text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
values |
|---|---|---|
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 `226`):
Maximum sequence length in encoded prompt. Must be consistent wi... | 240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/consisid/pipeline_consisid.py |
the local facial extractor. This is crucial for the model to edit facial features If not provided, the
local facial extractor will not run normally.
kps_cond (`Optional[torch.Tensor]`, *optional*):
A tensor that determines whether the global facial extractor use keypoint info... | 240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/consisid/pipeline_consisid.py |
Examples:
Returns:
[`~pipelines.consisid.pipeline_output.ConsisIDPipelineOutput`] or `tuple`:
[`~pipelines.consisid.pipeline_output.ConsisIDPipelineOutput`] if `return_dict` is True, otherwise a
`tuple`. When returning a tuple, the first element is a list with the generated ... | 240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/consisid/pipeline_consisid.py |
# 1. Check inputs. Raise error if not correct
self.check_inputs(
image=image,
prompt=prompt,
height=height,
width=width,
negative_prompt=negative_prompt,
callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
lat... | 240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/consisid/pipeline_consisid.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.
do_classifier_free_guidance = guidance_scale > 1.0
# 3. Encode inpu... | 240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/consisid/pipeline_consisid.py |
# 4. Prepare timesteps
timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device)
self._num_timesteps = len(timesteps)
# 5. Prepare latents
is_kps = getattr(self.transformer.config, "is_kps", False)
kps_cond = kps_cond if is_kps else None
... | 240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/consisid/pipeline_consisid.py |
latent_channels = self.transformer.config.in_channels // 2
latents, image_latents = self.prepare_latents(
image,
batch_size * num_videos_per_prompt,
latent_channels,
num_frames,
height,
width,
prompt_embeds.dtype,
de... | 240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/consisid/pipeline_consisid.py |
with self.progress_bar(total=num_inference_steps) as progress_bar:
# for DPM-solver++
old_pred_original_sample = None
timesteps_cpu = timesteps.cpu()
for i, t in enumerate(timesteps):
if self.interrupt:
continue
latent_... | 240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/consisid/pipeline_consisid.py |
# predict noise model_output
noise_pred = self.transformer(
hidden_states=latent_model_input,
encoder_hidden_states=prompt_embeds,
timestep=timestep,
image_rotary_emb=image_rotary_emb,
attention_kwargs=at... | 240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/consisid/pipeline_consisid.py |
# perform guidance
if use_dynamic_cfg:
self._guidance_scale = 1 + guidance_scale * (
(
1
- math.cos(
math.pi
* ((num_inference_steps - times... | 240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/consisid/pipeline_consisid.py |
# compute the previous noisy sample x_t -> x_t-1
if not isinstance(self.scheduler, CogVideoXDPMScheduler):
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
else:
latents, old_pred_original_sample = se... | 240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/consisid/pipeline_consisid.py |
# call the callback, if provided
if callback_on_step_end is not None:
callback_kwargs = {}
for k in callback_on_step_end_tensor_inputs:
callback_kwargs[k] = locals()[k]
callback_outputs = callback_on_step_end(self, i, t,... | 240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/consisid/pipeline_consisid.py |
# Offload all models
self.maybe_free_model_hooks()
if not return_dict:
return (video,)
return ConsisIDPipelineOutput(frames=video) | 240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/consisid/pipeline_consisid.py |
class ConsisIDPipelineOutput(BaseOutput):
r"""
Output class for ConsisID pipelines.
Args:
frames (`torch.Tensor`, `np.ndarray`, or List[List[PIL.Image.Image]]):
List of video outputs - It can be a nested list of length `batch_size,` with each sub-list containing
denoised PIL... | 241 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/consisid/pipeline_output.py |
class DiffEditInversionPipelineOutput(BaseOutput):
"""
Output class for Stable Diffusion pipelines.
