diff --git a/examples/community/README.md b/examples/community/README.md
index ee823873c7b0..3b1218dc2727 100644
--- a/examples/community/README.md
+++ b/examples/community/README.md
@@ -86,6 +86,7 @@ PIXART-α Controlnet pipeline | Implementation of the controlnet model for pixar
| Perturbed-Attention Guidance |StableDiffusionPAGPipeline is a modification of StableDiffusionPipeline to support Perturbed-Attention Guidance (PAG).|[Perturbed-Attention Guidance](#perturbed-attention-guidance)|[Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/perturbed_attention_guidance.ipynb)|[Hyoungwon Cho](https://github.com/HyoungwonCho)|
| CogVideoX DDIM Inversion Pipeline | Implementation of DDIM inversion and guided attention-based editing denoising process on CogVideoX. | [CogVideoX DDIM Inversion Pipeline](#cogvideox-ddim-inversion-pipeline) | - | [LittleNyima](https://github.com/LittleNyima) |
| FaithDiff Stable Diffusion XL Pipeline | Implementation of [(CVPR 2025) FaithDiff: Unleashing Diffusion Priors for Faithful Image Super-resolutionUnleashing Diffusion Priors for Faithful Image Super-resolution](https://arxiv.org/abs/2411.18824) - FaithDiff is a faithful image super-resolution method that leverages latent diffusion models by actively adapting the diffusion prior and jointly fine-tuning its components (encoder and diffusion model) with an alignment module to ensure high fidelity and structural consistency. | [FaithDiff Stable Diffusion XL Pipeline](#faithdiff-stable-diffusion-xl-pipeline) | [](https://huggingface.co/jychen9811/FaithDiff) | [Junyang Chen, Jinshan Pan, Jiangxin Dong, IMAG Lab, (Adapted by Eliseu Silva)](https://github.com/JyChen9811/FaithDiff) |
+| Stable Diffusion 3 InstructPix2Pix Pipeline | Implementation of Stable Diffusion 3 InstructPix2Pix Pipeline | [Stable Diffusion 3 InstructPix2Pix Pipeline](#stable-diffusion-3-instructpix2pix-pipeline) | [](https://huggingface.co/BleachNick/SD3_UltraEdit_freeform) [](https://huggingface.co/CaptainZZZ/sd3-instructpix2pix) | [Jiayu Zhang](https://github.com/xduzhangjiayu) and [Haozhe Zhao](https://github.com/HaozheZhao)|
To load a custom pipeline you just need to pass the `custom_pipeline` argument to `DiffusionPipeline`, as one of the files in `diffusers/examples/community`. Feel free to send a PR with your own pipelines, we will merge them quickly.
```py
@@ -5432,4 +5433,50 @@ cropped_image = gen_image.crop((0, 0, width_init, height_init))
cropped_image.save("data/result.png")
````
### Result
-[
](https://imgsli.com/MzY1NzE2)
\ No newline at end of file
+[](https://imgsli.com/MzY1NzE2)
+
+
+# Stable Diffusion 3 InstructPix2Pix Pipeline
+This the implementation of the Stable Diffusion 3 InstructPix2Pix Pipeline, based on the HuggingFace Diffusers.
+
+## Example Usage
+This pipeline aims to edit image based on user's instruction by using SD3
+````py
+import torch
+from diffusers import SD3Transformer2DModel
+from diffusers import DiffusionPipeline
+from diffusers.utils import load_image
+
+
+resolution = 512
+image = load_image("https://hf.co/datasets/diffusers/diffusers-images-docs/resolve/main/mountain.png").resize(
+ (resolution, resolution)
+)
+edit_instruction = "Turn sky into a sunny one"
+
+
+pipe = DiffusionPipeline.from_pretrained(
+ "stabilityai/stable-diffusion-3-medium-diffusers", custom_pipeline="pipeline_stable_diffusion_3_instruct_pix2pix", torch_dtype=torch.float16).to('cuda')
+
+pipe.transformer = SD3Transformer2DModel.from_pretrained("CaptainZZZ/sd3-instructpix2pix",torch_dtype=torch.float16).to('cuda')
+
+edited_image = pipe(
+ prompt=edit_instruction,
+ image=image,
+ height=resolution,
+ width=resolution,
+ guidance_scale=7.5,
+ image_guidance_scale=1.5,
+ num_inference_steps=30,
+).images[0]
+
+edited_image.save("edited_image.png")
+````
+|Original|Edited|
+|---|---|
+||
+
+### Note
+This model is trained on 512x512, so input size is better on 512x512.
+For better editing performance, please refer to this powerful model https://huggingface.co/BleachNick/SD3_UltraEdit_freeform and Paper "UltraEdit: Instruction-based Fine-Grained Image
+Editing at Scale", many thanks to their contribution!
