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if self.sem_guidance is None:
self.sem_guidance = torch.zeros((num_inference_steps + 1, *noise_pred_text.shape))
if edit_momentum is None:
edit_momentum = torch.zeros_like(noise_guidance) | 208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py |
if enable_edit_guidance:
concept_weights = torch.zeros(
(len(noise_pred_edit_concepts), noise_guidance.shape[0]),
device=device,
dtype=noise_guidance.dtype,
)
noise_guidance_edit = torch.zeros(
(len(noise_pred_edit_concepts), *noise_guidance.shape),
device=device,
dtype=noise_guidance.dtype,
)
# noise_guidance_edit = torch.zeros_like(noise_guidance)
warmup_inds = []
for c, noise_pred_edit_concept in enumerate(noise_pred_edit_concepts):
self.edit_estimates[i, c] = noise_pred_edit_concept
if isinstance(edit_guidance_scale, list):
edit_guidance_scale_c = edit_guidance_scale[c]
else: | 208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py |
edit_guidance_scale_c = edit_guidance_scale | 208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py |
if isinstance(edit_threshold, list):
edit_threshold_c = edit_threshold[c]
else:
edit_threshold_c = edit_threshold
if isinstance(reverse_editing_direction, list):
reverse_editing_direction_c = reverse_editing_direction[c]
else:
reverse_editing_direction_c = reverse_editing_direction
if edit_weights:
edit_weight_c = edit_weights[c]
else:
edit_weight_c = 1.0
if isinstance(edit_warmup_steps, list):
edit_warmup_steps_c = edit_warmup_steps[c]
else:
edit_warmup_steps_c = edit_warmup_steps | 208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py |
if isinstance(edit_cooldown_steps, list):
edit_cooldown_steps_c = edit_cooldown_steps[c]
elif edit_cooldown_steps is None:
edit_cooldown_steps_c = i + 1
else:
edit_cooldown_steps_c = edit_cooldown_steps
if i >= edit_warmup_steps_c:
warmup_inds.append(c)
if i >= edit_cooldown_steps_c:
noise_guidance_edit[c, :, :, :, :] = torch.zeros_like(noise_pred_edit_concept)
continue
noise_guidance_edit_tmp = noise_pred_edit_concept - noise_pred_uncond
# tmp_weights = (noise_pred_text - noise_pred_edit_concept).sum(dim=(1, 2, 3))
tmp_weights = (noise_guidance - noise_pred_edit_concept).sum(dim=(1, 2, 3)) | 208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py |
tmp_weights = torch.full_like(tmp_weights, edit_weight_c) # * (1 / enabled_editing_prompts)
if reverse_editing_direction_c:
noise_guidance_edit_tmp = noise_guidance_edit_tmp * -1
concept_weights[c, :] = tmp_weights
noise_guidance_edit_tmp = noise_guidance_edit_tmp * edit_guidance_scale_c | 208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py |
# torch.quantile function expects float32
if noise_guidance_edit_tmp.dtype == torch.float32:
tmp = torch.quantile(
torch.abs(noise_guidance_edit_tmp).flatten(start_dim=2),
edit_threshold_c,
dim=2,
keepdim=False,
)
else:
tmp = torch.quantile(
torch.abs(noise_guidance_edit_tmp).flatten(start_dim=2).to(torch.float32),
edit_threshold_c,
dim=2,
keepdim=False,
).to(noise_guidance_edit_tmp.dtype) | 208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py |
noise_guidance_edit_tmp = torch.where(
torch.abs(noise_guidance_edit_tmp) >= tmp[:, :, None, None],
noise_guidance_edit_tmp,
torch.zeros_like(noise_guidance_edit_tmp),
)
noise_guidance_edit[c, :, :, :, :] = noise_guidance_edit_tmp
# noise_guidance_edit = noise_guidance_edit + noise_guidance_edit_tmp
warmup_inds = torch.tensor(warmup_inds).to(device)
if len(noise_pred_edit_concepts) > warmup_inds.shape[0] > 0:
concept_weights = concept_weights.to("cpu") # Offload to cpu
noise_guidance_edit = noise_guidance_edit.to("cpu") | 208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py |
concept_weights_tmp = torch.index_select(concept_weights.to(device), 0, warmup_inds)
concept_weights_tmp = torch.where(
concept_weights_tmp < 0, torch.zeros_like(concept_weights_tmp), concept_weights_tmp
)
concept_weights_tmp = concept_weights_tmp / concept_weights_tmp.sum(dim=0)
# concept_weights_tmp = torch.nan_to_num(concept_weights_tmp)
noise_guidance_edit_tmp = torch.index_select(noise_guidance_edit.to(device), 0, warmup_inds)
noise_guidance_edit_tmp = torch.einsum(
"cb,cbijk->bijk", concept_weights_tmp, noise_guidance_edit_tmp
)
noise_guidance = noise_guidance + noise_guidance_edit_tmp
self.sem_guidance[i] = noise_guidance_edit_tmp.detach().cpu() | 208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py |
del noise_guidance_edit_tmp
del concept_weights_tmp
concept_weights = concept_weights.to(device)
noise_guidance_edit = noise_guidance_edit.to(device)
concept_weights = torch.where(
concept_weights < 0, torch.zeros_like(concept_weights), concept_weights
)
concept_weights = torch.nan_to_num(concept_weights)
noise_guidance_edit = torch.einsum("cb,cbijk->bijk", concept_weights, noise_guidance_edit)
noise_guidance_edit = noise_guidance_edit.to(edit_momentum.device)
noise_guidance_edit = noise_guidance_edit + edit_momentum_scale * edit_momentum
edit_momentum = edit_mom_beta * edit_momentum + (1 - edit_mom_beta) * noise_guidance_edit | 208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py |
if warmup_inds.shape[0] == len(noise_pred_edit_concepts):
noise_guidance = noise_guidance + noise_guidance_edit
self.sem_guidance[i] = noise_guidance_edit.detach().cpu()
if sem_guidance is not None:
edit_guidance = sem_guidance[i].to(device)
noise_guidance = noise_guidance + edit_guidance
noise_pred = noise_pred_uncond + noise_guidance
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
step_idx = i // getattr(self.scheduler, "order", 1)
callback(step_idx, t, latents)
if XLA_AVAILABLE:
xm.mark_step() | 208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py |
# 8. Post-processing
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
image, has_nsfw_concept = self.run_safety_checker(image, device, text_embeddings.dtype)
else:
image = latents
has_nsfw_concept = None
if has_nsfw_concept is None:
do_denormalize = [True] * image.shape[0]
else:
do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
if not return_dict:
return (image, has_nsfw_concept)
return SemanticStableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept) | 208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py |
class StableCascadeDecoderPipeline(DiffusionPipeline):
"""
Pipeline for generating images from the Stable Cascade model.
