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add pipeline
This commit is contained in:
Chong
@@ -168,6 +168,7 @@ else:
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ShapEImg2ImgPipeline,
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ShapEPipeline,
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StableDiffusionAdapterPipeline,
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StableDiffusionXLAdapterPipeline,
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StableDiffusionAttendAndExcitePipeline,
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StableDiffusionControlNetImg2ImgPipeline,
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StableDiffusionControlNetInpaintPipeline,
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@@ -117,6 +117,7 @@ else:
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StableDiffusionXLPipeline,
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)
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from .t2i_adapter import StableDiffusionAdapterPipeline
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from .t2i_adapter import StableDiffusionAdapterPipeline
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from .text_to_video_synthesis import TextToVideoSDPipeline, TextToVideoZeroPipeline, VideoToVideoSDPipeline
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from .unclip import UnCLIPImageVariationPipeline, UnCLIPPipeline
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from .unidiffuser import ImageTextPipelineOutput, UniDiffuserModel, UniDiffuserPipeline, UniDiffuserTextDecoder
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@@ -12,3 +12,4 @@ except OptionalDependencyNotAvailable:
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from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
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else:
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from .pipeline_stable_diffusion_adapter import StableDiffusionAdapterPipeline
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from .pipeline_stable_diffusion_xl_adapter import StableDiffusionXLAdapterPipeline
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@@ -0,0 +1,939 @@
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# Copyright 2023 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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import os
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from typing import Any, Callable, Dict, List, Optional, Tuple, Union
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from dataclasses import dataclass
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import torch
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from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
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import PIL
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import numpy as np
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from ...image_processor import VaeImageProcessor
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from ...loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin
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from ...models import AutoencoderKL, MultiAdapter, T2IAdapter, UNet2DConditionModel
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from ...models.attention_processor import (
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AttnProcessor2_0,
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LoRAAttnProcessor2_0,
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LoRAXFormersAttnProcessor,
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XFormersAttnProcessor,
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)
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from ...schedulers import KarrasDiffusionSchedulers
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from ...utils import (
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PIL_INTERPOLATION,
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BaseOutput,
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is_accelerate_available,
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is_accelerate_version,
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logging,
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randn_tensor,
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replace_example_docstring,
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)
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from ..pipeline_utils import DiffusionPipeline
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from basicsr.utils import tensor2img
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@dataclass
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class StableDiffusionAdapterXLPipelineOutput(BaseOutput):
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"""
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Output class for Stable Diffusion pipelines.
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Args:
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images (`List[PIL.Image.Image]` or `np.ndarray`)
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List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width,
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num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline.
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"""
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images: Union[List[PIL.Image.Image], np.ndarray]
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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 StableDiffusionXLPipeline
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>>> pipe = StableDiffusionXLPipeline.from_pretrained(
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... "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
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... )
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>>> pipe = pipe.to("cuda")
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>>> prompt = "a photo of an astronaut riding a horse on mars"
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>>> image = pipe(prompt).images[0]
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```
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"""
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def _preprocess_adapter_image(image, height, width):
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if isinstance(image, torch.Tensor):
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return image
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elif isinstance(image, PIL.Image.Image):
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image = [image]
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if isinstance(image[0], PIL.Image.Image):
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image = [np.array(i.resize((width, height), resample=PIL_INTERPOLATION["lanczos"])) for i in image]
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image = [
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i[None, ..., None] if i.ndim == 2 else i[None, ...] for i in image
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] # expand [h, w] or [h, w, c] to [b, h, w, c]
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image = np.concatenate(image, axis=0)
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image = np.array(image).astype(np.float32) / 255.0
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image = image.transpose(0, 3, 1, 2)
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image = torch.from_numpy(image)
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elif isinstance(image[0], torch.Tensor):
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if image[0].ndim == 3:
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image = torch.stack(image, dim=0)
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elif image[0].ndim == 4:
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image = torch.cat(image, dim=0)
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else:
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raise ValueError(
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f"Invalid image tensor! Expecting image tensor with 3 or 4 dimension, but recive: {image[0].ndim}"
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)
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return image
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg
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def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
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"""
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Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
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Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4
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"""
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std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
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std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
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# rescale the results from guidance (fixes overexposure)
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noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
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# mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
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noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
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return noise_cfg
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class StableDiffusionXLAdapterPipeline(DiffusionPipeline, FromSingleFileMixin, LoraLoaderMixin):
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r"""
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Pipeline for text-to-image generation using Stable Diffusion augmented with T2I-Adapter
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https://arxiv.org/abs/2302.08453
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This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
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library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
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Args:
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adapter ([`T2IAdapter`] or [`MultiAdapter`] or `List[T2IAdapter]`):
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Provides additional conditioning to the unet during the denoising process. If you set multiple Adapter as a
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list, the outputs from each Adapter are added together to create one combined additional conditioning.
