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# 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:
callback(i, t, intermediate_images)
if XLA_AVAILABLE:
xm.mark_step()
image = intermediate_images
if output_type == "pil":
# 9. Post-processing
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
# 10. Run safety checker
image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype)
# 11. Convert to PIL
image = self.numpy_to_pil(image) | 313 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_superresolution.py |
# 12. Apply watermark
if self.watermarker is not None:
self.watermarker.apply_watermark(image, self.unet.config.sample_size)
elif output_type == "pt":
nsfw_detected = None
watermark_detected = None
if hasattr(self, "unet_offload_hook") and self.unet_offload_hook is not None:
self.unet_offload_hook.offload()
else:
# 9. Post-processing
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
# 10. Run safety checker
image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype)
# Offload all models
self.maybe_free_model_hooks()
if not return_dict:
return (image, nsfw_detected, watermark_detected)
return IFPipelineOutput(images=image, nsfw_detected=nsfw_detected, watermark_detected=watermark_detected) | 313 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_superresolution.py |
class IFInpaintingPipeline(DiffusionPipeline, StableDiffusionLoraLoaderMixin):
tokenizer: T5Tokenizer
text_encoder: T5EncoderModel
unet: UNet2DConditionModel
scheduler: DDPMScheduler
feature_extractor: Optional[CLIPImageProcessor]
safety_checker: Optional[IFSafetyChecker]
watermarker: Optional[IFWatermarker]
bad_punct_regex = re.compile(
r"["
+ "#®•©™&@·º½¾¿¡§~"
+ r"\)"
+ r"\("
+ r"\]"
+ r"\["
+ r"\}"
+ r"\{"
+ r"\|"
+ "\\"
+ r"\/"
+ r"\*"
+ r"]{1,}"
) # noqa
_optional_components = ["tokenizer", "text_encoder", "safety_checker", "feature_extractor", "watermarker"]
model_cpu_offload_seq = "text_encoder->unet"
_exclude_from_cpu_offload = ["watermarker"] | 314 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py |
def __init__(
self,
tokenizer: T5Tokenizer,
text_encoder: T5EncoderModel,
unet: UNet2DConditionModel,
scheduler: DDPMScheduler,
safety_checker: Optional[IFSafetyChecker],
feature_extractor: Optional[CLIPImageProcessor],
watermarker: Optional[IFWatermarker],
requires_safety_checker: bool = True,
):
super().__init__() | 314 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.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 IF 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 ."
) | 314 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.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.register_modules(
tokenizer=tokenizer,
text_encoder=text_encoder,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
watermarker=watermarker,
)
self.register_to_config(requires_safety_checker=requires_safety_checker) | 314 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py |
@torch.no_grad()
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.encode_prompt
def encode_prompt(
self,
prompt: Union[str, List[str]],
do_classifier_free_guidance: bool = True,
num_images_per_prompt: int = 1,
device: Optional[torch.device] = None,
negative_prompt: Optional[Union[str, List[str]]] = None,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
clean_caption: bool = False,
):
r"""
Encodes the prompt into text encoder hidden states. | 314 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py |
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
whether to use classifier free guidance or not
num_images_per_prompt (`int`, *optional*, defaults to 1):
number of images that should be generated per prompt
device: (`torch.device`, *optional*):
torch device to place the resulting embeddings on
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. 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*): | 314 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py |
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.
clean_caption (bool, defaults to `False`):
If `True`, the function will preprocess and clean the provided caption before encoding.
"""
if prompt is not None and negative_prompt is not None:
if 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)}."
