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[docs] ControlNet guide (#4640) 

* first draft

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docs/source/en/_toctree.yml
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title: Overview title: Overview
- local: using-diffusers/sdxl - local: using-diffusers/sdxl
title: Stable Diffusion XL title: Stable Diffusion XL
- local: using-diffusers/controlnet
title: ControlNet
- local: using-diffusers/distilled_sd - local: using-diffusers/distilled_sd
title: Distilled Stable Diffusion inference title: Distilled Stable Diffusion inference
- local: using-diffusers/reproducibility - local: using-diffusers/reproducibility
......
docs/source/en/api/pipelines/controlnet.md
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# ControlNet # ControlNet
[Adding Conditional Control to Text-to-Image Diffusion Models](https://huggingface.co/papers/2302.05543) by Lvmin Zhang and Maneesh Agrawala. ControlNet was introduced in [Adding Conditional Control to Text-to-Image Diffusion Models](https://huggingface.co/papers/2302.05543) by Lvmin Zhang and Maneesh Agrawala.
Using a pretrained model, we can provide control images (for example, a depth map) to control Stable Diffusion text-to-image generation so that it follows the structure of the depth image and fills in the details. With a ControlNet model, you can provide an additional control image to condition and control Stable Diffusion generation. For example, if you provide a depth map, the ControlNet model generates an image that'll preserve the spatial information from the depth map. It is a more flexible and accurate way to control the image generation process.
The abstract from the paper is: The abstract from the paper is:
...@@ -22,290 +22,13 @@ The abstract from the paper is: ...@@ -22,290 +22,13 @@ The abstract from the paper is:
This model was contributed by [takuma104](https://huggingface.co/takuma104). ❤️ This model was contributed by [takuma104](https://huggingface.co/takuma104). ❤️
The original codebase can be found at [lllyasviel/ControlNet](https://github.com/lllyasviel/ControlNet). The original codebase can be found at [lllyasviel/ControlNet](https://github.com/lllyasviel/ControlNet), and you can find official ControlNet checkpoints on [lllyasviel's](https://huggingface.co/lllyasviel) Hub profile.
## Usage example <Tip>
In the following we give a simple example of how to use a *ControlNet* checkpoint with Diffusers for inference. Make sure to check out the Schedulers [guide](/using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](/using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.
The inference pipeline is the same for all pipelines:
* 1. Take an image and run it through a pre-conditioning processor. </Tip>
* 2. Run the pre-processed image through the [`StableDiffusionControlNetPipeline`].
Let's have a look at a simple example using the [Canny Edge ControlNet](https://huggingface.co/lllyasviel/sd-controlnet-canny).
```python
from diffusers import StableDiffusionControlNetPipeline
from diffusers.utils import load_image
# Let's load the popular vermeer image
image = load_image(
"https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png"
)
```
![img](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png)
Next, we process the image to get the canny image. This is step *1.* - running the pre-conditioning processor. The pre-conditioning processor is different for every ControlNet. Please see the model cards of the [official checkpoints](#controlnet-with-stable-diffusion-1.5) for more information about other models.
First, we need to install opencv:
```
pip install opencv-contrib-python
```
Next, let's also install all required Hugging Face libraries:
```
pip install diffusers transformers git+https://github.com/huggingface/accelerate.git
```
Then we can retrieve the canny edges of the image.
```python
import cv2
from PIL import Image
import numpy as np
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)
```
Let's take a look at the processed image.
![img](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/vermeer_canny_edged.png)
Now, we load the official [Stable Diffusion 1.5 Model](runwayml/stable-diffusion-v1-5) as well as the ControlNet for canny edges.
```py
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
import torch
controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
)
```
To speed-up things and reduce memory, let's enable model offloading and use the fast [`UniPCMultistepScheduler`].
```py
from diffusers import UniPCMultistepScheduler
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
# this command loads the individual model components on GPU on-demand.
pipe.enable_model_cpu_offload()
```
Finally, we can run the pipeline:
```py
generator = torch.manual_seed(0)
out_image = pipe(
"disco dancer with colorful lights", num_inference_steps=20, generator=generator, image=canny_image
).images[0]
```
This should take only around 3-4 seconds on GPU (depending on hardware). The output image then looks as follows:
![img](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/vermeer_disco_dancing.png)
**Note**: To see how to run all other ControlNet checkpoints, please have a look at [ControlNet with Stable Diffusion 1.5](#controlnet-with-stable-diffusion-1.5).
<!-- TODO: add space -->
## Combining multiple conditionings
Multiple ControlNet conditionings can be combined for a single image generation. Pass a list of ControlNets to the pipeline's constructor and a corresponding list of conditionings to `__call__`.
When combining conditionings, it is helpful to mask conditionings such that they do not overlap. In the example, we mask the middle of the canny map where the pose conditioning is located.
It can also be helpful to vary the `controlnet_conditioning_scales` to emphasize one conditioning over the other.
### Canny conditioning
The original image:
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/landscape.png"/>
Prepare the conditioning:
```python
from diffusers.utils import load_image
from PIL import Image
import cv2
import numpy as np
from diffusers.utils import load_image
canny_image = load_image(
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/landscape.png"
)
canny_image = np.array(canny_image)
low_threshold = 100
high_threshold = 200
canny_image = cv2.Canny(canny_image, low_threshold, high_threshold)
# zero out middle columns of image where pose will be overlayed
zero_start = canny_image.shape[1] // 4
zero_end = zero_start + canny_image.shape[1] // 2
canny_image[:, zero_start:zero_end] = 0
canny_image = canny_image[:, :, None]
canny_image = np.concatenate([canny_image, canny_image, canny_image], axis=2)
canny_image = Image.fromarray(canny_image)
```
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/controlnet/landscape_canny_masked.png"/>
### Openpose conditioning
The original image:
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/person.png" width=600/>
Prepare the conditioning:
```python
from controlnet_aux import OpenposeDetector
from diffusers.utils import load_image
openpose = OpenposeDetector.from_pretrained("lllyasviel/ControlNet")
openpose_image = load_image(
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/person.png"
)
openpose_image = openpose(openpose_image)
```
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/controlnet/person_pose.png" width=600/>
### Running ControlNet with multiple conditionings
```python
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
import torch
controlnet = [
ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-openpose", torch_dtype=torch.float16),
ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16),
]
pipe = StableDiffusionControlNetPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
)
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
pipe.enable_xformers_memory_efficient_attention()
pipe.enable_model_cpu_offload()
prompt = "a giant standing in a fantasy landscape, best quality"
negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
generator = torch.Generator(device="cpu").manual_seed(1)
images = [openpose_image, canny_image]
image = pipe(
prompt,
images,
num_inference_steps=20,
generator=generator,
negative_prompt=negative_prompt,
controlnet_conditioning_scale=[1.0, 0.8],
).images[0]
image.save("./multi_controlnet_output.png")
```
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/controlnet/multi_controlnet_output.png" width=600/>
### Guess Mode
Guess Mode is [a ControlNet feature that was implemented](https://github.com/lllyasviel/ControlNet#guess-mode--non-prompt-mode) after the publication of [the paper](https://arxiv.org/abs/2302.05543). The description states:
>In this mode, the ControlNet encoder will try best to recognize the content of the input control map, like depth map, edge map, scribbles, etc, even if you remove all prompts.
#### The core implementation:
It adjusts the scale of the output residuals from ControlNet by a fixed ratio depending on the block depth. The shallowest DownBlock corresponds to `0.1`. As the blocks get deeper, the scale increases exponentially, and the scale for the output of the MidBlock becomes `1.0`.
Since the core implementation is just this, **it does not have any impact on prompt conditioning**. While it is common to use it without specifying any prompts, it is also possible to provide prompts if desired.
#### Usage:
Just specify `guess_mode=True` in the pipe() function. A `guidance_scale` between 3.0 and 5.0 is [recommended](https://github.com/lllyasviel/ControlNet#guess-mode--non-prompt-mode).
