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if (
expected_add_embed_dim > passed_add_embed_dim
and (expected_add_embed_dim - passed_add_embed_dim) == self.unet.config.addition_time_embed_dim
):
raise ValueError(
f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. Please make sure to enable `requires_aesthetics_score` with `pipe.register_to_config(requires_aesthetics_score=True)` to make sure `aesthetic_score` {aesthetic_score} and `negative_aesthetic_score` {negative_aesthetic_score} is correctly used by the model."
)
elif (
expected_add_embed_dim < passed_add_embed_dim
and (passed_add_embed_dim - expected_add_embed_dim) == self.unet.config.addition_time_embed_dim
):
raise ValueError( | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. Please make sure to disable `requires_aesthetics_score` with `pipe.register_to_config(requires_aesthetics_score=False)` to make sure `target_size` {target_size} is correctly used by the model."
)
elif expected_add_embed_dim != passed_add_embed_dim:
raise ValueError(
f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`."
) | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
add_time_ids = torch.tensor([add_time_ids], dtype=dtype)
add_neg_time_ids = torch.tensor([add_neg_time_ids], dtype=dtype)
return add_time_ids, add_neg_time_ids
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae
def upcast_vae(self):
dtype = self.vae.dtype
self.vae.to(dtype=torch.float32)
use_torch_2_0_or_xformers = isinstance(
self.vae.decoder.mid_block.attentions[0].processor,
(
AttnProcessor2_0,
XFormersAttnProcessor,
),
)
# if xformers or torch_2_0 is used attention block does not need
# to be in float32 which can save lots of memory
if use_torch_2_0_or_xformers:
self.vae.post_quant_conv.to(dtype)
self.vae.decoder.conv_in.to(dtype)
self.vae.decoder.mid_block.to(dtype) | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
@property
def guidance_scale(self):
return self._guidance_scale
@property
def clip_skip(self):
return self._clip_skip
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
@property
def do_classifier_free_guidance(self):
return self._guidance_scale > 1
@property
def cross_attention_kwargs(self):
return self._cross_attention_kwargs
@property
def num_timesteps(self):
return self._num_timesteps
@property
def interrupt(self):
return self._interrupt | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
prompt_2: Optional[Union[str, List[str]]] = None,
image: PipelineImageInput = None,
control_image: PipelineImageInput = None,
height: Optional[int] = None,
width: Optional[int] = None,
strength: float = 0.8,
num_inference_steps: int = 50,
guidance_scale: float = 5.0,
negative_prompt: Optional[Union[str, List[str]]] = None,
negative_prompt_2: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.Tensor] = None,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
pooled_prompt_embeds: Optional[torch.Tensor] = None, | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,
ip_adapter_image: Optional[PipelineImageInput] = None,
ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
controlnet_conditioning_scale: Union[float, List[float]] = 0.8,
guess_mode: bool = False,
control_guidance_start: Union[float, List[float]] = 0.0,
control_guidance_end: Union[float, List[float]] = 1.0,
control_mode: Optional[Union[int, List[int]]] = None,
original_size: Tuple[int, int] = None,
crops_coords_top_left: Tuple[int, int] = (0, 0),
target_size: Tuple[int, int] = None,
negative_original_size: Optional[Tuple[int, int]] = None,
negative_crops_coords_top_left: Tuple[int, int] = (0, 0),
negative_target_size: Optional[Tuple[int, int]] = None, | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
aesthetic_score: float = 6.0,
negative_aesthetic_score: float = 2.5,
clip_skip: Optional[int] = None,
callback_on_step_end: Optional[
Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
] = None,
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
**kwargs,
):
r"""
Function invoked when calling the pipeline for generation. | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
instead.
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
used in both text-encoders
image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:
`List[List[torch.Tensor]]`, `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
image latents as `image`, if passing latents directly, it will not be encoded again.
control_image (`PipelineImageInput`): | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
The ControlNet input condition. ControlNet uses this input condition to generate guidance to Unet. 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 according to them. 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.
height (`int`, *optional*, defaults to the size of control_image):
The height in pixels of the generated image. Anything below 512 pixels won't work well for
[stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
and checkpoints that are not specifically fine-tuned on low resolutions.
width (`int`, *optional*, defaults to the size of control_image):
The width in pixels of the generated image. Anything below 512 pixels won't work well for
[stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
and checkpoints that are not specifically fine-tuned on low resolutions.
strength (`float`, *optional*, defaults to 0.8):
Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a
starting point and more noise is added the higher the `strength`. The number of denoising steps depends
on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising
process runs for the full number of iterations specified in `num_inference_steps`. A value of 1 | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
essentially ignores `image`.
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
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
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*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
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
`text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
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
[`schedulers.DDIMScheduler`], will be ignored for others.