\ No newline at end of file
diff --git a/examples/community/pipeline_stable_diffusion_3_instruct_pix2pix.py b/examples/community/pipeline_stable_diffusion_3_instruct_pix2pix.py
new file mode 100644
index 000000000000..97491a85970b
--- /dev/null
+++ b/examples/community/pipeline_stable_diffusion_3_instruct_pix2pix.py
@@ -0,0 +1,1266 @@
+# Copyright 2024 Stability AI, The HuggingFace Team and The InstantX 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 PIL.Image
+import torch
+from transformers import (
+ CLIPTextModelWithProjection,
+ CLIPTokenizer,
+ SiglipImageProcessor,
+ SiglipVisionModel,
+ T5EncoderModel,
+ T5TokenizerFast,
+)
+
+from ...image_processor import PipelineImageInput, VaeImageProcessor
+from ...loaders import FromSingleFileMixin, SD3IPAdapterMixin, SD3LoraLoaderMixin
+from ...models.autoencoders import AutoencoderKL
+from ...models.transformers import SD3Transformer2DModel
+from ...schedulers import FlowMatchEulerDiscreteScheduler
+from ...utils import (
+ USE_PEFT_BACKEND,
+ deprecate,
+ is_torch_xla_available,
+ logging,
+ replace_example_docstring,
+ scale_lora_layers,
+ unscale_lora_layers,
+)
+from ...utils.torch_utils import randn_tensor
+from ..pipeline_utils import DiffusionPipeline
+from .pipeline_output import StableDiffusion3PipelineOutput
+
+
+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 StableDiffusion3InstructPix2PixPipeline
+ >>> from diffusers.utils import load_image
+
+ >>> resolution = 1024
+ >>> image = load_image(
+ ... "https://hf.co/datasets/diffusers/diffusers-images-docs/resolve/main/mountain.png"
+ ... ).resize((resolution, resolution))
+ >>> edit_instruction = "Turn sky into a cloudy one"
+
+ >>> pipe = StableDiffusion3InstructPix2PixPipeline.from_pretrained(
+ ... "your_own_model_path", torch_dtype=torch.float16
+ ... ).to("cuda")
+
+ >>> edited_image = pipe(
+ ... prompt=edit_instruction,
+ ... image=image,
+ ... height=resolution,
+ ... width=resolution,
+ ... guidance_scale=7.5,
+ ... image_guidance_scale=1.5,
+ ... num_inference_steps=30,
+ ... ).images[0]
+ >>> edited_image
+ ```
+"""
+
+
+# Copied from diffusers.pipelines.flux.pipeline_flux.calculate_shift
+def calculate_shift(
+ image_seq_len,
+ base_seq_len: int = 256,
+ max_seq_len: int = 4096,
+ base_shift: float = 0.5,
+ max_shift: float = 1.15,
+):
+ m = (max_shift - base_shift) / (max_seq_len - base_seq_len)
+ b = base_shift - m * base_seq_len
+ mu = image_seq_len * m + b
+ return mu
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents
+def retrieve_latents(
+ encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "sample"
+):
+ if hasattr(encoder_output, "latent_dist") and sample_mode == "sample":
+ return encoder_output.latent_dist.sample(generator)
+ elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax":
+ return encoder_output.latent_dist.mode()
+ elif hasattr(encoder_output, "latents"):
+ return encoder_output.latents
+ else:
+ raise AttributeError("Could not access latents of provided encoder_output")
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
+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,
+):
+ r"""
+ 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 StableDiffusion3InstructPix2PixPipeline(
+ DiffusionPipeline, SD3LoraLoaderMixin, FromSingleFileMixin, SD3IPAdapterMixin
+):
+ r"""
+ Args:
+ transformer ([`SD3Transformer2DModel`]):
+ Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
+ scheduler ([`FlowMatchEulerDiscreteScheduler`]):
+ A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
+ vae ([`AutoencoderKL`]):
+ Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+ text_encoder ([`CLIPTextModelWithProjection`]):
+ [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),
+ specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant,
+ with an additional added projection layer that is initialized with a diagonal matrix with the `hidden_size`
+ as its dimension.
+ text_encoder_2 ([`CLIPTextModelWithProjection`]):
+ [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),
+ specifically the
+ [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)
+ variant.
+ text_encoder_3 ([`T5EncoderModel`]):
+ Frozen text-encoder. Stable Diffusion 3 uses
+ [T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel), specifically the
+ [t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl) variant.
+ tokenizer (`CLIPTokenizer`):
+ Tokenizer of class
+ [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).
+ tokenizer_3 (`T5TokenizerFast`):
+ Tokenizer of class
+ [T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer).
+ image_encoder (`SiglipVisionModel`, *optional*):
+ Pre-trained Vision Model for IP Adapter.
+ feature_extractor (`SiglipImageProcessor`, *optional*):
+ Image processor for IP Adapter.
