ControlNet

ControlNet conditions the stable diffusion model with an additional input image. In Optimum Neuron, we support the compilation of one or multiple ControlNet(s) along with the stable diffusion checkpoint. Then you can use the compiled artifacts to generate styled images.

Export to Neuron

We can either compile one or multiple ControlNet via the Optimum CLI or programatically via the NeuronStableDiffusionControlNetPipeline class by passing the controlnet_ids.

Option 1: CLI

optimum-cli export neuron -m runwayml/stable-diffusion-v1-5 --batch_size 1 --height 512 --width 512 --controlnet_ids lllyasviel/sd-controlnet-canny --num_images_per_prompt 1 sd_neuron_controlnet/

Option 2: Python API

from optimum.neuron import NeuronStableDiffusionControlNetPipeline

model_id = "runwayml/stable-diffusion-v1-5"
controlnet_id = "lllyasviel/sd-controlnet-canny"

# [Neuron] pipeline
input_shapes = {"batch_size": 1, "height": 512, "width": 512, "num_images_per_prompt": 1}
compiler_args = {"auto_cast": "matmul", "auto_cast_type": "bf16"}
pipe = NeuronStableDiffusionControlNetPipeline.from_pretrained(
    model_id,
    controlnet_ids=controlnet_id,
    export=True,
    **input_shapes,
    **compiler_args,
)
pipe.save_pretrained("sd_neuron_controlnet")

Text-to-Image

For text-to-image, we can specify an additional conditioning input.

Here is an example with a canny image, a white outline of an image on a black background. The ControlNet will use the canny image as a control to guide the model to generate an image with the same outline.

import cv2
import numpy as np
from diffusers import UniPCMultistepScheduler
from diffusers.utils import load_image, make_image_grid
from PIL import Image

from optimum.neuron import NeuronStableDiffusionControlNetPipeline


# prepare canny image
original_image = load_image(
    "https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png"
)

image = np.array(original_image)

low_threshold = 100
high_threshold = 200

image = cv2.Canny(image, low_threshold, high_threshold)
image = image[:, :, None]
image = np.concatenate([image, image, image], axis=2)
canny_image = Image.fromarray(image)

# load pre-compiled neuron model
pipe = NeuronStableDiffusionControlNetPipeline.from_pretrained("sd_neuron_controlnet")
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)

# inference
output = pipe("the mona lisa", image=canny_image).images[0]
compare = make_image_grid([original_image, canny_image, output], rows=1, cols=3)
compare.save("compare.png")

stable diffusion 1.5 generated image with controlnet.

MultiControlNet

With Optimum Neuron, you can also compose multiple ControlNet conditionings from different image inputs:

Compile multiple ControlNet for SD1.5

optimum-cli export neuron --inline-weights-neff --model jyoung105/stable-diffusion-v1-5 --task stable-diffusion --auto_cast matmul --auto_cast_type bf16 --batch_size 1 --num_images_per_prompt 1 --controlnet_ids lllyasviel/control_v11p_sd15_openpose lllyasviel/control_v11f1p_sd15_depth --height 512 --width 512 sd15-512x512-bf16-openpose-depth

Run SD1.5 with OpenPose and Depth conditionings:

import numpy as np
import torch
from PIL import Image

from controlnet_aux import OpenposeDetector
from transformers import pipeline
from diffusers import UniPCMultistepScheduler
from diffusers.utils import load_image
from optimum.neuron import NeuronStableDiffusionControlNetPipeline


# OpenPose+Depth ControlNet
model_id = "sd15-512x512-bf16-openpose-depth"

# Load ControlNet images

# 1. openpose
image = load_image("https://huggingface.co/lllyasviel/control_v11p_sd15_openpose/resolve/main/images/input.png")
processor = OpenposeDetector.from_pretrained('lllyasviel/ControlNet')
openpose_image = processor(image)

# 2. depth
image = load_image("https://huggingface.co/lllyasviel/control_v11p_sd15_depth/resolve/main/images/input.png")
depth_estimator = pipeline('depth-estimation')
image = depth_estimator(image)['depth']
image = np.array(image)
image = image[:, :, None]
image = np.concatenate([image, image, image], axis=2)
depth_image = Image.fromarray(image)

images = [openpose_image.resize((512, 512)), depth_image.resize((512, 512))]

# 3. inference
pipe = NeuronStableDiffusionControlNetPipeline.from_pretrained(model_id)
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
prompt = "a giant in a fantasy landscape, best quality"
negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"

image = pipe(prompt=prompt, image=images).images[0]
image.save('out.png')

stable diffusion 1.5 generated image with OpenPose and Depth controlnet.