Args:
latents (`torch.Tensor`)
inverted latents tensor
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `num_timesteps * batch_si... | 242 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
class StableDiffusionDiffEditPipeline(
DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, StableDiffusionLoraLoaderMixin
):
r"""
<Tip warning={true}>
This is an experimental feature!
</Tip>
Pipeline for text-guided image inpainting using Stable Diffusion and DiffEdit.
... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.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))... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for
more details about a model's potential harms.
feature_extractor ([`~transformer... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
model_cpu_offload_seq = "text_encoder->unet->vae"
_optional_components = ["safety_checker", "feature_extractor", "inverse_scheduler"]
_exclude_from_cpu_offload = ["safety_checker"]
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
if scheduler is not None and getattr(scheduler.config, "steps_offset", 1) != 1:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
if scheduler is not None and getattr(scheduler.config, "skip_prk_steps", True) is False:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} has not set the configuration"
" `skip_prk_steps`. `skip_prk_steps` should be set to True in the configurat... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.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"
... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.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... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
is_unet_version_less_0_9_0 = (
unet is not None
and hasattr(unet.config, "_diffusers_version")
and version.parse(version.parse(unet.config._diffusers_version).base_version) < version.parse("0.9.0.dev0")
)
is_unet_sample_size_less_64 = (
unet is not None an... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
" \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
" in the config might lead to incorrect results in future versions. If you have do... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
inverse_scheduler=inverse_scheduler,
... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torc... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
prompt_embeds_tuple = self.encode_prompt(
prompt=prompt,
device=device,
num_images_per_prompt=num_images_per_prompt,
do_classifier_free_guidance=do_classifier_free_guidance,
negative_prompt=negative_prompt,
prompt_embeds=prompt_embeds,
... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.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.... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.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... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.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... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.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... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.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... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.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 | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.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_... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds) | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.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... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.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... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.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(
... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.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... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.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):
... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.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... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
def decode_latents(self, latents):
deprecation_message = "The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead"
d... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
def check_inputs(
self,
prompt,
strength,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
):
if (strength is None) or (strength is not None and (strength < 0 or strength > 1)):
raise ValueError(
... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.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:
... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.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... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
def check_source_inputs(
self,
source_prompt=None,
source_negative_prompt=None,
source_prompt_embeds=None,
source_negative_prompt_embeds=None,
):
if source_prompt is not None and source_prompt_embeds is not None:
raise ValueError(
f"Cannot ... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
if source_negative_prompt is not None and source_negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `source_negative_prompt`: {source_negative_prompt} and `source_negative_prompt_embeds`:"
f" {source_negative_prompt_embeds}. Please make sure to only f... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
def get_timesteps(self, num_inference_steps, strength, device):
# get the original timestep using init_timestep
init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
t_start = max(num_inference_steps - init_timestep, 0)
timesteps = self.scheduler.timesteps[t_star... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
shape = (
batch_size,
num_channels_latents,
... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
def prepare_image_latents(self, image, batch_size, dtype, device, generator=None):
if not isinstance(image, (torch.Tensor, PIL.Image.Image, list))... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
if isinstance(generator, list):
latents = [
self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)
]
latents = torch.cat(latents, dim=0)
else:
latents = self.vae.encode(image).latent_di... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
if batch_size != latents.shape[0]:
if batch_size % latents.shape[0] == 0:
# expand image_latents for batch_size
deprecation_message = (
f"You have passed {batch_size} text prompts (`prompt`), but only {latents.shape[0]} initial"
" image... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
f"Cannot duplicate `image` of batch size {latents.shape[0]} to {batch_size} text prompts."