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) | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
to make generation deterministic.
latents (`torch.Tensor`, *optional*):
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 will ge generated by sampling using the supplied random `generator`.
prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument. | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
pooled_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
If not provided, pooled text embeddings will be generated from `prompt` input argument.
negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
input argument.
ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):
Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
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`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
`self.processor` in | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.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):
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`, *optional*, defaults to `False`):
In this mode, the ControlNet encoder will try best to recognize the content of the input image even if
you remove all prompts. The `guidance_scale` between 3.0 and 5.0 is recommended.
control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0):
The percentage of total steps at which the controlnet starts applying. | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0):
The percentage of total steps at which the controlnet stops applying.
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.
`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](https://huggingface.co/papers/2307.01952).
crops_coords_top_left (`Tuple[int]`, *optional*, 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 | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
`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](https://huggingface.co/papers/2307.01952).
target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
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](https://huggingface.co/papers/2307.01952).
negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
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 | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
[https://huggingface.co/papers/2307.01952](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]`, *optional*, 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](https://huggingface.co/papers/2307.01952). For more
information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
To negatively condition the generation process based on a target image resolution. It should be as same | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
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](https://huggingface.co/papers/2307.01952). For more
information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
aesthetic_score (`float`, *optional*, defaults to 6.0):
Used to simulate an aesthetic score of the generated image by influencing the positive text condition.
Part of SDXL's micro-conditioning as explained in section 2.2 of
[https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
negative_aesthetic_score (`float`, *optional*, defaults to 2.5):
Part of SDXL's micro-conditioning as explained in section 2.2 of
[https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). Can be used to | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
simulate an aesthetic score of the generated image by influencing the negative text condition.
clip_skip (`int`, *optional*):
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 (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*):
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`, *optional*): | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
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. | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
Examples:
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple`
containing the output images.
"""
callback = kwargs.pop("callback", None)
callback_steps = kwargs.pop("callback_steps", None)
if callback is not None:
deprecate(
"callback",
"1.0.0",
"Passing `callback` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`",
)
if callback_steps is not None:
deprecate(
"callback_steps",
"1.0.0",
"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`",
) | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet
# align format for control guidance
if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):
control_guidance_start = len(control_guidance_end) * [control_guidance_start]
elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):
control_guidance_end = len(control_guidance_start) * [control_guidance_end]
if not isinstance(control_image, list):
control_image = [control_image]
if not isinstance(control_mode, list):
control_mode = [control_mode]
if len(control_image) != len(control_mode):
raise ValueError("Expected len(control_image) == len(control_type)") | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
num_control_type = controlnet.config.num_control_type
# 1. Check inputs
control_type = [0 for _ in range(num_control_type)]
for _image, control_idx in zip(control_image, control_mode):
control_type[control_idx] = 1
self.check_inputs(
prompt,
prompt_2,
_image,
strength,
num_inference_steps,
callback_steps,
negative_prompt,
negative_prompt_2,
prompt_embeds,
negative_prompt_embeds,
pooled_prompt_embeds,
negative_pooled_prompt_embeds,
ip_adapter_image,
ip_adapter_image_embeds,
controlnet_conditioning_scale,
control_guidance_start,
control_guidance_end,
callback_on_step_end_tensor_inputs,
)
control_type = torch.Tensor(control_type) | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
self._guidance_scale = guidance_scale
self._clip_skip = clip_skip
