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535 lines
20 KiB
535 lines
20 KiB
2 years ago
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#
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# For licensing see accompanying LICENSE.md file.
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# Copyright (C) 2022 Apple Inc. All Rights Reserved.
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#
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import argparse
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from diffusers.pipeline_utils import DiffusionPipeline
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from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
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from diffusers.schedulers import (
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DDIMScheduler,
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DPMSolverMultistepScheduler,
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EulerAncestralDiscreteScheduler,
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EulerDiscreteScheduler,
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LMSDiscreteScheduler,
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PNDMScheduler,
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)
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from diffusers.schedulers.scheduling_utils import SchedulerMixin
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import gc
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import inspect
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import logging
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logging.basicConfig()
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logger = logging.getLogger(__name__)
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logger.setLevel(logging.INFO)
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import numpy as np
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import os
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from python_coreml_stable_diffusion.coreml_model import (
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CoreMLModel,
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_load_mlpackage,
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get_available_compute_units,
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)
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import time
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import torch # Only used for `torch.from_tensor` in `pipe.scheduler.step()`
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from transformers import CLIPFeatureExtractor, CLIPTokenizer
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from typing import Union, Optional
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class CoreMLStableDiffusionPipeline(DiffusionPipeline):
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""" Core ML version of
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`diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline`
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"""
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def __init__(
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self,
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text_encoder: CoreMLModel,
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unet: CoreMLModel,
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vae_decoder: CoreMLModel,
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feature_extractor: CLIPFeatureExtractor,
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safety_checker: Optional[CoreMLModel],
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scheduler: Union[DDIMScheduler,
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DPMSolverMultistepScheduler,
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EulerAncestralDiscreteScheduler,
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EulerDiscreteScheduler,
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LMSDiscreteScheduler,
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PNDMScheduler],
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tokenizer: CLIPTokenizer,
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):
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super().__init__()
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# Register non-Core ML components of the pipeline similar to the original pipeline
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self.register_modules(
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tokenizer=tokenizer,
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scheduler=scheduler,
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feature_extractor=feature_extractor,
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)
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if safety_checker is None:
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# Reproduce original warning:
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# https://github.com/huggingface/diffusers/blob/v0.9.0/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py#L119
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logger.warning(
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f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
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" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
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" results in services or applications open to the public. Both the diffusers team and Hugging Face"
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" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
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" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
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" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
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)
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# Register Core ML components of the pipeline
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self.safety_checker = safety_checker
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self.text_encoder = text_encoder
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self.unet = unet
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self.unet.in_channels = self.unet.expected_inputs["sample"]["shape"][1]
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self.vae_decoder = vae_decoder
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VAE_DECODER_UPSAMPLE_FACTOR = 8
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# In PyTorch, users can determine the tensor shapes dynamically by default
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# In CoreML, tensors have static shapes unless flexible shapes were used during export
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# See https://coremltools.readme.io/docs/flexible-inputs
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latent_h, latent_w = self.unet.expected_inputs["sample"]["shape"][2:]
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self.height = latent_h * VAE_DECODER_UPSAMPLE_FACTOR
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self.width = latent_w * VAE_DECODER_UPSAMPLE_FACTOR
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logger.info(
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f"Stable Diffusion configured to generate {self.height}x{self.width} images"
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)
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def _encode_prompt(self, prompt, num_images_per_prompt,
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do_classifier_free_guidance, negative_prompt):
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batch_size = len(prompt) if isinstance(prompt, list) else 1
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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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return_tensors="np",
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)
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text_input_ids = text_inputs.input_ids
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if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
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removed_text = self.tokenizer.batch_decode(
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text_input_ids[:, self.tokenizer.model_max_length:])
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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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text_input_ids = text_input_ids[:, :self.tokenizer.
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model_max_length]
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text_embeddings = self.text_encoder(
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input_ids=text_input_ids.astype(np.float32))["last_hidden_state"]
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if do_classifier_free_guidance:
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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 type(prompt) is not type(negative_prompt):
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raise TypeError(
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"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
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" {type(prompt)}.")
