image,
control_image=control_image,
).images[0]
make_image_grid([init_image, mask_image, output], rows=1, cols=3)
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet-inpaint-result.png"/>
</div> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#inpainting | #inpainting | .md | 64_4 |
[Guess mode](https://github.com/lllyasviel/ControlNet/discussions/188) does not require supplying a prompt to a ControlNet at all! This forces the ControlNet encoder to do its best to "guess" the contents of the input control map (depth map, pose estimation, canny edge, etc.).
Guess mode adjusts the scale of the output residuals from a ControlNet by a fixed ratio depending on the block depth. The shallowest `DownBlock` corresponds to 0.1, and as the blocks get deeper, the scale increases exponentially such that the scale of the `MidBlock` output becomes 1.0.
<Tip>
Guess mode does not have any impact on prompt conditioning and you can still provide a prompt if you want.
</Tip>
Set `guess_mode=True` in the pipeline, and it is [recommended](https://github.com/lllyasviel/ControlNet#guess-mode--non-prompt-mode) to set the `guidance_scale` value between 3.0 and 5.0.
```py
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
from diffusers.utils import load_image, make_image_grid
import numpy as np
import torch
from PIL import Image
import cv2
controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", use_safetensors=True)
pipe = StableDiffusionControlNetPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", controlnet=controlnet, use_safetensors=True).to("cuda")
original_image = load_image("https://huggingface.co/takuma104/controlnet_dev/resolve/main/bird_512x512.png")
image = np.array(original_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)
image = pipe("", image=canny_image, guess_mode=True, guidance_scale=3.0).images[0]
make_image_grid([original_image, canny_image, image], rows=1, cols=3)
```
<div class="flex gap-4">
<div>
<img class="rounded-xl" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare_guess_mode/output_images/diffusers/output_bird_canny_0.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">regular mode with prompt</figcaption>
</div>
<div>
<img class="rounded-xl" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare_guess_mode/output_images/diffusers/output_bird_canny_0_gm.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">guess mode without prompt</figcaption>
</div>
</div> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#guess-mode | #guess-mode | .md | 64_5 |
There aren't too many ControlNet models compatible with Stable Diffusion XL (SDXL) at the moment, but we've trained two full-sized ControlNet models for SDXL conditioned on canny edge detection and depth maps. We're also experimenting with creating smaller versions of these SDXL-compatible ControlNet models so it is easier to run on resource-constrained hardware. You can find these checkpoints on the [🤗 Diffusers Hub organization](https://huggingface.co/diffusers)!
Let's use a SDXL ControlNet conditioned on canny images to generate an image. Start by loading an image and prepare the canny image:
```py
from diffusers import StableDiffusionXLControlNetPipeline, ControlNetModel, AutoencoderKL
from diffusers.utils import load_image, make_image_grid
from PIL import Image
import cv2
import numpy as np
import torch
original_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/hf-logo.png"
)
image = np.array(original_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)
make_image_grid([original_image, canny_image], rows=1, cols=2)
```
<div class="flex gap-4">
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/hf-logo.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">original image</figcaption>
</div>
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/hf-logo-canny.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">canny image</figcaption>
</div>
</div>
Load a SDXL ControlNet model conditioned on canny edge detection and pass it to the [`StableDiffusionXLControlNetPipeline`]. You can also enable model offloading to reduce memory usage.
```py
controlnet = ControlNetModel.from_pretrained(
"diffusers/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16,
use_safetensors=True
)
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16, use_safetensors=True)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
torch_dtype=torch.float16,
use_safetensors=True
)
pipe.enable_model_cpu_offload()
```
Now pass your prompt (and optionally a negative prompt if you're using one) and canny image to the pipeline:
<Tip>
The [`controlnet_conditioning_scale`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/controlnet#diffusers.StableDiffusionControlNetPipeline.__call__.controlnet_conditioning_scale) parameter determines how much weight to assign to the conditioning inputs. A value of 0.5 is recommended for good generalization, but feel free to experiment with this number!
</Tip>
```py
prompt = "aerial view, a futuristic research complex in a bright foggy jungle, hard lighting"
negative_prompt = 'low quality, bad quality, sketches'
image = pipe(
prompt,
negative_prompt=negative_prompt,
image=canny_image,
controlnet_conditioning_scale=0.5,
).images[0]
make_image_grid([original_image, canny_image, image], rows=1, cols=3)
```
<div class="flex justify-center">
<img class="rounded-xl" src="https://huggingface.co/diffusers/controlnet-canny-sdxl-1.0/resolve/main/out_hug_lab_7.png"/>
</div>
You can use [`StableDiffusionXLControlNetPipeline`] in guess mode as well by setting the parameter to `True`:
```py
from diffusers import StableDiffusionXLControlNetPipeline, ControlNetModel, AutoencoderKL
from diffusers.utils import load_image, make_image_grid
import numpy as np
import torch
import cv2
from PIL import Image
prompt = "aerial view, a futuristic research complex in a bright foggy jungle, hard lighting"
negative_prompt = "low quality, bad quality, sketches"
original_image = load_image(
"https://hf.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/hf-logo.png"
)
controlnet = ControlNetModel.from_pretrained(
"diffusers/controlnet-canny-sdxl-1.0", torch_dtype=torch.float16, use_safetensors=True
)
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16, use_safetensors=True)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet, vae=vae, torch_dtype=torch.float16, use_safetensors=True
)
pipe.enable_model_cpu_offload()
image = np.array(original_image)
image = cv2.Canny(image, 100, 200)
image = image[:, :, None]
image = np.concatenate([image, image, image], axis=2)
canny_image = Image.fromarray(image)
image = pipe(
prompt, negative_prompt=negative_prompt, controlnet_conditioning_scale=0.5, image=canny_image, guess_mode=True,
).images[0]
make_image_grid([original_image, canny_image, image], rows=1, cols=3)
```
<Tip>
You can use a refiner model with `StableDiffusionXLControlNetPipeline` to improve image quality, just like you can with a regular `StableDiffusionXLPipeline`.
See the [Refine image quality](./sdxl#refine-image-quality) section to learn how to use the refiner model.
Make sure to use `StableDiffusionXLControlNetPipeline` and pass `image` and `controlnet_conditioning_scale`.
