From 28d1b0a845cff750c37e36fa46999c98e0c7efdf Mon Sep 17 00:00:00 2001 From: ZIFENG278 <zifengzhang18@gmail.com> Date: Tue, 23 Jan 2024 10:46:36 +0800 Subject: [PATCH] sd lcm 1.0 --- .gitignore | 4 +- flagged/log.csv | 3 + gr.py | 19 +- nohup.out | 0 prepare.sh | 31 - requirements.txt | 8 +- sd/__init__.py | 7 +- sd/libuntpu.so | Bin 3401224 -> 0 bytes sd/{newtool.py => npuengine.md} | 299 +- sd/npuengine.py | 50 + sd/npuengine2.md | 63 + sd/pipeline152device.py | 923 + sd/pipeline2.py | 84 +- sd/pipelineXL.py | 706 + sd/pipelinexl2device.py | 773 + sd/scheduler.py | 2 +- sd/untool.py | 440 +- test.py | 33 + tokenizerV21/merges.txt | 48895 +++++++++++++++ tokenizerV21/special_tokens_map.json | 24 + tokenizerV21/tokenizer_config.json | 34 + tokenizerV21/vocab.json | 49410 ++++++++++++++++ tokenizer_2/merges.txt | 0 tokenizer_2/special_tokens_map.json | 0 tokenizer_2/tokenizer_config.json | 0 tokenizer_2/vocab.json | 0 ...-1.2.17-py3-none-manylinux2014_aarch64.whl | Bin 287828 -> 0 bytes ...f-1.2.31-py3-none-manylinux2014_x86_64.whl | Bin 0 -> 305041 bytes 28 files changed, 101071 insertions(+), 737 deletions(-) create mode 100644 flagged/log.csv create mode 100644 nohup.out delete mode 100644 prepare.sh mode change 100644 => 100755 sd/__init__.py delete mode 100644 sd/libuntpu.so rename sd/{newtool.py => npuengine.md} (65%) create mode 100644 sd/npuengine.py create mode 100755 sd/npuengine2.md create mode 100644 sd/pipeline152device.py create mode 100644 sd/pipelineXL.py create mode 100644 sd/pipelinexl2device.py create mode 100644 test.py create mode 100644 tokenizerV21/merges.txt create mode 100644 tokenizerV21/special_tokens_map.json create mode 100644 tokenizerV21/tokenizer_config.json create mode 100644 tokenizerV21/vocab.json mode change 100644 => 100755 tokenizer_2/merges.txt mode change 100644 => 100755 tokenizer_2/special_tokens_map.json mode change 100644 => 100755 tokenizer_2/tokenizer_config.json mode change 100644 => 100755 tokenizer_2/vocab.json delete mode 100644 tpu_perf-1.2.17-py3-none-manylinux2014_aarch64.whl create mode 100644 tpu_perf-1.2.31-py3-none-manylinux2014_x86_64.whl diff --git a/.gitignore b/.gitignore index 3bf1682..2d4f3c6 100644 --- a/.gitignore +++ b/.gitignore @@ -2,4 +2,6 @@ *.png *.bmodel *.log - +#tpu_perf-1.2.31-py3-none-manylinux2014_x86_64.whl +.venv +.idea diff --git a/flagged/log.csv b/flagged/log.csv new file mode 100644 index 0000000..670f90c --- /dev/null +++ b/flagged/log.csv @@ -0,0 +1,3 @@ +text,output,flag,username,timestamp +"a girl, 4k","{""path"":""flagged/output/7bdfe86367a3b675d7cf/image.png"",""url"":null,""size"":null,""orig_name"":""image.png"",""mime_type"":null}",,,2023-12-04 17:55:59.243892 +美女,"{""path"":""flagged/output/bdd1c889144ba8f3f558/1701704395.png"",""url"":null,""size"":null,""orig_name"":""1701704395.png"",""mime_type"":null}",,,2023-12-04 23:40:03.485464 diff --git a/gr.py b/gr.py index a1b6163..05acef2 100644 --- a/gr.py +++ b/gr.py @@ -1,12 +1,13 @@ import gradio as gr -from sd import StableDiffusionPipeline +from sd import StableDiffusionPipeline, StableDiffusionXLPipeline, SD2_1_MODELS, SD_XL_MODELS +from sd import SD15Parallel, SDXL2Parallel +from sd import UpscaleModel from PIL import Image import numpy as np import os import time import random import torch - def seed_torch(seed=1029): seed=seed%4294967296 random.seed(seed) @@ -27,22 +28,28 @@ def seed_torch(seed=1029): def generate_image(text): + # 这里是一个虚构的图像生成函数 + # 在实际应用中,您需要替换为实际的图像生成模型代码 + # 比如调用 DALL-E、Imagen 或其他模型 + # 这个函数应该根据输入的文本生成并返回图像 img_pil = pipe( prompt=text, negative_prompt="low resolution", num_inference_steps=4, scheduler=scheduler, - guidance_scale=0, - seeds=[random.randint(0,1000000)] + guidance_scale=1.05, + seeds=[random.randint(0,100000)] ) - return img_pil #"{}.png".format(time_stamp) + time_stamp = str(time.time()).split(".")