459 lines
15 KiB
Python
459 lines
15 KiB
Python
from logging import getLogger
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from os import path
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from typing import Any, List, Optional, Tuple
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import numpy as np
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import torch
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from onnx import load_model
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from transformers import CLIPTokenizer
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from ..constants import ONNX_MODEL
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from ..convert.diffusion.lora import blend_loras, buffer_external_data_tensors
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from ..convert.diffusion.textual_inversion import blend_textual_inversions
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from ..diffusers.utils import expand_prompt
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from ..models.meta import NetworkModel
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from ..params import DeviceParams, Size
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from ..server import ServerContext
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from ..utils import run_gc
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from .pipelines.controlnet import OnnxStableDiffusionControlNetPipeline
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from .pipelines.lpw import OnnxStableDiffusionLongPromptWeightingPipeline
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from .pipelines.pix2pix import OnnxStableDiffusionInstructPix2PixPipeline
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from .version_safe_diffusers import (
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DDIMScheduler,
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DDPMScheduler,
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DEISMultistepScheduler,
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DPMSolverMultistepScheduler,
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DPMSolverSinglestepScheduler,
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EulerAncestralDiscreteScheduler,
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EulerDiscreteScheduler,
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HeunDiscreteScheduler,
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IPNDMScheduler,
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KarrasVeScheduler,
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KDPM2AncestralDiscreteScheduler,
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KDPM2DiscreteScheduler,
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LMSDiscreteScheduler,
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OnnxRuntimeModel,
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OnnxStableDiffusionImg2ImgPipeline,
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OnnxStableDiffusionInpaintPipeline,
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OnnxStableDiffusionPipeline,
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PNDMScheduler,
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StableDiffusionPipeline,
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UniPCMultistepScheduler,
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)
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logger = getLogger(__name__)
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latent_channels = 4
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latent_factor = 8
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available_pipelines = {
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"controlnet": OnnxStableDiffusionControlNetPipeline,
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"img2img": OnnxStableDiffusionImg2ImgPipeline,
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"inpaint": OnnxStableDiffusionInpaintPipeline,
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"lpw": OnnxStableDiffusionLongPromptWeightingPipeline,
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"pix2pix": OnnxStableDiffusionInstructPix2PixPipeline,
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"txt2img": OnnxStableDiffusionPipeline,
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}
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pipeline_schedulers = {
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"ddim": DDIMScheduler,
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"ddpm": DDPMScheduler,
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"deis-multi": DEISMultistepScheduler,
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"dpm-multi": DPMSolverMultistepScheduler,
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"dpm-single": DPMSolverSinglestepScheduler,
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"euler": EulerDiscreteScheduler,
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"euler-a": EulerAncestralDiscreteScheduler,
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"heun": HeunDiscreteScheduler,
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"ipndm": IPNDMScheduler,
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"k-dpm-2-a": KDPM2AncestralDiscreteScheduler,
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"k-dpm-2": KDPM2DiscreteScheduler,
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"karras-ve": KarrasVeScheduler,
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"lms-discrete": LMSDiscreteScheduler,
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"pndm": PNDMScheduler,
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"unipc-multi": UniPCMultistepScheduler,
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}
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def get_available_pipelines() -> List[str]:
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return list(available_pipelines.keys())
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def get_pipeline_schedulers() -> List[str]:
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return list(pipeline_schedulers.keys())
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def get_scheduler_name(scheduler: Any) -> Optional[str]:
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for k, v in pipeline_schedulers.items():
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if scheduler == v or scheduler == v.__name__:
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return k
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return None
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def get_latents_from_seed(seed: int, size: Size, batch: int = 1) -> np.ndarray:
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"""
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From https://www.travelneil.com/stable-diffusion-updates.html.
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This one needs to use np.random because of the return type.