```py
base = StableDiffusionXLControlNetPipeline(...)
image = base(
prompt=prompt,
controlnet_conditioning_scale=0.5,
image=canny_image,
num_inference_steps=40,
denoising_end=0.8,
output_type="latent",
).images
# rest exactly as with StableDiffusionXLPipeline
```
</Tip> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#controlnet-with-stable-diffusion-xl | #controlnet-with-stable-diffusion-xl | .md | 64_6 |
<Tip>
Replace the SDXL model with a model like [stable-diffusion-v1-5/stable-diffusion-v1-5](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) to use multiple conditioning inputs with Stable Diffusion models.
</Tip>
You can compose multiple ControlNet conditionings from different image inputs to create a *MultiControlNet*. To get better results, it is often helpful to:
1. mask conditionings such that they don't overlap (for example, mask the area of a canny image where the pose conditioning is located)
2. experiment with the [`controlnet_conditioning_scale`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/controlnet#diffusers.StableDiffusionControlNetPipeline.__call__.controlnet_conditioning_scale) parameter to determine how much weight to assign to each conditioning input
In this example, you'll combine a canny image and a human pose estimation image to generate a new image.
Prepare the canny image conditioning:
```py
from diffusers.utils import load_image, make_image_grid
from PIL import Image
import numpy as np
import cv2
original_image = load_image(
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/landscape.png"
)
image = np.array(original_image)
low_threshold = 100
high_threshold = 200
image = cv2.Canny(image, low_threshold, high_threshold)
# zero out middle columns of image where pose will be overlaid
zero_start = image.shape[1] // 4
zero_end = zero_start + image.shape[1] // 2
image[:, zero_start:zero_end] = 0
image = image[:, :, None]
image = np.concatenate([image, image, image], axis=2)
canny_image = Image.fromarray(image)
make_image_grid([original_image, canny_image], rows=1, cols=2)
```
<div class="flex gap-4">
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/landscape.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">original image</figcaption>
</div>
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/controlnet/landscape_canny_masked.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">canny image</figcaption>
</div>
</div>
For human pose estimation, install [controlnet_aux](https://github.com/patrickvonplaten/controlnet_aux):
```py
# uncomment to install the necessary library in Colab
#!pip install -q controlnet-aux
```
Prepare the human pose estimation conditioning:
```py
from controlnet_aux import OpenposeDetector
openpose = OpenposeDetector.from_pretrained("lllyasviel/ControlNet")
original_image = load_image(
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/person.png"
)
openpose_image = openpose(original_image)
make_image_grid([original_image, openpose_image], rows=1, cols=2)
```
<div class="flex gap-4">
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/person.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">original image</figcaption>
</div>
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/controlnet/person_pose.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">human pose image</figcaption>
</div>
</div>
Load a list of ControlNet models that correspond to each conditioning, and pass them to the [`StableDiffusionXLControlNetPipeline`]. Use the faster [`UniPCMultistepScheduler`] and enable model offloading to reduce memory usage.
```py
from diffusers import StableDiffusionXLControlNetPipeline, ControlNetModel, AutoencoderKL, UniPCMultistepScheduler
import torch
controlnets = [
ControlNetModel.from_pretrained(
"thibaud/controlnet-openpose-sdxl-1.0", torch_dtype=torch.float16
),
ControlNetModel.from_pretrained(
"diffusers/controlnet-canny-sdxl-1.0", torch_dtype=torch.float16, use_safetensors=True
),
]
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16, use_safetensors=True)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnets, vae=vae, torch_dtype=torch.float16, use_safetensors=True
)
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()
```
Now you can pass your prompt (an optional negative prompt if you're using one), canny image, and pose image to the pipeline:
```py
prompt = "a giant standing in a fantasy landscape, best quality"
negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
generator = torch.manual_seed(1)
images = [openpose_image.resize((1024, 1024)), canny_image.resize((1024, 1024))]
images = pipe(
prompt,
image=images,
num_inference_steps=25,
generator=generator,
negative_prompt=negative_prompt,
num_images_per_prompt=3,
controlnet_conditioning_scale=[1.0, 0.8],
).images
make_image_grid([original_image, canny_image, openpose_image,
images[0].resize((512, 512)), images[1].resize((512, 512)), images[2].resize((512, 512))], rows=2, cols=3)
```
<div class="flex justify-center">
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/multicontrolnet.png"/>
</div> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#multicontrolnet | #multicontrolnet | .md | 64_7 |
<!--Copyright 2024 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
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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.
--> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/diffedit.md | https://huggingface.co/docs/diffusers/en/using-diffusers/diffedit/ | .md | 65_0 |
|
[[open-in-colab]]
Image editing typically requires providing a mask of the area to be edited. DiffEdit automatically generates the mask for you based on a text query, making it easier overall to create a mask without image editing software. The DiffEdit algorithm works in three steps:
1. the diffusion model denoises an image conditioned on some query text and reference text which produces different noise estimates for different areas of the image; the difference is used to infer a mask to identify which area of the image needs to be changed to match the query text
2. the input image is encoded into latent space with DDIM
3. the latents are decoded with the diffusion model conditioned on the text query, using the mask as a guide such that pixels outside the mask remain the same as in the input image
This guide will show you how to use DiffEdit to edit images without manually creating a mask.
Before you begin, make sure you have the following libraries installed:
```py
# uncomment to install the necessary libraries in Colab
#!pip install -q diffusers transformers accelerate
```
The [`StableDiffusionDiffEditPipeline`] requires an image mask and a set of partially inverted latents. The image mask is generated from the [`~StableDiffusionDiffEditPipeline.generate_mask`] function, and includes two parameters, `source_prompt` and `target_prompt`. These parameters determine what to edit in the image. For example, if you want to change a bowl of *fruits* to a bowl of *pears*, then:
```py
source_prompt = "a bowl of fruits"
target_prompt = "a bowl of pears"
```
The partially inverted latents are generated from the [`~StableDiffusionDiffEditPipeline.invert`] function, and it is generally a good idea to include a `prompt` or *caption* describing the image to help guide the inverse latent sampling process. The caption can often be your `source_prompt`, but feel free to experiment with other text descriptions!