self._cross_attention_kwargs = cross_attention_kwargs
self._interrupt = False
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
device = self._execution_device
global_pool_conditions = controlnet.config.global_pool_conditions
guess_mode = guess_mode or global_pool_conditions | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
# 3.1. Encode input prompt
text_encoder_lora_scale = (
self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
)
(
prompt_embeds,
negative_prompt_embeds,
pooled_prompt_embeds,
negative_pooled_prompt_embeds,
) = self.encode_prompt(
prompt,
prompt_2,
device,
num_images_per_prompt,
self.do_classifier_free_guidance,
negative_prompt,
negative_prompt_2,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
pooled_prompt_embeds=pooled_prompt_embeds,
negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
lora_scale=text_encoder_lora_scale,
clip_skip=self.clip_skip,
) | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
# 3.2 Encode ip_adapter_image
if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
image_embeds = self.prepare_ip_adapter_image_embeds(
ip_adapter_image,
ip_adapter_image_embeds,
device,
batch_size * num_images_per_prompt,
self.do_classifier_free_guidance,
)
# 4. Prepare image and controlnet_conditioning_image
image = self.image_processor.preprocess(image, height=height, width=width).to(dtype=torch.float32) | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
for idx, _ in enumerate(control_image):
control_image[idx] = self.prepare_control_image(
image=control_image[idx],
width=width,
height=height,
batch_size=batch_size * num_images_per_prompt,
num_images_per_prompt=num_images_per_prompt,
device=device,
dtype=controlnet.dtype,
do_classifier_free_guidance=self.do_classifier_free_guidance,
guess_mode=guess_mode,
)
height, width = control_image[idx].shape[-2:]
# 5. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
self._num_timesteps = len(timesteps) | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
# 6. Prepare latent variables
if latents is None:
latents = self.prepare_latents(
image,
latent_timestep,
batch_size,
num_images_per_prompt,
prompt_embeds.dtype,
device,
generator,
True,
)
# 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 7.1 Create tensor stating which controlnets to keep
controlnet_keep = []
for i in range(len(timesteps)):
controlnet_keep.append(
1.0
- float(i / len(timesteps) < control_guidance_start or (i + 1) / len(timesteps) > control_guidance_end)
) | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
# 7.2 Prepare added time ids & embeddings
original_size = original_size or (height, width)
target_size = target_size or (height, width)
for _image in control_image:
if isinstance(_image, torch.Tensor):
original_size = original_size or _image.shape[-2:]
if negative_original_size is None:
negative_original_size = original_size
if negative_target_size is None:
negative_target_size = target_size
add_text_embeds = pooled_prompt_embeds
if self.text_encoder_2 is None:
text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])
else:
text_encoder_projection_dim = self.text_encoder_2.config.projection_dim | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
add_time_ids, add_neg_time_ids = self._get_add_time_ids(
original_size,
crops_coords_top_left,
target_size,
aesthetic_score,
negative_aesthetic_score,
negative_original_size,
negative_crops_coords_top_left,
negative_target_size,
dtype=prompt_embeds.dtype,
text_encoder_projection_dim=text_encoder_projection_dim,
)
add_time_ids = add_time_ids.repeat(batch_size * num_images_per_prompt, 1)
if self.do_classifier_free_guidance:
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)
add_neg_time_ids = add_neg_time_ids.repeat(batch_size * num_images_per_prompt, 1)
add_time_ids = torch.cat([add_neg_time_ids, add_time_ids], dim=0) | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
prompt_embeds = prompt_embeds.to(device)
add_text_embeds = add_text_embeds.to(device)
add_time_ids = add_time_ids.to(device)
control_type = (
control_type.reshape(1, -1)
.to(device, dtype=prompt_embeds.dtype)
.repeat(batch_size * num_images_per_prompt * 2, 1)
)
# 8. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
if self.interrupt:
continue
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
added_cond_kwargs = {
"text_embeds": add_text_embeds,
"time_ids": add_time_ids,
}
# controlnet(s) inference
if guess_mode and self.do_classifier_free_guidance:
# Infer ControlNet only for the conditional batch.
control_model_input = latents
control_model_input = self.scheduler.scale_model_input(control_model_input, t)
controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]
controlnet_added_cond_kwargs = {
"text_embeds": add_text_embeds.chunk(2)[1],
"time_ids": add_time_ids.chunk(2)[1],
}
else:
control_model_input = latent_model_input
controlnet_prompt_embeds = prompt_embeds
controlnet_added_cond_kwargs = added_cond_kwargs | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
if isinstance(controlnet_keep[i], list):
cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]
else:
controlnet_cond_scale = controlnet_conditioning_scale
if isinstance(controlnet_cond_scale, list):
controlnet_cond_scale = controlnet_cond_scale[0]
cond_scale = controlnet_cond_scale * controlnet_keep[i]
down_block_res_samples, mid_block_res_sample = self.controlnet(
control_model_input,
t,
encoder_hidden_states=controlnet_prompt_embeds,
controlnet_cond=control_image,
control_type=control_type,
control_type_idx=control_mode,
conditioning_scale=cond_scale,
guess_mode=guess_mode,
added_cond_kwargs=controlnet_added_cond_kwargs, | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
return_dict=False,
) | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
if guess_mode and self.do_classifier_free_guidance:
