text
stringlengths 1
1.02k
| class_index
int64 0
1.38k
| source
stringclasses 431
values |
|---|---|---|
if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None:
raise ValueError(
"If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`."
)
# `prompt` needs more sophisticated handling when there are multiple
# conditionings.
if isinstance(self.controlnet, MultiControlNetModel):
if isinstance(prompt, list):
logger.warning(
f"You have {len(self.controlnet.nets)} ControlNets and you have passed {len(prompt)}"
" prompts. The conditionings will be fixed across the prompts."
) | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py |
# Check `image`
is_compiled = hasattr(F, "scaled_dot_product_attention") and isinstance(
self.controlnet, torch._dynamo.eval_frame.OptimizedModule
)
if (
isinstance(self.controlnet, ControlNetModel)
or is_compiled
and isinstance(self.controlnet._orig_mod, ControlNetModel)
):
self.check_image(image, prompt, prompt_embeds)
elif (
isinstance(self.controlnet, MultiControlNetModel)
or is_compiled
and isinstance(self.controlnet._orig_mod, MultiControlNetModel)
):
if not isinstance(image, list):
raise TypeError("For multiple controlnets: `image` must be type `list`") | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py |
# When `image` is a nested list:
# (e.g. [[canny_image_1, pose_image_1], [canny_image_2, pose_image_2]])
elif any(isinstance(i, list) for i in image):
raise ValueError("A single batch of multiple conditionings are supported at the moment.")
elif len(image) != len(self.controlnet.nets):
raise ValueError(
f"For multiple controlnets: `image` must have the same length as the number of controlnets, but got {len(image)} images and {len(self.controlnet.nets)} ControlNets."
)
for image_ in image:
self.check_image(image_, prompt, prompt_embeds)
else:
assert False | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py |
# Check `controlnet_conditioning_scale`
if (
isinstance(self.controlnet, ControlNetModel)
or is_compiled
and isinstance(self.controlnet._orig_mod, ControlNetModel)
):
if not isinstance(controlnet_conditioning_scale, float):
raise TypeError("For single controlnet: `controlnet_conditioning_scale` must be type `float`.")
elif (
isinstance(self.controlnet, MultiControlNetModel)
or is_compiled
and isinstance(self.controlnet._orig_mod, MultiControlNetModel)
):
if isinstance(controlnet_conditioning_scale, list):
if any(isinstance(i, list) for i in controlnet_conditioning_scale):
raise ValueError("A single batch of multiple conditionings are supported at the moment.")
elif isinstance(controlnet_conditioning_scale, list) and len(controlnet_conditioning_scale) != len(
self.controlnet.nets
): | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py |
raise ValueError(
"For multiple controlnets: When `controlnet_conditioning_scale` is specified as `list`, it must have"
" the same length as the number of controlnets"
)
else:
assert False | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py |
if not isinstance(control_guidance_start, (tuple, list)):
control_guidance_start = [control_guidance_start]
if not isinstance(control_guidance_end, (tuple, list)):
control_guidance_end = [control_guidance_end]
if len(control_guidance_start) != len(control_guidance_end):
raise ValueError(
f"`control_guidance_start` has {len(control_guidance_start)} elements, but `control_guidance_end` has {len(control_guidance_end)} elements. Make sure to provide the same number of elements to each list."
)
if isinstance(self.controlnet, MultiControlNetModel):
if len(control_guidance_start) != len(self.controlnet.nets):
raise ValueError(
f"`control_guidance_start`: {control_guidance_start} has {len(control_guidance_start)} elements but there are {len(self.controlnet.nets)} controlnets available. Make sure to provide {len(self.controlnet.nets)}."
) | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py |
for start, end in zip(control_guidance_start, control_guidance_end):
if start >= end:
raise ValueError(
f"control guidance start: {start} cannot be larger or equal to control guidance end: {end}."
