756 lines
36 KiB
Python
756 lines
36 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 inspect
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import warnings
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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
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import torch
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from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
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from ...image_processor import 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.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 DDPMScheduler, KarrasDiffusionSchedulers
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from ...utils import deprecate, is_accelerate_available, is_accelerate_version, logging, randn_tensor
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from ..pipeline_utils import DiffusionPipeline
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from . import StableDiffusionPipelineOutput
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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def preprocess(image):
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warnings.warn(
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"The preprocess method is deprecated and will be removed in a future version. Please"
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" use VaeImageProcessor.preprocess instead",
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FutureWarning,
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)
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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 % 64 for x in (w, h)) # resize to integer multiple of 64
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image = [np.array(i.resize((w, h)))[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 StableDiffusionUpscalePipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin):
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r"""
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Pipeline for text-guided image super-resolution using Stable Diffusion 2.
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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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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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low_res_scheduler ([`SchedulerMixin`]):
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A scheduler used to add initial noise to the low resolution conditioning image. It must be an instance of
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[`DDPMScheduler`].
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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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_optional_components = ["watermarker", "safety_checker", "feature_extractor"]
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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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low_res_scheduler: DDPMScheduler,
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scheduler: KarrasDiffusionSchedulers,
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safety_checker: Optional[Any] = None,
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feature_extractor: Optional[CLIPImageProcessor] = None,
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watermarker: Optional[Any] = None,
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max_noise_level: int = 350,
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):
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super().__init__()
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if hasattr(
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vae, "config"
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): # check if vae has a config attribute `scaling_factor` and if it is set to 0.08333, else set it to 0.08333 and deprecate
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is_vae_scaling_factor_set_to_0_08333 = (
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hasattr(vae.config, "scaling_factor") and vae.config.scaling_factor == 0.08333
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)
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if not is_vae_scaling_factor_set_to_0_08333:
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deprecation_message = (
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"The configuration file of the vae does not contain `scaling_factor` or it is set to"
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f" {vae.config.scaling_factor}, which seems highly unlikely. If your checkpoint is a fine-tuned"
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" version of `stabilityai/stable-diffusion-x4-upscaler` you should change 'scaling_factor' to"
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" 0.08333 Please make sure to update the config accordingly, as not doing so might lead to"
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" incorrect results in future versions. If you have downloaded this checkpoint from the Hugging"
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" Face Hub, it would be very nice if you could open a Pull Request for the `vae/config.json` file"
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)
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deprecate("wrong scaling_factor", "1.0.0", deprecation_message, standard_warn=False)
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vae.register_to_config(scaling_factor=0.08333)
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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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low_res_scheduler=low_res_scheduler,
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scheduler=scheduler,
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safety_checker=safety_checker,
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watermarker=watermarker,
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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, resample="bicubic")
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self.register_to_config(max_noise_level=max_noise_level)
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def enable_model_cpu_offload(self, gpu_id=0):
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r"""
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Offload all models to CPU to reduce memory usage with a low impact on performance. Moves one whole model at a
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time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs.
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Memory savings are lower than using `enable_sequential_cpu_offload`, but performance is much better due to the
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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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hook = None
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for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]:
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if cpu_offloaded_model is not None:
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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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# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.run_safety_checker
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def run_safety_checker(self, image, device, dtype):
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if self.safety_checker is not None:
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safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
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image, nsfw_detected, watermark_detected = self.safety_checker(
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images=image,
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clip_input=safety_checker_input.pixel_values.to(dtype=dtype),
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)
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else:
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nsfw_detected = None
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watermark_detected = None
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if hasattr(self, "unet_offload_hook") and self.unet_offload_hook is not None:
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self.unet_offload_hook.offload()
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return image, nsfw_detected, watermark_detected
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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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):
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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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"""
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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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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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prompt_embeds = self.text_encoder(
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text_input_ids.to(device),
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attention_mask=attention_mask,
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)
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prompt_embeds = prompt_embeds[0]
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prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.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=self.text_encoder.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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# For classifier free guidance, we need to do two forward passes.