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adapter_weights (`List[float]`, *optional*, defaults to None):
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List of floats representing the weight which will be multiply to each adapter's output before adding them
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together.
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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 ([`CLIPTextModel`]):
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Frozen text-encoder. Stable Diffusion uses the text portion of
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[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
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the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) 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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unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
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scheduler ([`SchedulerMixin`]):
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A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
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[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
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safety_checker ([`StableDiffusionSafetyChecker`]):
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Classification module that estimates whether generated images could be considered offensive or harmful.
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Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
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feature_extractor ([`CLIPFeatureExtractor`]):
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Model that extracts features from generated images to be used as inputs for the `safety_checker`.
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"""
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def __init__(
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self,
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vae: AutoencoderKL,
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text_encoder: CLIPTextModel,
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text_encoder_2: CLIPTextModelWithProjection,
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tokenizer: CLIPTokenizer,
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tokenizer_2: CLIPTokenizer,
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unet: UNet2DConditionModel,
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adapter: Union[T2IAdapter, MultiAdapter, List[T2IAdapter]],
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scheduler: KarrasDiffusionSchedulers,
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force_zeros_for_empty_prompt: bool = True,
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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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tokenizer=tokenizer,
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tokenizer_2=tokenizer_2,
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unet=unet,
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adapter=adapter,
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scheduler=scheduler,
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)
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self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)
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self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
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self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
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self.default_sample_size = self.unet.config.sample_size
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing
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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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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_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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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_tiling
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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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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_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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def enable_model_cpu_offload(self, gpu_id=0):
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r"""
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Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
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to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
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method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
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`enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
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"""
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if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
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from accelerate import cpu_offload_with_hook
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else:
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raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
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device = torch.device(f"cuda:{gpu_id}")
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if self.device.type != "cpu":
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self.to("cpu", silence_dtype_warnings=True)
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torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
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model_sequence = (
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[self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]
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)
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model_sequence.extend([self.unet, self.vae, self.adapter])
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hook = None
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for cpu_offloaded_model in model_sequence:
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_, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
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# We'll offload the last model manually.
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self.final_offload_hook = hook
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def encode_prompt(
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self,
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prompt: str,
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prompt_2: Optional[str] = None,
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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[str] = None,
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negative_prompt_2: Optional[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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lora_scale: Optional[float] = None,
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):
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r"""
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Encodes the prompt into text encoder hidden states.
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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 both 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 both 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*):
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Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
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weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
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argument.
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pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
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Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
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If not provided, pooled text embeddings will be generated from `prompt` input argument.
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negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
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Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
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weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
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input argument.
