text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
values |
|---|---|---|
# 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_imag... | 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.un... | 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: Opt... | 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]... | 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"
" re... | 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'` i... | 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,
... | 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):
... | 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 b... | 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]
... | 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... | 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):
... | 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,
... | 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 ... | 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)
ima... | 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 ... | 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 is... | 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:
... | 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: `pr... | 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.ndarra... | 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... | 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
... | 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:
... | 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... | 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... | 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)... | 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 = ... | 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... | 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_)
... | 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).unsque... | 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_im... | 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_infer... | 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."
... | 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:... | 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 generatio... | 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 ba... | 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, a... | 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 ... | 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*):
... | 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/sta... | 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 r... | 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 elemen... | 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
... | 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_embe... | 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(d... | 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 n... | 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]
... | 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_ima... | 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... | 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... | 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 ... | 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 ([cli... | 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 ... | 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"]
_callbac... | 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: Un... | 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,
... | 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"
... | 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'` i... | 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,
... | 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 g... | 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 wil... | 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_l... | 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 =... | 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.re... | 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 no... | 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_i... | 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):
... | 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 w... | 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,
n... | 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 cal... | 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:
... | 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 spe... | 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:
... | 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,
... | 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_i... | 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._g... | 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,
... | 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... | 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... | 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... | 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_con... | 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:/... | 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... | 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... | 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 Noi... | 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 t... | 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 ... | 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}")
... | 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
... | 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... | 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,
... | 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,
... | 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_... | 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,
... | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.py |
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