# Inferred ControlNet only for the conditional batch.
# To apply the output of ControlNet to both the unconditional and conditional batches,
# add 0 to the unconditional batch to keep it unchanged.
down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]
mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])
if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
added_cond_kwargs["image_embeds"] = image_embeds | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
# predict the noise residual
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=self.cross_attention_kwargs,
down_block_additional_residuals=down_block_res_samples,
mid_block_additional_residual=mid_block_res_sample,
added_cond_kwargs=added_cond_kwargs,
return_dict=False,
)[0]
# perform guidance
if self.do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
if callback_on_step_end is not None:
callback_kwargs = {}
for k in callback_on_step_end_tensor_inputs:
callback_kwargs[k] = locals()[k]
callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
latents = callback_outputs.pop("latents", latents)
prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
add_text_embeds = callback_outputs.pop("add_text_embeds", add_text_embeds)
add_time_ids = callback_outputs.pop("add_time_ids", add_time_ids) | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
step_idx = i // getattr(self.scheduler, "order", 1)
callback(step_idx, t, latents)
if XLA_AVAILABLE:
xm.mark_step()
# If we do sequential model offloading, let's offload unet and controlnet
# manually for max memory savings
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
self.unet.to("cpu")
self.controlnet.to("cpu")
torch.cuda.empty_cache()
if not output_type == "latent":
# make sure the VAE is in float32 mode, as it overflows in float16
needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
if needs_upcasting:
self.upcast_vae()
latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype) | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
# unscale/denormalize the latents
# denormalize with the mean and std if available and not None
has_latents_mean = hasattr(self.vae.config, "latents_mean") and self.vae.config.latents_mean is not None
has_latents_std = hasattr(self.vae.config, "latents_std") and self.vae.config.latents_std is not None
if has_latents_mean and has_latents_std:
latents_mean = (
torch.tensor(self.vae.config.latents_mean).view(1, 4, 1, 1).to(latents.device, latents.dtype)
)
latents_std = (
torch.tensor(self.vae.config.latents_std).view(1, 4, 1, 1).to(latents.device, latents.dtype)
)
latents = latents * latents_std / self.vae.config.scaling_factor + latents_mean
else:
latents = latents / self.vae.config.scaling_factor
image = self.vae.decode(latents, return_dict=False)[0] | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
# cast back to fp16 if needed
if needs_upcasting:
self.vae.to(dtype=torch.float16)
else:
image = latents
return StableDiffusionXLPipelineOutput(images=image)
# apply watermark if available
if self.watermark is not None:
image = self.watermark.apply_watermark(image)
image = self.image_processor.postprocess(image, output_type=output_type)
# Offload all models
self.maybe_free_model_hooks()
if not return_dict:
return (image,)
return StableDiffusionXLPipelineOutput(images=image) | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
class BlipDiffusionControlNetPipeline(DiffusionPipeline):
"""
Pipeline for Canny Edge based Controlled subject-driven generation using Blip Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) | 92 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_blip_diffusion.py |
Args:
tokenizer ([`CLIPTokenizer`]):
Tokenizer for the text encoder
text_encoder ([`ContextCLIPTextModel`]):
Text encoder to encode the text prompt
vae ([`AutoencoderKL`]):
VAE model to map the latents to the image
unet ([`UNet2DConditionModel`]):
Conditional U-Net architecture to denoise the image embedding.
scheduler ([`PNDMScheduler`]):
A scheduler to be used in combination with `unet` to generate image latents.
qformer ([`Blip2QFormerModel`]):
QFormer model to get multi-modal embeddings from the text and image.
controlnet ([`ControlNetModel`]):
ControlNet model to get the conditioning image embedding.
image_processor ([`BlipImageProcessor`]):
Image Processor to preprocess and postprocess the image.
ctx_begin_pos (int, `optional`, defaults to 2):
Position of the context token in the text encoder.
""" | 92 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_blip_diffusion.py |
model_cpu_offload_seq = "qformer->text_encoder->unet->vae"
def __init__(
self,
tokenizer: CLIPTokenizer,
text_encoder: ContextCLIPTextModel,
vae: AutoencoderKL,
unet: UNet2DConditionModel,
scheduler: PNDMScheduler,
qformer: Blip2QFormerModel,
controlnet: ControlNetModel,
image_processor: BlipImageProcessor,
ctx_begin_pos: int = 2,
mean: List[float] = None,
std: List[float] = None,
):
super().__init__()
self.register_modules(
tokenizer=tokenizer,
text_encoder=text_encoder,
vae=vae,
unet=unet,
scheduler=scheduler,
qformer=qformer,
controlnet=controlnet,
image_processor=image_processor,
)
self.register_to_config(ctx_begin_pos=ctx_begin_pos, mean=mean, std=std) | 92 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_blip_diffusion.py |
def get_query_embeddings(self, input_image, src_subject):
return self.qformer(image_input=input_image, text_input=src_subject, return_dict=False)
# from the original Blip Diffusion code, speciefies the target subject and augments the prompt by repeating it
def _build_prompt(self, prompts, tgt_subjects, prompt_strength=1.0, prompt_reps=20):
rv = []
for prompt, tgt_subject in zip(prompts, tgt_subjects):
prompt = f"a {tgt_subject} {prompt.strip()}"
# a trick to amplify the prompt
rv.append(", ".join([prompt] * int(prompt_strength * prompt_reps)))
return rv | 92 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_blip_diffusion.py |
# Copied from diffusers.pipelines.consistency_models.pipeline_consistency_models.ConsistencyModelPipeline.prepare_latents
def prepare_latents(self, batch_size, num_channels, height, width, dtype, device, generator, latents=None):
shape = (batch_size, num_channels, height, width)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
