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# 8. 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... | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.py |
if self.do_classifier_free_guidance:
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
prompt_attention_mask = torch.cat([negative_prompt_attention_mask, prompt_attention_mask])
prompt_embeds_2 = torch.cat([negative_prompt_embeds_2, prompt_embeds_2])
prom... | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.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... | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.py |
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# expand scalar t to 1-D tensor to ma... | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.py |
# controlnet(s) inference
control_block_samples = self.controlnet(
latent_model_input,
t_expand,
encoder_hidden_states=prompt_embeds,
text_embedding_mask=prompt_attention_mask,
encoder_hidden_states_t5=pr... | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.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... | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.py |
if self.do_classifier_free_guidance and guidance_rescale > 0.0:
# Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
# compute the previous noisy sample x_t -> x_t-1... | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_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)
prompt_embeds_2 = callback_outputs.pop("prom... | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.py |
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:
image = latents
has_nsfw_concept = None
if ... | 315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.py |
class StableDiffusion3Pipeline(DiffusionPipeline, SD3LoraLoaderMixin, FromSingleFileMixin, SD3IPAdapterMixin):
r"""
Args:
transformer ([`SD3Transformer2DModel`]):
Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
scheduler ([`FlowMatchEulerDiscreteSch... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
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-14-laion2B-39B-b160k)
... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.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).
image_encoder (`PreTr... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.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,... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
self.register_modules(
vae=vae,
text_encoder=text_encoder,
text_encoder_2=text_encoder_2,
text_encoder_3=text_encoder_3,
tokenizer=tokenizer,
tokenizer_2=tokenizer_2,
tokenizer_3=tokenizer_3,
transformer=transformer,
... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
self.patch_size = (
self.transformer.config.patch_size if hasattr(self, "transformer") and self.transformer is not None else 2
) | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
def _get_t5_prompt_embeds(
self,
prompt: Union[str, List[str]] = None,
num_images_per_prompt: int = 1,
max_sequence_length: int = 256,
device: Optional[torch.device] = None,
dtype: Optional[torch.dtype] = None,
):
device = device or self._execution_device
... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.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... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.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... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
prompt = [prompt] if isinstance(prompt, str) else prompt
batch_size = len(prompt)
text_inputs = tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer_max_length,
truncation=True,
return_tensors="pt",
) | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
text_input_ids = text_inputs.input_ids
untruncated_ids = tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
removed_text = tokenizer.batch_decode(untruncated_ids[:,... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
_, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
pooled_prompt_e... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
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] = None,
num_images_per_prompt: int = 1,
do_classifier_free_guidance: bool = True,
negative_prompt: Opti... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
prompt_2 (`str` or `List[str]`, *optional*):
The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
used in all text-encoders
... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
negative_prompt_2 (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
the output of the pre-final layer will be used for computing the prompt embeddings.
lora_scale (`float`, *optional*):
A lora scale that will be applied to all LoRA layers o... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
# set lora scale so that monkey patched LoRA
# function of text encoder can correctly access it
if lora_scale is not None and isinstance(self, SD3LoraLoaderMixin):
self._lora_scale = lora_scale
# dynamically adjust the LoRA scale
if self.text_encoder is not None and ... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
prompt_embed, pooled_prompt_embed = self._get_clip_prompt_embeds(
prompt=prompt,
device=device,
num_images_per_prompt=num_images_per_prompt,
clip_skip=clip_skip,
clip_model_index=0,
)
prompt_2_embed, pooled_prompt_2_... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
clip_prompt_embeds = torch.nn.functional.pad(
clip_prompt_embeds, (0, t5_prompt_embed.shape[-1] - clip_prompt_embeds.shape[-1])
)
prompt_embeds = torch.cat([clip_prompt_embeds, t5_prompt_embed], dim=-2)
pooled_prompt_embeds = torch.cat([pooled_prompt_embed, pooled_pr... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
# normalize str to list
negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
negative_prompt_2 = (
batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2
)
negative... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
if prompt is not None and type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif batch_size != len(negative_pro... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
negative_prompt_embed, negative_pooled_prompt_embed = self._get_clip_prompt_embeds(
negative_prompt,
device=device,
num_images_per_prompt=num_images_per_prompt,
clip_skip=None,
clip_model_index=0,
)
negative_prompt_2... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
negative_clip_prompt_embeds = torch.nn.functional.pad(
negative_clip_prompt_embeds,
(0, t5_negative_prompt_embed.shape[-1] - negative_clip_prompt_embeds.shape[-1]),
)
negative_prompt_embeds = torch.cat([negative_clip_prompt_embeds, t5_negative_prompt_embed], dim=... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
def check_inputs(
self,
prompt,
prompt_2,
prompt_3,
height,
width,
negative_prompt=None,
negative_prompt_2=None,
negative_prompt_3=None,
prompt_embeds=None,
negative_prompt_embeds=None,
pooled_prompt_embeds=None,
neg... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
if callback_on_step_end_tensor_inputs is not None and not all(
k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
):
raise ValueError(
f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in cal... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt_2 is not None and prompt_embeds is not ... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `pr... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None:
raise ValueError(
"If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
shape = (
batch_size,
num_channels_latents,
int(height) // self.vae_scale_factor,
int(width) // self.vae_scale_factor,
)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a lis... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
@property
def do_classifier_free_guidance(self):
return self._guidance_scale... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.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... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
# Adapted from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.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,... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.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*):
... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.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:
... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
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.warning(
"`pipe.enable_sequential_cpu_offload()` might fail for `image_encoder` if it uses "
"`torch.nn.Multih... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
prompt_2: Optional[Union[str, List[str]]] = None,
prompt_3: Optional[Union[str, List[str]]] = None,
height: Optional[int] = None,
width: Optio... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
ip_adapter_image: Optional[PipelineImageInput] = None,
ip_adapter_image_embeds: Optional[torch.Tensor] = None,
output_type: Optional[str] = "pil",
return_d... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
instead.
