mirror of
https://github.com/huggingface/diffusers.git
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1267 lines
62 KiB
Python
1267 lines
62 KiB
Python
# Copyright 2025 Stability AI, The HuggingFace Team and The InstantX Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import inspect
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from typing import Any, Callable, Dict, List, Optional, Union
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import PIL.Image
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import torch
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from transformers import (
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CLIPTextModelWithProjection,
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CLIPTokenizer,
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SiglipImageProcessor,
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SiglipVisionModel,
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T5EncoderModel,
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T5TokenizerFast,
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)
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from ...image_processor import PipelineImageInput, VaeImageProcessor
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from ...loaders import FromSingleFileMixin, SD3IPAdapterMixin, SD3LoraLoaderMixin
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from ...models.autoencoders import AutoencoderKL
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from ...models.transformers import SD3Transformer2DModel
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from ...schedulers import FlowMatchEulerDiscreteScheduler
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from ...utils import (
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USE_PEFT_BACKEND,
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deprecate,
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is_torch_xla_available,
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logging,
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replace_example_docstring,
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scale_lora_layers,
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unscale_lora_layers,
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)
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from ...utils.torch_utils import randn_tensor
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from ..pipeline_utils import DiffusionPipeline
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from .pipeline_output import StableDiffusion3PipelineOutput
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if is_torch_xla_available():
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import torch_xla.core.xla_model as xm
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XLA_AVAILABLE = True
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else:
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XLA_AVAILABLE = False
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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EXAMPLE_DOC_STRING = """
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Examples:
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```py
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>>> import torch
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>>> from diffusers import StableDiffusion3InstructPix2PixPipeline
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>>> from diffusers.utils import load_image
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>>> resolution = 1024
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>>> image = load_image(
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... "https://hf.co/datasets/diffusers/diffusers-images-docs/resolve/main/mountain.png"
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... ).resize((resolution, resolution))
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>>> edit_instruction = "Turn sky into a cloudy one"
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>>> pipe = StableDiffusion3InstructPix2PixPipeline.from_pretrained(
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... "your_own_model_path", torch_dtype=torch.float16
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... ).to("cuda")
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>>> edited_image = pipe(
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... prompt=edit_instruction,
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... image=image,
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... height=resolution,
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... width=resolution,
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... guidance_scale=7.5,
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... image_guidance_scale=1.5,
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... num_inference_steps=30,
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... ).images[0]
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>>> edited_image
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```
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"""
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# Copied from diffusers.pipelines.flux.pipeline_flux.calculate_shift
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def calculate_shift(
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image_seq_len,
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base_seq_len: int = 256,
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max_seq_len: int = 4096,
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base_shift: float = 0.5,
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max_shift: float = 1.15,
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):
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m = (max_shift - base_shift) / (max_seq_len - base_seq_len)
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b = base_shift - m * base_seq_len
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mu = image_seq_len * m + b
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return mu
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents
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def retrieve_latents(
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encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "sample"
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):
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if hasattr(encoder_output, "latent_dist") and sample_mode == "sample":
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return encoder_output.latent_dist.sample(generator)
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elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax":
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return encoder_output.latent_dist.mode()
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elif hasattr(encoder_output, "latents"):
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return encoder_output.latents
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else:
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raise AttributeError("Could not access latents of provided encoder_output")
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
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def retrieve_timesteps(
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scheduler,
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num_inference_steps: Optional[int] = None,
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device: Optional[Union[str, torch.device]] = None,
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timesteps: Optional[List[int]] = None,
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sigmas: Optional[List[float]] = None,
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**kwargs,
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):
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r"""
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Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
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custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
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Args:
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scheduler (`SchedulerMixin`):
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The scheduler to get timesteps from.
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num_inference_steps (`int`):
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The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
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must be `None`.
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device (`str` or `torch.device`, *optional*):
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The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
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timesteps (`List[int]`, *optional*):
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Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
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`num_inference_steps` and `sigmas` must be `None`.
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sigmas (`List[float]`, *optional*):
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Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
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`num_inference_steps` and `timesteps` must be `None`.
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Returns:
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`Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
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second element is the number of inference steps.
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"""
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if timesteps is not None and sigmas is not None:
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raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
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if timesteps is not None:
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accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
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if not accepts_timesteps:
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raise ValueError(
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f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
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f" timestep schedules. Please check whether you are using the correct scheduler."
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)
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scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
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timesteps = scheduler.timesteps
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num_inference_steps = len(timesteps)
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elif sigmas is not None:
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accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
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if not accept_sigmas:
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raise ValueError(
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f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
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f" sigmas schedules. Please check whether you are using the correct scheduler."
