diff --git a/CHANGELOG.md b/CHANGELOG.md
index 38f1966bd..ce5254e13 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -70,8 +70,10 @@ And other goodies like multiple *XYZ grid* improvements, additional *Flux Contro
- supports sdxl in both txt2img and img2img, simply select from scripts
- [APG: Adaptive Projected Guidance](https://arxiv.org/pdf/2410.02416)
- - latest algo to provide guidance for image generation, can be used instead of existing guidance rescale and/or PAG
- - in addtion to stronger guidance it can also increase image details using negative momentum
+ - latest algo to provide better guidance for image generation, can be used instead of existing guidance rescale and/or PAG
+ - in addtion to stronger guidance and reduction of burn at high guidance values, it can also increase image details
+ - compatible with *sd15/sdxl/sc*
+ - select in scripts -> apg
- **flux**
- avoid unet load if unchanged
diff --git a/modules/apg/__init__.py b/modules/apg/__init__.py
index e081b4f31..0bf6f1c8d 100644
--- a/modules/apg/__init__.py
+++ b/modules/apg/__init__.py
@@ -4,6 +4,7 @@ import torch
import diffusers
from .pipeline_stable_diffision_xl_apg import StableDiffusionXLPipelineAPG
from .pipeline_stable_cascade_prior_apg import StableCascadePriorPipelineAPG
+from .pipeline_stable_diffusion_apg import StableDiffusionPipelineAPG
class MomentumBuffer:
diff --git a/modules/apg/pipeline_stable_diffision_xl_apg.py b/modules/apg/pipeline_stable_diffision_xl_apg.py
index 8a29ecc50..eda0e3cf0 100644
--- a/modules/apg/pipeline_stable_diffision_xl_apg.py
+++ b/modules/apg/pipeline_stable_diffision_xl_apg.py
@@ -1179,6 +1179,7 @@ class StableDiffusionXLPipelineAPG(
return_dict=False,
)[0]
+ # perform guidance
if self.do_classifier_free_guidance:
noise_pred_uncond, noise_pred_cond = noise_pred.chunk(2)
noise_pred = apg.normalized_guidance(noise_pred_cond, noise_pred_uncond, guidance_scale=self.guidance_scale)
diff --git a/modules/apg/pipeline_stable_diffusion_apg.py b/modules/apg/pipeline_stable_diffusion_apg.py
new file mode 100644
index 000000000..4b615ffb8
--- /dev/null
+++ b/modules/apg/pipeline_stable_diffusion_apg.py
@@ -0,0 +1,1059 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import inspect
+from typing import Any, Callable, Dict, List, Optional, Union
+import torch
+from packaging import version
+
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
+from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback
+from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
+from diffusers.loaders import FromSingleFileMixin, IPAdapterMixin, StableDiffusionLoraLoaderMixin, TextualInversionLoaderMixin
+from diffusers.models import AutoencoderKL, ImageProjection, UNet2DConditionModel
+from diffusers.models.lora import adjust_lora_scale_text_encoder
+from diffusers.schedulers import KarrasDiffusionSchedulers
+from diffusers.configuration_utils import FrozenDict
+from diffusers.utils import USE_PEFT_BACKEND, deprecate, is_torch_xla_available, logging, replace_example_docstring, scale_lora_layers, unscale_lora_layers
+from diffusers.utils.torch_utils import randn_tensor
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin
+from diffusers.pipelines.stable_diffusion.pipeline_output import StableDiffusionPipelineOutput
+from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
+from modules import apg
+
+if is_torch_xla_available():
+ import torch_xla.core.xla_model as xm
+ XLA_AVAILABLE = True
+else:
+ XLA_AVAILABLE = False
+
+logger = logging.get_logger(__name__) # pylint: disable=invalid-name
+
+EXAMPLE_DOC_STRING = """
+ Examples:
+ ```py
+ >>> import torch
+ >>> from diffusers import StableDiffusionPipeline
+
+ >>> pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
+ >>> pipe = pipe.to("cuda")
+
+ >>> prompt = "a photo of an astronaut riding a horse on mars"
+ >>> image = pipe(prompt).images[0]
+ ```
+"""
+
+
+def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
+ """
+ Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
+ Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4
+ """
+ std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
+ std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
+ # rescale the results from guidance (fixes overexposure)
+ noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
+ # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
+ noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
+ return noise_cfg
+
+
+def retrieve_timesteps(
+ scheduler,
+ num_inference_steps: Optional[int] = None,
+ device: Optional[Union[str, torch.device]] = None,
+ timesteps: Optional[List[int]] = None,
+ sigmas: Optional[List[float]] = None,
+ **kwargs,
+):
+ """
+ Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
+ custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
+
+ Args:
+ scheduler (`SchedulerMixin`):
+ The scheduler to get timesteps from.
