mirror of
https://github.com/mudler/LocalAI.git
synced 2024-06-07 19:40:48 +00:00
5d1018495f
* feat(intel): add diffusers support * try to consume upstream container image * Debug * Manually install deps * Map transformers/hf cache dir to modelpath if not specified * fix(compel): update initialization, pass by all gRPC options * fix: add dependencies, implement transformers for xpu * base it from the oneapi image * Add pillow * set threads if specified when launching the API * Skip conda install if intel * defaults to non-intel * ci: add to pipelines * prepare compel only if enabled * Skip conda install if intel * fix cleanup * Disable compel by default * Install torch 2.1.0 with Intel * Skip conda on some setups * Detect python * Quiet output * Do not override system python with conda * Prefer python3 * Fixups * exllama2: do not install without conda (overrides pytorch version) * exllama/exllama2: do not install if not using cuda * Add missing dataset dependency * Small fixups, symlink to python, add requirements * Add neural_speed to the deps * correctly handle model offloading * fix: device_map == xpu * go back at calling python, fixed at dockerfile level * Exllama2 restricted to only nvidia gpus * Tokenizer to xpu
452 lines
18 KiB
Python
Executable File
452 lines
18 KiB
Python
Executable File
#!/usr/bin/env python3
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from concurrent import futures
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import argparse
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from collections import defaultdict
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from enum import Enum
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import signal
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import sys
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import time
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import os
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from PIL import Image
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import torch
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import backend_pb2
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import backend_pb2_grpc
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import grpc
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from diffusers import StableDiffusionXLPipeline, StableDiffusionDepth2ImgPipeline, DPMSolverMultistepScheduler, StableDiffusionPipeline, DiffusionPipeline, EulerAncestralDiscreteScheduler
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from diffusers import StableDiffusionImg2ImgPipeline, AutoPipelineForText2Image, ControlNetModel, StableVideoDiffusionPipeline
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from diffusers.pipelines.stable_diffusion import safety_checker
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from diffusers.utils import load_image,export_to_video
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from compel import Compel, ReturnedEmbeddingsType
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from transformers import CLIPTextModel
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from safetensors.torch import load_file
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_ONE_DAY_IN_SECONDS = 60 * 60 * 24
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COMPEL=os.environ.get("COMPEL", "0") == "1"
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XPU=os.environ.get("XPU", "0") == "1"
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CLIPSKIP=os.environ.get("CLIPSKIP", "1") == "1"
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SAFETENSORS=os.environ.get("SAFETENSORS", "1") == "1"
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CHUNK_SIZE=os.environ.get("CHUNK_SIZE", "8")
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FPS=os.environ.get("FPS", "7")
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DISABLE_CPU_OFFLOAD=os.environ.get("DISABLE_CPU_OFFLOAD", "0") == "1"
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FRAMES=os.environ.get("FRAMES", "64")
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if XPU:
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import intel_extension_for_pytorch as ipex
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print(ipex.xpu.get_device_name(0))
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# If MAX_WORKERS are specified in the environment use it, otherwise default to 1
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MAX_WORKERS = int(os.environ.get('PYTHON_GRPC_MAX_WORKERS', '1'))
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# https://github.com/CompVis/stable-diffusion/issues/239#issuecomment-1627615287
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def sc(self, clip_input, images) : return images, [False for i in images]
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# edit the StableDiffusionSafetyChecker class so that, when called, it just returns the images and an array of True values
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safety_checker.StableDiffusionSafetyChecker.forward = sc
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from diffusers.schedulers import (
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DDIMScheduler,
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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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KDPM2AncestralDiscreteScheduler,
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KDPM2DiscreteScheduler,
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LMSDiscreteScheduler,
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PNDMScheduler,
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UniPCMultistepScheduler,
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)
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# The scheduler list mapping was taken from here: https://github.com/neggles/animatediff-cli/blob/6f336f5f4b5e38e85d7f06f1744ef42d0a45f2a7/src/animatediff/schedulers.py#L39
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# Credits to https://github.com/neggles
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# See https://github.com/huggingface/diffusers/issues/4167 for more details on sched mapping from A1111
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class DiffusionScheduler(str, Enum):
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ddim = "ddim" # DDIM
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pndm = "pndm" # PNDM
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heun = "heun" # Heun
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unipc = "unipc" # UniPC
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euler = "euler" # Euler
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euler_a = "euler_a" # Euler a
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lms = "lms" # LMS
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k_lms = "k_lms" # LMS Karras
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dpm_2 = "dpm_2" # DPM2
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k_dpm_2 = "k_dpm_2" # DPM2 Karras
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dpm_2_a = "dpm_2_a" # DPM2 a
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k_dpm_2_a = "k_dpm_2_a" # DPM2 a Karras
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dpmpp_2m = "dpmpp_2m" # DPM++ 2M
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k_dpmpp_2m = "k_dpmpp_2m" # DPM++ 2M Karras
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dpmpp_sde = "dpmpp_sde" # DPM++ SDE
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k_dpmpp_sde = "k_dpmpp_sde" # DPM++ SDE Karras
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dpmpp_2m_sde = "dpmpp_2m_sde" # DPM++ 2M SDE
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k_dpmpp_2m_sde = "k_dpmpp_2m_sde" # DPM++ 2M SDE Karras
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def get_scheduler(name: str, config: dict = {}):
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is_karras = name.startswith("k_")
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if is_karras:
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# strip the k_ prefix and add the karras sigma flag to config
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name = name.lstrip("k_")
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config["use_karras_sigmas"] = True
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if name == DiffusionScheduler.ddim:
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sched_class = DDIMScheduler
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elif name == DiffusionScheduler.pndm:
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sched_class = PNDMScheduler
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elif name == DiffusionScheduler.heun:
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sched_class = HeunDiscreteScheduler
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elif name == DiffusionScheduler.unipc:
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sched_class = UniPCMultistepScheduler
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elif name == DiffusionScheduler.euler:
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sched_class = EulerDiscreteScheduler
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elif name == DiffusionScheduler.euler_a:
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sched_class = EulerAncestralDiscreteScheduler
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elif name == DiffusionScheduler.lms:
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sched_class = LMSDiscreteScheduler
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elif name == DiffusionScheduler.dpm_2:
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# Equivalent to DPM2 in K-Diffusion
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sched_class = KDPM2DiscreteScheduler
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elif name == DiffusionScheduler.dpm_2_a:
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# Equivalent to `DPM2 a`` in K-Diffusion
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sched_class = KDPM2AncestralDiscreteScheduler
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elif name == DiffusionScheduler.dpmpp_2m:
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# Equivalent to `DPM++ 2M` in K-Diffusion
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sched_class = DPMSolverMultistepScheduler
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config["algorithm_type"] = "dpmsolver++"
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config["solver_order"] = 2
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elif name == DiffusionScheduler.dpmpp_sde:
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# Equivalent to `DPM++ SDE` in K-Diffusion
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sched_class = DPMSolverSinglestepScheduler
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elif name == DiffusionScheduler.dpmpp_2m_sde:
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# Equivalent to `DPM++ 2M SDE` in K-Diffusion
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sched_class = DPMSolverMultistepScheduler
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config["algorithm_type"] = "sde-dpmsolver++"
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else:
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raise ValueError(f"Invalid scheduler '{'k_' if is_karras else ''}{name}'")
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return sched_class.from_config(config)
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# Implement the BackendServicer class with the service methods
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class BackendServicer(backend_pb2_grpc.BackendServicer):
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def Health(self, request, context):
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return backend_pb2.Reply(message=bytes("OK", 'utf-8'))
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def LoadModel(self, request, context):
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try:
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print(f"Loading model {request.Model}...", file=sys.stderr)
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print(f"Request {request}", file=sys.stderr)
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torchType = torch.float32
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variant = None
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if request.F16Memory:
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torchType = torch.float16
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variant="fp16"
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local = False
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modelFile = request.Model
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self.cfg_scale = 7
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if request.CFGScale != 0:
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self.cfg_scale = request.CFGScale
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clipmodel = "runwayml/stable-diffusion-v1-5"
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if request.CLIPModel != "":
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clipmodel = request.CLIPModel
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clipsubfolder = "text_encoder"
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if request.CLIPSubfolder != "":
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clipsubfolder = request.CLIPSubfolder
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# Check if ModelFile exists
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if request.ModelFile != "":
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if os.path.exists(request.ModelFile):
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local = True
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modelFile = request.ModelFile
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fromSingleFile = request.Model.startswith("http") or request.Model.startswith("/") or local
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self.img2vid=False
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self.txt2vid=False
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## img2img
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if (request.PipelineType == "StableDiffusionImg2ImgPipeline") or (request.IMG2IMG and request.PipelineType == ""):
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if fromSingleFile:
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self.pipe = StableDiffusionImg2ImgPipeline.from_single_file(modelFile,
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torch_dtype=torchType)
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else:
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self.pipe = StableDiffusionImg2ImgPipeline.from_pretrained(request.Model,
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torch_dtype=torchType)
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elif request.PipelineType == "StableDiffusionDepth2ImgPipeline":
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self.pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(request.Model,
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torch_dtype=torchType)
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## img2vid
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elif request.PipelineType == "StableVideoDiffusionPipeline":
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self.img2vid=True
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self.pipe = StableVideoDiffusionPipeline.from_pretrained(
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request.Model, torch_dtype=torchType, variant=variant
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)
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if not DISABLE_CPU_OFFLOAD:
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self.pipe.enable_model_cpu_offload()
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## text2img
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elif request.PipelineType == "AutoPipelineForText2Image" or request.PipelineType == "":
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self.pipe = AutoPipelineForText2Image.from_pretrained(request.Model,
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torch_dtype=torchType,
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use_safetensors=SAFETENSORS,
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variant=variant)
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elif request.PipelineType == "StableDiffusionPipeline":
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if fromSingleFile:
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self.pipe = StableDiffusionPipeline.from_single_file(modelFile,
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torch_dtype=torchType)
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else:
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self.pipe = StableDiffusionPipeline.from_pretrained(request.Model,
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torch_dtype=torchType)
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elif request.PipelineType == "DiffusionPipeline":
