860 lines
42 KiB
Python
860 lines
42 KiB
Python
# 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 contextlib
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import inspect
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from typing import Any, Callable, Dict, List, Optional, Union
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import numpy as np
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import PIL.Image
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import torch
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from packaging import version
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from transformers import CLIPTextModel, CLIPTokenizer, DPTFeatureExtractor, DPTForDepthEstimation
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from ...configuration_utils import FrozenDict
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from ...image_processor import PipelineImageInput, VaeImageProcessor
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from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
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from ...models import AutoencoderKL, UNet2DConditionModel
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from ...models.lora import adjust_lora_scale_text_encoder
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from ...schedulers import KarrasDiffusionSchedulers
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from ...utils import PIL_INTERPOLATION, USE_PEFT_BACKEND, deprecate, logging, scale_lora_layers, unscale_lora_layers
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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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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents
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def retrieve_latents(
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encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "sample"
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):
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if hasattr(encoder_output, "latent_dist") and sample_mode == "sample":
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return encoder_output.latent_dist.sample(generator)
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elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax":
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return encoder_output.latent_dist.mode()
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elif hasattr(encoder_output, "latents"):
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return encoder_output.latents
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else:
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raise AttributeError("Could not access latents of provided encoder_output")
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.preprocess
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def preprocess(image):
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deprecation_message = "The preprocess method is deprecated and will be removed in diffusers 1.0.0. Please use VaeImageProcessor.preprocess(...) instead"
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deprecate("preprocess", "1.0.0", deprecation_message, standard_warn=False)
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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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w, h = image[0].size
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w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8
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image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image]
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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 = 2.0 * image - 1.0
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image = torch.from_numpy(image)
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elif isinstance(image[0], torch.Tensor):
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image = torch.cat(image, dim=0)
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return image
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class StableDiffusionDepth2ImgPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin):
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r"""
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Pipeline for text-guided depth-based image-to-image generation using Stable Diffusion.
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This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
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implemented for all pipelines (downloading, saving, running on a particular device, etc.).
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The pipeline also inherits the following loading methods:
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- [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
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- [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights
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- [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights
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Args:
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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 ([`~transformers.CLIPTextModel`]):
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Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
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tokenizer ([`~transformers.CLIPTokenizer`]):
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A `CLIPTokenizer` to tokenize text.
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unet ([`UNet2DConditionModel`]):
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A `UNet2DConditionModel` 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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"""
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model_cpu_offload_seq = "text_encoder->unet->vae"
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_callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds", "depth_mask"]
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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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tokenizer: CLIPTokenizer,
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unet: UNet2DConditionModel,
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scheduler: KarrasDiffusionSchedulers,
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depth_estimator: DPTForDepthEstimation,
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feature_extractor: DPTFeatureExtractor,
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):
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super().__init__()
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is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
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version.parse(unet.config._diffusers_version).base_version
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) < version.parse("0.9.0.dev0")
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is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
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if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
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deprecation_message = (
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"The configuration file of the unet has set the default `sample_size` to smaller than"
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" 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the"
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" following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
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" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
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" \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
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" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
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" in the config might lead to incorrect results in future versions. If you have downloaded this"
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" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
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" the `unet/config.json` file"
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)
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deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
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new_config = dict(unet.config)
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new_config["sample_size"] = 64
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unet._internal_dict = FrozenDict(new_config)
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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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tokenizer=tokenizer,
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unet=unet,
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scheduler=scheduler,
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depth_estimator=depth_estimator,
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feature_extractor=feature_extractor,
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)
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self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
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self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
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def _encode_prompt(
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self,
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prompt,
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device,
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num_images_per_prompt,
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do_classifier_free_guidance,
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negative_prompt=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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lora_scale: Optional[float] = None,
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**kwargs,
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):
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deprecation_message = "`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple."