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elif isinstance(negative_prompt, str):
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uncond_tokens = [negative_prompt] * batch_size
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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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else:
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uncond_tokens = negative_prompt
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max_length = text_input_ids.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="np",
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)
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uncond_embeddings = self.text_encoder(
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input_ids=uncond_input.input_ids.astype(
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np.float32))["last_hidden_state"]
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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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text_embeddings = np.concatenate(
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[uncond_embeddings, text_embeddings])
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text_embeddings = text_embeddings.transpose(0, 2, 1)[:, :, None, :]
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return text_embeddings
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def run_safety_checker(self, image):
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if self.safety_checker is not None:
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safety_checker_input = self.feature_extractor(
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self.numpy_to_pil(image),
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return_tensors="np",
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)
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safety_checker_outputs = self.safety_checker(
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clip_input=safety_checker_input.pixel_values.astype(
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np.float16),
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images=image.astype(np.float16),
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adjustment=np.array([0.]).astype(
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np.float16), # defaults to 0 in original pipeline
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)
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# Unpack dict
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has_nsfw_concept = safety_checker_outputs["has_nsfw_concepts"]
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image = safety_checker_outputs["filtered_images"]
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concept_scores = safety_checker_outputs["concept_scores"]
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logger.info(
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f"Generated image has nsfw concept={has_nsfw_concept.any()}")
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else:
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has_nsfw_concept = None
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return image, has_nsfw_concept
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def decode_latents(self, latents):
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latents = 1 / 0.18215 * latents
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image = self.vae_decoder(z=latents.astype(np.float16))["image"]
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image = np.clip(image / 2 + 0.5, 0, 1)
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image = image.transpose((0, 2, 3, 1))
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return image
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def prepare_latents(self,
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batch_size,
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num_channels_latents,
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height,
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width,
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latents=None):
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latents_shape = (batch_size, num_channels_latents, self.height // 8,
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self.width // 8)
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if latents is None:
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latents = np.random.randn(*latents_shape).astype(np.float16)
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elif latents.shape != latents_shape:
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raise ValueError(
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f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"
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)
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latents = latents * self.scheduler.init_noise_sigma
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return latents
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def check_inputs(self, prompt, height, width, callback_steps):
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if height != self.height or width != self.width:
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logger.warning(
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"`height` and `width` dimensions (of the output image tensor) are fixed when exporting the Core ML models " \
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"unless flexible shapes are used during export (https://coremltools.readme.io/docs/flexible-inputs). " \
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"This pipeline was provided with Core ML models that generate {self.height}x{self.width} images (user requested {height}x{width})"
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)
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if not isinstance(prompt, str) and not isinstance(prompt, list):
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raise ValueError(
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f"`prompt` has to be of type `str` or `list` but is {type(prompt)}"
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)
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if height % 8 != 0 or width % 8 != 0:
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raise ValueError(
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f"`height` and `width` have to be divisible by 8 but are {height} and {width}."
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)
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if (callback_steps is None) or (callback_steps is not None and
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(not isinstance(callback_steps, int)
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or callback_steps <= 0)):
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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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def prepare_extra_step_kwargs(self, 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(
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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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return extra_step_kwargs
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def __call__(
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self,
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prompt,
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height=512,
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width=512,
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num_inference_steps=50,
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guidance_scale=7.5,
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negative_prompt=None,
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num_images_per_prompt=1,
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eta=0.0,
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latents=None,
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output_type="pil",
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return_dict=True,
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callback=None,
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callback_steps=1,
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**kwargs,
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):
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# 1. Check inputs. Raise error if not correct
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self.check_inputs(prompt, height, width, callback_steps)
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# 2. Define call parameters
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batch_size = 1 if isinstance(prompt, str) else len(prompt)
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if batch_size > 1 or num_images_per_prompt > 1:
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raise NotImplementedError(
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"For batched generation of multiple images and/or multiple prompts, please refer to the Swift package."
