text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
values |
|---|---|---|
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... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.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... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
shape = (
batch_size,
num_channels_latents,
int(height) // self.vae_scale_factor,
int(width) // self.vae_scale_factor,
)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a lis... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
def prepare_image(
self,
image,
width,
height,
batch_size,
num_images_per_prompt,
device,
dtype,
do_classifier_free_guidance=False,
guess_mode=False,
):
if isinstance(image, torch.Tensor):
pass
else:
... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.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... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
Args:
image (`PipelineImageInput`):
Input image to be encoded.
device: (`torch.device`):
Torch device.
Returns:
`torch.Tensor`: The encoded image feature representation.
"""
if not isinstance(image, torch.Tensor):
i... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
# Copied from diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3.StableDiffusion3Pipeline.prepare_ip_adapter_image_embeds
def prepare_ip_adapter_image_embeds(
self,
ip_adapter_image: Optional[PipelineImageInput] = None,
ip_adapter_image_embeds: Optional[torch.Tensor] = None,
... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
Args:
ip_adapter_image (`PipelineImageInput`, *optional*):
The input image to extract features from for IP-Adapter.
ip_adapter_image_embeds (`torch.Tensor`, *optional*):
Precomputed image embeddings.
device: (`torch.device`, *optional*):
... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
if ip_adapter_image_embeds is not None:
if do_classifier_free_guidance:
single_negative_image_embeds, single_image_embeds = ip_adapter_image_embeds.chunk(2)
else:
single_image_embeds = ip_adapter_image_embeds
elif ip_adapter_image is not None:
... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
# Copied from diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3.StableDiffusion3Pipeline.enable_sequential_cpu_offload
def enable_sequential_cpu_offload(self, *args, **kwargs):
if self.image_encoder is not None and "image_encoder" not in self._exclude_from_cpu_offload:
logger.wa... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.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... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
num_images_per_prompt: Optional[int] = 1,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
pooled_... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
Function invoked when calling the pipeline for generation. | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.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... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
The width in pixels of the generated image. This is set to 1024 by default for the best results.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower infere... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
control_guidance_start (`f... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
specified as `torch.Tensor`, it is passed to ControlNet as is. `PIL.Image.Image` can also be accepted
as an image. The dimensions of the output image defaults to `image`'s dimensions. If height and/or
width are passed, `image` is resized accordingly. If multiple ControlNets are specified... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
Embeddings projected from the embeddings of controlnet input conditions.
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 guida... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
The number of images to generate per prompt.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
latents (`tor... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
Optional image input to work with IP Adapters.
ip_adapter_image_embeds (`torch.Tensor`, *optional*):
Pre-generated image embeddings for IP-Adapter. Should be a tensor of shape `(batch_size, num_images,
emb_dim)`. It should contain the negative image embedding if `do_classifie... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
`self.processor` in
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
callback_on_step_end ... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
`._callback_tensor_inputs` attribute of your pipeline class.
max_sequence_length (`int` defaults to 256): Maximum sequence length to use with the `prompt`. | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
Examples:
Returns:
[`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a
`tuple`. When returning a tuple, the first element is a list with the ge... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.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,... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt,
prompt_2,
prompt_3,
height,
width,
negative_prompt=negative_prompt,
negative_prompt_2=negative_prompt_2,
negative_prompt_3=negative_prompt_3,
... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
device = self._execution_device
... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
(
prompt_embeds,
negative_prompt_embeds,
pooled_prompt_embeds,
negative_pooled_prompt_embeds,
) = self.encode_prompt(
prompt=prompt,
prompt_2=prompt_2,
prompt_3=prompt_3,
negative_prompt=negative_prompt,
... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
if self.do_classifier_free_guidance:
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
pooled_prompt_embeds = torch.cat([negative_pooled_prompt_embeds, pooled_prompt_embeds], dim=0) | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
# 3. Prepare control image
if controlnet_config.force_zeros_for_pooled_projection:
# instantx sd3 controlnet does not apply shift factor
vae_shift_factor = 0
else:
vae_shift_factor = self.vae.config.shift_factor
if isinstance(self.controlnet, SD3ControlNetMode... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
control_image = self.vae.encode(control_image).latent_dist.sample()
control_image = (control_image - vae_shift_factor) * self.vae.config.scaling_factor
elif isinstance(self.controlnet, SD3MultiControlNetModel):
control_images = []
for control_image_ in control_image:
... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
control_images.append(control_image_)
control_image = control_images
else:
assert False
# 4. Prepare timesteps
timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, sigmas=sigmas)
num_warmup_steps = max(len(timesteps) -... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
# 6. 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)
]
... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
if controlnet_config.joint_attention_dim is not None:
controlnet_encoder_hidden_states = prompt_embeds
else:
# SD35 official 8b controlnet does not use encoder_hidden_states
controlnet_encoder_hidden_states = None
# 7. Prepare image embeddings
if (ip_adapter_... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
# 8. Denoising loop
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 =... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
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 = controlnet_conditioning_scale
if isinstance(controlnet_cond_scale, list):
... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
# controlnet(s) inference
control_block_samples = self.controlnet(
hidden_states=latent_model_input,
timestep=timestep,
encoder_hidden_states=controlnet_encoder_hidden_states,
pooled_projections=controlnet_pooled_projections... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
noise_pred = self.transformer(
hidden_states=latent_model_input,
timestep=timestep,
encoder_hidden_states=prompt_embeds,
pooled_projections=pooled_prompt_embeds,
block_controlnet_hidden_states=control_block_samples,
... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.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_... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.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... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.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 not return_dict:
return (image,)
return StableDiffusion3PipelineOutput(images=i... | 82 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py |
class StableDiffusion3ControlNetInpaintingPipeline(
DiffusionPipeline, SD3LoraLoaderMixin, FromSingleFileMixin, SD3IPAdapterMixin
):
r"""
Args:
transformer ([`SD3Transformer2DModel`]):
Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
scheduler ([... | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py |
as its dimension.