Let's load the pipeline, scheduler, inverse scheduler, and enable some optimizations to reduce memory usage:
```py
import torch
from diffusers import DDIMScheduler, DDIMInverseScheduler, StableDiffusionDiffEditPipeline
pipeline = StableDiffusionDiffEditPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-1",
torch_dtype=torch.float16,
safety_checker=None,
use_safetensors=True,
)
pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
pipeline.inverse_scheduler = DDIMInverseScheduler.from_config(pipeline.scheduler.config)
pipeline.enable_model_cpu_offload()
pipeline.enable_vae_slicing()
```
Load the image to edit:
```py
from diffusers.utils import load_image, make_image_grid
img_url = "https://github.com/Xiang-cd/DiffEdit-stable-diffusion/raw/main/assets/origin.png"
raw_image = load_image(img_url).resize((768, 768))
raw_image
```
Use the [`~StableDiffusionDiffEditPipeline.generate_mask`] function to generate the image mask. You'll need to pass it the `source_prompt` and `target_prompt` to specify what to edit in the image:
```py
from PIL import Image
source_prompt = "a bowl of fruits"
target_prompt = "a basket of pears"
mask_image = pipeline.generate_mask(
image=raw_image,
source_prompt=source_prompt,
target_prompt=target_prompt,
)
Image.fromarray((mask_image.squeeze()*255).astype("uint8"), "L").resize((768, 768))
```
Next, create the inverted latents and pass it a caption describing the image:
```py
inv_latents = pipeline.invert(prompt=source_prompt, image=raw_image).latents
```
Finally, pass the image mask and inverted latents to the pipeline. The `target_prompt` becomes the `prompt` now, and the `source_prompt` is used as the `negative_prompt`:
```py
output_image = pipeline(
prompt=target_prompt,
mask_image=mask_image,
image_latents=inv_latents,
negative_prompt=source_prompt,
).images[0]
mask_image = Image.fromarray((mask_image.squeeze()*255).astype("uint8"), "L").resize((768, 768))
make_image_grid([raw_image, mask_image, output_image], rows=1, cols=3)
```
<div class="flex gap-4">
<div>
<img class="rounded-xl" src="https://github.com/Xiang-cd/DiffEdit-stable-diffusion/raw/main/assets/origin.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">original image</figcaption>
</div>
<div>
<img class="rounded-xl" src="https://github.com/Xiang-cd/DiffEdit-stable-diffusion/blob/main/assets/target.png?raw=true"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">edited image</figcaption>
</div>
</div> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/diffedit.md | https://huggingface.co/docs/diffusers/en/using-diffusers/diffedit/#diffedit | #diffedit | .md | 65_1 |
The source and target embeddings can be automatically generated with the [Flan-T5](https://huggingface.co/docs/transformers/model_doc/flan-t5) model instead of creating them manually.
Load the Flan-T5 model and tokenizer from the 🤗 Transformers library:
```py
import torch
from transformers import AutoTokenizer, T5ForConditionalGeneration
tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-large")
model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-large", device_map="auto", torch_dtype=torch.float16)
```
Provide some initial text to prompt the model to generate the source and target prompts.
```py
source_concept = "bowl"
target_concept = "basket"
source_text = f"Provide a caption for images containing a {source_concept}. "
"The captions should be in English and should be no longer than 150 characters."
target_text = f"Provide a caption for images containing a {target_concept}. "
"The captions should be in English and should be no longer than 150 characters."
```
Next, create a utility function to generate the prompts:
```py
@torch.no_grad()
def generate_prompts(input_prompt):
input_ids = tokenizer(input_prompt, return_tensors="pt").input_ids.to("cuda")
outputs = model.generate(
input_ids, temperature=0.8, num_return_sequences=16, do_sample=True, max_new_tokens=128, top_k=10
)
return tokenizer.batch_decode(outputs, skip_special_tokens=True)
source_prompts = generate_prompts(source_text)
target_prompts = generate_prompts(target_text)
print(source_prompts)
print(target_prompts)
```
<Tip>
Check out the [generation strategy](https://huggingface.co/docs/transformers/main/en/generation_strategies) guide if you're interested in learning more about strategies for generating different quality text.
</Tip>
Load the text encoder model used by the [`StableDiffusionDiffEditPipeline`] to encode the text. You'll use the text encoder to compute the text embeddings:
```py
import torch
from diffusers import StableDiffusionDiffEditPipeline
pipeline = StableDiffusionDiffEditPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16, use_safetensors=True
)
pipeline.enable_model_cpu_offload()
pipeline.enable_vae_slicing()
@torch.no_grad()
def embed_prompts(sentences, tokenizer, text_encoder, device="cuda"):
embeddings = []
for sent in sentences:
text_inputs = tokenizer(
sent,
padding="max_length",
max_length=tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
prompt_embeds = text_encoder(text_input_ids.to(device), attention_mask=None)[0]
embeddings.append(prompt_embeds)
return torch.concatenate(embeddings, dim=0).mean(dim=0).unsqueeze(0)
source_embeds = embed_prompts(source_prompts, pipeline.tokenizer, pipeline.text_encoder)
target_embeds = embed_prompts(target_prompts, pipeline.tokenizer, pipeline.text_encoder)
```
Finally, pass the embeddings to the [`~StableDiffusionDiffEditPipeline.generate_mask`] and [`~StableDiffusionDiffEditPipeline.invert`] functions, and pipeline to generate the image:
```diff
from diffusers import DDIMInverseScheduler, DDIMScheduler
from diffusers.utils import load_image, make_image_grid
from PIL import Image
pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
pipeline.inverse_scheduler = DDIMInverseScheduler.from_config(pipeline.scheduler.config)
img_url = "https://github.com/Xiang-cd/DiffEdit-stable-diffusion/raw/main/assets/origin.png"
raw_image = load_image(img_url).resize((768, 768))
mask_image = pipeline.generate_mask(
image=raw_image,
- source_prompt=source_prompt,
- target_prompt=target_prompt,
+ source_prompt_embeds=source_embeds,
+ target_prompt_embeds=target_embeds,
)
inv_latents = pipeline.invert(
- prompt=source_prompt,
+ prompt_embeds=source_embeds,
image=raw_image,
).latents
output_image = pipeline(
mask_image=mask_image,
image_latents=inv_latents,
- prompt=target_prompt,
- negative_prompt=source_prompt,
+ prompt_embeds=target_embeds,
+ negative_prompt_embeds=source_embeds,
).images[0]
mask_image = Image.fromarray((mask_image.squeeze()*255).astype("uint8"), "L")
make_image_grid([raw_image, mask_image, output_image], rows=1, cols=3)
``` | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/diffedit.md | https://huggingface.co/docs/diffusers/en/using-diffusers/diffedit/#generate-source-and-target-embeddings | #generate-source-and-target-embeddings | .md | 65_2 |
While you can use the `source_prompt` as a caption to help generate the partially inverted latents, you can also use the [BLIP](https://huggingface.co/docs/transformers/model_doc/blip) model to automatically generate a caption.