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.) | 209 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade.py |
Args:
tokenizer (`CLIPTokenizer`):
The CLIP tokenizer.
text_encoder (`CLIPTextModel`):
The CLIP text encoder.
decoder ([`StableCascadeUNet`]):
The Stable Cascade decoder unet.
vqgan ([`PaellaVQModel`]):
The VQGAN model.
scheduler ([`DDPMWuerstchenScheduler`]):
A scheduler to be used in combination with `prior` to generate image embedding.
latent_dim_scale (float, `optional`, defaults to 10.67):
Multiplier to determine the VQ latent space size from the image embeddings. If the image embeddings are
height=24 and width=24, the VQ latent shape needs to be height=int(24*10.67)=256 and
width=int(24*10.67)=256 in order to match the training conditions.
""" | 209 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade.py |
unet_name = "decoder"
text_encoder_name = "text_encoder"
model_cpu_offload_seq = "text_encoder->decoder->vqgan"
_callback_tensor_inputs = [
"latents",
"prompt_embeds_pooled",
"negative_prompt_embeds",
"image_embeddings",
]
def __init__(
self,
decoder: StableCascadeUNet,
tokenizer: CLIPTokenizer,
text_encoder: CLIPTextModel,
scheduler: DDPMWuerstchenScheduler,
vqgan: PaellaVQModel,
latent_dim_scale: float = 10.67,
) -> None:
super().__init__()
self.register_modules(
decoder=decoder,
tokenizer=tokenizer,
text_encoder=text_encoder,
scheduler=scheduler,
vqgan=vqgan,
)
self.register_to_config(latent_dim_scale=latent_dim_scale) | 209 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade.py |
def prepare_latents(
self, batch_size, image_embeddings, num_images_per_prompt, dtype, device, generator, latents, scheduler
):
_, channels, height, width = image_embeddings.shape
latents_shape = (
batch_size * num_images_per_prompt,
4,
int(height * self.config.latent_dim_scale),
int(width * self.config.latent_dim_scale),
)
if latents is None:
latents = randn_tensor(latents_shape, generator=generator, device=device, dtype=dtype)
else:
if latents.shape != latents_shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
latents = latents.to(device)
latents = latents * scheduler.init_noise_sigma
return latents | 209 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade.py |
def encode_prompt(
self,
device,
batch_size,
num_images_per_prompt,
do_classifier_free_guidance,
prompt=None,
negative_prompt=None,
prompt_embeds: Optional[torch.Tensor] = None,
prompt_embeds_pooled: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds_pooled: Optional[torch.Tensor] = None,
):
if prompt_embeds is None:
# get prompt text embeddings
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
attention_mask = text_inputs.attention_mask
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids | 209 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade.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[:, self.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" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
attention_mask = attention_mask[:, : self.tokenizer.model_max_length] | 209 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade.py |
text_encoder_output = self.text_encoder(
text_input_ids.to(device), attention_mask=attention_mask.to(device), output_hidden_states=True
)
prompt_embeds = text_encoder_output.hidden_states[-1]
if prompt_embeds_pooled is None:
prompt_embeds_pooled = text_encoder_output.text_embeds.unsqueeze(1)
prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
prompt_embeds_pooled = prompt_embeds_pooled.to(dtype=self.text_encoder.dtype, device=device)
prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0)
prompt_embeds_pooled = prompt_embeds_pooled.repeat_interleave(num_images_per_prompt, dim=0) | 209 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade.py |
if negative_prompt_embeds is None and do_classifier_free_guidance:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif 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"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt | 209 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade.py |
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
negative_prompt_embeds_text_encoder_output = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=uncond_input.attention_mask.to(device),
output_hidden_states=True,
)
negative_prompt_embeds = negative_prompt_embeds_text_encoder_output.hidden_states[-1]
negative_prompt_embeds_pooled = negative_prompt_embeds_text_encoder_output.text_embeds.unsqueeze(1) | 209 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade.py |
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=self.text_encoder.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) | 209 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade.py |
seq_len = negative_prompt_embeds_pooled.shape[1]
negative_prompt_embeds_pooled = negative_prompt_embeds_pooled.to(
dtype=self.text_encoder.dtype, device=device
)
negative_prompt_embeds_pooled = negative_prompt_embeds_pooled.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds_pooled = negative_prompt_embeds_pooled.view(
batch_size * num_images_per_prompt, seq_len, -1
)
# done duplicates
return prompt_embeds, prompt_embeds_pooled, negative_prompt_embeds, negative_prompt_embeds_pooled | 209 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade.py |
def check_inputs(
self,
prompt,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
callback_on_step_end_tensor_inputs=None,
):
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]}"
) | 209 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade.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."
