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LocalAI/extra/grpc/diffusers/backend_diffusers.py
Ettore Di Giacinto afdc0ebfd7
feat: add --single-active-backend to allow only one backend active at the time (#925) 
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2023-08-19 01:49:33 +02:00

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#!/usr/bin/env python3
import grpc
from concurrent import futures
import time
import backend_pb2
import backend_pb2_grpc
import argparse
import signal
import sys
import os
# import diffusers
import torch
from torch import autocast
from diffusers import StableDiffusionXLPipeline, StableDiffusionDepth2ImgPipeline, DPMSolverMultistepScheduler, StableDiffusionPipeline, DiffusionPipeline, EulerAncestralDiscreteScheduler
from diffusers.pipelines.stable_diffusion import safety_checker
from compel import Compel
from PIL import Image
from io import BytesIO
from diffusers import StableDiffusionImg2ImgPipeline
from transformers import CLIPTextModel
from enum import Enum
_ONE_DAY_IN_SECONDS = 60 * 60 * 24
COMPEL=os.environ.get("COMPEL", "1") == "1"
CLIPSKIP=os.environ.get("CLIPSKIP", "1") == "1"
# https://github.com/CompVis/stable-diffusion/issues/239#issuecomment-1627615287
def sc(self, clip_input, images) : return images, [False for i in images]
# edit the StableDiffusionSafetyChecker class so that, when called, it just returns the images and an array of True values
safety_checker.StableDiffusionSafetyChecker.forward = sc
from diffusers.schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
HeunDiscreteScheduler,
KDPM2AncestralDiscreteScheduler,
KDPM2DiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UniPCMultistepScheduler,
)
# The scheduler list mapping was taken from here: https://github.com/neggles/animatediff-cli/blob/6f336f5f4b5e38e85d7f06f1744ef42d0a45f2a7/src/animatediff/schedulers.py#L39
# Credits to https://github.com/neggles
# See https://github.com/huggingface/diffusers/issues/4167 for more details on sched mapping from A1111
class DiffusionScheduler(str, Enum):
ddim = "ddim" # DDIM
pndm = "pndm" # PNDM
heun = "heun" # Heun
unipc = "unipc" # UniPC
euler = "euler" # Euler
euler_a = "euler_a" # Euler a
lms = "lms" # LMS
k_lms = "k_lms" # LMS Karras
dpm_2 = "dpm_2" # DPM2
k_dpm_2 = "k_dpm_2" # DPM2 Karras
dpm_2_a = "dpm_2_a" # DPM2 a
k_dpm_2_a = "k_dpm_2_a" # DPM2 a Karras
dpmpp_2m = "dpmpp_2m" # DPM++ 2M
k_dpmpp_2m = "k_dpmpp_2m" # DPM++ 2M Karras
dpmpp_sde = "dpmpp_sde" # DPM++ SDE
k_dpmpp_sde = "k_dpmpp_sde" # DPM++ SDE Karras
dpmpp_2m_sde = "dpmpp_2m_sde" # DPM++ 2M SDE
k_dpmpp_2m_sde = "k_dpmpp_2m_sde" # DPM++ 2M SDE Karras
def get_scheduler(name: str, config: dict = {}):
is_karras = name.startswith("k_")
if is_karras:
# strip the k_ prefix and add the karras sigma flag to config
name = name.lstrip("k_")
config["use_karras_sigmas"] = True
if name == DiffusionScheduler.ddim:
sched_class = DDIMScheduler
elif name == DiffusionScheduler.pndm:
sched_class = PNDMScheduler
elif name == DiffusionScheduler.heun:
sched_class = HeunDiscreteScheduler
elif name == DiffusionScheduler.unipc:
sched_class = UniPCMultistepScheduler
elif name == DiffusionScheduler.euler:
sched_class = EulerDiscreteScheduler
elif name == DiffusionScheduler.euler_a:
sched_class = EulerAncestralDiscreteScheduler
elif name == DiffusionScheduler.lms:
sched_class = LMSDiscreteScheduler
elif name == DiffusionScheduler.dpm_2:
# Equivalent to DPM2 in K-Diffusion
sched_class = KDPM2DiscreteScheduler
elif name == DiffusionScheduler.dpm_2_a:
# Equivalent to `DPM2 a`` in K-Diffusion
sched_class = KDPM2AncestralDiscreteScheduler
elif name == DiffusionScheduler.dpmpp_2m:
# Equivalent to `DPM++ 2M` in K-Diffusion
sched_class = DPMSolverMultistepScheduler
config["algorithm_type"] = "dpmsolver++"
config["solver_order"] = 2
elif name == DiffusionScheduler.dpmpp_sde:
# Equivalent to `DPM++ SDE` in K-Diffusion
sched_class = DPMSolverSinglestepScheduler
elif name == DiffusionScheduler.dpmpp_2m_sde:
# Equivalent to `DPM++ 2M SDE` in K-Diffusion
sched_class = DPMSolverMultistepScheduler
config["algorithm_type"] = "sde-dpmsolver++"
else:
raise ValueError(f"Invalid scheduler '{'k_' if is_karras else ''}{name}'")
return sched_class.from_config(config)
# Implement the BackendServicer class with the service methods
class BackendServicer(backend_pb2_grpc.BackendServicer):
def Health(self, request, context):
return backend_pb2.Reply(message=bytes("OK", 'utf-8'))
def LoadModel(self, request, context):
try:
print(f"Loading model {request.Model}...", file=sys.stderr)
print(f"Request {request}", file=sys.stderr)
torchType = torch.float32
if request.F16Memory:
torchType = torch.float16
local = False
modelFile = request.Model
cfg_scale = 7