)
if start < 0.0:
raise ValueError(f"control guidance start: {start} can't be smaller than 0.")
if end > 1.0:
raise ValueError(f"control guidance end: {end} can't be larger than 1.0.")
if ip_adapter_image is not None and ip_adapter_image_embeds is not None:
raise ValueError(
"Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined."
) | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py |
if ip_adapter_image_embeds is not None:
if not isinstance(ip_adapter_image_embeds, list):
raise ValueError(
f"`ip_adapter_image_embeds` has to be of type `list` but is {type(ip_adapter_image_embeds)}"
)
elif ip_adapter_image_embeds[0].ndim not in [3, 4]:
raise ValueError(
f"`ip_adapter_image_embeds` has to be a list of 3D or 4D tensors but is {ip_adapter_image_embeds[0].ndim}D"
) | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py |
# Copied from diffusers.pipelines.controlnet.pipeline_controlnet_sd_xl.StableDiffusionXLControlNetPipeline.check_image
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) | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py |
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)}"
)
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] | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py |
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}"
)
# Copied from diffusers.pipelines.controlnet.pipeline_controlnet_sd_xl.StableDiffusionXLControlNetPipeline.prepare_image
def prepare_control_image(
self,
image,
width,
height,
batch_size,
num_images_per_prompt,
device,
dtype,
do_classifier_free_guidance=False,
guess_mode=False,
):
image = self.control_image_processor.preprocess(image, height=height, width=width).to(dtype=torch.float32)
image_batch_size = image.shape[0] | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py |
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)
if do_classifier_free_guidance and not guess_mode:
image = torch.cat([image] * 2)
return image
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps
def get_timesteps(self, num_inference_steps, strength, device):
# get the original timestep using init_timestep
init_timestep = min(int(num_inference_steps * strength), num_inference_steps) | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py |
t_start = max(num_inference_steps - init_timestep, 0)
timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
if hasattr(self.scheduler, "set_begin_index"):
self.scheduler.set_begin_index(t_start * self.scheduler.order)
return timesteps, num_inference_steps - t_start
# Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl_img2img.StableDiffusionXLImg2ImgPipeline.prepare_latents
def prepare_latents(
self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None, add_noise=True
):
if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
raise ValueError(
f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
) | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py |
latents_mean = latents_std = None
if hasattr(self.vae.config, "latents_mean") and self.vae.config.latents_mean is not None:
latents_mean = torch.tensor(self.vae.config.latents_mean).view(1, 4, 1, 1)
if hasattr(self.vae.config, "latents_std") and self.vae.config.latents_std is not None:
latents_std = torch.tensor(self.vae.config.latents_std).view(1, 4, 1, 1)
# Offload text encoder if `enable_model_cpu_offload` was enabled
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
self.text_encoder_2.to("cpu")
torch.cuda.empty_cache()
image = image.to(device=device, dtype=dtype)
batch_size = batch_size * num_images_per_prompt
if image.shape[1] == 4:
init_latents = image | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py |
else:
# make sure the VAE is in float32 mode, as it overflows in float16
if self.vae.config.force_upcast:
image = image.float()
self.vae.to(dtype=torch.float32)
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."
) | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py |
elif isinstance(generator, list):
if image.shape[0] < batch_size and batch_size % image.shape[0] == 0:
image = torch.cat([image] * (batch_size // image.shape[0]), dim=0)
elif image.shape[0] < batch_size and batch_size % image.shape[0] != 0:
raise ValueError(
f"Cannot duplicate `image` of batch size {image.shape[0]} to effective batch_size {batch_size} "
)
init_latents = [
retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])
for i in range(batch_size)
]
init_latents = torch.cat(init_latents, dim=0)
else:
init_latents = retrieve_latents(self.vae.encode(image), generator=generator)
if self.vae.config.force_upcast:
self.vae.to(dtype) | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py |
init_latents = init_latents.to(dtype)
if latents_mean is not None and latents_std is not None:
latents_mean = latents_mean.to(device=device, dtype=dtype)
latents_std = latents_std.to(device=device, dtype=dtype)
init_latents = (init_latents - latents_mean) * self.vae.config.scaling_factor / latents_std
else:
init_latents = self.vae.config.scaling_factor * init_latents | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py |
if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:
# expand init_latents for batch_size
additional_image_per_prompt = batch_size // init_latents.shape[0]
init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0)
elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:
raise ValueError(
f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
)
else:
init_latents = torch.cat([init_latents], dim=0)
if add_noise:
shape = init_latents.shape
noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
# get latents
init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
latents = init_latents
return latents | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py |
# Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl_img2img.StableDiffusionXLImg2ImgPipeline._get_add_time_ids
def _get_add_time_ids(
self,
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,
text_encoder_projection_dim=None,
):
if self.config.requires_aesthetics_score:
add_time_ids = list(original_size + crops_coords_top_left + (aesthetic_score,))
add_neg_time_ids = list(
negative_original_size + negative_crops_coords_top_left + (negative_aesthetic_score,)
)
else:
add_time_ids = list(original_size + crops_coords_top_left + target_size)
add_neg_time_ids = list(negative_original_size + crops_coords_top_left + negative_target_size) | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py |
passed_add_embed_dim = (
self.unet.config.addition_time_embed_dim * len(add_time_ids) + text_encoder_projection_dim
)
expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py |
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( | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_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`."
) | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_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) | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_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 | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_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, | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_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,
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,
aesthetic_score: float = 6.0,
negative_aesthetic_score: float = 2.5, | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py |
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"],
pag_scale: float = 3.0,
pag_adaptive_scale: float = 0.0,
):
r"""
Function invoked when calling the pipeline for generation. | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_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. | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py |
control_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 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): | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py |
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)
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 | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py |
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
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`, | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py |
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
`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 | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py |
[`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)
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. | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py |
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.
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. | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py |
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
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`): | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py |
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
[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`): | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py |
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.
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 | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py |
[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
`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 | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py |
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
[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 | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py |
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
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 | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py |
[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
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 | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py |
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*):
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.
pag_scale (`float`, *optional*, defaults to 3.0):
The scale factor for the perturbed attention guidance. If it is set to 0.0, the perturbed attention
guidance will not be used.
pag_adaptive_scale (`float`, *optional*, defaults to 0.0): | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py |
The adaptive scale factor for the perturbed attention guidance. If it is set to 0.0, `pag_scale` is
used. | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py |
Examples:
Returns:
[`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a
`tuple` containing the output images.
"""
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 | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py |
# 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]
elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):
mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1
control_guidance_start, control_guidance_end = (
mult * [control_guidance_start],
mult * [control_guidance_end],
) | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py |
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt,
prompt_2,
control_image,
strength,
num_inference_steps,
None,
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,
)
self._guidance_scale = guidance_scale
self._clip_skip = clip_skip