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)
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# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
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# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
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# corresponds to doing no classifier free guidance.
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do_classifier_free_guidance = guidance_scale > 1.0
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# 3. Encode input prompt
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text_embeddings = self._encode_prompt(
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prompt,
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num_images_per_prompt,
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do_classifier_free_guidance,
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negative_prompt,
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)
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# 4. Prepare timesteps
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self.scheduler.set_timesteps(num_inference_steps)
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timesteps = self.scheduler.timesteps
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# 5. Prepare latent variables
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num_channels_latents = self.unet.in_channels
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latents = self.prepare_latents(
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batch_size * num_images_per_prompt,
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num_channels_latents,
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height,
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width,
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latents,
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)
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# 6. Prepare extra step kwargs
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extra_step_kwargs = self.prepare_extra_step_kwargs(eta)
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# 7. Denoising loop
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for i, t in enumerate(self.progress_bar(timesteps)):
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# expand the latents if we are doing classifier free guidance
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latent_model_input = np.concatenate(
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[latents] * 2) if do_classifier_free_guidance else latents
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latent_model_input = self.scheduler.scale_model_input(
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latent_model_input, t)
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# predict the noise residual
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noise_pred = self.unet(
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sample=latent_model_input.astype(np.float16),
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timestep=np.array([t, t], np.float16),
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encoder_hidden_states=text_embeddings.astype(np.float16),
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)["noise_pred"]
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# perform guidance
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if do_classifier_free_guidance:
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noise_pred_uncond, noise_pred_text = np.split(noise_pred, 2)
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noise_pred = noise_pred_uncond + guidance_scale * (
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noise_pred_text - noise_pred_uncond)
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# compute the previous noisy sample x_t -> x_t-1
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latents = self.scheduler.step(torch.from_numpy(noise_pred),