ControlNet with Stable Diffusion XL

Export to Neuron

optimum-cli export neuron -m stabilityai/stable-diffusion-xl-base-1.0 --task stable-diffusion-xl --batch_size 1 --height 1024 --width 1024 --controlnet_ids diffusers/controlnet-canny-sdxl-1.0-small --num_images_per_prompt 1 sdxl_neuron_controlnet/

Text-to-Image

import cv2
import numpy as np
from diffusers.utils import load_image
from PIL import Image
from optimum.neuron import NeuronStableDiffusionXLControlNetPipeline

# Inputs
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://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/hf-logo.png"
)
image = np.array(image)
image = cv2.Canny(image, 100, 200)
image = image[:, :, None]
image = np.concatenate([image, image, image], axis=2)
image = Image.fromarray(image)

controlnet_conditioning_scale = 0.5  # recommended for good generalization

pipe = NeuronStableDiffusionXLControlNetPipeline.from_pretrained("sdxl_neuron_controlnet")

images = pipe(
    prompt,
    negative_prompt=negative_prompt,
    image=image,
    controlnet_conditioning_scale=controlnet_conditioning_scale,
).images
images[0].save("hug_lab.png")

stable diffusion xl generated image with controlnet.

NeuronStableDiffusionControlNetPipeline

class optimum.neuron.NeuronStableDiffusionControlNetPipeline

< source >

( config: typing.Dict[str, typing.Any] configs: typing.Dict[str, ForwardRef('PretrainedConfig')] neuron_configs: typing.Dict[str, ForwardRef('NeuronDefaultConfig')] data_parallel_mode: typing.Literal['none', 'unet', 'transformer', 'all'] scheduler: typing.Optional[diffusers.schedulers.scheduling_utils.SchedulerMixin] vae_decoder: typing.Union[torch.jit._script.ScriptModule, ForwardRef('NeuronModelVaeDecoder')] text_encoder: typing.Union[torch.jit._script.ScriptModule, ForwardRef('NeuronModelTextEncoder'), NoneType] = None text_encoder_2: typing.Union[torch.jit._script.ScriptModule, ForwardRef('NeuronModelTextEncoder'), NoneType] = None unet: typing.Union[torch.jit._script.ScriptModule, ForwardRef('NeuronModelUnet'), NoneType] = None transformer: typing.Union[torch.jit._script.ScriptModule, ForwardRef('NeuronModelTransformer'), NoneType] = None vae_encoder: typing.Union[torch.jit._script.ScriptModule, ForwardRef('NeuronModelVaeEncoder'), NoneType] = None image_encoder: typing.Optional[torch.jit._script.ScriptModule] = None safety_checker: typing.Optional[torch.jit._script.ScriptModule] = None tokenizer: typing.Union[transformers.models.clip.tokenization_clip.CLIPTokenizer, transformers.utils.dummy_sentencepiece_objects.T5Tokenizer, NoneType] = None tokenizer_2: typing.Optional[transformers.models.clip.tokenization_clip.CLIPTokenizer] = None feature_extractor: typing.Optional[transformers.models.clip.feature_extraction_clip.CLIPFeatureExtractor] = None controlnet: typing.Union[torch.jit._script.ScriptModule, typing.List[torch.jit._script.ScriptModule], ForwardRef('NeuronControlNetModel'), ForwardRef('NeuronMultiControlNetModel'), NoneType] = None requires_aesthetics_score: bool = False force_zeros_for_empty_prompt: bool = True add_watermarker: typing.Optional[bool] = None model_save_dir: typing.Union[str, pathlib.Path, tempfile.TemporaryDirectory, NoneType] = None model_and_config_save_paths: typing.Optional[typing.Dict[str, typing.Tuple[str, pathlib.Path]]] = None )

