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# Copied from diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3_img2img.StableDiffusion3Img2ImgPipeline.check_inputs
def check_inputs(
self,
prompt,
prompt_2,
prompt_3,
height,
width,
strength,
negative_prompt=None,
negative_pr... | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.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)}."
) | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.py |
if strength < 0 or strength > 1:
raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
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 Va... | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.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 ... | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.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)}")
... | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.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."
)
... | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.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... | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.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... | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.py |
t_start = int(max(num_inference_steps - init_timestep, 0))
timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
if hasattr(self.scheduler, "set_begin_index"):
self.scheduler.set_begin_index(t_start * self.scheduler.order)
return timesteps, num_inference_steps - t_s... | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.py |
else:
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the ... | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.py |
if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:
# expand init_latents for batch_size
additional_image_per_prompt = batch_size // init_latents.shape[0]
init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0)
elif batch_s... | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.py |
@property
def clip_skip(self):
return self._clip_skip
# 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... | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.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... | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.py |
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
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[... | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.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... | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.py |
or tensors, the expected shape should be `(B, C, H, W)` or `(C, H, W)`. If it is a numpy array or a
list of arrays, the expected shape should be `(B, H, W, C)` or `(H, W, C)` It can also accept image
latents as `image`, but if passing latents directly it is not encoded again.
... | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.py |
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
sigmas (`List[float]`, *optional*):
Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
... | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.py |
usually at the expense of lower image quality.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `gui... | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.py |
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generat... | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.py |
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
P... | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.py |
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead
of a plain tuple.
joint_at... | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.py |
callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
`callback_on_step_end_tensor_inputs`.
callback_on_step_end_tensor_inputs (`List`, *optional*):
The list of tensor inputs for the `callback_on_step_end` function. The tensors specifie... | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.py |
The adaptive scale factor for the perturbed attention guidance. If it is set to 0.0, `pag_scale` is
used. | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.py |
Examples: | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.py |
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 generated images.
... | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.py |
negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
max_sequence_length=max_sequence_length,
) | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.py |
self._guidance_scale = guidance_scale
self._clip_skip = clip_skip
self._joint_attention_kwargs = joint_attention_kwargs
self._interrupt = False
self._pag_scale = pag_scale
self._pag_adaptive_scale = pag_adaptive_scale
# 2. Define call parameters
if prompt is not ... | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.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(
... | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.py |
max_sequence_length=max_sequence_length,
lora_scale=lora_scale,
) | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.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... | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.py |
# 4. Prepare timesteps
timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, sigmas=sigmas)
timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_pro... | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.py |
if self.do_perturbed_attention_guidance:
original_attn_proc = self.transformer.attn_processors
self._set_pag_attn_processor(
pag_applied_layers=self.pag_applied_layers,
do_classifier_free_guidance=self.do_classifier_free_guidance,
)
# 6. Denoi... | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.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... | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.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... | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.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_... | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.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... | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.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_... | 348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.py |
class StableDiffusionControlNetPAGInpaintPipeline(
DiffusionPipeline,
StableDiffusionMixin,
TextualInversionLoaderMixin,
StableDiffusionLoraLoaderMixin,
IPAdapterMixin,
FromSingleFileMixin,
PAGMixin,
):
r"""
Pipeline for image inpainting using Stable Diffusion with ControlNet guidanc... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.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... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.py |
This pipeline can be used with checkpoints that have been specifically fine-tuned for inpainting
([runwayml/stable-diffusion-inpainting](https://huggingface.co/runwayml/stable-diffusion-inpainting)) as well as
default text-to-image Stable Diffusion checkpoints
([runwayml/stable-diffusion-v1-5](https://huggi... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.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))... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.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... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.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,
... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.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"
... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.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... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.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... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.py |
self.set_pag_applied_layers(pag_applied_layers) | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.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.... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.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... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.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... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.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... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.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... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.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 | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.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_... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.py |
prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds) | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.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... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.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... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.py |
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.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(
... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.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... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.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):
... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.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... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.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 = ... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.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... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.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]] * ... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.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):
... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.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... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps
def get_timesteps(self, num_inference_steps, strength, device):
# get the original timestep using init_timestep
init_timestep = min(int(num_inference_steps * strength), n... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.py |
def check_inputs(
self,
prompt,
image,
mask_image,
height,
width,
output_type,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
ip_adapter_image=None,
ip_adapter_image_embeds=None,
controlnet_co... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.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... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.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:
... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.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... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.py |
if padding_mask_crop is not None:
if not isinstance(image, PIL.Image.Image):
raise ValueError(
f"The image should be a PIL image when inpainting mask crop, but is of type" f" {type(image)}."