+ num_inference_steps (`int`):
+ The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
+ must be `None`.
+ device (`str` or `torch.device`, *optional*):
+ The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
+ timesteps (`List[int]`, *optional*):
+ Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
+ `num_inference_steps` and `sigmas` must be `None`.
+ sigmas (`List[float]`, *optional*):
+ Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
+ `num_inference_steps` and `timesteps` must be `None`.
+
+ Returns:
+ `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
+ second element is the number of inference steps.
+ """
+ if timesteps is not None and sigmas is not None:
+ raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
+ if timesteps is not None:
+ accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+ if not accepts_timesteps:
+ raise ValueError(
+ f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+ f" timestep schedules. Please check whether you are using the correct scheduler."
+ )
+ scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
+ timesteps = scheduler.timesteps
+ num_inference_steps = len(timesteps)
+ elif sigmas is not None:
+ accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+ if not accept_sigmas:
+ raise ValueError(
+ f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+ f" sigmas schedules. Please check whether you are using the correct scheduler."
+ )
+ scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
+ timesteps = scheduler.timesteps
+ num_inference_steps = len(timesteps)
+ else:
+ scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
+ timesteps = scheduler.timesteps
+ return timesteps, num_inference_steps
+
+
+class StableDiffusionPipelineAPG(
+ DiffusionPipeline,
+ StableDiffusionMixin,
+ TextualInversionLoaderMixin,
+ StableDiffusionLoraLoaderMixin,
+ IPAdapterMixin,
+ FromSingleFileMixin,
+):
+ r"""
+ Pipeline for text-to-image generation using Stable Diffusion.
+
+ This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
+ implemented for all pipelines (downloading, saving, running on a particular device, etc.).
+
+ The pipeline also inherits the following loading methods:
+ - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
+ - [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
+ - [`~loaders.StableDiffusionLoraLoaderMixin.save_lora_weights`] for saving LoRA weights
+ - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
+ - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
+
+ 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)).
+ tokenizer ([`~transformers.CLIPTokenizer`]):
+ A `CLIPTokenizer` to tokenize text.
+ unet ([`UNet2DConditionModel`]):
+ A `UNet2DConditionModel` to denoise the encoded image latents.
+ scheduler ([`SchedulerMixin`]):
+ A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
+ [`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/runwayml/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 inputs to the `safety_checker`.
+ """
+
+ model_cpu_offload_seq = "text_encoder->image_encoder->unet->vae"
+ _optional_components = ["safety_checker", "feature_extractor", "image_encoder"]
+ _exclude_from_cpu_offload = ["safety_checker"]
+ _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
+
+ def __init__(
+ self,
+ vae: AutoencoderKL,
+ text_encoder: CLIPTextModel,
+ tokenizer: CLIPTokenizer,
+ unet: UNet2DConditionModel,
+ scheduler: KarrasDiffusionSchedulers,
+ safety_checker: StableDiffusionSafetyChecker,
+ feature_extractor: CLIPImageProcessor,
+ image_encoder: CLIPVisionModelWithProjection = None,
+ requires_safety_checker: bool = True,
+ ):
+ super().__init__()
+
+ if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 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 "
+ "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
+ " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
+ " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
+ " file"
+ )
+ deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
+ new_config = dict(scheduler.config)
+ new_config["steps_offset"] = 1
+ scheduler._internal_dict = FrozenDict(new_config)
+
+ if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample 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. Please make sure to update the"
+ " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
+ " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
+ " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
+ )
+ deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
+ new_config = dict(scheduler.config)
+ new_config["clip_sample"] = False
+ scheduler._internal_dict = FrozenDict(new_config)
+
+ 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"
+ " results in services or applications open to the public. Both the diffusers team and Hugging Face"
+ " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
+ " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
+ " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
+ )
+
+ 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'` instead."