call

< source >

( prompt: typing.Union[str, typing.List[str]] = None image: typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]] = None num_inference_steps: int = 50 timesteps: typing.Optional[typing.List[int]] = None sigmas: typing.Optional[typing.List[float]] = None guidance_scale: float = 7.5 negative_prompt: typing.Union[str, typing.List[str], NoneType] = None num_images_per_prompt: typing.Optional[int] = 1 eta: float = 0.0 generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None latents: typing.Optional[torch.Tensor] = None prompt_embeds: typing.Optional[torch.Tensor] = None negative_prompt_embeds: typing.Optional[torch.Tensor] = None ip_adapter_image: typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor], NoneType] = None ip_adapter_image_embeds: typing.Optional[typing.List[torch.Tensor]] = None output_type: str = 'pil' return_dict: bool = True cross_attention_kwargs: typing.Optional[typing.Dict[str, typing.Any]] = None controlnet_conditioning_scale: typing.Union[float, typing.List[float]] = 1.0 guess_mode: bool = False control_guidance_start: typing.Union[float, typing.List[float]] = 0.0 control_guidance_end: typing.Union[float, typing.List[float]] = 1.0 clip_skip: typing.Optional[int] = None callback_on_step_end: typing.Union[typing.Callable[[int, int, typing.Dict], NoneType], diffusers.callbacks.PipelineCallback, diffusers.callbacks.MultiPipelineCallbacks, NoneType] = None callback_on_step_end_tensor_inputs: typing.List[str] = ['latents'] **kwargs ) → diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput or tuple

Parameters

prompt (Optional[Union[str, List[str]]], defaults to None) — The prompt or prompts to guide image generation. If not defined, you need to pass prompt_embeds.
image (Optional["PipelineImageInput"], defaults to None) — The ControlNet input condition to provide guidance to the unet for generation. If the type is specified as torch.Tensor, it is passed to ControlNet as is. PIL.Image.Image can also be accepted as an image. The dimensions of the output image defaults to image’s dimensions. If height and/or width are passed, image is resized accordingly. If multiple ControlNets are specified in init, images must be passed as a list such that each element of the list can be correctly batched for input to a single ControlNet. When prompt is a list, and if a list of images is passed for a single ControlNet, each will be paired with each prompt in the prompt list. This also applies to multiple ControlNets, where a list of image lists can be passed to batch for each prompt and each ControlNet.
num_inference_steps (int, defaults to 50) — The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference.
timesteps (Optional[List[int]], defaults to None) — Custom timesteps to use for the denoising process with schedulers which support a timesteps argument in their set_timesteps method. If not defined, the default behavior when num_inference_steps is passed will be used. Must be in descending order.
sigmas (Optional[List[int]], defaults to None) — Custom sigmas to use for the denoising process with schedulers which support a sigmas argument in their set_timesteps method. If not defined, the default behavior when num_inference_steps is passed will be used.
guidance_scale (float, defaults to 7.5) — A higher guidance scale value encourages the model to generate images closely linked to the text prompt at the expense of lower image quality. Guidance scale is enabled when guidance_scale > 1.
negative_prompt (Optional[Union[str, List[str]]], defaults to None) — The prompt or prompts to guide what to not include in image generation. If not defined, you need to pass negative_prompt_embeds instead. Ignored when not using guidance (guidance_scale < 1).
num_images_per_prompt (int, defaults to 1) — The number of images to generate per prompt. If it is different from the batch size used for the compiltaion, it will be overriden by the static batch size of neuron (except for dynamic batching).
eta (float, defaults to 0.0) — Corresponds to parameter eta (η) from the DDIM paper. Only applies to the diffusers.schedulers.DDIMScheduler, and is ignored in other schedulers.
generator (Optional[Union[torch.Generator, List[torch.Generator]]], defaults to None) — A torch.Generator to make generation deterministic.
latents (Optional[torch.Tensor], defaults to None) — 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 tensor is generated by sampling using the supplied random generator.
prompt_embeds (Optional[torch.Tensor], defaults to None) — Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not provided, text embeddings are generated from the prompt input argument.
negative_prompt_embeds (Optional[torch.Tensor], defaults to None) — Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not provided, negative_prompt_embeds are generated from the negative_prompt input argument.
ip_adapter_image — (Optional[PipelineImageInput], defaults to None): Optional image input to work with IP Adapters.
ip_adapter_image_embeds (Optional[List[torch.Tensor]], defaults to None) — Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of IP-adapters. Each element should be a tensor of shape (batch_size, num_images, emb_dim). It should contain the negative image embedding if do_classifier_free_guidance is set to True. If not provided, embeddings are computed from the ip_adapter_image input argument.
output_type (str, defaults to "pil") — The output format of the generated image. Choose between PIL.Image or np.array.
return_dict (bool, defaults to True) — Whether or not to return a diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput instead of a plain tuple.
cross_attention_kwargs (Optional[Dict[str, Any]], defaults to None) — A kwargs dictionary that if specified is passed along to the AttentionProcessor as defined in self.processor.
controlnet_conditioning_scale (Union[float, List[float]], defaults to 1.0) — 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 corresponding scale as a list.
guess_mode (bool, defaults to False) — The ControlNet encoder tries to recognize the content of the input image even if you remove all prompts. A guidance_scale value between 3.0 and 5.0 is recommended.
control_guidance_start (Union[float, List[float]], defaults to 0.0) — The percentage of total steps at which the ControlNet starts applying.
control_guidance_end (Union[float, List[float]], optional, defaults to 1.0) — The percentage of total steps at which the ControlNet stops applying.
clip_skip (Optional[int], defaults to None) — Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that the output of the pre-final layer will be used for computing the prompt embeddings.
callback_on_step_end (Optional[Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]], defaults to None) — A function or a subclass of PipelineCallback or MultiPipelineCallbacks that is called at the end of each denoising step during the inference. with the following arguments: callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict). callback_kwargs will include a list of all tensors as specified by callback_on_step_end_tensor_inputs.
callback_on_step_end_tensor_inputs (List[str], defaults to ["latents"]) — The list of tensor inputs for the callback_on_step_end function. The tensors specified in the list will be passed as callback_kwargs argument. You will only be able to include variables listed in the ._callback_tensor_inputs attribute of your pipeline class.

