text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
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|---|---|---|
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... | 319 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_inpaint.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
... | 319 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_inpaint.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... | 319 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_inpaint.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... | 319 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_inpaint.py |
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt,
prompt_2,
prompt_3,
height,
width,
strength,
negative_prompt=negative_prompt,
negative_prompt_2=negative_prompt_2,
negative_prompt_... | 319 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_inpaint.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 | 319 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_inpaint.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,
... | 319 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_inpaint.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) | 319 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_inpaint.py |
# 3. Prepare timesteps
scheduler_kwargs = {}
if self.scheduler.config.get("use_dynamic_shifting", None) and mu is None:
image_seq_len = (int(height) // self.vae_scale_factor // self.transformer.config.patch_size) * (
int(width) // self.vae_scale_factor // self.transformer.con... | 319 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_inpaint.py |
timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
# check that number of inference steps is not < 1 - as this doesn't make sense
if num_inference_steps < 1:
raise ValueError(
f"After adjusting the num_inference_steps by strength parame... | 319 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_inpaint.py |
# create a boolean to check if the strength is set to 1. if so then initialise the latents with pure noise
is_strength_max = strength == 1.0
# 4. Preprocess mask and image
if padding_mask_crop is not None:
crops_coords = self.mask_processor.get_crop_region(mask_image, width, height,... | 319 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_inpaint.py |
latents_outputs = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
prompt_embeds.dtype,
device,
generator,
latents,
image=init_image,
timestep=latent_time... | 319 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_inpaint.py |
mask, masked_image_latents = self.prepare_mask_latents(
mask_condition,
masked_image,
batch_size,
num_images_per_prompt,
height,
width,
prompt_embeds.dtype,
device,
generator,
self.do_classifier_free_... | 319 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_inpaint.py |
# match the inpainting pipeline and will be updated with input + mask inpainting model later
if num_channels_transformer == 33:
# default case for runwayml/stable-diffusion-inpainting
num_channels_mask = mask.shape[1]
num_channels_masked_image = masked_image_latents.shape[1]
... | 319 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_inpaint.py |
f" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of"
" `pipeline.transformer` or your `mask_image` or `image` input."
)
elif num_channels_transformer != 16:
raise ValueError(
f"The transformer {self.... | 319 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_inpaint.py |
# 7. 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,
... | 319 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_inpaint.py |
# 8. Denoising loop
num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
self._num_timesteps = len(timesteps)
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
if self.interrupt:
... | 319 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_inpaint.py |
noise_pred = self.transformer(
hidden_states=latent_model_input,
timestep=timestep,
encoder_hidden_states=prompt_embeds,
pooled_projections=pooled_prompt_embeds,
joint_attention_kwargs=self.joint_attention_kwargs,
... | 319 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_inpaint.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 num_channels_transformer == 16:
init_latents_proper = image_latents
if sel... | 319 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_inpaint.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_... | 319 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_inpaint.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... | 319 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_inpaint.py |
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False, generator=generator)[
0
]
else:
image = latents
do_denormalize = [True] * image.shape[0]
image = self.image_processor.postpro... | 319 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_inpaint.py |
class StableDiffusionXLWatermarker:
def __init__(self):
self.watermark = WATERMARK_BITS
self.encoder = WatermarkEncoder()
self.encoder.set_watermark("bits", self.watermark)
def apply_watermark(self, images: torch.Tensor):
# can't encode images that are smaller than 256
... | 320 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/watermark.py |
class StableDiffusionXLPipelineOutput(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)`. ... | 321 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_output.py |
class FlaxStableDiffusionXLPipelineOutput(BaseOutput):
"""
Output class for Flax Stable Diffusion XL pipelines.
Args:
images (`np.ndarray`)
Array of shape `(batch_size, height, width, num_channels)` with images from the diffusion pipeline.