```py
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
import torch
controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny")
pipe = StableDiffusionControlNetPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", controlnet=controlnet).to(
"cuda"
)
image = pipe("", image=canny_image, guess_mode=True, guidance_scale=3.0).images[0]
image.save("guess_mode_generated.png")
```
#### Output image comparison:
Canny Control Example
|no guess_mode with prompt|guess_mode without prompt|
|---|---|
|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare_guess_mode/output_images/diffusers/output_bird_canny_0.png"><img width="128" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare_guess_mode/output_images/diffusers/output_bird_canny_0.png"/></a>|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare_guess_mode/output_images/diffusers/output_bird_canny_0_gm.png"><img width="128" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare_guess_mode/output_images/diffusers/output_bird_canny_0_gm.png"/></a>|
## Available checkpoints
ControlNet requires a *control image* in addition to the text-to-image *prompt*.
Each pretrained model is trained using a different conditioning method that requires different images for conditioning the generated outputs. For example, Canny edge conditioning requires the control image to be the output of a Canny filter, while depth conditioning requires the control image to be a depth map. See the overview and image examples below to know more.
All checkpoints can be found under the authors' namespace [lllyasviel](https://huggingface.co/lllyasviel).
**13.04.2024 Update**: The author has released improved controlnet checkpoints v1.1 - see [here](#controlnet-v1.1).
### ControlNet v1.0
| Model Name | Control Image Overview| Control Image Example | Generated Image Example |
|---|---|---|---|
|[lllyasviel/sd-controlnet-canny](https://huggingface.co/lllyasviel/sd-controlnet-canny)<br/> *Trained with canny edge detection* | A monochrome image with white edges on a black background.|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_bird_canny.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_bird_canny.png"/></a>|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_bird_canny_1.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_bird_canny_1.png"/></a>|
|[lllyasviel/sd-controlnet-depth](https://huggingface.co/lllyasviel/sd-controlnet-depth)<br/> *Trained with Midas depth estimation* |A grayscale image with black representing deep areas and white representing shallow areas.|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_vermeer_depth.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_vermeer_depth.png"/></a>|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_vermeer_depth_2.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_vermeer_depth_2.png"/></a>|
|[lllyasviel/sd-controlnet-hed](https://huggingface.co/lllyasviel/sd-controlnet-hed)<br/> *Trained with HED edge detection (soft edge)* |A monochrome image with white soft edges on a black background.|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_bird_hed.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_bird_hed.png"/></a>|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_bird_hed_1.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_bird_hed_1.png"/></a> |
|[lllyasviel/sd-controlnet-mlsd](https://huggingface.co/lllyasviel/sd-controlnet-mlsd)<br/> *Trained with M-LSD line detection* |A monochrome image composed only of white straight lines on a black background.|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_room_mlsd.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_room_mlsd.png"/></a>|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_room_mlsd_0.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_room_mlsd_0.png"/></a>|
|[lllyasviel/sd-controlnet-normal](https://huggingface.co/lllyasviel/sd-controlnet-normal)<br/> *Trained with normal map* |A [normal mapped](https://en.wikipedia.org/wiki/Normal_mapping) image.|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_human_normal.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_human_normal.png"/></a>|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_human_normal_1.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_human_normal_1.png"/></a>|
|[lllyasviel/sd-controlnet-openpose](https://huggingface.co/lllyasviel/sd-controlnet_openpose)<br/> *Trained with OpenPose bone image* |A [OpenPose bone](https://github.com/CMU-Perceptual-Computing-Lab/openpose) image.|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_human_openpose.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_human_openpose.png"/></a>|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_human_openpose_0.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_human_openpose_0.png"/></a>|
|[lllyasviel/sd-controlnet-scribble](https://huggingface.co/lllyasviel/sd-controlnet_scribble)<br/> *Trained with human scribbles* |A hand-drawn monochrome image with white outlines on a black background.|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_vermeer_scribble.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_vermeer_scribble.png"/></a>|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_vermeer_scribble_0.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_vermeer_scribble_0.png"/></a> |
|[lllyasviel/sd-controlnet-seg](https://huggingface.co/lllyasviel/sd-controlnet_seg)<br/>*Trained with semantic segmentation* |An [ADE20K](https://groups.csail.mit.edu/vision/datasets/ADE20K/)'s segmentation protocol image.|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_room_seg.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_room_seg.png"/></a>|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_room_seg_1.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_room_seg_1.png"/></a> |
### ControlNet v1.1
| Model Name | Control Image Overview| Condition Image | Control Image Example | Generated Image Example |
|---|---|---|---|---|
|[lllyasviel/control_v11p_sd15_canny](https://huggingface.co/lllyasviel/control_v11p_sd15_canny)<br/> | *Trained with canny edge detection* | A monochrome image with white edges on a black background.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_canny/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_canny/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_canny/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_canny/resolve/main/images/image_out.png"/></a>|
|[lllyasviel/control_v11e_sd15_ip2p](https://huggingface.co/lllyasviel/control_v11e_sd15_ip2p)<br/> | *Trained with pixel to pixel instruction* | No condition .|<a href="https://huggingface.co/lllyasviel/control_v11e_sd15_ip2p/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11e_sd15_ip2p/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11e_sd15_ip2p/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11e_sd15_ip2p/resolve/main/images/image_out.png"/></a>|
|[lllyasviel/control_v11p_sd15_inpaint](https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint)<br/> | Trained with image inpainting | No condition.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint/resolve/main/images/output.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint/resolve/main/images/output.png"/></a>|
|[lllyasviel/control_v11p_sd15_mlsd](https://huggingface.co/lllyasviel/control_v11p_sd15_mlsd)<br/> | Trained with multi-level line segment detection | An image with annotated line segments.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_mlsd/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_mlsd/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_mlsd/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_mlsd/resolve/main/images/image_out.png"/></a>|
|[lllyasviel/control_v11f1p_sd15_depth](https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth)<br/> | Trained with depth estimation | An image with depth information, usually represented as a grayscale image.|<a href="https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth/resolve/main/images/image_out.png"/></a>|
|[lllyasviel/control_v11p_sd15_normalbae](https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae)<br/> | Trained with surface normal estimation | An image with surface normal information, usually represented as a color-coded image.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae/resolve/main/images/image_out.png"/></a>|
|[lllyasviel/control_v11p_sd15_seg](https://huggingface.co/lllyasviel/control_v11p_sd15_seg)<br/> | Trained with image segmentation | An image with segmented regions, usually represented as a color-coded image.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_seg/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_seg/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_seg/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_seg/resolve/main/images/image_out.png"/></a>|
|[lllyasviel/control_v11p_sd15_lineart](https://huggingface.co/lllyasviel/control_v11p_sd15_lineart)<br/> | Trained with line art generation | An image with line art, usually black lines on a white background.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_lineart/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_lineart/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_lineart/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_lineart/resolve/main/images/image_out.png"/></a>|
|[lllyasviel/control_v11p_sd15s2_lineart_anime](https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime)<br/> | Trained with anime line art generation | An image with anime-style line art.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime/resolve/main/images/image_out.png"/></a>|
|[lllyasviel/control_v11p_sd15_openpose](https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime)<br/> | Trained with human pose estimation | An image with human poses, usually represented as a set of keypoints or skeletons.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_openpose/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_openpose/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_openpose/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_openpose/resolve/main/images/image_out.png"/></a>|
|[lllyasviel/control_v11p_sd15_scribble](https://huggingface.co/lllyasviel/control_v11p_sd15_scribble)<br/> | Trained with scribble-based image generation | An image with scribbles, usually random or user-drawn strokes.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_scribble/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_scribble/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_scribble/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_scribble/resolve/main/images/image_out.png"/></a>|
|[lllyasviel/control_v11p_sd15_softedge](https://huggingface.co/lllyasviel/control_v11p_sd15_softedge)<br/> | Trained with soft edge image generation | An image with soft edges, usually to create a more painterly or artistic effect.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_softedge/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_softedge/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_softedge/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_softedge/resolve/main/images/image_out.png"/></a>|
|[lllyasviel/control_v11e_sd15_shuffle](https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle)<br/> | Trained with image shuffling | An image with shuffled patches or regions.|<a href="https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle/resolve/main/images/image_out.png"/></a>|
|[lllyasviel/control_v11f1e_sd15_tile](https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile)<br/> | Trained with image tiling | A blurry image or part of an image .|<a href="https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile/resolve/main/images/original.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile/resolve/main/images/original.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile/resolve/main/images/output.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile/resolve/main/images/output.png"/></a>|
## StableDiffusionControlNetPipeline ## StableDiffusionControlNetPipeline
[[autodoc]] StableDiffusionControlNetPipeline [[autodoc]] StableDiffusionControlNetPipeline
...@@ -343,8 +66,15 @@ All checkpoints can be found under the authors' namespace [lllyasviel](https://h ...@@ -343,8 +66,15 @@ All checkpoints can be found under the authors' namespace [lllyasviel](https://h
- disable_xformers_memory_efficient_attention - disable_xformers_memory_efficient_attention
- load_textual_inversion - load_textual_inversion
## StableDiffusionPipelineOutput
[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput
## FlaxStableDiffusionControlNetPipeline ## FlaxStableDiffusionControlNetPipeline
[[autodoc]] FlaxStableDiffusionControlNetPipeline [[autodoc]] FlaxStableDiffusionControlNetPipeline
- all - all
- __call__ - __call__
## FlaxStableDiffusionControlNetPipelineOutput
[[autodoc]] pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput
\ No newline at end of file
docs/source/en/api/pipelines/controlnet_sdxl.md
View file @ aedd7876
...@@ -12,151 +12,35 @@ specific language governing permissions and limitations under the License. ...@@ -12,151 +12,35 @@ specific language governing permissions and limitations under the License.