self._cross_attention_kwargs = cross_attention_kwargs
self._pag_scale = pag_scale
self._pag_adaptive_scale = pag_adaptive_scale | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py |
# 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
if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):
controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets) | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_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,
) | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_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:
ip_adapter_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) | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py |
if isinstance(controlnet, ControlNetModel):
control_image = self.prepare_control_image(
image=control_image,
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=False,
)
height, width = control_image.shape[-2:]
elif isinstance(controlnet, MultiControlNetModel):
control_images = [] | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py |
for control_image_ in control_image:
control_image_ = self.prepare_control_image(
image=control_image_,
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=False,
)
control_images.append(control_image_)
control_image = control_images
height, width = control_image[0].shape[-2:]
else:
assert False | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py |
# 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)
# 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) | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py |
# 7.1 Create tensor stating which controlnets to keep
controlnet_keep = []
for i in range(len(timesteps)):
keeps = [
1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)
for s, e in zip(control_guidance_start, control_guidance_end)
]
controlnet_keep.append(keeps[0] if isinstance(controlnet, ControlNetModel) else keeps)
# 7.2 Prepare added time ids & embeddings
if isinstance(control_image, list):
original_size = original_size or control_image[0].shape[-2:]
else:
original_size = original_size or control_image.shape[-2:]
target_size = target_size or (height, width)
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 | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py |
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
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)
add_neg_time_ids = add_neg_time_ids.repeat(batch_size * num_images_per_prompt, 1) | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py |
control_images = control_image if isinstance(control_image, list) else [control_image]
for i, single_image in enumerate(control_images):
if self.do_classifier_free_guidance:
single_image = single_image.chunk(2)[0]
if self.do_perturbed_attention_guidance:
single_image = self._prepare_perturbed_attention_guidance(
single_image, single_image, self.do_classifier_free_guidance
)
elif self.do_classifier_free_guidance:
single_image = torch.cat([single_image] * 2)
single_image = single_image.to(device)
control_images[i] = single_image
control_image = control_images if isinstance(control_image, list) else control_images[0] | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py |
if ip_adapter_image_embeds is not None:
for i, image_embeds in enumerate(ip_adapter_image_embeds):
negative_image_embeds = None
if self.do_classifier_free_guidance:
negative_image_embeds, image_embeds = image_embeds.chunk(2)
if self.do_perturbed_attention_guidance:
image_embeds = self._prepare_perturbed_attention_guidance(
image_embeds, negative_image_embeds, self.do_classifier_free_guidance
)
elif self.do_classifier_free_guidance:
image_embeds = torch.cat([negative_image_embeds, image_embeds], dim=0)
image_embeds = image_embeds.to(device)
ip_adapter_image_embeds[i] = image_embeds | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py |
if self.do_perturbed_attention_guidance:
prompt_embeds = self._prepare_perturbed_attention_guidance(
prompt_embeds, negative_prompt_embeds, self.do_classifier_free_guidance
)
add_text_embeds = self._prepare_perturbed_attention_guidance(
add_text_embeds, negative_pooled_prompt_embeds, self.do_classifier_free_guidance
)
add_time_ids = self._prepare_perturbed_attention_guidance(
add_time_ids, add_neg_time_ids, self.do_classifier_free_guidance
)
elif 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_time_ids = torch.cat([add_neg_time_ids, add_time_ids], dim=0) | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_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)
added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
controlnet_prompt_embeds = prompt_embeds
controlnet_added_cond_kwargs = added_cond_kwargs
# 8. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
if self.do_perturbed_attention_guidance:
original_attn_proc = self.unet.attn_processors
self._set_pag_attn_processor(
pag_applied_layers=self.pag_applied_layers,
do_classifier_free_guidance=self.do_classifier_free_guidance,
) | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py |
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * (prompt_embeds.shape[0] // latents.shape[0]))
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# controlnet(s) inference
control_model_input = latent_model_input | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_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,
conditioning_scale=cond_scale,
guess_mode=False,
added_cond_kwargs=controlnet_added_cond_kwargs,
return_dict=False,
) | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py |
if ip_adapter_image_embeds is not None:
added_cond_kwargs["image_embeds"] = ip_adapter_image_embeds
# 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] | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py |
# perform guidance
if self.do_perturbed_attention_guidance:
noise_pred = self._apply_perturbed_attention_guidance(
noise_pred, self.do_classifier_free_guidance, self.guidance_scale, t
)
elif 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]
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) | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py |
latents = callback_outputs.pop("latents", latents)
prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
add_text_embeds = callback_outputs.pop("add_text_embeds", add_text_embeds)
negative_pooled_prompt_embeds = callback_outputs.pop(
"negative_pooled_prompt_embeds", negative_pooled_prompt_embeds
)
add_time_ids = callback_outputs.pop("add_time_ids", add_time_ids)
add_neg_time_ids = callback_outputs.pop("add_neg_time_ids", add_neg_time_ids)
# 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 XLA_AVAILABLE:
xm.mark_step() | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py |
# 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
if needs_upcasting:
self.upcast_vae()
latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype) | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_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] | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_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 self.do_perturbed_attention_guidance:
self.unet.set_attn_processor(original_attn_proc)
if not return_dict:
return (image,)
return StableDiffusionXLPipelineOutput(images=image) | 342 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py |
class HunyuanDiTPAGPipeline(DiffusionPipeline, PAGMixin):
r"""
Pipeline for English/Chinese-to-image generation using HunyuanDiT and [Perturbed Attention
Guidance](https://huggingface.co/docs/diffusers/en/using-diffusers/pag).