"""
images... | 322 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_output.py |
class FlaxStableDiffusionXLPipeline(FlaxDiffusionPipeline):
def __init__(
self,
text_encoder: FlaxCLIPTextModel,
text_encoder_2: FlaxCLIPTextModel,
vae: FlaxAutoencoderKL,
tokenizer: CLIPTokenizer,
tokenizer_2: CLIPTokenizer,
unet: FlaxUNet2DConditionModel,
... | 323 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_flax_stable_diffusion_xl.py |
def prepare_inputs(self, prompt: Union[str, List[str]]):
if not isinstance(prompt, (str, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
# Assume we have the two encoders
inputs = []
for tokenizer in [self.tokenizer, self.tokeniz... | 323 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_flax_stable_diffusion_xl.py |
def __call__(
self,
prompt_ids: jax.Array,
params: Union[Dict, FrozenDict],
prng_seed: jax.Array,
num_inference_steps: int = 50,
guidance_scale: Union[float, jax.Array] = 7.5,
height: Optional[int] = None,
width: Optional[int] = None,
latents: jnp.... | 323 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_flax_stable_diffusion_xl.py |
if jit:
images = _p_generate(
self,
prompt_ids,
params,
prng_seed,
num_inference_steps,
height,
width,
guidance_scale,
latents,
neg_prompt_ids,
... | 323 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_flax_stable_diffusion_xl.py |
# bs, encoder_input, seq_length
te_1_inputs = prompt_ids[:, 0, :]
te_2_inputs = prompt_ids[:, 1, :]
prompt_embeds = self.text_encoder(te_1_inputs, params=params["text_encoder"], output_hidden_states=True)
prompt_embeds = prompt_embeds["hidden_states"][-2]
prompt_embeds_2_out = s... | 323 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_flax_stable_diffusion_xl.py |
def _generate(
self,
prompt_ids: jnp.array,
params: Union[Dict, FrozenDict],
prng_seed: jax.Array,
num_inference_steps: int,
height: int,
width: int,
guidance_scale: float,
latents: Optional[jnp.array] = None,
neg_prompt_ids: Optional[jnp.a... | 323 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_flax_stable_diffusion_xl.py |
add_time_ids = self._get_add_time_ids(
(height, width), (0, 0), (height, width), prompt_embeds.shape[0], dtype=prompt_embeds.dtype
)
prompt_embeds = jnp.concatenate([neg_prompt_embeds, prompt_embeds], axis=0) # (2, 77, 2048)
add_text_embeds = jnp.concatenate([negative_pooled_embeds... | 323 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_flax_stable_diffusion_xl.py |
# Create random latents
latents_shape = (
batch_size,
self.unet.config.in_channels,
height // self.vae_scale_factor,
width // self.vae_scale_factor,
)
if latents is None:
latents = jax.random.normal(prng_seed, shape=latents_shape, dtype... | 323 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_flax_stable_diffusion_xl.py |
# Denoising loop
def loop_body(step, args):
latents, scheduler_state = args
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
... | 323 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_flax_stable_diffusion_xl.py |
# predict the noise residual
noise_pred = self.unet.apply(
{"params": params["unet"]},
jnp.array(latents_input),
jnp.array(timestep, dtype=jnp.int32),
encoder_hidden_states=prompt_embeds,
added_cond_kwargs=added_cond_kwargs,
... | 323 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_flax_stable_diffusion_xl.py |
if DEBUG:
# run with python for loop
for i in range(num_inference_steps):
latents, scheduler_state = loop_body(i, (latents, scheduler_state))
else:
latents, _ = jax.lax.fori_loop(0, num_inference_steps, loop_body, (latents, scheduler_state))
if return... | 323 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_flax_stable_diffusion_xl.py |
class StableDiffusionXLInpaintPipeline(
DiffusionPipeline,
StableDiffusionMixin,
TextualInversionLoaderMixin,
StableDiffusionXLLoraLoaderMixin,
FromSingleFileMixin,
IPAdapterMixin,
):
r"""
Pipeline for text-to-image generation using Stable Diffusion XL.
This model inherits from [`Di... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
The pipeline also inherits the following loading methods:
- [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
- [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
- [`~loaders.StableDiffusionXLLoraLoaderMixin.load_lo... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]):
Frozen text-encoder. Stable Diffusion XL uses the text portion of
[CLIP](https://huggingface.co/docs/t... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
tokenizer_2 (`CLIPTokenizer`):
Second Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
Whether the negative prompt embeddings shall be forced to always be set to 0. Also see the config of
`stabilityai/stable-diffusion-xl-base-1-0`.