) | 209 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade.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}."
)
@property
def guidance_scale(self):
return self._guidance_scale
@property
def do_classifier_free_guidance(self):
return self._guidance_scale > 1
@property
def num_timesteps(self):
return self._num_timesteps | 209 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade.py |
def get_timestep_ratio_conditioning(self, t, alphas_cumprod):
s = torch.tensor([0.008])
clamp_range = [0, 1]
min_var = torch.cos(s / (1 + s) * torch.pi * 0.5) ** 2
var = alphas_cumprod[t]
var = var.clamp(*clamp_range)
s, min_var = s.to(var.device), min_var.to(var.device)
ratio = (((var * min_var) ** 0.5).acos() / (torch.pi * 0.5)) * (1 + s) - s
return ratio | 209 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
image_embeddings: Union[torch.Tensor, List[torch.Tensor]],
prompt: Union[str, List[str]] = None,
num_inference_steps: int = 10,
guidance_scale: float = 0.0,
negative_prompt: Optional[Union[str, List[str]]] = None,
prompt_embeds: Optional[torch.Tensor] = None,
prompt_embeds_pooled: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds_pooled: Optional[torch.Tensor] = None,
num_images_per_prompt: int = 1,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.Tensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
): | 209 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade.py |
"""
Function invoked when calling the pipeline for generation. | 209 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade.py |
Args:
image_embedding (`torch.Tensor` or `List[torch.Tensor]`):
Image Embeddings either extracted from an image or generated by a Prior Model.
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
num_inference_steps (`int`, *optional*, defaults to 12):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 0.0):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`decoder_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
`decoder_guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely | 209 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade.py |
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. Ignored when not using guidance (i.e., ignored
if `decoder_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
provided, text embeddings will be generated from `prompt` input argument.
prompt_embeds_pooled (`torch.Tensor`, *optional*):
Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
If not provided, pooled text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.Tensor`, *optional*): | 209 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade.py |
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.
negative_prompt_embeds_pooled (`torch.Tensor`, *optional*):
Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds_pooled will be generated from `negative_prompt`
input argument.
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)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
latents (`torch.Tensor`, *optional*): | 209 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade.py |
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between: `"pil"` (`PIL.Image.Image`), `"np"`
(`np.array`) or `"pt"` (`torch.Tensor`).
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
callback_on_step_end (`Callable`, *optional*):
A function that calls at the end of each denoising steps during the inference. The function is called
with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, | 209 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade.py |
callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
`callback_on_step_end_tensor_inputs`.
callback_on_step_end_tensor_inputs (`List`, *optional*):
The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
`._callback_tensor_inputs` attribute of your pipeline class. | 209 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade.py |
Examples:
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple` [`~pipelines.ImagePipelineOutput`] if `return_dict` is True,
otherwise a `tuple`. When returning a tuple, the first element is a list with the generated image
embeddings.
"""
# 0. Define commonly used variables
device = self._execution_device
dtype = self.decoder.dtype
self._guidance_scale = guidance_scale
if is_torch_version("<", "2.2.0") and dtype == torch.bfloat16:
raise ValueError("`StableCascadeDecoderPipeline` requires torch>=2.2.0 when using `torch.bfloat16` dtype.") | 209 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade.py |
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt,
negative_prompt=negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
)
if isinstance(image_embeddings, list):
image_embeddings = torch.cat(image_embeddings, dim=0)
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] | 209 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade.py |
