text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
values |
|---|---|---|
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... | 231 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_prior.py |
# 1. Check inputs. Raise error if not correct
if prompt is not None and not isinstance(prompt, list):
if isinstance(prompt, str):
prompt = [prompt]
else:
raise TypeError(f"'prompt' must be of type 'list' or 'str', but got {type(prompt)}.")
if self... | 231 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_prior.py |
# 2. Encode caption
prompt_embeds, negative_prompt_embeds = self.encode_prompt(
prompt=prompt,
device=device,
num_images_per_prompt=num_images_per_prompt,
do_classifier_free_guidance=self.do_classifier_free_guidance,
negative_prompt=negative_prompt,
... | 231 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_prior.py |
# 3. Determine latent shape of image embeddings
dtype = text_encoder_hidden_states.dtype
latent_height = ceil(height / self.config.resolution_multiple)
latent_width = ceil(width / self.config.resolution_multiple)
num_channels = self.prior.config.c_in
effnet_features_shape = (num_... | 231 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_prior.py |
# 6. Run denoising loop
self._num_timesteps = len(timesteps[:-1])
for i, t in enumerate(self.progress_bar(timesteps[:-1])):
ratio = t.expand(latents.size(0)).to(dtype)
# 7. Denoise image embeddings
predicted_image_embedding = self.prior(
torch.cat([la... | 231 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_prior.py |
# 9. Renoise latents to next timestep
latents = self.scheduler.step(
model_output=predicted_image_embedding,
timestep=ratio,
sample=latents,
generator=generator,
).prev_sample
if callback_on_step_end is not None:
... | 231 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_prior.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()
# 10. Denormalize the latents
latents = latents * self.config.latent_mean... | 231 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_prior.py |
class WuerstchenCombinedPipeline(DiffusionPipeline):
"""
Combined Pipeline for text-to-image generation using Wuerstchen
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... | 232 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_combined.py |
Args:
tokenizer (`CLIPTokenizer`):
The decoder tokenizer to be used for text inputs.
text_encoder (`CLIPTextModel`):
The decoder text encoder to be used for text inputs.
decoder (`WuerstchenDiffNeXt`):
The decoder model to be used for decoder image generation ... | 232 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_combined.py |
_load_connected_pipes = True
def __init__(
self,
tokenizer: CLIPTokenizer,
text_encoder: CLIPTextModel,
decoder: WuerstchenDiffNeXt,
scheduler: DDPMWuerstchenScheduler,
vqgan: PaellaVQModel,
prior_tokenizer: CLIPTokenizer,
prior_text_encoder: CLIPText... | 232 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_combined.py |
self.register_modules(
text_encoder=text_encoder,
tokenizer=tokenizer,
decoder=decoder,
scheduler=scheduler,
vqgan=vqgan,
prior_prior=prior_prior,
prior_text_encoder=prior_text_encoder,
prior_tokenizer=prior_tokenizer,
... | 232 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_combined.py |
def enable_model_cpu_offload(self, gpu_id: Optional[int] = None, device: Union[torch.device, str] = "cuda"):
r"""
Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
to `enable_sequential_cpu_offload`, this method moves one whole model at... | 232 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_combined.py |
def enable_sequential_cpu_offload(self, gpu_id: Optional[int] = None, device: Union[torch.device, str] = "cuda"):
r"""
Offloads all models (`unet`, `text_encoder`, `vae`, and `safety checker` state dicts) to CPU using 🤗
Accelerate, significantly reducing memory usage. Models are moved to a `tor... | 232 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_combined.py |
def set_progress_bar_config(self, **kwargs):
self.prior_pipe.set_progress_bar_config(**kwargs)
self.decoder_pipe.set_progress_bar_config(**kwargs) | 232 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_combined.py |
@torch.no_grad()
@replace_example_docstring(TEXT2IMAGE_EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Optional[Union[str, List[str]]] = None,
height: int = 512,
width: int = 512,
prior_num_inference_steps: int = 60,
prior_timesteps: Optional[List[float]] = None,
... | 232 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_combined.py |
prior_callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
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... | 232 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_combined.py |
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation for the prior and decoder.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ... | 232 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_combined.py |
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
height (`int`, *optional*, defaults to 512):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to 512):
The width i... | 232 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_combined.py |
prior_num_inference_steps (`Union[int, Dict[float, int]]`, *optional*, defaults to 60):
The number of prior denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference. For more specific timestep spacing, you can pass customized
... | 232 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_combined.py |
Custom timesteps to use for the denoising process for the decoder. If not defined, equal spaced
`num_inference_steps` timesteps are used. Must be in descending order.