)
else:
latents = torch.cat([latents], dim=0) | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
return latents
def get_epsilon(self, model_output: torch.Tensor, sample: torch.Tensor, timestep: int):
pred_type = self.inverse_scheduler.config.prediction_type
alpha_prod_t = self.inverse_scheduler.alphas_cumprod[timestep]
beta_prod_t = 1 - alpha_prod_t
if pred_type == "epsilon":... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def generate_mask(
self,
image: Union[torch.Tensor, PIL.Image.Image] = None,
target_prompt: Optional[Union[str, List[str]]] = None,
target_negative_prompt: Optional[Union[str, List[str]]] = None,
target_promp... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
output_type: Optional[str] = "np",
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
):
r"""
Generate a latent mask given a mask prompt, a target prompt, and an image. | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
Args:
image (`PIL.Image.Image`):
`Image` or tensor representing an image batch to be used for computing the mask.
target_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide semantic mask generation. If not defined, you need to pass
... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
source_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to gu... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
inputs (prompt weighting). If not provided, text embeddings are generated from `source_prompt` input
argument.
source_negative_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated text embeddings to negatively guide the semantic mask generation. Can be used to easily
... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
The maximum multiple of the mean absolute difference used to clamp the semantic guidance map before
mask binarization.
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
... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the
[`~models.attention_processor.AttnProcessor`] as defined in
[`self.pro... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
Examples:
Returns:
`List[PIL.Image.Image]` or `np.array`:
When returning a `List[PIL.Image.Image]`, the list consists of a batch of single-channel binary images
with dimensions `(height // self.vae_scale_factor, width // self.vae_scale_factor)`. If it's
... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
if (num_maps_per_mask is None) or (
num_maps_per_mask is not None and (not isinstance(num_maps_per_mask, int) or num_maps_per_mask <= 0)
):
raise ValueError(
f"`num_maps_per_mask` has to be a positive integer but is {num_maps_per_mask} of type"
f" {type(nu... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
# 2. Define call parameters
if target_prompt is not None and isinstance(target_prompt, str):
batch_size = 1
elif target_prompt is not None and isinstance(target_prompt, list):
batch_size = len(target_prompt)
else:
batch_size = target_prompt_embeds.shape[0]
... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
# 3. Encode input prompts
(cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None)
target_negative_prompt_embeds, target_prompt_embeds = self.encode_prompt(
target_prompt,
device,
num_maps_per_mask,
do_classifier_free_gui... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
source_negative_prompt_embeds, source_prompt_embeds = self.encode_prompt(
source_prompt,
device,
num_maps_per_mask,
do_classifier_free_guidance,
source_negative_prompt,
prompt_embeds=source_prompt_embeds,
negative_prompt_embeds=source_n... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
# 6. Prepare image latents and add noise with specified strength
image_latents = self.prepare_image_latents(
image, batch_size * num_maps_per_mask, self.vae.dtype, device, generator
)
noise = randn_tensor(image_latents.shape, generator=generator, device=device, dtype=self.vae.dtype)
... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
if do_classifier_free_guidance:
noise_pred_neg_src, noise_pred_source, noise_pred_uncond, noise_pred_target = noise_pred.chunk(4)
noise_pred_source = noise_pred_neg_src + guidance_scale * (noise_pred_source - noise_pred_neg_src)
noise_pred_target = noise_pred_uncond + guidance_scale ... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
# 8. Compute the mask from the absolute difference of predicted noise residuals
# TODO: Consider smoothing mask guidance map
mask_guidance_map = (
torch.abs(noise_pred_target - noise_pred_source)
.reshape(batch_size, num_maps_per_mask, *noise_pred_target.shape[-3:])
.... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_INVERT_DOC_STRING)
def invert(
self,
prompt: Optional[Union[str, List[str]]] = None,
image: Union[torch.Tensor, PIL.Image.Image] = None,
num_inference_steps: int = 50,
inpaint_strength: float = 0.8,
guidance_scal... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
num_auto_corr_rolls: int = 5,
):
r"""
Generate inverted latents given a prompt and image. | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.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 (`PIL.Image.Image`):
`Image` or tensor representing an image batch to produce the inverted latents guided by `... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
A higher guidance scale value encourages the model to generate images closely linked to the te... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
provided, text embeddings are generated from the `prompt` input argument.