# Compute the effective number of images per prompt
# We must account for the fact that the image embeddings from the prior can be generated with num_images_per_prompt > 1
# This results in a case where a single prompt is associated with multiple image embeddings
# Divide the number of image embeddings by the batch size to determine if this is the case.
num_images_per_prompt = num_images_per_prompt * (image_embeddings.shape[0] // batch_size) | 209 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade.py |
# 2. Encode caption
if prompt_embeds is None and negative_prompt_embeds is None:
_, prompt_embeds_pooled, _, negative_prompt_embeds_pooled = self.encode_prompt(
prompt=prompt,
device=device,
batch_size=batch_size,
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,
prompt_embeds_pooled=prompt_embeds_pooled,
negative_prompt_embeds=negative_prompt_embeds,
negative_prompt_embeds_pooled=negative_prompt_embeds_pooled,
) | 209 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade.py |
# The pooled embeds from the prior are pooled again before being passed to the decoder
prompt_embeds_pooled = (
torch.cat([prompt_embeds_pooled, negative_prompt_embeds_pooled])
if self.do_classifier_free_guidance
else prompt_embeds_pooled
)
effnet = (
torch.cat([image_embeddings, torch.zeros_like(image_embeddings)])
if self.do_classifier_free_guidance
else image_embeddings
)
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
# 5. Prepare latents
latents = self.prepare_latents(
batch_size, image_embeddings, num_images_per_prompt, dtype, device, generator, latents, self.scheduler
) | 209 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade.py |
if isinstance(self.scheduler, DDPMWuerstchenScheduler):
timesteps = timesteps[:-1]
else:
if hasattr(self.scheduler.config, "clip_sample") and self.scheduler.config.clip_sample:
self.scheduler.config.clip_sample = False # disample sample clipping
logger.warning(" set `clip_sample` to be False")
# 6. Run denoising loop
if hasattr(self.scheduler, "betas"):
alphas = 1.0 - self.scheduler.betas
alphas_cumprod = torch.cumprod(alphas, dim=0)
else:
alphas_cumprod = [] | 209 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade.py |
self._num_timesteps = len(timesteps)
for i, t in enumerate(self.progress_bar(timesteps)):
if not isinstance(self.scheduler, DDPMWuerstchenScheduler):
if len(alphas_cumprod) > 0:
timestep_ratio = self.get_timestep_ratio_conditioning(t.long().cpu(), alphas_cumprod)
timestep_ratio = timestep_ratio.expand(latents.size(0)).to(dtype).to(device)
else:
timestep_ratio = t.float().div(self.scheduler.timesteps[-1]).expand(latents.size(0)).to(dtype)
else:
timestep_ratio = t.expand(latents.size(0)).to(dtype) | 209 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade.py |
# 7. Denoise latents
predicted_latents = self.decoder(
sample=torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents,
timestep_ratio=torch.cat([timestep_ratio] * 2) if self.do_classifier_free_guidance else timestep_ratio,
clip_text_pooled=prompt_embeds_pooled,
effnet=effnet,
return_dict=False,
)[0]
# 8. Check for classifier free guidance and apply it
if self.do_classifier_free_guidance:
predicted_latents_text, predicted_latents_uncond = predicted_latents.chunk(2)
predicted_latents = torch.lerp(predicted_latents_uncond, predicted_latents_text, self.guidance_scale) | 209 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade.py |
# 9. Renoise latents to next timestep
if not isinstance(self.scheduler, DDPMWuerstchenScheduler):
timestep_ratio = t
latents = self.scheduler.step(
model_output=predicted_latents,
timestep=timestep_ratio,
sample=latents,
generator=generator,
).prev_sample
if callback_on_step_end is not None:
callback_kwargs = {}
for k in callback_on_step_end_tensor_inputs:
callback_kwargs[k] = locals()[k]
callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
latents = callback_outputs.pop("latents", latents)
prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
if XLA_AVAILABLE:
xm.mark_step() | 209 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade.py |
if output_type not in ["pt", "np", "pil", "latent"]:
raise ValueError(
f"Only the output types `pt`, `np`, `pil` and `latent` are supported not output_type={output_type}"
)
if not output_type == "latent":
# 10. Scale and decode the image latents with vq-vae
latents = self.vqgan.config.scale_factor * latents
images = self.vqgan.decode(latents).sample.clamp(0, 1)
if output_type == "np":
images = images.permute(0, 2, 3, 1).cpu().float().numpy() # float() as bfloat16-> numpy doesnt work
elif output_type == "pil":
images = images.permute(0, 2, 3, 1).cpu().float().numpy() # float() as bfloat16-> numpy doesnt work
images = self.numpy_to_pil(images)
else:
images = latents
# Offload all models
self.maybe_free_model_hooks() | 209 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade.py |
if not return_dict:
return images
return ImagePipelineOutput(images) | 209 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade.py |
class StableCascadeCombinedPipeline(DiffusionPipeline):
"""
Combined Pipeline for text-to-image generation using Stable Cascade.
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.) | 210 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_combined.py |
Args:
tokenizer (`CLIPTokenizer`):
The decoder tokenizer to be used for text inputs.
text_encoder (`CLIPTextModel`):
The decoder text encoder to be used for text inputs.
decoder (`StableCascadeUNet`):
The decoder model to be used for decoder image generation pipeline.
scheduler (`DDPMWuerstchenScheduler`):
The scheduler to be used for decoder image generation pipeline.
vqgan (`PaellaVQModel`):
The VQGAN model to be used for decoder image generation pipeline.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
Model that extracts features from generated images to be used as inputs for the `image_encoder`.
image_encoder ([`CLIPVisionModelWithProjection`]):
Frozen CLIP image-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
prior_prior (`StableCascadeUNet`):
The prior model to be used for prior pipeline. | 210 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_combined.py |
prior_scheduler (`DDPMWuerstchenScheduler`):
The scheduler to be used for prior pipeline.