add_watermarker (`bool`, *optional*):
Whether to use the [invisible_watermark library](https://github.com/ShieldMnt/invisible-watermark/) to
... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
model_cpu_offload_seq = "text_encoder->text_encoder_2->image_encoder->unet->vae"
_optional_components = [
"tokenizer",
"tokenizer_2",
"text_encoder",
"text_encoder_2",
"image_encoder",
"feature_extractor",
]
_callback_tensor_inputs = [
"latents",
... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
text_encoder_2: CLIPTextModelWithProjection,
tokenizer: CLIPTokenizer,
tokenizer_2: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: KarrasDiffusionSchedulers,
image_encoder:... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
self.register_modules(
vae=vae,
text_encoder=text_encoder,
text_encoder_2=text_encoder_2,
tokenizer=tokenizer,
tokenizer_2=tokenizer_2,
unet=unet,
image_encoder=image_encoder,
feature_extractor=feature_extractor,
... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available()
if add_watermarker:
self.watermark = StableDiffusionXLWatermarker()
else:
self.watermark = None
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffus... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
image = image.to(device=device, dtype=dtype)
if output_hidden_states:
image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]
image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
uncond_imag... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds
def prepare_ip_adapter_image_embeds(
self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance
):
image_embeds = ... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
for single_ip_adapter_image, image_proj_layer in zip(
ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers
):
output_hidden_state = not isinstance(image_proj_layer, ImageProjection)
single_image_embeds, single_negative_image_embeds = self.encod... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
ip_adapter_image_embeds = []
for i, single_image_embeds in enumerate(image_embeds):
single_image_embeds = torch.cat([single_image_embeds] * num_images_per_prompt, dim=0)
if do_classifier_free_guidance:
single_negative_image_embeds = torch.cat([negative_image_embeds[i]] * ... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
# Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.encode_prompt
def encode_prompt(
self,
prompt: str,
prompt_2: Optional[str] = None,
device: Optional[torch.device] = None,
num_images_per_prompt: int = 1,
do_c... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.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 both text-encoders
... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
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 in both text-encoders
prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak tex... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
If not provided, pooled text embeddings will be generated from `prompt` input argument.
negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.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, StableDiffusionXLLoraLoaderMixin):
self._lora_scale = lora_scale
# dynamically adjust the LoRA scale
if self.text_encoder is... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
# Define tokenizers and text encoders
tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]
text_encoders = (
[self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]
)
if prompt_embe... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
text_inputs = tokenizer(
prompt,
padding="max_length",
max_length=tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)
# We are only ALWAYS interested in the pooled output of the final text encoder
if pooled_prompt_embeds is None and prompt_embeds[0].ndim == 2:
pooled_prompt_embeds = prompt_embeds[0]
... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
# get unconditional embeddings for classifier free guidance
zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt
if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:
negative_prompt_embeds = torch.zeros_lik... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
uncond_tokens: List[str]
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)}."
)
... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
negative_prompt_embeds_list = []
for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):
if isinstance(self, TextualInversionLoaderMixin):
negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)
max_l... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
# We are only ALWAYS interested in the pooled output of the final text encoder
if negative_pooled_prompt_embeds is None and negative_prompt_embeds[0].ndim == 2:
negative_pooled_prompt_embeds = negative_prompt_embeds[0]
negative_prompt_embeds = negative_prompt_embeds.h... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
bs_embed, 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(bs_embed * num_images_per_prompt, seq_len, -1)
if do_clas... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
bs_embed * num_images_per_prompt, -1
)
if do_classifier_free_guidance:
negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
bs_embed ... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
# 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 fo... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
def check_inputs(
self,
prompt,
... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):
raise ValueError(
f"`callback_steps` ha... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt_2 is not None and prompt_embeds is not ... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):
raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}") | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.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."
)
... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `pr... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
if output_type != "pil":
raise ValueError(f"The output type should be PIL when inpainting mask crop, but is" f" {output_type}.") | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
if ip_adapter_image is not None and ip_adapter_image_embeds is not None:
raise ValueError(
"Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined."
)
if ip_adapter_image_embeds is not No... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
def prepare_latents(
self,
batch_size,
num_channels_latents,
height,
width,
dtype,
device,
generator,
latents=None,
image=None,
timestep=None,
is_strength_max=True,
add_noise=True,
return_noise=False,
... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
if (image is None or timestep is None) and not is_strength_max:
raise ValueError(
"Since strength < 1. initial latents are to be initialised as a combination of Image + Noise."
"However, either the image or the noise timestep has not been provided."