Returns

diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput or tuple

If return_dict is True, diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput is returned, otherwise a tuple is returned where the first element is a list with the generated images and the second element is a list of bools indicating whether the corresponding generated image contains “not-safe-for-work” (nsfw) content.

The call function to the pipeline for generation.

NeuronStableDiffusionXLControlNetPipeline

class optimum.neuron.NeuronStableDiffusionXLControlNetPipeline

< source >

call

< source >

( prompt: typing.Union[str, typing.List[str], NoneType] = None prompt_2: typing.Union[str, typing.List[str], NoneType] = None image: typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]] = None num_inference_steps: int = 50 timesteps: typing.List[int] = None sigmas: typing.List[float] = None denoising_end: typing.Optional[float] = None guidance_scale: float = 5.0 negative_prompt: typing.Union[str, typing.List[str], NoneType] = None negative_prompt_2: typing.Union[str, typing.List[str], NoneType] = None num_images_per_prompt: typing.Optional[int] = 1 eta: float = 0.0 generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None latents: typing.Optional[torch.Tensor] = None prompt_embeds: typing.Optional[torch.Tensor] = None negative_prompt_embeds: typing.Optional[torch.Tensor] = None pooled_prompt_embeds: typing.Optional[torch.Tensor] = None negative_pooled_prompt_embeds: typing.Optional[torch.Tensor] = None ip_adapter_image: typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor], NoneType] = None ip_adapter_image_embeds: typing.Optional[typing.List[torch.Tensor]] = None output_type: typing.Optional[str] = 'pil' return_dict: bool = True cross_attention_kwargs: typing.Optional[typing.Dict[str, typing.Any]] = None controlnet_conditioning_scale: typing.Union[float, typing.List[float]] = 1.0 guess_mode: bool = False control_guidance_start: typing.Union[float, typing.List[float]] = 0.0 control_guidance_end: typing.Union[float, typing.List[float]] = 1.0 original_size: typing.Optional[typing.Tuple[int, int]] = None crops_coords_top_left: typing.Tuple[int, int] = (0, 0) target_size: typing.Optional[typing.Tuple[int, int]] = None negative_original_size: typing.Optional[typing.Tuple[int, int]] = None negative_crops_coords_top_left: typing.Tuple[int, int] = (0, 0) negative_target_size: typing.Optional[typing.Tuple[int, int]] = None clip_skip: typing.Optional[int] = None callback_on_step_end: typing.Union[typing.Callable[[int, int, typing.Dict], NoneType], diffusers.callbacks.PipelineCallback, diffusers.callbacks.MultiPipelineCallbacks, NoneType] = None callback_on_step_end_tensor_inputs: typing.List[str] = ['latents'] **kwargs ) → diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput or tuple