[0] + img_pil.save("{}.png".format(time_stamp)) + return "{}.png".format(time_stamp) # 创建 Gradio 接口 iface = gr.Interface( fn=generate_image, # 指定处理函数 inputs="text", # 输入类型为文本 outputs="image", # 输出类型为图像 - title="Text-to-Image Generator", # 界面标题 + title="SDv1.5 Text-to-Image Generator🛸", # 界面标题 description="Generate images from text descriptions." # 描述 ) diff --git a/nohup.out b/nohup.out new file mode 100644 index 0000000..e69de29 diff --git a/prepare.sh b/prepare.sh deleted file mode 100644 index e7aa5e5..0000000 --- a/prepare.sh +++ /dev/null @@ -1,31 +0,0 @@ -# wget https://github.com/sophgo/tpu-perf/releases/download/v1.2.17/tpu_perf-1.2.17-py3-none-manylinux2014_aarch64.whl -# 判断操作系统是arm还是x86 -if [ $(uname -m) = "x86_64" ]; then - echo "Your current operating system is based on x86_64" - echo "you need to install the x86_64 version of sophon" - echo "check your package now ..." - pip3 install opencv-python -i https://pypi.tuna.tsinghua.edu.cn/simple - python3 -c "import sophon.sail" || echo "check package failed" && exit 1 - echo "check package success" -else - echo "Your current operating system is based on arm64" - python3 -m dfn --url http://disk-sophgo-vip.quickconnect.to/sharing/LiM00jkBJ && pip3 install sophon_arm-0.0.0-py3-none-any.whl && rm -rf ./sophon_arm-0.0.0-py3-none-any.whl - pip3 install tpu_perf-1.2.17-py3-none-manylinux2014_aarch64.whl -fi - -pip3 install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple -if [ ! -d "./models" ]; then - mkdir models -fi -if [ ! -d "./models/basic" ]; then - mkdir models/basic -fi -if [ ! -d "./models/controlnet" ]; then - mkdir models/controlnet -fi -if [ ! -d "./models/basic/babes20" ]; then - python3 -m dfn --url http://disk-sophgo-vip.quickconnect.to/sharing/QWKCQ7Pxa && unzip babes20.zip && rm -rf babes20.zip && mv babes20 models/basic/ -fi - -python3 -m dfn --url http://disk-sophgo-vip.quickconnect.to/sharing/63dydmQ6q && mv canny_multize.bmodel models/controlnet/ -python3 -m dfn --url http://disk-sophgo-vip.quickconnect.to/sharing/86Rm1E7cl && mv tile_multize.bmodel models/controlnet/ diff --git a/requirements.txt b/requirements.txt index 28e94e8..f4afaf9 100644 --- a/requirements.txt +++ b/requirements.txt @@ -3,10 +3,12 @@ torchsde transformers tqdm diffusers -flask==2.2.2 +#flask==2.2.2 pillow dfn nasops -flask_cors==4.0.0 +#flask_cors lark -Werkzeug>=2.2.0 \ No newline at end of file +gradio +#Werkzeug==2.2.2 +#anyio==3.7.1 diff --git a/sd/__init__.py b/sd/__init__.py old mode 100644 new mode 100755 index f173cac..4ca2f0a --- a/sd/__init__.py +++ b/sd/__init__.py @@ -1 +1,6 @@ -from .pipeline2 import StableDiffusionPipeline \ No newline at end of file +from .pipeline2 import StableDiffusionPipeline, SD2_1_MODELS, SD_XL_MODELS +from .pipelineXL import StableDiffusionXLPipeline +from .npuengine import EngineOV +from .upscale import UpscaleModel +from .pipeline152device import SD15Parallel +from .pipelinexl2device import SDXL2Parallel \ No newline at end of file diff --git a/sd/libuntpu.so b/sd/libuntpu.so deleted file mode 100644 index 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z4k3=d2Q%<LJ*M^W`}Kzz)zT1uI0OIn7c#OL^Yhb|#6KbaL<aua_lmFWOEX}IKk4vc z{rz+ES)1qQpJ;n#0f;Ak-QgKdy4gBnotLD6dLQEGCC}*+YDn?OQR`T`sG*1Ow1}~U q|3BOHB>c%ciqzl!jpwZ+I>wfx{$c*|FAc;0aq|TWcqae^kLF)|kgPfY diff --git a/sd/newtool.py b/sd/npuengine.md similarity index 