""" | 210 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_combined.py |
_load_connected_pipes = True
_optional_components = ["prior_feature_extractor", "prior_image_encoder"]
def __init__(
self,
tokenizer: CLIPTokenizer,
text_encoder: CLIPTextModel,
decoder: StableCascadeUNet,
scheduler: DDPMWuerstchenScheduler,
vqgan: PaellaVQModel,
prior_prior: StableCascadeUNet,
prior_text_encoder: CLIPTextModel,
prior_tokenizer: CLIPTokenizer,
prior_scheduler: DDPMWuerstchenScheduler,
prior_feature_extractor: Optional[CLIPImageProcessor] = None,
prior_image_encoder: Optional[CLIPVisionModelWithProjection] = None,
):
super().__init__() | 210 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_combined.py |
self.register_modules(
text_encoder=text_encoder,
tokenizer=tokenizer,
decoder=decoder,
scheduler=scheduler,
vqgan=vqgan,
prior_text_encoder=prior_text_encoder,
prior_tokenizer=prior_tokenizer,
prior_prior=prior_prior,
prior_scheduler=prior_scheduler,
prior_feature_extractor=prior_feature_extractor,
prior_image_encoder=prior_image_encoder,
)
self.prior_pipe = StableCascadePriorPipeline(
prior=prior_prior,
text_encoder=prior_text_encoder,
tokenizer=prior_tokenizer,
scheduler=prior_scheduler,
image_encoder=prior_image_encoder,
feature_extractor=prior_feature_extractor,
)
self.decoder_pipe = StableCascadeDecoderPipeline(
text_encoder=text_encoder,
tokenizer=tokenizer,
decoder=decoder,
scheduler=scheduler, | 210 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_combined.py |
vqgan=vqgan,
) | 210 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_combined.py |
def enable_xformers_memory_efficient_attention(self, attention_op: Optional[Callable] = None):
self.decoder_pipe.enable_xformers_memory_efficient_attention(attention_op)
def enable_model_cpu_offload(self, gpu_id: Optional[int] = None, device: Union[torch.device, str] = "cuda"):
r"""
Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
`enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
"""
self.prior_pipe.enable_model_cpu_offload(gpu_id=gpu_id, device=device)
self.decoder_pipe.enable_model_cpu_offload(gpu_id=gpu_id, device=device) | 210 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_combined.py |
def enable_sequential_cpu_offload(self, gpu_id: Optional[int] = None, device: Union[torch.device, str] = "cuda"):
r"""
Offloads all models (`unet`, `text_encoder`, `vae`, and `safety checker` state dicts) to CPU using 🤗
Accelerate, significantly reducing memory usage. Models are moved to a `torch.device('meta')` and loaded on a
GPU only when their specific submodule's `forward` method is called. Offloading happens on a submodule basis.
Memory savings are higher than using `enable_model_cpu_offload`, but performance is lower.
"""
self.prior_pipe.enable_sequential_cpu_offload(gpu_id=gpu_id, device=device)
self.decoder_pipe.enable_sequential_cpu_offload(gpu_id=gpu_id, device=device)
def progress_bar(self, iterable=None, total=None):
self.prior_pipe.progress_bar(iterable=iterable, total=total)
self.decoder_pipe.progress_bar(iterable=iterable, total=total) | 210 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_combined.py |
def set_progress_bar_config(self, **kwargs):
self.prior_pipe.set_progress_bar_config(**kwargs)
self.decoder_pipe.set_progress_bar_config(**kwargs) | 210 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_combined.py |
@torch.no_grad()
@replace_example_docstring(TEXT2IMAGE_EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Optional[Union[str, List[str]]] = None,
images: Union[torch.Tensor, PIL.Image.Image, List[torch.Tensor], List[PIL.Image.Image]] = None,
height: int = 512,
width: int = 512,
prior_num_inference_steps: int = 60,
prior_guidance_scale: float = 4.0,
num_inference_steps: int = 12,
decoder_guidance_scale: float = 0.0,
negative_prompt: Optional[Union[str, List[str]]] = None,
prompt_embeds: Optional[torch.Tensor] = None,
prompt_embeds_pooled: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds_pooled: Optional[torch.Tensor] = None,
num_images_per_prompt: int = 1,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.Tensor] = None, | 210 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_combined.py |
output_type: Optional[str] = "pil",
return_dict: bool = True,
prior_callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
prior_callback_on_step_end_tensor_inputs: List[str] = ["latents"],
callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
):
"""
Function invoked when calling the pipeline for generation. | 210 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_combined.py |
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation for the prior and decoder.
images (`torch.Tensor`, `PIL.Image.Image`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, *optional*):
The images to guide the image generation for the prior.
negative_prompt (`str` or `List[str]`, *optional*):
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_embeds (`torch.Tensor`, *optional*):
Pre-generated text embeddings for the prior. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, text embeddings will be generated from `prompt` input argument.
prompt_embeds_pooled (`torch.Tensor`, *optional*): | 210 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_combined.py |
Pre-generated text embeddings for the prior. 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 for the prior. 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.
negative_prompt_embeds_pooled (`torch.Tensor`, *optional*):
Pre-generated negative text embeddings for the prior. 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.
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt. | 210 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_combined.py |
height (`int`, *optional*, defaults to 512):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to 512):
The width in pixels of the generated image.
prior_guidance_scale (`float`, *optional*, defaults to 4.0):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`prior_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
`prior_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.
prior_num_inference_steps (`Union[int, Dict[float, int]]`, *optional*, defaults to 60): | 210 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_combined.py |
The number of prior denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference. For more specific timestep spacing, you can pass customized
`prior_timesteps`
num_inference_steps (`int`, *optional*, defaults to 12):
The number of decoder denoising steps. More denoising steps usually lead to a higher quality image at
the expense of slower inference. For more specific timestep spacing, you can pass customized
`timesteps`
decoder_guidance_scale (`float`, *optional*, defaults to 0.0):
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 > | 210 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_combined.py |
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
latents (`torch.Tensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between: `"pil"` (`PIL.Image.Image`), `"np"` | 210 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_combined.py |
(`np.array`) or `"pt"` (`torch.Tensor`).
