417 lines
15 KiB
Python
417 lines
15 KiB
Python
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import inspect
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from logging import getLogger
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from typing import Callable, List, Optional, Union
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import numpy as np
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import torch
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import torch.nn.functional as F
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from diffusers.models.unet_2d_condition import UNet2DConditionOutput
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from diffusers.pipelines.onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
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from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
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from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
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from transformers import CLIPImageProcessor, CLIPTokenizer
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from ...constants import LATENT_CHANNELS, LATENT_FACTOR, ONNX_MODEL
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from ...convert.utils import onnx_export
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from .base import OnnxStableDiffusionBasePipeline
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logger = getLogger(__name__)
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class OnnxStableDiffusionHighresPipeline(OnnxStableDiffusionBasePipeline):
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upscaler: OnnxRuntimeModel
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def __init__(
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self,
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vae_encoder: OnnxRuntimeModel,
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vae_decoder: OnnxRuntimeModel,
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text_encoder: OnnxRuntimeModel,
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tokenizer: CLIPTokenizer,
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unet: OnnxRuntimeModel,
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scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
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safety_checker: OnnxRuntimeModel,
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feature_extractor: CLIPImageProcessor,
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requires_safety_checker: bool = True,
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upscaler: OnnxRuntimeModel = None,
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):
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super().__init__(
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vae_encoder=vae_encoder,
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vae_decoder=vae_decoder,
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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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safety_checker=safety_checker,
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feature_extractor=feature_extractor,
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requires_safety_checker=requires_safety_checker,
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)
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self.upscaler = upscaler
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@torch.no_grad()
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def text2img(
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self,
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prompt: Union[str, List[str]] = None,
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height: Optional[int] = 512,
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width: Optional[int] = 512,
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num_inference_steps: Optional[int] = 50,
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num_upscale_steps: Optional[int] = 50,
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guidance_scale: Optional[float] = 7.5,
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negative_prompt: Optional[Union[str, List[str]]] = None,
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num_images_per_prompt: Optional[int] = 1,
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eta: Optional[float] = 0.0,
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generator: Optional[np.random.RandomState] = None,
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latents: Optional[np.ndarray] = None,
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prompt_embeds: Optional[np.ndarray] = None,
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negative_prompt_embeds: Optional[np.ndarray] = None,
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output_type: Optional[str] = "pil",
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return_dict: bool = True,
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callback: Optional[Callable[[int, int, np.ndarray], None]] = None,
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callback_steps: int = 1,
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):
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# check inputs. Raise error if not correct
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self.check_inputs(
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prompt,
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height,
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width,
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callback_steps,
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negative_prompt,
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prompt_embeds,
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negative_prompt_embeds,
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)
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# define call parameters
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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 generator is None:
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generator = np.random
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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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prompt_embeds, text_pooler_out = 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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prompt_embeds=prompt_embeds,
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negative_prompt_embeds=negative_prompt_embeds,
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)
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# get the initial random noise unless the user supplied it
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latents_dtype = prompt_embeds.dtype
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latents_shape = (
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batch_size * num_images_per_prompt,
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LATENT_CHANNELS,
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height // LATENT_FACTOR,
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width // LATENT_FACTOR,
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)
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if latents is None:
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latents = generator.randn(*latents_shape).astype(latents_dtype)
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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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# set timesteps
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self.scheduler.set_timesteps(num_inference_steps)
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latents = latents * np.float64(self.scheduler.init_noise_sigma)
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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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)
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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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timestep_dtype = next(
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(
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input.type
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for input in self.unet.model.get_inputs()
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if input.name == "timestep"
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),
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"tensor(float)",
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)
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timestep_dtype = ORT_TO_NP_TYPE[timestep_dtype]
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for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
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# expand the latents if we are doing classifier free guidance
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latent_model_input = (
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np.concatenate([latents] * 2)
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if do_classifier_free_guidance
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else latents
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)
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latent_model_input = self.scheduler.scale_model_input(
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torch.from_numpy(latent_model_input), t
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)
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latent_model_input = latent_model_input.cpu().numpy()
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# predict the noise residual
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timestep = np.array([t], dtype=timestep_dtype)
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noise_pred = self.unet(
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sample=latent_model_input,
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timestep=timestep,
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encoder_hidden_states=prompt_embeds,
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)
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noise_pred = noise_pred[0]
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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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)
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# compute the previous noisy sample x_t -> x_t-1
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scheduler_output = self.scheduler.step(
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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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)
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latents = scheduler_output.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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if self.upscaler is not None:
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# 5. set upscale timesteps
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self.scheduler.set_timesteps(num_upscale_steps)
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timesteps = self.scheduler.timesteps
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batch_multiplier = 2 if do_classifier_free_guidance else 1
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image = np.concatenate([latents] * batch_multiplier)
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# 5. Add noise to image (set to be 0):
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# (see below notes from the author):
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# "the This step theoretically can make the model work better on out-of-distribution inputs, but mostly
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# just seems to make it match the input less, so it's turned off by default."
