text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
values |
|---|---|---|
Examples:
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`
"""
callback = kwargs.pop("callback", None)
callback_steps = kwargs.pop("callback_steps", None)
if callback is not None:
deprecate(
"callback",
"1.0.0",
... | 180 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2.py |
if callback_on_step_end_tensor_inputs is not None and not all(
k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
):
raise ValueError(
f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in cal... | 180 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2.py |
if self.do_classifier_free_guidance:
image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
negative_image_embeds = negative_image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
image_embeds = torch.cat([negative_image_embeds, image_embeds], dim=0).to(
... | 180 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2.py |
self._num_timesteps = len(timesteps)
for i, t in enumerate(self.progress_bar(timesteps)):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
added_cond_kwargs = {"image_... | 180 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2.py |
if self.do_classifier_free_guidance:
noise_pred, variance_pred = noise_pred.split(latents.shape[1], dim=1)
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
_, variance_pred_text = variance_pred.chunk(2)
noise_pred = noise_pred_uncond + self.guidanc... | 180 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2.py |
if callback_on_step_end is not None:
callback_kwargs = {}
for k in callback_on_step_end_tensor_inputs:
callback_kwargs[k] = locals()[k]
callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
latents = callback_outputs.pop... | 180 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2.py |
if not output_type == "latent":
# post-processing
image = self.movq.decode(latents, force_not_quantize=True)["sample"]
if output_type in ["np", "pil"]:
image = image * 0.5 + 0.5
image = image.clamp(0, 1)
image = image.cpu().permute(0, 2... | 180 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2.py |
class KandinskyV22InpaintPipeline(DiffusionPipeline):
"""
Pipeline for text-guided image inpainting using Kandinsky2.1
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, ... | 181 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_inpainting.py |
def __init__(
self,
unet: UNet2DConditionModel,
scheduler: DDPMScheduler,
movq: VQModel,
):
super().__init__()
self.register_modules(
unet=unet,
scheduler=scheduler,
movq=movq,
)
self.movq_scale_factor = 2 ** (len(s... | 181 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_inpainting.py |
@property
def guidance_scale(self):
return self._guidance_scale
@property
def do_classifier_free_guidance(self):
return self._guidance_scale > 1
@property
def num_timesteps(self):
return self._num_timesteps | 181 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_inpainting.py |
@torch.no_grad()
def __call__(
self,
image_embeds: Union[torch.Tensor, List[torch.Tensor]],
image: Union[torch.Tensor, PIL.Image.Image],
mask_image: Union[torch.Tensor, PIL.Image.Image, np.ndarray],
negative_image_embeds: Union[torch.Tensor, List[torch.Tensor]],
heigh... | 181 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_inpainting.py |
Args:
image_embeds (`torch.Tensor` or `List[torch.Tensor]`):
The clip image embeddings for text prompt, that will be used to condition the image generation.
image (`PIL.Image.Image`):
`Image`, or tensor representing an image batch which will be inpainted, *i.e.* p... | 181 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_inpainting.py |
The clip image embeddings for negative text prompt, will be used to condition the image generation.
height (`int`, *optional*, defaults to 512):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to 512):
The width in pixels of the gen... | 181 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_inpainting.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.
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
generator (`... | 181 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_inpainting.py |
The output format of the generate image. Choose between: `"pil"` (`PIL.Image.Image`), `"np"`
(`np.array`) or `"pt"` (`torch.Tensor`).