) | 314 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py |
if device is None:
device = self._execution_device
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]
# while T5 can handle much longer input sequences than 77, the text encoder was trained with a max length of 77 for IF
max_length = 77
if prompt_embeds is None:
prompt = self._text_preprocessing(prompt, clean_caption=clean_caption)
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=max_length,
truncation=True,
add_special_tokens=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids | 314 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py |
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
text_input_ids, untruncated_ids
):
removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_length - 1 : -1])
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {max_length} tokens: {removed_text}"
)
attention_mask = text_inputs.attention_mask.to(device)
prompt_embeds = self.text_encoder(
text_input_ids.to(device),
attention_mask=attention_mask,
)
prompt_embeds = prompt_embeds[0]
if self.text_encoder is not None:
dtype = self.text_encoder.dtype
elif self.unet is not None:
dtype = self.unet.dtype
else:
dtype = None
prompt_embeds = prompt_embeds.to(dtype=dtype, device=device) | 314 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.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) | 314 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.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 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"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt | 314 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py |
uncond_tokens = self._text_preprocessing(uncond_tokens, clean_caption=clean_caption)
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_attention_mask=True,
add_special_tokens=True,
return_tensors="pt",
)
attention_mask = uncond_input.attention_mask.to(device)
negative_prompt_embeds = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
negative_prompt_embeds = negative_prompt_embeds[0]
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1] | 314 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py |
negative_prompt_embeds = negative_prompt_embeds.to(dtype=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)
# 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
else:
negative_prompt_embeds = None
return prompt_embeds, negative_prompt_embeds | 314 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py |
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.run_safety_checker
def run_safety_checker(self, image, device, dtype):
if self.safety_checker is not None:
safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
image, nsfw_detected, watermark_detected = self.safety_checker(
images=image,
clip_input=safety_checker_input.pixel_values.to(dtype=dtype),
)
else:
nsfw_detected = None
watermark_detected = None
return image, nsfw_detected, watermark_detected | 314 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py |
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.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 | 314 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py |
def check_inputs(
self,
prompt,
image,
mask_image,
batch_size,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
):
if (callback_steps is None) or (
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)}."
) | 314 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.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 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)}")
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."
) | 314 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.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}."
)
# image
if isinstance(image, list):
check_image_type = image[0]
else:
check_image_type = image | 314 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py |
if (
not isinstance(check_image_type, torch.Tensor)
and not isinstance(check_image_type, PIL.Image.Image)
and not isinstance(check_image_type, np.ndarray)
):
raise ValueError(
"`image` has to be of type `torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, or List[...] but is"
f" {type(check_image_type)}"
)
if isinstance(image, list):
image_batch_size = len(image)
elif isinstance(image, torch.Tensor):
image_batch_size = image.shape[0]
elif isinstance(image, PIL.Image.Image):
image_batch_size = 1
elif isinstance(image, np.ndarray):
image_batch_size = image.shape[0]
else:
assert False
if batch_size != image_batch_size:
raise ValueError(f"image batch size: {image_batch_size} must be same as prompt batch size {batch_size}")
# mask_image | 314 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py |
if isinstance(mask_image, list):
check_image_type = mask_image[0]
else:
check_image_type = mask_image
if (
not isinstance(check_image_type, torch.Tensor)
and not isinstance(check_image_type, PIL.Image.Image)
and not isinstance(check_image_type, np.ndarray)
):
raise ValueError(
"`mask_image` has to be of type `torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, or List[...] but is"
f" {type(check_image_type)}"
)
if isinstance(mask_image, list):
image_batch_size = len(mask_image)
elif isinstance(mask_image, torch.Tensor):
image_batch_size = mask_image.shape[0]
elif isinstance(mask_image, PIL.Image.Image):
image_batch_size = 1
elif isinstance(mask_image, np.ndarray):
image_batch_size = mask_image.shape[0]
else:
assert False | 314 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py |
if image_batch_size != 1 and batch_size != image_batch_size:
raise ValueError(
f"mask_image batch size: {image_batch_size} must be `1` or the same as prompt batch size {batch_size}"
)
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._text_preprocessing
def _text_preprocessing(self, text, clean_caption=False):
if clean_caption and not is_bs4_available():
logger.warning(BACKENDS_MAPPING["bs4"][-1].format("Setting `clean_caption=True`"))
logger.warning("Setting `clean_caption` to False...")
clean_caption = False
if clean_caption and not is_ftfy_available():
logger.warning(BACKENDS_MAPPING["ftfy"][-1].format("Setting `clean_caption=True`"))
logger.warning("Setting `clean_caption` to False...")