65% rename from sd/newtool.py rename to sd/npuengine.md index baf41e4..b38854f 100644 --- a/sd/newtool.py +++ b/sd/npuengine.md @@ -7,10 +7,8 @@ mylog = logging.getLogger(__name__) MLIR_START = 4294967296 -# print better -np.set_printoptions(precision=6, suppress=True) -def cpu(self, force=True, record=True): - self.npy + +def cpu(self, force=True): copy_tensor_to_host(self, force) def malloc_device_addr(self): @@ -18,7 +16,7 @@ def malloc_device_addr(self): self.get_bm_handle() malloc_device(self) -def npu(self, force=True, record=True): +def npu(self, force=True): copy_tensor_to_device(self, force) def malloc_host_addr(self): @@ -40,19 +38,6 @@ def npy(self): shape = list(self.shape)[:self.dims] data_type = type_map[self.dtype] self.npy__ = make_c2np(self.data, shape, data_type) - self.is_from_np = True - else: - if self.size == 0: - size = self.max_size // np.dtype(type_map[self.dtype]).itemsize - self.npy__ = np.zeros(size, dtype=type_map[self.dtype]) - self.is_from_np = True - self.data = make_np2c(self.npy__) - else: - shape = list(self.shape)[:self.dims] - data_type = type_map[self.dtype] - self.npy__ = np.zeros(shape, dtype=data_type) - self.data = make_np2c(self.npy__) - self.is_from_np = True return self.npy__ else: return self.npy__ @@ -128,47 +113,16 @@ def set_numpy(self, npy, do_necessary=True): assert self.shape[i] == npy.shape[i] self.data = make_np2c(npy) -def set_dtype_shape(self, dtype, shape, regen_npy=True): +def set_dtype_shape(self, dtype, shape): self.dtype = dtype self.dims = len(shape) for i in range(self.dims): self.shape[i] = shape[i] self.size = np.prod(shape) * np.dtype(type_map[dtype]).itemsize - if self.is_from_np and regen_npy is True: - self.npy__ = np.zeros(shape, dtype=type_map[dtype]) - self.data = make_np2c(self.npy__) - -def find_father(self) -> UntensorS: - if self.is_copy: - cur_tensor = ctypes.cast(self.copy_tensor, ctypes.POINTER(UntensorS)).contents - return cur_tensor.find_father() - else: - return self -def set_copy_tensor(self, copy_tensor:UntensorS): +def set_copy_tensor(self, copy_tensor): self.is_copy = True - self.copy_tensor = ctypes.addressof(copy_tensor) - -def diff_set_with_flag(self, npdata, flag=0, do_necessary=True): - # 0: force copy, 1: simple check copy , 2: force not copy - tensor = self.find_father() - if npdata.dtype != type_map[self.dtype] and do_necessary: - npdata = npdata.astype(type_map[self.dtype]) - if flag == 0: - tensor.data = make_np2c(npdata) - tensor.npy__ = npdata - tensor.is_from_np = True - tensor.size = npdata.size * npdata.dtype.itemsize - tensor.npu() - else: - ValueError("not support") - -def copy_tensor_data(self, tensor:UntensorS, flag=0): - tensor = tensor.find_father() - if flag == 0: - copy_data_from_tensor_with_flag(self, tensor, 0) - else: - ValueError("not support") + self.copy_tensor = copy_tensor def __del__(self): pass @@ -185,9 +139,6 @@ def __del__(self): UntensorS.set_copy_tensor = set_copy_tensor UntensorS.set_numpy = set_numpy UntensorS.set_dtype_shape = set_dtype_shape -UntensorS.diff_set_with_flag = diff_set_with_flag -UntensorS.copy_tensor_data = copy_tensor_data -UntensorS.find_father = find_father UntensorS.