+ )
+
+ is_unet_version_less_0_9_0 = 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 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
+ if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
+ deprecation_message = (
+ "The configuration file of the unet has set the default `sample_size` to smaller than"
+ " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
+ " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
+ " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
+ " \n- runwayml/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 downloaded this"
+ " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
+ " the `unet/config.json` file"
+ )
+ deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
+ new_config = dict(unet.config)
+ new_config["sample_size"] = 64
+ unet._internal_dict = FrozenDict(new_config)
+
+ 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,
+ )
+ self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+ self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
+ self.register_to_config(requires_safety_checker=requires_safety_checker)
+
+ def _encode_prompt(
+ self,
+ prompt,
+ device,
+ num_images_per_prompt,
+ do_classifier_free_guidance,
+ negative_prompt=None,
+ prompt_embeds: Optional[torch.Tensor] = None,
+ negative_prompt_embeds: Optional[torch.Tensor] = None,
+ lora_scale: Optional[float] = None,
+ **kwargs,
+ ):
+ deprecation_message = "`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple."
+ deprecate("_encode_prompt()", "1.0.0", deprecation_message, standard_warn=False)
+
+ 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,
+ negative_prompt_embeds=negative_prompt_embeds,
+ lora_scale=lora_scale,
+ **kwargs,
+ )
+
+ # concatenate for backwards comp
+ prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])
+
+ return prompt_embeds
+
+ def encode_prompt(
+ self,
+ prompt,
+ device,
+ num_images_per_prompt,
+ do_classifier_free_guidance,
+ negative_prompt=None,
+ prompt_embeds: Optional[torch.Tensor] = None,
+ negative_prompt_embeds: Optional[torch.Tensor] = None,
+ lora_scale: Optional[float] = None,
+ clip_skip: Optional[int] = None,
+ ):
+ r"""
+ Encodes the prompt into text encoder hidden states.
+
+ 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 (`bool`):
+ whether to use classifier free guidance or not
+ negative_prompt (`str` or `List[str]`, *optional*):
+ The prompt or prompts not to guide the image generation. If not defined, one has to pass
+ `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+ less than `1`).
+ prompt_embeds (`torch.Tensor`, *optional*):
+ Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+ provided, text embeddings will be generated from `prompt` input argument.
+ 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_scale (`float`, *optional*):
+ A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
+ clip_skip (`int`, *optional*):
+ Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
+ the output of the pre-final layer will be used for computing the prompt embeddings.
+ """
+ # 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, StableDiffusionLoraLoaderMixin):
+ self._lora_scale = lora_scale
+
+ # 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
+ elif prompt is not None and isinstance(prompt, list):
+ batch_size = len(prompt)
+ else:
+ batch_size = prompt_embeds.shape[0]
+
+ if prompt_embeds is None:
+ # textual inversion: process multi-vector tokens if necessary
+ if isinstance(self, TextualInversionLoaderMixin):
+ prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
+
+ text_inputs = self.tokenizer(
+ prompt,
+ 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="longest", return_tensors="pt").input_ids
+
+ if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
+ text_input_ids, untruncated_ids
+ ):
+ removed_text = self.tokenizer.batch_decode(
+ untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
+ )
+ logger.warning(
+ "The following part of your input was truncated because CLIP can only handle sequences up to"
+ f" {self.tokenizer.model_max_length} tokens: {removed_text}"
+ )
+
+ 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
+
+ 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_mask, output_hidden_states=True
+ )
+ # Access the `hidden_states` first, that contains a tuple of
+ # all the hidden states from the encoder layers. Then index into
+ # the tuple to access the hidden states from the desired layer.
+ prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]
+ # We also need to apply the final LayerNorm here to not mess with the
+ # representations. The `last_hidden_states` that we typically use for
+ # obtaining the final prompt representations passes through the LayerNorm
+ # layer.
+ prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)
+
+ 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, device=device)
+
+ 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)
+
+ # 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 not type(negative_prompt):
+ raise TypeError(
+ f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+ f" {type(prompt)}."
+ )
+ elif isinstance(negative_prompt, str):
+ uncond_tokens = [negative_prompt]
+ elif batch_size != len(negative_prompt):
+ raise ValueError(
+ f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+ f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+ " the batch size of `prompt`."
+ )
+ else:
+ uncond_tokens = negative_prompt
+
+ # 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(
+ uncond_tokens,
+ padding="max_length",
+ max_length=max_length,
+ truncation=True,
+ return_tensors="pt",
+ )
+
+ if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
+ attention_mask = uncond_input.attention_mask.to(device)
+ else:
+ attention_mask = None
+
+ negative_prompt_embeds = self.text_encoder(
+ uncond_input.input_ids.to(device),
+ attention_mask=attention_mask,
+ )
+ negative_prompt_embeds = negative_prompt_embeds[0]
+
+ 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_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
+ negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
+
+ if self.text_encoder is not None:
+ if isinstance(self, StableDiffusionLoraLoaderMixin) and USE_PEFT_BACKEND:
+ # Retrieve the original scale by scaling back the LoRA layers
+ unscale_lora_layers(self.text_encoder, lora_scale)
+
+ return prompt_embeds, negative_prompt_embeds
+
+ 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):
+ image = self.feature_extractor(image, return_tensors="pt").pixel_values
+
+ 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_image_enc_hidden_states = self.image_encoder(
+ torch.zeros_like(image), output_hidden_states=True
+ ).hidden_states[-2]
+ uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(
+ num_images_per_prompt, dim=0
+ )
+ return image_enc_hidden_states, uncond_image_enc_hidden_states
+ else:
+ image_embeds = self.image_encoder(image).image_embeds
+ image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
+ uncond_image_embeds = torch.zeros_like(image_embeds)
+
+ return image_embeds, uncond_image_embeds
+
+ def prepare_ip_adapter_image_embeds(
+ self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance
+ ):
+ image_embeds = []
+ if do_classifier_free_guidance:
+ negative_image_embeds = []
+ if ip_adapter_image_embeds is None:
+ if not isinstance(ip_adapter_image, list):
+ ip_adapter_image = [ip_adapter_image]
+
+ if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers):
+ raise ValueError(
+ f"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters."
+ )
+
+ for single_ip_adapter_image, image_proj_layer in zip(
+ ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers
+ ):
+ output_hidden_state = not isinstance(image_proj_layer, ImageProjection)
+ single_image_embeds, single_negative_image_embeds = self.encode_image(
+ single_ip_adapter_image, device, 1, output_hidden_state
+ )
+
+ image_embeds.append(single_image_embeds[None, :])
+ if do_classifier_free_guidance:
+ negative_image_embeds.append(single_negative_image_embeds[None, :])
+ else:
+ for single_image_embeds in ip_adapter_image_embeds:
+ if do_classifier_free_guidance:
+ single_negative_image_embeds, single_image_embeds = single_image_embeds.chunk(2)
+ negative_image_embeds.append(single_negative_image_embeds)
+ image_embeds.append(single_image_embeds)
+
+ ip_adapter_image_embeds = []
+ for i, single_image_embeds in enumerate(image_embeds):
+ single_image_embeds = torch.cat([single_image_embeds] * num_images_per_prompt, dim=0)
+ if do_classifier_free_guidance:
+ single_negative_image_embeds = torch.cat([negative_image_embeds[i]] * num_images_per_prompt, dim=0)
+ single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds], dim=0)
+
+ single_image_embeds = single_image_embeds.to(device=device)
+ ip_adapter_image_embeds.append(single_image_embeds)
+
+ return ip_adapter_image_embeds
+
+ def run_safety_checker(self, image, device, dtype):
+ if self.safety_checker is None:
+ has_nsfw_concept = None
+ else:
+ if torch.is_tensor(image):
+ feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
+ else:
+ feature_extractor_input = self.image_processor.numpy_to_pil(image)
+ safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
+ image, has_nsfw_concept = self.safety_checker(
+ images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
+ )
+ return image, has_nsfw_concept
+
+ 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"
+ deprecate("decode_latents", "1.0.0", deprecation_message, standard_warn=False)
+
+ latents = 1 / self.vae.config.scaling_factor * latents
+ image = self.vae.decode(latents, return_dict=False)[0]
+ image = (image / 2 + 0.5).clamp(0, 1)
+ # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
+ image = image.cpu().permute(0, 2, 3, 1).float().numpy()
+ return image
+
+ 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 with the DDIMScheduler, it will be ignored for other schedulers.
+ # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+ # and should be between [0, 1]
+
+ accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+ extra_step_kwargs = {}
+ if accepts_eta:
+ extra_step_kwargs["eta"] = eta
+
+ # check if the scheduler accepts generator
+ accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
+ if accepts_generator:
+ extra_step_kwargs["generator"] = generator
+ return extra_step_kwargs
+
+ def check_inputs(
+ self,
+ prompt,
+ height,
+ width,
+ callback_steps,
+ negative_prompt=None,
+ prompt_embeds=None,
+ negative_prompt_embeds=None,
+ ip_adapter_image=None,
+ ip_adapter_image_embeds=None,
+ callback_on_step_end_tensor_inputs=None,
+ ):
+ if height % 8 != 0 or width % 8 != 0:
+ raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+ 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_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 callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
+ )
+
+ 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:
+ raise ValueError(
+ "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
+ )
+ elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
+ raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+ 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."
+ )
+
+ 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: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
+ f" {negative_prompt_embeds.shape}."
+ )
+
+ if ip_adapter_image is not None and ip_adapter_image_embeds is not None:
+ raise ValueError(
+ "Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined."
+ )
+
+ if ip_adapter_image_embeds is not None:
+ if not isinstance(ip_adapter_image_embeds, list):
+ raise ValueError(
+ f"`ip_adapter_image_embeds` has to be of type `list` but is {type(ip_adapter_image_embeds)}"
+ )
+ elif ip_adapter_image_embeds[0].ndim not in [3, 4]:
+ raise ValueError(
+ f"`ip_adapter_image_embeds` has to be a list of 3D or 4D tensors but is {ip_adapter_image_embeds[0].ndim}D"
+ )
+
+ 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 isinstance(generator, list) and len(generator) != batch_size:
+ raise ValueError(
+ f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+ f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+ )
+
+ if latents is None:
+ latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+ else:
+ latents = latents.to(device)
+
+ # scale the initial noise by the standard deviation required by the scheduler
+ latents = latents * self.scheduler.init_noise_sigma
+ return latents
+
+ # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
+ def get_guidance_scale_embedding(
+ self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32
+ ) -> torch.Tensor:
+ """
+ See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
+
+ Args:
+ w (`torch.Tensor`):
+ Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.
+ embedding_dim (`int`, *optional*, defaults to 512):
+ Dimension of the embeddings to generate.
+ dtype (`torch.dtype`, *optional*, defaults to `torch.float32`):
+ Data type of the generated embeddings.
+
+ Returns:
+ `torch.Tensor`: Embedding vectors with shape `(len(w), embedding_dim)`.