clean_caption = False
if not isinstance(text, (tuple, list)):
text = [text] | 314 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py |
def process(text: str):
if clean_caption:
text = self._clean_caption(text)
text = self._clean_caption(text)
else:
text = text.lower().strip()
return text
return [process(t) for t in text] | 314 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py |
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._clean_caption
def _clean_caption(self, caption):
caption = str(caption)
caption = ul.unquote_plus(caption)
caption = caption.strip().lower()
caption = re.sub("<person>", "person", caption)
# urls:
caption = re.sub(
r"\b((?:https?:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))", # noqa
"",
caption,
) # regex for urls
caption = re.sub(
r"\b((?:www:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))", # noqa
"",
caption,
) # regex for urls
# html:
caption = BeautifulSoup(caption, features="html.parser").text
# @<nickname>
caption = re.sub(r"@[\w\d]+\b", "", caption) | 314 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py |
# 31C0—31EF CJK Strokes
# 31F0—31FF Katakana Phonetic Extensions
# 3200—32FF Enclosed CJK Letters and Months
# 3300—33FF CJK Compatibility
# 3400—4DBF CJK Unified Ideographs Extension A
# 4DC0—4DFF Yijing Hexagram Symbols
# 4E00—9FFF CJK Unified Ideographs
caption = re.sub(r"[\u31c0-\u31ef]+", "", caption)
caption = re.sub(r"[\u31f0-\u31ff]+", "", caption)
caption = re.sub(r"[\u3200-\u32ff]+", "", caption)
caption = re.sub(r"[\u3300-\u33ff]+", "", caption)
caption = re.sub(r"[\u3400-\u4dbf]+", "", caption)
caption = re.sub(r"[\u4dc0-\u4dff]+", "", caption)
caption = re.sub(r"[\u4e00-\u9fff]+", "", caption)
####################################################### | 314 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py |
# все виды тире / all types of dash --> "-"
caption = re.sub(
r"[\u002D\u058A\u05BE\u1400\u1806\u2010-\u2015\u2E17\u2E1A\u2E3A\u2E3B\u2E40\u301C\u3030\u30A0\uFE31\uFE32\uFE58\uFE63\uFF0D]+", # noqa
"-",
caption,
)
# кавычки к одному стандарту
caption = re.sub(r"[`´«»“”¨]", '"', caption)
caption = re.sub(r"[‘’]", "'", caption)
# "
caption = re.sub(r""?", "", caption)
# &
caption = re.sub(r"&", "", caption)
# ip adresses:
caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption)
# article ids:
caption = re.sub(r"\d:\d\d\s+$", "", caption)
# \n
caption = re.sub(r"\\n", " ", caption) | 314 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py |
# "#123"
caption = re.sub(r"#\d{1,3}\b", "", caption)
# "#12345.."
caption = re.sub(r"#\d{5,}\b", "", caption)
# "123456.."
caption = re.sub(r"\b\d{6,}\b", "", caption)
# filenames:
caption = re.sub(r"[\S]+\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)", "", caption)
#
caption = re.sub(r"[\"\']{2,}", r'"', caption) # """AUSVERKAUFT"""
caption = re.sub(r"[\.]{2,}", r" ", caption) # """AUSVERKAUFT"""
caption = re.sub(self.bad_punct_regex, r" ", caption) # ***AUSVERKAUFT***, #AUSVERKAUFT
caption = re.sub(r"\s+\.\s+", r" ", caption) # " . "
# this-is-my-cute-cat / this_is_my_cute_cat
regex2 = re.compile(r"(?:\-|\_)")
if len(re.findall(regex2, caption)) > 3:
caption = re.sub(regex2, " ", caption)
caption = ftfy.fix_text(caption)
caption = html.unescape(html.unescape(caption)) | 314 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py |
caption = re.sub(r"\b[a-zA-Z]{1,3}\d{3,15}\b", "", caption) # jc6640
caption = re.sub(r"\b[a-zA-Z]+\d+[a-zA-Z]+\b", "", caption) # jc6640vc
caption = re.sub(r"\b\d+[a-zA-Z]+\d+\b", "", caption) # 6640vc231
caption = re.sub(r"(worldwide\s+)?(free\s+)?shipping", "", caption)
caption = re.sub(r"(free\s)?download(\sfree)?", "", caption)
caption = re.sub(r"\bclick\b\s(?:for|on)\s\w+", "", caption)
caption = re.sub(r"\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\simage[s]?)?", "", caption)
caption = re.sub(r"\bpage\s+\d+\b", "", caption)
caption = re.sub(r"\b\d*[a-zA-Z]+\d+[a-zA-Z]+\d+[a-zA-Z\d]*\b", r" ", caption) # j2d1a2a...