)
if not isinstance(mask_image, PIL.Image.Image):
... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.py |
# `prompt` needs more sophisticated handling when there are multiple
# conditionings.
if isinstance(self.controlnet, MultiControlNetModel):
if isinstance(prompt, list):
logger.warning(
f"You have {len(self.controlnet.nets)} ControlNets and you have passed ... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.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... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.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):
raise ValueError("A single batch of multiple conditionings are supported at the moment.")
elif len(image) != len(self... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.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(... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.py |
raise ValueError(
"For multiple controlnets: When `controlnet_conditioning_scale` is specified as `list`, it must have"
" the same length as the number of controlnets"
)
else:
assert False | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.py |
if len(control_guidance_start) != len(control_guidance_end):
raise ValueError(
f"`control_guidance_start` has {len(control_guidance_start)} elements, but `control_guidance_end` has {len(control_guidance_end)} elements. Make sure to provide the same number of elements to each list."
... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.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... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.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 ... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.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... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.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... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.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... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.py |
if image_batch_size == 1:
repeat_by = batch_size
else:
# image batch size is the same as prompt batch size
repeat_by = num_images_per_prompt
image = image.repeat_interleave(repeat_by, dim=0)
image = image.to(device=device, dtype=dtype)
if do_classif... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_inpaint.StableDiffusionInpaintPipeline.prepare_latents
def prepare_latents(
self,
batch_size,
num_channels_latents,
height,
width,
dtype,
device,
generator,
latents=No... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.py |
if (image is None or timestep is None) and not is_strength_max:
raise ValueError(
"Since strength < 1. initial latents are to be initialised as a combination of Image + Noise."
"However, either the image or the noise timestep has not been provided."
)
if ... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.py |
if latents is None:
noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
# if strength is 1. then initialise the latents to noise, else initial to image + noise
latents = noise if is_strength_max else self.scheduler.add_noise(image_latents, noise, timestep)
... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_inpaint.StableDiffusionInpaintPipeline.prepare_mask_latents
def prepare_mask_latents(
self, mask, masked_image, batch_size, height, width, dtype, device, generator, do_classifier_free_guidance
):
# resize the mask to la... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.py |
# duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method
if mask.shape[0] < batch_size:
if not batch_size % mask.shape[0] == 0:
raise ValueError(
"The passed mask and the required batch size don't match. Masks are suppose... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.py |
f" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed."
" Make sure the number of images that you pass is divisible by the total requested batch size."
)
masked_image_latents = masked_image_latents.repeat(batch_size // masked_ima... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.py |
mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask
masked_image_latents = (
torch.cat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents
)
# aligning device to prevent device errors when concating it with the latent model input
... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_inpaint.StableDiffusionInpaintPipeline._encode_vae_image
def _encode_vae_image(self, image: torch.Tensor, generator: torch.Generator):
if isinstance(generator, list):
image_latents = [
retrieve_latents(s... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.py |
# Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
def get_guidance_scale_embedding(
self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32
) -> torch.Tensor:
""... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.py |
Returns:
`torch.Tensor`: Embedding vectors with shape `(len(w), embedding_dim)`.
"""
assert len(w.shape) == 1
w = w * 1000.0
half_dim = embedding_dim // 2
emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, dtype=dty... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.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... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
image: PipelineImageInput = None,
mask_image: PipelineImageInput = None,
control_image: PipelineImageInput = None,
height: Optional[int] = Non... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.py |
ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
controlnet_conditioning_scale: Union[float, List[float]] = 0.5,
control_guidance_start: Union[float... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.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]`, or `List[... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.py |
`List[PIL.Image.Image]`, or `List[np.ndarray]`):
`Image`, NumPy array or tensor representing an image batch to mask `image`. White pixels in the mask
are repainted while black pixels are preserved. If `mask_image` is a PIL image, it is converted to a
single channel (lumin... | 349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.py |
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