+ """
+
+ model_cpu_offload_seq = "text_encoder->text_encoder_2->text_encoder_3->image_encoder->transformer->vae"
+ _optional_components = ["image_encoder", "feature_extractor"]
+ _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds", "negative_pooled_prompt_embeds"]
+
+ def __init__(
+ self,
+ transformer: SD3Transformer2DModel,
+ scheduler: FlowMatchEulerDiscreteScheduler,
+ vae: AutoencoderKL,
+ text_encoder: CLIPTextModelWithProjection,
+ tokenizer: CLIPTokenizer,
+ text_encoder_2: CLIPTextModelWithProjection,
+ tokenizer_2: CLIPTokenizer,
+ text_encoder_3: T5EncoderModel,
+ tokenizer_3: T5TokenizerFast,
+ image_encoder: SiglipVisionModel = None,
+ feature_extractor: SiglipImageProcessor = None,
+ ):
+ super().__init__()
+
+ self.register_modules(
+ vae=vae,
+ text_encoder=text_encoder,
+ text_encoder_2=text_encoder_2,
+ text_encoder_3=text_encoder_3,
+ tokenizer=tokenizer,
+ tokenizer_2=tokenizer_2,
+ tokenizer_3=tokenizer_3,
+ transformer=transformer,
+ scheduler=scheduler,
+ image_encoder=image_encoder,
+ feature_extractor=feature_extractor,
+ )
+ self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
+ self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
+ self.tokenizer_max_length = (
+ self.tokenizer.model_max_length if hasattr(self, "tokenizer") and self.tokenizer is not None else 77
+ )
+ self.default_sample_size = (
+ self.transformer.config.sample_size
+ if hasattr(self, "transformer") and self.transformer is not None
+ else 128
+ )
+ self.patch_size = (
+ self.transformer.config.patch_size if hasattr(self, "transformer") and self.transformer is not None else 2
+ )
+
+ def _get_t5_prompt_embeds(
+ self,
+ prompt: Union[str, List[str]] = None,
+ num_images_per_prompt: int = 1,
+ max_sequence_length: int = 256,
+ device: Optional[torch.device] = None,
+ dtype: Optional[torch.dtype] = None,
+ ):
+ device = device or self._execution_device
+ dtype = dtype or self.text_encoder.dtype
+
+ prompt = [prompt] if isinstance(prompt, str) else prompt
+ batch_size = len(prompt)
+
+ if self.text_encoder_3 is None:
+ return torch.zeros(
+ (
+ batch_size * num_images_per_prompt,
+ self.tokenizer_max_length,
+ self.transformer.config.joint_attention_dim,
+ ),
+ device=device,
+ dtype=dtype,
+ )
+
+ text_inputs = self.tokenizer_3(
+ prompt,
+ padding="max_length",
+ max_length=max_sequence_length,
+ truncation=True,
+ add_special_tokens=True,
+ return_tensors="pt",
+ )
+ text_input_ids = text_inputs.input_ids
+ untruncated_ids = self.tokenizer_3(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_3.batch_decode(untruncated_ids[:, self.tokenizer_max_length - 1 : -1])
+ logger.warning(
+ "The following part of your input was truncated because `max_sequence_length` is set to "
+ f" {max_sequence_length} tokens: {removed_text}"
+ )
+
+ prompt_embeds = self.text_encoder_3(text_input_ids.to(device))[0]
+
+ dtype = self.text_encoder_3.dtype
+ prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
+
+ _, seq_len, _ = prompt_embeds.shape
+
+ # duplicate text embeddings and attention mask 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(batch_size * num_images_per_prompt, seq_len, -1)
+
+ return prompt_embeds
+
+ def _get_clip_prompt_embeds(
+ self,
+ prompt: Union[str, List[str]],
+ num_images_per_prompt: int = 1,
+ device: Optional[torch.device] = None,
+ clip_skip: Optional[int] = None,
+ clip_model_index: int = 0,
+ ):
+ device = device or self._execution_device
+
+ clip_tokenizers = [self.tokenizer, self.tokenizer_2]
+ clip_text_encoders = [self.text_encoder, self.text_encoder_2]
+
+ tokenizer = clip_tokenizers[clip_model_index]
+ text_encoder = clip_text_encoders[clip_model_index]
+
+ prompt = [prompt] if isinstance(prompt, str) else prompt
+ batch_size = len(prompt)
+
+ text_inputs = tokenizer(
+ prompt,
+ padding="max_length",
+ max_length=self.tokenizer_max_length,
+ truncation=True,
+ return_tensors="pt",
+ )
+
+ text_input_ids = text_inputs.input_ids
+ untruncated_ids = 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 = tokenizer.batch_decode(untruncated_ids[:, self.tokenizer_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_max_length} tokens: {removed_text}"
+ )
+ prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)
+ pooled_prompt_embeds = prompt_embeds[0]
+
+ if clip_skip is None:
+ prompt_embeds = prompt_embeds.hidden_states[-2]
+ else:
+ prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 2)]
+
+ prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
+
+ _, 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(batch_size * num_images_per_prompt, seq_len, -1)
+
+ pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt, 1)
+ pooled_prompt_embeds = pooled_prompt_embeds.view(batch_size * num_images_per_prompt, -1)
+
+ return prompt_embeds, pooled_prompt_embeds
+
+ def encode_prompt(
+ self,
+ prompt: Union[str, List[str]],
+ prompt_2: Union[str, List[str]],
+ prompt_3: Union[str, List[str]],
+ device: Optional[torch.device] = None,
+ num_images_per_prompt: int = 1,
+ do_classifier_free_guidance: bool = True,
+ negative_prompt: Optional[Union[str, List[str]]] = None,
+ negative_prompt_2: Optional[Union[str, List[str]]] = None,
+ negative_prompt_3: Optional[Union[str, List[str]]] = None,
+ prompt_embeds: Optional[torch.FloatTensor] = None,
+ negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+ pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+ negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+ clip_skip: Optional[int] = None,
+ max_sequence_length: int = 256,
+ lora_scale: Optional[float] = None,
+ ):
+ r"""
+
+ 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 all text-encoders
+ prompt_3 (`str` or `List[str]`, *optional*):
+ The prompt or prompts to be sent to the `tokenizer_3` and `text_encoder_3`. If not defined, `prompt` is
+ used in all text-encoders
+ 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`).
+ negative_prompt_2 (`str` or `List[str]`, *optional*):
+ 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 all the text-encoders.
+ negative_prompt_3 (`str` or `List[str]`, *optional*):
+ The prompt or prompts not to guide the image generation to be sent to `tokenizer_3` and
+ `text_encoder_3`. If not defined, `negative_prompt` is used in all the text-encoders.
+ prompt_embeds (`torch.FloatTensor`, *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.FloatTensor`, *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.
+ pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+ Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
+ If not provided, pooled text embeddings will be generated from `prompt` input argument.
+ negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+ Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+ weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
+ input argument.
+ 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.