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
prior_callback_on_step_end (`Callable`, *optional*):
A function that calls at the end of each denoising steps during the inference. The function is called
with the following arguments: `prior_callback_on_step_end(self: DiffusionPipeline, step: int, timestep:
int, callback_kwargs: Dict)`.
prior_callback_on_step_end_tensor_inputs (`List`, *optional*):
The list of tensor inputs for the `prior_callback_on_step_end` function. The tensors specified in the
list will be passed as `callback_kwargs` argument. You will only be able to include variables listed in
the `._callback_tensor_inputs` attribute of your pipeline class. | 210 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_combined.py |
callback_on_step_end (`Callable`, *optional*):
A function that calls at the end of each denoising steps during the inference. The function is called
with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
`callback_on_step_end_tensor_inputs`.
callback_on_step_end_tensor_inputs (`List`, *optional*):
The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
`._callback_tensor_inputs` attribute of your pipeline class. | 210 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_combined.py |
Examples:
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple` [`~pipelines.ImagePipelineOutput`] if `return_dict` is True,
otherwise a `tuple`. When returning a tuple, the first element is a list with the generated images.
"""
dtype = self.decoder_pipe.decoder.dtype
if is_torch_version("<", "2.2.0") and dtype == torch.bfloat16:
raise ValueError(
"`StableCascadeCombinedPipeline` requires torch>=2.2.0 when using `torch.bfloat16` dtype."
) | 210 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_combined.py |
prior_outputs = self.prior_pipe(
prompt=prompt if prompt_embeds is None else None,
images=images,
height=height,
width=width,
num_inference_steps=prior_num_inference_steps,
guidance_scale=prior_guidance_scale,
negative_prompt=negative_prompt if negative_prompt_embeds is None else None,
prompt_embeds=prompt_embeds,
prompt_embeds_pooled=prompt_embeds_pooled,
negative_prompt_embeds=negative_prompt_embeds,
negative_prompt_embeds_pooled=negative_prompt_embeds_pooled,
num_images_per_prompt=num_images_per_prompt,
generator=generator,
latents=latents,
output_type="pt",
return_dict=True,
callback_on_step_end=prior_callback_on_step_end,
callback_on_step_end_tensor_inputs=prior_callback_on_step_end_tensor_inputs,
)
image_embeddings = prior_outputs.image_embeddings | 210 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_combined.py |
prompt_embeds = prior_outputs.get("prompt_embeds", None)
prompt_embeds_pooled = prior_outputs.get("prompt_embeds_pooled", None)
negative_prompt_embeds = prior_outputs.get("negative_prompt_embeds", None)
negative_prompt_embeds_pooled = prior_outputs.get("negative_prompt_embeds_pooled", None) | 210 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_combined.py |
outputs = self.decoder_pipe(
image_embeddings=image_embeddings,
prompt=prompt if prompt_embeds is None else None,
num_inference_steps=num_inference_steps,
guidance_scale=decoder_guidance_scale,
negative_prompt=negative_prompt if negative_prompt_embeds is None else None,
prompt_embeds=prompt_embeds,
prompt_embeds_pooled=prompt_embeds_pooled,
negative_prompt_embeds=negative_prompt_embeds,
negative_prompt_embeds_pooled=negative_prompt_embeds_pooled,
generator=generator,
output_type=output_type,
return_dict=return_dict,
callback_on_step_end=callback_on_step_end,
callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
)
return outputs | 210 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_combined.py |
class StableCascadePriorPipelineOutput(BaseOutput):
"""
Output class for WuerstchenPriorPipeline.
Args:
image_embeddings (`torch.Tensor` or `np.ndarray`)
Prior image embeddings for text prompt
prompt_embeds (`torch.Tensor`):
Text embeddings for the prompt.
negative_prompt_embeds (`torch.Tensor`):
Text embeddings for the negative prompt.
"""
image_embeddings: Union[torch.Tensor, np.ndarray]
prompt_embeds: Union[torch.Tensor, np.ndarray]
prompt_embeds_pooled: Union[torch.Tensor, np.ndarray]
negative_prompt_embeds: Union[torch.Tensor, np.ndarray]
negative_prompt_embeds_pooled: Union[torch.Tensor, np.ndarray] | 211 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_prior.py |
class StableCascadePriorPipeline(DiffusionPipeline):
"""
Pipeline for generating image prior for Stable Cascade.
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.) | 212 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_prior.py |
Args:
prior ([`StableCascadeUNet`]):
The Stable Cascade prior to approximate the image embedding from the text and/or image embedding.
text_encoder ([`CLIPTextModelWithProjection`]):
Frozen text-encoder
([laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)).
feature_extractor ([`~transformers.CLIPImageProcessor`]):
Model that extracts features from generated images to be used as inputs for the `image_encoder`.
image_encoder ([`CLIPVisionModelWithProjection`]):
Frozen CLIP image-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
scheduler ([`DDPMWuerstchenScheduler`]): | 212 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_prior.py |
A scheduler to be used in combination with `prior` to generate image embedding.
resolution_multiple ('float', *optional*, defaults to 42.67):
Default resolution for multiple images generated.
