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[docs] ControlNet guide (#4640) 

* first draft

* finish first draft

* feedback and remove sections from API pages

* clean docstrings

* add full code example
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docs/source/en/_toctree.yml
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...@@ -64,6 +64,8 @@ ...@@ -64,6 +64,8 @@
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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docs/source/en/api/pipelines/controlnet_sdxl.md
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...@@ -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__
\ No newline at end of file
## StableDiffusionPipelineOutput
[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput
\ No newline at end of file
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src/diffusers/pipelines/controlnet/pipeline_controlnet.py
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...@@ -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
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...@@ -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_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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