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# Here we concatenate the unconditional and text embeddings into a single batch
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# to avoid doing two forward passes
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prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
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return prompt_embeds
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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.StableDiffusionPipeline.decode_latents
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def decode_latents(self, latents):
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warnings.warn(
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"The decode_latents method is deprecated and will be removed in a future version. Please"
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" use VaeImageProcessor instead",
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FutureWarning,
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)
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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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def check_inputs(
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self,
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prompt,
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image,
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noise_level,
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callback_steps,
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negative_prompt=None,
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prompt_embeds=None,
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negative_prompt_embeds=None,
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):
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if (callback_steps is None) or (
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callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
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):
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raise ValueError(
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f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
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f" {type(callback_steps)}."
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)
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if prompt is not None and prompt_embeds is not None:
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raise ValueError(
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f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
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" only forward one of the two."
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)
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elif prompt is None and prompt_embeds is None:
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raise ValueError(
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"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
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)
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elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
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raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
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if negative_prompt is not None and negative_prompt_embeds is not None:
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raise ValueError(
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f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
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f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
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)
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if prompt_embeds is not None and negative_prompt_embeds is not None:
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if prompt_embeds.shape != negative_prompt_embeds.shape:
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raise ValueError(
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"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
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f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
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f" {negative_prompt_embeds.shape}."
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)
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if (
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not isinstance(image, torch.Tensor)
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and not isinstance(image, PIL.Image.Image)
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and not isinstance(image, np.ndarray)
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and not isinstance(image, list)
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):
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raise ValueError(
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f"`image` has to be of type `torch.Tensor`, `np.ndarray`, `PIL.Image.Image` or `list` but is {type(image)}"
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)
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# verify batch size of prompt and image are same if image is a list or tensor or numpy array
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if isinstance(image, list) or isinstance(image, torch.Tensor) or isinstance(image, np.ndarray):
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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 isinstance(image, list):
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image_batch_size = len(image)
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else:
|
|
image_batch_size = image.shape[0]
|
|
if batch_size != image_batch_size:
|
|
raise ValueError(
|
|
f"`prompt` has batch size {batch_size} and `image` has batch size {image_batch_size}."
|
|
" Please make sure that passed `prompt` matches the batch size of `image`."
|
|
)
|
|
|
|
# check noise level
|
|
if noise_level > self.config.max_noise_level:
|
|
raise ValueError(f"`noise_level` has to be <= {self.config.max_noise_level} but is {noise_level}")
|
|
|
|
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)}."
|
|
)
|
|
|
|
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
|
|
shape = (batch_size, num_channels_latents, height, width)
|
|
if latents is None:
|
|
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
|
|
else:
|
|
if latents.shape != shape:
|
|
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
|
|
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 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)
|
|
|
|
@torch.no_grad()
|
|
def __call__(
|
|
self,
|
|
prompt: Union[str, List[str]] = None,
|
|
image: Union[
|
|
torch.FloatTensor,
|
|
PIL.Image.Image,
|
|
np.ndarray,
|
|
List[torch.FloatTensor],
|
|
List[PIL.Image.Image],
|
|
List[np.ndarray],
|
|
] = None,
|
|
num_inference_steps: int = 75,
|
|
guidance_scale: float = 9.0,
|
|
noise_level: int = 20,
|
|
negative_prompt: 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,
|
|
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,
|
|
):
|
|
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 upscaled.
|
|
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.
|
|
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.
|
|
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 is 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 (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.
|
|
callback (`Callable`, *optional*):
|
|
A function that calls every `callback_steps` steps during inference. The function is 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 is called. If not specified, the callback is called at
|
|
every step.
|
|
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/cross_attention.py).
|
|
|
|
Examples:
|
|
```py
|
|
>>> import requests
|
|
>>> from PIL import Image
|
|
>>> from io import BytesIO
|
|
>>> from diffusers import StableDiffusionUpscalePipeline
|
|
>>> import torch
|
|
|
|
>>> # load model and scheduler
|
|
>>> model_id = "stabilityai/stable-diffusion-x4-upscaler"
|
|
>>> pipeline = StableDiffusionUpscalePipeline.from_pretrained(
|
|
... model_id, revision="fp16", torch_dtype=torch.float16
|
|
... )
|
|
>>> pipeline = pipeline.to("cuda")
|
|
|
|
>>> # let's download an image
|
|
>>> url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale/low_res_cat.png"
|
|
>>> response = requests.get(url)
|
|
>>> low_res_img = Image.open(BytesIO(response.content)).convert("RGB")
|
|
>>> low_res_img = low_res_img.resize((128, 128))
|
|
>>> prompt = "a white cat"
|
|
|
|
>>> upscaled_image = pipeline(prompt=prompt, image=low_res_img).images[0]
|
|
>>> upscaled_image.save("upsampled_cat.png")
|
|
```
|
|
|
|
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 and the
|
|
second element is a list of `bool`s indicating whether the corresponding generated image contains
|
|
"not-safe-for-work" (nsfw) content.