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noise_level = np.array([0.0], dtype=np.float32)
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noise_level = np.concatenate([noise_level] * image.shape[0])
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inv_noise_level = (noise_level**2 + 1) ** (-0.5)
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image_cond = (
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F.interpolate(torch.tensor(image), scale_factor=2, mode="nearest")
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* inv_noise_level[:, None, None, None]
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)
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image_cond = image_cond.numpy().astype(prompt_embeds.dtype)
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noise_level_embed = np.concatenate(
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[
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np.ones(
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(text_pooler_out.shape[0], 64), dtype=text_pooler_out.dtype
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),
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np.zeros(
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(text_pooler_out.shape[0], 64), dtype=text_pooler_out.dtype
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),
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],
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axis=1,
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)
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# upscaling latents
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latents_shape = (
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batch_size * num_images_per_prompt,
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LATENT_CHANNELS,
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height * 2 // LATENT_FACTOR,
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width * 2 // LATENT_FACTOR,
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)
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latents = generator.randn(*latents_shape).astype(latents_dtype)
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timestep_condition = np.concatenate(
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[noise_level_embed, text_pooler_out], axis=1
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)
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num_warmup_steps = 0
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with self.progress_bar(total=num_upscale_steps) as progress_bar:
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for i, t in enumerate(timesteps):
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sigma = self.scheduler.sigmas[i]
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# expand the latents if we are doing classifier free guidance
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latent_model_input = (
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np.concatenate([latents] * 2)
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if do_classifier_free_guidance
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else latents
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)
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scaled_model_input = self.scheduler.scale_model_input(
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latent_model_input, t
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)
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scaled_model_input = np.concatenate(
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[scaled_model_input, image_cond], axis=1
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)
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# preconditioning parameter based on Karras et al. (2022) (table 1)
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timestep = np.log(sigma) * 0.25
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noise_pred = self.upscaler(
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sample=scaled_model_input,
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timestep=timestep,
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encoder_hidden_states=prompt_embeds,
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timestep_cond=timestep_condition,
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).sample
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# in original repo, the output contains a variance channel that's not used
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noise_pred = noise_pred[:, :-1]
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# apply preconditioning, based on table 1 in Karras et al. (2022)
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inv_sigma = 1 / (sigma**2 + 1)
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noise_pred = (
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inv_sigma * latent_model_input
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+ self.scheduler.scale_model_input(sigma, t) * noise_pred
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)
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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 = noise_pred.chunk(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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)
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# compute the previous noisy sample x_t -> x_t-1
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scheduler_output = self.scheduler.step(
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noise_pred, t, torch.from_numpy(latents)
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)
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latents = scheduler_output.prev_sample.numpy()
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# call the callback, if provided
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if i == len(timesteps) - 1 or (
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(i + 1) > num_warmup_steps
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and (i + 1) % self.scheduler.order == 0
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):
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progress_bar.update()
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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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else:
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logger.debug("skipping latent upscaler, no model provided")
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# decode image
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latents = 1 / 0.18215 * latents
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# it seems likes there is a strange result for using half-precision vae decoder if batchsize>1
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image = np.concatenate(
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[
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self.vae_decoder(latent_sample=latents[i : i + 1])[0]
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for i in range(latents.shape[0])
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]
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)
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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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if output_type == "pil":
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image = self.numpy_to_pil(image)
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if not return_dict:
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return (image, None)
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return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=None)
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def export_unet(pipeline, output_path, unet_sample_size=1024):
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device = torch.device("cpu")
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dtype = torch.float32
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num_tokens = pipeline.text_encoder.config.max_position_embeddings
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text_hidden_size = pipeline.text_encoder.config.hidden_size
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unet_inputs = ["sample", "timestep", "encoder_hidden_states", "timestep_cond"]
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unet_in_channels = pipeline.unet.config.in_channels
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unet_path = output_path / "unet" / ONNX_MODEL
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logger.info("exporting UNet to %s", unet_path)
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onnx_export(
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pipeline.unet,
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model_args=(
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torch.randn(
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2,
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unet_in_channels,
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unet_sample_size // LATENT_FACTOR,
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unet_sample_size // LATENT_FACTOR,
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).to(device=device, dtype=dtype),
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torch.randn(2).to(device=device, dtype=dtype),
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torch.randn(2, num_tokens, text_hidden_size).to(device=device, dtype=dtype),
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torch.randn(2, 64, 64, 2).to(
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device=device, dtype=dtype
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), # TODO: not the right shape
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),
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output_path=unet_path,
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ordered_input_names=unet_inputs,
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# has to be different from "sample" for correct tracing
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output_names=["out_sample"],
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dynamic_axes={
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"sample": {0: "batch"}, # , 1: "channels", 2: "height", 3: "width"},
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"timestep": {0: "batch"},
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"encoder_hidden_states": {0: "batch", 1: "sequence"},
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},
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opset=14,
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|
|
half=False,
|
||
|
|
external_data=True,
|
||
|
|
v2=False,
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
def load_and_export(source="stabilityai/sd-x2-latent-upscaler"):
|
||
|
|
from pathlib import Path
|
||
|
|
|
||
|
|
from diffusers import StableDiffusionLatentUpscalePipeline
|
||
|
|
|
||
|
|
upscaler = StableDiffusionLatentUpscalePipeline.from_pretrained(
|
||
|
|
source, torch_dtype=torch.float32
|
||
|
|
)
|
||
|
|
export_unet(upscaler, Path("/tmp/latent-upscaler"))
|
||
|
|
|
||
|
|
|
||
|
|
def load_and_run(
|
||
|
|
prompt,
|
||
|
|
source="stabilityai/sd-x2-latent-upscaler",
|
||
|
|
checkpoint="../models/stable-diffusion-onnx-v1-5",
|
||
|
|
):
|
||
|
|
from diffusers import (
|
||
|
|
EulerAncestralDiscreteScheduler,
|
||
|
|
StableDiffusionLatentUpscalePipeline,
|
||
|
|
)
|
||
|
|
|
||
|
|
upscaler = StableDiffusionLatentUpscalePipeline.from_pretrained(source)
|
||
|
|
highres = OnnxStableDiffusionHighresPipeline.from_pretrained(checkpoint)
|
||
|
|
scheduler = EulerAncestralDiscreteScheduler.from_pretrained(
|
||
|
|
f"{checkpoint}/scheduler"
|
||
|
|
)
|
||
|
|
|
||
|
|
# combine them
|
||
|
|
highres.scheduler = scheduler
|
||
|
|
highres.upscaler = RetorchModel(upscaler.unet)
|
||
|
|
|
||
|
|
# run
|
||
|
|
result = highres.text2img(prompt, num_inference_steps=25, num_upscale_steps=25)
|
||
|
|
image = result.images[0]
|
||
|
|
image.save("/tmp/highres.png")
|
||
|
|
|
||
|
|
|
||
|
|
class RetorchModel:
|
||
|
|
"""
|
||
|
|
Shim back from ONNX to PyTorch
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(self, model) -> None:
|
||
|
|
self.model = model
|
||
|
|
|
||
|
|
def __call__(self, **kwargs):
|
||
|
|
inputs = {
|
||
|
|
k: torch.from_numpy(v) if isinstance(v, np.ndarray) else v
|
||
|
|
for k, v in kwargs.items()
|
||
|
|
}
|
||
|
|
outputs = self.model(**inputs)
|
||
|
|
return UNet2DConditionOutput(sample=outputs.sample.numpy())
|