if request.CFGScale != 0:
cfg_scale = request.CFGScale
clipmodel = "runwayml/stable-diffusion-v1-5"
if request.CLIPModel != "":
clipmodel = request.CLIPModel
clipsubfolder = "text_encoder"
if request.CLIPSubfolder != "":
clipsubfolder = request.CLIPSubfolder
# Check if ModelFile exists
if request.ModelFile != "":
if os.path.exists(request.ModelFile):
local = True
modelFile = request.ModelFile
fromSingleFile = request.Model.startswith("http") or request.Model.startswith("/") or local
if request.IMG2IMG and request.PipelineType == "":
request.PipelineType == "StableDiffusionImg2ImgPipeline"
if request.PipelineType == "":
request.PipelineType == "StableDiffusionPipeline"
## img2img
if request.PipelineType == "StableDiffusionImg2ImgPipeline":
if fromSingleFile:
self.pipe = StableDiffusionImg2ImgPipeline.from_single_file(modelFile,
torch_dtype=torchType,
guidance_scale=cfg_scale)
else:
self.pipe = StableDiffusionImg2ImgPipeline.from_pretrained(request.Model,
torch_dtype=torchType,
guidance_scale=cfg_scale)
if request.PipelineType == "StableDiffusionDepth2ImgPipeline":
self.pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(request.Model,
torch_dtype=torchType,
guidance_scale=cfg_scale)
## text2img
if request.PipelineType == "StableDiffusionPipeline":
if fromSingleFile:
self.pipe = StableDiffusionPipeline.from_single_file(modelFile,
torch_dtype=torchType,
guidance_scale=cfg_scale)
else:
self.pipe = StableDiffusionPipeline.from_pretrained(request.Model,
torch_dtype=torchType,
guidance_scale=cfg_scale)
if request.PipelineType == "DiffusionPipeline":
self.pipe = DiffusionPipeline.from_pretrained(request.Model,
torch_dtype=torchType,
guidance_scale=cfg_scale)
if request.PipelineType == "StableDiffusionXLPipeline":
if fromSingleFile:
self.pipe = StableDiffusionXLPipeline.from_single_file(modelFile,
torch_dtype=torchType, use_safetensors=True,
guidance_scale=cfg_scale)
else:
self.pipe = StableDiffusionXLPipeline.from_pretrained(
request.Model,
torch_dtype=torchType,
use_safetensors=True,
# variant="fp16"
guidance_scale=cfg_scale)
# https://github.com/huggingface/diffusers/issues/4446
# do not use text_encoder in the constructor since then
# https://github.com/huggingface/diffusers/issues/3212#issuecomment-1521841481
if CLIPSKIP and request.CLIPSkip != 0:
text_encoder = CLIPTextModel.from_pretrained(clipmodel, num_hidden_layers=request.CLIPSkip, subfolder=clipsubfolder, torch_dtype=torchType)
self.pipe.text_encoder=text_encoder
# torch_dtype needs to be customized. float16 for GPU, float32 for CPU
# TODO: this needs to be customized
if request.SchedulerType != "":
self.pipe.scheduler = get_scheduler(request.SchedulerType, self.pipe.scheduler.config)
self.compel = Compel(tokenizer=self.pipe.tokenizer, text_encoder=self.pipe.text_encoder)
if request.CUDA:
self.pipe.to('cuda')
except Exception as err:
return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
# Implement your logic here for the LoadModel service
# Replace this with your desired response
return backend_pb2.Result(message="Model loaded successfully", success=True)
def GenerateImage(self, request, context):
prompt = request.positive_prompt
# create a dictionary of values for the parameters
options = {
"negative_prompt": request.negative_prompt,
"width": request.width,
"height": request.height,
"num_inference_steps": request.step,
}
if request.src != "":
image = Image.open(request.src)
options["image"] = image
# Get the keys that we will build the args for our pipe for
keys = options.keys()
if request.EnableParameters != "":
keys = request.EnableParameters.split(",")
if request.EnableParameters == "none":
keys = []
# create a dictionary of parameters by using the keys from EnableParameters and the values from defaults
kwargs = {key: options[key] for key in keys}
image = {}
if COMPEL:
conditioning = self.compel.build_conditioning_tensor(prompt)
kwargs["prompt_embeds"]= conditioning
# pass the kwargs dictionary to the self.pipe method
image = self.pipe(
**kwargs
).images[0]
else:
# pass the kwargs dictionary to the self.pipe method
image = self.pipe(
prompt,
**kwargs
).images[0]
# save the result
image.save(request.dst)
return backend_pb2.Result(message="Model loaded successfully", success=True)
def serve(address):
server = grpc.server(futures.ThreadPoolExecutor(max_workers=1))
backend_pb2_grpc.add_BackendServicer_to_server(BackendServicer(), server)
server.add_insecure_port(address)
server.start()
print("Server started. Listening on: " + address, file=sys.stderr)
# Define the signal handler function
def signal_handler(sig, frame):
print("Received termination signal. Shutting down...")
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)
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