text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
values |
|---|---|---|
Args:
transformer ([`SD3Transformer2DModel`]):
Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
scheduler ([`FlowMatchEulerDiscreteScheduler`]):
A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
... | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),
specifically the
[laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)
variant.
text_encoder_3 ([`T5EncoderModel`]):... | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
Tokenizer of class
[T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer).
""" | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
model_cpu_offload_seq = "text_encoder->text_encoder_2->text_encoder_3->transformer->vae"
_optional_components = []
_callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds", "negative_pooled_prompt_embeds"]
def __init__(
self,
transformer: SD3Transformer2DModel,
... | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
self.register_modules(
vae=vae,
text_encoder=text_encoder,
text_encoder_2=text_encoder_2,
text_encoder_3=text_encoder_3,
tokenizer=tokenizer,
tokenizer_2=tokenizer_2,
tokenizer_3=tokenizer_3,
transformer=transformer,
... | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
self.transformer.config.patch_size if hasattr(self, "transformer") and self.transformer is not None else 2
) | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
self.set_pag_applied_layers(
pag_applied_layers, pag_attn_processors=(PAGCFGJointAttnProcessor2_0(), PAGJointAttnProcessor2_0())
)
# Copied from diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3.StableDiffusion3Pipeline._get_t5_prompt_embeds
def _get_t5_prompt_embeds(
... | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
if self.text_encoder_3 is None:
return torch.zeros(
(
batch_size * num_images_per_prompt,
self.tokenizer_max_length,
self.transformer.config.joint_attention_dim,
),
device=device,
dtyp... | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
removed_text = self.tokenizer_3.batch_decode(untruncated_ids[:, self.tokenizer_max_length - 1 : -1])
logger.warning(
"The following part of your input was truncated beca... | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
# Copied from diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3.StableDiffusion3Pipeline._get_clip_prompt_embeds
def _get_clip_prompt_embeds(
self,
prompt: Union[str, List[str]],
num_images_per_prompt: int = 1,
device: Optional[torch.device] = None,
clip_skip... | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
text_input_ids = text_inputs.input_ids
untruncated_ids = tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
removed_text = tokenizer.batch_decode(untruncated_ids[:,... | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
_, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
pooled_prompt_e... | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
# Copied from diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3.StableDiffusion3Pipeline.encode_prompt
def encode_prompt(
self,
prompt: Union[str, List[str]],
prompt_2: Union[str, List[str]],
prompt_3: Union[str, List[str]],
device: Optional[torch.device] = N... | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
r""" | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
prompt_2 (`str` or `List[str]`, *optional*):
The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
used in all text-encoders
... | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
negative_prompt_2 (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
... | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
... | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
the output of the pre-final layer will be used for computing the prompt embeddings.
lora_scale (`float`, *optional*):
A lora scale that will be applied to all LoRA layers o... | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
# set lora scale so that monkey patched LoRA
# function of text encoder can correctly access it
if lora_scale is not None and isinstance(self, SD3LoraLoaderMixin):
self._lora_scale = lora_scale
# dynamically adjust the LoRA scale
if self.text_encoder is not None and ... | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
prompt_embed, pooled_prompt_embed = self._get_clip_prompt_embeds(
prompt=prompt,
device=device,
num_images_per_prompt=num_images_per_prompt,
clip_skip=clip_skip,
clip_model_index=0,
)
prompt_2_embed, pooled_prompt_2_... | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
clip_prompt_embeds = torch.nn.functional.pad(
clip_prompt_embeds, (0, t5_prompt_embed.shape[-1] - clip_prompt_embeds.shape[-1])
)
prompt_embeds = torch.cat([clip_prompt_embeds, t5_prompt_embed], dim=-2)
pooled_prompt_embeds = torch.cat([pooled_prompt_embed, pooled_pr... | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
# normalize str to list
negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
negative_prompt_2 = (
batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2
)
negative... | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
if prompt is not None and type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif batch_size != len(negative_pro... | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
negative_prompt_embed, negative_pooled_prompt_embed = self._get_clip_prompt_embeds(
negative_prompt,
device=device,
num_images_per_prompt=num_images_per_prompt,
clip_skip=None,
clip_model_index=0,
)
negative_prompt_2... | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
negative_clip_prompt_embeds = torch.nn.functional.pad(
negative_clip_prompt_embeds,
(0, t5_negative_prompt_embed.shape[-1] - negative_clip_prompt_embeds.shape[-1]),
)
negative_prompt_embeds = torch.cat([negative_clip_prompt_embeds, t5_negative_prompt_embed], dim=... | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
# Copied from diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3.StableDiffusion3Pipeline.check_inputs
def check_inputs(
self,
prompt,
prompt_2,
prompt_3,
height,
width,
negative_prompt=None,
negative_prompt_2=None,
negative_pro... | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
f"You can use height {height - height % (self.vae_scale_factor * self.patch_size)} and width {width - width % (self.vae_scale_factor * self.patch_size)}."
) | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
if callback_on_step_end_tensor_inputs is not None and not all(
k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
):
raise ValueError(
f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in cal... | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.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_2 is not None and prompt_embeds is not ... | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
"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)}")
... | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
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."
)
... | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.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... | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None:
raise ValueError(
"If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used... | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
shape = (
batch_size,
num_channels_latents,
int(height) // self.vae_scale_factor,
int(width) // self.vae_scale_factor,
)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a lis... | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
@property
def do_classifier_free_guidance(self):
return self._guidance_scale... | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
prompt_2: Optional[Union[str, List[str]]] = None,
prompt_3: Optional[Union[str, List[str]]] = None,
height: Optional[int] = None,
width: Optio... | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
joint_attention_kwargs: Optional[Dict[str, Any]] = None,
clip_skip: Optional[int] = None,
... | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
instead.
prompt_2 (`str` or `List[str]`, *optional*):
The prompt or prompts to be sent to `tokeni... | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
The width in pixels of the generated image. This is set to 1024 by default for the best results.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower infere... | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
negative_prompt (`str` or ... | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
The prompt or prompts not to guide the image generation to be sent to `tokenizer_3` and
`text_encoder_3`. If not defined, `negative_prompt` is used instead
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
gener... | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. ... | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
input argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io... | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
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 sp... | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
The scale factor for the perturbed attention guidance. If it is set to 0.0, the perturbed attention
guidance will not be used.
pag_adaptive_scale (`float`, *optional*, defaults to 0.0):
The adaptive scale factor for the perturbed attention guidance. If it is set to 0.0, `pag_... | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
Examples:
Returns:
[`~pipelines.stable_diffusion_3.StableDiffusion3PipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion_3.StableDiffusion3PipelineOutput`] if `return_dict` is True, otherwise a
`tuple`. When returning a tuple, the first element is a list with the genera... | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt,
prompt_2,
prompt_3,
height,
width,
negative_prompt=negative_prompt,
negative_prompt_2=negative_prompt_2,
negative_prompt_3=negative_prompt_3,
... | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
device = self._execution_device | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
lora_scale = (
self.joint_attention_kwargs.get("scale", None) if self.joint_attention_kwargs is not None else None
)
(
prompt_embeds,
negative_prompt_embeds,
pooled_prompt_embeds,
negative_pooled_prompt_embeds,
) = self.encode_prompt(
... | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
max_sequence_length=max_sequence_length,
lora_scale=lora_scale,
) | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
if self.do_perturbed_attention_guidance:
prompt_embeds = self._prepare_perturbed_attention_guidance(
prompt_embeds, negative_prompt_embeds, self.do_classifier_free_guidance
)
pooled_prompt_embeds = self._prepare_perturbed_attention_guidance(
pooled_pro... | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
# 5. Prepare latent variables
num_channels_latents = self.transformer.config.in_channels
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
prompt_embeds.dtype,
device,
... | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
# expand the latents if we are doing classifier free guidance, perturbed-attention guidance, or both
latent_model_input = torch.cat([latents] * (prompt_embeds.shape[0] // latents.shape[0]))
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
timest... | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
# perform guidance
if self.do_perturbed_attention_guidance:
noise_pred = self._apply_perturbed_attention_guidance(
noise_pred, self.do_classifier_free_guidance, self.guidance_scale, t
)
elif self.do_classifier_free_guidance... | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
if latents.dtype != latents_dtype:
if torch.backends.mps.is_available():
# some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
latents = latents.to(latents_dtype)
if callback_on_... | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
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)
negative_pooled_prompt_embeds = callback_out... | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
image = self.vae.decode(latents, return_dict=False)[0]
image = self.image_processor.postprocess(image, output_type=output_type)
# Offload all models
self.maybe_free_model_hooks()
if self.do_perturbed_attention_guidance:
self.transformer.set_attn_processor(original_attn_... | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
class StableDiffusionPAGPipeline(
DiffusionPipeline,
StableDiffusionMixin,
TextualInversionLoaderMixin,
StableDiffusionLoraLoaderMixin,
IPAdapterMixin,
FromSingleFileMixin,
PAGMixin,
):
r"""
Pipeline for text-to-image generation using Stable Diffusion.