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.)
HunyuanDiT uses two text encoders: [mT5](https://huggingface.co/google/mt5-base) and [bilingual CLIP](fine-tuned by
ourselves) | 343 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.py |
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. We use
`sdxl-vae-fp16-fix`.
text_encoder (Optional[`~transformers.BertModel`, `~transformers.CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
HunyuanDiT uses a fine-tuned [bilingual CLIP].
tokenizer (Optional[`~transformers.BertTokenizer`, `~transformers.CLIPTokenizer`]):
A `BertTokenizer` or `CLIPTokenizer` to tokenize text.
transformer ([`HunyuanDiT2DModel`]):
The HunyuanDiT model designed by Tencent Hunyuan.
text_encoder_2 (`T5EncoderModel`):
The mT5 embedder. Specifically, it is 't5-v1_1-xxl'.
tokenizer_2 (`MT5Tokenizer`):
The tokenizer for the mT5 embedder.
scheduler ([`DDPMScheduler`]): | 343 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.py |
A scheduler to be used in combination with HunyuanDiT to denoise the encoded image latents.
""" | 343 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.py |
model_cpu_offload_seq = "text_encoder->text_encoder_2->transformer->vae"
_optional_components = [
"safety_checker",
"feature_extractor",
"text_encoder_2",
"tokenizer_2",
"text_encoder",
"tokenizer",
]
_exclude_from_cpu_offload = ["safety_checker"]
_callback_tensor_inputs = [
"latents",
"prompt_embeds",
"negative_prompt_embeds",
"prompt_embeds_2",
"negative_prompt_embeds_2",
] | 343 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.py |
def __init__(
self,
vae: AutoencoderKL,
text_encoder: BertModel,
tokenizer: BertTokenizer,
transformer: HunyuanDiT2DModel,
scheduler: DDPMScheduler,
safety_checker: Optional[StableDiffusionSafetyChecker] = None,
feature_extractor: Optional[CLIPImageProcessor] = None,
requires_safety_checker: bool = True,
text_encoder_2: Optional[T5EncoderModel] = None,
tokenizer_2: Optional[MT5Tokenizer] = None,
pag_applied_layers: Union[str, List[str]] = "blocks.1", # "blocks.16.attn1", "blocks.16", "16", 16
):
super().__init__()
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
tokenizer_2=tokenizer_2,
transformer=transformer,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
text_encoder_2=text_encoder_2,
) | 343 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.py |
if safety_checker is None and requires_safety_checker:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
) | 343 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.py |
if safety_checker is not None and feature_extractor is None:
raise ValueError(
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
)
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.register_to_config(requires_safety_checker=requires_safety_checker)
self.default_sample_size = (
self.transformer.config.sample_size
if hasattr(self, "transformer") and self.transformer is not None
else 128
)
self.set_pag_applied_layers(
pag_applied_layers, pag_attn_processors=(PAGCFGHunyuanAttnProcessor2_0(), PAGHunyuanAttnProcessor2_0())
) | 343 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.py |
# Copied from diffusers.pipelines.hunyuandit.pipeline_hunyuandit.HunyuanDiTPipeline.encode_prompt
def encode_prompt(
self,
prompt: str,
device: torch.device = None,
dtype: torch.dtype = None,
num_images_per_prompt: int = 1,
do_classifier_free_guidance: bool = True,
negative_prompt: Optional[str] = None,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
prompt_attention_mask: Optional[torch.Tensor] = None,
negative_prompt_attention_mask: Optional[torch.Tensor] = None,
max_sequence_length: Optional[int] = None,
text_encoder_index: int = 0,
):
r"""
Encodes the prompt into text encoder hidden states. | 343 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.py |
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
device: (`torch.device`):
torch device
dtype (`torch.dtype`):
torch dtype
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`).
prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not | 343 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.py |
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.
prompt_attention_mask (`torch.Tensor`, *optional*):
Attention mask for the prompt. Required when `prompt_embeds` is passed directly.
negative_prompt_attention_mask (`torch.Tensor`, *optional*):
Attention mask for the negative prompt. Required when `negative_prompt_embeds` is passed directly.
max_sequence_length (`int`, *optional*): maximum sequence length to use for the prompt.
text_encoder_index (`int`, *optional*):
Index of the text encoder to use. `0` for clip and `1` for T5.
"""
if dtype is None: | 343 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.py |
if self.text_encoder_2 is not None:
dtype = self.text_encoder_2.dtype
elif self.transformer is not None:
dtype = self.transformer.dtype
else:
dtype = None | 343 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.py |
if device is None:
device = self._execution_device
tokenizers = [self.tokenizer, self.tokenizer_2]
text_encoders = [self.text_encoder, self.text_encoder_2]
tokenizer = tokenizers[text_encoder_index]
text_encoder = text_encoders[text_encoder_index]
if max_sequence_length is None:
if text_encoder_index == 0:
max_length = 77
if text_encoder_index == 1:
max_length = 256
else:
max_length = max_sequence_length
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] | 343 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.py |
if prompt_embeds is None:
text_inputs = tokenizer(
prompt,
padding="max_length",
max_length=max_length,
truncation=True,
return_attention_mask=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}"
) | 343 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.py |
prompt_attention_mask = text_inputs.attention_mask.to(device)
prompt_embeds = text_encoder(
text_input_ids.to(device),
attention_mask=prompt_attention_mask,
)
prompt_embeds = prompt_embeds[0]
prompt_attention_mask = prompt_attention_mask.repeat(num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
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) | 343 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.py |
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif 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 isinstance(negative_prompt, str):
uncond_tokens = [negative_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" | 343 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.py |
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt | 343 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.py |
max_length = prompt_embeds.shape[1]
uncond_input = tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
negative_prompt_attention_mask = uncond_input.attention_mask.to(device)
negative_prompt_embeds = text_encoder(
uncond_input.input_ids.to(device),
attention_mask=negative_prompt_attention_mask,
)
negative_prompt_embeds = negative_prompt_embeds[0]
negative_prompt_attention_mask = negative_prompt_attention_mask.repeat(num_images_per_prompt, 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]
negative_prompt_embeds = negative_prompt_embeds.to(dtype=dtype, device=device) | 343 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.py |
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)
return prompt_embeds, negative_prompt_embeds, prompt_attention_mask, negative_prompt_attention_mask | 343 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
def run_safety_checker(self, image, device, dtype):
if self.safety_checker is None:
has_nsfw_concept = None
else:
if torch.is_tensor(image):
feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
else:
feature_extractor_input = self.image_processor.numpy_to_pil(image)
safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
image, has_nsfw_concept = self.safety_checker(
images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
)
return image, has_nsfw_concept | 343 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.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 | 343 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.py |
# Copied from diffusers.pipelines.hunyuandit.pipeline_hunyuandit.HunyuanDiTPipeline.check_inputs
def check_inputs(
self,
prompt,
height,
width,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
prompt_attention_mask=None,
negative_prompt_attention_mask=None,
prompt_embeds_2=None,
negative_prompt_embeds_2=None,
prompt_attention_mask_2=None,
negative_prompt_attention_mask_2=None,
callback_on_step_end_tensor_inputs=None,
):
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") | 343 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.py |
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]}"
) | 343 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.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 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 None and prompt_embeds_2 is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds_2`. Cannot leave both `prompt` and `prompt_embeds_2` 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)}") | 343 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.py |
if prompt_embeds is not None and prompt_attention_mask is None:
raise ValueError("Must provide `prompt_attention_mask` when specifying `prompt_embeds`.")
if prompt_embeds_2 is not None and prompt_attention_mask_2 is None:
raise ValueError("Must provide `prompt_attention_mask_2` when specifying `prompt_embeds_2`.")