decoder_guidance_scale (`float`, *optional*, defaults to 0.0):
Guidance scale as defined in [Classifier-Free ... | 232 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_combined.py |
latents (`torch.Tensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by samp... | 232 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_combined.py |
with the following arguments: `prior_callback_on_step_end(self: DiffusionPipeline, step: int, timestep:
int, callback_kwargs: Dict)`.
prior_callback_on_step_end_tensor_inputs (`List`, *optional*):
The list of tensor inputs for the `prior_callback_on_step_end` function. The te... | 232 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_combined.py |
callback_on_step_end_tensor_inputs (`List`, *optional*):
The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
`._callback... | 232 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_combined.py |
Examples: | 232 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_combined.py |
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple` [`~pipelines.ImagePipelineOutput`] if `return_dict` is True,
otherwise a `tuple`. When returning a tuple, the first element is a list with the generated images.
"""
prior_kwargs = {}
if kwargs.get("prior_callback", No... | 232 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_combined.py |
prior_kwargs["callback_steps"] = kwargs.pop("prior_callback_steps") | 232 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_combined.py |
prior_outputs = self.prior_pipe(
prompt=prompt if prompt_embeds is None else None,
height=height,
width=width,
num_inference_steps=prior_num_inference_steps,
timesteps=prior_timesteps,
guidance_scale=prior_guidance_scale,
negative_promp... | 232 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_combined.py |
outputs = self.decoder_pipe(
image_embeddings=image_embeddings,
prompt=prompt if prompt is not None else "",
num_inference_steps=num_inference_steps,
timesteps=decoder_timesteps,
guidance_scale=decoder_guidance_scale,
negative_prompt=negative_promp... | 232 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_combined.py |
class SafeStableDiffusionSafetyChecker(PreTrainedModel):
config_class = CLIPConfig
_no_split_modules = ["CLIPEncoderLayer"]
def __init__(self, config: CLIPConfig):
super().__init__(config)
self.vision_model = CLIPVisionModel(config.vision_config)
self.visual_projection = nn.Linear... | 233 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/safety_checker.py |
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
special_cos_dist = cosine_distance(image_embeds, self.special_care_embeds).cpu().float().numpy()
cos_dist = cosine_distance(image_embeds, self.concept_embeds).cpu().float().numpy()
result = [... | 233 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/safety_checker.py |
for concept_idx in range(len(special_cos_dist[0])):
concept_cos = special_cos_dist[i][concept_idx]
concept_threshold = self.special_care_embeds_weights[concept_idx].item()
result_img["special_scores"][concept_idx] = round(concept_cos - concept_threshold + adjustment, 3)
... | 233 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/safety_checker.py |
result.append(result_img)
has_nsfw_concepts = [len(res["bad_concepts"]) > 0 for res in result]
return images, has_nsfw_concepts
@torch.no_grad()
def forward_onnx(self, clip_input: torch.Tensor, images: torch.Tensor):
pooled_output = self.vision_model(clip_input)[1] # pooled_output
... | 233 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/safety_checker.py |
special_scores = special_cos_dist - self.special_care_embeds_weights + adjustment
# special_scores = special_scores.round(decimals=3)
special_care = torch.any(special_scores > 0, dim=1)
special_adjustment = special_care * 0.01
special_adjustment = special_adjustment.unsqueeze(1).expand(-... | 233 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/safety_checker.py |
class StableDiffusionSafePipelineOutput(BaseOutput):
"""
Output class for Safe Stable Diffusion pipelines. | 234 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_output.py |
Args:
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width,
num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline.