# ControlNet with Stable Diffusion XL # ControlNet with Stable Diffusion XL
[Adding Conditional Control to Text-to-Image Diffusion Models](https://huggingface.co/papers/2302.05543) by Lvmin Zhang and Maneesh Agrawala. ControlNet was introduced in [Adding Conditional Control to Text-to-Image Diffusion Models](https://huggingface.co/papers/2302.05543) by Lvmin Zhang and Maneesh Agrawala.
Using a pretrained model, we can provide control images (for example, a depth map) to control Stable Diffusion text-to-image generation so that it follows the structure of the depth image and fills in the details. With a ControlNet model, you can provide an additional control image to condition and control Stable Diffusion generation. For example, if you provide a depth map, the ControlNet model generates an image that'll preserve the spatial information from the depth map. It is a more flexible and accurate way to control the image generation process.
The abstract from the paper is: The abstract from the paper is:
*We present a neural network structure, ControlNet, to control pretrained large diffusion models to support additional input conditions. The ControlNet learns task-specific conditions in an end-to-end way, and the learning is robust even when the training dataset is small (< 50k). Moreover, training a ControlNet is as fast as fine-tuning a diffusion model, and the model can be trained on a personal devices. Alternatively, if powerful computation clusters are available, the model can scale to large amounts (millions to billions) of data. We report that large diffusion models like Stable Diffusion can be augmented with ControlNets to enable conditional inputs like edge maps, segmentation maps, keypoints, etc. This may enrich the methods to control large diffusion models and further facilitate related applications.* *We present a neural network structure, ControlNet, to control pretrained large diffusion models to support additional input conditions. The ControlNet learns task-specific conditions in an end-to-end way, and the learning is robust even when the training dataset is small (< 50k). Moreover, training a ControlNet is as fast as fine-tuning a diffusion model, and the model can be trained on a personal devices. Alternatively, if powerful computation clusters are available, the model can scale to large amounts (millions to billions) of data. We report that large diffusion models like Stable Diffusion can be augmented with ControlNets to enable conditional inputs like edge maps, segmentation maps, keypoints, etc. This may enrich the methods to control large diffusion models and further facilitate related applications.*
We provide support using ControlNets with [Stable Diffusion XL](./stable_diffusion/stable_diffusion_xl.md) (SDXL). You can find additional smaller Stable Diffusion XL (SDXL) ControlNet checkpoints from the 🤗 [Diffusers](https://huggingface.co/diffusers) Hub organization, and browse [community-trained](https://huggingface.co/models?other=stable-diffusion-xl&other=controlnet) checkpoints on the Hub.
You can find numerous SDXL ControlNet checkpoints from [this link](https://huggingface.co/models?other=stable-diffusion-xl&other=controlnet). There are some smaller ControlNet checkpoints too: <Tip warning={true}>
* [controlnet-canny-sdxl-1.0-small](https://huggingface.co/diffusers/controlnet-canny-sdxl-1.0-small) 🧪 Many of the SDXL ControlNet checkpoints are experimental, and there is a lot of room for improvement. Feel free to open an [Issue](https://github.com/huggingface/diffusers/issues/new/choose) and leave us feedback on how we can improve!
* [controlnet-canny-sdxl-1.0-mid](https://huggingface.co/diffusers/controlnet-canny-sdxl-1.0-mid)
* [controlnet-depth-sdxl-1.0-small](https://huggingface.co/diffusers/controlnet-depth-sdxl-1.0-small)
* [controlnet-depth-sdxl-1.0-mid](https://huggingface.co/diffusers/controlnet-depth-sdxl-1.0-mid)
We also encourage you to train custom ControlNets; we provide a [training script](https://github.com/huggingface/diffusers/blob/main/examples/controlnet/README_sdxl.md) for this. </Tip>
You can find some results below: If you don't see a checkpoint you're interested in, you can train your own SDXL ControlNet with our [training script](https://github.com/huggingface/diffusers/blob/main/examples/controlnet/README_sdxl.md).
<img src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/sd_xl/sdxl_controlnet_canny_grid.png" width=600/> <Tip>
🚨 At the time of this writing, many of these SDXL ControlNet checkpoints are experimental and there is a lot of room for improvement. We encourage our users to provide feedback. 🚨 Make sure to check out the Schedulers [guide](/using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](/using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.
## MultiControlNet </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
from PIL import Image
import numpy as np
import cv2
canny_image = load_image(
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/landscape.png"
)
canny_image = np.array(canny_image)
low_threshold = 100
high_threshold = 200
canny_image = cv2.Canny(canny_image, low_threshold, high_threshold)
# zero out middle columns of image where pose will be overlayed
zero_start = canny_image.shape[1] // 4
zero_end = zero_start + canny_image.shape[1] // 2
canny_image[:, zero_start:zero_end] = 0
canny_image = canny_image[:, :, None]
canny_image = np.concatenate([canny_image, canny_image, canny_image], axis=2)
canny_image = Image.fromarray(canny_image).resize((1024, 1024))
```
<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>
Prepare the human pose estimation conditioning:
```py
from controlnet_aux import OpenposeDetector
from diffusers.utils import load_image
openpose = OpenposeDetector.from_pretrained("lllyasviel/ControlNet")
openpose_image = load_image(
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/person.png"
)
openpose_image = openpose(openpose_image).resize((1024, 1024))
```
<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 nable 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, use_safetensors=True
),
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, canny_image]
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[0]
```
<div class="flex justify-center">
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/multicontrolnet.png"/>
</div>
## StableDiffusionXLControlNetPipeline ## StableDiffusionXLControlNetPipeline
[[autodoc]] StableDiffusionXLControlNetPipeline [[autodoc]] StableDiffusionXLControlNetPipeline
- all - all
- __call__ - __call__
## StableDiffusionPipelineOutput
[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput
\ No newline at end of file
docs/source/en/using-diffusers/controlnet.md 0 → 100644
View file @ aedd7876
# ControlNet
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 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 diffusers transformers accelerate safetensors opencv-python
```
## Text-to-image
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 import StableDiffusionControlNetPipeline
from diffusers.utils import load_image
from PIL import Image
import cv2
import numpy as np
image = load_image(
"https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png"
)
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)
```
<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(
"runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16, use_safetensors=True
).to("cuda")
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]
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet-text2img.png"/>
</div>
## Image-to-image
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
image = load_image(
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet-img2img.jpg"
).resize((768, 768))
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(
"runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16, use_safetensors=True
).to("cuda")
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]
```
<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>
## Inpainting
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 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 initial image and a mask image:
```py
from diffusers import StableDiffusionControlNetInpaintPipeline, ControlNetModel, UniPCMultistepScheduler
from diffusers.utils import load_image
import numpy as np
import torch
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))
```
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
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
import torch
controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_inpaint", torch_dtype=torch.float16, use_safetensors=True)
pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16, use_safetensors=True
).to("cuda")
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]
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet-inpaint-result.png"/>
</div>
## Guess mode
[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 it's 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
import torch
controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", use_safetensors=True)
pipe = StableDiffusionControlNetPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", controlnet=controlnet, use_safetensors=True).to(
"cuda"
)
image = pipe("", image=canny_image, guess_mode=True, guidance_scale=3.0).images[0]
image
```
<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>
## ControlNet with Stable Diffusion XL
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](https://huggingface.co/diffusers) Hub organization!