latents = latents.to(device=device, dtype=dtype)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents | 92 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_blip_diffusion.py |
def encode_prompt(self, query_embeds, prompt, device=None):
device = device or self._execution_device
# embeddings for prompt, with query_embeds as context
max_len = self.text_encoder.text_model.config.max_position_embeddings
max_len -= self.qformer.config.num_query_tokens
tokenized_prompt = self.tokenizer(
prompt,
padding="max_length",
truncation=True,
max_length=max_len,
return_tensors="pt",
).to(device)
batch_size = query_embeds.shape[0]
ctx_begin_pos = [self.config.ctx_begin_pos] * batch_size
text_embeddings = self.text_encoder(
input_ids=tokenized_prompt.input_ids,
ctx_embeddings=query_embeds,
ctx_begin_pos=ctx_begin_pos,
)[0]
return text_embeddings | 92 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_blip_diffusion.py |
# Adapted from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.prepare_image
def prepare_control_image(
self,
image,
width,
height,
batch_size,
num_images_per_prompt,
device,
dtype,
do_classifier_free_guidance=False,
):
image = self.image_processor.preprocess(
image,
size={"width": width, "height": height},
do_rescale=True,
do_center_crop=False,
do_normalize=False,
return_tensors="pt",
)["pixel_values"].to(device)
image_batch_size = image.shape[0]
if image_batch_size == 1:
repeat_by = batch_size
else:
# image batch size is the same as prompt batch size
repeat_by = num_images_per_prompt
image = image.repeat_interleave(repeat_by, dim=0)
image = image.to(device=device, dtype=dtype) | 92 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_blip_diffusion.py |
if do_classifier_free_guidance:
image = torch.cat([image] * 2)
return image
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: List[str],
reference_image: PIL.Image.Image,
condtioning_image: PIL.Image.Image,
source_subject_category: List[str],
target_subject_category: List[str],
latents: Optional[torch.Tensor] = None,
guidance_scale: float = 7.5,
height: int = 512,
width: int = 512,
num_inference_steps: int = 50,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
neg_prompt: Optional[str] = "",
prompt_strength: float = 1.0,
prompt_reps: int = 20,
output_type: Optional[str] = "pil",
return_dict: bool = True,
):
"""
Function invoked when calling the pipeline for generation. | 92 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_blip_diffusion.py |
Args:
prompt (`List[str]`):
The prompt or prompts to guide the image generation.
reference_image (`PIL.Image.Image`):
The reference image to condition the generation on.
condtioning_image (`PIL.Image.Image`):
The conditioning canny edge image to condition the generation on.
source_subject_category (`List[str]`):
The source subject category.
target_subject_category (`List[str]`):
The target subject category.
latents (`torch.Tensor`, *optional*):
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 will ge generated by random sampling.
guidance_scale (`float`, *optional*, defaults to 7.5): | 92 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_blip_diffusion.py |
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
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.
height (`int`, *optional*, defaults to 512):
The height of the generated image.
width (`int`, *optional*, defaults to 512):
The width of the generated image.
seed (`int`, *optional*, defaults to 42):
The seed to use for random generation.
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 | 92 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_blip_diffusion.py |
expense of slower inference.
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)
to make generation deterministic.
neg_prompt (`str`, *optional*, defaults to ""):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
prompt_strength (`float`, *optional*, defaults to 1.0):
The strength of the prompt. Specifies the number of times the prompt is repeated along with prompt_reps
to amplify the prompt.
prompt_reps (`int`, *optional*, defaults to 20):
The number of times the prompt is repeated along with prompt_strength to amplify the prompt.
Examples: | 92 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_blip_diffusion.py |
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`
"""
device = self._execution_device
reference_image = self.image_processor.preprocess(
reference_image, image_mean=self.config.mean, image_std=self.config.std, return_tensors="pt"
)["pixel_values"]
reference_image = reference_image.to(device)
if isinstance(prompt, str):
prompt = [prompt]
if isinstance(source_subject_category, str):
source_subject_category = [source_subject_category]
if isinstance(target_subject_category, str):
target_subject_category = [target_subject_category]
batch_size = len(prompt) | 92 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_blip_diffusion.py |
prompt = self._build_prompt(
prompts=prompt,
tgt_subjects=target_subject_category,
prompt_strength=prompt_strength,
prompt_reps=prompt_reps,
)
query_embeds = self.get_query_embeddings(reference_image, source_subject_category)
text_embeddings = self.encode_prompt(query_embeds, prompt, device)
# 3. unconditional embedding
do_classifier_free_guidance = guidance_scale > 1.0
if do_classifier_free_guidance:
max_length = self.text_encoder.text_model.config.max_position_embeddings | 92 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_blip_diffusion.py |
uncond_input = self.tokenizer(
[neg_prompt] * batch_size,
padding="max_length",
max_length=max_length,
return_tensors="pt",
)
uncond_embeddings = self.text_encoder(
input_ids=uncond_input.input_ids.to(device),
ctx_embeddings=None,
)[0]
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
scale_down_factor = 2 ** (len(self.unet.config.block_out_channels) - 1)
latents = self.prepare_latents(
batch_size=batch_size,
num_channels=self.unet.config.in_channels,
height=height // scale_down_factor,
width=width // scale_down_factor,
generator=generator, | 92 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_blip_diffusion.py |
latents=latents,
dtype=self.unet.dtype,
device=device,
)
# set timesteps
extra_set_kwargs = {}
self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs) | 92 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_blip_diffusion.py |