caption = re.sub(r"\b\d+\.?\d*[xх×]\d+\.?\d*\b", "", caption)
caption = re.sub(r"\b\s+\:\s+", r": ", caption)
caption = re.sub(r"(\D[,\./])\b", r"\1 ", caption)
caption = re.sub(r"\s+", " ", caption)
caption.strip() | 314 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py |
caption = re.sub(r"^[\"\']([\w\W]+)[\"\']$", r"\1", caption)
caption = re.sub(r"^[\'\_,\-\:;]", r"", caption)
caption = re.sub(r"[\'\_,\-\:\-\+]$", r"", caption)
caption = re.sub(r"^\.\S+$", "", caption)
return caption.strip()
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_img2img.IFImg2ImgPipeline.preprocess_image
def preprocess_image(self, image: PIL.Image.Image) -> torch.Tensor:
if not isinstance(image, list):
image = [image]
def numpy_to_pt(images):
if images.ndim == 3:
images = images[..., None]
images = torch.from_numpy(images.transpose(0, 3, 1, 2))
return images
if isinstance(image[0], PIL.Image.Image):
new_image = [] | 314 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py |
for image_ in image:
image_ = image_.convert("RGB")
image_ = resize(image_, self.unet.config.sample_size)
image_ = np.array(image_)
image_ = image_.astype(np.float32)
image_ = image_ / 127.5 - 1
new_image.append(image_)
image = new_image
image = np.stack(image, axis=0) # to np
image = numpy_to_pt(image) # to pt
elif isinstance(image[0], np.ndarray):
image = np.concatenate(image, axis=0) if image[0].ndim == 4 else np.stack(image, axis=0)
image = numpy_to_pt(image)
elif isinstance(image[0], torch.Tensor):
image = torch.cat(image, axis=0) if image[0].ndim == 4 else torch.stack(image, axis=0)
return image
def preprocess_mask_image(self, mask_image) -> torch.Tensor:
if not isinstance(mask_image, list):
mask_image = [mask_image] | 314 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py |
if isinstance(mask_image[0], torch.Tensor):
mask_image = torch.cat(mask_image, axis=0) if mask_image[0].ndim == 4 else torch.stack(mask_image, axis=0)
if mask_image.ndim == 2:
# Batch and add channel dim for single mask
mask_image = mask_image.unsqueeze(0).unsqueeze(0)
elif mask_image.ndim == 3 and mask_image.shape[0] == 1:
# Single mask, the 0'th dimension is considered to be
# the existing batch size of 1
mask_image = mask_image.unsqueeze(0)
elif mask_image.ndim == 3 and mask_image.shape[0] != 1:
# Batch of mask, the 0'th dimension is considered to be
# the batching dimension
mask_image = mask_image.unsqueeze(1)
mask_image[mask_image < 0.5] = 0
mask_image[mask_image >= 0.5] = 1
elif isinstance(mask_image[0], PIL.Image.Image):
new_mask_image = [] | 314 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py |
for mask_image_ in mask_image:
mask_image_ = mask_image_.convert("L")
mask_image_ = resize(mask_image_, self.unet.config.sample_size)
mask_image_ = np.array(mask_image_)
mask_image_ = mask_image_[None, None, :]
new_mask_image.append(mask_image_)
mask_image = new_mask_image
mask_image = np.concatenate(mask_image, axis=0)
mask_image = mask_image.astype(np.float32) / 255.0
mask_image[mask_image < 0.5] = 0
mask_image[mask_image >= 0.5] = 1
mask_image = torch.from_numpy(mask_image)
elif isinstance(mask_image[0], np.ndarray):
mask_image = np.concatenate([m[None, None, :] for m in mask_image], axis=0)
mask_image[mask_image < 0.5] = 0
mask_image[mask_image >= 0.5] = 1
mask_image = torch.from_numpy(mask_image)
return mask_image | 314 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps
def get_timesteps(self, num_inference_steps, strength):
# get the original timestep using init_timestep
init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
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
def prepare_intermediate_images(
self, image, timestep, batch_size, num_images_per_prompt, dtype, device, mask_image, generator=None
):
image_batch_size, channels, height, width = image.shape
batch_size = batch_size * num_images_per_prompt
shape = (batch_size, channels, height, width) | 314 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py |
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."