Load the BLIP model and processor from the 🤗 Transformers library:
```py
import torch
from transformers import BlipForConditionalGeneration, BlipProcessor
processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base", torch_dtype=torch.float16, low_cpu_mem_usage=True)
```
Create a utility function to generate a caption from the input image:
```py
@torch.no_grad()
def generate_caption(images, caption_generator, caption_processor):
text = "a photograph of"
inputs = caption_processor(images, text, return_tensors="pt").to(device="cuda", dtype=caption_generator.dtype)
caption_generator.to("cuda")
outputs = caption_generator.generate(**inputs, max_new_tokens=128)
# offload caption generator
caption_generator.to("cpu")
caption = caption_processor.batch_decode(outputs, skip_special_tokens=True)[0]
return caption
```
Load an input image and generate a caption for it using the `generate_caption` function:
```py
from diffusers.utils import load_image
img_url = "https://github.com/Xiang-cd/DiffEdit-stable-diffusion/raw/main/assets/origin.png"
raw_image = load_image(img_url).resize((768, 768))
caption = generate_caption(raw_image, model, processor)
```
<div class="flex justify-center">
<figure>
<img class="rounded-xl" src="https://github.com/Xiang-cd/DiffEdit-stable-diffusion/raw/main/assets/origin.png"/>
<figcaption class="text-center">generated caption: "a photograph of a bowl of fruit on a table"</figcaption>
</figure>
</div>
Now you can drop the caption into the [`~StableDiffusionDiffEditPipeline.invert`] function to generate the partially inverted latents! | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/diffedit.md | https://huggingface.co/docs/diffusers/en/using-diffusers/diffedit/#generate-a-caption-for-inversion | #generate-a-caption-for-inversion | .md | 65_3 |
<!--Copyright 2024 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
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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.
--> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/overview_techniques.md | https://huggingface.co/docs/diffusers/en/using-diffusers/overview_techniques/ | .md | 66_0 |
|
The inference pipeline supports and enables a wide range of techniques that are divided into two categories:
* Pipeline functionality: these techniques modify the pipeline or extend it for other applications. For example, pipeline callbacks add new features to a pipeline and a pipeline can also be extended for distributed inference.
* Improve inference quality: these techniques increase the visual quality of the generated images. For example, you can enhance your prompts with GPT2 to create better images with lower effort. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/overview_techniques.md | https://huggingface.co/docs/diffusers/en/using-diffusers/overview_techniques/#overview | #overview | .md | 66_1 |
<!--Copyright 2024 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
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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.
--> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlling_generation.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlling_generation/ | .md | 67_0 |
|
Controlling outputs generated by diffusion models has been long pursued by the community and is now an active research topic. In many popular diffusion models, subtle changes in inputs, both images and text prompts, can drastically change outputs. In an ideal world we want to be able to control how semantics are preserved and changed.
Most examples of preserving semantics reduce to being able to accurately map a change in input to a change in output. I.e. adding an adjective to a subject in a prompt preserves the entire image, only modifying the changed subject. Or, image variation of a particular subject preserves the subject's pose.
Additionally, there are qualities of generated images that we would like to influence beyond semantic preservation. I.e. in general, we would like our outputs to be of good quality, adhere to a particular style, or be realistic.
We will document some of the techniques `diffusers` supports to control generation of diffusion models. Much is cutting edge research and can be quite nuanced. If something needs clarifying or you have a suggestion, don't hesitate to open a discussion on the [forum](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/63) or a [GitHub issue](https://github.com/huggingface/diffusers/issues).
We provide a high level explanation of how the generation can be controlled as well as a snippet of the technicals. For more in depth explanations on the technicals, the original papers which are linked from the pipelines are always the best resources.
Depending on the use case, one should choose a technique accordingly. In many cases, these techniques can be combined. For example, one can combine Textual Inversion with SEGA to provide more semantic guidance to the outputs generated using Textual Inversion.
Unless otherwise mentioned, these are techniques that work with existing models and don't require their own weights.
1. [InstructPix2Pix](#instruct-pix2pix)
2. [Pix2Pix Zero](#pix2pix-zero)
3. [Attend and Excite](#attend-and-excite)
4. [Semantic Guidance](#semantic-guidance-sega)
5. [Self-attention Guidance](#self-attention-guidance-sag)
6. [Depth2Image](#depth2image)
7. [MultiDiffusion Panorama](#multidiffusion-panorama)
8. [DreamBooth](#dreambooth)
9. [Textual Inversion](#textual-inversion)
10. [ControlNet](#controlnet)
11. [Prompt Weighting](#prompt-weighting)
12. [Custom Diffusion](#custom-diffusion)
13. [Model Editing](#model-editing)
14. [DiffEdit](#diffedit)
15. [T2I-Adapter](#t2i-adapter)
16. [FABRIC](#fabric)
For convenience, we provide a table to denote which methods are inference-only and which require fine-tuning/training.
| **Method** | **Inference only** | **Requires training /<br> fine-tuning** | **Comments** |
| :-------------------------------------------------: | :----------------: | :-------------------------------------: | :---------------------------------------------------------------------------------------------: |
| [InstructPix2Pix](#instruct-pix2pix) | ✅ | ❌ | Can additionally be<br>fine-tuned for better <br>performance on specific <br>edit instructions. |
| [Pix2Pix Zero](#pix2pix-zero) | ✅ | ❌ | |
| [Attend and Excite](#attend-and-excite) | ✅ | ❌ | |
| [Semantic Guidance](#semantic-guidance-sega) | ✅ | ❌ | |
| [Self-attention Guidance](#self-attention-guidance-sag) | ✅ | ❌ | |
| [Depth2Image](#depth2image) | ✅ | ❌ | |
| [MultiDiffusion Panorama](#multidiffusion-panorama) | ✅ | ❌ | |
| [DreamBooth](#dreambooth) | ❌ | ✅ | |
| [Textual Inversion](#textual-inversion) | ❌ | ✅ | |
| [ControlNet](#controlnet) | ✅ | ❌ | A ControlNet can be <br>trained/fine-tuned on<br>a custom conditioning. |
| [Prompt Weighting](#prompt-weighting) | ✅ | ❌ | |
| [Custom Diffusion](#custom-diffusion) | ❌ | ✅ | |
| [Model Editing](#model-editing) | ✅ | ❌ | |
| [DiffEdit](#diffedit) | ✅ | ❌ | |
| [T2I-Adapter](#t2i-adapter) | ✅ | ❌ | |
| [Fabric](#fabric) | ✅ | ❌ | | | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlling_generation.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlling_generation/#controlled-generation | #controlled-generation | .md | 67_1 |
[Paper](https://arxiv.org/abs/2211.09800)
[InstructPix2Pix](../api/pipelines/pix2pix) is fine-tuned from Stable Diffusion to support editing input images. It takes as inputs an image and a prompt describing an edit, and it outputs the edited image.
