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# reverse the timestep since Lumina uses t=0 as the noise and t=1 as the image
current_timestep = 1 - current_timestep / self.scheduler.config.num_train_timesteps | 247 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/lumina/pipeline_lumina.py |
# prepare image_rotary_emb for positional encoding
# dynamic scaling_factor for different resolution.
# NOTE: For `Time-aware` denosing mechanism from Lumina-Next
# https://arxiv.org/abs/2406.18583, Sec 2.3
# NOTE: We should compute different image_rotary_... | 247 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/lumina/pipeline_lumina.py |
noise_pred = self.transformer(
hidden_states=latent_model_input,
timestep=current_timestep,
encoder_hidden_states=prompt_embeds,
encoder_mask=prompt_attention_mask,
image_rotary_emb=image_rotary_emb,
... | 247 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/lumina/pipeline_lumina.py |
# perform guidance scale
# NOTE: For exact reproducibility reasons, we apply classifier-free guidance on only
# three channels by default. The standard approach to cfg applies it to all channels.
# This can be done by uncommenting the following line and commenting-out the... | 247 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/lumina/pipeline_lumina.py |
noise_pred_eps = torch.cat([noise_pred_half, noise_pred_half], dim=0) | 247 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/lumina/pipeline_lumina.py |
noise_pred = torch.cat([noise_pred_eps, noise_pred_rest], dim=1)
noise_pred, _ = noise_pred.chunk(2, dim=0)
# compute the previous noisy sample x_t -> x_t-1
latents_dtype = latents.dtype
noise_pred = -noise_pred
latents = self.schedule... | 247 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/lumina/pipeline_lumina.py |
if not output_type == "latent":
latents = latents / self.vae.config.scaling_factor
image = self.vae.decode(latents, return_dict=False)[0]
image = self.image_processor.postprocess(image, output_type=output_type)
else:
image = latents
# Offload all models
... | 247 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/lumina/pipeline_lumina.py |
class CogView3PipelineOutput(BaseOutput):
"""
Output class for CogView3 pipelines.
Args:
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width,
num_channels)`. PIL images or num... | 248 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogview3/pipeline_output.py |
class CogView3PlusPipeline(DiffusionPipeline):
r"""
Pipeline for text-to-image generation using CogView3Plus.
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 o... | 249 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogview3/pipeline_cogview3plus.py |
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`T5EncoderModel`]):
Frozen text-encoder. CogView3Plus uses
[T5](https://huggingface.co/docs/transformers/model_doc/t5#tra... | 249 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogview3/pipeline_cogview3plus.py |
_optional_components = []
model_cpu_offload_seq = "text_encoder->transformer->vae"
_callback_tensor_inputs = [
"latents",
"prompt_embeds",
"negative_prompt_embeds",
]
def __init__(
self,
tokenizer: T5Tokenizer,
text_encoder: T5EncoderModel,
vae: ... | 249 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogview3/pipeline_cogview3plus.py |
# Copied from diffusers.pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipeline._get_t5_prompt_embeds with num_videos_per_prompt->num_images_per_prompt
def _get_t5_prompt_embeds(
self,
prompt: Union[str, List[str]] = None,
num_images_per_prompt: int = 1,
max_sequence_length: int = 22... | 249 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogview3/pipeline_cogview3plus.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_sequence_length - 1 : -1])
logger.warning(
"The following part of your input was truncated because `max... | 249 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogview3/pipeline_cogview3plus.py |
def encode_prompt(
self,
prompt: Union[str, List[str]],
negative_prompt: Optional[Union[str, List[str]]] = None,
do_classifier_free_guidance: bool = True,
num_images_per_prompt: int = 1,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optiona... | 249 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogview3/pipeline_cogview3plus.py |
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
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. Ign... | 249 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogview3/pipeline_cogview3plus.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... | 249 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogview3/pipeline_cogview3plus.py |
prompt = [prompt] if isinstance(prompt, str) else prompt
if prompt is not None:
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if prompt_embeds is None:
prompt_embeds = self._get_t5_prompt_embeds(
prompt=prompt,
... | 249 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogview3/pipeline_cogview3plus.py |
if prompt is not None and type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif batch_size != len(negative_pro... | 249 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogview3/pipeline_cogview3plus.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,
... | 249 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogview3/pipeline_cogview3plus.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.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_... | 249 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogview3/pipeline_cogview3plus.py |
# 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
# Copied from diffusers.pipelines.latte.pipeline_... | 249 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogview3/pipeline_cogview3plus.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... | 249 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogview3/pipeline_cogview3plus.py |
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") | 249 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogview3/pipeline_cogview3plus.py |
if prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
if negative_prompt is ... | 249 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogview3/pipeline_cogview3plus.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... | 249 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogview3/pipeline_cogview3plus.py |
@property
def interrupt(self):
return self._interrupt | 249 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogview3/pipeline_cogview3plus.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Optional[Union[str, List[str]]] = None,
negative_prompt: Optional[Union[str, List[str]]] = None,
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_st... | 249 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogview3/pipeline_cogview3plus.py |
Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
] = None,
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
max_sequence_length: int = 224,
) -> Union[CogView3PipelineOutput, Tuple]:
"""
Function invoked when calling the pipeline f... | 249 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogview3/pipeline_cogview3plus.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`.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If n... | 249 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogview3/pipeline_cogview3plus.py |
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 with schedulers which support a `timesteps` argument
... | 249 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogview3/pipeline_cogview3plus.py |
usually at the expense of lower image quality.
