text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
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|---|---|---|
>>> # load model and scheduler
>>> pipe = DDPMPipeline.from_pretrained("google/ddpm-cat-256")
>>> # run pipeline in inference (sample random noise and denoise)
>>> image = pipe().images[0]
>>> # save image
>>> image.save("ddpm_generated_image.png")
``` | 331 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ddpm/pipeline_ddpm.py |
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
returned where the first element is a list with the generated images
"""
# Sample gaussian noise to begin... | 331 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ddpm/pipeline_ddpm.py |
if self.device.type == "mps":
# randn does not work reproducibly on mps
image = randn_tensor(image_shape, generator=generator, dtype=self.unet.dtype)
image = image.to(self.device)
else:
image = randn_tensor(image_shape, generator=generator, device=self.device, dty... | 331 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ddpm/pipeline_ddpm.py |
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image) | 331 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ddpm/pipeline_ddpm.py |
class ModelWrapper:
def __init__(self, model, alphas_cumprod):
self.model = model
self.alphas_cumprod = alphas_cumprod
def apply_model(self, *args, **kwargs):
if len(args) == 3:
encoder_hidden_states = args[-1]
args = args[:2]
if kwargs.get("cond", None) ... | 332 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_k_diffusion.py |
class StableDiffusionKDiffusionPipeline(
DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, StableDiffusionLoraLoaderMixin
):
r"""
Pipeline for text-to-image generation using Stable Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the ... | 333 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_k_diffusion.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... | 333 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_k_diffusion.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... | 333 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_k_diffusion.py |
model_cpu_offload_seq = "text_encoder->unet->vae"
_optional_components = ["safety_checker", "feature_extractor"]
_exclude_from_cpu_offload = ["safety_checker"]
def __init__(
self,
vae,
text_encoder,
tokenizer,
unet,
scheduler,
safety_checker,
... | 333 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_k_diffusion.py |
# get correct sigmas from LMS
scheduler = LMSDiscreteScheduler.from_config(scheduler.config)
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_check... | 333 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_k_diffusion.py |
def set_scheduler(self, scheduler_type: str):
library = importlib.import_module("k_diffusion")
sampling = getattr(library, "sampling")
try:
self.sampler = getattr(sampling, scheduler_type)
except Exception:
valid_samplers = []
for s in dir(sampling):
... | 333 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_k_diffusion.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torc... | 333 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_k_diffusion.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,
... | 333 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_k_diffusion.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.... | 333 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_k_diffusion.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... | 333 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_k_diffusion.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... | 333 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_k_diffusion.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... | 333 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_k_diffusion.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... | 333 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_k_diffusion.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 | 333 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_k_diffusion.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_... | 333 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_k_diffusion.py |
prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds) | 333 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_k_diffusion.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... | 333 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_k_diffusion.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... | 333 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_k_diffusion.py |
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt | 333 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_k_diffusion.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(
... | 333 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_k_diffusion.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... | 333 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_k_diffusion.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
def run_safety_checker(self, image, device, dtype):
if self.safety_checker is None:
has_nsfw_concept = None
else:
if torch.is_tensor(image):
... | 333 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_k_diffusion.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... | 333 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_k_diffusion.py |
def check_inputs(
self,
prompt,
height,
width,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
callback_on_step_end_tensor_inputs=None,
):
if height % 8 != 0 or width % 8 != 0:
raise Va... | 333 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_k_diffusion.py |
if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if callback_on_step_end_tensor_... | 333 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_k_diffusion.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:
... | 333 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_k_diffusion.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... | 333 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_k_diffusion.py |
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
shape = (
batch_size,
num_channels_latents,
int(height) // self.vae_scale_factor,
int(width) // self.vae_scale_factor,
)
if latents ... | 333 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_k_diffusion.py |
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]] = None,
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
n... | 333 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_k_diffusion.py |
Function invoked when calling the pipeline for generation. | 333 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_k_diffusion.py |
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
instead.
height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
... | 333 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_k_diffusion.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 ... | 333 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_k_diffusion.py |
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`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`, *o... | 333 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_k_diffusion.py |
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.