)
if ... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
if latents is None and add_noise:
noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
# if strength is 1. then initialise the latents to noise, else initial to image + noise
latents = noise if is_strength_max else self.scheduler.add_noise(image_latents, noise... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
def _encode_vae_image(self, image: torch.Tensor, generator: torch.Generator):
dtype = image.dtype
if self.vae.config.force_upcast:
image = image.float()
self.vae.to(dtype=torch.float32)
if isinstance(generator, list):
image_latents = [
retriev... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
def prepare_mask_latents(
self, mask, masked_image, batch_size, height, width, dtype, device, generator, do_classifier_free_guidance
):
# resize the mask to latents shape as we concatenate the mask to the latents
# we do that before converting to dtype to avoid breaking in case we're using c... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
# duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method
if mask.shape[0] < batch_size:
if not batch_size % mask.shape[0] == 0:
raise ValueError(
"The passed mask and the required batch size don't match. Masks are suppose... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
if masked_image is not None:
if masked_image_latents is None:
masked_image = masked_image.to(device=device, dtype=dtype)
masked_image_latents = self._encode_vae_image(masked_image, generator=generator)
if masked_image_latents.shape[0] < batch_size:
... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
masked_image_latents = (
torch.cat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents
)
# aligning device to prevent device errors when concating it with the latent model input
masked_image_latents = masked_image_latents.to(device=de... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
if hasattr(self.scheduler, "set_begin_index"):
self.scheduler.set_begin_index(t_start * self.scheduler.order)
return timesteps, num_inference_steps - t_start
else:
# Strength is irrelevan... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
num_inference_steps = (self.scheduler.timesteps < discrete_timestep_cutoff).sum().item()
if self.scheduler.order == 2 and num_inference_steps % 2 == 0:
# if the scheduler is a 2nd order scheduler we might have to do +1
# because `num_inference_steps` might be even given that ... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
# because t_n+1 >= t_n, we slice the timesteps starting from the end
t_start = len(self.scheduler.timesteps) - num_inference_steps
timesteps = self.scheduler.timesteps[t_start:]
if hasattr(self.scheduler, "set_begin_index"):
self.scheduler.set_begin_index(t_start)
... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
# Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl_img2img.StableDiffusionXLImg2ImgPipeline._get_add_time_ids
def _get_add_time_ids(
self,
original_size,
crops_coords_top_left,
target_size,
aesthetic_score,
negative_aesthetic_score,
... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
passed_add_embed_dim = (
self.unet.config.addition_time_embed_dim * len(add_time_ids) + text_encoder_projection_dim
)
expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
if (
expected_add_embed_dim > passed_add_embed_dim
and (expected_add_embed_dim - passed_add_embed_dim) == self.unet.config.addition_time_embed_dim
):
raise ValueError(
f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vec... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. Please make sure to disable `requires_aesthetics_score` with `pipe.register_to_config(requires_aesthetics_score=False)` to make sure `target_size` {target_size} is correctly used by the... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
add_time_ids = torch.tensor([add_time_ids], dtype=dtype)
add_neg_time_ids = torch.tensor([add_neg_time_ids], dtype=dtype)
return add_time_ids, add_neg_time_ids
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae
def upc... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
# Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
def get_guidance_scale_embedding(
self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32
) -> torch.Tensor:
""... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
Returns:
`torch.Tensor`: Embedding vectors with shape `(len(w), embedding_dim)`.
"""
assert len(w.shape) == 1
w = w * 1000.0
half_dim = embedding_dim // 2
emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, dtype=dty... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
@property
def do_classifier_free_guidance(self):
return self._guidance_scale... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.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,
image: PipelineImageInput = None,
mask_image: PipelineImageInput = None,
masked_image_latent... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.Tensor] = None,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
pooled_prompt_embeds: Optional[torch.Tensor] = None,
negative_po... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
negative_target_size: Optional[Tuple[int, int]] = None,
aesthetic_score: float = 6.0,
negative_aesthetic_score: float = 2.5,
clip_skip: Optional[int] = None,
callback_on_step_end: Optional[
Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.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 the `to... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L)
instead of 3, so the expected shape would be `(B, H, W, 1)`.
height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
The height in pixel... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
[stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
and checkpoints that are not specifically fine-tuned on low resolutions.
padding_mask_crop (`int`, *optional*, defaults to `None`):
The size of margin in the crop to be... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
strength (`float`, *optional*, defaults to 0.9999):
Conceptually, indicates how much to transform the masked portion of the reference `image`. Must be
between 0 and 1. `image` will be used as a starting point, adding more noise to it the larger the
`strength`. The number ... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
expense of slower inference.
timesteps (`List[int]`, *optional*):
Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
it is assumed that the passed `image` is a partly denoised image. Note that when this is specified,
strength will be ignored. The `denoising_start` parameter is particularly beneficial when this pipeline
is integrated into a "Mixture of Denoisers" multi-pipeline setup, as detailed in [**... | 324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py |
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