514 lines
23 KiB
Python
514 lines
23 KiB
Python
# Copyright 2025 OmniGen team and The HuggingFace 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 Callable, Dict, List, Optional, Union
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import numpy as np
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import torch
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from transformers import LlamaTokenizer
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from ...image_processor import PipelineImageInput, VaeImageProcessor
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from ...models.autoencoders import AutoencoderKL
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from ...models.transformers import OmniGenTransformer2DModel
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from ...schedulers import FlowMatchEulerDiscreteScheduler
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from ...utils import is_torch_xla_available, is_torchvision_available, logging, replace_example_docstring
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from ...utils.torch_utils import randn_tensor
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from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
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if is_torchvision_available():
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from .processor_omnigen import OmniGenMultiModalProcessor
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if is_torch_xla_available():
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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 OmniGenPipeline
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>>> pipe = OmniGenPipeline.from_pretrained("Shitao/OmniGen-v1-diffusers", torch_dtype=torch.bfloat16)
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>>> pipe.to("cuda")
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>>> prompt = "A cat holding a sign that says hello world"
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>>> # Depending on the variant being used, the pipeline call will slightly vary.
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>>> # Refer to the pipeline documentation for more details.
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>>> image = pipe(prompt, num_inference_steps=50, guidance_scale=2.5).images[0]
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>>> image.save("output.png")
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```
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"""
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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 OmniGenPipeline(
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DiffusionPipeline,
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):
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r"""
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The OmniGen pipeline for multimodal-to-image generation.
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Reference: https://huggingface.co/papers/2409.11340
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Args:
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transformer ([`OmniGenTransformer2DModel`]):
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Autoregressive Transformer architecture for OmniGen.
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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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tokenizer (`LlamaTokenizer`):
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Text tokenizer of class.
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[LlamaTokenizer](https://huggingface.co/docs/transformers/main/model_doc/llama#transformers.LlamaTokenizer).
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"""
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model_cpu_offload_seq = "transformer->vae"
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_optional_components = []
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_callback_tensor_inputs = ["latents"]
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def __init__(
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self,
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transformer: OmniGenTransformer2DModel,
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scheduler: FlowMatchEulerDiscreteScheduler,
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vae: AutoencoderKL,
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tokenizer: LlamaTokenizer,
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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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tokenizer=tokenizer,
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transformer=transformer,
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scheduler=scheduler,
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)
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self.vae_scale_factor = (
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2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) is not None else 8
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)
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# OmniGen latents are turned into 2x2 patches and packed. This means the latent width and height has to be divisible
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# by the patch size. So the vae scale factor is multiplied by the patch size to account for this
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self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor * 2)
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self.multimodal_processor = OmniGenMultiModalProcessor(tokenizer, max_image_size=1024)
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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 120000
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)
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self.default_sample_size = 128
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def encode_input_images(
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self,
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input_pixel_values: List[torch.Tensor],
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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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"""
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get the continue embedding of input images by VAE
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Args:
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input_pixel_values: normalized pixel of input images
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device:
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Returns: torch.Tensor
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"""
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device = device or self._execution_device
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dtype = dtype or self.vae.dtype
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input_img_latents = []
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for img in input_pixel_values:
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img = self.vae.encode(img.to(device, dtype)).latent_dist.sample().mul_(self.vae.config.scaling_factor)
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input_img_latents.append(img)
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return input_img_latents
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def check_inputs(
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self,
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prompt,
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input_images,
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height,
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width,
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use_input_image_size_as_output,
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callback_on_step_end_tensor_inputs=None,
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):
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if input_images is not None:
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if len(input_images) != len(prompt):
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raise ValueError(
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f"The number of prompts: {len(prompt)} does not match the number of input images: {len(input_images)}."
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)
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for i in range(len(input_images)):
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if input_images[i] is not None:
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if not all(f"<img><|image_{k + 1}|></img>" in prompt[i] for k in range(len(input_images[i]))):
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raise ValueError(
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f"prompt `{prompt[i]}` doesn't have enough placeholders for the input images `{input_images[i]}`"
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)
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if height % (self.vae_scale_factor * 2) != 0 or width % (self.vae_scale_factor * 2) != 0:
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logger.warning(
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f"`height` and `width` have to be divisible by {self.vae_scale_factor * 2} but are {height} and {width}. Dimensions will be resized accordingly"
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)
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if use_input_image_size_as_output:
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if input_images is None or input_images[0] is None:
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raise ValueError(
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"`use_input_image_size_as_output` is set to True, but no input image was found. If you are performing a text-to-image task, please set it to False."
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)
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if callback_on_step_end_tensor_inputs is not None and not all(
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k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
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):
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raise ValueError(
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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]}"
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)
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def enable_vae_slicing(self):
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r"""
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Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
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compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
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"""
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self.vae.enable_slicing()
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def disable_vae_slicing(self):
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r"""
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Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
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computing decoding in one step.