output_type (`str`, *optional*, defaults to `"pil"`):
The... | 333 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_k_diffusion.py |
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
use_karras_sigmas (`bool`, *optional*, defaults to `False`):
Use karras sigmas. For e... | 333 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_k_diffusion.py |
[`~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 element is a
... | 333 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_k_diffusion.py |
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds
)
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
... | 333 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_k_diffusion.py |
# 3. Encode input prompt
prompt_embeds, negative_prompt_embeds = self.encode_prompt(
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negati... | 333 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_k_diffusion.py |
# 5. Prepare sigmas
if use_karras_sigmas:
sigma_min: float = self.k_diffusion_model.sigmas[0].item()
sigma_max: float = self.k_diffusion_model.sigmas[-1].item()
sigmas = get_sigmas_karras(n=num_inference_steps, sigma_min=sigma_min, sigma_max=sigma_max)
else:
... | 333 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_k_diffusion.py |
# 6. Prepare latent variables
num_channels_latents = self.unet.config.in_channels
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
prompt_embeds.dtype,
device,
gene... | 333 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_k_diffusion.py |
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
return noise_pred
# 8. Run k-diffusion solver
sampler_kwargs = {}
if "noise_sampler" in inspect.signature(self.sampler).param... | 333 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_k_diffusion.py |
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
else:
image = latents
has_nsfw_concept = None
if ... | 333 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_k_diffusion.py |
class ModelWrapper:
def __init__(self, model, alphas_cumprod):
self.model = model
self.alphas_cumprod = alphas_cumprod
def apply_model(self, *args, **kwargs):
if len(args) == 3:
encoder_hidden_states = args[-1]
args = args[:2]
if kwargs.get("cond", None) ... | 334 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py |
class StableDiffusionXLKDiffusionPipeline(
DiffusionPipeline,
StableDiffusionMixin,
FromSingleFileMixin,
StableDiffusionXLLoraLoaderMixin,
TextualInversionLoaderMixin,
IPAdapterMixin,
):
r"""
Pipeline for text-to-image generation using Stable Diffusion XL and k-diffusion.
This model... | 335 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py |
The pipeline also inherits the following loading methods:
- [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
- [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
- [`~loaders.StableDiffusionXLLoraLoaderMixin.load_lo... | 335 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py |
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]):
Frozen text-encoder. Stable Diffusion XL uses the text portion of
[CLIP](https://huggingface.co/docs/t... | 335 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py |
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
tokenizer_2 (`CLIPTokenizer`):
Second Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
... | 335 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py |
model_cpu_offload_seq = "text_encoder->text_encoder_2->unet->vae"
_optional_components = [
"tokenizer",
"tokenizer_2",
"text_encoder",
"text_encoder_2",
"feature_extractor",
]
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel... | 335 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py |
# get correct sigmas from LMS
scheduler = LMSDiscreteScheduler.from_config(scheduler.config)
self.register_modules(
vae=vae,
text_encoder=text_encoder,
text_encoder_2=text_encoder_2,
tokenizer=tokenizer,
tokenizer_2=tokenizer_2,
une... | 335 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py |
model = ModelWrapper(unet, scheduler.alphas_cumprod)
if scheduler.config.prediction_type == "v_prediction":
self.k_diffusion_model = CompVisVDenoiser(model)
else:
self.k_diffusion_model = CompVisDenoiser(model)
# Copied from diffusers.pipelines.stable_diffusion_k_diffusion.p... | 335 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py |
# Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.encode_prompt
def encode_prompt(
self,
prompt: str,
prompt_2: Optional[str] = None,
device: Optional[torch.device] = None,
num_images_per_prompt: int = 1,
do_c... | 335 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py |
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
prompt_2 (`str` or `List[str]`, *optional*):
The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
used in both text-encoders
... | 335 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py |
The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
`text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak tex... | 335 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py |
If not provided, pooled text embeddings will be generated from `prompt` input argument.
negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided... | 335 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py |
# set lora scale so that monkey patched LoRA
# function of text encoder can correctly access it
if lora_scale is not None and isinstance(self, StableDiffusionXLLoraLoaderMixin):
self._lora_scale = lora_scale
# dynamically adjust the LoRA scale
if self.text_encoder is... | 335 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py |
# Define tokenizers and text encoders
tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]
text_encoders = (
[self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]
)
if prompt_embe... | 335 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py |
text_inputs = tokenizer(
prompt,
padding="max_length",
max_length=tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
... | 335 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py |
prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)
# We are only ALWAYS interested in the pooled output of the final text encoder
if pooled_prompt_embeds is None and prompt_embeds[0].ndim == 2:
pooled_prompt_embeds = prompt_embeds[0]
... | 335 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py |
# get unconditional embeddings for classifier free guidance
zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt
if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:
negative_prompt_embeds = torch.zeros_lik... | 335 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py |
uncond_tokens: List[str]
if prompt is not None and type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
... | 335 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py |
negative_prompt_embeds_list = []
for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):
if isinstance(self, TextualInversionLoaderMixin):
negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)
max_l... | 335 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py |
# We are only ALWAYS interested in the pooled output of the final text encoder
if negative_pooled_prompt_embeds is None and negative_prompt_embeds[0].ndim == 2:
negative_pooled_prompt_embeds = negative_prompt_embeds[0]
negative_prompt_embeds = negative_prompt_embeds.h... | 335 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py |
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
if do_clas... | 335 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py |
pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
bs_embed * num_images_per_prompt, -1
)
if do_classifier_free_guidance:
negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
bs_embed ... | 335 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py |
return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
def check_inputs(
self,
prompt,
prompt_2,
height,
width,
negative_prompt=None,
negative_prompt_2=None,
prompt_embeds=None,
negative_prompt_embeds... | 335 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py |
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt_2 is not None and prompt_embeds is not ... | 335 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py |
elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):
raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}") | 335 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.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."