Parameters

prompt (Optional[Union[str, List[str]]], defaults to None) — The prompt or prompts to guide image generation. If not defined, you need to pass prompt_embeds.
prompt_2 (Optional[Union[str, List[str]]], defaults to None) — The prompt or prompts to be sent to tokenizer_2 and text_encoder_2. If not defined, prompt is used in both text-encoders.
image (Optional["PipelineImageInput"], defaults to None) — The ControlNet input condition to provide guidance to the unet for generation. If the type is specified as torch.Tensor, it is passed to ControlNet as is. PIL.Image.Image can also be accepted as an image. The dimensions of the output image defaults to image’s dimensions. If height and/or width are passed, image is resized accordingly. If multiple ControlNets are specified in init, images must be passed as a list such that each element of the list can be correctly batched for input to a single ControlNet.
num_inference_steps (int, defaults to 50) — The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference.
timesteps (Optional[List[int]], defaults to None) — Custom timesteps to use for the denoising process with schedulers which support a timesteps argument in their set_timesteps method. If not defined, the default behavior when num_inference_steps is passed will be used. Must be in descending order.
sigmas (Optional[List[int]], defaults to None) — Custom sigmas to use for the denoising process with schedulers which support a sigmas argument in their set_timesteps method. If not defined, the default behavior when num_inference_steps is passed will be used.
denoising_end (Optional[float], defaults to None) — When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be completed before it is intentionally prematurely terminated. As a result, the returned sample will still retain a substantial amount of noise as determined by the discrete timesteps selected by the scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a “Mixture of Denoisers” multi-pipeline setup, as elaborated in Refining the Image Output
guidance_scale (float, defaults to 5.0) — A higher guidance scale value encourages the model to generate images closely linked to the text prompt at the expense of lower image quality. Guidance scale is enabled when guidance_scale > 1.
negative_prompt (Optional[Union[str, List[str]]], defaults to None) — The prompt or prompts to guide what to not include in image generation. If not defined, you need to pass negative_prompt_embeds instead. Ignored when not using guidance (guidance_scale < 1).
negative_prompt_2 (Optional[Union[str, List[str]]], defaults to None) — The prompt or prompts to guide what to not include in image generation. This is sent to tokenizer_2 and text_encoder_2. If not defined, negative_prompt is used in both text-encoders.
num_images_per_prompt (int, defaults to 1) — The number of images to generate per prompt.
eta (float, defaults to 0.0) — Corresponds to parameter eta (η) from the DDIM paper. Only applies to the diffusers.schedulers.DDIMScheduler, and is ignored in other schedulers.
generator (Optional[Union[torch.Generator, List[torch.Generator]]], defaults to None) — A torch.Generator to make generation deterministic.
latents (Optional[torch.Tensor], defaults to None) — 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 tensor is generated by sampling using the supplied random generator.
prompt_embeds (Optional[torch.Tensor], defaults to None) — Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not provided, text embeddings are generated from the prompt input argument.
negative_prompt_embeds (Optional[torch.Tensor], defaults to None) — Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not provided, negative_prompt_embeds are generated from the negative_prompt input argument.
pooled_prompt_embeds (Optional[torch.Tensor], defaults to None) — Pre-generated pooled text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not provided, pooled text embeddings are generated from prompt input argument.
negative_pooled_prompt_embeds (Optional[torch.Tensor], defaults to None) — Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not provided, pooled negative_prompt_embeds are generated from negative_prompt input argument.
ip_adapter_image — (Optional[PipelineImageInput], defaults to None): Optional image input to work with IP Adapters.
ip_adapter_image_embeds (Optional[List[torch.Tensor]], defaults to None) — Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of IP-adapters. Each element should be a tensor of shape (batch_size, num_images, emb_dim). It should contain the negative image embedding if do_classifier_free_guidance is set to True. If not provided, embeddings are computed from the ip_adapter_image input argument.
output_type (Optional[str], defaults to "pil") — The output format of the generated image. Choose between PIL.Image or np.array.
