text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
values |
|---|---|---|
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback_on_step_end (`Callable[[int, int, Dict], None]`, `PipelineCallback`, `MultiPipelineCallbacks`, *op... | 343 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.py |
original_size (`Tuple[int, int]`, *optional*, defaults to `(1024, 1024)`):
The original size of the image. Used to calculate the time ids.
target_size (`Tuple[int, int]`, *optional*):
The target size of the image. Used to calculate the time ids.
crops_coords_top_l... | 343 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.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_... | 343 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.py |
Examples:
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
otherwise a `tuple` is returned where the first element is a list with ... | 343 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.py |
if use_resolution_binning and (height, width) not in SUPPORTED_SHAPE:
width, height = map_to_standard_shapes(width, height)
height = int(height)
width = int(width)
logger.warning(f"Reshaped to (height, width)=({height}, {width}), Supported shapes are {SUPPORTED_SHAPE}") | 343 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.py |
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt,
height,
width,
negative_prompt,
prompt_embeds,
negative_prompt_embeds,
prompt_attention_mask,
negative_prompt_attention_mask,
promp... | 343 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.py |
device = self._execution_device | 343 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.py |
# 3. Encode input prompt
(
prompt_embeds,
negative_prompt_embeds,
prompt_attention_mask,
negative_prompt_attention_mask,
) = self.encode_prompt(
prompt=prompt,
device=device,
dtype=self.transformer.dtype,
num... | 343 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.py |
device=device,
dtype=self.transformer.dtype,
num_images_per_prompt=num_images_per_prompt,
do_classifier_free_guidance=self.do_classifier_free_guidance,
negative_prompt=negative_prompt,
prompt_embeds=prompt_embeds_2,
negative_prompt_embeds=negative_... | 343 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.py |
# 4. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
# 5. Prepare latent variables
num_channels_latents = self.transformer.config.in_channels
latents = self.prepare_latents(
batch_size * num_imag... | 343 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.py |
# 7. Create image_rotary_emb, style embedding & time ids
grid_height = height // 8 // self.transformer.config.patch_size
grid_width = width // 8 // self.transformer.config.patch_size
base_size = 512 // 8 // self.transformer.config.patch_size
grid_crops_coords = get_resize_crop_region_for... | 343 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.py |
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
if self.do_perturbed_attention_guidance:
prompt_embeds = self._prepare_perturbed_attention_guidance(
... | 343 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.py |
add_time_ids = torch.cat([add_time_ids] * 3, dim=0)
style = torch.cat([style] * 3, dim=0)
elif self.do_classifier_free_guidance:
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
prompt_attention_mask = torch.cat([negative_prompt_attention_mask, prompt_attent... | 343 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.py |
prompt_embeds = prompt_embeds.to(device=device)
prompt_attention_mask = prompt_attention_mask.to(device=device)
prompt_embeds_2 = prompt_embeds_2.to(device=device)
prompt_attention_mask_2 = prompt_attention_mask_2.to(device=device)
add_time_ids = add_time_ids.to(dtype=prompt_embeds.dtype... | 343 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.py |
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
if self.interrupt:
continue
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * (prom... | 343 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.py |
# predict the noise residual
noise_pred = self.transformer(
latent_model_input,
t_expand,
encoder_hidden_states=prompt_embeds,
text_embedding_mask=prompt_attention_mask,
encoder_hidden_states_t5=prompt_em... | 343 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.py |
# perform guidance
if self.do_perturbed_attention_guidance:
noise_pred, noise_pred_text = self._apply_perturbed_attention_guidance(
noise_pred, self.do_classifier_free_guidance, self.guidance_scale, t, True
)
elif self.do_cl... | 343 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.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 = ... | 343 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.py |
if XLA_AVAILABLE:
xm.mark_step()
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, prompt_embeds.dtype)
else:
im... | 343 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.py |
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept) | 343 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.py |
class StableDiffusionControlNetPAGPipeline(
DiffusionPipeline,
StableDiffusionMixin,
TextualInversionLoaderMixin,
StableDiffusionLoraLoaderMixin,
IPAdapterMixin,
FromSingleFileMixin,
PAGMixin,
):
r"""
Pipeline for text-to-image generation using Stable Diffusion with ControlNet guidan... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_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... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_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))... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py |
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/runwayml/s... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_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,
... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_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"
... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_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... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py |
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
controlnet=controlnet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
image_enc... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_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.... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_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... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_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... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_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... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_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... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_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 | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_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_... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py |
prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds) | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_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... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_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... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py |
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_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(
... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_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... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_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):
... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_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... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_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 = ... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_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... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_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]] * ... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_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):
... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_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... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py |
def check_inputs(
self,
prompt,
image,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
ip_adapter_image=None,
ip_adapter_image_embeds=None,
controlnet_conditioning_scale=1.0,
control_guidance_start=0.0,
contro... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_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:
... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_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... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py |
# Check `image`
is_compiled = hasattr(F, "scaled_dot_product_attention") and isinstance(
self.controlnet, torch._dynamo.eval_frame.OptimizedModule
)
if (
isinstance(self.controlnet, ControlNetModel)
or is_compiled
and isinstance(self.controlnet._or... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py |
# When `image` is a nested list:
# (e.g. [[canny_image_1, pose_image_1], [canny_image_2, pose_image_2]])
elif any(isinstance(i, list) for i in image):
transposed_image = [list(t) for t in zip(*image)]
if len(transposed_image) != len(self.controlnet.nets):
... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py |
f"For multiple controlnets: `image` must have the same length as the number of controlnets, but got {len(image)} images and {len(self.controlnet.nets)} ControlNets."
)
else:
for image_ in image:
self.check_image(image_, prompt, prompt_embeds)
else:... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py |
# Check `controlnet_conditioning_scale`
if (
isinstance(self.controlnet, ControlNetModel)
or is_compiled
and isinstance(self.controlnet._orig_mod, ControlNetModel)
):
if not isinstance(controlnet_conditioning_scale, float):
raise TypeError(... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py |
)
elif isinstance(controlnet_conditioning_scale, list) and len(controlnet_conditioning_scale) != len(
self.controlnet.nets
):
raise ValueError(
"For multiple controlnets: When `controlnet_conditioning_scale` is specified as `list`, it must have... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py |
if not isinstance(control_guidance_start, (tuple, list)):
control_guidance_start = [control_guidance_start]
if not isinstance(control_guidance_end, (tuple, list)):
control_guidance_end = [control_guidance_end]
if len(control_guidance_start) != len(control_guidance_end):
... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py |
for start, end in zip(control_guidance_start, control_guidance_end):
if start >= end:
raise ValueError(
f"control guidance start: {start} cannot be larger or equal to control guidance end: {end}."
)
if start < 0.0:
raise ValueEr... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_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 ... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py |
# Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.check_image
def check_image(self, image, prompt, prompt_embeds):
image_is_pil = isinstance(image, PIL.Image.Image)
image_is_tensor = isinstance(image, torch.Tensor)
image_is_np = isinstance(ima... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py |
if (
not image_is_pil
and not image_is_tensor
and not image_is_np
and not image_is_pil_list
and not image_is_tensor_list
and not image_is_np_list
):
raise TypeError(
f"image must be passed and be one of PIL image... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py |
if image_batch_size != 1 and image_batch_size != prompt_batch_size:
raise ValueError(
f"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}"
)
# Copied from diffusers.p... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py |
image = image.repeat_interleave(repeat_by, dim=0)
image = image.to(device=device, dtype=dtype)
if do_classifier_free_guidance and not guess_mode:
image = torch.cat([image] * 2)
return image
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDif... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py |
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
latents = latents.to(device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_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... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_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 ... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py |
@property
def cross_attention_kwargs(self):
return self._cross_attention_kwargs
@property
def num_timesteps(self):
return self._num_timesteps | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
image: PipelineImageInput = None,
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
timesteps:... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py |
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
controlnet_conditioning_scale: Union[float, List[float]] = 1.0,
guess_mode: bool = False,
control_guidance_start: Union[float, List[float]] = 0.0,
control_guidance_end: Union[float, List[float]] = 1.0,
clip_skip: Optional[i... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py |
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:
... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py |
to a single ControlNet. When `prompt` is a list, and if a list of images is passed for a single
ControlNet, each will be paired with each prompt in the `prompt` list. This also applies to multiple
ControlNets, where a list of image lists can be passed to batch for each prompt and each Co... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py |
Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
passed will be used. Must be in descending order.