nsfw_content_detected (`... | 234 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_output.py |
images: Union[List[PIL.Image.Image], np.ndarray]
nsfw_content_detected: Optional[List[bool]]
unsafe_images: Optional[Union[List[PIL.Image.Image], np.ndarray]]
applied_safety_concept: Optional[str] | 234 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_output.py |
class SafetyConfig(object):
WEAK = {
"sld_warmup_steps": 15,
"sld_guidance_scale": 20,
"sld_threshold": 0.0,
"sld_momentum_scale": 0.0,
"sld_mom_beta": 0.0,
}
MEDIUM = {
"sld_warmup_steps": 10,
"sld_guidance_scale": 1000,
"sld_threshold": 0.01,... | 235 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/__init__.py |
class StableDiffusionPipelineSafe(DiffusionPipeline, StableDiffusionMixin, IPAdapterMixin):
r"""
Pipeline based on the [`StableDiffusionPipeline`] for text-to-image generation using Safe Latent Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic met... | 236 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py |
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`~transformers.CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14))... | 236 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py |
Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for
more details about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as input... | 236 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py |
model_cpu_offload_seq = "text_encoder->unet->vae"
_optional_components = ["safety_checker", "feature_extractor", "image_encoder"]
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler:... | 236 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.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 "
... | 236 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.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... | 236 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.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"
... | 236 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.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... | 236 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py |
is_unet_version_less_0_9_0 = (
unet is not None
and hasattr(unet.config, "_diffusers_version")
and version.parse(version.parse(unet.config._diffusers_version).base_version) < version.parse("0.9.0.dev0")
)
is_unet_sample_size_less_64 = (
unet is not None an... | 236 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py |
" \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
" in the config might lead to incorrect results in future versions. If you have do... | 236 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py |
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
image_encoder=image_encoder,
)
... | 236 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py |
Args:
concept (`str`):
The text of the new safety concept
"""
self._safety_text_concept = concept
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt,
enable... | 236 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py |
Args:
prompt (`str` or `List[str]`):
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 (`bool`):
... | 236 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.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 = self.tokenizer(prompt, padding="max... | 236 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py |
prompt_embeds = self.text_encoder(
text_input_ids.to(device),
attention_mask=attention_mask,
)
prompt_embeds = prompt_embeds[0]
# duplicate text embeddings for each generation per prompt, using mps friendly method
bs_embed, seq_len, _ = prompt_embeds.shape
... | 236 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py |
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif type(prompt) is not type(negative_prompt):
raise TypeError(
... | 236 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py |
uncond_tokens = negative_prompt | 236 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py |
max_length = text_input_ids.shape[-1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
if hasattr(self.text_encoder.confi... | 236 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py |
# 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.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size... | 236 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py |
# duplicate safety embeddings for each generation per prompt, using mps friendly method
seq_len = safety_embeddings.shape[1]
safety_embeddings = safety_embeddings.repeat(batch_size, num_images_per_prompt, 1)
safety_embeddings = safety_embeddings.view(batch_size * num_imag... | 236 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py |
else:
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
... | 236 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py |
def run_safety_checker(self, image, device, dtype, enable_safety_guidance):
if self.safety_checker is not None:
images = image.copy()
safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
image, has_nsfw_concept = self.safety_... | 236 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py |
if has_nsfw_concept:
flagged_images[idx] = images[idx]
image[idx] = np.zeros(image[idx].shape) # black image
else:
has_nsfw_concept = None
flagged_images = None
return image, has_nsfw_concept, flagged_images | 236 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.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... | 236 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.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... | 236 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py |
# Copied from diffusers.pipelines.stable_diffusion_k_diffusion.pipeline_stable_diffusion_k_diffusion.StableDiffusionKDiffusionPipeline.check_inputs
def check_inputs(
self,
prompt,
height,
width,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
... | 236 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.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_... | 236 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.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:
... | 236 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.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... | 236 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.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,
... | 236 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py |
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
def perform_safety_guidance(
self,
enable_safety_guidance,
safety_momentum,
noise_guidance,
noise_pred_out,
... | 236 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py |
# Equation 6
safety_concept_scale = torch.where(
(noise_pred_text - noise_pred_safety_concept) >= sld_threshold, torch.zeros_like(scale), scale
)
# Equation 4
noise_guidance_safety = torch.mul((noise_pred_safety_concept - noise_pred_uncond), safety_concep... | 236 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image
def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):
dtype = next(self.image_encoder.parameters()).dtype
if not isinstance(image, torch.Tensor):
... | 236 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py |
image = image.to(device=device, dtype=dtype)
if output_hidden_states:
image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]
image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
uncond_imag... | 236 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py |
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]],
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,
num_imag... | 236 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py |
r"""
The call function to the pipeline for generation. | 236 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py |
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The height in pixels of the gene... | 236 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py |
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
pass `negative_prompt_e... | 236 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py |
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 not provided, a latents
... | 236 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py |
A function that calls every `callback_steps` steps during inference. The function is called with the
following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` func... | 236 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py |
sld_momentum_scale (`float`, *optional*, defaults to 0.3):
Scale of the SLD momentum to be added to the safety guidance at each diffusion step. If set to 0.0,
momentum is disabled. Momentum is built up during warmup for diffusion steps smaller than
`sld_warmup_steps`.