InstructPix2Pix has been explicitly trained to work well with [InstructGPT](https://openai.com/blog/instruction-following/)-like prompts. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlling_generation.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlling_generation/#instructpix2pix | #instructpix2pix | .md | 67_2 |
[Paper](https://arxiv.org/abs/2302.03027)
[Pix2Pix Zero](../api/pipelines/pix2pix_zero) allows modifying an image so that one concept or subject is translated to another one while preserving general image semantics.
The denoising process is guided from one conceptual embedding towards another conceptual embedding. The intermediate latents are optimized during the denoising process to push the attention maps towards reference attention maps. The reference attention maps are from the denoising process of the input image and are used to encourage semantic preservation.
Pix2Pix Zero can be used both to edit synthetic images as well as real images.
- To edit synthetic images, one first generates an image given a caption.
Next, we generate image captions for the concept that shall be edited and for the new target concept. We can use a model like [Flan-T5](https://huggingface.co/docs/transformers/model_doc/flan-t5) for this purpose. Then, "mean" prompt embeddings for both the source and target concepts are created via the text encoder. Finally, the pix2pix-zero algorithm is used to edit the synthetic image.
- To edit a real image, one first generates an image caption using a model like [BLIP](https://huggingface.co/docs/transformers/model_doc/blip). Then one applies DDIM inversion on the prompt and image to generate "inverse" latents. Similar to before, "mean" prompt embeddings for both source and target concepts are created and finally the pix2pix-zero algorithm in combination with the "inverse" latents is used to edit the image.
<Tip>
Pix2Pix Zero is the first model that allows "zero-shot" image editing. This means that the model
can edit an image in less than a minute on a consumer GPU as shown [here](../api/pipelines/pix2pix_zero#usage-example).
</Tip>
As mentioned above, Pix2Pix Zero includes optimizing the latents (and not any of the UNet, VAE, or the text encoder) to steer the generation toward a specific concept. This means that the overall
pipeline might require more memory than a standard [StableDiffusionPipeline](../api/pipelines/stable_diffusion/text2img).
<Tip>
An important distinction between methods like InstructPix2Pix and Pix2Pix Zero is that the former
involves fine-tuning the pre-trained weights while the latter does not. This means that you can
apply Pix2Pix Zero to any of the available Stable Diffusion models.
</Tip> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlling_generation.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlling_generation/#pix2pix-zero | #pix2pix-zero | .md | 67_3 |
[Paper](https://arxiv.org/abs/2301.13826)
[Attend and Excite](../api/pipelines/attend_and_excite) allows subjects in the prompt to be faithfully represented in the final image.
A set of token indices are given as input, corresponding to the subjects in the prompt that need to be present in the image. During denoising, each token index is guaranteed to have a minimum attention threshold for at least one patch of the image. The intermediate latents are iteratively optimized during the denoising process to strengthen the attention of the most neglected subject token until the attention threshold is passed for all subject tokens.
Like Pix2Pix Zero, Attend and Excite also involves a mini optimization loop (leaving the pre-trained weights untouched) in its pipeline and can require more memory than the usual [StableDiffusionPipeline](../api/pipelines/stable_diffusion/text2img). | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlling_generation.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlling_generation/#attend-and-excite | #attend-and-excite | .md | 67_4 |
[Paper](https://arxiv.org/abs/2301.12247)
[SEGA](../api/pipelines/semantic_stable_diffusion) allows applying or removing one or more concepts from an image. The strength of the concept can also be controlled. I.e. the smile concept can be used to incrementally increase or decrease the smile of a portrait.
Similar to how classifier free guidance provides guidance via empty prompt inputs, SEGA provides guidance on conceptual prompts. Multiple of these conceptual prompts can be applied simultaneously. Each conceptual prompt can either add or remove their concept depending on if the guidance is applied positively or negatively.
Unlike Pix2Pix Zero or Attend and Excite, SEGA directly interacts with the diffusion process instead of performing any explicit gradient-based optimization. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlling_generation.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlling_generation/#semantic-guidance-sega | #semantic-guidance-sega | .md | 67_5 |
[Paper](https://arxiv.org/abs/2210.00939)
[Self-attention Guidance](../api/pipelines/self_attention_guidance) improves the general quality of images.
SAG provides guidance from predictions not conditioned on high-frequency details to fully conditioned images. The high frequency details are extracted out of the UNet self-attention maps. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlling_generation.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlling_generation/#self-attention-guidance-sag | #self-attention-guidance-sag | .md | 67_6 |
[Project](https://huggingface.co/stabilityai/stable-diffusion-2-depth)
[Depth2Image](../api/pipelines/stable_diffusion/depth2img) is fine-tuned from Stable Diffusion to better preserve semantics for text guided image variation.
It conditions on a monocular depth estimate of the original image. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlling_generation.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlling_generation/#depth2image | #depth2image | .md | 67_7 |
[Paper](https://arxiv.org/abs/2302.08113)
[MultiDiffusion Panorama](../api/pipelines/panorama) defines a new generation process over a pre-trained diffusion model. This process binds together multiple diffusion generation methods that can be readily applied to generate high quality and diverse images. Results adhere to user-provided controls, such as desired aspect ratio (e.g., panorama), and spatial guiding signals, ranging from tight segmentation masks to bounding boxes.