+ """
+ assert len(w.shape) == 1
+ w = w * 1000.0
+
+ half_dim = embedding_dim // 2
+ emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
+ emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
+ emb = w.to(dtype)[:, None] * emb[None, :]
+ emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
+ if embedding_dim % 2 == 1: # zero pad
+ emb = torch.nn.functional.pad(emb, (0, 1))
+ assert emb.shape == (w.shape[0], embedding_dim)
+ return emb
+
+ @property
+ def guidance_scale(self):
+ return self._guidance_scale
+
+ @property
+ def guidance_rescale(self):
+ return self._guidance_rescale
+
+ @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 do_classifier_free_guidance(self):
+ return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None
+
+ @property
+ def cross_attention_kwargs(self):
+ return self._cross_attention_kwargs
+
+ @property
+ def num_timesteps(self):
+ return self._num_timesteps
+
+ @property
+ def interrupt(self):
+ return self._interrupt
+
+ @torch.no_grad()
+ @replace_example_docstring(EXAMPLE_DOC_STRING)
+ def __call__(
+ self,
+ prompt: Union[str, List[str]] = None,
+ height: Optional[int] = None,
+ width: Optional[int] = None,
+ num_inference_steps: int = 50,
+ timesteps: List[int] = None,
+ sigmas: List[float] = None,
+ guidance_scale: float = 7.5,
+ negative_prompt: Optional[Union[str, List[str]]] = None,
+ num_images_per_prompt: Optional[int] = 1,
+ eta: float = 0.0,
+ generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+ latents: Optional[torch.Tensor] = None,
+ prompt_embeds: Optional[torch.Tensor] = None,
+ negative_prompt_embeds: Optional[torch.Tensor] = None,
+ ip_adapter_image: Optional[PipelineImageInput] = None,
+ ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,
+ output_type: Optional[str] = "pil",
+ return_dict: bool = True,
+ cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+ guidance_rescale: float = 0.0,
+ clip_skip: Optional[int] = None,
+ callback_on_step_end: Optional[
+ Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
+ ] = None,
+ callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+ **kwargs,
+ ):
+ r"""
+ The call function to the pipeline for generation.
+
+ Args:
+ prompt (`str` or `List[str]`, *optional*):
+ 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 generated image.
+ width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
+ The width in pixels of the generated image.
+ num_inference_steps (`int`, *optional*, defaults to 50):
+ The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+ expense of slower inference.
+ timesteps (`List[int]`, *optional*):
+ Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
+ in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
+ passed will be used. Must be in descending order.
+ sigmas (`List[float]`, *optional*):
+ Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
+ their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
+ will be used.
+ guidance_scale (`float`, *optional*, defaults to 7.5):
+ A higher guidance scale value encourages the model to generate images closely linked to the text
+ `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
+ negative_prompt (`str` or `List[str]`, *optional*):
+ The prompt or prompts to guide what to not include in image generation. If not defined, you need to
+ pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
+ num_images_per_prompt (`int`, *optional*, defaults to 1):
+ The number of images to generate per prompt.
+ eta (`float`, *optional*, defaults to 0.0):
+ Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
+ to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
+ generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+ A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
+ generation deterministic.
+ latents (`torch.Tensor`, *optional*):
+ Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
+ generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+ tensor is generated by sampling using the supplied random `generator`.
+ prompt_embeds (`torch.Tensor`, *optional*):
+ Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
+ provided, text embeddings are generated from the `prompt` input argument.
+ negative_prompt_embeds (`torch.Tensor`, *optional*):
+ Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
+ not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
+ ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
+ ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):
+ Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of
+ IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should
+ contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not
+ provided, embeddings are computed from the `ip_adapter_image` input argument.
+ output_type (`str`, *optional*, defaults to `"pil"`):
+ The output format of the generated image. Choose between `PIL.Image` or `np.array`.
+ return_dict (`bool`, *optional*, defaults to `True`):
+ Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+ plain tuple.
+ cross_attention_kwargs (`dict`, *optional*):
+ A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
+ [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+ guidance_rescale (`float`, *optional*, defaults to 0.0):
+ Guidance rescale factor from [Common Diffusion Noise Schedules and Sample Steps are
+ Flawed](https://arxiv.org/pdf/2305.08891.pdf). Guidance rescale factor should fix overexposure when
+ using zero terminal SNR.
+ clip_skip (`int`, *optional*):
+ Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
+ the output of the pre-final layer will be used for computing the prompt embeddings.