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deprecate("_encode_prompt()", "1.0.0", deprecation_message, standard_warn=False)
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prompt_embeds_tuple = self.encode_prompt(
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prompt=prompt,
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device=device,
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num_images_per_prompt=num_images_per_prompt,
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do_classifier_free_guidance=do_classifier_free_guidance,
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negative_prompt=negative_prompt,
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prompt_embeds=prompt_embeds,
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negative_prompt_embeds=negative_prompt_embeds,
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lora_scale=lora_scale,
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**kwargs,
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)
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# concatenate for backwards comp
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prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])
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return prompt_embeds
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt
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def encode_prompt(
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self,
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prompt,
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device,
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num_images_per_prompt,
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do_classifier_free_guidance,
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negative_prompt=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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lora_scale: Optional[float] = None,
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clip_skip: Optional[int] = 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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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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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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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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clip_skip (`int`, *optional*):
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Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
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the output of the pre-final layer will be used for computing the prompt embeddings.
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"""
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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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# dynamically adjust the LoRA scale
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if not USE_PEFT_BACKEND:
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adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
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else:
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scale_lora_layers(self.text_encoder, 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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if prompt_embeds is None:
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# textual inversion: procecss multi-vector tokens if necessary
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if isinstance(self, TextualInversionLoaderMixin):
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prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
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text_inputs = self.tokenizer(
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prompt,
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padding="max_length",
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max_length=self.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 = self.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 = self.tokenizer.batch_decode(
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untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
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)
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logger.warning(
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"The following part of your input was truncated because CLIP can only handle sequences up to"
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f" {self.tokenizer.model_max_length} tokens: {removed_text}"
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)
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if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
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attention_mask = text_inputs.attention_mask.to(device)
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else:
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attention_mask = None
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if clip_skip is None:
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prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)
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prompt_embeds = prompt_embeds[0]
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else:
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prompt_embeds = self.text_encoder(
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text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True
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)
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# Access the `hidden_states` first, that contains a tuple of
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# all the hidden states from the encoder layers. Then index into
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# the tuple to access the hidden states from the desired layer.
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prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]
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# We also need to apply the final LayerNorm here to not mess with the
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# representations. The `last_hidden_states` that we typically use for
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# obtaining the final prompt representations passes through the LayerNorm
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# layer.
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prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)
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if self.text_encoder is not None:
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prompt_embeds_dtype = self.text_encoder.dtype
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elif self.unet is not None:
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prompt_embeds_dtype = self.unet.dtype
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else:
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prompt_embeds_dtype = prompt_embeds.dtype
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prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
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bs_embed, seq_len, _ = prompt_embeds.shape
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# duplicate text embeddings for each generation per prompt, using mps friendly method
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prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
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prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
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# get unconditional embeddings for classifier free guidance
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if do_classifier_free_guidance and negative_prompt_embeds is None:
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uncond_tokens: List[str]
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if negative_prompt is None:
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uncond_tokens = [""] * batch_size
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elif prompt is not None and type(prompt) is not type(negative_prompt):
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raise TypeError(
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f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
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f" {type(prompt)}."
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)
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elif isinstance(negative_prompt, str):
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uncond_tokens = [negative_prompt]
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elif batch_size != len(negative_prompt):
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raise ValueError(
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f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
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f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
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" the batch size of `prompt`."
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)
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else:
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uncond_tokens = negative_prompt
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# textual inversion: procecss multi-vector tokens if necessary
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if isinstance(self, TextualInversionLoaderMixin):
