text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
values |
|---|---|---|
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.z... | 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_... | 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_... | 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
noi... | 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,
... | 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_edi... | 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_weight... | 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, t... | 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(d... | 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_n... | 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... | 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 ([... | 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:... | 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.c... | 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_... | 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]
)
logg... | 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... | 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_p... | 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(
... | 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)
negati... | 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)
... | 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 ... | 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:
... | 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: `pr... | 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)
... | 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: Option... | 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... | 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`... | 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*):
... | 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`.
... | 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 specifie... | 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 common... | 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,... | 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 emb... | 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=... | 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 = (
... | 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.wa... | 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)
... | 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=pr... | 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,
... | 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
... | 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 o... | 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 p... | 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,... | 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,
... | 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"""
Offload... | 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 `tor... | 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 = 5... | 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,
... | 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 ... | 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 emb... | 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 ... | 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):
... | 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... | 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... | 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: Dic... | 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
... | 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_p... | 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 ... | 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.
ne... | 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, runnin... | 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.c... | 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... | 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),
... | 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_... | 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]
)
logg... | 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... | 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_p... | 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(
... | 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)
negati... | 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)
... | 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_encod... | 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,
... | 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:
... | 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: `pr... | 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 genera... | 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(imag... | 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)
... | 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,
... | 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 ... | 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 gene... | 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.
nega... | 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 (`tor... | 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... | 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 co... | 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... | 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_imag... | 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,
)
eli... | 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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