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"""
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latents_shape = (
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batch,
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latent_channels,
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size.height // latent_factor,
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size.width // latent_factor,
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)
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rng = np.random.default_rng(seed)
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image_latents = rng.standard_normal(latents_shape).astype(np.float32)
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return image_latents
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def get_tile_latents(
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full_latents: np.ndarray, dims: Tuple[int, int, int]
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) -> np.ndarray:
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x, y, tile = dims
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t = tile // latent_factor
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x = x // latent_factor
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y = y // latent_factor
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xt = x + t
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yt = y + t
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return full_latents[:, :, y:yt, x:xt]
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def load_pipeline(
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server: ServerContext,
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pipeline: str,
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model: str,
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scheduler_name: str,
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device: DeviceParams,
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control: Optional[NetworkModel] = None,
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inversions: Optional[List[Tuple[str, float]]] = None,
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loras: Optional[List[Tuple[str, float]]] = None,
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):
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inversions = inversions or []
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loras = loras or []
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control_key = control.name if control is not None else None
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torch_dtype = (
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torch.float16 if "torch-fp16" in server.optimizations else torch.float32
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)
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logger.debug("using Torch dtype %s for pipeline", torch_dtype)
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pipe_key = (
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pipeline,
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model,
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device.device,
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device.provider,
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control_key,
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inversions,
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loras,
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)
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scheduler_key = (scheduler_name, model)
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scheduler_type = pipeline_schedulers[scheduler_name]
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cache_pipe = server.cache.get("diffusion", pipe_key)
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if cache_pipe is not None:
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logger.debug("reusing existing diffusion pipeline")
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pipe = cache_pipe
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cache_scheduler = server.cache.get("scheduler", scheduler_key)
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if cache_scheduler is None:
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logger.debug("loading new diffusion scheduler")
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scheduler = scheduler_type.from_pretrained(
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model,
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provider=device.ort_provider(),
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sess_options=device.sess_options(),
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subfolder="scheduler",
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torch_dtype=torch_dtype,
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)
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if device is not None and hasattr(scheduler, "to"):
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scheduler = scheduler.to(device.torch_str())
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pipe.scheduler = scheduler
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server.cache.set("scheduler", scheduler_key, scheduler)
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run_gc([device])
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else:
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if server.cache.drop("diffusion", pipe_key) > 0:
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logger.debug("unloading previous diffusion pipeline")
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run_gc([device])
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logger.debug("loading new diffusion pipeline from %s", model)
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components = {
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"scheduler": scheduler_type.from_pretrained(
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model,
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provider=device.ort_provider(),
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sess_options=device.sess_options(),
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subfolder="scheduler",
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torch_dtype=torch_dtype,
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)
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}
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# shared components
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text_encoder = None
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unet_type = "unet"
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# ControlNet component
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if pipeline == "controlnet" and control is not None:
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cnet_path = path.join(server.model_path, "control", f"{control.name}.onnx")
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logger.debug("loading ControlNet weights from %s", cnet_path)
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components["controlnet"] = OnnxRuntimeModel(
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OnnxRuntimeModel.load_model(
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cnet_path,
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provider=device.ort_provider(),
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sess_options=device.sess_options(),
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)
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)
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unet_type = "cnet"
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# Textual Inversion blending
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if inversions is not None and len(inversions) > 0:
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logger.debug("blending Textual Inversions from %s", inversions)
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inversion_names, inversion_weights = zip(*inversions)
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inversion_models = [
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path.join(server.model_path, "inversion", name)
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for name in inversion_names
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]
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text_encoder = load_model(path.join(model, "text_encoder", ONNX_MODEL))
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tokenizer = CLIPTokenizer.from_pretrained(
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model,
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subfolder="tokenizer",
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torch_dtype=torch_dtype,
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)
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text_encoder, tokenizer = blend_textual_inversions(
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server,
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text_encoder,
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tokenizer,
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list(
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zip(
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inversion_models,
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inversion_weights,
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inversion_names,
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[None] * len(inversion_models),
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)
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),
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)
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components["tokenizer"] = tokenizer
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# should be pretty small and should not need external data
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if loras is None or len(loras) == 0:
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components["text_encoder"] = OnnxRuntimeModel(
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OnnxRuntimeModel.load_model(
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text_encoder.SerializeToString(),
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provider=device.ort_provider(),
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sess_options=device.sess_options(),
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)
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)
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# LoRA blending
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if loras is not None and len(loras) > 0:
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lora_names, lora_weights = zip(*loras)
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lora_models = [
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path.join(server.model_path, "lora", name) for name in lora_names
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]
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logger.info(
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"blending base model %s with LoRA models: %s", model, lora_models
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)
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# blend and load text encoder
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text_encoder = text_encoder or path.join(model, "text_encoder", ONNX_MODEL)
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text_encoder = blend_loras(
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server,
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text_encoder,
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list(zip(lora_models, lora_weights)),
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"text_encoder",
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)
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(text_encoder, text_encoder_data) = buffer_external_data_tensors(
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text_encoder
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)
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text_encoder_names, text_encoder_values = zip(*text_encoder_data)
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text_encoder_opts = device.sess_options(cache=False)
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text_encoder_opts.add_external_initializers(
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list(text_encoder_names), list(text_encoder_values)
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)