return_dict (bool, defaults to True) — Whether or not to return a ~pipelines.stable_diffusion.StableDiffusionPipelineOutput instead of a plain tuple.
cross_attention_kwargs (Optional[Dict[str, Any]], defaults to None) — A kwargs dictionary that if specified is passed along to the AttentionProcessor as defined in self.processor.
controlnet_conditioning_scale (Union[float, List[float]], defaults to 1.0) — 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 corresponding scale as a list.
guess_mode (bool, defaults to False) — The ControlNet encoder tries to recognize the content of the input image even if you remove all prompts. A guidance_scale value between 3.0 and 5.0 is recommended.
control_guidance_start (Union[float, List[float]], defaults to 0.0) — The percentage of total steps at which the ControlNet starts applying.
control_guidance_end (Union[float, List[float]], defaults to 1.0) — The percentage of total steps at which the ControlNet stops applying.
original_size (Optional[Tuple[int, int]], defaults to (1024, 1024)) — If original_size is not the same as target_size the image will appear to be down- or upsampled. original_size defaults to (height, width) if not specified. Part of SDXL’s micro-conditioning as explained in section 2.2 of https://huggingface.co/papers/2307.01952.
crops_coords_top_left (Tuple[int, int], defaults to (0, 0)) — crops_coords_top_left can be used to generate an image that appears to be “cropped” from the position crops_coords_top_left downwards. Favorable, well-centered images are usually achieved by setting crops_coords_top_left to (0, 0). Part of SDXL’s micro-conditioning as explained in section 2.2 of https://huggingface.co/papers/2307.01952.
target_size (Optional[Tuple[int, int]], defaults to None) — For most cases, target_size should be set to the desired height and width of the generated image. If not specified it will default to (height, width). Part of SDXL’s micro-conditioning as explained in section 2.2 of https://huggingface.co/papers/2307.01952.
negative_original_size (Optional[Tuple[int, int]], defaults to None) — To negatively condition the generation process based on a specific image resolution. Part of SDXL’s micro-conditioning as explained in section 2.2 of https://huggingface.co/papers/2307.01952. For more information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
negative_crops_coords_top_left (Tuple[int, int], defaults to (0, 0)) — To negatively condition the generation process based on a specific crop coordinates. Part of SDXL’s micro-conditioning as explained in section 2.2 of https://huggingface.co/papers/2307.01952. For more information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
negative_target_size (Optional[Tuple[int, int]], defaults to None) — To negatively condition the generation process based on a target image resolution. It should be as same as the target_size for most cases. Part of SDXL’s micro-conditioning as explained in section 2.2 of https://huggingface.co/papers/2307.01952. For more information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
clip_skip (Optional[int], defaults to None) — Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that the output of the pre-final layer will be used for computing the prompt embeddings.
callback_on_step_end (Optional[Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]], defaults to None) — A function or a subclass of PipelineCallback or MultiPipelineCallbacks that is called at the end of each denoising step during the inference. with the following arguments: callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict). callback_kwargs will include a list of all tensors as specified by callback_on_step_end_tensor_inputs.
callback_on_step_end_tensor_inputs (List[str], defaults to ["latents"]) — The list of tensor inputs for the callback_on_step_end function. The tensors specified in the list will be passed as callback_kwargs argument. You will only be able to include variables listed in the ._callback_tensor_inputs attribute of your pipeline class.

Returns

diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput or tuple

If return_dict is True, diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput is returned, otherwise a tuple is returned containing the output images.

The call function to the pipeline for generation.

Examples:

Are there any other diffusion features that you want us to support in 🤗Optimum-neuron? Please file an issue to Optimum-neuron Github repo or discuss with us on HuggingFace’s community forum, cheers 🤗 !

AWS Trainium & Inferentia

ControlNet

Export to Neuron

Option 1: CLI

Option 2: Python API

Text-to-Image

MultiControlNet

ControlNet with Stable Diffusion XL

Export to Neuron

Text-to-Image

NeuronStableDiffusionControlNetPipeline

class optimum.neuron.NeuronStableDiffusionControlNetPipeline

__call__

NeuronStableDiffusionXLControlNetPipeline

class optimum.neuron.NeuronStableDiffusionXLControlNetPipeline

__call__

call

call