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Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
image (`PIL.Image.Image`):
`Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will
be masked out with `mask_ima... | 296 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint.py |
The width in pixels of the generated image.
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 inference.
guidance_scale (`float`, *optional*,... | 296 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint.py |
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *opt... | 296 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint.py |
tensor will ge generated by sampling using the supplied random `generator`.
prompt_embeds (`np.ndarray`, *optional*):
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 `prom... | 296 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint.py |
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with... | 296 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint.py |
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
When returning a tuple, the first element is a list with the generated images, and the second... | 296 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint.py |
if generator is None:
generator = np.random
# set timesteps
self.scheduler.set_timesteps(num_inference_steps)
# 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 =... | 296 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint.py |
num_channels_latents = NUM_LATENT_CHANNELS
latents_shape = (batch_size * num_images_per_prompt, num_channels_latents, height // 8, width // 8)
latents_dtype = prompt_embeds.dtype
if latents is None:
latents = generator.randn(*latents_shape).astype(latents_dtype)
else:
... | 296 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint.py |
# duplicate mask and masked_image_latents for each generation per prompt
mask = mask.repeat(batch_size * num_images_per_prompt, 0)
masked_image_latents = masked_image_latents.repeat(batch_size * num_images_per_prompt, 0)
mask = np.concatenate([mask] * 2) if do_classifier_free_guidance else mask... | 296 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint.py |
unet_input_channels = NUM_UNET_INPUT_CHANNELS
if num_channels_latents + num_channels_mask + num_channels_masked_image != unet_input_channels:
raise ValueError(
"Incorrect configuration settings! The config of `pipeline.unet` expects"
f" {unet_input_channels} but recei... | 296 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint.py |
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
ac... | 296 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint.py |
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
# expand the latents if we are doing classifier free guidance
latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents
# concat latents, mask, masked_image_latnets in the channel... | 296 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint.py |
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = np.split(noise_pred, 2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
sc... | 296 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint.py |
latents = 1 / 0.18215 * latents
# image = self.vae_decoder(latent_sample=latents)[0]
# it seems likes there is a strange result for using half-precision vae decoder if batchsize>1
image = np.concatenate(
[self.vae_decoder(latent_sample=latents[i : i + 1])[0] for i in range(latents.sh... | 296 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint.py |
if self.safety_checker is not None:
safety_checker_input = self.feature_extractor(
self.numpy_to_pil(image), return_tensors="np"
).pixel_values.astype(image.dtype)
# safety_checker does not support batched inputs yet
images, has_nsfw_concept = [], []
... | 296 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint.py |
class StableDiffusionLatentUpscalePipeline(DiffusionPipeline, StableDiffusionMixin, FromSingleFileMixin):
r"""
Pipeline for upscaling Stable Diffusion output image resolution by a factor of 2.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
imp... | 297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.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))... | 297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py |
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
self.image... | 297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py |
def _encode_prompt(
self,
prompt,
device,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
pooled_prompt_embeds: Optional[torch.Tensor] = None,
n... | 297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py |
(
prompt_embeds,
negative_prompt_embeds,
pooled_prompt_embeds,
negative_pooled_prompt_embeds,
) = self.encode_prompt(
prompt=prompt,
device=device,
do_classifier_free_guidance=do_classifier_free_guidance,
negative_pr... | 297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py |
def encode_prompt(
self,
prompt,
device,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
pooled_prompt_embeds: Optional[torch.Tensor] = None,
ne... | 297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py |
Args:
prompt (`str` or `list(int)`):
prompt to be encoded
device: (`torch.device`):
torch device
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`):
... | 297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py |
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
pooled_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
... | 297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py |
if prompt_embeds is None or pooled_prompt_embeds is None:
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_length=True,
return_tensor... | 297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py |
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
text_input_ids, untruncated_ids
):
removed_text = self.tokenizer.batch_decode(
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
)
logg... | 297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py |
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
if negative_prompt_embeds is None or negative_pooled_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batc... | 297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py |
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt | 297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py |
max_length = text_input_ids.shape[-1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_length=True,
return_tensors="pt... | 297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.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... | 297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py |
def check_inputs(
self,
prompt,
image,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
pooled_prompt_embeds=None,
negative_pooled_prompt_embeds=None,
):
if prompt is not None and prompt_embeds is n... | 297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.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."
)
... | 297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py |
if prompt_embeds is not None and pooled_prompt_embeds is None:
raise ValueError(
"If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`."