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uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
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max_length = prompt_embeds.shape[1]
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uncond_input = self.tokenizer(
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uncond_tokens,
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padding="max_length",
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max_length=max_length,
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truncation=True,
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return_tensors="pt",
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)
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if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
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attention_mask = uncond_input.attention_mask.to(device)
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else:
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attention_mask = None
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negative_prompt_embeds = self.text_encoder(
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uncond_input.input_ids.to(device),
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attention_mask=attention_mask,
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)
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negative_prompt_embeds = negative_prompt_embeds[0]
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if do_classifier_free_guidance:
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# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
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seq_len = negative_prompt_embeds.shape[1]
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negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
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negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
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negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
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if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:
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# Retrieve the original scale by scaling back the LoRA layers
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unscale_lora_layers(self.text_encoder, lora_scale)
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return prompt_embeds, negative_prompt_embeds
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
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def run_safety_checker(self, image, device, dtype):
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if self.safety_checker is None:
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has_nsfw_concept = None
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else:
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if torch.is_tensor(image):
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feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
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else:
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feature_extractor_input = self.image_processor.numpy_to_pil(image)
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safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
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image, has_nsfw_concept = self.safety_checker(
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images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
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)
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return image, has_nsfw_concept
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
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def decode_latents(self, latents):
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deprecation_message = "The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead"
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deprecate("decode_latents", "1.0.0", deprecation_message, standard_warn=False)
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latents = 1 / self.vae.config.scaling_factor * latents
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image = self.vae.decode(latents, return_dict=False)[0]
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image = (image / 2 + 0.5).clamp(0, 1)
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# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
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image = image.cpu().permute(0, 2, 3, 1).float().numpy()
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return image
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
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def prepare_extra_step_kwargs(self, generator, eta):
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# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
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# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
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# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
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# and should be between [0, 1]
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accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
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extra_step_kwargs = {}
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if accepts_eta:
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extra_step_kwargs["eta"] = eta
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# check if the scheduler accepts generator
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accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
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if accepts_generator:
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extra_step_kwargs["generator"] = generator
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return extra_step_kwargs
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.check_inputs
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def check_inputs(
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self,
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prompt,
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|
strength,
|
|
callback_steps,
|
|
negative_prompt=None,
|
|
prompt_embeds=None,
|
|
negative_prompt_embeds=None,
|
|
callback_on_step_end_tensor_inputs=None,
|
|
):
|
|
if strength < 0 or strength > 1:
|
|
raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
|
|
|
|
if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):
|
|
raise ValueError(
|
|
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
|
|
f" {type(callback_steps)}."
|
|
)
|
|
|
|
if callback_on_step_end_tensor_inputs is not None and not all(
|
|
k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
|
|
):
|
|
raise ValueError(
|
|
f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
|
|
)
|
|
if prompt is not None and prompt_embeds is not None:
|
|
raise ValueError(
|
|
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
|
|
" only forward one of the two."
|
|
)
|
|
elif prompt is None and prompt_embeds is None:
|
|
raise ValueError(
|
|
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
|
|
)
|
|
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
|
|
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
|
|
|
|
if negative_prompt is not None and negative_prompt_embeds is not None:
|
|
raise ValueError(
|
|
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
|
|
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
|
|
)
|
|
|
|
if prompt_embeds is not None and negative_prompt_embeds is not None:
|
|
if prompt_embeds.shape != negative_prompt_embeds.shape:
|
|
raise ValueError(
|
|
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
|
|
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
|
|
f" {negative_prompt_embeds.shape}."
|
|
)
|
|
|
|
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps
|
|
def get_timesteps(self, num_inference_steps, strength, device):
|
|
# get the original timestep using init_timestep
|
|
init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
|
|
|
|
t_start = max(num_inference_steps - init_timestep, 0)
|
|
timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
|
|
|
|
return timesteps, num_inference_steps - t_start
|
|
|
|
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.prepare_latents
|
|
def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None):
|
|
if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
|
|
raise ValueError(
|
|
f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
|
|
)
|
|
|
|
image = image.to(device=device, dtype=dtype)
|
|
|
|
batch_size = batch_size * num_images_per_prompt
|
|
|
|
if image.shape[1] == 4:
|
|
init_latents = image
|
|
|
|
else:
|
|
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."
|
|
)
|
|
|
|
elif isinstance(generator, list):
|
|
init_latents = [
|
|
retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])
|
|
for i in range(batch_size)
|
|
]
|
|
init_latents = torch.cat(init_latents, dim=0)
|
|
else:
|
|
init_latents = retrieve_latents(self.vae.encode(image), generator=generator)
|
|
|
|
init_latents = self.vae.config.scaling_factor * init_latents
|
|
|
|
if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:
|
|
# expand init_latents for batch_size
|
|
deprecation_message = (
|
|
f"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial"
|
|
" images (`image`). Initial images are now duplicating to match the number of text prompts. Note"
|
|
" that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update"
|
|
" your script to pass as many initial images as text prompts to suppress this warning."