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
... | 181 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_inpainting.py |
will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
`._callback_tensor_inputs` attribute of your pipeline class. | 181 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_inpainting.py |
Examples: | 181 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_inpainting.py |
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`
"""
if not self._warn_has_been_called and version.parse(version.parse(__version__).base_version) < version.parse(
"0.23.0.dev0"
):
logger.warning(
"Please note that the expected format of `... | 181 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_inpainting.py |
)
self._warn_has_been_called = True | 181 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_inpainting.py |
callback = kwargs.pop("callback", None)
callback_steps = kwargs.pop("callback_steps", None)
if callback is not None:
deprecate(
"callback",
"1.0.0",
"Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_o... | 181 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_inpainting.py |
if callback_on_step_end_tensor_inputs is not None and not all(
k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
):
raise ValueError(
f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in cal... | 181 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_inpainting.py |
if self.do_classifier_free_guidance:
image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
negative_image_embeds = negative_image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
image_embeds = torch.cat([negative_image_embeds, image_embeds], dim=0).to(
... | 181 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_inpainting.py |
image_shape = tuple(image.shape[-2:])
mask_image = F.interpolate(
mask_image,
image_shape,
mode="nearest",
)
mask_image = prepare_mask(mask_image)
masked_image = image * mask_image
mask_image = mask_image.repeat_interleave(num_images_per_promp... | 181 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_inpainting.py |
# create initial latent
latents = self.prepare_latents(
(batch_size, num_channels_latents, height, width),
image_embeds.dtype,
device,
generator,
latents,
self.scheduler,
)
noise = torch.clone(latents)
self._num_tim... | 181 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_inpainting.py |
if self.do_classifier_free_guidance:
noise_pred, variance_pred = noise_pred.split(latents.shape[1], dim=1)
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
_, variance_pred_text = variance_pred.chunk(2)
noise_pred = noise_pred_uncond + self.guidanc... | 181 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_inpainting.py |
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(
noise_pred,
t,
latents,
generator=generator,
)[0]
init_latents_proper = image[:1]
init_mask = mask_image[:1]
if i <... | 181 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_inpainting.py |
latents = callback_outputs.pop("latents", latents)
image_embeds = callback_outputs.pop("image_embeds", image_embeds)
negative_image_embeds = callback_outputs.pop("negative_image_embeds", negative_image_embeds)
masked_image = callback_outputs.pop("masked_image", masked_ima... | 181 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_inpainting.py |
if not output_type == "latent":
image = self.movq.decode(latents, force_not_quantize=True)["sample"]
if output_type in ["np", "pil"]:
image = image * 0.5 + 0.5
image = image.clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
... | 181 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_inpainting.py |
class KandinskyV22ControlnetImg2ImgPipeline(DiffusionPipeline):
"""
Pipeline for image-to-image generation using Kandinsky
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or savi... | 182 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_controlnet_img2img.py |
self.register_modules(
unet=unet,
scheduler=scheduler,
movq=movq,
)
self.movq_scale_factor = 2 ** (len(self.movq.config.block_out_channels) - 1)
# Copied from diffusers.pipelines.kandinsky.pipeline_kandinsky_img2img.KandinskyImg2ImgPipeline.get_timesteps
def ... | 182 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_controlnet_img2img.py |
# Copied from diffusers.pipelines.kandinsky2_2.pipeline_kandinsky2_2_img2img.KandinskyV22Img2ImgPipeline.prepare_latents
def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None):
if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
... | 182 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_controlnet_img2img.py |
elif isinstance(generator, list):
init_latents = [
self.movq.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)
]
init_latents = torch.cat(init_latents, dim=0)
else:
init_latents = self.movq... | 182 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_controlnet_img2img.py |
@torch.no_grad()
def __call__(
self,
image_embeds: Union[torch.Tensor, List[torch.Tensor]],
image: Union[torch.Tensor, PIL.Image.Image, List[torch.Tensor], List[PIL.Image.Image]],
negative_image_embeds: Union[torch.Tensor, List[torch.Tensor]],
hint: torch.Tensor,
heig... | 182 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_controlnet_img2img.py |
Args:
image_embeds (`torch.Tensor` or `List[torch.Tensor]`):
The clip image embeddings for text prompt, that will be used to condition the image generation.
image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]... | 182 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_controlnet_img2img.py |
be maximum and the denoising process will run for the full number of iterations specified in
`num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
hint (`torch.Tensor`):
The controlnet condition.
negative_image_embeds (`torch.Tensor` or `Lis... | 182 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_controlnet_img2img.py |
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Highe... | 182 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_controlnet_img2img.py |
The output format of the generate image. Choose between: `"pil"` (`PIL.Image.Image`), `"np"`
(`np.array`) or `"pt"` (`torch.Tensor`).