)
... | 335 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.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... | 335 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py |
if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None:
raise ValueError(
"If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used... | 335 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py |
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
shape = (
batch_size,
num_channels_latents,
int(height) // self.vae_scale_factor,
int(width) // self.vae_scale_factor,
)
if isinstan... | 335 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py |
# Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline._get_add_time_ids
def _get_add_time_ids(
self, original_size, crops_coords_top_left, target_size, dtype, text_encoder_projection_dim=None
):
add_time_ids = list(original_size + crops_coor... | 335 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py |
add_time_ids = torch.tensor([add_time_ids], dtype=dtype)
return add_time_ids
# Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.upcast_vae
def upcast_vae(self):
dtype = self.vae.dtype
self.vae.to(dtype=torch.float32)
use_... | 335 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py |
@property
def clip_skip(self):
return self._clip_skip
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
@property
def... | 335 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
prompt_2: Optional[Union[str, List[str]]] = None,
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
... | 335 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py |
original_size: Optional[Tuple[int, int]] = None,
crops_coords_top_left: Tuple[int, int] = (0, 0),
target_size: Optional[Tuple[int, int]] = None,
negative_original_size: Optional[Tuple[int, int]] = None,
negative_crops_coords_top_left: Tuple[int, int] = (0, 0),
negative_target_siz... | 335 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py |
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
instead.
prompt_2 (`str` or `List[str]`, *optional*):
The prompt or prompts to be sent to the `to... | 335 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py |
width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
The width in pixels of the generated image. This is set to 1024 by default for the best results.
Anything below 512 pixels won't work well for
[stabilityai/stable-diffusion-xl-bas... | 335 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py |
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 at the expense of lower image quality.
negative_prompt (`str` or ... | 335 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.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... | 335 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py |
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.
pooled_prompt_embeds (`torch.Tensor`, *optional*):
Pre-ge... | 335 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py |
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead
of a plain tuple.
original... | 335 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py |
`crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
`crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of
[https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
... | 335 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py |
micro-conditioning as explained in section 2.2 of
[https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
negative_crops_coords_top_left (`Tup... | 335 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py |
as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of
[https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/42... | 335 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py |
Examples:
Returns:
[`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a
`tuple`. When returning a tuple, the first element is a list with the ge... | 335 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py |
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt,
prompt_2,
height,
width,
negative_prompt,
negative_prompt_2,
prompt_embeds,
negative_prompt_embeds,
pooled_prompt_embeds,
... | 335 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py |
(
prompt_embeds,
negative_prompt_embeds,
pooled_prompt_embeds,
negative_pooled_prompt_embeds,
) = self.encode_prompt(
prompt=prompt,
prompt_2=prompt_2,
device=device,
num_images_per_prompt=num_images_per_prompt,
... | 335 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py |
# 5. Prepare sigmas
if use_karras_sigmas:
sigma_min: float = self.k_diffusion_model.sigmas[0].item()
sigma_max: float = self.k_diffusion_model.sigmas[-1].item()
sigmas = get_sigmas_karras(n=num_inference_steps, sigma_min=sigma_min, sigma_max=sigma_max)
else:
... | 335 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py |
# 7. Prepare added time ids & embeddings
add_text_embeds = pooled_prompt_embeds
if self.text_encoder_2 is None:
text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])
else:
text_encoder_projection_dim = self.text_encoder_2.config.projection_dim | 335 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py |
add_time_ids = self._get_add_time_ids(
original_size,
crops_coords_top_left,
target_size,
dtype=prompt_embeds.dtype,
text_encoder_projection_dim=text_encoder_projection_dim,
)
if negative_original_size is not None and negative_target_size is no... | 335 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py |
if self.do_classifier_free_guidance:
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)
add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0)
prompt_e... | 335 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py |
# 8. Optionally get Guidance Scale Embedding
timestep_cond = None
if self.unet.config.time_cond_proj_dim is not None:
guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)
timestep_cond = self.get_guidance_scale_embedding(
... | 335 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py |
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
return noise_pred
# 10. Run k-diffusion solver
sampler_kwargs = {}
if "noise_sampler" in inspect.signature(self.sampler).para... | 335 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py |
if needs_upcasting:
self.upcast_vae()
latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
# cast back to fp16 if needed
if needs_upcasti... | 335 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py |
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