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lora_scale (`float`, *optional*):
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A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
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"""
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device = device or self._execution_device
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# set lora scale so that monkey patched LoRA
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# function of text encoder can correctly access it
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if lora_scale is not None and isinstance(self, LoraLoaderMixin):
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self._lora_scale = lora_scale
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if prompt is not None and isinstance(prompt, str):
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batch_size = 1
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elif prompt is not None and isinstance(prompt, list):
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batch_size = len(prompt)
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else:
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batch_size = prompt_embeds.shape[0]
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# Define tokenizers and text encoders
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tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]
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text_encoders = (
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[self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]
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)
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if prompt_embeds is None:
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prompt_2 = prompt_2 or prompt
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# textual inversion: procecss multi-vector tokens if necessary
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prompt_embeds_list = []
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prompts = [prompt, prompt_2]
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for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders):
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if isinstance(self, TextualInversionLoaderMixin):
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prompt = self.maybe_convert_prompt(prompt, tokenizer)
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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=tokenizer.model_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(
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text_input_ids, untruncated_ids
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):
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removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_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" {tokenizer.model_max_length} tokens: {removed_text}"
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)
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prompt_embeds = text_encoder(
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text_input_ids.to(device),
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output_hidden_states=True,
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)
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# We are only ALWAYS interested in the pooled output of the final text encoder
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pooled_prompt_embeds = prompt_embeds[0]
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prompt_embeds = prompt_embeds.hidden_states[-2]
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prompt_embeds_list.append(prompt_embeds)
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prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)
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# get unconditional embeddings for classifier free guidance
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zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt
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if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:
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negative_prompt_embeds = torch.zeros_like(prompt_embeds)
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negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)
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elif do_classifier_free_guidance and negative_prompt_embeds is None:
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negative_prompt = negative_prompt or ""
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negative_prompt_2 = negative_prompt_2 or negative_prompt
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uncond_tokens: List[str]
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if prompt is not None and type(prompt) is not type(negative_prompt):
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raise TypeError(
|
||||
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
|
||||
f" {type(prompt)}."
|
||||
)
|
||||
elif isinstance(negative_prompt, str):
|
||||
uncond_tokens = [negative_prompt, negative_prompt_2]
|
||||
elif batch_size != len(negative_prompt):
|
||||
raise ValueError(
|
||||
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
|
||||
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
|
||||
" the batch size of `prompt`."
|
||||
)
|
||||
else:
|
||||
uncond_tokens = [negative_prompt, negative_prompt_2]
|
||||
|
||||
negative_prompt_embeds_list = []
|
||||
for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):
|
||||
if isinstance(self, TextualInversionLoaderMixin):
|
||||
negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)
|
||||
|
||||
max_length = prompt_embeds.shape[1]
|
||||
uncond_input = tokenizer(
|
||||
negative_prompt,
|
||||
padding="max_length",
|
||||
max_length=max_length,
|
||||
truncation=True,
|
||||
return_tensors="pt",
|
||||
)
|
||||
|
||||
negative_prompt_embeds = text_encoder(
|
||||
uncond_input.input_ids.to(device),
|
||||
output_hidden_states=True,
|
||||
)
|
||||
# We are only ALWAYS interested in the pooled output of the final text encoder
|
||||
negative_pooled_prompt_embeds = negative_prompt_embeds[0]
|
||||
negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]
|
||||
|
||||
negative_prompt_embeds_list.append(negative_prompt_embeds)
|
||||
|
||||
negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)
|
||||
|
||||
prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
|
||||
bs_embed, seq_len, _ = prompt_embeds.shape
|
||||
# duplicate text embeddings for each generation per prompt, using mps friendly method
|
||||
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
|
||||
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
|
||||
|
||||
if do_classifier_free_guidance:
|
||||
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
|
||||
seq_len = negative_prompt_embeds.shape[1]
|
||||
negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
|
||||
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
|
||||
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
|
||||
|
||||
pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
|
||||
bs_embed * num_images_per_prompt, -1
|
||||
)
|
||||
if do_classifier_free_guidance:
|
||||
negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
|
||||
bs_embed * num_images_per_prompt, -1
|
||||
)
|
||||
|
||||
return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
|
||||
|
||||
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
|
||||
def prepare_extra_step_kwargs(self, generator, eta):
|
||||
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
|
||||
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
|
||||
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
|
||||
# and should be between [0, 1]
|
||||
|
||||
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
|
||||
extra_step_kwargs = {}
|
||||
if accepts_eta:
|
||||
extra_step_kwargs["eta"] = eta
|
||||
|
||||
# check if the scheduler accepts generator
|
||||
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
|
||||
if accepts_generator:
|
||||
extra_step_kwargs["generator"] = generator
|
||||
return extra_step_kwargs
|
||||
|
||||
def check_inputs(
|
||||
self,
|
||||
prompt,
|
||||
prompt_2,
|
||||
height,
|
||||
width,
|
||||
callback_steps,
|
||||
negative_prompt=None,
|
||||
negative_prompt_2=None,
|
||||
prompt_embeds=None,
|
||||
negative_prompt_embeds=None,
|
||||
pooled_prompt_embeds=None,
|
||||
negative_pooled_prompt_embeds=None,
|
||||
):
|
||||
if height % 8 != 0 or width % 8 != 0:
|
||||
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
|
||||
|
||||
if (callback_steps is None) or (
|
||||
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
|
||||
):
|
||||
raise ValueError(
|
||||
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
|
||||
f" {type(callback_steps)}."
