text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
values |
|---|---|---|
if self.do_classifier_free_guidance:
image_embeds = torch.cat([image_embeds_pooled, uncond_image_embeds_pooled], dim=0)
else:
image_embeds = image_embeds_pooled
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and ... | 212 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_prior.py |
# 5. Prepare latents
latents = self.prepare_latents(
batch_size, height, width, num_images_per_prompt, dtype, device, generator, latents, self.scheduler
)
if isinstance(self.scheduler, DDPMWuerstchenScheduler):
timesteps = timesteps[:-1]
else:
if hasa... | 212 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_prior.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)
... | 212 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_prior.py |
clip_text=text_encoder_hidden_states,
clip_img=image_embeds,
return_dict=False,
)[0] | 212 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_prior.py |
# 8. Check for classifier free guidance and apply it
if self.do_classifier_free_guidance:
predicted_image_embedding_text, predicted_image_embedding_uncond = predicted_image_embedding.chunk(2)
predicted_image_embedding = torch.lerp(
predicted_image_embeddin... | 212 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_prior.py |
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... | 212 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_prior.py |
if output_type == "np":
latents = latents.cpu().float().numpy() # float() as bfloat16-> numpy doesnt work
prompt_embeds = prompt_embeds.cpu().float().numpy() # float() as bfloat16-> numpy doesnt work
negative_prompt_embeds = (
negative_prompt_embeds.cpu().float().nu... | 212 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_prior.py |
class StableDiffusionAdapterPipelineOutput(BaseOutput):
"""
Args:
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width,
num_channels)`. PIL images or numpy array present the denoise... | 213 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.py |
class StableDiffusionAdapterPipeline(DiffusionPipeline, StableDiffusionMixin):
r"""
Pipeline for text-to-image generation using Stable Diffusion augmented with T2I-Adapter
https://arxiv.org/abs/2302.08453
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic... | 214 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.py |
Args:
adapter ([`T2IAdapter`] or [`MultiAdapter`] or `List[T2IAdapter]`):
Provides additional conditioning to the unet during the denoising process. If you set multiple Adapter as a
list, the outputs from each Adapter are added together to create one combined additional conditioning.
... | 214 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.py |
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
scheduler ([`Sched... | 214 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.py |
model_cpu_offload_seq = "text_encoder->adapter->unet->vae"
_optional_components = ["safety_checker", "feature_extractor"]
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
adapter: Union[T2I... | 214 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.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"
... | 214 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.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... | 214 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.py |
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
adapter=adapter,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
self.vae_... | 214 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.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[torc... | 214 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.py |
prompt_embeds_tuple = self.encode_prompt(
prompt=prompt,
device=device,
num_images_per_prompt=num_images_per_prompt,
do_classifier_free_guidance=do_classifier_free_guidance,
negative_prompt=negative_prompt,
prompt_embeds=prompt_embeds,
... | 214 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.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.... | 214 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.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... | 214 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.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... | 214 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.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... | 214 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.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... | 214 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.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 | 214 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.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_... | 214 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.py |
prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds) | 214 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.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... | 214 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.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... | 214 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.py |
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt | 214 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.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(
... | 214 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.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... | 214 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.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):
... | 214 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
def decode_latents(self, latents):
deprecation_message = "The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead"
d... | 214 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.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... | 214 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.py |
def check_inputs(
self,
prompt,
height,
width,
callback_steps,
image,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
):
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` ha... | 214 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.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:
... | 214 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.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... | 214 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.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,
... | 214 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.py |
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
def _default_height_width(self, height, width, image):
# NOTE: It is possible that a list of images have different
# dimensions for each i... | 214 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.py |
if width is None:
if isinstance(image, PIL.Image.Image):
width = image.width
elif isinstance(image, torch.Tensor):
width = image.shape[-1]
# round down to nearest multiple of `self.adapter.downscale_factor`
width = (width // self.adapter.d... | 214 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.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... | 214 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.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 ... | 214 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
image: Union[torch.Tensor, PIL.Image.Image, List[PIL.Image.Image]] = None,
height: Optional[int] = None,
width: Optional[int] = None,
num_infe... | 214 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.py |
callback_steps: int = 1,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
adapter_conditioning_scale: Union[float, List[float]] = 1.0,
clip_skip: Optional[int] = None,
):
r"""
Function invoked when calling the pipeline for generation. | 214 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.py |
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
instead.