)
noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
image = image.repeat_interleave(num_images_per_prompt, dim=0)
noised_image = self.scheduler.add_noise(image, noise, timestep)
image = (1 - mask_image) * image + mask_image * noised_image
return image | 314 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
image: Union[
PIL.Image.Image, torch.Tensor, np.ndarray, List[PIL.Image.Image], List[torch.Tensor], List[np.ndarray]
] = None,
mask_image: Union[
PIL.Image.Image, torch.Tensor, np.ndarray, List[PIL.Image.Image], List[torch.Tensor], List[np.ndarray]
] = None,
strength: float = 1.0,
num_inference_steps: int = 50,
timesteps: List[int] = None,
guidance_scale: float = 7.0,
negative_prompt: 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,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
output_type: Optional[str] = "pil", | 314 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py |
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,
callback_steps: int = 1,
clean_caption: bool = True,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
):
"""
Function invoked when calling the pipeline for generation. | 314 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.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.
image (`torch.Tensor` or `PIL.Image.Image`):
`Image`, or tensor representing an image batch, that will be used as the starting point for the
process.
mask_image (`PIL.Image.Image`):
`Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted
to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L)
instead of 3, so the expected shape would be `(B, H, W, 1)`.
strength (`float`, *optional*, defaults to 1.0): | 314 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py |
Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`
will be used as a starting point, adding more noise to it the larger the `strength`. The number of
denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will
be maximum and the denoising process will run for the full number of iterations specified in
`num_inference_steps`. A value of 1, therefore, 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.
timesteps (`List[int]`, *optional*):
Custom timesteps to use for the denoising process. If not defined, equal spaced `num_inference_steps`
timesteps are used. Must be in descending order. | 314 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py |
guidance_scale (`float`, *optional*, defaults to 7.0):
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
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt. | 314 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py |
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)
to make generation deterministic.
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 | 314 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py |
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` 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.IFPipelineOutput`] instead of a plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be | 314 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py |
called at every step.
clean_caption (`bool`, *optional*, defaults to `True`):
Whether or not to clean the caption before creating embeddings. Requires `beautifulsoup4` and `ftfy` to
be installed. If the dependencies are not installed, the embeddings will be created from the raw
prompt.
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). | 314 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py |
Examples:
Returns:
[`~pipelines.stable_diffusion.IFPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.IFPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When
returning a tuple, the first element is a list with the generated images, and the second element is a list
of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw)
or watermarked content, according to the `safety_checker`.