prompt_2 (`str` or `List[str]`, *optional*):
The prompt or prompts to be sent to `tokeni... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
The width in pixels of the generated image. This is set to 1024 by default for the best results.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower infere... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
negative_prompt (`str` or ... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
The prompt or prompts not to guide the image generation to be sent to `tokenizer_3` and
`text_encoder_3`. If not defined, `negative_prompt` is used instead
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
gener... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. ... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
input argument.
ip_adapter_image (`PipelineImageInput`, *optional*):
Optional image input to work with IP Adapters.
ip_adapter_image_embeds (`torch.Tensor`, *optiona... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
Whether or not to return a [`~pipelines.stable_diffusion_3.StableDiffusion3PipelineOutput`] instead of
a plain tuple.
joint_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.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... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
`skip_guidance_layers`. The guidance will be applied to the layers specified in `skip_guidance_layers`
with a scale of `skip_layer_guidance_scale`. The guidance will be applied to the rest of the layers
with a scale of `1`.
skip_layer_guidance_stop (`int`, *optional*): The st... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
`skip_guidance_layers` from the fraction specified in `skip_layer_guidance_start`. Recommended value by
StabiltyAI for Stable Diffusion 3.5 Medium is 0.01.
mu (`float`, *optional*): `mu` value used for `dynamic_shifting`. | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
Examples:
Returns:
[`~pipelines.stable_diffusion_3.StableDiffusion3PipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion_3.StableDiffusion3PipelineOutput`] if `return_dict` is True, otherwise a
`tuple`. When returning a tuple, the first element is a list with the genera... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt,
prompt_2,
prompt_3,
height,
width,
negative_prompt=negative_prompt,
negative_prompt_2=negative_prompt_2,
negative_prompt_3=negative_prompt_3,
... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
device = self._execution_device | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
lora_scale = (
self.joint_attention_kwargs.get("scale", None) if self.joint_attention_kwargs is not None else None
)
(
prompt_embeds,
negative_prompt_embeds,
pooled_prompt_embeds,
negative_pooled_prompt_embeds,
) = self.encode_prompt(
... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
max_sequence_length=max_sequence_length,
lora_scale=lora_scale,
) | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
if self.do_classifier_free_guidance:
if skip_guidance_layers is not None:
original_prompt_embeds = prompt_embeds
original_pooled_prompt_embeds = pooled_prompt_embeds
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
pooled_promp... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
# 5. Prepare timesteps
scheduler_kwargs = {}
if self.scheduler.config.get("use_dynamic_shifting", None) and mu is None:
_, _, height, width = latents.shape
image_seq_len = (height // self.transformer.config.patch_size) * (
width // self.transformer.config.patch_si... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
self._num_timesteps = len(timesteps) | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
# 6. Prepare image embeddings
if (ip_adapter_image is not None and self.is_ip_adapter_active) 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,
... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
timestep = t.expand(latent_model_input.... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
# perform guidance
if self.do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
should_skip_layers = (
... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
pooled_projections=original_pooled_prompt_embeds,
joint_attention_kwargs=self.joint_attention_kwargs,
return_dict=False,
skip_layers=skip_guidance_layers,
)[0]
noise_pred = (
... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
# compute the previous noisy sample x_t -> x_t-1
latents_dtype = latents.dtype
latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
if latents.dtype != latents_dtype:
if torch.backends.mps.is_available():
... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
latents = callback_outputs.pop("latents", latents)
prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
negative_pooled_prompt_embeds = callback_out... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
image = self.vae.decode(latents, return_dict=False)[0]
image = self.image_processor.postprocess(image, output_type=output_type)
# Offload all models
self.maybe_free_model_hooks()
if not return_dict:
return (image,)
return StableDiffusion3PipelineOutput(images=i... | 316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py |
class StableDiffusion3PipelineOutput(BaseOutput):
"""
Output class for Stable Diffusion pipelines.
Args:
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width,
num_channels)`. P... | 317 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_output.py |
class StableDiffusion3Img2ImgPipeline(DiffusionPipeline, SD3LoraLoaderMixin, FromSingleFileMixin, SD3IPAdapterMixin):
r"""
Args:
transformer ([`SD3Transformer2DModel`]):
Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
scheduler ([`FlowMatchEulerDisc... | 318 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_img2img.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... | 318 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_img2img.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).
""" | 318 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_img2img.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,... | 318 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_img2img.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,
... | 318 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_img2img.py |
self.transformer.config.sample_size
if hasattr(self, "transformer") and self.transformer is not None
else 128
)
self.patch_size = (
self.transformer.config.patch_size if hasattr(self, "transformer") and self.transformer is not None else 2
) | 318 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_img2img.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... | 318 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_img2img.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... | 318 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_img2img.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... | 318 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_img2img.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... | 318 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_img2img.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[:,... | 318 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_img2img.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... | 318 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_img2img.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... | 318 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_img2img.py |
r""" | 318 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_img2img.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
... | 318 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_img2img.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
... | 318 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_img2img.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.
... | 318 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_img2img.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... | 318 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_img2img.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 ... | 318 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_img2img.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_... | 318 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_img2img.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... | 318 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_img2img.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... | 318 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_img2img.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... | 318 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_img2img.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... | 318 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_img2img.py |
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