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"""
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self.vae.disable_slicing()
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def enable_vae_tiling(self):
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r"""
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Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
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compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
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processing larger images.
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"""
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self.vae.enable_tiling()
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def disable_vae_tiling(self):
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r"""
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Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
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computing decoding in one step.
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"""
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self.vae.disable_tiling()
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# Copied from diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3.StableDiffusion3Pipeline.prepare_latents
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def prepare_latents(
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self,
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batch_size,
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num_channels_latents,
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height,
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width,
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dtype,
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device,
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generator,
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latents=None,
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):
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if latents is not None:
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return latents.to(device=device, dtype=dtype)
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shape = (
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batch_size,
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num_channels_latents,
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int(height) // self.vae_scale_factor,
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int(width) // self.vae_scale_factor,
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)
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if isinstance(generator, list) and len(generator) != batch_size:
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raise ValueError(
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f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
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f" size of {batch_size}. Make sure the batch size matches the length of the generators."
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)
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latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
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return latents
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@property
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def guidance_scale(self):
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return self._guidance_scale
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@property
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def num_timesteps(self):
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return self._num_timesteps
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@property
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def interrupt(self):
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return self._interrupt
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@torch.no_grad()
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@replace_example_docstring(EXAMPLE_DOC_STRING)
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def __call__(
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self,
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prompt: Union[str, List[str]],
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input_images: Union[PipelineImageInput, List[PipelineImageInput]] = None,
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height: Optional[int] = None,
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width: Optional[int] = None,
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num_inference_steps: int = 50,
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max_input_image_size: int = 1024,
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timesteps: List[int] = None,
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guidance_scale: float = 2.5,
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img_guidance_scale: float = 1.6,
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use_input_image_size_as_output: bool = False,
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num_images_per_prompt: Optional[int] = 1,
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generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
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latents: Optional[torch.Tensor] = None,
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output_type: Optional[str] = "pil",
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return_dict: bool = True,
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callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
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callback_on_step_end_tensor_inputs: List[str] = ["latents"],
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):
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r"""
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Function invoked when calling the pipeline for generation.
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Args:
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prompt (`str` or `List[str]`, *optional*):
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The prompt or prompts to guide the image generation. If the input includes images, need to add
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placeholders `<img><|image_i|></img>` in the prompt to indicate the position of the i-th images.
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input_images (`PipelineImageInput` or `List[PipelineImageInput]`, *optional*):
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The list of input images. We will replace the "<|image_i|>" in prompt with the i-th image in list.
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height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
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The height in pixels of the generated image. This is set to 1024 by default for the best results.
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width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
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The width in pixels of the generated image. This is set to 1024 by default for the best results.
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num_inference_steps (`int`, *optional*, defaults to 50):
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The number of denoising steps. More denoising steps usually lead to a higher quality image at the
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expense of slower inference.
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max_input_image_size (`int`, *optional*, defaults to 1024):
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the maximum size of input image, which will be used to crop the input image to the maximum size
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timesteps (`List[int]`, *optional*):
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Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
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in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
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passed will be used. Must be in descending order.
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guidance_scale (`float`, *optional*, defaults to 2.5):
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Guidance scale as defined in [Classifier-Free Diffusion
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Guidance](https://huggingface.co/papers/2207.12598). `guidance_scale` is defined as `w` of equation 2.
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of [Imagen Paper](https://huggingface.co/papers/2205.11487). Guidance scale is enabled by setting
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`guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to
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the text `prompt`, usually at the expense of lower image quality.
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img_guidance_scale (`float`, *optional*, defaults to 1.6):
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Defined as equation 3 in [Instrucpix2pix](https://huggingface.co/papers/2211.09800).
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use_input_image_size_as_output (bool, defaults to False):
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whether to use the input image size as the output image size, which can be used for single-image input,
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e.g., image editing task
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num_images_per_prompt (`int`, *optional*, defaults to 1):
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The number of images to generate per prompt.
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generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
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One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
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to make generation deterministic.
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latents (`torch.Tensor`, *optional*):
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Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
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generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
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tensor will ge generated by sampling using the supplied random `generator`.
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output_type (`str`, *optional*, defaults to `"pil"`):
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The output format of the generate image. Choose between
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[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
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return_dict (`bool`, *optional*, defaults to `True`):
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Whether or not to return a [`~pipelines.flux.FluxPipelineOutput`] instead of a plain tuple.
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callback_on_step_end (`Callable`, *optional*):
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A function that calls at the end of each denoising steps during the inference. The function is called
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with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
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callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
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`callback_on_step_end_tensor_inputs`.
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callback_on_step_end_tensor_inputs (`List`, *optional*):
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The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
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will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
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`._callback_tensor_inputs` attribute of your pipeline class.
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Examples:
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Returns: [`~pipelines.ImagePipelineOutput`] or `tuple`:
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If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is returned
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where the first element is a list with the generated images.