"""
# 1. Check inputs. Raise error if not correct
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] | 314 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py |
self.check_inputs(
prompt,
image,
mask_image,
batch_size,
callback_steps,
negative_prompt,
prompt_embeds,
negative_prompt_embeds,
)
# 2. Define call parameters
device = self._execution_device
# 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.
do_classifier_free_guidance = guidance_scale > 1.0 | 314 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py |
# 3. Encode input prompt
prompt_embeds, negative_prompt_embeds = self.encode_prompt(
prompt,
do_classifier_free_guidance,
num_images_per_prompt=num_images_per_prompt,
device=device,
negative_prompt=negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
clean_caption=clean_caption,
)
if do_classifier_free_guidance:
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
dtype = prompt_embeds.dtype
# 4. Prepare timesteps
if timesteps is not None:
self.scheduler.set_timesteps(timesteps=timesteps, device=device)
timesteps = self.scheduler.timesteps
num_inference_steps = len(timesteps)
else:
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps | 314 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py |
timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength)
# 5. Prepare intermediate images
image = self.preprocess_image(image)
image = image.to(device=device, dtype=dtype)
mask_image = self.preprocess_mask_image(mask_image)
mask_image = mask_image.to(device=device, dtype=dtype)
if mask_image.shape[0] == 1:
mask_image = mask_image.repeat_interleave(batch_size * num_images_per_prompt, dim=0)
else:
mask_image = mask_image.repeat_interleave(num_images_per_prompt, dim=0)
noise_timestep = timesteps[0:1]
noise_timestep = noise_timestep.repeat(batch_size * num_images_per_prompt)
intermediate_images = self.prepare_intermediate_images(
image, noise_timestep, batch_size, num_images_per_prompt, dtype, device, mask_image, generator
) | 314 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py |
# 6. 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)
# HACK: see comment in `enable_model_cpu_offload`
if hasattr(self, "text_encoder_offload_hook") and self.text_encoder_offload_hook is not None:
self.text_encoder_offload_hook.offload()
# 7. 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):
model_input = (
torch.cat([intermediate_images] * 2) if do_classifier_free_guidance else intermediate_images
)
model_input = self.scheduler.scale_model_input(model_input, t) | 314 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py |
# predict the noise residual
noise_pred = self.unet(
model_input,
t,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs,
return_dict=False,
)[0]
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred_uncond, _ = noise_pred_uncond.split(model_input.shape[1], dim=1)
noise_pred_text, predicted_variance = noise_pred_text.split(model_input.shape[1], dim=1)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
noise_pred = torch.cat([noise_pred, predicted_variance], dim=1) | 314 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py |
if self.scheduler.config.variance_type not in ["learned", "learned_range"]:
noise_pred, _ = noise_pred.split(model_input.shape[1], dim=1)
# compute the previous noisy sample x_t -> x_t-1
prev_intermediate_images = intermediate_images
intermediate_images = self.scheduler.step(
noise_pred, t, intermediate_images, **extra_step_kwargs, return_dict=False
)[0]
intermediate_images = (1 - mask_image) * prev_intermediate_images + mask_image * intermediate_images
# 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:
callback(i, t, intermediate_images)
if XLA_AVAILABLE:
xm.mark_step() | 314 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py |
image = intermediate_images
if output_type == "pil":
# 8. Post-processing
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
# 9. Run safety checker
image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype)
# 10. Convert to PIL
image = self.numpy_to_pil(image)
# 11. Apply watermark
if self.watermarker is not None:
self.watermarker.apply_watermark(image, self.unet.config.sample_size)
elif output_type == "pt":
nsfw_detected = None
watermark_detected = None | 314 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py |
if hasattr(self, "unet_offload_hook") and self.unet_offload_hook is not None:
self.unet_offload_hook.offload()
else:
# 8. Post-processing
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
# 9. Run safety checker
image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype)
# Offload all models
self.maybe_free_model_hooks()
if not return_dict:
return (image, nsfw_detected, watermark_detected)
return IFPipelineOutput(images=image, nsfw_detected=nsfw_detected, watermark_detected=watermark_detected) | 314 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py |
class HunyuanDiTControlNetPipeline(DiffusionPipeline):
r"""
Pipeline for English/Chinese-to-image generation using HunyuanDiT.
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) | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.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`]): | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.py |
A scheduler to be used in combination with HunyuanDiT to denoise the encoded image latents.
controlnet ([`HunyuanDiT2DControlNetModel`] or `List[HunyuanDiT2DControlNetModel]` or [`HunyuanDiT2DControlNetModel`]):
Provides additional conditioning to the `unet` during the denoising process. If you set multiple
ControlNets as a list, the outputs from each ControlNet are added together to create one combined
additional conditioning.
""" | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.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",
] | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.py |
def __init__(
self,
vae: AutoencoderKL,
text_encoder: BertModel,
tokenizer: BertTokenizer,
transformer: HunyuanDiT2DModel,
scheduler: DDPMScheduler,
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPImageProcessor,
controlnet: Union[
HunyuanDiT2DControlNetModel,
List[HunyuanDiT2DControlNetModel],
Tuple[HunyuanDiT2DControlNetModel],
HunyuanDiT2DMultiControlNetModel,
],
text_encoder_2=T5EncoderModel,
tokenizer_2=MT5Tokenizer,
requires_safety_checker: bool = True,
):
super().__init__()
if isinstance(controlnet, (list, tuple)):
controlnet = HunyuanDiT2DMultiControlNetModel(controlnet) | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.py |
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,
controlnet=controlnet,
) | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.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 ."
) | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.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
) | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.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. | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.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 | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.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: | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.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 | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.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] | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.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}"
) | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.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) | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.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" | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.py |
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.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) | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.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 | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.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 | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.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 | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.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}.") | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.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]}"
) | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.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)}") | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.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`.") | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.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" | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.py |
f" got: `prompt_embeds_2` {prompt_embeds_2.shape} != `negative_prompt_embeds_2`"
f" {negative_prompt_embeds_2.shape}."
) | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.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) | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.py |
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
# Copied from diffusers.pipelines.controlnet_sd3.pipeline_stable_diffusion_3_controlnet.StableDiffusion3ControlNetPipeline.prepare_image
def prepare_image(
self,
image,
width,
height,
batch_size,
num_images_per_prompt,
device,
dtype,
do_classifier_free_guidance=False,
guess_mode=False,
):
if isinstance(image, torch.Tensor):
pass
else:
image = self.image_processor.preprocess(image, height=height, width=width)
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) | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.py |
image = image.to(device=device, dtype=dtype)
if do_classifier_free_guidance and not guess_mode:
image = torch.cat([image] * 2)
return image
@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 | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.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,
control_image: PipelineImageInput = None,
controlnet_conditioning_scale: Union[float, List[float]] = 1.0,
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, | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.py |
prompt_attention_mask: Optional[torch.Tensor] = None,
prompt_attention_mask_2: Optional[torch.Tensor] = None,
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,
):
r"""
The call function to the pipeline for generation with HunyuanDiT. | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.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`.
control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0): | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.py |
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.
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 to provide guidance to the `unet` for generation. If the type is
specified as `torch.Tensor`, it is passed to ControlNet as is. `PIL.Image.Image` can also be accepted
as an image. The dimensions of the output image defaults to `image`'s dimensions. If height and/or
width are passed, `image` is resized accordingly. If multiple ControlNets are specified in `init`, | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.py |
images must be passed as a list such that each element of the list can be correctly batched for input
to a single ControlNet.
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.
negative_prompt (`str` or `List[str]`, *optional*):
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): | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.py |
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*):
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. | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.py |
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
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*): | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.py |
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`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback_on_step_end (`Callable[[int, int, Dict], None]`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*):
A callback function or a list of callback functions to be called at the end of each denoising step.
callback_on_step_end_tensor_inputs (`List[str]`, *optional*): | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.py |
A list of tensor inputs that should be passed to the callback function. If not defined, all tensor
inputs will be passed.
guidance_rescale (`float`, *optional*, defaults to 0.0):
Rescale the noise_cfg according to `guidance_rescale`. Based on findings of [Common Diffusion Noise
Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4
original_size (`Tuple[int, int]`, *optional*, defaults to `(1024, 1024)`):
The original size of the image. Used to calculate the time ids.
target_size (`Tuple[int, int]`, *optional*):
The target size of the image. Used to calculate the time ids.
crops_coords_top_left (`Tuple[int, int]`, *optional*, defaults to `(0, 0)`):
The top left coordinates of the crop. Used to calculate the time ids.
use_resolution_binning (`bool`, *optional*, defaults to `True`): | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.py |
Whether to use resolution binning or not. If `True`, the input resolution will be mapped to the closest
standard resolution. Supported resolutions are 1024x1024, 1280x1280, 1024x768, 1152x864, 1280x960,
768x1024, 864x1152, 960x1280, 1280x768, and 768x1280. It is recommended to set this to `True`. | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.py |
Examples:
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
otherwise a `tuple` is returned where the first element is a list with the generated images and the
second element is a list of `bool`s indicating whether the corresponding generated image contains
"not-safe-for-work" (nsfw) content.