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t,
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torch.from_numpy(latents),
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**extra_step_kwargs,
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).prev_sample.numpy()
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# call the callback, if provided
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if callback is not None and i % callback_steps == 0:
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callback(i, t, latents)
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# 8. Post-processing
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image = self.decode_latents(latents)
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# 9. Run safety checker
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image, has_nsfw_concept = self.run_safety_checker(image)
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|
|
# 10. Convert to PIL
|
||
|
|
if output_type == "pil":
|
||
|
|
image = self.numpy_to_pil(image)
|
||
|
|
|
||
|
|
if not return_dict:
|
||
|
|
return (image, has_nsfw_concept)
|
||
|
|
|
||
|
|
return StableDiffusionPipelineOutput(
|
||
|
|
images=image, nsfw_content_detected=has_nsfw_concept)
|
||
|
|
|
||
|
|
|
||
|
|
def get_available_schedulers():
|
||
|
|
schedulers = {}
|
||
|
|
for scheduler in [DDIMScheduler,
|
||
|
|
DPMSolverMultistepScheduler,
|
||
|
|
EulerAncestralDiscreteScheduler,
|
||
|
|
EulerDiscreteScheduler,
|
||
|
|
LMSDiscreteScheduler,
|
||
|
|
PNDMScheduler]:
|
||
|
|
schedulers[scheduler().__class__.__name__.replace("Scheduler", "")] = scheduler
|
||
|
|
return schedulers
|
||
|
|
|
||
|
|
SCHEDULER_MAP = get_available_schedulers()
|
||
|
|
|
||
|
|
def get_coreml_pipe(pytorch_pipe,
|
||
|
|
mlpackages_dir,
|
||
|
|
model_version,
|
||
|
|
compute_unit,
|
||
|
|
delete_original_pipe=True,
|
||
|
|
scheduler_override=None):
|
||
|
|
""" Initializes and returns a `CoreMLStableDiffusionPipeline` from an original
|
||
|
|
diffusers PyTorch pipeline
|
||
|
|
"""
|
||
|
|
# Ensure `scheduler_override` object is of correct type if specified
|
||
|
|
if scheduler_override is not None:
|
||
|
|
assert isinstance(scheduler_override, SchedulerMixin)
|
||
|
|
logger.warning(
|
||
|
|
"Overriding scheduler in pipeline: "
|
||
|
|
f"Default={pytorch_pipe.scheduler}, Override={scheduler_override}")
|
||
|
|
|
||
|
|
# Gather configured tokenizer and scheduler attributes from the original pipe
|
||
|
|
coreml_pipe_kwargs = {
|
||
|
|
"tokenizer": pytorch_pipe.tokenizer,
|
||
|
|
"scheduler": pytorch_pipe.scheduler if scheduler_override is None else scheduler_override,
|
||
|
|
"feature_extractor": pytorch_pipe.feature_extractor,
|
||
|
|
}
|
||
|
|
|
||
|
|
model_names_to_load = ["text_encoder", "unet", "vae_decoder"]
|
||
|
|
if getattr(pytorch_pipe, "safety_checker", None) is not None:
|
||
|
|
model_names_to_load.append("safety_checker")
|
||
|
|
else:
|
||
|
|
logger.warning(
|
||
|
|
f"Original diffusers pipeline for {model_version} does not have a safety_checker, "
|
||
|
|
"Core ML pipeline will mirror this behavior.")
|
||
|
|
coreml_pipe_kwargs["safety_checker"] = None
|
||
|
|
|
||
|
|
if delete_original_pipe:
|
||
|
|
del pytorch_pipe
|
||
|
|
gc.collect()
|
||
|
|
logger.info("Removed PyTorch pipe to reduce peak memory consumption")
|
||
|
|
|
||
|
|
# Load Core ML models
|
||
|
|
logger.info(f"Loading Core ML models in memory from {mlpackages_dir}")
|
||
|
|
coreml_pipe_kwargs.update({
|
||
|
|
model_name: _load_mlpackage(
|
||
|
|
model_name,
|
||
|
|
mlpackages_dir,
|
||
|
|
model_version,
|
||
|
|
compute_unit,
|
||
|
|
)
|
||
|
|
for model_name in model_names_to_load
|
||
|
|
})
|
||
|
|
logger.info("Done.")
|
||
|
|
|
||
|
|
logger.info("Initializing Core ML pipe for image generation")
|
||
|
|
coreml_pipe = CoreMLStableDiffusionPipeline(**coreml_pipe_kwargs)
|
||
|
|
logger.info("Done.")
|
||
|
|
|
||
|
|
return coreml_pipe
|
||
|
|
|
||
|
|
|