... | 236 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py |
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
otherwise a `tuple` is returned where the first element is a list with the generated image... | 236 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py |
pipeline = StableDiffusionPipelineSafe.from_pretrained(
"AIML-TUDA/stable-diffusion-safe", torch_dtype=torch.float16
).to("cuda")
prompt = "the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c. leyendecker"
image = pipeline(prompt=pr... | 236 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py |
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
enable_safety_gu... | 236 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py |
if ip_adapter_image is not None:
output_hidden_state = False if isinstance(self.unet.encoder_hid_proj, ImageProjection) else True
image_embeds, negative_image_embeds = self.encode_image(
ip_adapter_image, device, num_images_per_prompt, output_hidden_state
)
... | 236 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py |
# 5. 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... | 236 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py |
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
latent_model_input = (
... | 236 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py |
# perform guidance
if do_classifier_free_guidance:
noise_pred_out = noise_pred.chunk((3 if enable_safety_guidance else 2))
noise_pred_uncond, noise_pred_text = noise_pred_out[0], noise_pred_out[1]
# default classifier free guidance
... | 236 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py |
# Equation 6
safety_concept_scale = torch.where(
(noise_pred_text - noise_pred_safety_concept) >= sld_threshold,
torch.zeros_like(scale),
scale,
)
# Equation 4
... | 236 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py |
noise_pred = noise_pred_uncond + guidance_scale * noise_guidance
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
# call the callback, if provided
if i == len(time... | 236 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py |
# 10. Convert to PIL
if output_type == "pil":
image = self.numpy_to_pil(image)
if flagged_images is not None:
flagged_images = self.numpy_to_pil(flagged_images)
if not return_dict:
return (
image,
has_nsfw_concept,
... | 236 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py |
class SanaPipelineOutput(BaseOutput):
"""
Output class for Sana pipelines.
Args:
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width,
num_channels)`. PIL images or numpy array... | 237 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/sana/pipeline_output.py |
class SanaPipeline(DiffusionPipeline, SanaLoraLoaderMixin):
r"""
Pipeline for text-to-image generation using [Sana](https://huggingface.co/papers/2410.10629).
"""
# fmt: off
bad_punct_regex = re.compile(r"[" + "#®•©™&@·º½¾¿¡§~" + r"\)" + r"\(" + r"\]" + r"\[" + r"\}" + r"\{" + r"\|" + "\\" + r"\/" ... | 238 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/sana/pipeline_sana.py |
self.vae_scale_factor = (
2 ** (len(self.vae.config.encoder_block_out_channels) - 1)
if hasattr(self, "vae") and self.vae is not None
else 32
)
self.image_processor = PixArtImageProcessor(vae_scale_factor=self.vae_scale_factor)
def enable_vae_slicing(self):
... | 238 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/sana/pipeline_sana.py |
def enable_vae_tiling(self):
r"""
Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
processing larger images.
""... | 238 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/sana/pipeline_sana.py |
def encode_prompt(
self,
prompt: Union[str, List[str]],
do_classifier_free_guidance: bool = True,
negative_prompt: str = "",
num_images_per_prompt: int = 1,
device: Optional[torch.device] = None,
prompt_embeds: Optional[torch.Tensor] = None,
negative_promp... | 238 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/sana/pipeline_sana.py |
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
negative_prompt (`str` or `List[str]`, *optional*):
The prompt not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds`
instead. Ignored when n... | 238 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/sana/pipeline_sana.py |
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated negative text embeddings. For S... | 238 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/sana/pipeline_sana.py |
if device is None:
device = self._execution_device
# 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, SanaLoraLoaderMixin):
self._lora_scale = lora_scale
# dynamicall... | 238 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/sana/pipeline_sana.py |
if prompt_embeds is None:
prompt = self._text_preprocessing(prompt, clean_caption=clean_caption)
# prepare complex human instruction
if not complex_human_instruction:
max_length_all = max_length
else:
chi_prompt = "\n".join(complex_human_i... | 238 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/sana/pipeline_sana.py |
prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=prompt_attention_mask)
prompt_embeds = prompt_embeds[0][:, select_index]
prompt_attention_mask = prompt_attention_mask[:, select_index]
if self.transformer is not None:
dtype = self.transformer.dtype... | 238 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/sana/pipeline_sana.py |
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens = [negative_prompt] * batch_size if isinstance(negative_prompt, str) else negative_prompt
uncond_tokens = self._text_preprocessing(uncond_tokens, c... | 238 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/sana/pipeline_sana.py |
negative_prompt_embeds = self.text_encoder(
uncond_input.input_ids.to(device), attention_mask=negative_prompt_attention_mask
)
negative_prompt_embeds = negative_prompt_embeds[0]
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each gen... | 238 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/sana/pipeline_sana.py |
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