|
||||
)
|
||||
|
||||
if 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 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)}")
|
||||
|
||||
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."
|
||||
)
|
||||
|
||||
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`."
|
||||
)
|
||||
|
||||
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
|
||||
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
|
||||
shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
|
||||
if isinstance(generator, list) and len(generator) != batch_size:
|
||||
raise ValueError(
|
||||
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
|
||||
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
|
||||
)
|
||||
|
||||
if latents is None:
|
||||
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
|
||||
else:
|
||||
latents = latents.to(device)
|
||||
|
||||
# scale the initial noise by the standard deviation required by the scheduler
|
||||
latents = latents * self.scheduler.init_noise_sigma
|
||||
return latents
|
||||
|
||||
def _get_add_time_ids(self, original_size, crops_coords_top_left, target_size, dtype):
|
||||
add_time_ids = list(original_size + crops_coords_top_left + target_size)
|
||||
|
||||
passed_add_embed_dim = (
|
||||
self.unet.config.addition_time_embed_dim * len(add_time_ids) + self.text_encoder_2.config.projection_dim
|
||||
)
|
||||
expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features
|
||||
|
||||
if expected_add_embed_dim != passed_add_embed_dim:
|
||||
raise ValueError(
|
||||
f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`."
|
||||
)
|
||||
|
||||
add_time_ids = torch.tensor([add_time_ids], dtype=dtype)
|
||||
return add_time_ids
|
||||
|
||||
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae
|
||||
def upcast_vae(self):
|
||||
dtype = self.vae.dtype
|
||||
self.vae.to(dtype=torch.float32)
|
||||
use_torch_2_0_or_xformers = isinstance(
|
||||
self.vae.decoder.mid_block.attentions[0].processor,
|
||||
(
|
||||
AttnProcessor2_0,
|
||||
XFormersAttnProcessor,
|
||||
LoRAXFormersAttnProcessor,
|
||||
LoRAAttnProcessor2_0,
|
||||
),
|
||||
)
|
||||
# if xformers or torch_2_0 is used attention block does not need
|
||||
# to be in float32 which can save lots of memory
|
||||
if use_torch_2_0_or_xformers:
|
||||
self.vae.post_quant_conv.to(dtype)
|
||||
self.vae.decoder.conv_in.to(dtype)
|
||||
self.vae.decoder.mid_block.to(dtype)
|
||||
|
||||
def _default_height_width(self, height, width, image):