+ lora_scale (`float`, *optional*):
+ A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
+ """
+ device = device or 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, SD3LoraLoaderMixin):
+ self._lora_scale = lora_scale
+
+ # dynamically adjust the LoRA scale
+ if self.text_encoder is not None and USE_PEFT_BACKEND:
+ scale_lora_layers(self.text_encoder, lora_scale)
+ if self.text_encoder_2 is not None and USE_PEFT_BACKEND:
+ scale_lora_layers(self.text_encoder_2, lora_scale)
+
+ prompt = [prompt] if isinstance(prompt, str) else prompt
+ if prompt is not None:
+ batch_size = len(prompt)
+ else:
+ batch_size = prompt_embeds.shape[0]
+
+ if prompt_embeds is None:
+ prompt_2 = prompt_2 or prompt
+ prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2
+
+ prompt_3 = prompt_3 or prompt
+ prompt_3 = [prompt_3] if isinstance(prompt_3, str) else prompt_3
+
+ prompt_embed, pooled_prompt_embed = self._get_clip_prompt_embeds(
+ prompt=prompt,
+ device=device,
+ num_images_per_prompt=num_images_per_prompt,
+ clip_skip=clip_skip,
+ clip_model_index=0,
+ )
+ prompt_2_embed, pooled_prompt_2_embed = self._get_clip_prompt_embeds(
+ prompt=prompt_2,
+ device=device,
+ num_images_per_prompt=num_images_per_prompt,
+ clip_skip=clip_skip,
+ clip_model_index=1,
+ )
+ clip_prompt_embeds = torch.cat([prompt_embed, prompt_2_embed], dim=-1)
+
+ t5_prompt_embed = self._get_t5_prompt_embeds(
+ prompt=prompt_3,
+ num_images_per_prompt=num_images_per_prompt,
+ max_sequence_length=max_sequence_length,
+ device=device,
+ )
+
+ clip_prompt_embeds = torch.nn.functional.pad(
+ clip_prompt_embeds, (0, t5_prompt_embed.shape[-1] - clip_prompt_embeds.shape[-1])
+ )
+
+ prompt_embeds = torch.cat([clip_prompt_embeds, t5_prompt_embed], dim=-2)
+ pooled_prompt_embeds = torch.cat([pooled_prompt_embed, pooled_prompt_2_embed], dim=-1)
+
+ if do_classifier_free_guidance and negative_prompt_embeds is None:
+ negative_prompt = negative_prompt or ""
+ negative_prompt_2 = negative_prompt_2 or negative_prompt
+ negative_prompt_3 = negative_prompt_3 or negative_prompt
+
+ # normalize str to list
+ negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
+ negative_prompt_2 = (
+ batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2
+ )
+ negative_prompt_3 = (
+ batch_size * [negative_prompt_3] if isinstance(negative_prompt_3, str) else negative_prompt_3
+ )
+
+ 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)}."
+ )
+ 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`."
+ )
+
+ negative_prompt_embed, negative_pooled_prompt_embed = self._get_clip_prompt_embeds(
+ negative_prompt,
+ device=device,
+ num_images_per_prompt=num_images_per_prompt,
+ clip_skip=None,
+ clip_model_index=0,
+ )
+ negative_prompt_2_embed, negative_pooled_prompt_2_embed = self._get_clip_prompt_embeds(
+ negative_prompt_2,
+ device=device,
+ num_images_per_prompt=num_images_per_prompt,
+ clip_skip=None,
+ clip_model_index=1,
+ )
+ negative_clip_prompt_embeds = torch.cat([negative_prompt_embed, negative_prompt_2_embed], dim=-1)
+
+ t5_negative_prompt_embed = self._get_t5_prompt_embeds(
+ prompt=negative_prompt_3,
+ num_images_per_prompt=num_images_per_prompt,
+ max_sequence_length=max_sequence_length,
+ device=device,
+ )
+
+ negative_clip_prompt_embeds = torch.nn.functional.pad(
+ negative_clip_prompt_embeds,
+ (0, t5_negative_prompt_embed.shape[-1] - negative_clip_prompt_embeds.shape[-1]),
+ )
+
+ negative_prompt_embeds = torch.cat([negative_clip_prompt_embeds, t5_negative_prompt_embed], dim=-2)
+ negative_pooled_prompt_embeds = torch.cat(
+ [negative_pooled_prompt_embed, negative_pooled_prompt_2_embed], dim=-1
+ )
+
+ if self.text_encoder is not None:
+ if isinstance(self, SD3LoraLoaderMixin) and USE_PEFT_BACKEND:
+ # Retrieve the original scale by scaling back the LoRA layers
+ unscale_lora_layers(self.text_encoder, lora_scale)
+
+ if self.text_encoder_2 is not None:
+ if isinstance(self, SD3LoraLoaderMixin) and USE_PEFT_BACKEND:
+ # Retrieve the original scale by scaling back the LoRA layers
+ unscale_lora_layers(self.text_encoder_2, lora_scale)
+
+ return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
+
+ def check_inputs(
+ self,
+ prompt,
+ prompt_2,
+ prompt_3,
+ height,
+ width,
+ negative_prompt=None,
+ negative_prompt_2=None,
+ negative_prompt_3=None,
+ prompt_embeds=None,
+ negative_prompt_embeds=None,
+ pooled_prompt_embeds=None,
+ negative_pooled_prompt_embeds=None,
+ callback_on_step_end_tensor_inputs=None,
+ max_sequence_length=None,
+ ):
+ if (
+ height % (self.vae_scale_factor * self.patch_size) != 0
+ or width % (self.vae_scale_factor * self.patch_size) != 0
+ ):
+ raise ValueError(
+ f"`height` and `width` have to be divisible by {self.vae_scale_factor * self.patch_size} but are {height} and {width}."