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
from PIL import Image
import cv2
import numpy as np
image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/hf-logo.png"
)
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)
canny_image
```
<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'
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=image,
controlnet_conditioning_scale=0.5,
).images[0]
images
```
<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
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"
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(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, controlnet_conditioning_scale=0.5, image=canny_image, guess_mode=True,
).images[0]
```
### MultiControlNet
<Tip>
Replace the SDXL model with a model like [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/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
from PIL import Image
import numpy as np
import cv2
canny_image = load_image(
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/landscape.png"
)
canny_image = np.array(canny_image)
low_threshold = 100
high_threshold = 200
canny_image = cv2.Canny(canny_image, low_threshold, high_threshold)
# zero out middle columns of image where pose will be overlayed
zero_start = canny_image.shape[1] // 4
zero_end = zero_start + canny_image.shape[1] // 2
canny_image[:, zero_start:zero_end] = 0
canny_image = canny_image[:, :, None]
canny_image = np.concatenate([canny_image, canny_image, canny_image], axis=2)
canny_image = Image.fromarray(canny_image).resize((1024, 1024))
```
<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>
Prepare the human pose estimation conditioning:
```py
from controlnet_aux import OpenposeDetector
from diffusers.utils import load_image
openpose = OpenposeDetector.from_pretrained("lllyasviel/ControlNet")
openpose_image = load_image(
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/person.png"
)
openpose_image = openpose(openpose_image).resize((1024, 1024))
```
<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, use_safetensors=True
),
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, canny_image]
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[0]
```
<div class="flex justify-center">
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/multicontrolnet.png"/>
</div>
\ No newline at end of file
src/diffusers/pipelines/controlnet/pipeline_controlnet.py
View file @ aedd7876
...@@ -96,35 +96,34 @@ class StableDiffusionControlNetPipeline( ...@@ -96,35 +96,34 @@ class StableDiffusionControlNetPipeline(
r""" r"""
Pipeline for text-to-image generation using Stable Diffusion with ControlNet guidance. Pipeline for text-to-image generation using Stable Diffusion with ControlNet guidance.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) implemented for all pipelines (downloading, saving, running on a particular device, etc.).
In addition the pipeline inherits the following loading methods: The pipeline also inherits the following loading methods:
- *Textual-Inversion*: [`loaders.TextualInversionLoaderMixin.load_textual_inversion`] - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
Args: Args:
vae ([`AutoencoderKL`]): vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]): text_encoder ([`~transformers.CLIPTextModel`]):
Frozen text-encoder. Stable Diffusion uses the text portion of Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically tokenizer ([`~transformers.CLIPTokenizer`]):
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. A `CLIPTokenizer` to tokenize text.
tokenizer (`CLIPTokenizer`): unet ([`UNet2DConditionModel`]):
Tokenizer of class A `UNet2DConditionModel` to denoise the encoded image latents.
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
controlnet ([`ControlNetModel`] or `List[ControlNetModel]`): controlnet ([`ControlNetModel`] or `List[ControlNetModel]`):
Provides additional conditioning to the unet during the denoising process. If you set multiple ControlNets Provides additional conditioning to the `unet` during the denoising process. If you set multiple
as a list, the outputs from each ControlNet are added together to create one combined additional ControlNets as a list, the outputs from each ControlNet are added together to create one combined
conditioning. additional conditioning.
scheduler ([`SchedulerMixin`]): scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]): safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful. Classification module that estimates whether generated images could be considered offensive or harmful.
Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details. Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
feature_extractor ([`CLIPImageProcessor`]): about a model's potential harms.
Model that extracts features from generated images to be used as inputs for the `safety_checker`. feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
""" """
_optional_components = ["safety_checker", "feature_extractor"] _optional_components = ["safety_checker", "feature_extractor"]
...@@ -725,92 +724,84 @@ class StableDiffusionControlNetPipeline( ...@@ -725,92 +724,84 @@ class StableDiffusionControlNetPipeline(
control_guidance_end: Union[float, List[float]] = 1.0, control_guidance_end: Union[float, List[float]] = 1.0,
): ):
r""" r"""
Function invoked when calling the pipeline for generation. The call function to the pipeline for generation.
Args: Args:
prompt (`str` or `List[str]`, *optional*): prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
instead.
image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,: image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:
`List[List[torch.FloatTensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`): `List[List[torch.FloatTensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`):
The ControlNet input condition. ControlNet uses this input condition to generate guidance to Unet. If The ControlNet input condition to provide guidance to the `unet` for generation. If the type is
the type is specified as `Torch.FloatTensor`, it is passed to ControlNet as is. `PIL.Image.Image` can specified as `torch.FloatTensor`, it is passed to ControlNet as is. `PIL.Image.Image` can also be
also be accepted as an image. The dimensions of the output image defaults to `image`'s dimensions. If accepted as an image. The dimensions of the output image defaults to `image`'s dimensions. If height
height and/or width are passed, `image` is resized according to them. If multiple ControlNets are and/or width are passed, `image` is resized accordingly. If multiple ControlNets are specified in
specified in init, images must be passed as a list such that each element of the list can be correctly `init`, images must be passed as a list such that each element of the list can be correctly batched for
batched for input to a single controlnet. input to a single ControlNet.
height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The height in pixels of the generated image. The height in pixels of the generated image.
width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The width in pixels of the generated image. The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50): num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference. expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5): guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). A higher guidance scale value encourages the model to generate images closely linked to the text
`guidance_scale` is defined as `w` of equation 2. of [Imagen `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
negative_prompt (`str` or `List[str]`, *optional*): negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass The prompt or prompts to guide what to not include in image generation. If not defined, you need to
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
less than `1`).
num_images_per_prompt (`int`, *optional*, defaults to 1): num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt. The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0): eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
[`schedulers.DDIMScheduler`], will be ignored for others. to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*): generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
to make generation deterministic. generation deterministic.
latents (`torch.FloatTensor`, *optional*): latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`. tensor is generated by sampling using the supplied random `generator`.
prompt_embeds (`torch.FloatTensor`, *optional*): prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
provided, text embeddings will be generated from `prompt` input argument. provided, text embeddings are generated from the `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*): negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
argument.
output_type (`str`, *optional*, defaults to `"pil"`): output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between The output format of the generated image. Choose between `PIL.Image` or `np.array`.
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`): return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple. plain tuple.
callback (`Callable`, *optional*): callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be A function that calls every `callback_steps` steps during inference. The function is called with the
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1): callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be The frequency at which the `callback` function is called. If not specified, the callback is called at
called at every step. every step.
cross_attention_kwargs (`dict`, *optional*): cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
`self.processor` in [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0): controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):
The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added The outputs of the ControlNet are multiplied by `controlnet_conditioning_scale` before they are added
to the residual in the original unet. If multiple ControlNets are specified in init, you can set the to the residual in the original `unet`. If multiple ControlNets are specified in `init`, you can set
corresponding scale as a list. the corresponding scale as a list.
guess_mode (`bool`, *optional*, defaults to `False`): guess_mode (`bool`, *optional*, defaults to `False`):
In this mode, the ControlNet encoder will try best to recognize the content of the input image even if The ControlNet encoder tries to recognize the content of the input image even if you remove all
you remove all prompts. The `guidance_scale` between 3.0 and 5.0 is recommended. prompts. A `guidance_scale` value between 3.0 and 5.0 is recommended.
control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0): control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0):
The percentage of total steps at which the controlnet starts applying. The percentage of total steps at which the ControlNet starts applying.
control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0): control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0):
The percentage of total steps at which the controlnet stops applying. The percentage of total steps at which the ControlNet stops applying.