)
... | 297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py |
if (
not isinstance(image, torch.Tensor)
and not isinstance(image, np.ndarray)
and not isinstance(image, PIL.Image.Image)
and not isinstance(image, list)
):
raise ValueError(
f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image`... | 297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py |
if isinstance(image, list):
image_batch_size = len(image)
else:
image_batch_size = image.shape[0] if image.ndim == 4 else 1
if batch_size != image_batch_size:
raise ValueError(
f"`prompt` has batch size {batch_size} and `image` ... | 297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.prepare_latents
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
shape = (batch_size, num_channels_latents, height, width)
... | 297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py |
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]] = None,
image: PipelineImageInput = None,
num_inference_steps: int = 75,
guidance_scale: float = 9.0,
negative_prompt: Optional[Union[str, List[str]]] = None,
generator: Optional[Union[torch.Ge... | 297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py |
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide image upscaling.
image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
`Image` or tensor representing an image batch to be ... | 297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py |
A higher guidance scale value encourages the model to generate images closely linked to the text
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to g... | 297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py |
latents (`torch.Tensor`, *optional*):
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling u... | 297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py |
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step. | 297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py |
Examples:
```py
>>> from diffusers import StableDiffusionLatentUpscalePipeline, StableDiffusionPipeline
>>> import torch
>>> pipeline = StableDiffusionPipeline.from_pretrained(
... "CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16
... )
>>> pipeline... | 297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py |
>>> upscaled_image = upscaler(
... prompt=prompt,
... image=low_res_latents,
... num_inference_steps=20,
... guidance_scale=0,
... generator=generator,
... ).images[0]
>>> upscaled_image.save("../images/a2.png")
```
Returns:
... | 297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py |
# 2. Define call parameters
if prompt is not None:
batch_size = 1 if isinstance(prompt, str) else len(prompt)
else:
batch_size = prompt_embeds.shape[0]
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation... | 297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py |
# 3. Encode input prompt
(
prompt_embeds,
negative_prompt_embeds,
pooled_prompt_embeds,
negative_pooled_prompt_embeds,
) = self.encode_prompt(
prompt,
device,
do_classifier_free_guidance,
negative_prompt,
... | 297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py |
# 4. Preprocess image
image = self.image_processor.preprocess(image)
image = image.to(dtype=prompt_embeds.dtype, device=device)
if image.shape[1] == 3:
# encode image if not in latent-space yet
image = retrieve_latents(self.vae.encode(image), generator=generator) * self.v... | 297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py |
# 5. Add noise to image (set to be 0):
# (see below notes from the author):
# "the This step theoretically can make the model work better on out-of-distribution inputs, but mostly just seems to make it match the input less, so it's turned off by default."
noise_level = torch.tensor([0.0], dtype=... | 297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py |
timestep_condition = torch.cat([noise_level_embed, pooled_prompt_embeds], dim=1)
# 6. Prepare latent variables
height, width = image.shape[2:]
num_channels_latents = self.vae.config.latent_channels
latents = self.prepare_latents(
batch_size,
num_channels_latents,... | 297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py |
# 7. Check that sizes of image and latents match
num_channels_image = image.shape[1]
if num_channels_latents + num_channels_image != self.unet.config.in_channels:
raise ValueError(
f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects"
... | 297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py |
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
sigma = self.scheduler.sigmas[i]
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_f... | 297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py |
# in original repo, the output contains a variance channel that's not used
noise_pred = noise_pred[:, :-1]
# apply preconditioning, based on table 1 in Karras et al. (2022)
inv_sigma = 1 / (sigma**2 + 1)
noise_pred = inv_sigma * latent_model_input + self.... | 297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py |
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
step_idx = i // getattr(self.sch... | 297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py |
class StableDiffusionInstructPix2PixPipeline(
DiffusionPipeline,
StableDiffusionMixin,
TextualInversionLoaderMixin,
StableDiffusionLoraLoaderMixin,
IPAdapterMixin,
FromSingleFileMixin,
):
r"""
Pipeline for pixel-level image editing by following text instructions (based on Stable Diffusio... | 298 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.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))... | 298 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py |
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 ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as input... | 298 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py |
model_cpu_offload_seq = "text_encoder->unet->vae"
_optional_components = ["safety_checker", "feature_extractor", "image_encoder"]
_exclude_from_cpu_offload = ["safety_checker"]
_callback_tensor_inputs = ["latents", "prompt_embeds", "image_latents"]
def __init__(
self,
vae: AutoencoderKL... | 298 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.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"
... | 298 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.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... | 298 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py |
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]] = None,
image: PipelineImageInput = None,
num_inference_steps: int = 100,
guidance_scale: float = 7.5,
image_guidance_scale: float = 1.5,
negative_prompt: Optional[Union[str, List[str]]] = None... | 298 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py |
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
**kwargs,
):
r"""
The call function to the pipeline for generation. | 298 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py |
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
image (`torch.Tensor` `np.ndarray`, `PIL.Image.Image`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
... | 298 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py |
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
image_guidance_scale (`float`, *optional*, defaults to 1.5):
Push the generated image towards the initial `image`. Image guidance scale is enabled by setting
`image_guidance_s... | 298 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py |
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
generator (`torch.Generator`, *optional*):
... | 298 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py |
provided, text embeddings are generated from the `prompt` input argument.