cond_image = self.prepare_control_image(
image=condtioning_image,
width=width,
height=height,
batch_size=batch_size,
num_images_per_prompt=1,
device=device,
dtype=self.controlnet.dtype,
do_classifier_free_guidance=do_classifier_free_guidance,
)
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
# expand the latents if we are doing classifier free guidance
do_classifier_free_guidance = guidance_scale > 1.0
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
down_block_res_samples, mid_block_res_sample = self.controlnet(
latent_model_input,
t,
encoder_hidden_states=text_embeddings,
controlnet_cond=cond_image,
return_dict=False,
) | 92 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_blip_diffusion.py |
noise_pred = self.unet(
latent_model_input,
timestep=t,
encoder_hidden_states=text_embeddings,
down_block_additional_residuals=down_block_res_samples,
mid_block_additional_residual=mid_block_res_sample,
)["sample"]
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
latents = self.scheduler.step(
noise_pred,
t,
latents,
)["prev_sample"]
if XLA_AVAILABLE:
xm.mark_step()
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
image = self.image_processor.postprocess(image, output_type=output_type) | 92 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_blip_diffusion.py |
# Offload all models
self.maybe_free_model_hooks()
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image) | 92 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_blip_diffusion.py |
class StableDiffusionXLControlNetInpaintPipeline(
DiffusionPipeline,
StableDiffusionMixin,
StableDiffusionXLLoraLoaderMixin,
FromSingleFileMixin,
IPAdapterMixin,
TextualInversionLoaderMixin,
):
r"""
Pipeline for text-to-image generation using Stable Diffusion XL.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
The pipeline also inherits the following loading methods:
- [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
- [`~loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
- [`~loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights`] for saving LoRA weights
- [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
- [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]):
Frozen text-encoder. Stable Diffusion XL uses the text portion of
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
text_encoder_2 ([` CLIPTextModelWithProjection`]):
Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),
specifically the
[laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)
variant.
tokenizer (`CLIPTokenizer`):
Tokenizer of class | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
[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.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
""" | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
model_cpu_offload_seq = "text_encoder->text_encoder_2->unet->vae"
_optional_components = [
"tokenizer",
"tokenizer_2",
"text_encoder",
"text_encoder_2",
"image_encoder",
"feature_extractor",
]
_callback_tensor_inputs = [
"latents",
"prompt_embeds",
"negative_prompt_embeds",
"add_text_embeds",
"add_time_ids",
"negative_pooled_prompt_embeds",
"add_neg_time_ids",
"mask",
"masked_image_latents",
] | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
text_encoder_2: CLIPTextModelWithProjection,
tokenizer: CLIPTokenizer,
tokenizer_2: CLIPTokenizer,
unet: UNet2DConditionModel,
controlnet: Union[ControlNetModel, List[ControlNetModel], Tuple[ControlNetModel], MultiControlNetModel],
scheduler: KarrasDiffusionSchedulers,
requires_aesthetics_score: bool = False,
force_zeros_for_empty_prompt: bool = True,
add_watermarker: Optional[bool] = None,
feature_extractor: Optional[CLIPImageProcessor] = None,
image_encoder: Optional[CLIPVisionModelWithProjection] = None,
):
super().__init__()
if isinstance(controlnet, (list, tuple)):
controlnet = MultiControlNetModel(controlnet) | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
self.register_modules(
vae=vae,
text_encoder=text_encoder,
text_encoder_2=text_encoder_2,
tokenizer=tokenizer,
tokenizer_2=tokenizer_2,
unet=unet,
controlnet=controlnet,
scheduler=scheduler,
feature_extractor=feature_extractor,
image_encoder=image_encoder,
)
self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)
self.register_to_config(requires_aesthetics_score=requires_aesthetics_score)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
self.mask_processor = VaeImageProcessor(
vae_scale_factor=self.vae_scale_factor, do_normalize=False, do_binarize=True, do_convert_grayscale=True
)
self.control_image_processor = VaeImageProcessor( | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=False
) | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available()
if add_watermarker:
self.watermark = StableDiffusionXLWatermarker()
else:
self.watermark = None | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
# Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.encode_prompt
def encode_prompt(
self,
prompt: str,
prompt_2: Optional[str] = None,
device: Optional[torch.device] = None,
num_images_per_prompt: int = 1,
do_classifier_free_guidance: bool = True,
negative_prompt: Optional[str] = None,
negative_prompt_2: Optional[str] = None,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
pooled_prompt_embeds: Optional[torch.Tensor] = None,
negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,
lora_scale: Optional[float] = None,
clip_skip: Optional[int] = None,
):
r"""
Encodes the prompt into text encoder hidden states. | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
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
used in both text-encoders
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
negative_prompt_2 (`str` or `List[str]`, *optional*): | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
`text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
pooled_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
If not provided, pooled text embeddings will be generated from `prompt` input argument.
negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
input argument.
lora_scale (`float`, *optional*):
A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
clip_skip (`int`, *optional*):
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.
