#!/usr/bin/env python3 from concurrent import futures import traceback import argparse from collections import defaultdict from enum import Enum import signal import sys import time import os from PIL import Image import torch import backend_pb2 import backend_pb2_grpc import grpc from diffusers import StableDiffusion3Pipeline, StableDiffusionXLPipeline, StableDiffusionDepth2ImgPipeline, DPMSolverMultistepScheduler, StableDiffusionPipeline, DiffusionPipeline, \ EulerAncestralDiscreteScheduler, FluxPipeline, FluxTransformer2DModel from diffusers import StableDiffusionImg2ImgPipeline, AutoPipelineForText2Image, ControlNetModel, StableVideoDiffusionPipeline from diffusers.pipelines.stable_diffusion import safety_checker from diffusers.utils import load_image, export_to_video from compel import Compel, ReturnedEmbeddingsType from optimum.quanto import freeze, qfloat8, quantize from transformers import CLIPTextModel, T5EncoderModel from safetensors.torch import load_file _ONE_DAY_IN_SECONDS = 60 * 60 * 24 COMPEL = os.environ.get("COMPEL", "0") == "1" XPU = os.environ.get("XPU", "0") == "1" CLIPSKIP = os.environ.get("CLIPSKIP", "1") == "1" SAFETENSORS = os.environ.get("SAFETENSORS", "1") == "1" CHUNK_SIZE = os.environ.get("CHUNK_SIZE", "8") FPS = os.environ.get("FPS", "7") DISABLE_CPU_OFFLOAD = os.environ.get("DISABLE_CPU_OFFLOAD", "0") == "1" FRAMES = os.environ.get("FRAMES", "64") if XPU: import intel_extension_for_pytorch as ipex print(ipex.xpu.get_device_name(0)) # If MAX_WORKERS are specified in the environment use it, otherwise default to 1 MAX_WORKERS = int(os.environ.get('PYTHON_GRPC_MAX_WORKERS', '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 variant = None if request.F16Memory: torchType = torch.float16 variant = "fp16" local = False modelFile = request.Model self.cfg_scale = 7 self.PipelineType = request.PipelineType if request.CFGScale != 0: self.cfg_scale = request.CFGScale clipmodel = "Lykon/dreamshaper-8" 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 self.img2vid = False self.txt2vid = False ## img2img if (request.PipelineType == "StableDiffusionImg2ImgPipeline") or (request.IMG2IMG and request.PipelineType == ""): if fromSingleFile: self.pipe = StableDiffusionImg2ImgPipeline.from_single_file(modelFile, torch_dtype=torchType) else: self.pipe = StableDiffusionImg2ImgPipeline.from_pretrained(request.Model, torch_dtype=torchType) elif request.PipelineType == "StableDiffusionDepth2ImgPipeline": self.pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(request.Model, torch_dtype=torchType) ## img2vid elif request.PipelineType == "StableVideoDiffusionPipeline": self.img2vid = True self.pipe = StableVideoDiffusionPipeline.from_pretrained( request.Model, torch_dtype=torchType, variant=variant ) if not DISABLE_CPU_OFFLOAD: self.pipe.enable_model_cpu_offload() ## text2img elif request.PipelineType == "AutoPipelineForText2Image" or request.PipelineType == "": self.pipe = AutoPipelineForText2Image.from_pretrained(request.Model, torch_dtype=torchType, use_safetensors=SAFETENSORS, variant=variant) elif request.PipelineType == "StableDiffusionPipeline": if fromSingleFile: self.pipe = StableDiffusionPipeline.from_single_file(modelFile, torch_dtype=torchType) else: self.pipe = StableDiffusionPipeline.from_pretrained(request.Model, torch_dtype=torchType) elif request.PipelineType == "DiffusionPipeline": self.pipe = DiffusionPipeline.from_pretrained(request.Model, torch_dtype=torchType) elif request.PipelineType == "VideoDiffusionPipeline": self.txt2vid = True self.pipe = DiffusionPipeline.from_pretrained(request.Model, torch_dtype=torchType) elif request.PipelineType == "StableDiffusionXLPipeline": if fromSingleFile: self.pipe = StableDiffusionXLPipeline.from_single_file(modelFile, torch_dtype=torchType, use_safetensors=True) else: self.pipe = StableDiffusionXLPipeline.from_pretrained( request.Model, torch_dtype=torchType, use_safetensors=True, variant=variant) elif request.PipelineType == "StableDiffusion3Pipeline": if fromSingleFile: self.pipe = StableDiffusion3Pipeline.from_single_file(modelFile, torch_dtype=torchType, use_safetensors=True) else: self.pipe = StableDiffusion3Pipeline.from_pretrained( request.Model, torch_dtype=torchType, use_safetensors=True, variant=variant) elif request.PipelineType == "FluxPipeline": if fromSingleFile: self.pipe = FluxPipeline.from_single_file(modelFile, torch_dtype=torchType, use_safetensors=True) else: self.pipe = FluxPipeline.from_pretrained( request.Model, torch_dtype=torch.bfloat16) if request.LowVRAM: self.pipe.enable_model_cpu_offload() elif request.PipelineType == "FluxTransformer2DModel": dtype = torch.bfloat16 # specify from environment or default to "ChuckMcSneed/FLUX.1-dev" bfl_repo = os.environ.get("BFL_REPO", "ChuckMcSneed/FLUX.1-dev") transformer = FluxTransformer2DModel.from_single_file(modelFile, torch_dtype=dtype) quantize(transformer, weights=qfloat8) freeze(transformer) text_encoder_2 = T5EncoderModel.from_pretrained(bfl_repo, subfolder="text_encoder_2", torch_dtype=dtype) quantize(text_encoder_2, weights=qfloat8) freeze(text_encoder_2) self.pipe = FluxPipeline.from_pretrained(bfl_repo, transformer=None, text_encoder_2=None, torch_dtype=dtype) self.pipe.transformer = transformer self.pipe.text_encoder_2 = text_encoder_2 if request.LowVRAM: self.pipe.enable_model_cpu_offload() if CLIPSKIP and request.CLIPSkip != 0: self.clip_skip = request.CLIPSkip else: self.clip_skip = 0 # 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) if COMPEL: self.compel = Compel( tokenizer=[self.pipe.tokenizer, self.pipe.tokenizer_2], text_encoder=[self.pipe.text_encoder, self.pipe.text_encoder_2], returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED, requires_pooled=[False, True] ) if request.ControlNet: self.controlnet = ControlNetModel.from_pretrained( request.ControlNet, torch_dtype=torchType, variant=variant ) self.pipe.controlnet = self.controlnet else: self.controlnet = None if request.LoraAdapter and not os.path.isabs(request.LoraAdapter): # modify LoraAdapter to be relative to modelFileBase request.LoraAdapter = os.path.join(request.ModelPath, request.LoraAdapter) device = "cpu" if not request.CUDA else "cuda" self.device = device if request.LoraAdapter: # Check if its a local file and not a directory ( we load lora differently for a safetensor file ) if os.path.exists(request.LoraAdapter) and not os.path.isdir(request.LoraAdapter): self.pipe.load_lora_weights(request.LoraAdapter) else: self.pipe.unet.load_attn_procs(request.LoraAdapter) if len(request.LoraAdapters) > 0: i = 0 adapters_name = [] adapters_weights = [] for adapter in request.LoraAdapters: if not os.path.isabs(adapter): adapter = os.path.join(request.ModelPath, adapter) self.pipe.load_lora_weights(adapter, adapter_name=f"adapter_{i}") adapters_name.append(f"adapter_{i}") i += 1 for adapters_weight in request.LoraScales: adapters_weights.append(adapters_weight) self.pipe.set_adapters(adapters_name, adapter_weights=adapters_weights) if request.CUDA: self.pipe.to('cuda') if self.controlnet: self.controlnet.to('cuda') if XPU: self.pipe = self.pipe.to("xpu") 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) # https://github.com/huggingface/diffusers/issues/3064 def load_lora_weights(self, checkpoint_path, multiplier, device, dtype): LORA_PREFIX_UNET = "lora_unet" LORA_PREFIX_TEXT_ENCODER = "lora_te" # load LoRA weight from .safetensors state_dict = load_file(checkpoint_path, device=device) updates = defaultdict(dict) for key, value in