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components["text_encoder"] = OnnxRuntimeModel(
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OnnxRuntimeModel.load_model(
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text_encoder.SerializeToString(),
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provider=device.ort_provider(),
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sess_options=text_encoder_opts,
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)
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)
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# blend and load unet
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unet = path.join(model, unet_type, ONNX_MODEL)
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blended_unet = blend_loras(
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server,
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unet,
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list(zip(lora_models, lora_weights)),
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"unet",
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)
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(unet_model, unet_data) = buffer_external_data_tensors(blended_unet)
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unet_names, unet_values = zip(*unet_data)
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unet_opts = device.sess_options(cache=False)
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unet_opts.add_external_initializers(list(unet_names), list(unet_values))
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components["unet"] = OnnxRuntimeModel(
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OnnxRuntimeModel.load_model(
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unet_model.SerializeToString(),
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provider=device.ort_provider(),
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sess_options=unet_opts,
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)
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)
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# make sure a UNet has been loaded
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if "unet" not in components:
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unet = path.join(model, unet_type, ONNX_MODEL)
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logger.debug("loading UNet (%s) from %s", unet_type, unet)
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components["unet"] = OnnxRuntimeModel(
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OnnxRuntimeModel.load_model(
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unet,
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provider=device.ort_provider(),
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sess_options=device.sess_options(),
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)
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)
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pipeline_class = available_pipelines.get(pipeline, OnnxStableDiffusionPipeline)
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logger.debug("loading pretrained SD pipeline for %s", pipeline_class.__name__)
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pipe = pipeline_class.from_pretrained(
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model,
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provider=device.ort_provider(),
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sess_options=device.sess_options(),
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safety_checker=None,
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torch_dtype=torch_dtype,
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**components,
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)
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if not server.show_progress:
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pipe.set_progress_bar_config(disable=True)
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optimize_pipeline(server, pipe)
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# TODO: CPU VAE, etc
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if device is not None and hasattr(pipe, "to"):
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pipe = pipe.to(device.torch_str())
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# monkey-patch pipeline
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patch_pipeline(server, pipe, pipeline)
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server.cache.set("diffusion", pipe_key, pipe)
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server.cache.set("scheduler", scheduler_key, components["scheduler"])
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return pipe
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def optimize_pipeline(
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server: ServerContext,
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pipe: StableDiffusionPipeline,
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) -> None:
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if "diffusers-attention-slicing" in server.optimizations:
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logger.debug("enabling attention slicing on SD pipeline")
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try:
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pipe.enable_attention_slicing()
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except Exception as e:
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logger.warning("error while enabling attention slicing: %s", e)
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if "diffusers-vae-slicing" in server.optimizations:
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logger.debug("enabling VAE slicing on SD pipeline")
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try:
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pipe.enable_vae_slicing()
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except Exception as e:
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logger.warning("error while enabling VAE slicing: %s", e)
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if "diffusers-cpu-offload-sequential" in server.optimizations:
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logger.debug("enabling sequential CPU offload on SD pipeline")
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try:
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pipe.enable_sequential_cpu_offload()
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except Exception as e:
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logger.warning("error while enabling sequential CPU offload: %s", e)
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elif "diffusers-cpu-offload-model" in server.optimizations:
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# TODO: check for accelerate
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logger.debug("enabling model CPU offload on SD pipeline")
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try:
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pipe.enable_model_cpu_offload()
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except Exception as e:
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logger.warning("error while enabling model CPU offload: %s", e)
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if "diffusers-memory-efficient-attention" in server.optimizations:
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# TODO: check for xformers
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logger.debug("enabling memory efficient attention for SD pipeline")
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try:
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pipe.enable_xformers_memory_efficient_attention()
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except Exception as e:
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logger.warning("error while enabling memory efficient attention: %s", e)
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timestep_dtype = None
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class UNetWrapper(object):
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def __init__(self, server, wrapped):
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self.server = server
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self.wrapped = wrapped
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def __call__(
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self, sample=None, timestep=None, encoder_hidden_states=None, **kwargs
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):
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global timestep_dtype
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timestep_dtype = timestep.dtype
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logger.trace(
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"UNet parameter types: %s, %s, %s",
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sample.dtype,
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timestep.dtype,
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encoder_hidden_states.dtype,
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)
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if sample.dtype != timestep.dtype:
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logger.trace("converting UNet sample to timestep dtype")
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sample = sample.astype(timestep.dtype)
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if encoder_hidden_states.dtype != timestep.dtype:
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logger.trace("converting UNet hidden states to timestep dtype")
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encoder_hidden_states = encoder_hidden_states.astype(timestep.dtype)
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return self.wrapped(
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sample=sample,
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timestep=timestep,
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encoder_hidden_states=encoder_hidden_states,
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**kwargs,
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)
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def __getattr__(self, attr):
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return getattr(self.wrapped, attr)
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class VAEWrapper(object):
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def __init__(self, server, wrapped):
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self.server = server
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self.wrapped = wrapped
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def __call__(self, latent_sample=None, **kwargs):
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global timestep_dtype
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logger.trace("VAE parameter types: %s", latent_sample.dtype)
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if latent_sample.dtype != timestep_dtype:
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logger.info("converting VAE sample dtype")
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latent_sample = latent_sample.astype(timestep_dtype)
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return self.wrapped(latent_sample=latent_sample, **kwargs)
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def __getattr__(self, attr):
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return getattr(self.wrapped, attr)
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def patch_pipeline(
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server: ServerContext,
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pipe: StableDiffusionPipeline,
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pipeline: Any,
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) -> None:
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logger.debug("patching SD pipeline")
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pipe._encode_prompt = expand_prompt.__get__(pipe, pipeline)
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original_unet = pipe.unet
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original_vae = pipe.vae_decoder
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pipe.unet = UNetWrapper(server, original_unet)
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pipe.vae_decoder = VAEWrapper(server, original_vae)
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