num_images_per_prompt (`int`, *optional*, defaults to `1`):
The number of images to generate per prompt.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](http... | 249 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogview3/pipeline_cogview3plus.py |
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embed... | 249 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogview3/pipeline_cogview3plus.py |
`crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position
`crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
`crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in ... | 249 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogview3/pipeline_cogview3plus.py |
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).
callback_on_step_end ... | 249 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogview3/pipeline_cogview3plus.py |
will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
`._callback_tensor_inputs` attribute of your pipeline class.
max_sequence_length (`int`, defaults to `224`):
Maximum sequence length in encoded prompt. Can be set to other v... | 249 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogview3/pipeline_cogview3plus.py |
Examples:
Returns:
[`~pipelines.cogview3.pipeline_cogview3plus.CogView3PipelineOutput`] or `tuple`:
[`~pipelines.cogview3.pipeline_cogview3plus.CogView3PipelineOutput`] if `return_dict` is True, otherwise a
`tuple`. When returning a tuple, the first element is a list with th... | 249 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogview3/pipeline_cogview3plus.py |
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt,
height,
width,
negative_prompt,
callback_on_step_end_tensor_inputs,
prompt_embeds,
negative_prompt_embeds,
)
self._guidance_scale = guidanc... | 249 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogview3/pipeline_cogview3plus.py |
# 3. Encode input prompt
prompt_embeds, negative_prompt_embeds = self.encode_prompt(
prompt,
negative_prompt,
self.do_classifier_free_guidance,
num_images_per_prompt=num_images_per_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds... | 249 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogview3/pipeline_cogview3plus.py |
# 5. Prepare latents.
latent_channels = self.transformer.config.in_channels
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
latent_channels,
height,
width,
prompt_embeds.dtype,
device,
generator,
... | 249 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogview3/pipeline_cogview3plus.py |
if self.do_classifier_free_guidance:
original_size = torch.cat([original_size, original_size])
target_size = torch.cat([target_size, target_size])
crops_coords_top_left = torch.cat([crops_coords_top_left, crops_coords_top_left])
original_size = original_size.to(device).repea... | 249 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogview3/pipeline_cogview3plus.py |
latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
timestep = t.expand(lat... | 249 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogview3/pipeline_cogview3plus.py |
# perform guidance
if self.do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -... | 249 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogview3/pipeline_cogview3plus.py |
# call the callback, if provided
if callback_on_step_end is not None:
callback_kwargs = {}
for k in callback_on_step_end_tensor_inputs:
callback_kwargs[k] = locals()[k]
callback_outputs = callback_on_step_end(self, i, t,... | 249 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogview3/pipeline_cogview3plus.py |
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False, generator=generator)[
0
]
else:
image = latents
image = self.image_processor.postprocess(image, output_type=output_type)
# Of... | 249 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/cogview3/pipeline_cogview3plus.py |
class BlipImageProcessor(BaseImageProcessor):
r"""
Constructs a BLIP image processor. | 250 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/blip_image_processing.py |
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by the
`do_resize` parameter in the `preprocess` method.