MultiDiffusion Panorama allows to generate high-quality images at arbitrary aspect ratios (e.g., panoramas). | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlling_generation.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlling_generation/#multidiffusion-panorama | #multidiffusion-panorama | .md | 67_8 |
In addition to pre-trained models, Diffusers has training scripts for fine-tuning models on user-provided data. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlling_generation.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlling_generation/#fine-tuning-your-own-models | #fine-tuning-your-own-models | .md | 67_9 |
[Project](https://dreambooth.github.io/)
[DreamBooth](../training/dreambooth) fine-tunes a model to teach it about a new subject. I.e. a few pictures of a person can be used to generate images of that person in different styles. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlling_generation.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlling_generation/#dreambooth | #dreambooth | .md | 67_10 |
[Paper](https://arxiv.org/abs/2208.01618)
[Textual Inversion](../training/text_inversion) fine-tunes a model to teach it about a new concept. I.e. a few pictures of a style of artwork can be used to generate images in that style. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlling_generation.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlling_generation/#textual-inversion | #textual-inversion | .md | 67_11 |
[Paper](https://arxiv.org/abs/2302.05543)
[ControlNet](../api/pipelines/controlnet) is an auxiliary network which adds an extra condition.
There are 8 canonical pre-trained ControlNets trained on different conditionings such as edge detection, scribbles,
depth maps, and semantic segmentations. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlling_generation.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlling_generation/#controlnet | #controlnet | .md | 67_12 |
[Prompt weighting](../using-diffusers/weighted_prompts) is a simple technique that puts more attention weight on certain parts of the text
input. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlling_generation.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlling_generation/#prompt-weighting | #prompt-weighting | .md | 67_13 |
[Paper](https://arxiv.org/abs/2212.04488)
[Custom Diffusion](../training/custom_diffusion) only fine-tunes the cross-attention maps of a pre-trained
text-to-image diffusion model. It also allows for additionally performing Textual Inversion. It supports
multi-concept training by design. Like DreamBooth and Textual Inversion, Custom Diffusion is also used to
teach a pre-trained text-to-image diffusion model about new concepts to generate outputs involving the
concept(s) of interest. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlling_generation.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlling_generation/#custom-diffusion | #custom-diffusion | .md | 67_14 |
[Paper](https://arxiv.org/abs/2303.08084)
The [text-to-image model editing pipeline](../api/pipelines/model_editing) helps you mitigate some of the incorrect implicit assumptions a pre-trained text-to-image
diffusion model might make about the subjects present in the input prompt. For example, if you prompt Stable Diffusion to generate images for "A pack of roses", the roses in the generated images
are more likely to be red. This pipeline helps you change that assumption. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlling_generation.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlling_generation/#model-editing | #model-editing | .md | 67_15 |
[Paper](https://arxiv.org/abs/2210.11427)
[DiffEdit](../api/pipelines/diffedit) allows for semantic editing of input images along with
input prompts while preserving the original input images as much as possible. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlling_generation.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlling_generation/#diffedit | #diffedit | .md | 67_16 |
[Paper](https://arxiv.org/abs/2302.08453)
[T2I-Adapter](../api/pipelines/stable_diffusion/adapter) is an auxiliary network which adds an extra condition.
There are 8 canonical pre-trained adapters trained on different conditionings such as edge detection, sketch,
depth maps, and semantic segmentations. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlling_generation.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlling_generation/#t2i-adapter | #t2i-adapter | .md | 67_17 |
[Paper](https://arxiv.org/abs/2307.10159)
[Fabric](https://github.com/huggingface/diffusers/tree/442017ccc877279bcf24fbe92f92d3d0def191b6/examples/community#stable-diffusion-fabric-pipeline) is a training-free
approach applicable to a wide range of popular diffusion models, which exploits
the self-attention layer present in the most widely used architectures to condition
the diffusion process on a set of feedback images. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlling_generation.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlling_generation/#fabric | #fabric | .md | 67_18 |
<!--Copyright 2024 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
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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.
--> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/write_own_pipeline.md | https://huggingface.co/docs/diffusers/en/using-diffusers/write_own_pipeline/ | .md | 68_0 |
|
[[open-in-colab]]
🧨 Diffusers is designed to be a user-friendly and flexible toolbox for building diffusion systems tailored to your use-case. At the core of the toolbox are models and schedulers. While the [`DiffusionPipeline`] bundles these components together for convenience, you can also unbundle the pipeline and use the models and schedulers separately to create new diffusion systems.
In this tutorial, you'll learn how to use models and schedulers to assemble a diffusion system for inference, starting with a basic pipeline and then progressing to the Stable Diffusion pipeline. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/write_own_pipeline.md | https://huggingface.co/docs/diffusers/en/using-diffusers/write_own_pipeline/#understanding-pipelines-models-and-schedulers | #understanding-pipelines-models-and-schedulers | .md | 68_1 |
A pipeline is a quick and easy way to run a model for inference, requiring no more than four lines of code to generate an image:
```py
>>> from diffusers import DDPMPipeline
>>> ddpm = DDPMPipeline.from_pretrained("google/ddpm-cat-256", use_safetensors=True).to("cuda")
>>> image = ddpm(num_inference_steps=25).images[0]
>>> image
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/ddpm-cat.png" alt="Image of cat created from DDPMPipeline"/>
</div>
That was super easy, but how did the pipeline do that? Let's breakdown the pipeline and take a look at what's happening under the hood.
In the example above, the pipeline contains a [`UNet2DModel`] model and a [`DDPMScheduler`]. The pipeline denoises an image by taking random noise the size of the desired output and passing it through the model several times. At each timestep, the model predicts the *noise residual* and the scheduler uses it to predict a less noisy image. The pipeline repeats this process until it reaches the end of the specified number of inference steps.
To recreate the pipeline with the model and scheduler separately, let's write our own denoising process.
1. Load the model and scheduler:
```py
>>> from diffusers import DDPMScheduler, UNet2DModel
>>> scheduler = DDPMScheduler.from_pretrained("google/ddpm-cat-256")
>>> model = UNet2DModel.from_pretrained("google/ddpm-cat-256", use_safetensors=True).to("cuda")
```
2. Set the number of timesteps to run the denoising process for:
```py
>>> scheduler.set_timesteps(50)
```
3. Setting the scheduler timesteps creates a tensor with evenly spaced elements in it, 50 in this example. Each element corresponds to a timestep at which the model denoises an image. When you create the denoising loop later, you'll iterate over this tensor to denoise an image:
```py
>>> scheduler.timesteps
tensor([980, 960, 940, 920, 900, 880, 860, 840, 820, 800, 780, 760, 740, 720,
700, 680, 660, 640, 620, 600, 580, 560, 540, 520, 500, 480, 460, 440,
420, 400, 380, 360, 340, 320, 300, 280, 260, 240, 220, 200, 180, 160,
140, 120, 100, 80, 60, 40, 20, 0])
```
4. Create some random noise with the same shape as the desired output:
```py
>>> import torch
>>> sample_size = model.config.sample_size
>>> noise = torch.randn((1, 3, sample_size, sample_size), device="cuda")
```
5. Now write a loop to iterate over the timesteps. At each timestep, the model does a [`UNet2DModel.forward`] pass and returns the noisy residual. The scheduler's [`~DDPMScheduler.step`] method takes the noisy residual, timestep, and input and it predicts the image at the previous timestep. This output becomes the next input to the model in the denoising loop, and it'll repeat until it reaches the end of the `timesteps` array.