+ callback_on_step_end (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*):
+ A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of
+ each denoising step during the inference. with the following arguments: `callback_on_step_end(self:
+ DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a
+ list of all tensors as specified by `callback_on_step_end_tensor_inputs`.
+ 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_tensor_inputs` attribute of your pipeline class.
+
+ Examples:
+
+ 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 images and the
+ second element is a list of `bool`s indicating whether the corresponding generated image contains
+ "not-safe-for-work" (nsfw) content.
+ """
+
+ 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 using `callback_on_step_end`",
+ )
+ if callback_steps is not None:
+ deprecate(
+ "callback_steps",
+ "1.0.0",
+ "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`",
+ )
+
+ if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
+ callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
+
+ # 0. Default height and width to unet
+ height = height or self.unet.config.sample_size * self.vae_scale_factor
+ width = width or self.unet.config.sample_size * self.vae_scale_factor
+ # to deal with lora scaling and other possible forward hooks
+
+ # 1. Check inputs. Raise error if not correct
+ self.check_inputs(
+ prompt,
+ height,
+ width,
+ callback_steps,
+ negative_prompt,
+ prompt_embeds,
+ negative_prompt_embeds,
+ ip_adapter_image,
+ ip_adapter_image_embeds,
+ callback_on_step_end_tensor_inputs,
+ )
+
+ self._guidance_scale = guidance_scale
+ self._guidance_rescale = guidance_rescale
+ self._clip_skip = clip_skip
+ self._cross_attention_kwargs = cross_attention_kwargs
+ self._interrupt = False
+
+ # 2. Define call parameters
+ if prompt is not None and isinstance(prompt, str):
+ batch_size = 1
+ elif prompt is not None and isinstance(prompt, list):
+ batch_size = len(prompt)
+ else:
+ batch_size = prompt_embeds.shape[0]
+
+ device = self._execution_device
+
+ # 3. Encode input prompt
+ lora_scale = (
+ self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
+ )
+
+ prompt_embeds, negative_prompt_embeds = self.encode_prompt(
+ prompt,
+ device,
+ num_images_per_prompt,
+ self.do_classifier_free_guidance,
+ negative_prompt,
+ prompt_embeds=prompt_embeds,
+ negative_prompt_embeds=negative_prompt_embeds,
+ lora_scale=lora_scale,
+ clip_skip=self.clip_skip,
+ )
+
+ # 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
+ if self.do_classifier_free_guidance:
+ prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
+
+ if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
+ image_embeds = self.prepare_ip_adapter_image_embeds(
+ ip_adapter_image,
+ ip_adapter_image_embeds,
+ device,
+ batch_size * num_images_per_prompt,
+ self.do_classifier_free_guidance,
+ )
+
+ # 4. Prepare timesteps
+ timesteps, num_inference_steps = retrieve_timesteps(
+ self.scheduler, num_inference_steps, device, timesteps, sigmas
+ )
+
+ # 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,
+ generator,
+ latents,
+ )
+
+ # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+ extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+ # 6.1 Add image embeds for IP-Adapter
+ added_cond_kwargs = (
+ {"image_embeds": image_embeds}
+ if (ip_adapter_image is not None or ip_adapter_image_embeds is not None)
+ else None
+ )
+
+ # 6.2 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(
+ guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim
+ ).to(device=device, dtype=latents.dtype)
+
+ # 7. Denoising loop
+ num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
+ self._num_timesteps = len(timesteps)
+ with self.progress_bar(total=num_inference_steps) as progress_bar:
+ for i, t in enumerate(timesteps):
+ if self.interrupt:
+ continue
+
+ # 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
+ latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+ # predict the noise residual
+ noise_pred = self.unet(
+ latent_model_input,