|
||||
# NOTE: It is possible that a list of images have different
|
||||
# dimensions for each image, so just checking the first image
|
||||
# is not _exactly_ correct, but it is simple.
|
||||
while isinstance(image, list):
|
||||
image = image[0]
|
||||
|
||||
if height is None:
|
||||
if isinstance(image, PIL.Image.Image):
|
||||
height = image.height
|
||||
elif isinstance(image, torch.Tensor):
|
||||
height = image.shape[-2]
|
||||
|
||||
# round down to nearest multiple of `self.adapter.total_downscale_factor`
|
||||
height = (height // self.adapter.total_downscale_factor) * self.adapter.total_downscale_factor
|
||||
|
||||
if width is None:
|
||||
if isinstance(image, PIL.Image.Image):
|
||||
width = image.width
|
||||
elif isinstance(image, torch.Tensor):
|
||||
width = image.shape[-1]
|
||||
|
||||
# round down to nearest multiple of `self.adapter.total_downscale_factor`
|
||||
width = (width // self.adapter.total_downscale_factor) * self.adapter.total_downscale_factor
|
||||
|
||||
return height, width
|
||||
|
||||
@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,
|
||||
image: Union[torch.Tensor, PIL.Image.Image, List[PIL.Image.Image]] = None,
|
||||
height: Optional[int] = None,
|
||||
width: Optional[int] = None,
|
||||
num_inference_steps: int = 50,
|
||||
denoising_end: Optional[float] = None,
|
||||
guidance_scale: float = 5.0,
|
||||
negative_prompt: Optional[Union[str, List[str]]] = None,
|
||||
negative_prompt_2: Optional[Union[str, List[str]]] = None,
|
||||
num_images_per_prompt: Optional[int] = 1,
|
||||
eta: float = 0.0,
|
||||
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
|
||||
latents: Optional[torch.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,
|
||||
output_type: Optional[str] = "pil",
|
||||
return_dict: bool = True,
|
||||
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
|
||||
callback_steps: int = 1,
|
||||
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
||||
guidance_rescale: float = 0.0,
|
||||
original_size: Optional[Tuple[int, int]] = None,
|
||||
crops_coords_top_left: Tuple[int, int] = (0, 0),
|
||||
target_size: Optional[Tuple[int, int]] = None,
|
||||
adapter_conditioning_scale: Union[float, List[float]] = 1.0,
|
||||
):
|
||||
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 the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
|
||||
used in both text-encoders
|
||||
image (`torch.FloatTensor`, `PIL.Image.Image`, `List[torch.FloatTensor]` or `List[PIL.Image.Image]` or `List[List[PIL.Image.Image]]`):
|
||||
The Adapter input condition. Adapter uses this input condition to generate guidance to Unet. If the
|
||||
type is specified as `Torch.FloatTensor`, it is passed to Adapter as is. PIL.Image.Image` can also be
|
||||
accepted as an image. The control image is automatically resized to fit the output image.
|
||||
height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
|
||||
The height in pixels of the generated image.
|
||||
width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
|
||||
The width in pixels of the generated image.
|
||||
num_inference_steps (`int`, *optional*, defaults to 50):
|
||||
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
|
||||
expense of slower inference.
|
||||
denoising_end (`float`, *optional*):
|
||||
When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be
|
||||
completed before it is intentionally prematurely terminated. As a result, the returned sample will
|
||||
still retain a substantial amount of noise as determined by the discrete timesteps selected by the
|
||||
scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a
|
||||
"Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image
|
||||
Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output)
|
||||
guidance_scale (`float`, *optional*, defaults to 5.0):
|
||||
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
|
||||
`guidance_scale` is defined as `w` of equation 2. of [Imagen
|
||||
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
|
||||
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
|
||||
usually at the expense of lower image quality.
|
||||
negative_prompt (`str` or `List[str]`, *optional*):
|
||||
The prompt or prompts not to guide the image generation. If not defined, one has to pass
|
||||
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
|
||||
less than `1`).
|
||||
negative_prompt_2 (`str` or `List[str]`, *optional*):
|
||||
The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
|
||||
`text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
|
||||
num_images_per_prompt (`int`, *optional*, defaults to 1):
|
||||
The number of images to generate per prompt.
|
||||
eta (`float`, *optional*, defaults to 0.0):
|
||||
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
|
||||
[`schedulers.DDIMScheduler`], will be ignored for others.
|
||||
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
|
||||
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
|
||||
to make generation deterministic.
|
||||
latents (`torch.FloatTensor`, *optional*):
|
||||
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
|
||||
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
|
||||
tensor will ge generated by sampling using the supplied random `generator`.
|
||||
prompt_embeds (`torch.FloatTensor`, *optional*):
|
||||
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
|
||||
provided, text embeddings will be generated from `prompt` input argument.
|
||||
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
|
||||
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
|
||||
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
|
||||
argument.
|
||||
pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
|
||||
Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
|
||||
If not provided, pooled text embeddings will be generated from `prompt` input argument.
|
||||
negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
|
||||
Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
|
||||
weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
|
||||
input argument.
|
||||
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_xl.StableDiffusionAdapterPipelineOutput`] instead
|
||||
of a plain tuple.