size (`dict`, *optional*, defaults to `{"height": 384, "width": 3... | 250 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/blip_image_processing.py |
rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
Scale factor to use if rescaling the image. Only has an effect if `do_rescale` is set to `True`. Can be
overridden by the `rescale_factor` parameter in the `preprocess` method.
do_normalize (`bool`, *optional*, defaults ... | 250 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/blip_image_processing.py |
image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`):
Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` metho... | 250 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/blip_image_processing.py |
model_input_names = ["pixel_values"]
def __init__(
self,
do_resize: bool = True,
size: Dict[str, int] = None,
resample: PILImageResampling = PILImageResampling.BICUBIC,
do_rescale: bool = True,
rescale_factor: Union[int, float] = 1 / 255,
do_normalize: bool =... | 250 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/blip_image_processing.py |
self.do_resize = do_resize
self.size = size
self.resample = resample
self.do_rescale = do_rescale
self.rescale_factor = rescale_factor
self.do_normalize = do_normalize
self.image_mean = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
self.image_std = im... | 250 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/blip_image_processing.py |
# Copy-pasted from transformers.models.vit.image_processing_vit.ViTImageProcessor.resize with PILImageResampling.BILINEAR->PILImageResampling.BICUBIC
def resize(
self,
image: np.ndarray,
size: Dict[str, int],
resample: PILImageResampling = PILImageResampling.BICUBIC,
data_for... | 250 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/blip_image_processing.py |
Args:
image (`np.ndarray`):
Image to resize.
size (`Dict[str, int]`):
Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image.
resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`... | 250 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/blip_image_processing.py |
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels,... | 250 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/blip_image_processing.py |
Returns:
`np.ndarray`: The resized image.
"""
size = get_size_dict(size)
if "height" not in size or "width" not in size:
raise ValueError(f"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}")
output_size = (size["height"], size["w... | 250 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/blip_image_processing.py |
def preprocess(
self,
images: ImageInput,
do_resize: Optional[bool] = None,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = None,
do_rescale: Optional[bool] = None,
do_center_crop: Optional[bool] = None,
rescale_factor: Optional[float... | 250 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/blip_image_processing.py |
Args:
images (`ImageInput`):
Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
passing in images with pixel values between 0 and 1, set `do_rescale=False`.
do_resize (`bool`, *optional*, defaults to `self.do_resiz... | 250 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/blip_image_processing.py |
Resampling filter to use if resizing the image. Only has an effect if `do_resize` is set to `True`.
do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
Whether to rescale the image values between [0 - 1].
rescale_factor (`float`, *optional*, defaults to `self.rescale_... | 250 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/blip_image_processing.py |
do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`):
Whether to convert the image to RGB.
return_tensors (`str` or `TensorType`, *optional*):
The type of tensors to return. Can be one of:
- Unset: Return a list of `np.ndarray`.
... | 250 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/blip_image_processing.py |
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- Unset: Use the channel dimension format of the input image.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format for the input image. If unset... | 250 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/blip_image_processing.py |
rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
do_normalize = do_normalize if do_normalize is not None else self.do_normalize
image_mean = image_mean if image_mean is not None else self.image_mean
image_std = image_std if image_std is not None else self.im... | 250 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/blip_image_processing.py |
size = size if size is not None else self.size
size = get_size_dict(size, default_to_square=False)
images = make_list_of_images(images)
if not valid_images(images):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
... | 250 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/blip_image_processing.py |