+ t,
+ encoder_hidden_states=prompt_embeds,
+ timestep_cond=timestep_cond,
+ cross_attention_kwargs=self.cross_attention_kwargs,
+ added_cond_kwargs=added_cond_kwargs,
+ return_dict=False,
+ )[0]
+
+ # perform guidance
+ if self.do_classifier_free_guidance:
+ noise_pred_uncond, noise_pred_cond = noise_pred.chunk(2)
+ noise_pred = apg.normalized_guidance(noise_pred_cond, noise_pred_uncond, guidance_scale=self.guidance_scale)
+
+ # compute the previous noisy sample x_t -> x_t-1
+ latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
+
+ if callback_on_step_end is not None:
+ callback_kwargs = {}
+ for k in callback_on_step_end_tensor_inputs:
+ callback_kwargs[k] = locals()[k]
+ callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+ latents = callback_outputs.pop("latents", latents)
+ prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+ negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
+
+ # call the callback, if provided
+ if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+ progress_bar.update()
+ if callback is not None and i % callback_steps == 0:
+ step_idx = i // getattr(self.scheduler, "order", 1)
+ callback(step_idx, t, latents)
+
+ if XLA_AVAILABLE:
+ xm.mark_step()
+
+ if not output_type == "latent":
+ image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False, generator=generator)[
+ 0
+ ]
+ image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
+ else:
+ image = latents
+ has_nsfw_concept = None
+
+ if has_nsfw_concept is None:
+ do_denormalize = [True] * image.shape[0]
+ else:
+ do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
+
+ image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
+
+ # Offload all models
+ self.maybe_free_model_hooks()
+
+ if not return_dict:
+ return (image, has_nsfw_concept)
+
+ return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
diff --git a/scripts/apg.py b/scripts/apg.py
index 8a7314521..3325d9333 100644
--- a/scripts/apg.py
+++ b/scripts/apg.py
@@ -18,9 +18,9 @@ class Script(scripts.Script):
with gr.Row():
gr.HTML(' APG: Adaptive projected guidance
')
with gr.Row():
- eta = gr.Slider(label="ETA", value=1.0, minimum=0, maximum=2.0, step=0.01)
- momentum = gr.Slider(label="Momentum", value=0.0, minimum=-1.0, maximum=1.0, step=0.01)
- threshold = gr.Slider(label="Threshold", value=0.0, minimum=0.0, maximum=5.0, step=0.01)
+ eta = gr.Slider(label="ETA", value=1.0, minimum=0, maximum=2.0, step=0.05)
+ momentum = gr.Slider(label="Momentum", value=-0.50, minimum=-1.0, maximum=1.0, step=0.05)
+ threshold = gr.Slider(label="Threshold", value=0.0, minimum=0.0, maximum=10.0, step=0.05)
return [eta, momentum, threshold]
def register(self): # register xyz grid elements
@@ -37,9 +37,9 @@ class Script(scripts.Script):
xyz_classes.axis_options.append(xyz_classes.AxisOption("[APG] Threshold", float, apply_field("apg_threshold")))
def run(self, p: processing.StableDiffusionProcessing, eta = 0.0, momentum = 0.0, threshold = 0.0): # pylint: disable=arguments-differ
- supported_model_list = ['sdxl', 'sc']
+ supported_model_list = ['sd', 'sdxl', 'sc']
if shared.sd_model_type not in supported_model_list:
- shared.log.warning(f'APG: pipeline={shared.sd_model_type} required={supported_model_list}')
+ shared.log.warning(f'APG: class={shared.sd_model.__class__.__name__} model={shared.sd_model_type} required={supported_model_list}')
return None
from modules import apg
apg.eta = getattr(p, 'apg_eta', eta) # use values set by xyz grid or via ui
@@ -47,6 +47,9 @@ class Script(scripts.Script):
apg.threshold = getattr(p, 'apg_threshold', threshold)
apg.buffer = apg.MomentumBuffer(apg.momentum) # recreate buffer
# pipelines with call to apg.normalized_guidance instead of default
+ if shared.sd_model_type == "sd":
+ self.orig_pipe = shared.sd_model
+ shared.sd_model = sd_models.switch_pipe(apg.StableDiffusionPipelineAPG, shared.sd_model)
if shared.sd_model_type == "sdxl":
self.orig_pipe = shared.sd_model
shared.sd_model = sd_models.switch_pipe(apg.StableDiffusionXLPipelineAPG, shared.sd_model)