+ f"You can use height {height - height % (self.vae_scale_factor * self.patch_size)} and width {width - width % (self.vae_scale_factor * self.patch_size)}."
+ )
+
+ 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_2 is not None and prompt_embeds is not None:
+ raise ValueError(
+ f"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+ " only forward one of the two."
+ )
+ elif prompt_3 is not None and prompt_embeds is not None:
+ raise ValueError(
+ f"Cannot forward both `prompt_3`: {prompt_2} 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)}")
+ 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)}")
+ elif prompt_3 is not None and (not isinstance(prompt_3, str) and not isinstance(prompt_3, list)):
+ raise ValueError(f"`prompt_3` has to be of type `str` or `list` but is {type(prompt_3)}")
+
+ 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."
+ )
+ elif negative_prompt_2 is not None and negative_prompt_embeds is not None:
+ raise ValueError(
+ f"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:"
+ f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+ )
+ elif negative_prompt_3 is not None and negative_prompt_embeds is not None:
+ raise ValueError(
+ f"Cannot forward both `negative_prompt_3`: {negative_prompt_3} 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 prompt_embeds is not None and pooled_prompt_embeds is None:
+ raise ValueError(
+ "If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`."
+ )
+
+ 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 to generate `negative_prompt_embeds`."
+ )
+
+ if max_sequence_length is not None and max_sequence_length > 512:
+ raise ValueError(f"`max_sequence_length` cannot be greater than 512 but is {max_sequence_length}")
+
+ def prepare_latents(
+ self,
+ batch_size,
+ num_channels_latents,
+ height,
+ width,
+ dtype,
+ device,
+ generator,
+ latents=None,
+ ):
+ if latents is not None:
+ return latents.to(device=device, dtype=dtype)
+
+ 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."
+ )
+
+ latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+
+ return latents
+
+ def prepare_image_latents(
+ self,
+ image,
+ batch_size,
+ num_images_per_prompt,
+ dtype,
+ device,
+ generator,
+ do_classifier_free_guidance,
+ ):
+ if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
+ raise ValueError(
+ f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
+ )
+
+ image = image.to(device=device, dtype=dtype)
+
+ batch_size = batch_size * num_images_per_prompt
+
+ if image.shape[1] == self.vae.config.latent_channels:
+ image_latents = image
+ else:
+ image_latents = retrieve_latents(self.vae.encode(image), sample_mode="argmax", generator=generator)
+
+ image_latents = (image_latents - self.vae.config.shift_factor) * self.vae.config.scaling_factor
+
+ if batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] == 0:
+ # expand image_latents for batch_size
+ deprecation_message = (
+ f"You have passed {batch_size} text prompts (`prompt`), but only {image_latents.shape[0]} initial"
+ " images (`image`). Initial images are now duplicating to match the number of text prompts. Note"
+ " that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update"
+ " your script to pass as many initial images as text prompts to suppress this warning."
+ )
+ deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False)
+ additional_image_per_prompt = batch_size // image_latents.shape[0]
+ image_latents = torch.cat([image_latents] * additional_image_per_prompt, dim=0)
+ elif batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] != 0:
+ raise ValueError(
+ f"Cannot duplicate `image` of batch size {image_latents.shape[0]} to {batch_size} text prompts."
+ )
+ else:
+ image_latents = torch.cat([image_latents], dim=0)
+
+ if do_classifier_free_guidance:
+ uncond_image_latents = torch.zeros_like(image_latents)
+ image_latents = torch.cat([image_latents, image_latents, uncond_image_latents], dim=0)
+
+ return image_latents
+
+ @property
+ def guidance_scale(self):
+ return self._guidance_scale
+
+ @property
+ def image_guidance_scale(self):
+ return self._image_guidance_scale
+
+ @property
+ def skip_guidance_layers(self):
+ return self._skip_guidance_layers
+
+ @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 > 1.0 and self.image_guidance_scale >= 1.0
+
+ @property
+ def joint_attention_kwargs(self):
+ return self._joint_attention_kwargs
+
+ @property
+ def num_timesteps(self):
+ return self._num_timesteps
+
+ @property
+ def interrupt(self):
+ return self._interrupt
+
+ # Adapted from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.encode_image
+ def encode_image(self, image: PipelineImageInput, device: torch.device) -> torch.Tensor:
+ """Encodes the given image into a feature representation using a pre-trained image encoder.
+
+ Args:
+ image (`PipelineImageInput`):
+ Input image to be encoded.
+ device: (`torch.device`):
+ Torch device.
+
+ Returns:
+ `torch.Tensor`: The encoded image feature representation.
+ """
+ if not isinstance(image, torch.Tensor):
+ image = self.feature_extractor(image, return_tensors="pt").pixel_values
+
+ image = image.to(device=device, dtype=self.dtype)
+
+ return self.image_encoder(image, output_hidden_states=True).hidden_states[-2]
+
+ # Adapted from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.prepare_ip_adapter_image_embeds
+ def prepare_ip_adapter_image_embeds(
+ self,
+ ip_adapter_image: Optional[PipelineImageInput] = None,
+ ip_adapter_image_embeds: Optional[torch.Tensor] = None,
+ device: Optional[torch.device] = None,
+ num_images_per_prompt: int = 1,
+ do_classifier_free_guidance: bool = True,
+ ) -> torch.Tensor:
+ """Prepares image embeddings for use in the IP-Adapter.