# All transformations expect numpy arrays.
images = [to_numpy_array(image) for image in images]
if is_scaled_image(images[0]) and do_rescale:
logger.warning_once(
"It looks like you are trying to rescale already rescaled images. If the input"
" images have pi... | 250 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/blip_image_processing.py |
if do_rescale:
images = [
self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format)
for image in images
]
if do_normalize:
images = [
self.normalize(image=image, mean=image_mean, std=image_std, input_d... | 250 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/blip_image_processing.py |
# Follows diffusers.VaeImageProcessor.postprocess
def postprocess(self, sample: torch.Tensor, output_type: str = "pil"):
if output_type not in ["pt", "np", "pil"]:
raise ValueError(
f"output_type={output_type} is not supported. Make sure to choose one of ['pt', 'np', or 'pil']"
... | 250 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/blip_image_processing.py |
class BlipDiffusionPipeline(DiffusionPipeline):
"""
Pipeline for Zero-Shot Subject Driven Generation using Blip Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or savi... | 251 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/pipeline_blip_diffusion.py |
Args:
tokenizer ([`CLIPTokenizer`]):
Tokenizer for the text encoder
text_encoder ([`ContextCLIPTextModel`]):
Text encoder to encode the text prompt
vae ([`AutoencoderKL`]):
VAE model to map the latents to the image
unet ([`UNet2DConditionModel`]):
... | 251 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/pipeline_blip_diffusion.py |
def __init__(
self,
tokenizer: CLIPTokenizer,
text_encoder: ContextCLIPTextModel,
vae: AutoencoderKL,
unet: UNet2DConditionModel,
scheduler: PNDMScheduler,
qformer: Blip2QFormerModel,
image_processor: BlipImageProcessor,
ctx_begin_pos: int = 2,
... | 251 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/pipeline_blip_diffusion.py |
# from the original Blip Diffusion code, speciefies the target subject and augments the prompt by repeating it
def _build_prompt(self, prompts, tgt_subjects, prompt_strength=1.0, prompt_reps=20):
rv = []
for prompt, tgt_subject in zip(prompts, tgt_subjects):
prompt = f"a {tgt_subject} {p... | 251 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/pipeline_blip_diffusion.py |
# Copied from diffusers.pipelines.consistency_models.pipeline_consistency_models.ConsistencyModelPipeline.prepare_latents
def prepare_latents(self, batch_size, num_channels, height, width, dtype, device, generator, latents=None):
shape = (batch_size, num_channels, height, width)
if isinstance(genera... | 251 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/pipeline_blip_diffusion.py |
def encode_prompt(self, query_embeds, prompt, device=None):
device = device or self._execution_device
# embeddings for prompt, with query_embeds as context
max_len = self.text_encoder.text_model.config.max_position_embeddings
max_len -= self.qformer.config.num_query_tokens
toke... | 251 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/pipeline_blip_diffusion.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: List[str],
reference_image: PIL.Image.Image,
source_subject_category: List[str],
target_subject_category: List[str],
latents: Optional[torch.Tensor] = None,
guidanc... | 251 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/pipeline_blip_diffusion.py |
Args:
prompt (`List[str]`):
The prompt or prompts to guide the image generation.
reference_image (`PIL.Image.Image`):
The reference image to condition the generation on.
source_subject_category (`List[str]`):
The source subject category... | 251 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/pipeline_blip_diffusion.py |
`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 ... | 251 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/pipeline_blip_diffusion.py |
to make generation deterministic.
neg_prompt (`str`, *optional*, defaults to ""):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
prompt_strength (`float`, *optional*, d... | 251 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/pipeline_blip_diffusion.py |
Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
Examples: | 251 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/pipeline_blip_diffusion.py |
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`
"""
device = self._execution_device
reference_image = self.image_processor.preprocess(
reference_image, image_mean=self.config.mean, image_std=self.config.std, return_tensors="pt"
)["pixel_values"]
re... | 251 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/pipeline_blip_diffusion.py |
prompt = self._build_prompt(
prompts=prompt,
tgt_subjects=target_subject_category,
prompt_strength=prompt_strength,
prompt_reps=prompt_reps,
)
query_embeds = self.get_query_embeddings(reference_image, source_subject_category)
text_embeddings = self... | 251 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/pipeline_blip_diffusion.py |
uncond_input = self.tokenizer(
[neg_prompt] * batch_size,
padding="max_length",
max_length=max_length,
return_tensors="pt",
)
uncond_embeddings = self.text_encoder(
input_ids=uncond_input.input_ids.to(device),
... | 251 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/pipeline_blip_diffusion.py |
scale_down_factor = 2 ** (len(self.unet.config.block_out_channels) - 1)
latents = self.prepare_latents(
batch_size=batch_size,
num_channels=self.unet.config.in_channels,
height=height // scale_down_factor,
width=width // scale_down_factor,
generator=ge... | 251 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/pipeline_blip_diffusion.py |
noise_pred = self.unet(
latent_model_input,
timestep=t,
encoder_hidden_states=text_embeddings,
down_block_additional_residuals=None,
mid_block_additional_residual=None,
)["sample"]
# perform guidance
if ... | 251 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/pipeline_blip_diffusion.py |
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image) | 251 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/pipeline_blip_diffusion.py |
class Blip2TextEmbeddings(nn.Module):
"""Construct the embeddings from word and position embeddings."""