Examples: Examples:
Returns: Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
When returning a tuple, the first element is a list with the generated images, and the second element is a otherwise a `tuple` is returned where the first element is a list with the generated images and the
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" second element is a list of `bool`s indicating whether the corresponding generated image contains
(nsfw) content, according to the `safety_checker`. "not-safe-for-work" (nsfw) content.
""" """
controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet
......
src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py
View file @ aedd7876
...@@ -119,37 +119,36 @@ class StableDiffusionControlNetImg2ImgPipeline( ...@@ -119,37 +119,36 @@ class StableDiffusionControlNetImg2ImgPipeline(
DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, FromSingleFileMixin DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, FromSingleFileMixin
): ):
r""" r"""
Pipeline for text-to-image generation using Stable Diffusion with ControlNet guidance. Pipeline for image-to-image generation using Stable Diffusion with ControlNet guidance.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) implemented for all pipelines (downloading, saving, running on a particular device, etc.).
In addition the pipeline inherits the following loading methods: The pipeline also inherits the following loading methods:
- *Textual-Inversion*: [`loaders.TextualInversionLoaderMixin.load_textual_inversion`] - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
Args: Args:
vae ([`AutoencoderKL`]): vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]): text_encoder ([`~transformers.CLIPTextModel`]):
Frozen text-encoder. Stable Diffusion uses the text portion of Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically tokenizer ([`~transformers.CLIPTokenizer`]):
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. A `CLIPTokenizer` to tokenize text.
tokenizer (`CLIPTokenizer`): unet ([`UNet2DConditionModel`]):
Tokenizer of class A `UNet2DConditionModel` to denoise the encoded image latents.
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
controlnet ([`ControlNetModel`] or `List[ControlNetModel]`): controlnet ([`ControlNetModel`] or `List[ControlNetModel]`):
Provides additional conditioning to the unet during the denoising process. If you set multiple ControlNets Provides additional conditioning to the `unet` during the denoising process. If you set multiple
as a list, the outputs from each ControlNet are added together to create one combined additional ControlNets as a list, the outputs from each ControlNet are added together to create one combined
conditioning. additional conditioning.
scheduler ([`SchedulerMixin`]): scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]): safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful. Classification module that estimates whether generated images could be considered offensive or harmful.
Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details. Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
feature_extractor ([`CLIPImageProcessor`]): about a model's potential harms.
Model that extracts features from generated images to be used as inputs for the `safety_checker`. feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
""" """
_optional_components = ["safety_checker", "feature_extractor"] _optional_components = ["safety_checker", "feature_extractor"]
...@@ -798,97 +797,88 @@ class StableDiffusionControlNetImg2ImgPipeline( ...@@ -798,97 +797,88 @@ class StableDiffusionControlNetImg2ImgPipeline(
control_guidance_end: Union[float, List[float]] = 1.0, control_guidance_end: Union[float, List[float]] = 1.0,
): ):
r""" r"""
Function invoked when calling the pipeline for generation. The call function to the pipeline for generation.
Args: Args:
prompt (`str` or `List[str]`, *optional*): prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
instead.
image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,: image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:
`List[List[torch.FloatTensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`): `List[List[torch.FloatTensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`):
The initial image will be used as the starting point for the image generation process. Can also accept The initial image to be used as the starting point for the image generation process. Can also accept
image latents as `image`, if passing latents directly, it will not be encoded again. image latents as `image`, and if passing latents directly they are not encoded again.
control_image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,: control_image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:
`List[List[torch.FloatTensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`): `List[List[torch.FloatTensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`):
The ControlNet input condition. ControlNet uses this input condition to generate guidance to Unet. If The ControlNet input condition to provide guidance to the `unet` for generation. If the type is
the type is specified as `Torch.FloatTensor`, it is passed to ControlNet as is. `PIL.Image.Image` can specified as `torch.FloatTensor`, it is passed to ControlNet as is. `PIL.Image.Image` can also be
also be accepted as an image. The dimensions of the output image defaults to `image`'s dimensions. If accepted as an image. The dimensions of the output image defaults to `image`'s dimensions. If height
height and/or width are passed, `image` is resized according to them. If multiple ControlNets are and/or width are passed, `image` is resized accordingly. If multiple ControlNets are specified in
specified in init, images must be passed as a list such that each element of the list can be correctly `init`, images must be passed as a list such that each element of the list can be correctly batched for
batched for input to a single controlnet. input to a single ControlNet.
height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The height in pixels of the generated image. The height in pixels of the generated image.
width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The width in pixels of the generated image. The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50): num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference. expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5): guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). A higher guidance scale value encourages the model to generate images closely linked to the text
`guidance_scale` is defined as `w` of equation 2. of [Imagen `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
negative_prompt (`str` or `List[str]`, *optional*): negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass The prompt or prompts to guide what to not include in image generation. If not defined, you need to
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
less than `1`).
num_images_per_prompt (`int`, *optional*, defaults to 1): num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt. The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0): eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
[`schedulers.DDIMScheduler`], will be ignored for others. to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*): generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
to make generation deterministic. generation deterministic.
latents (`torch.FloatTensor`, *optional*): latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`. tensor is generated by sampling using the supplied random `generator`.
prompt_embeds (`torch.FloatTensor`, *optional*): prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
provided, text embeddings will be generated from `prompt` input argument. provided, text embeddings are generated from the `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*): negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
argument.
output_type (`str`, *optional*, defaults to `"pil"`): output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between The output format of the generated image. Choose between `PIL.Image` or `np.array`.
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`): return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple. plain tuple.
callback (`Callable`, *optional*): callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be A function that calls every `callback_steps` steps during inference. The function is called with the
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1): callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be The frequency at which the `callback` function is called. If not specified, the callback is called at
called at every step. every step.
cross_attention_kwargs (`dict`, *optional*): cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
`self.processor` in [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0): controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):
The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added The outputs of the ControlNet are multiplied by `controlnet_conditioning_scale` before they are added
to the residual in the original unet. If multiple ControlNets are specified in init, you can set the to the residual in the original `unet`. If multiple ControlNets are specified in `init`, you can set
corresponding scale as a list. Note that by default, we use a smaller conditioning scale for inpainting the corresponding scale as a list.
than for [`~StableDiffusionControlNetPipeline.__call__`].
guess_mode (`bool`, *optional*, defaults to `False`): guess_mode (`bool`, *optional*, defaults to `False`):
In this mode, the ControlNet encoder will try best to recognize the content of the input image even if The ControlNet encoder tries to recognize the content of the input image even if you remove all
you remove all prompts. The `guidance_scale` between 3.0 and 5.0 is recommended. prompts. A `guidance_scale` value between 3.0 and 5.0 is recommended.
control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0): control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0):
The percentage of total steps at which the controlnet starts applying. The percentage of total steps at which the ControlNet starts applying.
control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0): control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0):
The percentage of total steps at which the controlnet stops applying. The percentage of total steps at which the ControlNet stops applying.
Examples: Examples:
Returns: Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
When returning a tuple, the first element is a list with the generated images, and the second element is a otherwise a `tuple` is returned where the first element is a list with the generated images and the
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" second element is a list of `bool`s indicating whether the corresponding generated image contains
(nsfw) content, according to the `safety_checker`. "not-safe-for-work" (nsfw) content.
""" """
controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet
......
src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint.py
View file @ aedd7876
...@@ -231,48 +231,47 @@ class StableDiffusionControlNetInpaintPipeline( ...@@ -231,48 +231,47 @@ class StableDiffusionControlNetInpaintPipeline(
DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, FromSingleFileMixin DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, FromSingleFileMixin
): ):
r""" r"""
Pipeline for text-to-image generation using Stable Diffusion with ControlNet guidance. Pipeline for image inpainting using Stable Diffusion with ControlNet guidance.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) implemented for all pipelines (downloading, saving, running on a particular device, etc.).