""" | 212 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_prior.py |
unet_name = "prior"
text_encoder_name = "text_encoder"
model_cpu_offload_seq = "image_encoder->text_encoder->prior"
_optional_components = ["image_encoder", "feature_extractor"]
_callback_tensor_inputs = ["latents", "text_encoder_hidden_states", "negative_prompt_embeds"] | 212 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_prior.py |
def __init__(
self,
tokenizer: CLIPTokenizer,
text_encoder: CLIPTextModelWithProjection,
prior: StableCascadeUNet,
scheduler: DDPMWuerstchenScheduler,
resolution_multiple: float = 42.67,
feature_extractor: Optional[CLIPImageProcessor] = None,
image_encoder: Optional[CLIPVisionModelWithProjection] = None,
) -> None:
super().__init__()
self.register_modules(
tokenizer=tokenizer,
text_encoder=text_encoder,
image_encoder=image_encoder,
feature_extractor=feature_extractor,
prior=prior,
scheduler=scheduler,
)
self.register_to_config(resolution_multiple=resolution_multiple) | 212 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_prior.py |
def prepare_latents(
self, batch_size, height, width, num_images_per_prompt, dtype, device, generator, latents, scheduler
):
latent_shape = (
num_images_per_prompt * batch_size,
self.prior.config.in_channels,
ceil(height / self.config.resolution_multiple),
ceil(width / self.config.resolution_multiple),
)
if latents is None:
latents = randn_tensor(latent_shape, generator=generator, device=device, dtype=dtype)
else:
if latents.shape != latent_shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latent_shape}")
latents = latents.to(device)
latents = latents * scheduler.init_noise_sigma
return latents | 212 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_prior.py |
def encode_prompt(
self,
device,
batch_size,
num_images_per_prompt,
do_classifier_free_guidance,
prompt=None,
negative_prompt=None,
prompt_embeds: Optional[torch.Tensor] = None,
prompt_embeds_pooled: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds_pooled: Optional[torch.Tensor] = None,
):
if prompt_embeds is None:
# get prompt text embeddings
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
attention_mask = text_inputs.attention_mask
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids | 212 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_prior.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[:, self.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" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
attention_mask = attention_mask[:, : self.tokenizer.model_max_length] | 212 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_prior.py |
text_encoder_output = self.text_encoder(
text_input_ids.to(device), attention_mask=attention_mask.to(device), output_hidden_states=True
)
prompt_embeds = text_encoder_output.hidden_states[-1]
if prompt_embeds_pooled is None:
prompt_embeds_pooled = text_encoder_output.text_embeds.unsqueeze(1)
prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
prompt_embeds_pooled = prompt_embeds_pooled.to(dtype=self.text_encoder.dtype, device=device)
prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0)
prompt_embeds_pooled = prompt_embeds_pooled.repeat_interleave(num_images_per_prompt, dim=0) | 212 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_prior.py |
if negative_prompt_embeds is None and do_classifier_free_guidance:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif 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"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt | 212 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_prior.py |
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
negative_prompt_embeds_text_encoder_output = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=uncond_input.attention_mask.to(device),
output_hidden_states=True,
)
negative_prompt_embeds = negative_prompt_embeds_text_encoder_output.hidden_states[-1]
negative_prompt_embeds_pooled = negative_prompt_embeds_text_encoder_output.text_embeds.unsqueeze(1) | 212 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_prior.py |
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=self.text_encoder.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) | 212 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_prior.py |
seq_len = negative_prompt_embeds_pooled.shape[1]
negative_prompt_embeds_pooled = negative_prompt_embeds_pooled.to(
dtype=self.text_encoder.dtype, device=device
)
negative_prompt_embeds_pooled = negative_prompt_embeds_pooled.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds_pooled = negative_prompt_embeds_pooled.view(
batch_size * num_images_per_prompt, seq_len, -1
)
# done duplicates
return prompt_embeds, prompt_embeds_pooled, negative_prompt_embeds, negative_prompt_embeds_pooled | 212 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_prior.py |
def encode_image(self, images, device, dtype, batch_size, num_images_per_prompt):
image_embeds = []
for image in images:
image = self.feature_extractor(image, return_tensors="pt").pixel_values
image = image.to(device=device, dtype=dtype)
image_embed = self.image_encoder(image).image_embeds.unsqueeze(1)
image_embeds.append(image_embed)
image_embeds = torch.cat(image_embeds, dim=1)
image_embeds = image_embeds.repeat(batch_size * num_images_per_prompt, 1, 1)
negative_image_embeds = torch.zeros_like(image_embeds)
return image_embeds, negative_image_embeds | 212 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_prior.py |
def check_inputs(
self,
prompt,
images=None,
image_embeds=None,
negative_prompt=None,
prompt_embeds=None,
prompt_embeds_pooled=None,
negative_prompt_embeds=None,
negative_prompt_embeds_pooled=None,
callback_on_step_end_tensor_inputs=None,
):
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]}"
) | 212 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_prior.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."
) | 212 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_prior.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}."
)
if prompt_embeds is not None and prompt_embeds_pooled is None:
raise ValueError(
"If `prompt_embeds` are provided, `prompt_embeds_pooled` must also be provided. Make sure to generate `prompt_embeds_pooled` from the same text encoder that was used to generate `prompt_embeds`"
) | 212 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_prior.py |
if negative_prompt_embeds is not None and negative_prompt_embeds_pooled is None:
raise ValueError(
"If `negative_prompt_embeds` are provided, `negative_prompt_embeds_pooled` must also be provided. Make sure to generate `prompt_embeds_pooled` from the same text encoder that was used to generate `prompt_embeds`"
)
if prompt_embeds_pooled is not None and negative_prompt_embeds_pooled is not None:
if prompt_embeds_pooled.shape != negative_prompt_embeds_pooled.shape:
raise ValueError(
"`prompt_embeds_pooled` and `negative_prompt_embeds_pooled` must have the same shape when passed"
f"directly, but got: `prompt_embeds_pooled` {prompt_embeds_pooled.shape} !="
f"`negative_prompt_embeds_pooled` {negative_prompt_embeds_pooled.shape}."
) | 212 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_prior.py |
if image_embeds is not None and images is not None:
raise ValueError(
f"Cannot forward both `images`: {images} and `image_embeds`: {image_embeds}. Please make sure to"
" only forward one of the two."