|
|
)
|
|
deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False)
|
|
additional_image_per_prompt = batch_size // init_latents.shape[0]
|
|
init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0)
|
|
elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:
|
|
raise ValueError(
|
|
f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
|
|
)
|
|
else:
|
|
init_latents = torch.cat([init_latents], dim=0)
|
|
|
|
shape = init_latents.shape
|
|
noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
|
|
|
|
# get latents
|
|
init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
|
|
latents = init_latents
|
|
|
|
return latents
|
|
|
|
def prepare_depth_map(self, image, depth_map, batch_size, do_classifier_free_guidance, dtype, device):
|
|
if isinstance(image, PIL.Image.Image):
|
|
image = [image]
|
|
else:
|
|
image = list(image)
|
|
|
|
if isinstance(image[0], PIL.Image.Image):
|
|
width, height = image[0].size
|
|
elif isinstance(image[0], np.ndarray):
|
|
width, height = image[0].shape[:-1]
|
|
else:
|
|
height, width = image[0].shape[-2:]
|
|
|
|
if depth_map is None:
|
|
pixel_values = self.feature_extractor(images=image, return_tensors="pt").pixel_values
|
|
pixel_values = pixel_values.to(device=device)
|
|
# The DPT-Hybrid model uses batch-norm layers which are not compatible with fp16.
|
|
# So we use `torch.autocast` here for half precision inference.
|
|
context_manger = torch.autocast("cuda", dtype=dtype) if device.type == "cuda" else contextlib.nullcontext()
|
|
with context_manger:
|
|
depth_map = self.depth_estimator(pixel_values).predicted_depth
|
|
else:
|
|
depth_map = depth_map.to(device=device, dtype=dtype)
|
|
|
|
depth_map = torch.nn.functional.interpolate(
|
|
depth_map.unsqueeze(1),
|
|
size=(height // self.vae_scale_factor, width // self.vae_scale_factor),
|
|
mode="bicubic",
|
|
align_corners=False,
|
|
)
|
|
|
|
depth_min = torch.amin(depth_map, dim=[1, 2, 3], keepdim=True)
|
|
depth_max = torch.amax(depth_map, dim=[1, 2, 3], keepdim=True)
|
|
depth_map = 2.0 * (depth_map - depth_min) / (depth_max - depth_min) - 1.0
|
|
depth_map = depth_map.to(dtype)
|
|
|
|
# duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method
|
|
if depth_map.shape[0] < batch_size:
|
|
repeat_by = batch_size // depth_map.shape[0]
|
|
depth_map = depth_map.repeat(repeat_by, 1, 1, 1)
|
|
|
|
depth_map = torch.cat([depth_map] * 2) if do_classifier_free_guidance else depth_map
|
|
return depth_map
|
|
|
|
@property
|
|
def guidance_scale(self):
|
|
return self._guidance_scale
|
|
|
|
@property
|
|
def clip_skip(self):
|
|
return self._clip_skip
|
|
|
|
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
|
|
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
|
|
# corresponds to doing no classifier free guidance.
|
|
@property
|
|
def do_classifier_free_guidance(self):
|
|
return self._guidance_scale > 1
|
|
|
|
@property
|
|
def cross_attention_kwargs(self):
|
|
return self._cross_attention_kwargs
|
|
|
|
@property
|
|
def num_timesteps(self):
|
|
return self._num_timesteps
|
|
|
|
@torch.no_grad()
|
|
def __call__(
|
|
self,
|
|
prompt: Union[str, List[str]] = None,
|
|
image: PipelineImageInput = None,
|
|
depth_map: Optional[torch.FloatTensor] = None,
|
|
strength: float = 0.8,
|
|
num_inference_steps: Optional[int] = 50,
|
|
guidance_scale: Optional[float] = 7.5,
|
|
negative_prompt: Optional[Union[str, List[str]]] = None,
|
|
num_images_per_prompt: Optional[int] = 1,
|
|
eta: Optional[float] = 0.0,
|
|
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
|
|
prompt_embeds: Optional[torch.FloatTensor] = None,
|
|
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
|
|
output_type: Optional[str] = "pil",
|
|
return_dict: bool = True,
|
|
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
|
clip_skip: Optional[int] = None,
|
|
callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
|
|
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
|
|
**kwargs,
|
|
):
|
|
r"""
|
|
The call function to the pipeline for generation.
|
|
|
|
Args:
|
|
prompt (`str` or `List[str]`, *optional*):
|
|
The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
|
|
image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
|
|
`Image` or tensor representing an image batch to be used as the starting point. Can accept image
|
|
latents as `image` only if `depth_map` is not `None`.