|
||||
callback (`Callable`, *optional*):
|
||||
A function that will be called every `callback_steps` steps during inference. The function will be
|
||||
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
|
||||
callback_steps (`int`, *optional*, defaults to 1):
|
||||
The frequency at which the `callback` function will be called. If not specified, the callback will be
|
||||
called at every step.
|
||||
cross_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).
|
||||
guidance_rescale (`float`, *optional*, defaults to 0.7):
|
||||
Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are
|
||||
Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of
|
||||
[Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).
|
||||
Guidance rescale factor should fix overexposure when using zero terminal SNR.
|
||||
original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
|
||||
If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.
|
||||
`original_size` defaults to `(width, height)` if not specified. Part of SDXL's micro-conditioning as
|
||||
explained in section 2.2 of
|
||||
[https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
|
||||
crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
|
||||
`crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position
|
||||
`crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
|
||||
`crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of
|
||||
[https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
|
||||
target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
|
||||
For most cases, `target_size` should be set to the desired height and width of the generated image. If
|
||||
not specified it will default to `(width, height)`. Part of SDXL's micro-conditioning as explained in
|
||||
section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
|
||||
adapter_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):
|
||||
The outputs of the adapter are multiplied by `adapter_conditioning_scale` before they are added to the
|
||||
residual in the original unet. If multiple adapters are specified in init, you can set the
|
||||
corresponding scale as a list.
|
||||
Examples:
|
||||
|
||||
Returns:
|
||||
[`~pipelines.stable_diffusion.StableDiffusionAdapterPipelineOutput`] or `tuple`:
|
||||
[`~pipelines.stable_diffusion.StableDiffusionAdapterPipelineOutput`] if `return_dict` is True, otherwise a
|
||||
`tuple`. When returning a tuple, the first element is a list with the generated images.
|
||||
"""
|
||||
# 0. Default height and width to unet
|
||||
# height = height or self.default_sample_size * self.vae_scale_factor
|
||||
# width = width or self.default_sample_size * self.vae_scale_factor
|
||||
|
||||
height, width = self._default_height_width(height, width, image)
|
||||
device = self._execution_device
|
||||
|
||||
adapter_input = _preprocess_adapter_image(image, height, width).to(device)
|
||||
|
||||
original_size = (height, width) #original_size or (height, width)
|
||||
target_size = (height, width) #target_size or (height, width)
|
||||
|
||||
# 1. Check inputs. Raise error if not correct
|
||||
self.check_inputs(
|
||||
prompt,
|
||||
prompt_2,
|
||||
height,
|
||||
width,
|
||||
callback_steps,
|
||||
negative_prompt,
|
||||
negative_prompt_2,
|
||||
prompt_embeds,
|
||||
negative_prompt_embeds,
|
||||
pooled_prompt_embeds,
|
||||
negative_pooled_prompt_embeds,
|
||||
)
|
||||
|
||||
# 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
|
||||
|
||||
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
|
||||
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
|
||||
# corresponds to doing no classifier free guidance.
|
||||
do_classifier_free_guidance = guidance_scale > 1.0
|
||||
|
||||
# 3. Encode input prompt
|
||||
# text_encoder_lora_scale = (
|
||||
# cross_attention_kwargs.get("scale", None) if cross_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,
|
||||
device=device,
|
||||
num_images_per_prompt=num_images_per_prompt,
|
||||
do_classifier_free_guidance=do_classifier_free_guidance,
|
||||
negative_prompt=negative_prompt,
|
||||
negative_prompt_2=negative_prompt_2,
|
||||
prompt_embeds=prompt_embeds,
|
||||
negative_prompt_embeds=negative_prompt_embeds,
|
||||
pooled_prompt_embeds=pooled_prompt_embeds,
|
||||
negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
|
||||
# lora_scale=text_encoder_lora_scale,
|
||||
)
|
||||
|
||||
# 4. Prepare timesteps
|
||||
self.scheduler.set_timesteps(num_inference_steps, device=device)