This model inherits from [... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
The pipeline also inherits the following loading methods:
- [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
- [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
- [`~loaders.StableDiffusionLoraLoaderMixi... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`~transformers.CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14))... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `saf... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
model_cpu_offload_seq = "text_encoder->image_encoder->unet->vae"
_optional_components = ["safety_checker", "feature_extractor", "image_encoder"]
_exclude_from_cpu_offload = ["safety_checker"]
_callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
def __init__(
self,
... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
if scheduler is not None and getattr(scheduler.config, "steps_offset", 1) != 1:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
if scheduler is not None and getattr(scheduler.config, "clip_sample", False) is True:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
" `clip_sample` should be set to False in the configuration file... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
if safety_checker is None and requires_safety_checker:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
if safety_checker is not None and feature_extractor is None:
raise ValueError(
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` i... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
is_unet_version_less_0_9_0 = (
unet is not None
and hasattr(unet.config, "_diffusers_version")
and version.parse(version.parse(unet.config._diffusers_version).base_version) < version.parse("0.9.0.dev0")
)
is_unet_sample_size_less_64 = (
unet is not None an... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
" \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
" in the config might lead to incorrect results in future versions. If you have downloaded this... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
image_encoder=image_encoder,
)
... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt
def encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torch.... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`b... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.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.
lora... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
# dynamically adjust the LoRA scale
if not USE_PEFT_BACKEND:
adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
else:
scale_lora_layers(self.text_encoder, lora_scale)
if prompt is not None and isinstance(prompt, str):
batch_size = 1... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
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
untruncated_ids = sel... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = text_inputs.attention_mask.to(device)
else:
attention_mask = None | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
if clip_skip is None:
prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)
prompt_embeds = prompt_embeds[0]
else:
prompt_embeds = self.text_encoder(
text_input_ids.to(device), attention_mask=attention_... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds) | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
if self.text_encoder is not None:
prompt_embeds_dtype = self.text_encoder.dtype
elif self.unet is not None:
prompt_embeds_dtype = self.unet.dtype
else:
prompt_embeds_dtype = prompt_embeds.dtype
prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, devic... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif prompt is not None and type(prompt) is no... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
# textual inversion: process multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.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=prompt_embeds_dtype, device=device)
negative... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image
def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):
dtype = next(self.image_encoder.parameters()).dtype
if not isinstance(image, torch.Tensor):
... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
image = image.to(device=device, dtype=dtype)
if output_hidden_states:
image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]
image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
uncond_imag... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds
def prepare_ip_adapter_image_embeds(
self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance
):
image_embeds = ... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
for single_ip_adapter_image, image_proj_layer in zip(
ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers
):
output_hidden_state = not isinstance(image_proj_layer, ImageProjection)
single_image_embeds, single_negative_image_embeds = self.encod... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
ip_adapter_image_embeds = []
for i, single_image_embeds in enumerate(image_embeds):
single_image_embeds = torch.cat([single_image_embeds] * num_images_per_prompt, dim=0)
if do_classifier_free_guidance:
single_negative_image_embeds = torch.cat([negative_image_embeds[i]] * ... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
def run_safety_checker(self, image, device, dtype):
if self.safety_checker is None:
has_nsfw_concept = None
else:
if torch.is_tensor(image):
... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used w... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs
def check_inputs(
self,
prompt,
height,
width,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if callback_on_step_end_tensor_... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.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:
... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.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... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
if ip_adapter_image_embeds is not None:
if not isinstance(ip_adapter_image_embeds, list):
raise ValueError(
f"`ip_adapter_image_embeds` has to be of type `list` but is {type(ip_adapter_image_embeds)}"
)
elif ip_adapter_image_embeds[0].ndim not ... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
shape = (
batch_size,
num_channels_latents,
... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
# Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embeddi... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
Args:
w (`torch.Tensor`):
Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.
embedding_dim (`int`, *optional*, defaults to 512):
Dimension of the embeddings to generate.
dtype (`torch.dtype`, *optiona... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
half_dim = embedding_dim // 2
emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
emb = w.to(dtype)[:, None] * emb[None, :]
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
if embedding_dim % 2 == 1: # zero ... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
@property
def do_classifier_free_guidance(self):
return self._guidance_scale... | 346 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd.py |
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