In addition the pipeline inherits the following loading methods: The pipeline also inherits the following loading methods:
- *Textual-Inversion*: [`loaders.TextualInversionLoaderMixin.load_textual_inversion`] - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
<Tip> <Tip>
This pipeline can be used both with checkpoints that have been specifically fine-tuned for inpainting, such as This pipeline can be used with checkpoints that have been specifically fine-tuned for inpainting
[runwayml/stable-diffusion-inpainting](https://huggingface.co/runwayml/stable-diffusion-inpainting) ([runwayml/stable-diffusion-inpainting](https://huggingface.co/runwayml/stable-diffusion-inpainting)) as well as
as well as default text-to-image stable diffusion checkpoints, such as default text-to-image Stable Diffusion checkpoints
[runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5). ([runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5)). Default text-to-image
Default text-to-image stable diffusion checkpoints might be preferable for controlnets that have been fine-tuned on Stable Diffusion checkpoints might be preferable for ControlNets that have been fine-tuned on those, such as
those, such as [lllyasviel/control_v11p_sd15_inpaint](https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint). [lllyasviel/control_v11p_sd15_inpaint](https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint).
</Tip> </Tip>
Args: Args:
vae ([`AutoencoderKL`]): vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]): text_encoder ([`~transformers.CLIPTextModel`]):
Frozen text-encoder. Stable Diffusion uses the text portion of Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically tokenizer ([`~transformers.CLIPTokenizer`]):
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. A `CLIPTokenizer` to tokenize text.
tokenizer (`CLIPTokenizer`): unet ([`UNet2DConditionModel`]):
Tokenizer of class A `UNet2DConditionModel` to denoise the encoded image latents.
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
controlnet ([`ControlNetModel`] or `List[ControlNetModel]`): controlnet ([`ControlNetModel`] or `List[ControlNetModel]`):
Provides additional conditioning to the unet during the denoising process. If you set multiple ControlNets Provides additional conditioning to the `unet` during the denoising process. If you set multiple
as a list, the outputs from each ControlNet are added together to create one combined additional ControlNets as a list, the outputs from each ControlNet are added together to create one combined
conditioning. additional conditioning.
scheduler ([`SchedulerMixin`]): scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]): safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful. Classification module that estimates whether generated images could be considered offensive or harmful.
Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details. Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
feature_extractor ([`CLIPImageProcessor`]): about a model's potential harms.
Model that extracts features from generated images to be used as inputs for the `safety_checker`. feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
""" """
_optional_components = ["safety_checker", "feature_extractor"] _optional_components = ["safety_checker", "feature_extractor"]
...@@ -983,115 +982,105 @@ class StableDiffusionControlNetInpaintPipeline( ...@@ -983,115 +982,105 @@ class StableDiffusionControlNetInpaintPipeline(
control_guidance_end: Union[float, List[float]] = 1.0, control_guidance_end: Union[float, List[float]] = 1.0,
): ):
r""" r"""
Function invoked when calling the pipeline for generation. The call function to the pipeline for generation.
Args: Args:
prompt (`str` or `List[str]`, *optional*): prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
instead.
image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`,
`List[PIL.Image.Image]`, or `List[np.ndarray]`): `List[PIL.Image.Image]`, or `List[np.ndarray]`):
`Image`, numpy array or tensor representing an image batch to be inpainted (which parts of the image to `Image`, NumPy array or tensor representing an image batch to be used as the starting point. For both
be masked out with `mask_image` and repainted according to `prompt`). For both numpy array and pytorch NumPy array and PyTorch tensor, the expected value range is between `[0, 1]`. If it's a tensor or a
tensor, the expected value range is between `[0, 1]` If it's a tensor or a list or tensors, the list or tensors, the expected shape should be `(B, C, H, W)` or `(C, H, W)`. If it is a NumPy array or
expected shape should be `(B, C, H, W)` or `(C, H, W)`. If it is a numpy array or a list of arrays, the a list of arrays, the expected shape should be `(B, H, W, C)` or `(H, W, C)`. It can also accept image
expected shape should be `(B, H, W, C)` or `(H, W, C)` It can also accept image latents as `image`, but latents as `image`, but if passing latents directly it is not encoded again.
if passing latents directly it is not encoded again.
mask_image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, mask_image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`,
`List[PIL.Image.Image]`, or `List[np.ndarray]`): `List[PIL.Image.Image]`, or `List[np.ndarray]`):
`Image`, numpy array or tensor representing an image batch to mask `image`. White pixels in the mask `Image`, NumPy array or tensor representing an image batch to mask `image`. White pixels in the mask
are repainted while black pixels are preserved. If `mask_image` is a PIL image, it is converted to a are repainted while black pixels are preserved. If `mask_image` is a PIL image, it is converted to a
single channel (luminance) before use. If it's a numpy array or pytorch tensor, it should contain one single channel (luminance) before use. If it's a NumPy array or PyTorch tensor, it should contain one
color channel (L) instead of 3, so the expected shape for pytorch tensor would be `(B, 1, H, W)`, `(B, color channel (L) instead of 3, so the expected shape for PyTorch tensor would be `(B, 1, H, W)`, `(B,
H, W)`, `(1, H, W)`, `(H, W)`. And for numpy array would be for `(B, H, W, 1)`, `(B, H, W)`, `(H, W, H, W)`, `(1, H, W)`, `(H, W)`. And for NumPy array, it would be for `(B, H, W, 1)`, `(B, H, W)`, `(H,
1)`, or `(H, W)`. W, 1)`, or `(H, W)`.
control_image (`torch.FloatTensor`, `PIL.Image.Image`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, control_image (`torch.FloatTensor`, `PIL.Image.Image`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`,
`List[List[torch.FloatTensor]]`, or `List[List[PIL.Image.Image]]`): `List[List[torch.FloatTensor]]`, or `List[List[PIL.Image.Image]]`):
The ControlNet input condition. ControlNet uses this input condition to generate guidance to Unet. The The ControlNet input condition to provide guidance to the `unet` for generation. If the type is
dimensions of the output image defaults to `image`'s dimensions. If height and/or width are passed, specified as `torch.FloatTensor`, it is passed to ControlNet as is. `PIL.Image.Image` can also be
`image` is resized according to them. If multiple ControlNets are specified in init, images must be accepted as an image. The dimensions of the output image defaults to `image`'s dimensions. If height
passed as a list such that each element of the list can be correctly batched for input to a single and/or width are passed, `image` is resized accordingly. If multiple ControlNets are specified in
controlnet. `init`, images must be passed as a list such that each element of the list can be correctly batched for
height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): input to a single ControlNet.
height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The height in pixels of the generated image. The height in pixels of the generated image.
width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The width in pixels of the generated image. The width in pixels of the generated image.
strength (`float`, *optional*, defaults to 1.): strength (`float`, *optional*, defaults to 1.0):
Conceptually, indicates how much to transform the masked portion of the reference `image`. Must be Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a
between 0 and 1. `image` will be used as a starting point, adding more noise to it the larger the starting point and more noise is added the higher the `strength`. The number of denoising steps depends
`strength`. The number of denoising steps depends on the amount of noise initially added. When on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising
`strength` is 1, added noise will be maximum and the denoising process will run for the full number of process runs for the full number of iterations specified in `num_inference_steps`. A value of 1
iterations specified in `num_inference_steps`. A value of 1, therefore, essentially ignores the masked essentially ignores `image`.
portion of the reference `image`.
num_inference_steps (`int`, *optional*, defaults to 50): num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference. expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5): guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). A higher guidance scale value encourages the model to generate images closely linked to the text
`guidance_scale` is defined as `w` of equation 2. of [Imagen `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
negative_prompt (`str` or `List[str]`, *optional*): negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass The prompt or prompts to guide what to not include in image generation. If not defined, you need to
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
less than `1`).
num_images_per_prompt (`int`, *optional*, defaults to 1): num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt. The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0): eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
[`schedulers.DDIMScheduler`], will be ignored for others. to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*): generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
to make generation deterministic. generation deterministic.
latents (`torch.FloatTensor`, *optional*): latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`. tensor is generated by sampling using the supplied random `generator`.
prompt_embeds (`torch.FloatTensor`, *optional*): prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
provided, text embeddings will be generated from `prompt` input argument. provided, text embeddings are generated from the `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*): negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
argument.
output_type (`str`, *optional*, defaults to `"pil"`): output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between The output format of the generated image. Choose between `PIL.Image` or `np.array`.