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."
)
if negative_prompt_embeds is not None and negative_prompt_attention_mask is None:
raise ValueError("Must provide `negative_prompt_attention_mask` when specifying `negative_prompt_embeds`.") | 343 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.py |
if negative_prompt_embeds_2 is not None and negative_prompt_attention_mask_2 is None:
raise ValueError(
"Must provide `negative_prompt_attention_mask_2` when specifying `negative_prompt_embeds_2`."
)
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}."
)
if prompt_embeds_2 is not None and negative_prompt_embeds_2 is not None:
if prompt_embeds_2.shape != negative_prompt_embeds_2.shape:
raise ValueError(
"`prompt_embeds_2` and `negative_prompt_embeds_2` must have the same shape when passed directly, but" | 343 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.py |
f" got: `prompt_embeds_2` {prompt_embeds_2.shape} != `negative_prompt_embeds_2`"
f" {negative_prompt_embeds_2.shape}."
) | 343 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
shape = (
batch_size,
num_channels_latents,
int(height) // self.vae_scale_factor,
int(width) // self.vae_scale_factor,
)
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) | 343 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.py |
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
@property
def guidance_scale(self):
return self._guidance_scale
@property
def guidance_rescale(self):
return self._guidance_rescale
# 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 num_timesteps(self):
return self._num_timesteps
@property
def interrupt(self):
return self._interrupt | 343 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: Optional[int] = 50,
guidance_scale: Optional[float] = 5.0,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: Optional[float] = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.Tensor] = None,
prompt_embeds: Optional[torch.Tensor] = None,
prompt_embeds_2: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds_2: Optional[torch.Tensor] = None,
prompt_attention_mask: Optional[torch.Tensor] = None,
prompt_attention_mask_2: Optional[torch.Tensor] = None, | 343 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.py |
negative_prompt_attention_mask: Optional[torch.Tensor] = None,
negative_prompt_attention_mask_2: Optional[torch.Tensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback_on_step_end: Optional[
Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
] = None,
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
guidance_rescale: float = 0.0,
original_size: Optional[Tuple[int, int]] = (1024, 1024),
target_size: Optional[Tuple[int, int]] = None,
crops_coords_top_left: Tuple[int, int] = (0, 0),
use_resolution_binning: bool = True,
pag_scale: float = 3.0,
pag_adaptive_scale: float = 0.0,
):
r"""
The call function to the pipeline for generation with HunyuanDiT. | 343 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.py |
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
height (`int`):
The height in pixels of the generated image.
width (`int`):
The width in pixels of the generated 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. This parameter is modulated by `strength`.
guidance_scale (`float`, *optional*, 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 (`str` or `List[str]`, *optional*): | 343 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.py |
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`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
prompt_embeds (`torch.Tensor`, *optional*): | 343 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.py |
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.
prompt_embeds_2 (`torch.Tensor`, *optional*):
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 (`torch.Tensor`, *optional*):
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.
negative_prompt_embeds_2 (`torch.Tensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If | 343 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.py |
not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
prompt_attention_mask (`torch.Tensor`, *optional*):
Attention mask for the prompt. Required when `prompt_embeds` is passed directly.
prompt_attention_mask_2 (`torch.Tensor`, *optional*):
Attention mask for the prompt. Required when `prompt_embeds_2` is passed directly.
negative_prompt_attention_mask (`torch.Tensor`, *optional*):
Attention mask for the negative prompt. Required when `negative_prompt_embeds` is passed directly.
negative_prompt_attention_mask_2 (`torch.Tensor`, *optional*):
Attention mask for the negative prompt. Required when `negative_prompt_embeds_2` is passed directly.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`. | 343 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.