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
... | 182 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_controlnet_img2img.py |
Examples:
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`
"""
device = self._execution_device
do_classifier_free_guidance = guidance_scale > 1.0
if isinstance(image_embeds, list):
image_embeds = torch.cat(image_embeds, dim=0)
if isinstanc... | 182 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_controlnet_img2img.py |
image_embeds = torch.cat([negative_image_embeds, image_embeds], dim=0).to(
dtype=self.unet.dtype, device=device
)
hint = torch.cat([hint, hint], dim=0).to(dtype=self.unet.dtype, device=device)
if not isinstance(image, list):
image = [image]
if not all... | 182 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_controlnet_img2img.py |
latents = self.movq.encode(image)["latents"]
latents = latents.repeat_interleave(num_images_per_prompt, dim=0)
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
latent_timestep = tim... | 182 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_controlnet_img2img.py |
added_cond_kwargs = {"image_embeds": image_embeds, "hint": hint}
noise_pred = self.unet(
sample=latent_model_input,
timestep=t,
encoder_hidden_states=None,
added_cond_kwargs=added_cond_kwargs,
return_dict=False,
)[0]... | 182 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_controlnet_img2img.py |
if not (
hasattr(self.scheduler.config, "variance_type")
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
noise_pred, _ = noise_pred.split(latents.shape[1], dim=1)
# compute the previous noisy sample x_t -> x_t-1
... | 182 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_controlnet_img2img.py |
if output_type not in ["pt", "np", "pil"]:
raise ValueError(f"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}")
if output_type in ["np", "pil"]:
image = image * 0.5 + 0.5
image = image.clamp(0, 1)
image = image.cpu().permute... | 182 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_controlnet_img2img.py |
class KandinskyV22ControlnetPipeline(DiffusionPipeline):
"""
Pipeline for text-to-image generation using Kandinsky
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, runn... | 183 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_controlnet.py |
self.register_modules(
unet=unet,
scheduler=scheduler,
movq=movq,
)
self.movq_scale_factor = 2 ** (len(self.movq.config.block_out_channels) - 1)
# Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.prepare_latents
def prepare_latents(self, ... | 183 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_controlnet.py |
@torch.no_grad()
def __call__(
self,
image_embeds: Union[torch.Tensor, List[torch.Tensor]],
negative_image_embeds: Union[torch.Tensor, List[torch.Tensor]],
hint: torch.Tensor,
height: int = 512,
width: int = 512,
num_inference_steps: int = 100,
guidanc... | 183 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_controlnet.py |
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
hint (`torch.Tensor`):
The controlnet condition.
image_embeds (`torch.Tensor` or `List[torch.Tensor]`):
The clip image embeddings for text prompt,... | 183 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_controlnet.py |
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 100):
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*... | 183 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_controlnet.py |
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
latents (`torch.Tensor`, *optional*):
Pre-generated no... | 183 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_controlnet.py |
following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.
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.
return_dict (`boo... | 183 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_controlnet.py |
Examples:
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`
"""
device = self._execution_device
do_classifier_free_guidance = guidance_scale > 1.0
if isinstance(image_embeds, list):
image_embeds = torch.cat(image_embeds, dim=0)
if isinstanc... | 183 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_controlnet.py |
image_embeds = torch.cat([negative_image_embeds, image_embeds], dim=0).to(
dtype=self.unet.dtype, device=device
)
hint = torch.cat([hint, hint], dim=0).to(dtype=self.unet.dtype, device=device)
self.scheduler.set_timesteps(num_inference_steps, device=device)
times... | 183 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_controlnet.py |
for i, t in enumerate(self.progress_bar(timesteps_tensor)):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
added_cond_kwargs = {"image_embeds": image_embeds, "hint": hint}
... | 183 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_controlnet.py |
if do_classifier_free_guidance:
noise_pred, variance_pred = noise_pred.split(latents.shape[1], dim=1)
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
_, variance_pred_text = variance_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * ... | 183 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_controlnet.py |
if callback is not None and i % callback_steps == 0:
step_idx = i // getattr(self.scheduler, "order", 1)
callback(step_idx, t, latents)
if XLA_AVAILABLE:
xm.mark_step()
# post-processing
image = self.movq.decode(latents, force_not_quantize=Tr... | 183 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_controlnet.py |
class KandinskyV22CombinedPipeline(DiffusionPipeline):
"""
Combined Pipeline for text-to-image generation using Kandinsky
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or savin... | 184 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py |
Args:
scheduler (Union[`DDIMScheduler`,`DDPMScheduler`]):
A scheduler to be used in combination with `unet` to generate image latents.
unet ([`UNet2DConditionModel`]):
Conditional U-Net architecture to denoise the image embedding.
movq ([`VQModel`]):
MoVQ Deco... | 184 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py |
A scheduler to be used in combination with `prior` to generate image embedding.
prior_image_processor ([`CLIPImageProcessor`]):
A image_processor to be used to preprocess image from clip.