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`): return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple. plain tuple.
callback (`Callable`, *optional*): callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be A function that calls every `callback_steps` steps during inference. The function is called with the
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1): callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be The frequency at which the `callback` function is called. If not specified, the callback is called at
called at every step. every step.
cross_attention_kwargs (`dict`, *optional*): cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
`self.processor` in [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 0.5): controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 0.5):
The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added The outputs of the ControlNet are multiplied by `controlnet_conditioning_scale` before they are added
to the residual in the original unet. If multiple ControlNets are specified in init, you can set the to the residual in the original `unet`. If multiple ControlNets are specified in `init`, you can set
corresponding scale as a list. Note that by default, we use a smaller conditioning scale for inpainting the corresponding scale as a list.
than for [`~StableDiffusionControlNetPipeline.__call__`].
guess_mode (`bool`, *optional*, defaults to `False`): guess_mode (`bool`, *optional*, defaults to `False`):
In this mode, the ControlNet encoder will try best to recognize the content of the input image even if The ControlNet encoder tries to recognize the content of the input image even if you remove all
you remove all prompts. The `guidance_scale` between 3.0 and 5.0 is recommended. prompts. A `guidance_scale` value between 3.0 and 5.0 is recommended.
control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0): control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0):
The percentage of total steps at which the controlnet starts applying. The percentage of total steps at which the ControlNet starts applying.
control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0): control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0):
The percentage of total steps at which the controlnet stops applying. The percentage of total steps at which the ControlNet stops applying.
Examples: Examples:
Returns: Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
When returning a tuple, the first element is a list with the generated images, and the second element is a otherwise a `tuple` is returned where the first element is a list with the generated images and the
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" second element is a list of `bool`s indicating whether the corresponding generated image contains
(nsfw) content, according to the `safety_checker`. "not-safe-for-work" (nsfw) content.
""" """
controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet
......
src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl.py
View file @ aedd7876
...@@ -108,48 +108,42 @@ class StableDiffusionXLControlNetPipeline( ...@@ -108,48 +108,42 @@ class StableDiffusionXLControlNetPipeline(
r""" r"""
Pipeline for text-to-image generation using Stable Diffusion XL with ControlNet guidance. Pipeline for text-to-image generation using Stable Diffusion XL with ControlNet guidance.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) implemented for all pipelines (downloading, saving, running on a particular device, etc.).
In addition the pipeline inherits the following loading methods: The pipeline also inherits the following loading methods:
- *Textual-Inversion*: [`loaders.TextualInversionLoaderMixin.load_textual_inversion`] - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
- *LoRA*: [`loaders.LoraLoaderMixin.load_lora_weights`] - [`loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights
- *Ckpt*: [`loaders.FromSingleFileMixin.from_single_file`] - [`loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
Args: Args:
vae ([`AutoencoderKL`]): vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]): text_encoder ([`~transformers.CLIPTextModel`]):
Frozen text-encoder. Stable Diffusion uses the text portion of Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically text_encoder_2 ([`~transformers.CLIPTextModelWithProjection`]):
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. Second frozen text-encoder
text_encoder_2 ([` CLIPTextModelWithProjection`]): ([laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)).
Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of tokenizer ([`~transformers.CLIPTokenizer`]):
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection), A `CLIPTokenizer` to tokenize text.
specifically the tokenizer_2 ([`~transformers.CLIPTokenizer`]):
[laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k) A `CLIPTokenizer` to tokenize text.
variant. unet ([`UNet2DConditionModel`]):
tokenizer (`CLIPTokenizer`): A `UNet2DConditionModel` to denoise the encoded image latents.
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
tokenizer_2 (`CLIPTokenizer`):
Second Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
controlnet ([`ControlNetModel`] or `List[ControlNetModel]`): controlnet ([`ControlNetModel`] or `List[ControlNetModel]`):
Provides additional conditioning to the unet during the denoising process. If you set multiple ControlNets Provides additional conditioning to the `unet` during the denoising process. If you set multiple
as a list, the outputs from each ControlNet are added together to create one combined additional ControlNets as a list, the outputs from each ControlNet are added together to create one combined
conditioning. additional conditioning.
scheduler ([`SchedulerMixin`]): scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `"True"`): force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `"True"`):
Whether the negative prompt embeddings shall always be set to 0. Also see the config of Whether the negative prompt embeddings should always be set to 0. Also see the config of
`stabilityai/stable-diffusion-xl-base-1-0`. `stabilityai/stable-diffusion-xl-base-1-0`.
add_watermarker (`bool`, *optional*): add_watermarker (`bool`, *optional*):
Whether to use the [invisible_watermark](https://github.com/ShieldMnt/invisible-watermark/) library to Whether to use the [invisible_watermark](https://github.com/ShieldMnt/invisible-watermark/) library to
watermark output images. If not defined, it will default to `True` if the package is installed, otherwise watermark output images. If not defined, it defaults to `True` if the package is installed; otherwise no
no watermarker will be used. watermarker is used.
""" """
def __init__( def __init__(
...@@ -797,96 +791,88 @@ class StableDiffusionXLControlNetPipeline( ...@@ -797,96 +791,88 @@ class StableDiffusionXLControlNetPipeline(
negative_target_size: Optional[Tuple[int, int]] = None, negative_target_size: Optional[Tuple[int, int]] = None,
): ):
r""" r"""
Function invoked when calling the pipeline for generation. The call function to the pipeline for generation.
Args: Args:
prompt (`str` or `List[str]`, *optional*): prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
instead.
prompt_2 (`str` or `List[str]`, *optional*): prompt_2 (`str` or `List[str]`, *optional*):
The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is The prompt or prompts to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
used in both text-encoders used in both text-encoders.
image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,: image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:
`List[List[torch.FloatTensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`): `List[List[torch.FloatTensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`):
The ControlNet input condition. ControlNet uses this input condition to generate guidance to Unet. If The ControlNet input condition to provide guidance to the `unet` for generation. If the type is
the type is specified as `Torch.FloatTensor`, it is passed to ControlNet as is. `PIL.Image.Image` can specified as `torch.FloatTensor`, it is passed to ControlNet as is. `PIL.Image.Image` can also be
also be accepted as an image. The dimensions of the output image defaults to `image`'s dimensions. If accepted as an image. The dimensions of the output image defaults to `image`'s dimensions. If height
height and/or width are passed, `image` is resized according to them. If multiple ControlNets are and/or width are passed, `image` is resized accordingly. If multiple ControlNets are specified in
specified in init, images must be passed as a list such that each element of the list can be correctly `init`, images must be passed as a list such that each element of the list can be correctly batched for
batched for input to a single controlnet. input to a single ControlNet.
height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The height in pixels of the generated image. The height in pixels of the generated image.
width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The width in pixels of the generated image. The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50): num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference. expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5): guidance_scale (`float`, *optional*, defaults to 5.0):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). A higher guidance scale value encourages the model to generate images closely linked to the text
`guidance_scale` is defined as `w` of equation 2. of [Imagen `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
negative_prompt (`str` or `List[str]`, *optional*): negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass The prompt or prompts to guide what to not include in image generation. If not defined, you need to
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
less than `1`).
negative_prompt_2 (`str` or `List[str]`, *optional*): negative_prompt_2 (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and The prompt or prompts to guide what to not include in image generation. This is sent to `tokenizer_2`
`text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders and `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders.
num_images_per_prompt (`int`, *optional*, defaults to 1): num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt. The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0): eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
[`schedulers.DDIMScheduler`], will be ignored for others. to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*): generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
to make generation deterministic. generation deterministic.
latents (`torch.FloatTensor`, *optional*): latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`. tensor is generated by sampling using the supplied random `generator`.
prompt_embeds (`torch.FloatTensor`, *optional*): prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
provided, text embeddings will be generated from `prompt` input argument. provided, text embeddings are generated from the `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*): negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
argument.
pooled_prompt_embeds (`torch.FloatTensor`, *optional*): pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. Pre-generated pooled text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
If not provided, pooled text embeddings will be generated from `prompt` input argument. not provided, pooled text embeddings are generated from `prompt` input argument.
negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*): negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs (prompt
weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt` weighting). If not provided, pooled `negative_prompt_embeds` are generated from `negative_prompt` input
input argument. argument.
output_type (`str`, *optional*, defaults to `"pil"`): output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between The output format of the generated image. Choose between `PIL.Image` or `np.array`.