|
||||
|
||||
timesteps = self.scheduler.timesteps
|
||||
|
||||
# 5. Prepare latent variables
|
||||
num_channels_latents = self.unet.config.in_channels
|
||||
latents = self.prepare_latents(
|
||||
batch_size * num_images_per_prompt,
|
||||
num_channels_latents,
|
||||
height,
|
||||
width,
|
||||
prompt_embeds.dtype,
|
||||
device,
|
||||
generator,
|
||||
latents,
|
||||
)
|
||||
|
||||
# 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
|
||||
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
|
||||
|
||||
# 7. Prepare added time ids & embeddings & adapter features
|
||||
adapter_input = adapter_input.type(latents.dtype)
|
||||
adapter_state = self.adapter(adapter_input)
|
||||
for k, v in enumerate(adapter_state):
|
||||
adapter_state[k] = v * adapter_conditioning_scale
|
||||
if num_images_per_prompt > 1:
|
||||
for k, v in enumerate(adapter_state):
|
||||
adapter_state[k] = v.repeat(num_images_per_prompt, 1, 1, 1)
|
||||
if do_classifier_free_guidance:
|
||||
for k, v in enumerate(adapter_state):
|
||||
adapter_state[k] = torch.cat([v] * 2, dim=0)
|
||||
|
||||
add_text_embeds = pooled_prompt_embeds
|
||||
add_time_ids = self._get_add_time_ids(
|
||||
original_size, crops_coords_top_left, target_size, dtype=prompt_embeds.dtype
|
||||
)
|
||||
|
||||
if do_classifier_free_guidance:
|
||||
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
|
||||
add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)
|
||||
add_time_ids = torch.cat([add_time_ids, add_time_ids], dim=0)
|
||||
|
||||
prompt_embeds = prompt_embeds.to(device)
|
||||
add_text_embeds = add_text_embeds.to(device)
|
||||
add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)
|
||||
|
||||
# 8. Denoising loop
|
||||
num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
|
||||
|
||||
# 7.1 Apply denoising_end
|
||||
if denoising_end is not None and type(denoising_end) == float and denoising_end > 0 and denoising_end < 1:
|
||||
discrete_timestep_cutoff = int(
|
||||
round(
|
||||
self.scheduler.config.num_train_timesteps
|
||||
- (denoising_end * self.scheduler.config.num_train_timesteps)
|
||||
)
|
||||
)
|
||||
num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))
|
||||
timesteps = timesteps[:num_inference_steps]
|
||||
|
||||
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] * 2) if do_classifier_free_guidance else latents
|
||||
|
||||
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
|
||||
|
||||
# predict the noise residual
|
||||
added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
|
||||
noise_pred = self.unet(
|
||||
latent_model_input,
|
||||
t,
|
||||
encoder_hidden_states=prompt_embeds,
|
||||
cross_attention_kwargs=cross_attention_kwargs,
|
||||
added_cond_kwargs=added_cond_kwargs,
|
||||
return_dict=False,
|
||||
down_block_additional_residuals=[state.clone() for state in adapter_state],
|
||||
)[0]
|
||||
|
||||
# perform guidance
|
||||
if do_classifier_free_guidance:
|
||||
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
|
||||
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
|
||||
|
||||
if do_classifier_free_guidance and guidance_rescale > 0.0:
|
||||
# Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
|
||||
noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
|
||||
|
||||
# compute the previous noisy sample x_t -> x_t-1
|
||||
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
|
||||
|
||||
# call the callback, if provided
|
||||
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
|
||||
progress_bar.update()
|
||||
if callback is not None and i % callback_steps == 0:
|
||||
callback(i, t, latents)
|
||||
|
||||
# make sure the VAE is in float32 mode, as it overflows in float16
|
||||
if self.vae.dtype == torch.float16 and self.vae.config.force_upcast:
|
||||
self.upcast_vae()
|
||||
latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
|
||||
|
||||
if not output_type == "latent":
|
||||
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
|
||||
else:
|
||||
image = latents
|
||||
return StableDiffusionAdapterXLPipelineOutput(images=image)
|
||||
|
||||
image = self.image_processor.postprocess(image, output_type=output_type)
|
||||
|
||||
# Offload last model to CPU
|
||||
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
|
||||
self.final_offload_hook.offload()
|
||||
|
||||
if not return_dict:
|
||||
return (image,)
|
||||
|
||||
return StableDiffusionAdapterXLPipelineOutput(images=image)
|
||||
Reference in New Issue
Block a user