""" | 184 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py |
model_cpu_offload_seq = "prior_text_encoder->prior_image_encoder->unet->movq"
_load_connected_pipes = True
_exclude_from_cpu_offload = ["prior_prior"]
def __init__(
self,
unet: UNet2DConditionModel,
scheduler: DDPMScheduler,
movq: VQModel,
prior_prior: PriorTransform... | 184 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py |
self.register_modules(
unet=unet,
scheduler=scheduler,
movq=movq,
prior_prior=prior_prior,
prior_image_encoder=prior_image_encoder,
prior_text_encoder=prior_text_encoder,
prior_tokenizer=prior_tokenizer,
prior_scheduler=prio... | 184 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py |
def enable_xformers_memory_efficient_attention(self, attention_op: Optional[Callable] = None):
self.decoder_pipe.enable_xformers_memory_efficient_attention(attention_op)
def enable_sequential_cpu_offload(self, gpu_id: Optional[int] = None, device: Union[torch.device, str] = "cuda"):
r"""
Of... | 184 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py |
def progress_bar(self, iterable=None, total=None):
self.prior_pipe.progress_bar(iterable=iterable, total=total)
self.decoder_pipe.progress_bar(iterable=iterable, total=total)
self.decoder_pipe.enable_model_cpu_offload()
def set_progress_bar_config(self, **kwargs):
self.prior_pipe.se... | 184 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py |
@torch.no_grad()
@replace_example_docstring(TEXT2IMAGE_EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]],
negative_prompt: Optional[Union[str, List[str]]] = None,
num_inference_steps: int = 100,
guidance_scale: float = 4.0,
num_images_per_promp... | 184 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py |
callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
):
"""
Function invoked when calling the pipeline for generation. | 184 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py |
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if... | 184 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py |
prior_guidance_scale (`float`, *optional*, defaults to 4.0):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Gui... | 184 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py |
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually ... | 184 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py |
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between: `"pil"` (`PIL.Image.Image`), `"np"`
(`np.array`) or `"pt"` (`torch.Tensor`).
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to ... | 184 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py |
list will be passed as `callback_kwargs` argument. You will only be able to include variables listed in
the `._callback_tensor_inputs` attribute of your prior pipeline class.
callback_on_step_end (`Callable`, *optional*):
A function that calls at the end of each denoising ste... | 184 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py |
`._callback_tensor_inputs` attribute of your pipeline class. | 184 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py |
Examples:
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`
"""
prior_outputs = self.prior_pipe(
prompt=prompt,
negative_prompt=negative_prompt,
num_images_per_prompt=num_images_per_prompt,
num_inference_steps=prior_num_inference_... | 184 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py |
outputs = self.decoder_pipe(
image_embeds=image_embeds,
negative_image_embeds=negative_image_embeds,
width=width,
height=height,
num_inference_steps=num_inference_steps,
generator=generator,
guidance_scale=guidance_scale,
ou... | 184 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py |
class KandinskyV22Img2ImgCombinedPipeline(DiffusionPipeline):
"""
Combined Pipeline for image-to-image generation using Kandinsky
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading ... | 185 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py |
Args:
scheduler (Union[`DDIMScheduler`,`DDPMScheduler`]):
A scheduler to be used in combination with `unet` to generate image latents.
unet ([`UNet2DConditionModel`]):
Conditional U-Net architecture to denoise the image embedding.
movq ([`VQModel`]):
MoVQ Deco... | 185 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py |
A scheduler to be used in combination with `prior` to generate image embedding.
prior_image_processor ([`CLIPImageProcessor`]):
A image_processor to be used to preprocess image from clip.
""" | 185 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py |
model_cpu_offload_seq = "prior_text_encoder->prior_image_encoder->unet->movq"
_load_connected_pipes = True
_exclude_from_cpu_offload = ["prior_prior"]
def __init__(
self,
unet: UNet2DConditionModel,
scheduler: DDPMScheduler,
movq: VQModel,
prior_prior: PriorTransform... | 185 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py |
self.register_modules(
unet=unet,
scheduler=scheduler,
movq=movq,
prior_prior=prior_prior,
prior_image_encoder=prior_image_encoder,
prior_text_encoder=prior_text_encoder,
prior_tokenizer=prior_tokenizer,
prior_scheduler=prio... | 185 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py |
def enable_xformers_memory_efficient_attention(self, attention_op: Optional[Callable] = None):
self.decoder_pipe.enable_xformers_memory_efficient_attention(attention_op)
def enable_model_cpu_offload(self, gpu_id: Optional[int] = None, device: Union[torch.device, str] = "cuda"):
r"""
Offload... | 185 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py |
def enable_sequential_cpu_offload(self, gpu_id: Optional[int] = None, device: Union[torch.device, str] = "cuda"):
r"""
Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
text_encoder, vae and safety checker have their state dicts saved to CPU and... | 185 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py |
def progress_bar(self, iterable=None, total=None):
self.prior_pipe.progress_bar(iterable=iterable, total=total)
self.decoder_pipe.progress_bar(iterable=iterable, total=total)
self.decoder_pipe.enable_model_cpu_offload()
def set_progress_bar_config(self, **kwargs):
self.prior_pipe.se... | 185 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py |
@torch.no_grad()
@replace_example_docstring(IMAGE2IMAGE_EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]],
image: Union[torch.Tensor, PIL.Image.Image, List[torch.Tensor], List[PIL.Image.Image]],
negative_prompt: Optional[Union[str, List[str]]] = None,
... | 185 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py |
prior_callback_on_step_end_tensor_inputs: List[str] = ["latents"],
callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
):
"""
Function invoked when calling the pipeline for generation. | 185 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py |
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
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,... | 185 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py |
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Highe... | 185 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py |
`num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
num_inference_steps (`int`, *optional*, defaults to 100):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
he... | 185 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.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.
