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# 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,
... | 298 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.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, num_images_per_prompt, dtype, device, do_classifier_free_guidance, generator=None
):
... | 298 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py |
if batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] == 0:
# expand image_latents for batch_size
deprecation_message = (
f"You have passed {batch_size} text prompts (`prompt`), but only {image_latents.shape[0]} initial"
" images (`image`)... | 298 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py |
raise ValueError(
f"Cannot duplicate `image` of batch size {image_latents.shape[0]} to {batch_size} text prompts."
)
else:
image_latents = torch.cat([image_latents], dim=0) | 298 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py |
if do_classifier_free_guidance:
uncond_image_latents = torch.zeros_like(image_latents)
image_latents = torch.cat([image_latents, image_latents, uncond_image_latents], dim=0)
return image_latents
@property
def guidance_scale(self):
return self._guidance_scale
@prope... | 298 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py |
class OnnxStableDiffusionImg2ImgPipeline(DiffusionPipeline):
r"""
Pipeline for text-guided image to image generation using Stable Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as d... | 299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.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 uses the text portion of
[CLIP](https://huggingface.co/docs/tran... | 299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py |
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please, refer to the [model card](https://huggingface.co/stable-di... | 299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py |
vae_encoder: OnnxRuntimeModel
vae_decoder: OnnxRuntimeModel
text_encoder: OnnxRuntimeModel
tokenizer: CLIPTokenizer
unet: OnnxRuntimeModel
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]
safety_checker: OnnxRuntimeModel
feature_extractor: CLIPImageProcessor
_optiona... | 299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.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 "
... | 299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py |
if scheduler is not None and getattr(scheduler.config, "clip_sample", False) is True:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
" `clip_sample` should be set to False in the configuration file... | 299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.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"
... | 299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.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... | 299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_onnx_stable_diffusion.OnnxStableDiffusionPipeline._encode_prompt
def _encode_prompt(
self,
prompt: Union[str, List[str]],
num_images_per_prompt: Optional[int],
do_classifier_free_guidance: bool,
negative_prompt: Opti... | 299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py |
Args:
prompt (`str` or `List[str]`):
prompt to be encoded
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
... | 299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py |
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
"""
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)... | 299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py |
if prompt_embeds is None:
# get prompt text embeddings
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="np",
)
... | 299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py |
prompt_embeds = self.text_encoder(input_ids=text_input_ids.astype(np.int32))[0]
prompt_embeds = np.repeat(prompt_embeds, num_images_per_prompt, axis=0) | 299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.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)... | 299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py |
)
else:
uncond_tokens = negative_prompt | 299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py |
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="np",
)
negative_prompt_embeds = self.text_en... | 299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py |
def check_inputs(
self,
prompt: Union[str, List[str]],
callback_steps: int,
negative_prompt: Optional[Union[str, List[str]]] = None,
prompt_embeds: Optional[np.ndarray] = None,
negative_prompt_embeds: Optional[np.ndarray] = None,
):
if (callback_steps is None)... | 299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.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:
... | 299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.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... | 299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py |
def __call__(
self,
prompt: Union[str, List[str]],
image: Union[np.ndarray, PIL.Image.Image] = None,
strength: float = 0.8,
num_inference_steps: Optional[int] = 50,
guidance_scale: Optional[float] = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
... | 299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py |
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
image (`np.ndarray` or `PIL.Image.Image`):
`Image`, or tensor representing an image batch, that will be used as the starting point for the
process.
... | 299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py |
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference. This parameter will be modulated by `strength`.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free ... | 299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py |
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`scheduler... | 299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py |
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/sta... | 299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py |
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step. | 299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.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... | 299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py |
if strength < 0 or strength > 1:
raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
if generator is None:
generator = np.random
# set timesteps
self.scheduler.set_timesteps(num_inference_steps)
image = preprocess(image).cpu().nump... | 299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py |
latents_dtype = prompt_embeds.dtype
image = image.astype(latents_dtype)
# encode the init image into latents and scale the latents
init_latents = self.vae_encoder(sample=image)[0]
init_latents = 0.18215 * init_latents | 299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py |
if isinstance(prompt, str):
prompt = [prompt]
if len(prompt) > init_latents.shape[0] and len(prompt) % init_latents.shape[0] == 0:
# expand init_latents for batch_size
deprecation_message = (
f"You have passed {len(prompt)} text prompts (`prompt`), but only {i... | 299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py |
elif len(prompt) > init_latents.shape[0] and len(prompt) % init_latents.shape[0] != 0:
raise ValueError(
f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {len(prompt)} text prompts."