"""
if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
# 0. default height and width
height = height or self.default_sample_size * self.vae_scale_factor
width = width or self.default_sample_size * self.vae_scale_factor
height = int((height // 16) * 16)
width = int((width // 16) * 16) | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.py |
if use_resolution_binning and (height, width) not in SUPPORTED_SHAPE:
width, height = map_to_standard_shapes(width, height)
height = int(height)
width = int(width)
logger.warning(f"Reshaped to (height, width)=({height}, {width}), Supported shapes are {SUPPORTED_SHAPE}")
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt,
height,
width,
negative_prompt,
prompt_embeds,
negative_prompt_embeds,
prompt_attention_mask,
negative_prompt_attention_mask,
prompt_embeds_2,
negative_prompt_embeds_2,
prompt_attention_mask_2,
negative_prompt_attention_mask_2,
callback_on_step_end_tensor_inputs,
)
self._guidance_scale = guidance_scale
self._guidance_rescale = guidance_rescale
self._interrupt = False | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.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
# 3. Encode input prompt | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.py |
(
prompt_embeds,
negative_prompt_embeds,
prompt_attention_mask,
negative_prompt_attention_mask,
) = self.encode_prompt(
prompt=prompt,
device=device,
dtype=self.transformer.dtype,
num_images_per_prompt=num_images_per_prompt,
do_classifier_free_guidance=self.do_classifier_free_guidance,
negative_prompt=negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
prompt_attention_mask=prompt_attention_mask,
negative_prompt_attention_mask=negative_prompt_attention_mask,
max_sequence_length=77,
text_encoder_index=0,
)
(
prompt_embeds_2,
negative_prompt_embeds_2,
prompt_attention_mask_2,
negative_prompt_attention_mask_2,
) = self.encode_prompt(
prompt=prompt,
device=device, | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.py |
dtype=self.transformer.dtype,
num_images_per_prompt=num_images_per_prompt,
do_classifier_free_guidance=self.do_classifier_free_guidance,
negative_prompt=negative_prompt,
prompt_embeds=prompt_embeds_2,
negative_prompt_embeds=negative_prompt_embeds_2,
prompt_attention_mask=prompt_attention_mask_2,
negative_prompt_attention_mask=negative_prompt_attention_mask_2,
max_sequence_length=256,
text_encoder_index=1,
) | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.py |
# 4. Prepare control image
if isinstance(self.controlnet, HunyuanDiT2DControlNetModel):
control_image = self.prepare_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=self.dtype,
do_classifier_free_guidance=self.do_classifier_free_guidance,
guess_mode=False,
)
height, width = control_image.shape[-2:]
control_image = self.vae.encode(control_image).latent_dist.sample()
control_image = control_image * self.vae.config.scaling_factor
elif isinstance(self.controlnet, HunyuanDiT2DMultiControlNetModel):
control_images = [] | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.py |
for control_image_ in control_image:
control_image_ = self.prepare_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=self.dtype,
do_classifier_free_guidance=self.do_classifier_free_guidance,
guess_mode=False,
)
control_image_ = self.vae.encode(control_image_).latent_dist.sample()
control_image_ = control_image_ * self.vae.config.scaling_factor
control_images.append(control_image_)
control_image = control_images
else:
assert False
# 5. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.py |
# 6. Prepare latent variables
num_channels_latents = self.transformer.config.in_channels
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
prompt_embeds.dtype,
device,
generator,
latents,
)
# 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) | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.py |
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