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`): return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple. plain tuple.
callback (`Callable`, *optional*): callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be A function that calls every `callback_steps` steps during inference. The function is called with the
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1): callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be The frequency at which the `callback` function is called. If not specified, the callback is called at
called at every step. every step.
cross_attention_kwargs (`dict`, *optional*): cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
`self.processor` in [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0): controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):
The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added The outputs of the ControlNet are multiplied by `controlnet_conditioning_scale` before they are added
to the residual in the original unet. If multiple ControlNets are specified in init, you can set the to the residual in the original `unet`. If multiple ControlNets are specified in `init`, you can set
corresponding scale as a list. the corresponding scale as a list.
guess_mode (`bool`, *optional*, defaults to `False`): guess_mode (`bool`, *optional*, defaults to `False`):
In this mode, the ControlNet encoder will try best to recognize the content of the input image even if The ControlNet encoder tries to recognize the content of the input image even if you remove all
you remove all prompts. The `guidance_scale` between 3.0 and 5.0 is recommended. prompts. A `guidance_scale` value between 3.0 and 5.0 is recommended.
control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0): control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0):
The percentage of total steps at which the controlnet starts applying. The percentage of total steps at which the ControlNet starts applying.
control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0): control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0):
The percentage of total steps at which the controlnet stops applying. The percentage of total steps at which the ControlNet stops applying.
original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)): original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled. If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.
`original_size` defaults to `(width, height)` if not specified. Part of SDXL's micro-conditioning as `original_size` defaults to `(width, height)` if not specified. Part of SDXL's micro-conditioning as
...@@ -921,8 +907,8 @@ class StableDiffusionXLControlNetPipeline( ...@@ -921,8 +907,8 @@ class StableDiffusionXLControlNetPipeline(
Returns: Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple` If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
containing the output images. otherwise a `tuple` is returned containing the output images.
""" """
controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet
......
src/diffusers/pipelines/controlnet/pipeline_flax_controlnet.py
View file @ aedd7876
...@@ -110,33 +110,32 @@ EXAMPLE_DOC_STRING = """ ...@@ -110,33 +110,32 @@ EXAMPLE_DOC_STRING = """
class FlaxStableDiffusionControlNetPipeline(FlaxDiffusionPipeline): class FlaxStableDiffusionControlNetPipeline(FlaxDiffusionPipeline):
r""" r"""
Pipeline for text-to-image generation using Stable Diffusion with ControlNet Guidance. Flax-based pipeline for text-to-image generation using Stable Diffusion with ControlNet Guidance.
This model inherits from [`FlaxDiffusionPipeline`]. Check the superclass documentation for the generic methods the This model inherits from [`FlaxDiffusionPipeline`]. Check the superclass documentation for the generic methods
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) implemented for all pipelines (downloading, saving, running on a particular device, etc.).
Args: Args:
vae ([`FlaxAutoencoderKL`]): vae ([`FlaxAutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`FlaxCLIPTextModel`]): text_encoder ([`~transformers.FlaxCLIPTextModel`]):
Frozen text-encoder. Stable Diffusion uses the text portion of Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.FlaxCLIPTextModel), tokenizer ([`~transformers.CLIPTokenizer`]):
specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. A `CLIPTokenizer` to tokenize text.
tokenizer (`CLIPTokenizer`): unet ([`FlaxUNet2DConditionModel`]):
Tokenizer of class A `FlaxUNet2DConditionModel` to denoise the encoded image latents.
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
unet ([`FlaxUNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
controlnet ([`FlaxControlNetModel`]: controlnet ([`FlaxControlNetModel`]:
Provides additional conditioning to the unet during the denoising process. Provides additional conditioning to the `unet` during the denoising process.
scheduler ([`SchedulerMixin`]): scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`FlaxDDIMScheduler`], [`FlaxLMSDiscreteScheduler`], [`FlaxPNDMScheduler`], or [`FlaxDDIMScheduler`], [`FlaxLMSDiscreteScheduler`], [`FlaxPNDMScheduler`], or
[`FlaxDPMSolverMultistepScheduler`]. [`FlaxDPMSolverMultistepScheduler`].
safety_checker ([`FlaxStableDiffusionSafetyChecker`]): safety_checker ([`FlaxStableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful. Classification module that estimates whether generated images could be considered offensive or harmful.
Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details. Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
feature_extractor ([`CLIPFeatureExtractor`]): about a model's potential harms.
Model that extracts features from generated images to be used as inputs for the `safety_checker`. feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
""" """
def __init__( def __init__(
...@@ -362,47 +361,51 @@ class FlaxStableDiffusionControlNetPipeline(FlaxDiffusionPipeline): ...@@ -362,47 +361,51 @@ class FlaxStableDiffusionControlNetPipeline(FlaxDiffusionPipeline):
jit: bool = False, jit: bool = False,
): ):
r""" r"""
Function invoked when calling the pipeline for generation. The call function to the pipeline for generation.
Args: Args:
prompt_ids (`jnp.array`): prompt_ids (`jnp.array`):
The prompt or prompts to guide the image generation. The prompt or prompts to guide the image generation.
image (`jnp.array`): image (`jnp.array`):
Array representing the ControlNet input condition. ControlNet use this input condition to generate Array representing the ControlNet input condition to provide guidance to the `unet` for generation.
guidance to Unet. params (`Dict` or `FrozenDict`):
params (`Dict` or `FrozenDict`): Dictionary containing the model parameters/weights Dictionary containing the model parameters/weights.
prng_seed (`jax.random.KeyArray` or `jax.Array`): Array containing random number generator key prng_seed (`jax.random.KeyArray` or `jax.Array`):
Array containing random number generator key.
num_inference_steps (`int`, *optional*, defaults to 50): num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference. expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5): guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). A higher guidance scale value encourages the model to generate images closely linked to the text
`guidance_scale` is defined as `w` of equation 2. of [Imagen `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
latents (`jnp.array`, *optional*): latents (`jnp.array`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`. array is generated by sampling using the supplied random `generator`.
controlnet_conditioning_scale (`float` or `jnp.array`, *optional*, defaults to 1.0): controlnet_conditioning_scale (`float` or `jnp.array`, *optional*, defaults to 1.0):
The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added The outputs of the ControlNet are multiplied by `controlnet_conditioning_scale` before they are added
to the residual in the original unet. to the residual in the original `unet`.
return_dict (`bool`, *optional*, defaults to `True`): return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] instead of Whether or not to return a [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] instead of
a plain tuple. a plain tuple.
jit (`bool`, defaults to `False`): jit (`bool`, defaults to `False`):
Whether to run `pmap` versions of the generation and safety scoring functions. NOTE: This argument Whether to run `pmap` versions of the generation and safety scoring functions.
exists because `__call__` is not yet end-to-end pmap-able. It will be removed in a future release.
<Tip warning={true}>
This argument exists because `__call__` is not yet end-to-end pmap-able. It will be removed in a
future release.
</Tip>
Examples: Examples:
Returns: Returns:
[`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a If `return_dict` is `True`, [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] is
`tuple. When returning a tuple, the first element is a list with the generated images, and the second returned, otherwise a `tuple` is returned where the first element is a list with the generated images
element is a list of `bool`s denoting whether the corresponding generated image likely represents and the second element is a list of `bool`s indicating whether the corresponding generated image
"not-safe-for-work" (nsfw) content, according to the `safety_checker`. contains "not-safe-for-work" (nsfw) content.
""" """
height, width = image.shape[-2:] height, width = image.shape[-2:]
......
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