negative_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated negative text embeddings. Can be used... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
Whether or not to return a [`~pipelines.stable_diffusion.DiffEditInversionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
followi... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
lambda_auto_corr (`float`, *optional*, defaults to 20.0):
Lambda parameter to control auto correction.
lambda_kl (`float`, *optional*, defaults to 20.0):
Lambda parameter to control Kullback-Leibler divergence output.
num_reg_steps (`int`, *optional*, defaults to ... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
Examples:
Returns:
[`~pipelines.stable_diffusion.pipeline_stable_diffusion_diffedit.DiffEditInversionPipelineOutput`] or
`tuple`:
If `return_dict` is `True`,
[`~pipelines.stable_diffusion.pipeline_stable_diffusion_diffedit.DiffEditInversionPipelineOutput`... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.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]
if cross_attention_kwargs is None:
... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
# 5. Encode input prompt
prompt_embeds, negative_prompt_embeds = self.encode_prompt(
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negati... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
# 7. Noising loop where we obtain the intermediate noised latent image for each timestep.
num_warmup_steps = len(timesteps) - num_inference_steps * self.inverse_scheduler.order
inverted_latents = []
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumer... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# regularization of the noise prediction (not in or... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
l_ac = auto_corr_loss(var_epsilon, generator=generator)
l_ac.backward()
grad = var.grad.detach() / num_auto_corr_rolls
noise_pred = noise_pred - lambda_auto_corr * grad
if lambda_kl ... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
# compute the previous noisy sample x_t -> x_t-1
latents = self.inverse_scheduler.step(noise_pred, t, latents).prev_sample
inverted_latents.append(latents.detach().clone())
# call the callback, if provided
if i == len(timesteps) - 1 or (
... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
# 9. Convert to PIL.
if decode_latents and output_type == "pil":
image = self.image_processor.numpy_to_pil(image)
# Offload all models
self.maybe_free_model_hooks()
if not return_dict:
return (latents, image)
return DiffEditInversionPipelineOutput(laten... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Optional[Union[str, List[str]]] = None,
mask_image: Union[torch.Tensor, PIL.Image.Image] = None,
image_latents: Union[torch.Tensor, PIL.Image.Image] = None,
inpaint_strength: Optio... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
clip_skip: int = None,
):
r"""
The call function to the pipeline for generation. | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.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`.
mask_image (`PIL.Image.Image`):
`Image` or tensor representing an image batch to mask the generated image. White pix... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
denoising process is run on the masked area for the full number of iterations specified in
`num_inference_steps`. `image_latents` is used as a reference for the masked area, and adding more
noise to a region increases `inpaint_strength`. If `inpaint_strength` is 0, no inpainting occurs.
... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random `generator`.
prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated text embeddings. Can be used to easily twea... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
following... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.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.
Examples: | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
otherwise a `tuple` is returned where the first element is a list with the generated image... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
if mask_image is None:
raise ValueError(
"`mask_image` input cannot be undefined. Use `generate_mask()` to compute `mask_image` from text prompts."
)
if image_latents is None:
raise ValueError(
"`image_latents` input cannot be undefined. Use `i... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
device = self._execution_device
# 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.
do_classifier_free_guidance = guidan... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
# 3. Encode input prompt
text_encoder_lora_scale = (
cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
)
prompt_embeds, negative_prompt_embeds = self.encode_prompt(
prompt,
device,
num_images_per_prompt,
... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
# 4. Preprocess mask
mask_image = preprocess_mask(mask_image, batch_size)
latent_height, latent_width = mask_image.shape[-2:]
mask_image = torch.cat([mask_image] * num_images_per_prompt)
mask_image = mask_image.to(device=device, dtype=prompt_embeds.dtype)
# 5. Set timesteps
... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
latent_shape = (self.vae.config.latent_channels, latent_height, latent_width)
if image_latents.shape[-3:] != latent_shape:
raise ValueError(
f"Each latent image in `image_latents` must have shape {latent_shape}, "
f"but has shape {image_latents.shape[-3:]}"
... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
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