negative_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided, `negative_prompt_embeds` are gen... | 298 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py |
A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of
each denoising step during the inference. with the following arguments: `callback_on_step_end(self:
DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwar... | 298 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py |
[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). | 298 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py |
Examples:
```py
>>> import PIL
>>> import requests
>>> import torch
>>> from io import BytesIO
>>> from diffusers import StableDiffusionInstructPix2PixPipeline
>>> def download_image(url):
... response = requests.get(url)
... return PIL... | 298 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.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... | 298 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py |
if callback is not None:
deprecate(
"callback",
"1.0.0",
"Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
)
if callback_steps is not None:
deprecate(
"ca... | 298 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py |
# 0. Check inputs
self.check_inputs(
prompt,
callback_steps,
negative_prompt,
prompt_embeds,
negative_prompt_embeds,
ip_adapter_image,
ip_adapter_image_embeds,
callback_on_step_end_tensor_inputs,
)
se... | 298 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py |
# 2. Encode input prompt
prompt_embeds = self._encode_prompt(
prompt,
device,
num_images_per_prompt,
self.do_classifier_free_guidance,
negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
... | 298 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py |
# 5. Prepare Image latents
image_latents = self.prepare_image_latents(
image,
batch_size,
num_images_per_prompt,
prompt_embeds.dtype,
device,
self.do_classifier_free_guidance,
)
height, width = image_latents.shape[-2:]
... | 298 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py |
# 7. Check that shapes of latents and image match the UNet channels
num_channels_image = image_latents.shape[1]
if num_channels_latents + num_channels_image != self.unet.config.in_channels:
raise ValueError(
f"Incorrect configuration settings! The config of `pipeline.unet`: {... | 298 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py |
# 9. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
self._num_timesteps = len(timesteps)
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# Expand the latents if we are ... | 298 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py |
# predict the noise residual
noise_pred = self.unet(
scaled_latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
added_cond_kwargs=added_cond_kwargs,
cross_attention_kwargs=cross_attention_kwargs,
... | 298 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py |
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
if callback_on_step_end is not None:
callback_kwargs = {}
for k in callback_on_step_end_tensor_inputs... | 298 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py |
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
step_idx = i // getattr(self.sch... | 298 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py |
image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
# Offload all models
self.maybe_free_model_hooks()
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=image, nsfw_content_d... | 298 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.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 (`... | 298 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.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.
"""
... | 298 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py |
if prompt_embeds is None:
# textual inversion: process multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
text_inputs = self.tokenizer(
prompt,
... | 298 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py |
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
text_input_ids, untruncated_ids
):
removed_text = self.tokenizer.batch_decode(
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
)
logg... | 298 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py |
if self.text_encoder is not None:
prompt_embeds_dtype = self.text_encoder.dtype
else:
prompt_embeds_dtype = self.unet.dtype
prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text emb... | 298 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.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 type(prompt) is not type(negative_prompt)... | 298 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py |
else:
uncond_tokens = negative_prompt | 298 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.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(
... | 298 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.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... | 298 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py |
return prompt_embeds
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image
def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):
dtype = next(self.image_encoder.parameters()).dtype
if not isinstance(... | 298 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.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... | 298 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py |
def prepare_ip_adapter_image_embeds(
self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance
):
if ip_adapter_image_embeds is None:
if not isinstance(ip_adapter_image, list):
ip_adapter_image = [ip_adapter_image]
... | 298 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py |
image_embeds = []
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_nega... | 298 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py |
if do_classifier_free_guidance:
single_image_embeds = torch.cat(
[single_image_embeds, single_negative_image_embeds, single_negative_image_embeds]
)
single_image_embeds = single_image_embeds.to(device) | 298 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py |
image_embeds.append(single_image_embeds)
else:
repeat_dims = [1]
image_embeds = []
for single_image_embeds in ip_adapter_image_embeds:
if do_classifier_free_guidance:
(
single_image_embeds,
si... | 298 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py |
[single_image_embeds, single_negative_image_embeds, single_negative_image_embeds]
)
else:
single_image_embeds = single_image_embeds.repeat(
num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))
)
... | 298 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py |
return image_embeds
# 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_tenso... | 298 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.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... | 298 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.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... | 298 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py |
def check_inputs(
self,
prompt,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
ip_adapter_image=None,
ip_adapter_image_embeds=None,
callback_on_step_end_tensor_inputs=None,
):
if callback_steps is... | 298 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.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:
... | 298 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.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... | 298 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py |
if ip_adapter_image_embeds is not None:
if not isinstance(ip_adapter_image_embeds, list):
raise ValueError(
f"`ip_adapter_image_embeds` has to be of type `list` but is {type(ip_adapter_image_embeds)}"
)
elif ip_adapter_image_embeds[0].ndim not ... | 298 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py |
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