)
else:
init_latents = np.concatenate([init_latents] * num... | 299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py |
# get the original timestep using init_timestep
offset = self.scheduler.config.get("steps_offset", 0)
init_timestep = int(num_inference_steps * strength) + offset
init_timestep = min(init_timestep, num_inference_steps)
timesteps = self.scheduler.timesteps.numpy()[-init_timestep]
... | 299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.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... | 299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py |
for i, t in enumerate(self.progress_bar(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
latent_model_input = self.scheduler.scale_model_input(torch.from_numpy(lat... | 299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py |
# compute the previous noisy sample x_t -> x_t-1
scheduler_output = self.scheduler.step(
torch.from_numpy(noise_pred), t, torch.from_numpy(latents), **extra_step_kwargs
)
latents = scheduler_output.prev_sample.numpy()
# call the callback, if provided
... | 299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.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 = [], []
... | 299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py |
class OnnxStableDiffusionUpscalePipeline(DiffusionPipeline):
vae: OnnxRuntimeModel
text_encoder: OnnxRuntimeModel
tokenizer: CLIPTokenizer
unet: OnnxRuntimeModel
low_res_scheduler: DDPMScheduler
scheduler: KarrasDiffusionSchedulers
safety_checker: OnnxRuntimeModel
feature_extractor: CLIP... | 300 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_upscale.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 "
... | 300 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_upscale.py |
if scheduler is not None and getattr(scheduler.config, "clip_sample", False) is True:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
" `clip_sample` should be set to False in the configuration file... | 300 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_upscale.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"
... | 300 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_upscale.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... | 300 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_upscale.py |
def check_inputs(
self,
prompt: Union[str, List[str]],
image,
noise_level,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
):
if (callback_steps is None) or (
callback_steps is not None and (no... | 300 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_upscale.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:
... | 300 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_upscale.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... | 300 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_upscale.py |
# verify batch size of prompt and image are same if image is a list or tensor or numpy array
if isinstance(image, (list, np.ndarray)):
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
b... | 300 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_upscale.py |
# check noise level
if noise_level > self.config.max_noise_level:
raise ValueError(f"`noise_level` has to be <= {self.config.max_noise_level} but is {noise_level}")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_s... | 300 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_upscale.py |
def decode_latents(self, latents):
latents = 1 / 0.08333 * latents
image = self.vae(latent_sample=latents)[0]
image = np.clip(image / 2 + 0.5, 0, 1)
image = image.transpose((0, 2, 3, 1))
return image
def _encode_prompt(
self,
prompt: Union[str, List[str]],
... | 300 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_upscale.py |
Args:
prompt (`str` or `List[str]`):
prompt to be encoded
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
... | 300 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_upscale.py |
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
"""
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)... | 300 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_upscale.py |
if prompt_embeds is None:
# get prompt text embeddings
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="np",
)
... | 300 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_upscale.py |
prompt_embeds = self.text_encoder(input_ids=text_input_ids.astype(np.int32))[0]
prompt_embeds = np.repeat(prompt_embeds, num_images_per_prompt, axis=0) | 300 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_upscale.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)... | 300 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_upscale.py |
)
else:
uncond_tokens = negative_prompt | 300 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_upscale.py |
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="np",
)
negative_prompt_embeds = self.text_en... | 300 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_upscale.py |
def __call__(
self,
prompt: Union[str, List[str]],
image: Union[np.ndarray, PIL.Image.Image, List[PIL.Image.Image]],
num_inference_steps: int = 75,
guidance_scale: float = 9.0,
noise_level: int = 20,
negative_prompt: Optional[Union[str, List[str]]] = None,
... | 300 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_upscale.py |
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
image (`np.ndarray` or `PIL.Image.Image`):
`Image`, or tensor representing an image batch, that will be used as the starting point for the
process.
... | 300 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_upscale.py |
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
noise_level (`float`, defaults to 0.2):
Deteremines the amount of noise to add to the initial image before performing upscaling... | 300 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_upscale.py |
A np.random.RandomState to make generation deterministic.