image (`torch.Tensor`, `PIL.Image.Image`, `List[torch.Tensor]` or `List[PIL.Image.Image]` or `List[List[... | 214 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.py |
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 inference.
timesteps (`List[int]`, *optional*):
Custom timesteps to use for the denoisi... | 214 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.py |
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 >
1`. Highe... | 214 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.py |
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
... | 214 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.py |
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated negative text embeddings. Can b... | 214 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.py |
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.
callback_steps (`int`, *optional*, defaults ... | 214 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.py |
The outputs of the adapter are multiplied by `adapter_conditioning_scale` before they are added to the
residual in the original unet. If multiple adapters are specified in init, you can set the
corresponding scale as a list.
clip_skip (`int`, *optional*):
Numb... | 214 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.py |
Returns:
[`~pipelines.stable_diffusion.StableDiffusionAdapterPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.StableDiffusionAdapterPipelineOutput`] if `return_dict` is True, otherwise a
`tuple. When returning a tuple, the first element is a list with the generated images, ... | 214 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.py |
if isinstance(self.adapter, MultiAdapter):
adapter_input = []
for one_image in image:
one_image = _preprocess_adapter_image(one_image, height, width)
one_image = one_image.to(device=device, dtype=self.adapter.dtype)
adapter_input.append(one_image)... | 214 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.py |
# 3. Encode input prompt
prompt_embeds, negative_prompt_embeds = self.encode_prompt(
prompt,
device,
num_images_per_prompt,
self.do_classifier_free_guidance,
negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=n... | 214 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.py |
# 5. Prepare latent variables
num_channels_latents = self.unet.config.in_channels
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
prompt_embeds.dtype,
device,
gene... | 214 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.py |
# 7. Denoising loop
if isinstance(self.adapter, MultiAdapter):
adapter_state = self.adapter(adapter_input, adapter_conditioning_scale)
for k, v in enumerate(adapter_state):
adapter_state[k] = v
else:
adapter_state = self.adapter(adapter_input)
... | 214 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.py |
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch... | 214 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.py |
# perform guidance
if self.do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t... | 214 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.py |
if output_type == "latent":
image = latents
has_nsfw_concept = None
elif output_type == "pil":
# 8. Post-processing
image = self.decode_latents(latents)
# 9. Run safety checker
image, has_nsfw_concept = self.run_safety_checker(image, devic... | 214 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.py |
class StableDiffusionXLAdapterPipeline(
DiffusionPipeline,
StableDiffusionMixin,
TextualInversionLoaderMixin,
StableDiffusionXLLoraLoaderMixin,
IPAdapterMixin,
FromSingleFileMixin,
):
r"""
Pipeline for text-to-image generation using Stable Diffusion augmented with T2I-Adapter
https:/... | 215 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.py |
The pipeline also inherits the following loading methods:
- [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
- [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
- [`~loaders.StableDiffusionXLLoraLoaderMixin.load_lo... | 215 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.py |
Args:
adapter ([`T2IAdapter`] or [`MultiAdapter`] or `List[T2IAdapter]`):
Provides additional conditioning to the unet during the denoising process. If you set multiple Adapter as a
list, the outputs from each Adapter are added together to create one combined additional conditioning.