+
+ Either `ip_adapter_image` or `ip_adapter_image_embeds` must be passed.
+
+ Args:
+ ip_adapter_image (`PipelineImageInput`, *optional*):
+ The input image to extract features from for IP-Adapter.
+ ip_adapter_image_embeds (`torch.Tensor`, *optional*):
+ Precomputed image embeddings.
+ device: (`torch.device`, *optional*):
+ Torch device.
+ num_images_per_prompt (`int`, defaults to 1):
+ Number of images that should be generated per prompt.
+ do_classifier_free_guidance (`bool`, defaults to True):
+ Whether to use classifier free guidance or not.
+ """
+ device = device or self._execution_device
+
+ if ip_adapter_image_embeds is not None:
+ if do_classifier_free_guidance:
+ single_negative_image_embeds, single_image_embeds = ip_adapter_image_embeds.chunk(2)
+ else:
+ single_image_embeds = ip_adapter_image_embeds
+ elif ip_adapter_image is not None:
+ single_image_embeds = self.encode_image(ip_adapter_image, device)
+ if do_classifier_free_guidance:
+ single_negative_image_embeds = torch.zeros_like(single_image_embeds)
+ else:
+ raise ValueError("Neither `ip_adapter_image_embeds` or `ip_adapter_image_embeds` were provided.")
+
+ image_embeds = torch.cat([single_image_embeds] * num_images_per_prompt, dim=0)
+
+ if do_classifier_free_guidance:
+ negative_image_embeds = torch.cat([single_negative_image_embeds] * num_images_per_prompt, dim=0)
+ image_embeds = torch.cat([negative_image_embeds, image_embeds], dim=0)
+
+ return image_embeds.to(device=device)
+
+ def enable_sequential_cpu_offload(self, *args, **kwargs):
+ if self.image_encoder is not None and "image_encoder" not in self._exclude_from_cpu_offload:
+ logger.warning(
+ "`pipe.enable_sequential_cpu_offload()` might fail for `image_encoder` if it uses "
+ "`torch.nn.MultiheadAttention`. You can exclude `image_encoder` from CPU offloading by calling "
+ "`pipe._exclude_from_cpu_offload.append('image_encoder')` before `pipe.enable_sequential_cpu_offload()`."
+ )
+
+ super().enable_sequential_cpu_offload(*args, **kwargs)
+
+ @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,
+ prompt_3: Optional[Union[str, List[str]]] = None,
+ image: PipelineImageInput = None,
+ height: Optional[int] = None,
+ width: Optional[int] = None,
+ num_inference_steps: int = 28,
+ sigmas: Optional[List[float]] = None,
+ guidance_scale: float = 7.0,
+ image_guidance_scale: float = 1.5,
+ negative_prompt: Optional[Union[str, List[str]]] = None,
+ negative_prompt_2: Optional[Union[str, List[str]]] = None,
+ negative_prompt_3: Optional[Union[str, List[str]]] = None,
+ num_images_per_prompt: Optional[int] = 1,
+ generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+ latents: Optional[torch.FloatTensor] = None,
+ prompt_embeds: Optional[torch.FloatTensor] = None,
+ negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+ pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+ negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+ ip_adapter_image: Optional[PipelineImageInput] = None,
+ ip_adapter_image_embeds: Optional[torch.Tensor] = None,
+ output_type: Optional[str] = "pil",
+ return_dict: bool = True,
+ joint_attention_kwargs: Optional[Dict[str, Any]] = None,
+ clip_skip: Optional[int] = None,
+ callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
+ callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+ max_sequence_length: int = 256,
+ skip_guidance_layers: List[int] = None,
+ skip_layer_guidance_scale: float = 2.8,
+ skip_layer_guidance_stop: float = 0.2,
+ skip_layer_guidance_start: float = 0.01,
+ mu: Optional[float] = None,
+ ):
+ r"""
+ Function invoked when calling the pipeline for generation.
+
+ 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 `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
+ will be used instead
+ prompt_3 (`str` or `List[str]`, *optional*):
+ The prompt or prompts to be sent to `tokenizer_3` and `text_encoder_3`. If not defined, `prompt` is
+ will be used instead
+ height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+ The height in pixels of the generated image. This is set to 1024 by default for the best results.
+ 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.
+ 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.
+ 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 7.0):
+ Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+ `guidance_scale` is defined as `w` of equation 2. of [Imagen
+ Paper](https://arxiv.org/pdf/2205.11487.pdf). 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.
+ image_guidance_scale (`float`, *optional*, defaults to 1.5):
+ Image guidance scale is to push the generated image towards the initial image `image`. Image guidance
+ scale is enabled by setting `image_guidance_scale > 1`. Higher image guidance scale encourages to
+ generate images that are closely linked to the source image `image`, usually at the expense of lower
+ 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`).
+ negative_prompt_2 (`str` or `List[str]`, *optional*):
+ 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 instead
+ negative_prompt_3 (`str` or `List[str]`, *optional*):
+ The prompt or prompts not to guide the image generation to be sent to `tokenizer_3` and
+ `text_encoder_3`. If not defined, `negative_prompt` is used instead
+ num_images_per_prompt (`int`, *optional*, defaults to 1):
+ The number of images to generate per prompt.
+ 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.FloatTensor`, *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 sampling using the supplied random `generator`.
+ prompt_embeds (`torch.FloatTensor`, *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.FloatTensor`, *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.
+ pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+ Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
+ If not provided, pooled text embeddings will be generated from `prompt` input argument.
+ negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+ Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+ weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
+ input argument.