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)
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scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
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timesteps = scheduler.timesteps
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num_inference_steps = len(timesteps)
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else:
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scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
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timesteps = scheduler.timesteps
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return timesteps, num_inference_steps
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class StableDiffusion3InstructPix2PixPipeline(
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DiffusionPipeline, SD3LoraLoaderMixin, FromSingleFileMixin, SD3IPAdapterMixin
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):
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r"""
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Args:
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transformer ([`SD3Transformer2DModel`]):
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Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
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scheduler ([`FlowMatchEulerDiscreteScheduler`]):
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A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
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vae ([`AutoencoderKL`]):
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Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
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text_encoder ([`CLIPTextModelWithProjection`]):
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[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),
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specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant,
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with an additional added projection layer that is initialized with a diagonal matrix with the `hidden_size`
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as its dimension.
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text_encoder_2 ([`CLIPTextModelWithProjection`]):
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[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),
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specifically the
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[laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)
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variant.
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text_encoder_3 ([`T5EncoderModel`]):
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Frozen text-encoder. Stable Diffusion 3 uses
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[T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel), specifically the
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[t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl) variant.
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tokenizer (`CLIPTokenizer`):
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Tokenizer of class
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[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
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tokenizer_2 (`CLIPTokenizer`):
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Second Tokenizer of class
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[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
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tokenizer_3 (`T5TokenizerFast`):
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Tokenizer of class
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[T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer).
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image_encoder (`SiglipVisionModel`, *optional*):
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Pre-trained Vision Model for IP Adapter.
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feature_extractor (`SiglipImageProcessor`, *optional*):
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Image processor for IP Adapter.
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"""
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model_cpu_offload_seq = "text_encoder->text_encoder_2->text_encoder_3->image_encoder->transformer->vae"
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_optional_components = ["image_encoder", "feature_extractor"]
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_callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds", "negative_pooled_prompt_embeds"]
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def __init__(
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self,
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transformer: SD3Transformer2DModel,
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scheduler: FlowMatchEulerDiscreteScheduler,
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vae: AutoencoderKL,
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text_encoder: CLIPTextModelWithProjection,
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tokenizer: CLIPTokenizer,
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text_encoder_2: CLIPTextModelWithProjection,
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tokenizer_2: CLIPTokenizer,
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text_encoder_3: T5EncoderModel,
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tokenizer_3: T5TokenizerFast,
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image_encoder: SiglipVisionModel = None,
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feature_extractor: SiglipImageProcessor = None,
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):
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super().__init__()
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self.register_modules(
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vae=vae,
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text_encoder=text_encoder,
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text_encoder_2=text_encoder_2,
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text_encoder_3=text_encoder_3,
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tokenizer=tokenizer,
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tokenizer_2=tokenizer_2,
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tokenizer_3=tokenizer_3,
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transformer=transformer,
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scheduler=scheduler,
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image_encoder=image_encoder,
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feature_extractor=feature_extractor,
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)
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self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
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self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
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self.tokenizer_max_length = (
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self.tokenizer.model_max_length if hasattr(self, "tokenizer") and self.tokenizer is not None else 77
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)
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self.default_sample_size = (
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self.transformer.config.sample_size
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if hasattr(self, "transformer") and self.transformer is not None
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else 128
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)
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self.patch_size = (
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self.transformer.config.patch_size if hasattr(self, "transformer") and self.transformer is not None else 2
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)
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def _get_t5_prompt_embeds(
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self,
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prompt: Union[str, List[str]] = None,
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num_images_per_prompt: int = 1,
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max_sequence_length: int = 256,
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device: Optional[torch.device] = None,
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dtype: Optional[torch.dtype] = None,
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):
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device = device or self._execution_device
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dtype = dtype or self.text_encoder.dtype
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prompt = [prompt] if isinstance(prompt, str) else prompt
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batch_size = len(prompt)
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if self.text_encoder_3 is None:
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return torch.zeros(
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(
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batch_size * num_images_per_prompt,
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self.tokenizer_max_length,
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self.transformer.config.joint_attention_dim,
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),
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device=device,
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dtype=dtype,
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)
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text_inputs = self.tokenizer_3(
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prompt,
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padding="max_length",
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max_length=max_sequence_length,
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truncation=True,
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add_special_tokens=True,
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return_tensors="pt",
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)
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text_input_ids = text_inputs.input_ids
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untruncated_ids = self.tokenizer_3(prompt, padding="longest", return_tensors="pt").input_ids