||
|
|
def get_image_path(args, **override_kwargs):
|
||
|
|
""" mkdir output folder and encode metadata in the filename
|
||
|
|
"""
|
||
|
|
out_folder = os.path.join(args.o, "_".join(args.prompt.replace("/", "_").rsplit(" ")))
|
||
|
|
os.makedirs(out_folder, exist_ok=True)
|
||
|
|
|
||
|
|
out_fname = f"randomSeed_{override_kwargs.get('seed', None) or args.seed}"
|
||
|
|
out_fname += f"_computeUnit_{override_kwargs.get('compute_unit', None) or args.compute_unit}"
|
||
|
|
out_fname += f"_modelVersion_{override_kwargs.get('model_version', None) or args.model_version.replace('/', '_')}"
|
||
|
|
|
||
|
|
if args.scheduler is not None:
|
||
|
|
out_fname += f"_customScheduler_{override_kwargs.get('scheduler', None) or args.scheduler}"
|
||
|
|
out_fname += f"_numInferenceSteps{override_kwargs.get('num_inference_steps', None) or args.num_inference_steps}"
|
||
|
|
|
||
|
|
return os.path.join(out_folder, out_fname + ".png")
|
||
|
|
|
||
|
|
|
||
|
|
def main(args):
|
||
|
|
logger.info(f"Setting random seed to {args.seed}")
|
||
|
|
np.random.seed(args.seed)
|
||
|
|
|
||
|
|
logger.info("Initializing PyTorch pipe for reference configuration")
|
||
|
|
from diffusers import StableDiffusionPipeline
|
||
|
|
pytorch_pipe = StableDiffusionPipeline.from_pretrained(args.model_version,
|
||
|
|
use_auth_token=True)
|
||
|
|
|
||
|
|
user_specified_scheduler = None
|
||
|
|
if args.scheduler is not None:
|
||
|
|
user_specified_scheduler = SCHEDULER_MAP[
|
||
|
|
args.scheduler].from_config(pytorch_pipe.scheduler.config)
|
||
|
|
|
||
|
|
coreml_pipe = get_coreml_pipe(pytorch_pipe=pytorch_pipe,
|
||
|
|
mlpackages_dir=args.i,
|
||
|
|
model_version=args.model_version,
|
||
|
|
compute_unit=args.compute_unit,
|
||
|
|
scheduler_override=user_specified_scheduler)
|
||
|
|
|
||
|
|
logger.info("Beginning image generation.")
|
||
|
|
image = coreml_pipe(
|
||
|
|
prompt=args.prompt,
|
||
|
|
height=coreml_pipe.height,
|
||
|
|
width=coreml_pipe.width,
|
||
|
|
num_inference_steps=args.num_inference_steps,
|
||
|
|
)
|
||
|
|
|
||
|
|
out_path = get_image_path(args)
|
||
|
|
logger.info(f"Saving generated image to {out_path}")
|
||
|
|
image["images"][0].save(out_path)
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
parser = argparse.ArgumentParser()
|
||
|
|
|
||
|
|
parser.add_argument(
|
||
|
|
"--prompt",
|
||
|
|
required=True,
|
||
|
|
help="The text prompt to be used for text-to-image generation.")
|
||
|
|
parser.add_argument(
|
||
|
|
"-i",
|
||
|
|
required=True,
|
||
|
|
help=("Path to input directory with the .mlpackage files generated by "
|
||
|
|
"python_coreml_stable_diffusion.torch2coreml"))
|
||
|
|
parser.add_argument("-o", required=True)
|
||
|
|
parser.add_argument("--seed",
|
||
|
|
"-s",
|
||
|
|
default=93,
|
||
|
|
type=int,
|
||
|
|
help="Random seed to be able to reproduce results")
|
||
|
|
parser.add_argument(
|
||
|
|
"--model-version",
|
||
|
|
default="CompVis/stable-diffusion-v1-4",
|
||
|
|
help=
|
||
|
|
("The pre-trained model checkpoint and configuration to restore. "
|
||
|
|
"For available versions: https://huggingface.co/models?search=stable-diffusion"
|
||
|
|
))
|
||
|
|
parser.add_argument(
|
||
|
|
"--compute-unit",
|
||
|
|
choices=get_available_compute_units(),
|
||
|
|
default="ALL",
|
||
|
|
help=("The compute units to be used when executing Core ML models. "
|
||
|
|
f"Options: {get_available_compute_units()}"))
|
||
|
|
parser.add_argument(
|
||
|
|
"--scheduler",
|
||
|
|
choices=tuple(SCHEDULER_MAP.keys()),
|
||
|
|
default=None,
|
||
|
|
help=("The scheduler to use for running the reverse diffusion process. "
|
||
|
|
"If not specified, the default scheduler from the diffusers pipeline is utilized"))
|
||
|
|
parser.add_argument(
|
||
|
|
"--num-inference-steps",
|
||
|
|
default=50,
|
||
|
|
type=int,
|
||
|
|
help="The number of iterations the unet model will be executed throughout the reverse diffusion process")
|
||
|
|
|
||
|
|
args = parser.parse_args()
|
||
|
|
main(args)
|