state_dict.items(): # it is suggested to print out the key, it usually will be something like below # "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight" layer, elem = key.split('.', 1) updates[layer][elem] = value # directly update weight in diffusers model for layer, elems in updates.items(): if "text" in layer: layer_infos = layer.split(LORA_PREFIX_TEXT_ENCODER + "_")[-1].split("_") curr_layer = self.pipe.text_encoder else: layer_infos = layer.split(LORA_PREFIX_UNET + "_")[-1].split("_") curr_layer = self.pipe.unet # find the target layer temp_name = layer_infos.pop(0) while len(layer_infos) > -1: try: curr_layer = curr_layer.__getattr__(temp_name) if len(layer_infos) > 0: temp_name = layer_infos.pop(0) elif len(layer_infos) == 0: break except Exception: if len(temp_name) > 0: temp_name += "_" + layer_infos.pop(0) else: temp_name = layer_infos.pop(0) # get elements for this layer weight_up = elems['lora_up.weight'].to(dtype) weight_down = elems['lora_down.weight'].to(dtype) alpha = elems['alpha'] if 'alpha' in elems else None if alpha: alpha = alpha.item() / weight_up.shape[1] else: alpha = 1.0 # update weight if len(weight_up.shape) == 4: curr_layer.weight.data += multiplier * alpha * torch.mm(weight_up.squeeze(3).squeeze(2), weight_down.squeeze(3).squeeze(2)).unsqueeze(2).unsqueeze(3) else: curr_layer.weight.data += multiplier * alpha * torch.mm(weight_up, weight_down) def GenerateImage(self, request, context): prompt = request.positive_prompt steps = 1 if request.step != 0: steps = request.step # create a dictionary of values for the parameters options = { "negative_prompt": request.negative_prompt, "width": request.width, "height": request.height, "num_inference_steps": steps, } if request.src != "" and not self.controlnet and not self.img2vid: image = Image.open(request.src) options["image"] = image elif self.controlnet and request.src: pose_image = load_image(request.src) options["image"] = pose_image if CLIPSKIP and self.clip_skip != 0: options["clip_skip"] = self.clip_skip # 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} # Set seed if request.seed > 0: kwargs["generator"] = torch.Generator(device=self.device).manual_seed( request.seed ) if self.PipelineType == "FluxPipeline": kwargs["max_sequence_length"] = 256 if self.PipelineType == "FluxTransformer2DModel": kwargs["output_type"] = "pil" kwargs["generator"] = torch.Generator("cpu").manual_seed(0) if self.img2vid: # Load the conditioning image image = load_image(request.src) image = image.resize((1024, 576)) generator = torch.manual_seed(request.seed) frames = self.pipe(image, guidance_scale=self.cfg_scale, decode_chunk_size=CHUNK_SIZE, generator=generator).frames[0] export_to_video(frames, request.dst, fps=FPS) return backend_pb2.Result(message="Media generated successfully", success=True) if self.txt2vid: video_frames = self.pipe(prompt, guidance_scale=self.cfg_scale, num_inference_steps=steps, num_frames=int(FRAMES)).frames export_to_video(video_frames, request.dst) return backend_pb2.Result(message="Media generated successfully", success=True) image = {} if COMPEL: conditioning, pooled = self.compel.build_conditioning_tensor(prompt) kwargs["prompt_embeds"] = conditioning kwargs["pooled_prompt_embeds"] = pooled # pass the kwargs dictionary to the self.pipe method image = self.pipe( guidance_scale=self.cfg_scale, **kwargs ).images[0] else: # pass the kwargs dictionary to the self.pipe method image = self.pipe( prompt, guidance_scale=self.cfg_scale, **kwargs ).images[0] # save the result image.save(request.dst) return backend_pb2.Result(message="Media generated", success=True) def serve(address): server = grpc.server(futures.ThreadPoolExecutor(max_workers=MAX_WORKERS)) 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)