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 no... | 300 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_upscale.py |
argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
... | 300 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_upscale.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... | 300 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_upscale.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 generator is None:
ge... | 300 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_upscale.py |
latents_dtype = prompt_embeds.dtype
image = preprocess(image).cpu().numpy()
height, width = image.shape[2:]
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
self.config.num_latent_channels,
height,
width,
latents_dty... | 300 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_upscale.py |
batch_multiplier = 2 if do_classifier_free_guidance else 1
image = np.concatenate([image] * batch_multiplier * num_images_per_prompt)
noise_level = np.concatenate([noise_level] * image.shape[0])
# 7. Check that sizes of image and latents match
num_channels_image = image.shape[1]
... | 300 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_upscale.py |
# 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
timestep_dtype = next(
... | 300 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_upscale.py |
# concat latents, mask, masked_image_latents in the channel dimension
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
latent_model_input = np.concatenate([latent_model_input, image], axis=1)
# timestep to tensor
timestep = np.... | 300 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_upscale.py |
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(
torch.from_numpy(noise_pred), t, torch.from_numpy(latents), **extra_step_kwargs
).prev_sample
latents = latents.numpy()
# call the callback, if provided
... | 300 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_upscale.py |
images, has_nsfw_concept = [], []
for i in range(image.shape[0]):
image_i, has_nsfw_concept_i = self.safety_checker(
clip_input=safety_checker_input[i : i + 1], images=image[i : i + 1]
)
images.append(image_i)
has_nsfw_conce... | 300 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_upscale.py |
class StableUnCLIPImg2ImgPipeline(
DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, StableDiffusionLoraLoaderMixin
):
"""
Pipeline for text-guided image-to-image generation using stable unCLIP.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for t... | 301 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip_img2img.py |
Args:
feature_extractor ([`CLIPImageProcessor`]):
Feature extractor for image pre-processing before being encoded.
image_encoder ([`CLIPVisionModelWithProjection`]):
CLIP vision model for encoding images.
image_normalizer ([`StableUnCLIPImageNormalizer`]):
Use... | 301 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip_img2img.py |
A [`UNet2DConditionModel`] to denoise the encoded image latents.
scheduler ([`KarrasDiffusionSchedulers`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents.
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode i... | 301 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip_img2img.py |
model_cpu_offload_seq = "text_encoder->image_encoder->unet->vae"
_exclude_from_cpu_offload = ["image_normalizer"]
# image encoding components
feature_extractor: CLIPImageProcessor
image_encoder: CLIPVisionModelWithProjection
# image noising components
image_normalizer: StableUnCLIPImageNormali... | 301 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip_img2img.py |
def __init__(
self,
# image encoding components
feature_extractor: CLIPImageProcessor,
image_encoder: CLIPVisionModelWithProjection,
# image noising components
image_normalizer: StableUnCLIPImageNormalizer,
image_noising_scheduler: KarrasDiffusionSchedulers,
... | 301 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip_img2img.py |
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_promp... | 301 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip_img2img.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,
... | 301 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip_img2img.py |
if isinstance(image, PIL.Image.Image):
# the image embedding should repeated so it matches the total batch size of the prompt
repeat_by = batch_size
else:
# assume the image input is already properly batched and just needs to be repeated so
# it matches the num_im... | 301 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip_img2img.py |
image_embeds = self.noise_image_embeddings(
image_embeds=image_embeds,
noise_level=noise_level,
generator=generator,
)
# duplicate image embeddings for each generation per prompt, using mps friendly method
image_embeds = image_embeds.unsqueeze(1)
bs_e... | 301 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip_img2img.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.... | 301 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip_img2img.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... | 301 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip_img2img.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... | 301 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip_img2img.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... | 301 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip_img2img.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... | 301 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip_img2img.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 | 301 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip_img2img.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_... | 301 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip_img2img.py |
prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds) | 301 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip_img2img.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... | 301 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip_img2img.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... | 301 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip_img2img.py |
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt | 301 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip_img2img.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(
... | 301 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip_img2img.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... | 301 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip_img2img.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... | 301 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip_img2img.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... | 301 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip_img2img.py |
def check_inputs(
self,
prompt,
image,
height,
width,
callback_steps,
noise_level,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
image_embeds=None,
):
if height % 8 != 0 or width % 8 != 0:
... | 301 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip_img2img.py |
if prompt is None and prompt_embeds is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
if prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
... | 301 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip_img2img.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... | 301 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip_img2img.py |
if image is None and image_embeds is None:
raise ValueError(
"Provide either `image` or `image_embeds`. Cannot leave both `image` and `image_embeds` undefined."
)
if image is not None:
if (
not isinstance(image, torch.Tensor)
a... | 301 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip_img2img.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,
... | 301 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip_img2img.py |
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