... | 215 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.py |
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
scheduler ([`Sched... | 215 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.py |
model_cpu_offload_seq = "text_encoder->text_encoder_2->image_encoder->unet->vae"
_optional_components = [
"tokenizer",
"tokenizer_2",
"text_encoder",
"text_encoder_2",
"feature_extractor",
"image_encoder",
]
def __init__(
self,
vae: Autoencode... | 215 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.py |
self.register_modules(
vae=vae,
text_encoder=text_encoder,
text_encoder_2=text_encoder_2,
tokenizer=tokenizer,
tokenizer_2=tokenizer_2,
unet=unet,
adapter=adapter,
scheduler=scheduler,
feature_extractor=feature_e... | 215 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.py |
# Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.encode_prompt
def encode_prompt(
self,
prompt: str,
prompt_2: Optional[str] = None,
device: Optional[torch.device] = None,
num_images_per_prompt: int = 1,
do_c... | 215 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.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 both text-encoders
... | 215 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.py |
The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
`text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak tex... | 215 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.py |
If not provided, pooled text embeddings will be generated from `prompt` input argument.
negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided... | 215 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.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, StableDiffusionXLLoraLoaderMixin):
self._lora_scale = lora_scale
# dynamically adjust the LoRA scale
if self.text_encoder is... | 215 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.py |
# Define tokenizers and text encoders
tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]
text_encoders = (
[self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]
)
if prompt_embe... | 215 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.py |
text_inputs = tokenizer(
prompt,
padding="max_length",
max_length=tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
... | 215 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.py |
prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)
# We are only ALWAYS interested in the pooled output of the final text encoder
if pooled_prompt_embeds is None and prompt_embeds[0].ndim == 2:
pooled_prompt_embeds = prompt_embeds[0]
... | 215 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.py |
# get unconditional embeddings for classifier free guidance
zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt
if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:
negative_prompt_embeds = torch.zeros_lik... | 215 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.py |
uncond_tokens: List[str]
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)}."
)
... | 215 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.py |
negative_prompt_embeds_list = []
for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):
if isinstance(self, TextualInversionLoaderMixin):
negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)
max_l... | 215 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.py |
# We are only ALWAYS interested in the pooled output of the final text encoder
if negative_pooled_prompt_embeds is None and negative_prompt_embeds[0].ndim == 2:
negative_pooled_prompt_embeds = negative_prompt_embeds[0]
negative_prompt_embeds = negative_prompt_embeds.h... | 215 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.py |
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
if do_clas... | 215 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.py |
pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
bs_embed * num_images_per_prompt, -1
)
if do_classifier_free_guidance:
negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
bs_embed ... | 215 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.py |
return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
# 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):
dt... | 215 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.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... | 215 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.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 = ... | 215 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.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... | 215 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.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]] * ... | 215 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.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... | 215 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.py |
# Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.check_inputs
def check_inputs(
self,
prompt,
prompt_2,
height,
width,
callback_steps,
negative_prompt=None,
negative_prompt_2=None,
pro... | 215 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.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... | 215 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.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 ... | 215 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.py |
elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):
raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}") | 215 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.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."
)
... | 215 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.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... | 215 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.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... | 215 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.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 ... | 215 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.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,
... | 215 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.py |
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
# Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline._get_add_time_ids
def _get_add_time_ids(
... | 215 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.py |
if expected_add_embed_dim != passed_add_embed_dim:
raise ValueError(
f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `t... | 215 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae
def upcast_vae(self):
dtype = self.vae.dtype
self.vae.to(dtype=torch.float32)
use_torch_2_0_or_xformers = isinstance(
self.vae.decoder.mid_block.attentio... | 215 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.py |
# Copied from diffusers.pipelines.t2i_adapter.pipeline_stable_diffusion_adapter.StableDiffusionAdapterPipeline._default_height_width
def _default_height_width(self, height, width, image):
# NOTE: It is possible that a list of images have different
# dimensions for each image, so just checking the fi... | 215 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.py |
if width is None:
if isinstance(image, PIL.Image.Image):
width = image.width
elif isinstance(image, torch.Tensor):
width = image.shape[-1]
# round down to nearest multiple of `self.adapter.downscale_factor`
width = (width // self.adapter.d... | 215 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.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... | 215 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.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 ... | 215 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.py |
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