```py
>>> input = noise
>>> for t in scheduler.timesteps:
... with torch.no_grad():
... noisy_residual = model(input, t).sample
... previous_noisy_sample = scheduler.step(noisy_residual, t, input).prev_sample
... input = previous_noisy_sample
```
This is the entire denoising process, and you can use this same pattern to write any diffusion system.
6. The last step is to convert the denoised output into an image:
```py
>>> from PIL import Image
>>> import numpy as np
>>> image = (input / 2 + 0.5).clamp(0, 1).squeeze()
>>> image = (image.permute(1, 2, 0) * 255).round().to(torch.uint8).cpu().numpy()
>>> image = Image.fromarray(image)
>>> image
```
In the next section, you'll put your skills to the test and breakdown the more complex Stable Diffusion pipeline. The steps are more or less the same. You'll initialize the necessary components, and set the number of timesteps to create a `timestep` array. The `timestep` array is used in the denoising loop, and for each element in this array, the model predicts a less noisy image. The denoising loop iterates over the `timestep`'s, and at each timestep, it outputs a noisy residual and the scheduler uses it to predict a less noisy image at the previous timestep. This process is repeated until you reach the end of the `timestep` array.
Let's try it out! | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/write_own_pipeline.md | https://huggingface.co/docs/diffusers/en/using-diffusers/write_own_pipeline/#deconstruct-a-basic-pipeline | #deconstruct-a-basic-pipeline | .md | 68_2 |
Stable Diffusion is a text-to-image *latent diffusion* model. It is called a latent diffusion model because it works with a lower-dimensional representation of the image instead of the actual pixel space, which makes it more memory efficient. The encoder compresses the image into a smaller representation, and a decoder to convert the compressed representation back into an image. For text-to-image models, you'll need a tokenizer and an encoder to generate text embeddings. From the previous example, you already know you need a UNet model and a scheduler.
As you can see, this is already more complex than the DDPM pipeline which only contains a UNet model. The Stable Diffusion model has three separate pretrained models.
<Tip>
💡 Read the [How does Stable Diffusion work?](https://huggingface.co/blog/stable_diffusion#how-does-stable-diffusion-work) blog for more details about how the VAE, UNet, and text encoder models work.
</Tip>
Now that you know what you need for the Stable Diffusion pipeline, load all these components with the [`~ModelMixin.from_pretrained`] method. You can find them in the pretrained [`stable-diffusion-v1-5/stable-diffusion-v1-5`](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) checkpoint, and each component is stored in a separate subfolder:
```py
>>> from PIL import Image
>>> import torch
>>> from transformers import CLIPTextModel, CLIPTokenizer
>>> from diffusers import AutoencoderKL, UNet2DConditionModel, PNDMScheduler
>>> vae = AutoencoderKL.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="vae", use_safetensors=True)
>>> tokenizer = CLIPTokenizer.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="tokenizer")
>>> text_encoder = CLIPTextModel.from_pretrained(
... "CompVis/stable-diffusion-v1-4", subfolder="text_encoder", use_safetensors=True
... )
>>> unet = UNet2DConditionModel.from_pretrained(
... "CompVis/stable-diffusion-v1-4", subfolder="unet", use_safetensors=True
... )
```
Instead of the default [`PNDMScheduler`], exchange it for the [`UniPCMultistepScheduler`] to see how easy it is to plug a different scheduler in:
```py
>>> from diffusers import UniPCMultistepScheduler
>>> scheduler = UniPCMultistepScheduler.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="scheduler")
```
To speed up inference, move the models to a GPU since, unlike the scheduler, they have trainable weights:
```py
>>> torch_device = "cuda"
>>> vae.to(torch_device)
>>> text_encoder.to(torch_device)
>>> unet.to(torch_device)
``` | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/write_own_pipeline.md | https://huggingface.co/docs/diffusers/en/using-diffusers/write_own_pipeline/#deconstruct-the-stable-diffusion-pipeline | #deconstruct-the-stable-diffusion-pipeline | .md | 68_3 |
The next step is to tokenize the text to generate embeddings. The text is used to condition the UNet model and steer the diffusion process towards something that resembles the input prompt.
<Tip>
💡 The `guidance_scale` parameter determines how much weight should be given to the prompt when generating an image.
</Tip>
Feel free to choose any prompt you like if you want to generate something else!
```py
>>> prompt = ["a photograph of an astronaut riding a horse"]
>>> height = 512 # default height of Stable Diffusion
>>> width = 512 # default width of Stable Diffusion
>>> num_inference_steps = 25 # Number of denoising steps
>>> guidance_scale = 7.5 # Scale for classifier-free guidance
>>> generator = torch.manual_seed(0) # Seed generator to create the initial latent noise
>>> batch_size = len(prompt)
```
Tokenize the text and generate the embeddings from the prompt:
```py
>>> text_input = tokenizer(
... prompt, padding="max_length", max_length=tokenizer.model_max_length, truncation=True, return_tensors="pt"
... )
>>> with torch.no_grad():
... text_embeddings = text_encoder(text_input.input_ids.to(torch_device))[0]
```
You'll also need to generate the *unconditional text embeddings* which are the embeddings for the padding token. These need to have the same shape (`batch_size` and `seq_length`) as the conditional `text_embeddings`:
```py
>>> max_length = text_input.input_ids.shape[-1]
>>> uncond_input = tokenizer([""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt")
>>> uncond_embeddings = text_encoder(uncond_input.input_ids.to(torch_device))[0]
```
Let's concatenate the conditional and unconditional embeddings into a batch to avoid doing two forward passes:
```py
>>> text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
``` | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/write_own_pipeline.md | https://huggingface.co/docs/diffusers/en/using-diffusers/write_own_pipeline/#create-text-embeddings | #create-text-embeddings | .md | 68_4 |
Next, generate some initial random noise as a starting point for the diffusion process. This is the latent representation of the image, and it'll be gradually denoised. At this point, the `latent` image is smaller than the final image size but that's okay though because the model will transform it into the final 512x512 image dimensions later.