|
|
depth_map (`torch.FloatTensor`, *optional*):
|
|
Depth prediction to be used as additional conditioning for the image generation process. If not
|
|
defined, it automatically predicts the depth with `self.depth_estimator`.
|
|
strength (`float`, *optional*, defaults to 0.8):
|
|
Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a
|
|
starting point and more noise is added the higher the `strength`. The number of denoising steps depends
|
|
on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising
|
|
process runs for the full number of iterations specified in `num_inference_steps`. A value of 1
|
|
essentially ignores `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. This parameter is modulated by `strength`.
|
|
guidance_scale (`float`, *optional*, defaults to 7.5):
|
|
A higher guidance scale value encourages the model to generate images closely linked to the text
|
|
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
|
|
negative_prompt (`str` or `List[str]`, *optional*):
|
|
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
|
|
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
|
|
num_images_per_prompt (`int`, *optional*, defaults to 1):
|
|
The number of images to generate per prompt.
|
|
eta (`float`, *optional*, defaults to 0.0):
|
|
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
|
|
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
|
|
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
|
|
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
|
|
generation deterministic.
|
|
prompt_embeds (`torch.FloatTensor`, *optional*):
|
|
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
|
|
provided, text embeddings are generated from the `prompt` input argument.
|
|
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
|
|
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
|
|
not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
|
|
output_type (`str`, *optional*, defaults to `"pil"`):
|
|
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
|
|
return_dict (`bool`, *optional*, defaults to `True`):
|
|
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
|
|
plain tuple.
|
|
cross_attention_kwargs (`dict`, *optional*):
|
|
A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
|
|
[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
|
|
clip_skip (`int`, *optional*):
|
|
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
|
|
the output of the pre-final layer will be used for computing the prompt embeddings.
|
|
callback_on_step_end (`Callable`, *optional*):
|
|
A function that calls at the end of each denoising steps during the inference. The function is called
|
|
with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
|
|
callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
|
|
`callback_on_step_end_tensor_inputs`.
|
|
callback_on_step_end_tensor_inputs (`List`, *optional*):
|
|
The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
|
|
will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
|
|
`._callback_tensor_inputs` attribute of your pipeline class.
|
|
Examples:
|
|
|
|
```py
|
|
>>> import torch
|
|
>>> import requests
|
|
>>> from PIL import Image
|
|
|
|
>>> from diffusers import StableDiffusionDepth2ImgPipeline
|
|
|
|
>>> pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(
|
|
... "stabilityai/stable-diffusion-2-depth",
|
|
... torch_dtype=torch.float16,
|
|
... )
|
|
>>> pipe.to("cuda")
|
|
|
|
|
|
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
|
|
>>> init_image = Image.open(requests.get(url, stream=True).raw)
|
|
>>> prompt = "two tigers"
|
|
>>> n_propmt = "bad, deformed, ugly, bad anotomy"
|
|
>>> image = pipe(prompt=prompt, image=init_image, negative_prompt=n_propmt, strength=0.7).images[0]
|
|
```
|
|
|
|
Returns:
|
|
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
|
|
If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
|
|
otherwise a `tuple` is returned where the first element is a list with the generated images.
|
|
"""
|
|
|
|
callback = kwargs.pop("callback", None)
|
|
callback_steps = kwargs.pop("callback_steps", None)
|
|
|
|
if callback is not None:
|
|
deprecate(
|
|
"callback",
|
|
"1.0.0",
|
|
"Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
|
|
)
|
|
if callback_steps is not None:
|
|
deprecate(
|
|
"callback_steps",
|
|
"1.0.0",
|
|
"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
|
|
)
|
|
|
|
# 1. Check inputs
|
|
self.check_inputs(
|
|
prompt,
|
|
strength,
|
|
callback_steps,
|
|
negative_prompt=negative_prompt,
|
|
prompt_embeds=prompt_embeds,
|
|
negative_prompt_embeds=negative_prompt_embeds,
|
|
callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
|
|
)
|
|
|
|
self._guidance_scale = guidance_scale
|
|
self._clip_skip = clip_skip
|
|
self._cross_attention_kwargs = cross_attention_kwargs
|
|
|
|
if image is None:
|
|
raise ValueError("`image` input cannot be undefined.")