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self.pipe = DiffusionPipeline.from_pretrained(request.Model,
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torch_dtype=torchType)
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elif request.PipelineType == "VideoDiffusionPipeline":
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self.txt2vid=True
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self.pipe = DiffusionPipeline.from_pretrained(request.Model,
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torch_dtype=torchType)
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elif request.PipelineType == "StableDiffusionXLPipeline":
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if fromSingleFile:
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self.pipe = StableDiffusionXLPipeline.from_single_file(modelFile,
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torch_dtype=torchType,
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use_safetensors=True)
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else:
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self.pipe = StableDiffusionXLPipeline.from_pretrained(
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request.Model,
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torch_dtype=torchType,
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use_safetensors=True,
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variant=variant)
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if CLIPSKIP and request.CLIPSkip != 0:
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self.clip_skip = request.CLIPSkip
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else:
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self.clip_skip = 0
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# torch_dtype needs to be customized. float16 for GPU, float32 for CPU
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# TODO: this needs to be customized
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if request.SchedulerType != "":
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self.pipe.scheduler = get_scheduler(request.SchedulerType, self.pipe.scheduler.config)
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if COMPEL:
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self.compel = Compel(
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tokenizer=[self.pipe.tokenizer, self.pipe.tokenizer_2 ],
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text_encoder=[self.pipe.text_encoder, self.pipe.text_encoder_2],
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returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED,
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requires_pooled=[False, True]
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)
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if request.ControlNet:
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self.controlnet = ControlNetModel.from_pretrained(
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request.ControlNet, torch_dtype=torchType, variant=variant
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)
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self.pipe.controlnet = self.controlnet
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else:
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self.controlnet = None
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if request.CUDA:
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self.pipe.to('cuda')
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if self.controlnet:
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self.controlnet.to('cuda')
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if XPU:
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self.pipe = self.pipe.to("xpu")
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# Assume directory from request.ModelFile.
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# Only if request.LoraAdapter it's not an absolute path
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if request.LoraAdapter and request.ModelFile != "" and not os.path.isabs(request.LoraAdapter) and request.LoraAdapter:
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# get base path of modelFile
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modelFileBase = os.path.dirname(request.ModelFile)
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# modify LoraAdapter to be relative to modelFileBase
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request.LoraAdapter = os.path.join(modelFileBase, request.LoraAdapter)
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device = "cpu" if not request.CUDA else "cuda"
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self.device = device
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if request.LoraAdapter:
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# Check if its a local file and not a directory ( we load lora differently for a safetensor file )
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if os.path.exists(request.LoraAdapter) and not os.path.isdir(request.LoraAdapter):
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self.load_lora_weights(request.LoraAdapter, 1, device, torchType)
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else:
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self.pipe.unet.load_attn_procs(request.LoraAdapter)
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except Exception as err:
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return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
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# Implement your logic here for the LoadModel service
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# Replace this with your desired response
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return backend_pb2.Result(message="Model loaded successfully", success=True)
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# https://github.com/huggingface/diffusers/issues/3064
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def load_lora_weights(self, checkpoint_path, multiplier, device, dtype):
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LORA_PREFIX_UNET = "lora_unet"
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LORA_PREFIX_TEXT_ENCODER = "lora_te"
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# load LoRA weight from .safetensors
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state_dict = load_file(checkpoint_path, device=device)
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updates = defaultdict(dict)
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for key, value in state_dict.items():
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# it is suggested to print out the key, it usually will be something like below
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# "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"
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layer, elem = key.split('.', 1)
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updates[layer][elem] = value
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# directly update weight in diffusers model
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for layer, elems in updates.items():
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if "text" in layer:
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layer_infos = layer.split(LORA_PREFIX_TEXT_ENCODER + "_")[-1].split("_")
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curr_layer = self.pipe.text_encoder
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else:
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layer_infos = layer.split(LORA_PREFIX_UNET + "_")[-1].split("_")
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curr_layer = self.pipe.unet
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# find the target layer
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temp_name = layer_infos.pop(0)
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while len(layer_infos) > -1:
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try:
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curr_layer = curr_layer.__getattr__(temp_name)
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if len(layer_infos) > 0:
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temp_name = layer_infos.pop(0)
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elif len(layer_infos) == 0:
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break
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except Exception:
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if len(temp_name) > 0:
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temp_name += "_" + layer_infos.pop(0)
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else:
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temp_name = layer_infos.pop(0)
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# get elements for this layer
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weight_up = elems['lora_up.weight'].to(dtype)
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weight_down = elems['lora_down.weight'].to(dtype)
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alpha = elems['alpha'] if 'alpha' in elems else None
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if alpha:
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alpha = alpha.item() / weight_up.shape[1]
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else:
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alpha = 1.0
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# update weight
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if len(weight_up.shape) == 4:
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curr_layer.weight.data += multiplier * alpha * torch.mm(weight_up.squeeze(3).squeeze(2), weight_down.squeeze(3).squeeze(2)).unsqueeze(2).unsqueeze(3)
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else:
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curr_layer.weight.data += multiplier * alpha * torch.mm(weight_up, weight_down)
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def GenerateImage(self, request, context):
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prompt = request.positive_prompt
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steps = 1
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if request.step != 0:
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steps = request.step
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# create a dictionary of values for the parameters
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options = {
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"negative_prompt": request.negative_prompt,
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"width": request.width,
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"height": request.height,
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"num_inference_steps": steps,
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}
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if request.src != "" and not self.controlnet and not self.img2vid:
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image = Image.open(request.src)
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options["image"] = image
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elif self.controlnet and request.src:
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pose_image = load_image(request.src)
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options["image"] = pose_image
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if CLIPSKIP and self.clip_skip != 0:
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options["clip_skip"]=self.clip_skip
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# Get the keys that we will build the args for our pipe for
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keys = options.keys()
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if request.EnableParameters != "":
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keys = request.EnableParameters.split(",")
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if request.EnableParameters == "none":
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keys = []
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# create a dictionary of parameters by using the keys from EnableParameters and the values from defaults
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kwargs = {key: options[key] for key in keys}
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# Set seed
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if request.seed > 0:
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kwargs["generator"] = torch.Generator(device=self.device).manual_seed(
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request.seed
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)
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if self.img2vid:
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# Load the conditioning image
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image = load_image(request.src)
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image = image.resize((1024, 576))
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generator = torch.manual_seed(request.seed)
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frames = self.pipe(image, guidance_scale=self.cfg_scale, decode_chunk_size=CHUNK_SIZE, generator=generator).frames[0]
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export_to_video(frames, request.dst, fps=FPS)
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return backend_pb2.Result(message="Media generated successfully", success=True)
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if self.txt2vid:
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video_frames = self.pipe(prompt, guidance_scale=self.cfg_scale, num_inference_steps=steps, num_frames=int(FRAMES)).frames
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export_to_video(video_frames, request.dst)
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return backend_pb2.Result(message="Media generated successfully", success=True)
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image = {}
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if COMPEL:
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conditioning, pooled = self.compel.build_conditioning_tensor(prompt)
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kwargs["prompt_embeds"] = conditioning
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kwargs["pooled_prompt_embeds"] = pooled
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# pass the kwargs dictionary to the self.pipe method
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image = self.pipe(
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guidance_scale=self.cfg_scale,
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**kwargs
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).images[0]
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else:
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# pass the kwargs dictionary to the self.pipe method
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image = self.pipe(
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prompt,
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guidance_scale=self.cfg_scale,
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**kwargs
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).images[0]
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# save the result
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image.save(request.dst)
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return backend_pb2.Result(message="Media generated", success=True)
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def serve(address):
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server = grpc.server(futures.ThreadPoolExecutor(max_workers=MAX_WORKERS))
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backend_pb2_grpc.add_BackendServicer_to_server(BackendServicer(), server)
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server.add_insecure_port(address)
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server.start()
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print("Server started. Listening on: " + address, file=sys.stderr)
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# Define the signal handler function
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def signal_handler(sig, frame):
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print("Received termination signal. Shutting down...")
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server.stop(0)
|
|
sys.exit(0)
|
|
|
|
# Set the signal handlers for SIGINT and SIGTERM
|
|
signal.signal(signal.SIGINT, signal_handler)
|
|
signal.signal(signal.SIGTERM, signal_handler)
|
|
|
|
try:
|
|
while True:
|
|
time.sleep(_ONE_DAY_IN_SECONDS)
|
|
except KeyboardInterrupt:
|
|
server.stop(0)
|
|
|
|
if __name__ == "__main__":
|
|
parser = argparse.ArgumentParser(description="Run the gRPC server.")
|
|
parser.add_argument(
|
|
"--addr", default="localhost:50051", help="The address to bind the server to."
|
|
)
|
|
args = parser.parse_args()
|
|
|
|
serve(args.addr) |