<Tip>
💡 The height and width are divided by 8 because the `vae` model has 3 down-sampling layers. You can check by running the following:
```py
2 ** (len(vae.config.block_out_channels) - 1) == 8
```
</Tip>
```py
>>> latents = torch.randn(
... (batch_size, unet.config.in_channels, height // 8, width // 8),
... generator=generator,
... device=torch_device,
... )
``` | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/write_own_pipeline.md | https://huggingface.co/docs/diffusers/en/using-diffusers/write_own_pipeline/#create-random-noise | #create-random-noise | .md | 68_5 |
Start by scaling the input with the initial noise distribution, *sigma*, the noise scale value, which is required for improved schedulers like [`UniPCMultistepScheduler`]:
```py
>>> latents = latents * scheduler.init_noise_sigma
```
The last step is to create the denoising loop that'll progressively transform the pure noise in `latents` to an image described by your prompt. Remember, the denoising loop needs to do three things:
1. Set the scheduler's timesteps to use during denoising.
2. Iterate over the timesteps.
3. At each timestep, call the UNet model to predict the noise residual and pass it to the scheduler to compute the previous noisy sample.
```py
>>> from tqdm.auto import tqdm
>>> scheduler.set_timesteps(num_inference_steps)
>>> for t in tqdm(scheduler.timesteps):
... # expand the latents if we are doing classifier-free guidance to avoid doing two forward passes.
... latent_model_input = torch.cat([latents] * 2)
... latent_model_input = scheduler.scale_model_input(latent_model_input, timestep=t)
... # predict the noise residual
... with torch.no_grad():
... noise_pred = unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
... # perform guidance
... noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
... noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
... # compute the previous noisy sample x_t -> x_t-1
... latents = scheduler.step(noise_pred, t, latents).prev_sample
``` | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/write_own_pipeline.md | https://huggingface.co/docs/diffusers/en/using-diffusers/write_own_pipeline/#denoise-the-image | #denoise-the-image | .md | 68_6 |
The final step is to use the `vae` to decode the latent representation into an image and get the decoded output with `sample`:
```py
# scale and decode the image latents with vae
latents = 1 / 0.18215 * latents
with torch.no_grad():
image = vae.decode(latents).sample
```
Lastly, convert the image to a `PIL.Image` to see your generated image!
```py
>>> image = (image / 2 + 0.5).clamp(0, 1).squeeze()
>>> image = (image.permute(1, 2, 0) * 255).to(torch.uint8).cpu().numpy()
>>> image = Image.fromarray(image)
>>> image
```
<div class="flex justify-center">
<img src="https://huggingface.co/blog/assets/98_stable_diffusion/stable_diffusion_k_lms.png"/>
</div> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/write_own_pipeline.md | https://huggingface.co/docs/diffusers/en/using-diffusers/write_own_pipeline/#decode-the-image | #decode-the-image | .md | 68_7 |
From basic to complex pipelines, you've seen that all you really need to write your own diffusion system is a denoising loop. The loop should set the scheduler's timesteps, iterate over them, and alternate between calling the UNet model to predict the noise residual and passing it to the scheduler to compute the previous noisy sample.
This is really what 🧨 Diffusers is designed for: to make it intuitive and easy to write your own diffusion system using models and schedulers.
For your next steps, feel free to:
* Learn how to [build and contribute a pipeline](../using-diffusers/contribute_pipeline) to 🧨 Diffusers. We can't wait and see what you'll come up with!
* Explore [existing pipelines](../api/pipelines/overview) in the library, and see if you can deconstruct and build a pipeline from scratch using the models and schedulers separately. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/write_own_pipeline.md | https://huggingface.co/docs/diffusers/en/using-diffusers/write_own_pipeline/#next-steps | #next-steps | .md | 68_8 |
<!--Copyright 2024 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
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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.
--> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/consisid.md | https://huggingface.co/docs/diffusers/en/using-diffusers/consisid/ | .md | 69_0 |
|
[ConsisID](https://github.com/PKU-YuanGroup/ConsisID) is an identity-preserving text-to-video generation model that keeps the face consistent in the generated video by frequency decomposition. The main features of ConsisID are:
- Frequency decomposition: The characteristics of the DiT architecture are analyzed from the frequency domain perspective, and based on these characteristics, a reasonable control information injection method is designed.
- Consistency training strategy: A coarse-to-fine training strategy, dynamic masking loss, and dynamic cross-face loss further enhance the model's generalization ability and identity preservation performance.
- Inference without finetuning: Previous methods required case-by-case finetuning of the input ID before inference, leading to significant time and computational costs. In contrast, ConsisID is tuning-free.
This guide will walk you through using ConsisID for use cases. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/consisid.md | https://huggingface.co/docs/diffusers/en/using-diffusers/consisid/#consisid | #consisid | .md | 69_1 |
Model weights may be stored in separate subfolders on the Hub or locally, in which case, you should use the [`~DiffusionPipeline.from_pretrained`] method.
```python
# !pip install consisid_eva_clip insightface facexlib
import torch
from diffusers import ConsisIDPipeline
from diffusers.pipelines.consisid.consisid_utils import prepare_face_models, process_face_embeddings_infer
from huggingface_hub import snapshot_download
# Download ckpts
snapshot_download(repo_id="BestWishYsh/ConsisID-preview", local_dir="BestWishYsh/ConsisID-preview")
# Load face helper model to preprocess input face image
face_helper_1, face_helper_2, face_clip_model, face_main_model, eva_transform_mean, eva_transform_std = prepare_face_models("BestWishYsh/ConsisID-preview", device="cuda", dtype=torch.bfloat16)
# Load consisid base model
pipe = ConsisIDPipeline.from_pretrained("BestWishYsh/ConsisID-preview", torch_dtype=torch.bfloat16)
pipe.to("cuda")
``` | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/consisid.md | https://huggingface.co/docs/diffusers/en/using-diffusers/consisid/#load-model-checkpoints | #load-model-checkpoints | .md | 69_2 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.