+ ip_adapter_image (`PipelineImageInput`, *optional*):
+ Optional image input to work with IP Adapters.
+ ip_adapter_image_embeds (`torch.Tensor`, *optional*):
+ Pre-generated image embeddings for IP-Adapter. 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 generate image. Choose between
+ [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_3.StableDiffusion3PipelineOutput`] instead of
+ a plain tuple.
+ joint_attention_kwargs (`dict`, *optional*):
+ A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+ `self.processor` in
+ [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+ callback_on_step_end (`Callable`, *optional*):
+ A function that calls at the end of each denoising steps during the inference. The function is called
+ 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.
+ max_sequence_length (`int` defaults to 256): Maximum sequence length to use with the `prompt`.
+ skip_guidance_layers (`List[int]`, *optional*):
+ A list of integers that specify layers to skip during guidance. If not provided, all layers will be
+ used for guidance. If provided, the guidance will only be applied to the layers specified in the list.
+ Recommended value by StabiltyAI for Stable Diffusion 3.5 Medium is [7, 8, 9].
+ skip_layer_guidance_scale (`int`, *optional*): The scale of the guidance for the layers specified in
+ `skip_guidance_layers`. The guidance will be applied to the layers specified in `skip_guidance_layers`
+ with a scale of `skip_layer_guidance_scale`. The guidance will be applied to the rest of the layers
+ with a scale of `1`.
+ skip_layer_guidance_stop (`int`, *optional*): The step at which the guidance for the layers specified in
+ `skip_guidance_layers` will stop. The guidance will be applied to the layers specified in
+ `skip_guidance_layers` until the fraction specified in `skip_layer_guidance_stop`. Recommended value by
+ StabiltyAI for Stable Diffusion 3.5 Medium is 0.2.
+ skip_layer_guidance_start (`int`, *optional*): The step at which the guidance for the layers specified in
+ `skip_guidance_layers` will start. The guidance will be applied to the layers specified in
+ `skip_guidance_layers` from the fraction specified in `skip_layer_guidance_start`. Recommended value by
+ StabiltyAI for Stable Diffusion 3.5 Medium is 0.01.
+ mu (`float`, *optional*): `mu` value used for `dynamic_shifting`.
+
+ Examples:
+
+ Returns:
+ [`~pipelines.stable_diffusion_3.StableDiffusion3PipelineOutput`] or `tuple`:
+ [`~pipelines.stable_diffusion_3.StableDiffusion3PipelineOutput`] if `return_dict` is True, otherwise a
+ `tuple`. When returning a tuple, the first element is a list with the generated images.
+ """
+
+ height = height or self.default_sample_size * self.vae_scale_factor
+ width = width or self.default_sample_size * self.vae_scale_factor
+
+ # 1. Check inputs. Raise error if not correct
+ self.check_inputs(
+ prompt,
+ prompt_2,
+ prompt_3,
+ height,
+ width,
+ negative_prompt=negative_prompt,
+ negative_prompt_2=negative_prompt_2,
+ negative_prompt_3=negative_prompt_3,
+ prompt_embeds=prompt_embeds,
+ negative_prompt_embeds=negative_prompt_embeds,
+ pooled_prompt_embeds=pooled_prompt_embeds,
+ negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
+ callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
+ max_sequence_length=max_sequence_length,
+ )
+
+ self._guidance_scale = guidance_scale
+ self._image_guidance_scale = image_guidance_scale
+ self._skip_layer_guidance_scale = skip_layer_guidance_scale
+ self._clip_skip = clip_skip
+ self._joint_attention_kwargs = joint_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
+
+ lora_scale = (
+ self.joint_attention_kwargs.get("scale", None) if self.joint_attention_kwargs is not None else None
+ )
+ (
+ prompt_embeds,
+ negative_prompt_embeds,
+ pooled_prompt_embeds,
+ negative_pooled_prompt_embeds,
+ ) = self.encode_prompt(
+ prompt=prompt,
+ prompt_2=prompt_2,
+ prompt_3=prompt_3,
+ negative_prompt=negative_prompt,
+ negative_prompt_2=negative_prompt_2,
+ negative_prompt_3=negative_prompt_3,
+ do_classifier_free_guidance=self.do_classifier_free_guidance,
+ prompt_embeds=prompt_embeds,
+ negative_prompt_embeds=negative_prompt_embeds,
+ pooled_prompt_embeds=pooled_prompt_embeds,
+ negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
+ device=device,
+ clip_skip=self.clip_skip,
+ num_images_per_prompt=num_images_per_prompt,
+ max_sequence_length=max_sequence_length,
+ lora_scale=lora_scale,
+ )
+
+ if self.do_classifier_free_guidance:
+ if skip_guidance_layers is not None:
+ original_prompt_embeds = prompt_embeds
+ original_pooled_prompt_embeds = pooled_prompt_embeds
+ # The extra concat similar to how it's done in SD InstructPix2Pix.