"""
device = device or self._execution_device | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
# set lora scale so that monkey patched LoRA
# function of text encoder can correctly access it
if lora_scale is not None and isinstance(self, StableDiffusionXLLoraLoaderMixin):
self._lora_scale = lora_scale
# dynamically adjust the LoRA scale
if self.text_encoder is not None:
if not USE_PEFT_BACKEND:
adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
else:
scale_lora_layers(self.text_encoder, lora_scale)
if self.text_encoder_2 is not None:
if not USE_PEFT_BACKEND:
adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale)
else:
scale_lora_layers(self.text_encoder_2, lora_scale)
prompt = [prompt] if isinstance(prompt, str) else prompt
if prompt is not None:
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0] | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
# Define tokenizers and text encoders
tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]
text_encoders = (
[self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]
)
if prompt_embeds is None:
prompt_2 = prompt_2 or prompt
prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2
# textual inversion: process multi-vector tokens if necessary
prompt_embeds_list = []
prompts = [prompt, prompt_2]
for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders):
if isinstance(self, TextualInversionLoaderMixin):
prompt = self.maybe_convert_prompt(prompt, tokenizer) | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
text_inputs = tokenizer(
prompt,
padding="max_length",
max_length=tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
text_input_ids, untruncated_ids
):
removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1])
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {tokenizer.model_max_length} tokens: {removed_text}"
) | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)
# We are only ALWAYS interested in the pooled output of the final text encoder
if pooled_prompt_embeds is None and prompt_embeds[0].ndim == 2:
pooled_prompt_embeds = prompt_embeds[0]
if clip_skip is None:
prompt_embeds = prompt_embeds.hidden_states[-2]
else:
# "2" because SDXL always indexes from the penultimate layer.
prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 2)]
prompt_embeds_list.append(prompt_embeds)
prompt_embeds = torch.concat(prompt_embeds_list, dim=-1) | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
# get unconditional embeddings for classifier free guidance
zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt
if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:
negative_prompt_embeds = torch.zeros_like(prompt_embeds)
negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)
elif do_classifier_free_guidance and negative_prompt_embeds is None:
negative_prompt = negative_prompt or ""
negative_prompt_2 = negative_prompt_2 or negative_prompt
# normalize str to list
negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
negative_prompt_2 = (
batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2
) | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
uncond_tokens: List[str]
if prompt is not None and type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = [negative_prompt, negative_prompt_2] | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
negative_prompt_embeds_list = []
for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):
if isinstance(self, TextualInversionLoaderMixin):
negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)
max_length = prompt_embeds.shape[1]
uncond_input = tokenizer(
negative_prompt,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
negative_prompt_embeds = text_encoder(
uncond_input.input_ids.to(device),
output_hidden_states=True,
) | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
# We are only ALWAYS interested in the pooled output of the final text encoder
if negative_pooled_prompt_embeds is None and negative_prompt_embeds[0].ndim == 2:
negative_pooled_prompt_embeds = negative_prompt_embeds[0]
negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]
negative_prompt_embeds_list.append(negative_prompt_embeds)
negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)
if self.text_encoder_2 is not None:
prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
else:
prompt_embeds = prompt_embeds.to(dtype=self.unet.dtype, device=device) | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
if self.text_encoder_2 is not None:
negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
else:
negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.unet.dtype, device=device)
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
bs_embed * num_images_per_prompt, -1
)
if do_classifier_free_guidance:
negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
bs_embed * num_images_per_prompt, -1
)
if self.text_encoder is not None:
if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:
# Retrieve the original scale by scaling back the LoRA layers
unscale_lora_layers(self.text_encoder, lora_scale)
if self.text_encoder_2 is not None:
if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:
# Retrieve the original scale by scaling back the LoRA layers
unscale_lora_layers(self.text_encoder_2, lora_scale) | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image
def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):
dtype = next(self.image_encoder.parameters()).dtype
if not isinstance(image, torch.Tensor):
image = self.feature_extractor(image, return_tensors="pt").pixel_values | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
image = image.to(device=device, dtype=dtype)
if output_hidden_states:
image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]
image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
uncond_image_enc_hidden_states = self.image_encoder(
torch.zeros_like(image), output_hidden_states=True
).hidden_states[-2]
uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(
num_images_per_prompt, dim=0
)
return image_enc_hidden_states, uncond_image_enc_hidden_states
else:
image_embeds = self.image_encoder(image).image_embeds
image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
uncond_image_embeds = torch.zeros_like(image_embeds)
return image_embeds, uncond_image_embeds | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds
def prepare_ip_adapter_image_embeds(
self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance
):
image_embeds = []
if do_classifier_free_guidance:
negative_image_embeds = []
if ip_adapter_image_embeds is None:
if not isinstance(ip_adapter_image, list):
ip_adapter_image = [ip_adapter_image]
if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers):
raise ValueError(
f"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters."
) | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
for single_ip_adapter_image, image_proj_layer in zip(
ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers
):
output_hidden_state = not isinstance(image_proj_layer, ImageProjection)
single_image_embeds, single_negative_image_embeds = self.encode_image(
single_ip_adapter_image, device, 1, output_hidden_state
)
image_embeds.append(single_image_embeds[None, :])
if do_classifier_free_guidance:
negative_image_embeds.append(single_negative_image_embeds[None, :])
else:
for single_image_embeds in ip_adapter_image_embeds:
if do_classifier_free_guidance:
single_negative_image_embeds, single_image_embeds = single_image_embeds.chunk(2)
negative_image_embeds.append(single_negative_image_embeds)
image_embeds.append(single_image_embeds) | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
ip_adapter_image_embeds = []
for i, single_image_embeds in enumerate(image_embeds):
single_image_embeds = torch.cat([single_image_embeds] * num_images_per_prompt, dim=0)
if do_classifier_free_guidance:
single_negative_image_embeds = torch.cat([negative_image_embeds[i]] * num_images_per_prompt, dim=0)
single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds], dim=0)
single_image_embeds = single_image_embeds.to(device=device)
ip_adapter_image_embeds.append(single_image_embeds)
return ip_adapter_image_embeds | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
def check_image(self, image, prompt, prompt_embeds):
image_is_pil = isinstance(image, PIL.Image.Image)
image_is_tensor = isinstance(image, torch.Tensor)
image_is_np = isinstance(image, np.ndarray)
image_is_pil_list = isinstance(image, list) and isinstance(image[0], PIL.Image.Image)
image_is_tensor_list = isinstance(image, list) and isinstance(image[0], torch.Tensor)
image_is_np_list = isinstance(image, list) and isinstance(image[0], np.ndarray)
if (
not image_is_pil
and not image_is_tensor
and not image_is_np
and not image_is_pil_list
and not image_is_tensor_list
and not image_is_np_list
):
raise TypeError(
f"image must be passed and be one of PIL image, numpy array, torch tensor, list of PIL images, list of numpy arrays or list of torch tensors, but is {type(image)}"
) | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
if image_is_pil:
image_batch_size = 1
else:
image_batch_size = len(image)
if prompt is not None and isinstance(prompt, str):
prompt_batch_size = 1
elif prompt is not None and isinstance(prompt, list):
prompt_batch_size = len(prompt)
elif prompt_embeds is not None:
prompt_batch_size = prompt_embeds.shape[0]
if image_batch_size != 1 and image_batch_size != prompt_batch_size:
raise ValueError(
f"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}"
) | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
def check_inputs(
self,
prompt,
prompt_2,
image,
mask_image,
strength,
num_inference_steps,
callback_steps,
output_type,
negative_prompt=None,
negative_prompt_2=None,
prompt_embeds=None,
negative_prompt_embeds=None,
ip_adapter_image=None,
ip_adapter_image_embeds=None,
pooled_prompt_embeds=None,
negative_pooled_prompt_embeds=None,
controlnet_conditioning_scale=1.0,
control_guidance_start=0.0,
control_guidance_end=1.0,
callback_on_step_end_tensor_inputs=None,
padding_mask_crop=None,
):
if strength < 0 or strength > 1:
raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
if num_inference_steps is None:
raise ValueError("`num_inference_steps` cannot be None.")
elif not isinstance(num_inference_steps, int) or num_inference_steps <= 0: | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
raise ValueError(
f"`num_inference_steps` has to be a positive integer but is {num_inference_steps} of type"
f" {type(num_inference_steps)}."
) | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if callback_on_step_end_tensor_inputs is not None and not all(
k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
):
raise ValueError(
f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
) | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt_2 is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):
raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}") | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
elif negative_prompt_2 is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
) | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
) | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
if padding_mask_crop is not None:
if not isinstance(image, PIL.Image.Image):
raise ValueError(
f"The image should be a PIL image when inpainting mask crop, but is of type" f" {type(image)}."
)
if not isinstance(mask_image, PIL.Image.Image):
raise ValueError(
f"The mask image should be a PIL image when inpainting mask crop, but is of type"
f" {type(mask_image)}."
)
if output_type != "pil":
raise ValueError(f"The output type should be PIL when inpainting mask crop, but is" f" {output_type}.")
if prompt_embeds is not None and pooled_prompt_embeds is None:
raise ValueError(
"If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`."
) | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
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