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
self.position_embeddings = n... | 252 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/modeling_blip2.py |
def forward(
self,
input_ids=None,
position_ids=None,
query_embeds=None,
past_key_values_length=0,
):
if input_ids is not None:
seq_length = input_ids.size()[1]
else:
seq_length = 0
if position_ids is None:
position... | 252 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/modeling_blip2.py |
if query_embeds is not None:
batch_size = embeddings.shape[0]
# repeat the query embeddings for batch size
query_embeds = query_embeds.repeat(batch_size, 1, 1)
embeddings = torch.cat((query_embeds, embeddings), dim=1)
else:
embeddings =... | 252 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/modeling_blip2.py |
class Blip2VisionEmbeddings(nn.Module):
def __init__(self, config: Blip2VisionConfig):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.image_size = config.image_size
self.patch_size = config.patch_size
self.class_embedding = nn.Parame... | 253 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/modeling_blip2.py |
def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
batch_size = pixel_values.shape[0]
target_dtype = self.patch_embedding.weight.dtype
patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype)) # shape = [*, width, grid, grid]
patch_embeds = patch_embeds.flatte... | 253 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/modeling_blip2.py |
class Blip2QFormerEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList(
[Blip2QFormerLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
)
self.gradient_checkpointing = False
... | 254 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/modeling_blip2.py |
for i in range(self.config.num_hidden_layers):
layer_module = self.layer[i]
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_head_mask = head_mask[i] if head_mask is not None else None
past_key_value = past_key_value... | 254 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/modeling_blip2.py |
layer_outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(layer_module),
hidden_states,
attention_mask,
layer_head_mask,
encoder_hidden_states,
encoder_attention_mask,
)... | 254 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/modeling_blip2.py |
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[-1],)
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[1],)
if layer_module.has_cross_attention:
all_cross_atten... | 254 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/modeling_blip2.py |
if not return_dict:
return tuple(
v
for v in [
hidden_states,
next_decoder_cache,
all_hidden_states,
all_self_attentions,
all_cross_attentions,
]
... | 254 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/modeling_blip2.py |
class Blip2QFormerLayer(nn.Module):
def __init__(self, config, layer_idx):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = Blip2QFormerAttention(config)
self.layer_idx = layer_idx
if layer_idx % ... | 255 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/modeling_blip2.py |
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_value=None,
output_attentions=False,
query_length=0,
):
# decoder uni-directional self-attention ca... | 255 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/modeling_blip2.py |
if self.has_cross_attention:
if encoder_hidden_states is None:
raise ValueError("encoder_hidden_states must be given for cross-attention layers")
cross_attention_outputs = self.crossattention(
query_attention_output,
attention_m... | 255 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/modeling_blip2.py |
if attention_output.shape[1] > query_length:
layer_output_text = apply_chunking_to_forward(
self.feed_forward_chunk,
self.chunk_size_feed_forward,
self.seq_len_dim,
attention_output[:, query_length:, :],
)
... | 255 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/modeling_blip2.py |
def feed_forward_chunk_query(self, attention_output):
intermediate_output = self.intermediate_query(attention_output)
layer_output = self.output_query(intermediate_output, attention_output)
return layer_output | 255 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/modeling_blip2.py |
class ProjLayer(nn.Module):
def __init__(self, in_dim, out_dim, hidden_dim, drop_p=0.1, eps=1e-12):
super().__init__()
# Dense1 -> Act -> Dense2 -> Drop -> Res -> Norm
self.dense1 = nn.Linear(in_dim, hidden_dim)
self.act_fn = QuickGELU()
self.dense2 = nn.Linear(hidden_dim, o... | 256 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/modeling_blip2.py |
class Blip2VisionModel(Blip2PreTrainedModel):
main_input_name = "pixel_values"
config_class = Blip2VisionConfig
def __init__(self, config: Blip2VisionConfig):
super().__init__(config)
self.config = config
embed_dim = config.hidden_size
self.embeddings = Blip2VisionEmbeddings... | 257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/modeling_blip2.py |
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is ... | 257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/modeling_blip2.py |
pooled_output = last_hidden_state[:, 0, :]
pooled_output = self.post_layernorm(pooled_output)
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPooling(
last_hidden_state=last_hidden_state,
pooler_o... | 257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/blip_diffusion/modeling_blip2.py |
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