+ prompt_embeds = torch.cat([prompt_embeds, negative_prompt_embeds, negative_prompt_embeds], dim=0)
+ pooled_prompt_embeds = torch.cat(
+ [pooled_prompt_embeds, negative_pooled_prompt_embeds, negative_pooled_prompt_embeds], dim=0
+ )
+
+ # 4. Prepare latent variables
+ num_channels_latents = self.vae.config.latent_channels
+ latents = self.prepare_latents(
+ batch_size * num_images_per_prompt,
+ num_channels_latents,
+ height,
+ width,
+ prompt_embeds.dtype,
+ device,
+ generator,
+ latents,
+ )
+ # 5. Prepare image latents
+ image = self.image_processor.preprocess(image)
+ image_latents = self.prepare_image_latents(
+ image,
+ batch_size,
+ num_images_per_prompt,
+ prompt_embeds.dtype,
+ device,
+ generator,
+ self.do_classifier_free_guidance,
+ )
+
+ # 6. Check that shapes of latents and image match the DiT (SD3) in_channels
+ num_channels_image = image_latents.shape[1]
+ if num_channels_latents + num_channels_image != self.transformer.config.in_channels:
+ raise ValueError(
+ f"Incorrect configuration settings! The config of `pipeline.transformer`: {self.transformer.config} expects"
+ f" {self.transformer.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +"
+ f" `num_channels_image`: {num_channels_image} "
+ f" = {num_channels_latents + num_channels_image}. Please verify the config of"
+ " `pipeline.transformer` or your `image` input."
+ )
+
+ # 7. Prepare timesteps
+ scheduler_kwargs = {}
+ if self.scheduler.config.get("use_dynamic_shifting", None) and mu is None:
+ _, _, height, width = latents.shape
+ image_seq_len = (height // self.transformer.config.patch_size) * (
+ width // self.transformer.config.patch_size
+ )
+ mu = calculate_shift(
+ image_seq_len,
+ self.scheduler.config.get("base_image_seq_len", 256),
+ self.scheduler.config.get("max_image_seq_len", 4096),
+ self.scheduler.config.get("base_shift", 0.5),
+ self.scheduler.config.get("max_shift", 1.16),
+ )
+ scheduler_kwargs["mu"] = mu
+ elif mu is not None:
+ scheduler_kwargs["mu"] = mu
+ timesteps, num_inference_steps = retrieve_timesteps(
+ self.scheduler,
+ num_inference_steps,
+ device,
+ sigmas=sigmas,
+ **scheduler_kwargs,
+ )
+ num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+ self._num_timesteps = len(timesteps)
+
+ # 8. Prepare image embeddings
+ if (ip_adapter_image is not None and self.is_ip_adapter_active) or ip_adapter_image_embeds is not None:
+ ip_adapter_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,
+ )
+
+ if self.joint_attention_kwargs is None:
+ self._joint_attention_kwargs = {"ip_adapter_image_embeds": ip_adapter_image_embeds}
+ else:
+ self._joint_attention_kwargs.update(ip_adapter_image_embeds=ip_adapter_image_embeds)
+
+ # 9. Denoising loop
+ 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
+ # The latents are expanded 3 times because for pix2pix the guidance
+ # is applied for both the text and the input image.
+ latent_model_input = torch.cat([latents] * 3) if self.do_classifier_free_guidance else latents
+ # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+ timestep = t.expand(latent_model_input.shape[0])
+ scaled_latent_model_input = torch.cat([latent_model_input, image_latents], dim=1)
+
+ noise_pred = self.transformer(
+ hidden_states=scaled_latent_model_input,
+ timestep=timestep,
+ encoder_hidden_states=prompt_embeds,
+ pooled_projections=pooled_prompt_embeds,
+ joint_attention_kwargs=self.joint_attention_kwargs,
+ return_dict=False,
+ )[0]
+
+ # perform guidance
+ if self.do_classifier_free_guidance:
+ noise_pred_text, noise_pred_image, noise_pred_uncond = noise_pred.chunk(3)
+ noise_pred = (
+ noise_pred_uncond
+ + self.guidance_scale * (noise_pred_text - noise_pred_image)
+ + self.image_guidance_scale * (noise_pred_image - noise_pred_uncond)
+ )
+ should_skip_layers = (
+ True
+ if i > num_inference_steps * skip_layer_guidance_start
+ and i < num_inference_steps * skip_layer_guidance_stop
+ else False
+ )
+ if skip_guidance_layers is not None and should_skip_layers:
+ timestep = t.expand(latents.shape[0])
+ latent_model_input = latents
+ noise_pred_skip_layers = self.transformer(
+ hidden_states=latent_model_input,
+ timestep=timestep,
+ encoder_hidden_states=original_prompt_embeds,
+ pooled_projections=original_pooled_prompt_embeds,
+ joint_attention_kwargs=self.joint_attention_kwargs,
+ return_dict=False,
+ skip_layers=skip_guidance_layers,
+ )[0]
+ noise_pred = (
+ noise_pred + (noise_pred_text - noise_pred_skip_layers) * self._skip_layer_guidance_scale
+ )
+
+ # compute the previous noisy sample x_t -> x_t-1
+ latents_dtype = latents.dtype
+ latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
+
+ if latents.dtype != latents_dtype:
+ if torch.backends.mps.is_available():
+ # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
+ latents = latents.to(latents_dtype)
+
+ 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)
+ negative_pooled_prompt_embeds = callback_outputs.pop(
+ "negative_pooled_prompt_embeds", negative_pooled_prompt_embeds
+ )
+ image_latents = callback_outputs.pop("image_latents", image_latents)
+
+ # 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 XLA_AVAILABLE:
+ xm.mark_step()
+
+ if output_type == "latent":
+ image = latents
+
+ else:
+ latents = (latents / self.vae.config.scaling_factor) + self.vae.config.shift_factor
+ latents = latents.to(dtype=self.vae.dtype)
+
+ image = self.vae.decode(latents, return_dict=False)[0]
+ image = self.image_processor.postprocess(image, output_type=output_type)
+
+ # Offload all models
+ self.maybe_free_model_hooks()
+
+ if not return_dict:
+ return (image,)
+
+ return StableDiffusion3PipelineOutput(images=image)