sigmas (`List[float]`, ... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py |
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py |
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random `generator`.
prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated text embeddings. Can be used to easily twea... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py |
Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of
IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should
contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If n... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py |
[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):
The outputs of the ControlNet are multiplied by `controlnet_conditioning_scale` before th... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py |
control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0):
The percentage of total steps at which the ControlNet stops applying.
clip_skip (`int`, *optional*):
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 mean... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py |
callback_on_step_end_tensor_inputs (`List`, *optional*):
The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
`._callback... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py |
Examples:
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
otherwise a `tuple` is returned where the first element is a list with ... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py |
# align format for control guidance
if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):
control_guidance_start = len(control_guidance_end) * [control_guidance_start]
elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start,... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py |
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt,
image,
negative_prompt,
prompt_embeds,
negative_prompt_embeds,
ip_adapter_image,
ip_adapter_image_embeds,
controlnet_conditioning_scale,
... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py |
if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):
controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets)
global_pool_conditions = (
controlnet.config.global_pool_conditions
if isinstance(cont... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py |
# 3. Encode input prompt
text_encoder_lora_scale = (
self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
)
prompt_embeds, negative_prompt_embeds = self.encode_prompt(
prompt,
device,
num_images_per_pr... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py |
)
elif self.do_classifier_free_guidance:
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py |
# 4. Prepare image
if isinstance(controlnet, ControlNetModel):
image = self.prepare_image(
image=image,
width=width,
height=height,
batch_size=batch_size * num_images_per_prompt,
num_images_per_prompt=num_images_per_prom... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py |
for image_ in image:
image_ = self.prepare_image(
image=image_,
width=width,
height=height,
batch_size=batch_size * num_images_per_prompt,
num_images_per_prompt=num_images_per_prompt,
... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py |
# 6. 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... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py |
# 7.1 Add image embeds for IP-Adapter
if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
ip_adapter_image_embeds = self.prepare_ip_adapter_image_embeds(
ip_adapter_image,
ip_adapter_image_embeds,
device,
batch_size ... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py |
if self.do_perturbed_attention_guidance:
image_embeds = self._prepare_perturbed_attention_guidance(
image_embeds, negative_image_embeds, self.do_classifier_free_guidance
)
elif self.do_classifier_free_guidance:
image_emb... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py |
# 7.2 Create tensor stating which controlnets to keep
controlnet_keep = []
for i in range(len(timesteps)):
keeps = [
1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)
for s, e in zip(control_guidance_start, control_guidance_end)
]... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py |
if self.do_perturbed_attention_guidance:
single_image = self._prepare_perturbed_attention_guidance(
single_image, single_image, self.do_classifier_free_guidance
)
elif self.do_classifier_free_guidance:
single_image = torch.cat([single_image... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py |
# 8. Denoising loop
if self.do_perturbed_attention_guidance:
original_attn_proc = self.unet.attn_processors
self._set_pag_attn_processor(
pag_applied_layers=self.pag_applied_layers,
do_classifier_free_guidance=self.do_classifier_free_guidance,
... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py |
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * (prompt_embeds.shape[0] // latents.shape[0]))
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py |
# controlnet(s) inference
control_model_input = latent_model_input
if isinstance(controlnet_keep[i], list):
cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]
else:
controlnet_cond_scale = contr... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py |
if guess_mode and self.do_classifier_free_guidance:
# Inferred ControlNet only for the conditional batch.
# To apply the output of ControlNet to both the unconditional and conditional batches,
# add 0 to the unconditional batch to keep it unchanged.
... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py |
# predict the noise residual
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
timestep_cond=timestep_cond,
cross_attention_kwargs=self.cross_attention_kwargs,
... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.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:... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.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)
# call the callback, if provided
... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py |
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False, generator=generator)[
0
]
image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
else:
image = laten... | 344 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py |
class StableDiffusion3PAGPipeline(DiffusionPipeline, SD3LoraLoaderMixin, FromSingleFileMixin, PAGMixin):
r"""
[PAG pipeline](https://huggingface.co/docs/diffusers/main/en/using-diffusers/pag) for text-to-image generation
using Stable Diffusion 3. | 345 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3.py |
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