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"""
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height = height or self.default_sample_size * self.vae_scale_factor
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width = width or self.default_sample_size * self.vae_scale_factor
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num_cfg = 2 if input_images is not None else 1
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use_img_cfg = True if input_images is not None else False
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if isinstance(prompt, str):
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prompt = [prompt]
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input_images = [input_images]
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# 1. Check inputs. Raise error if not correct
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self.check_inputs(
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prompt,
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input_images,
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height,
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width,
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use_input_image_size_as_output,
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callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
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)
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self._guidance_scale = guidance_scale
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self._interrupt = False
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# 2. Define call parameters
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batch_size = len(prompt)
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device = self._execution_device
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# 3. process multi-modal instructions
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if max_input_image_size != self.multimodal_processor.max_image_size:
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self.multimodal_processor.reset_max_image_size(max_image_size=max_input_image_size)
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processed_data = self.multimodal_processor(
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prompt,
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input_images,
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height=height,
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width=width,
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use_img_cfg=use_img_cfg,
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use_input_image_size_as_output=use_input_image_size_as_output,
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num_images_per_prompt=num_images_per_prompt,
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)
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processed_data["input_ids"] = processed_data["input_ids"].to(device)
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processed_data["attention_mask"] = processed_data["attention_mask"].to(device)
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processed_data["position_ids"] = processed_data["position_ids"].to(device)
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# 4. Encode input images
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input_img_latents = self.encode_input_images(processed_data["input_pixel_values"], device=device)
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# 5. Prepare timesteps
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sigmas = np.linspace(1, 0, num_inference_steps + 1)[:num_inference_steps]
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timesteps, num_inference_steps = retrieve_timesteps(
|
|
self.scheduler, num_inference_steps, device, timesteps, sigmas=sigmas
|
|
)
|
|
self._num_timesteps = len(timesteps)
|
|
|
|
# 6. Prepare latents
|
|
transformer_dtype = self.transformer.dtype
|
|
if use_input_image_size_as_output:
|
|
height, width = processed_data["input_pixel_values"][0].shape[-2:]
|
|
latent_channels = self.transformer.config.in_channels
|
|
latents = self.prepare_latents(
|
|
batch_size * num_images_per_prompt,
|
|
latent_channels,
|
|
height,
|
|
width,
|
|
torch.float32,
|
|
device,
|
|
generator,
|
|
latents,
|
|
)
|
|
|
|
# 8. Denoising loop
|
|
with self.progress_bar(total=num_inference_steps) as progress_bar:
|
|
for i, t in enumerate(timesteps):
|
|
# expand the latents if we are doing classifier free guidance
|
|
latent_model_input = torch.cat([latents] * (num_cfg + 1))
|
|
latent_model_input = latent_model_input.to(transformer_dtype)
|
|
|
|
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
|
|
timestep = t.expand(latent_model_input.shape[0])
|
|
|
|
noise_pred = self.transformer(
|
|
hidden_states=latent_model_input,
|
|
timestep=timestep,
|
|
input_ids=processed_data["input_ids"],
|
|
input_img_latents=input_img_latents,
|
|
input_image_sizes=processed_data["input_image_sizes"],
|
|
attention_mask=processed_data["attention_mask"],
|
|
position_ids=processed_data["position_ids"],
|
|
return_dict=False,
|
|
)[0]
|
|
|
|
if num_cfg == 2:
|
|
cond, uncond, img_cond = torch.split(noise_pred, len(noise_pred) // 3, dim=0)
|
|
noise_pred = uncond + img_guidance_scale * (img_cond - uncond) + guidance_scale * (cond - img_cond)
|
|
else:
|
|
cond, uncond = torch.split(noise_pred, len(noise_pred) // 2, dim=0)
|
|
noise_pred = uncond + guidance_scale * (cond - uncond)
|
|
|
|
# compute the previous noisy sample x_t -> x_t-1
|
|
latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
|
|
|
|
if callback_on_step_end is not None:
|
|
callback_kwargs = {}
|
|
for k in callback_on_step_end_tensor_inputs:
|
|
callback_kwargs[k] = locals()[k]
|
|
callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
|
|
|
|
latents = callback_outputs.pop("latents", latents)
|
|
|
|
progress_bar.update()
|
|
|
|
if not output_type == "latent":
|
|
latents = latents.to(self.vae.dtype)
|
|
latents = latents / self.vae.config.scaling_factor
|
|
image = self.vae.decode(latents, return_dict=False)[0]
|
|
image = self.image_processor.postprocess(image, output_type=output_type)
|
|
else:
|
|
image = latents
|
|
|
|
# Offload all models
|
|
self.maybe_free_model_hooks()
|
|
|
|
if not return_dict:
|
|
return (image,)
|
|
|
|
return ImagePipelineOutput(images=image)
|