|
|
|
|
# 2. Define call parameters
|
|
if prompt is not None and isinstance(prompt, str):
|
|
batch_size = 1
|
|
elif prompt is not None and isinstance(prompt, list):
|
|
batch_size = len(prompt)
|
|
else:
|
|
batch_size = prompt_embeds.shape[0]
|
|
|
|
device = self._execution_device
|
|
|
|
# 3. Encode input prompt
|
|
text_encoder_lora_scale = (
|
|
self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
|
|
)
|
|
prompt_embeds, negative_prompt_embeds = self.encode_prompt(
|
|
prompt,
|
|
device,
|
|
num_images_per_prompt,
|
|
self.do_classifier_free_guidance,
|
|
negative_prompt,
|
|
prompt_embeds=prompt_embeds,
|
|
negative_prompt_embeds=negative_prompt_embeds,
|
|
lora_scale=text_encoder_lora_scale,
|
|
clip_skip=self.clip_skip,
|
|
)
|
|
# For classifier free guidance, we need to do two forward passes.
|
|
# Here we concatenate the unconditional and text embeddings into a single batch
|
|
# to avoid doing two forward passes
|
|
if self.do_classifier_free_guidance:
|
|
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
|
|
|
|
# 4. Prepare depth mask
|
|
depth_mask = self.prepare_depth_map(
|
|
image,
|
|
depth_map,
|
|
batch_size * num_images_per_prompt,
|
|
self.do_classifier_free_guidance,
|
|
prompt_embeds.dtype,
|
|
device,
|
|
)
|
|
|
|
# 5. Preprocess image
|
|
image = self.image_processor.preprocess(image)
|
|
|
|
# 6. Set timesteps
|
|
self.scheduler.set_timesteps(num_inference_steps, device=device)
|
|
timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
|
|
latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
|
|
|
|
# 7. Prepare latent variables
|
|
latents = self.prepare_latents(
|
|
image, latent_timestep, batch_size, num_images_per_prompt, prompt_embeds.dtype, device, generator
|
|
)
|
|
|
|
# 8. 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)
|
|
|
|
# 9. Denoising loop
|
|
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
|
|
self._num_timesteps = len(timesteps)
|
|
with self.progress_bar(total=num_inference_steps) as progress_bar:
|
|
for i, t in enumerate(timesteps):
|
|
# expand the latents if we are doing classifier free guidance
|
|
latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
|
|
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
|
|
latent_model_input = torch.cat([latent_model_input, depth_mask], dim=1)
|
|
|
|
# predict the noise residual
|
|
noise_pred = self.unet(
|
|
latent_model_input,
|
|
t,
|
|
encoder_hidden_states=prompt_embeds,
|
|
cross_attention_kwargs=self.cross_attention_kwargs,
|
|
return_dict=False,
|
|
)[0]
|
|
|
|
# perform guidance
|
|
if self.do_classifier_free_guidance:
|
|
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
|
|
noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
|
|
|
|
# compute the previous noisy sample x_t -> x_t-1
|
|
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
|
|
|
|
if callback_on_step_end is not None:
|
|
callback_kwargs = {}
|
|
for k in callback_on_step_end_tensor_inputs:
|
|
callback_kwargs[k] = locals()[k]
|
|
callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
|
|
|
|
latents = callback_outputs.pop("latents", latents)
|
|
prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
|
|
negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
|
|
depth_mask = callback_outputs.pop("depth_mask", depth_mask)
|
|
|
|
# call the callback, if provided
|
|
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
|
|
progress_bar.update()
|
|
if callback is not None and i % callback_steps == 0:
|
|
step_idx = i // getattr(self.scheduler, "order", 1)
|
|
callback(step_idx, t, latents)
|
|
|
|
if not output_type == "latent":
|
|
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
|
|
else:
|
|
image = latents
|
|
|
|
image = self.image_processor.postprocess(image, output_type=output_type)
|
|
self.maybe_free_model_hooks()
|
|
|
|
if not return_dict:
|
|
return (image,)
|
|
|
|
return ImagePipelineOutput(images=image)
|