text_encoder_2 ([`CLIPTextModelWithProjection`]):
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),
specifically the
[laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG... | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py |
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
tokenizer_3 (`T5TokenizerFast`):
Tokenizer of class
[T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer).
controlnet ([`SD3Cont... | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py |
model_cpu_offload_seq = "text_encoder->text_encoder_2->text_encoder_3->image_encoder->transformer->vae"
_optional_components = ["image_encoder", "feature_extractor"]
_callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds", "negative_pooled_prompt_embeds"]
def __init__(
self,... | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py |
self.register_modules(
vae=vae,
text_encoder=text_encoder,
text_encoder_2=text_encoder_2,
text_encoder_3=text_encoder_3,
tokenizer=tokenizer,
tokenizer_2=tokenizer_2,
tokenizer_3=tokenizer_3,
transformer=transformer,
... | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py |
self.tokenizer_max_length = (
self.tokenizer.model_max_length if hasattr(self, "tokenizer") and self.tokenizer is not None else 77
)
self.default_sample_size = (
self.transformer.config.sample_size
if hasattr(self, "transformer") and self.transformer is not None
... | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py |
# Copied from diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3.StableDiffusion3Pipeline._get_t5_prompt_embeds
def _get_t5_prompt_embeds(
self,
prompt: Union[str, List[str]] = None,
num_images_per_prompt: int = 1,
max_sequence_length: int = 256,
device: Optio... | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py |
text_inputs = self.tokenizer_3(
prompt,
padding="max_length",
max_length=max_sequence_length,
truncation=True,
add_special_tokens=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tok... | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py |
dtype = self.text_encoder_3.dtype
prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
_, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings and attention mask for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images... | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py |
clip_tokenizers = [self.tokenizer, self.tokenizer_2]
clip_text_encoders = [self.text_encoder, self.text_encoder_2]
tokenizer = clip_tokenizers[clip_model_index]
text_encoder = clip_text_encoders[clip_model_index]
prompt = [prompt] if isinstance(prompt, str) else prompt
batch_si... | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py |
text_input_ids = text_inputs.input_ids
untruncated_ids = tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
removed_text = tokenizer.batch_decode(untruncated_ids[:,... | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py |
_, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
pooled_prompt_e... | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py |
# Copied from diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3.StableDiffusion3Pipeline.encode_prompt
def encode_prompt(
self,
prompt: Union[str, List[str]],
prompt_2: Union[str, List[str]],
prompt_3: Union[str, List[str]],
device: Optional[torch.device] = N... | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py |
r""" | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py |
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
prompt_2 (`str` or `List[str]`, *optional*):
The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
used in all text-encoders
... | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py |
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
negative_prompt_2 (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
... | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py |
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
... | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py |
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
the output of the pre-final layer will be used for computing the prompt embeddings.
lora_scale (`float`, *optional*):
A lora scale that will be applied to all LoRA layers o... | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py |
# set lora scale so that monkey patched LoRA
# function of text encoder can correctly access it
if lora_scale is not None and isinstance(self, SD3LoraLoaderMixin):
self._lora_scale = lora_scale
# dynamically adjust the LoRA scale
if self.text_encoder is not None and ... | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py |
prompt_embed, pooled_prompt_embed = self._get_clip_prompt_embeds(
prompt=prompt,
device=device,
num_images_per_prompt=num_images_per_prompt,
clip_skip=clip_skip,
clip_model_index=0,
)
prompt_2_embed, pooled_prompt_2_... | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py |
clip_prompt_embeds = torch.nn.functional.pad(
clip_prompt_embeds, (0, t5_prompt_embed.shape[-1] - clip_prompt_embeds.shape[-1])
)
prompt_embeds = torch.cat([clip_prompt_embeds, t5_prompt_embed], dim=-2)
pooled_prompt_embeds = torch.cat([pooled_prompt_embed, pooled_pr... | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py |
# normalize str to list
negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
negative_prompt_2 = (
batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2
)
negative... | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py |
if prompt is not None and type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif batch_size != len(negative_pro... | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py |
negative_prompt_embed, negative_pooled_prompt_embed = self._get_clip_prompt_embeds(
negative_prompt,
device=device,
num_images_per_prompt=num_images_per_prompt,
clip_skip=None,
clip_model_index=0,
)
negative_prompt_2... | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py |
negative_clip_prompt_embeds = torch.nn.functional.pad(
negative_clip_prompt_embeds,
(0, t5_negative_prompt_embed.shape[-1] - negative_clip_prompt_embeds.shape[-1]),
)
negative_prompt_embeds = torch.cat([negative_clip_prompt_embeds, t5_negative_prompt_embed], dim=... | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py |
return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py |
# Copied from diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3.StableDiffusion3Pipeline.check_inputs
def check_inputs(
self,
prompt,
prompt_2,
prompt_3,
height,
width,
negative_prompt=None,
negative_prompt_2=None,
negative_pro... | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.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)}."
) | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.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... | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.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 ... | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.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)}")
... | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.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."
)
... | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.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... | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.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... | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py |
shape = (
batch_size,
num_channels_latents,
int(height) // self.vae_scale_factor,
int(width) // self.vae_scale_factor,
)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a lis... | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py |
def prepare_image_with_mask(
self,
image,
mask,
width,
height,
batch_size,
num_images_per_prompt,
device,
dtype,
do_classifier_free_guidance=False,
guess_mode=False,
):
if isinstance(image, torch.Tensor):
pas... | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py |
# Prepare mask
if isinstance(mask, torch.Tensor):
pass
else:
mask = self.mask_processor.preprocess(mask, height=height, width=width)
mask = mask.repeat_interleave(repeat_by, dim=0)
mask = mask.to(device=device, dtype=dtype)
# Get masked image
mask... | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py |
return control_image
@property
def guidance_scale(self):
return self._guidance_scale
@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/2205.114... | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py |
# Copied from diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3.StableDiffusion3Pipeline.encode_image
def encode_image(self, image: PipelineImageInput, device: torch.device) -> torch.Tensor:
"""Encodes the given image into a feature representation using a pre-trained image encoder.
... | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py |
# Copied from diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3.StableDiffusion3Pipeline.prepare_ip_adapter_image_embeds
def prepare_ip_adapter_image_embeds(
self,
ip_adapter_image: Optional[PipelineImageInput] = None,
ip_adapter_image_embeds: Optional[torch.Tensor] = None,
... | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py |
Args:
ip_adapter_image (`PipelineImageInput`, *optional*):
The input image to extract features from for IP-Adapter.
ip_adapter_image_embeds (`torch.Tensor`, *optional*):
Precomputed image embeddings.
device: (`torch.device`, *optional*):
... | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py |
if ip_adapter_image_embeds is not None:
if do_classifier_free_guidance:
single_negative_image_embeds, single_image_embeds = ip_adapter_image_embeds.chunk(2)
else:
single_image_embeds = ip_adapter_image_embeds
elif ip_adapter_image is not None:
... | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py |
# Copied from diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3.StableDiffusion3Pipeline.enable_sequential_cpu_offload
def enable_sequential_cpu_offload(self, *args, **kwargs):
if self.image_encoder is not None and "image_encoder" not in self._exclude_from_cpu_offload:
logger.wa... | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.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... | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py |
negative_prompt_3: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative... | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py |
):
r"""
Function invoked when calling the pipeline for generation. | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.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... | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py |
The width in pixels of the generated image. This is set to 1024 by default for the best results.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower infere... | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py |
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
control_guidance_start (`f... | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py |
be masked out with `control_mask` and repainted according to `prompt`). For both numpy array and
pytorch tensor, the expected value range is between `[0, 1]` If it's a tensor or a list or tensors, the
expected shape should be `(B, C, H, W)`. If it is a numpy array or a list of arrays, th... | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py |
for numpy array would be for `(B, H, W, 1)`, `(B, H, W)`, `(H, W, 1)`, or `(H, W)`.
controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):
The outputs of the ControlNet are multiplied by `controlnet_conditioning_scale` before they are added
to ... | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py |
negative_prompt_2 (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
`text_encoder_2`. If not defined, `negative_prompt` is used instead
negative_prompt_3 (`str` or `List[str]`, *optional*):
... | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py |
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
... | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py |
Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
If not provided, pooled text embeddings will be generated from `prompt` input argument.
negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negat... | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py |
`True`. If not provided, embeddings are computed from the `ip_adapter_image` input argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np... | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py |
A function that calls at the end of each denoising steps during the inference. The function is called
with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as sp... | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py |
Examples:
Returns:
[`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a
`tuple`. When returning a tuple, the first element is a list with the ge... | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.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,... | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py |
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt,
prompt_2,
prompt_3,
height,
width,
negative_prompt=negative_prompt,
negative_prompt_2=negative_prompt_2,
negative_prompt_3=negative_prompt_3,
... | 83 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py |
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