)
if images:
for i, image in enumerate(images):
if not isinstance(image, torch.Tensor) and not isinstance(image, PIL.Image.Image):
raise TypeError(
f"'images' must contain images of type 'torch.Tensor' or 'PIL.Image.Image, but got"
f"{type(image)} for image number {i}."
)
@property
def guidance_scale(self):
return self._guidance_scale
@property
def do_classifier_free_guidance(self):
return self._guidance_scale > 1
@property
def num_timesteps(self):
return self._num_timesteps | 212 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_prior.py |
def get_timestep_ratio_conditioning(self, t, alphas_cumprod):
s = torch.tensor([0.008])
clamp_range = [0, 1]
min_var = torch.cos(s / (1 + s) * torch.pi * 0.5) ** 2
var = alphas_cumprod[t]
var = var.clamp(*clamp_range)
s, min_var = s.to(var.device), min_var.to(var.device)
ratio = (((var * min_var) ** 0.5).acos() / (torch.pi * 0.5)) * (1 + s) - s
return ratio | 212 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_prior.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Optional[Union[str, List[str]]] = None,
images: Union[torch.Tensor, PIL.Image.Image, List[torch.Tensor], List[PIL.Image.Image]] = None,
height: int = 1024,
width: int = 1024,
num_inference_steps: int = 20,
timesteps: List[float] = None,
guidance_scale: float = 4.0,
negative_prompt: Optional[Union[str, List[str]]] = None,
prompt_embeds: Optional[torch.Tensor] = None,
prompt_embeds_pooled: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds_pooled: Optional[torch.Tensor] = None,
image_embeds: Optional[torch.Tensor] = None,
num_images_per_prompt: Optional[int] = 1,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.Tensor] = None, | 212 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_prior.py |
output_type: Optional[str] = "pt",
return_dict: bool = True,
callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
):
"""
Function invoked when calling the pipeline for generation. | 212 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_prior.py |
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
height (`int`, *optional*, defaults to 1024):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to 1024):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 60):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 8.0):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`decoder_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 | 212 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_prior.py |
`decoder_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. Ignored when not using guidance (i.e., ignored
if `decoder_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
provided, text embeddings will be generated from `prompt` input argument.
prompt_embeds_pooled (`torch.Tensor`, *optional*):
Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
If not provided, pooled text embeddings will be generated from `prompt` input argument. | 212 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_prior.py |
negative_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
negative_prompt_embeds_pooled (`torch.Tensor`, *optional*):
Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds_pooled will be generated from `negative_prompt`
input argument.
image_embeds (`torch.Tensor`, *optional*):
Pre-generated image embeddings. Can be used to easily tweak image inputs, *e.g.* prompt weighting. If
not provided, image embeddings will be generated from `image` input argument if existing.
num_images_per_prompt (`int`, *optional*, defaults to 1): | 212 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_prior.py |
The number of images to generate per prompt.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
latents (`torch.Tensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between: `"pil"` (`PIL.Image.Image`), `"np"`
(`np.array`) or `"pt"` (`torch.Tensor`).
return_dict (`bool`, *optional*, defaults to `True`): | 212 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_prior.py |
Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
callback_on_step_end (`Callable`, *optional*):
A function that calls at the end of each denoising steps during the inference. The function is called
with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
`callback_on_step_end_tensor_inputs`.
callback_on_step_end_tensor_inputs (`List`, *optional*):
The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
`._callback_tensor_inputs` attribute of your pipeline class. | 212 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_prior.py |
Examples:
Returns:
[`StableCascadePriorPipelineOutput`] or `tuple` [`StableCascadePriorPipelineOutput`] if `return_dict` is
True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated image
embeddings.
"""
# 0. Define commonly used variables
device = self._execution_device
dtype = next(self.prior.parameters()).dtype
self._guidance_scale = guidance_scale
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] | 212 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_prior.py |
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt,
images=images,
image_embeds=image_embeds,
negative_prompt=negative_prompt,
prompt_embeds=prompt_embeds,
prompt_embeds_pooled=prompt_embeds_pooled,
negative_prompt_embeds=negative_prompt_embeds,
negative_prompt_embeds_pooled=negative_prompt_embeds_pooled,
callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
) | 212 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_prior.py |
# 2. Encode caption + images
(
prompt_embeds,
prompt_embeds_pooled,
negative_prompt_embeds,
negative_prompt_embeds_pooled,
) = self.encode_prompt(
prompt=prompt,
device=device,
batch_size=batch_size,
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,
prompt_embeds_pooled=prompt_embeds_pooled,
negative_prompt_embeds=negative_prompt_embeds,
negative_prompt_embeds_pooled=negative_prompt_embeds_pooled,
) | 212 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_prior.py |
if images is not None:
image_embeds_pooled, uncond_image_embeds_pooled = self.encode_image(
images=images,
device=device,
dtype=dtype,
batch_size=batch_size,
num_images_per_prompt=num_images_per_prompt,
)
elif image_embeds is not None:
image_embeds_pooled = image_embeds.repeat(batch_size * num_images_per_prompt, 1, 1)
uncond_image_embeds_pooled = torch.zeros_like(image_embeds_pooled)
else:
image_embeds_pooled = torch.zeros(
batch_size * num_images_per_prompt,
1,
self.prior.config.clip_image_in_channels,
device=device,
dtype=dtype,
)
uncond_image_embeds_pooled = torch.zeros(
batch_size * num_images_per_prompt,
1,
self.prior.config.clip_image_in_channels,
device=device, | 212 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_prior.py |
dtype=dtype,
) | 212 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_prior.py |
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