prior_num_inference_steps (`int`, *optional*, defaults to 100):
The number of denoising steps. More denoising steps usually lea... | 185 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py |
tensor will ge generated by sampling using the supplied random `generator`.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between: `"pil"` (`PIL.Image.Image`), `"np"`
(`np.array`) or `"pt"` (`torch.Tensor`).
... | 185 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py |
Examples:
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`
"""
prior_outputs = self.prior_pipe(
prompt=prompt,
negative_prompt=negative_prompt,
num_images_per_prompt=num_images_per_prompt,
num_inference_steps=prior_num_inference_... | 185 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py |
if len(prompt) < image_embeds.shape[0] and image_embeds.shape[0] % len(prompt) == 0:
prompt = (image_embeds.shape[0] // len(prompt)) * prompt
if (
isinstance(image, (list, tuple))
and len(image) < image_embeds.shape[0]
and image_embeds.shape[0] % len(image) == 0
... | 185 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py |
outputs = self.decoder_pipe(
image=image,
image_embeds=image_embeds,
negative_image_embeds=negative_image_embeds,
width=width,
height=height,
strength=strength,
num_inference_steps=num_inference_steps,
generator=generator,
... | 185 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py |
class KandinskyV22InpaintCombinedPipeline(DiffusionPipeline):
"""
Combined Pipeline for inpainting generation using Kandinsky
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or s... | 186 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py |
Args:
scheduler (Union[`DDIMScheduler`,`DDPMScheduler`]):
A scheduler to be used in combination with `unet` to generate image latents.
unet ([`UNet2DConditionModel`]):
Conditional U-Net architecture to denoise the image embedding.
movq ([`VQModel`]):
MoVQ Deco... | 186 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py |
A scheduler to be used in combination with `prior` to generate image embedding.
prior_image_processor ([`CLIPImageProcessor`]):
A image_processor to be used to preprocess image from clip.
""" | 186 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py |
model_cpu_offload_seq = "prior_text_encoder->prior_image_encoder->unet->movq"
_load_connected_pipes = True
_exclude_from_cpu_offload = ["prior_prior"]
def __init__(
self,
unet: UNet2DConditionModel,
scheduler: DDPMScheduler,
movq: VQModel,
prior_prior: PriorTransform... | 186 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py |
self.register_modules(
unet=unet,
scheduler=scheduler,
movq=movq,
prior_prior=prior_prior,
prior_image_encoder=prior_image_encoder,
prior_text_encoder=prior_text_encoder,
prior_tokenizer=prior_tokenizer,
prior_scheduler=prio... | 186 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py |
def enable_xformers_memory_efficient_attention(self, attention_op: Optional[Callable] = None):
self.decoder_pipe.enable_xformers_memory_efficient_attention(attention_op)
def enable_sequential_cpu_offload(self, gpu_id: Optional[int] = None, device: Union[torch.device, str] = "cuda"):
r"""
Of... | 186 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py |
def progress_bar(self, iterable=None, total=None):
self.prior_pipe.progress_bar(iterable=iterable, total=total)
self.decoder_pipe.progress_bar(iterable=iterable, total=total)
self.decoder_pipe.enable_model_cpu_offload()
def set_progress_bar_config(self, **kwargs):
self.prior_pipe.se... | 186 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py |
@torch.no_grad()
@replace_example_docstring(INPAINT_EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]],
image: Union[torch.Tensor, PIL.Image.Image, List[torch.Tensor], List[PIL.Image.Image]],
mask_image: Union[torch.Tensor, PIL.Image.Image, List[torch.Tensor], ... | 186 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py |
prior_callback_on_step_end_tensor_inputs: List[str] = ["latents"],
callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
**kwargs,
):
"""
Function invoked when calling the pipeline for generation. | 186 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py |
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
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,... | 186 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py |
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