|
|
"""
|
|
|
|
# 1. Check inputs
|
|
self.check_inputs(
|
|
prompt,
|
|
image,
|
|
noise_level,
|
|
callback_steps,
|
|
negative_prompt,
|
|
prompt_embeds,
|
|
negative_prompt_embeds,
|
|
)
|
|
|
|
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
|
|
# 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 = self._encode_prompt(
|
|
prompt,
|
|
device,
|
|
num_images_per_prompt,
|
|
do_classifier_free_guidance,
|
|
negative_prompt,
|
|
prompt_embeds=prompt_embeds,
|
|
negative_prompt_embeds=negative_prompt_embeds,
|
|
lora_scale=text_encoder_lora_scale,
|
|
)
|
|
|
|
# 4. Preprocess image
|
|
image = self.image_processor.preprocess(image)
|
|
image = image.to(dtype=prompt_embeds.dtype, device=device)
|
|
|
|
# 5. set timesteps
|
|
self.scheduler.set_timesteps(num_inference_steps, device=device)
|
|
timesteps = self.scheduler.timesteps
|
|
|
|
# 5. Add noise to image
|
|
noise_level = torch.tensor([noise_level], dtype=torch.long, device=device)
|
|
noise = randn_tensor(image.shape, generator=generator, device=device, dtype=prompt_embeds.dtype)
|
|
image = self.low_res_scheduler.add_noise(image, noise, noise_level)
|
|
|
|
batch_multiplier = 2 if do_classifier_free_guidance else 1
|
|
image = torch.cat([image] * batch_multiplier * num_images_per_prompt)
|
|
noise_level = torch.cat([noise_level] * image.shape[0])
|
|
|
|
# 6. Prepare latent variables
|
|
height, width = image.shape[2:]
|
|
num_channels_latents = self.vae.config.latent_channels
|
|
latents = self.prepare_latents(
|
|
batch_size * num_images_per_prompt,
|
|
num_channels_latents,
|
|
height,
|
|
width,
|
|
prompt_embeds.dtype,
|
|
device,
|
|
generator,
|
|
latents,
|
|
)
|
|
|
|
# 7. Check that sizes of image and latents match
|
|
num_channels_image = image.shape[1]
|
|
if num_channels_latents + num_channels_image != self.unet.config.in_channels:
|
|
raise ValueError(
|
|
f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects"
|
|
f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +"
|
|
f" `num_channels_image`: {num_channels_image} "
|
|
f" = {num_channels_latents+num_channels_image}. Please verify the config of"
|
|
" `pipeline.unet` or your `image` input."
|
|
)
|
|
|
|
# 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
|
|
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
|
|
|
|
# concat latents, mask, masked_image_latents in the channel dimension
|
|
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
|
|
latent_model_input = torch.cat([latent_model_input, image], dim=1)
|
|
|
|
# predict the noise residual
|
|
noise_pred = self.unet(
|
|
latent_model_input,
|
|
t,
|
|
encoder_hidden_states=prompt_embeds,
|
|
cross_attention_kwargs=cross_attention_kwargs,
|
|
class_labels=noise_level,
|
|
return_dict=False,
|
|
)[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)
|
|
|
|
# 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)
|
|
|
|
# 10. Post-processing
|
|
# 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)
|
|
|
|
# post-processing
|
|
if not output_type == "latent":
|
|
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
|
|
image, has_nsfw_concept, _ = self.run_safety_checker(image, device, prompt_embeds.dtype)
|
|
else:
|
|
image = latents
|
|
has_nsfw_concept = None
|
|
|
|
if has_nsfw_concept is None:
|
|
do_denormalize = [True] * image.shape[0]
|
|
else:
|
|
do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
|
|
|
|
image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
|
|
|
|
# 11. Apply watermark
|
|
if output_type == "pil" and self.watermarker is not None:
|
|
image = self.watermarker.apply_watermark(image)
|
|
|
|
# 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, has_nsfw_concept)
|
|
|
|
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
|