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if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
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removed_text = self.tokenizer_3.batch_decode(untruncated_ids[:, self.tokenizer_max_length - 1 : -1])
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logger.warning(
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"The following part of your input was truncated because `max_sequence_length` is set to "
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f" {max_sequence_length} tokens: {removed_text}"
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)
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prompt_embeds = self.text_encoder_3(text_input_ids.to(device))[0]
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dtype = self.text_encoder_3.dtype
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prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
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_, seq_len, _ = prompt_embeds.shape
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# duplicate text embeddings and attention mask for each generation per prompt, using mps friendly method
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prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
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prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
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return prompt_embeds
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def _get_clip_prompt_embeds(
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self,
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prompt: Union[str, List[str]],
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num_images_per_prompt: int = 1,
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device: Optional[torch.device] = None,
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clip_skip: Optional[int] = None,
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clip_model_index: int = 0,
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):
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device = device or self._execution_device
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clip_tokenizers = [self.tokenizer, self.tokenizer_2]
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clip_text_encoders = [self.text_encoder, self.text_encoder_2]
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tokenizer = clip_tokenizers[clip_model_index]
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text_encoder = clip_text_encoders[clip_model_index]
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prompt = [prompt] if isinstance(prompt, str) else prompt
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batch_size = len(prompt)
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text_inputs = tokenizer(
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prompt,
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padding="max_length",
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max_length=self.tokenizer_max_length,
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truncation=True,
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return_tensors="pt",
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)
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text_input_ids = text_inputs.input_ids
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untruncated_ids = tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
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if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
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removed_text = tokenizer.batch_decode(untruncated_ids[:, self.tokenizer_max_length - 1 : -1])
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logger.warning(
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"The following part of your input was truncated because CLIP can only handle sequences up to"
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f" {self.tokenizer_max_length} tokens: {removed_text}"
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)
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prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)
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pooled_prompt_embeds = prompt_embeds[0]
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if clip_skip is None:
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prompt_embeds = prompt_embeds.hidden_states[-2]
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else:
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prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 2)]
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prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
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_, seq_len, _ = prompt_embeds.shape
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# duplicate text embeddings for each generation per prompt, using mps friendly method
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prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
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prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
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pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt, 1)
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pooled_prompt_embeds = pooled_prompt_embeds.view(batch_size * num_images_per_prompt, -1)
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return prompt_embeds, pooled_prompt_embeds
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def encode_prompt(
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self,
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prompt: Union[str, List[str]],
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prompt_2: Union[str, List[str]],
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prompt_3: Union[str, List[str]],
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device: Optional[torch.device] = None,
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num_images_per_prompt: int = 1,
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do_classifier_free_guidance: bool = True,
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negative_prompt: Optional[Union[str, List[str]]] = None,
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negative_prompt_2: Optional[Union[str, List[str]]] = None,
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negative_prompt_3: Optional[Union[str, List[str]]] = None,
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prompt_embeds: Optional[torch.FloatTensor] = None,
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negative_prompt_embeds: Optional[torch.FloatTensor] = None,
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pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
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negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
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clip_skip: Optional[int] = None,
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max_sequence_length: int = 256,
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lora_scale: Optional[float] = None,
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):
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r"""
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Args:
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prompt (`str` or `List[str]`, *optional*):
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prompt to be encoded
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prompt_2 (`str` or `List[str]`, *optional*):
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The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
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used in all text-encoders
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prompt_3 (`str` or `List[str]`, *optional*):
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The prompt or prompts to be sent to the `tokenizer_3` and `text_encoder_3`. If not defined, `prompt` is
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used in all text-encoders
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device: (`torch.device`):
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torch device
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num_images_per_prompt (`int`):
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number of images that should be generated per prompt
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do_classifier_free_guidance (`bool`):
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whether to use classifier free guidance or not
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negative_prompt (`str` or `List[str]`, *optional*):
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The prompt or prompts not to guide the image generation. If not defined, one has to pass
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`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
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less than `1`).
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negative_prompt_2 (`str` or `List[str]`, *optional*):
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The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
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`text_encoder_2`. If not defined, `negative_prompt` is used in all the text-encoders.
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negative_prompt_3 (`str` or `List[str]`, *optional*):
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The prompt or prompts not to guide the image generation to be sent to `tokenizer_3` and
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`text_encoder_3`. If not defined, `negative_prompt` is used in all the text-encoders.
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prompt_embeds (`torch.FloatTensor`, *optional*):
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Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
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provided, text embeddings will be generated from `prompt` input argument.
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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://huggingface.co/papers/2205.11487 . `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://huggingface.co/papers/2207.12598).
|
|
`guidance_scale` is defined as `w` of equation 2. of [Imagen
|
|
Paper](https://huggingface.co/papers/2205.11487). 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 be 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)
|