__del__ = __del__ def convert_tensor_c_into_dict(tensor_c): @@ -435,17 +386,13 @@ def load_lora_weight_npz(self, lora_path, alpha2=1, ready_reorder=False): for layer in tqdm(all_keys): if layer in self.match_dict: weight = updates[layer+".weight"] - alpha = 1/updates[layer+".alpha"] + alpha = updates[layer+".alpha"] self.lora_weight_save_unet_opt(layer, weight, alpha, alpha2, ready_reorder) -class UntoolEngineOV: - def __init__(self, model_path="", device_id=0, pre_malloc=True, default_io_mem=False, output_list=None, sg=False) : +class EngineOV: + def __init__(self, model_path="", device_id=0, pre_malloc=True) : self.model_path = model_path - self.sg = sg - if self.sg: - self.runtime = load_bmodel_sg(model_path, pre_malloc,default_io_mem, device_id) - else: - self.runtime = load_bmodel(model_path, pre_malloc,default_io_mem, device_id) + self.runtime = load_bmodel(model_path, pre_malloc, device_id) self.basic_info = self.model_info self.input_num = self.basic_info['input_num'] self.output_num = self.basic_info['output_num'] @@ -453,85 +400,17 @@ def __init__(self, model_path="", device_id=0, pre_malloc=True, default_io_mem=F self.outputs = self.output_num * [None] self.cur_stage = -1 self.init_io() - if pre_malloc and not self.sg: + if pre_malloc: self.check_and_move_to_device() - self.build_input_stage_map() - self.build_output_stage_map() - self.output_list = output_list def __str__(self): return self.model_path - - def fill_io_max(self): - if not self.sg: - fill_maxio(self.runtime) - else: - fill_maxio_sg(self.runtime) - self.init_io() - - def build_input_stage_map(self): - input_stage_map = {} - stage_num = self.basic_info['stage_num'] - for input_idx in range(self.input_num): - input_stage_map[input_idx] = {} - for stage_idx in range(stage_num): - for input_idx in range(self.input_num): - shape = tuple(self.basic_info['stage_info'][stage_idx]['input_tensor'][input_idx]['data_shape']) - input_stage_map[input_idx][shape] = stage_idx - self.input_stage_map = input_stage_map - - def build_output_stage_map(self): - output_stage_map = {} - stage_num = self.basic_info['stage_num'] - for output_idx in range(self.output_num): - output_stage_map[output_idx] = {} - for stage_idx in range(stage_num): - for output_idx in range(self.output_num): - shape = tuple(self.basic_info['stage_info'][stage_idx]['output_tensor'][output_idx]['data_shape']) - output_stage_map[output_idx][stage_idx] = shape - self.output_stage_map = output_stage_map - return self - - def default_input(self, default_input_map: dict={}, default_output_map: dict={}): - # default_input_map: 0/1: a single value - # if user want to set npy, please set manually - for idx, each_input in enumerate(self.inputs): - if each_input.is_copy: - continue - size = each_input.max_size // np.dtype(type_map[each_input.dtype]).itemsize - each_input.npy__ = np.zeros(size, dtype=type_map[each_input.dtype]) - each_input.is_from_np = True - each_input.size = each_input.max_size - if idx in default_input_map: - value = default_input_map[idx] - each_input.npy__ += value - each_input.data = make_np2c(each_input.npy__) - each_input.npu() - for idx, each_output in enumerate(self.outputs): - if each_output.is_copy: - continue - each_output.size = each_output.max_size // np.dtype(type_map[each_output.dtype]).itemsize - each_output.npy__ = np.zeros(each_output.size, dtype=type_map[each_output.dtype]) - each_output.is_from_np = True - if idx in default_output_map: - value = default_output_map[idx] - each_output.npy__ += value - each_output.data = make_np2c(each_output.npy__) - each_output.npu() - return self - def init_io(self): for i in range(self.input_num): - if not self.sg: - self.inputs[i] = get_input_tensor(self.runtime, i).contents - else: - self.inputs[i] = get_input_tensor_sg(self.runtime, i).contents + self.inputs[i] = get_input_tensor(self.runtime, i).contents for i in range(self.output_num): - if not self.sg: - self.outputs[i] = get_output_tensor(self.runtime, i).contents - else: - self.outputs[i] = get_output_tensor_sg(self.runtime, i).contents + self.outputs[i] = get_output_tensor(self.runtime, i).contents def init_output_with_np(self): assert self.cur_stage >= 0 @@ -557,7 +436,6 @@ def set_input_with_np(self, np_array): def check_and_move_to_device(self): check_move_to_device_fill_api(self.runtime, True) - return self def check_and_set_stage(self, shape_lists): cur_info = self.model_info @@ -579,7 +457,7 @@ def check_and_set_stage(self, shape_lists): break if self.cur_stage == -1: raise ValueError("can't find stage") - self.set_stage(self.cur_stage) + set_stage(self.runtime, self.cur_stage) # set input and output shape for i in range(self.input_num): self.inputs[i].set_dtype_shape(stage[self.cur_stage]['input_tensor'][i]['data_type'], stage[self.cur_stage]['input_tensor'][i]['data_shape']) @@ -615,30 +493,26 @@ def load_lora_file(self, lora_npz_path, match_csv, alpha=1, stage_idx=0): @property def model_info(self): model_info_c = ModelInfoSC() - if not self.sg: - convert_model_info_into_c(get_model_info(self.runtime), model_info_c) - else: - convert_sg_model_info_into_c(get_model_sg_info(self.runtime), model_info_c) + convert_model_info_into_c(get_model_info(self.runtime), model_info_c) res = convert_model_info_c_into_dict(model_info_c) return res - def run_copy(self, args,stage_diff_idx=0, output_list:list=[]): - if output_list is None: - output_list = [] - if self.output_list is not None and len(output_list) == 0: - output_list = self.output_list - + def run_copy(self, args, return_np_list=True, force=True, copy_force=False): + # args must be list or dicts value = [] if isinstance(args, list): value = args elif isinstance(args, dict): value = list(args.values()) - self.get_stage_by_shape(value[stage_diff_idx].shape, stage_diff_idx) - input_map = {} - for idx in range(self.input_num): - input_map[idx] = {"data":value[idx], "flag":0} - res = self.run_with_np(input_map, output_list) - return res + self.set_input_with_np(value) + self.move_input_into_device(force, copy_force) + run(self.runtime) + self.move_output_into_host(force) + if return_np_list: + res = [] + for i in self.outputs: + res.append(i.npy) + return res def fake_stage_run(self, stage_idx=0): stage = self.basic_info['stage_info'][stage_idx] @@ -647,7 +521,14 @@ def fake_stage_run(self, stage_idx=0): temp = stage['input_tensor'][i] temp_np = np.zeros(temp['data_shape']).astype(type_map[temp['data_type']]) value.append(temp_np) - res = self.run_copy(value, stage_diff_idx=0) + # print(value) + self.set_input_with_np(value) + self.move_input_into_device() + run(self.runtime) + self.move_output_into_host() + res = [] + for i in self.outputs: + res.append(i.npy) return res def replace_coeff(self, coeff, coeff_size, stage_idx=0): @@ -657,78 +538,8 @@ def replace_model(self, bmodel_file): pass - def set_stage(self, stage): - if self.sg: - set_stage_sg(self.runtime, stage) - else: - set_stage(self.runtime, stage) - self.cur_stage = stage - - def get_stage_by_shape(self, shape, idx): - if isinstance(shape, list): - shape = tuple(shape) - if shape not in self.input_stage_map[idx]: - ValueError("can't find stage") - stage = self.input_stage_map[idx][shape] - self.set_stage(stage) - return self - - def run_with_np(self, input_map_dict:dict={}, output_list:list=[], check_stage=True, stage_diff_idx=0, stage=-1) -> list: - # input_map_dict: {idx: { "data":np, "flag":0 } } - # output_map_dict: - # 这个函数假设所有的输入都已经在device上了 - # 0: force copy, 1: simple check copy , 2: force not copy - # get stage - if output_list is None: - output_list = [] - if self.output_list is not None and len(output_list) == 0: - output_list = self.output_list - - if not check_stage and stage >= 0: - self.set_stage(stage) - - for k,v in input_map_dict.items(): - input_tensor = self.inputs[k] - input_tensor.diff_set_with_flag(v['data'], v['flag'], True) - if check_stage and k == stage_diff_idx: - self.get_stage_by_shape(v['data'].shape, k) - if not self.sg: - run(self.runtime) - else: - run_sg(self.runtime) - res = [] - for idx in output_list: - self.outputs[idx].set_dtype_shape(self.basic_info['stage_info'][self.cur_stage]['output_tensor'][idx]['data_type'], self.basic_info['stage_info'][self.cur_stage]['output_tensor'][idx]['data_shape']) - self.outputs[idx].cpu() - res.append(self.outputs[idx].npy) - return res - - def __call__(self, - args=None, - stage_diff_idx=0, - input_map_dict_tensor:dict=None, - input_map_dict_np:dict=None, - output_list:list=None, - check_stage:bool=True): - if args is not None: - return self.run_copy(args, stage_diff_idx=stage_diff_idx) - if input_map_dict_np is not None: - return self.run_with_np(input_map_dict_np, output_list=output_list, check_stage=check_stage, stage_diff_idx=stage_diff_idx) - if input_map_dict_tensor is not None: - return self.run_with_tensor(input_map_dict_tensor, output_list=output_list, check_stage=check_stage, stage_diff_idx=stage_diff_idx) - return None - - def run_with_tensor(self, input_map_dict_tensor:dict={}, output_list:list=[], check_stage=True, stage_diff_idx=0): - if output_list is None: - output_list = [] - if self.output_list is not None and len(output_list) == 0: - output_list = self.output_list - - for k,v in input_map_dict_tensor.items(): - input_tensor = self.inputs[k] - # check address is same and same pass - # check address is not same and copy d2d - pass + def __call__(self, args, return_np_list=True, force=True, copy_force=False): + return self.run_copy(args, return_np_list, force, copy_force) # def __del__(self): # self.active_free() @@ -741,26 +552,20 @@ def active_free(self): self.outputs = None self.basic_info = None -def link_bmodel(modelsrc: UntoolEngineOV, modeldst: UntoolEngineOV, link_map:dict): - """link two models with map (map is (i/o, idx) : (i/o, idx) ) - Args: - modelsrc (EngineOV): source model - modeldst (EngineOV): dest model - link_map (dict): map is (i/o, idx) : (i/o, idx) i=0,o=1 - """ - for key, value in link_map.items(): - src_is_input = key[0] == 0 - src_idx = key[1] - dst_is_input = value[0] == 0 - dst_idx = value[1] - if src_is_input: - if dst_is_input: - modeldst.inputs[dst_idx].set_copy_tensor(modelsrc.inputs[src_idx]) - else: - modeldst.outputs[dst_idx].set_copy_tensor(modelsrc.inputs[src_idx]) - else: - if dst_is_input: - modeldst.inputs[dst_idx].set_copy_tensor(modelsrc.outputs[src_idx]) - else: - modeldst.outputs[dst_idx].set_copy_tensor(modelsrc.outputs[src_idx]) +if __name__=="__main__": + # for large unet (sdxl) will be error when double batch + # 观察到某一些模型推理会失败 + # model_path = "/workspace/aa/allmodel/basic/sd152parallel/tae1_5.bmodel" + # model = EngineOV(model_path, 11, True) + # print(model.model_info) + # res = model.fake_stage_run(0) + # del model + bmodel_path = "/workspace/autocompiler/autotrace/pt_models/v1-5-pruned-test/unet_2_1684x_f16.bmodel" + npz_file = "/workspace/demos/tpukern/test/mw/coeffex/lcm_lora_reorder.npz" + match_file = "/workspace/demos/tpukern/test/mw/coeffex/unet_match.csv" + model = EngineOV(bmodel_path, 11, True) + model.load_lora_file(npz_file, match_file, 1, 0) + + res = model.fake_stage_run(0) + # import pdb;pdb.set_trace() \ No newline at end of file diff --git a/sd/npuengine.py b/sd/npuengine.py new file mode 100644 index 0000000..0bfe437 --- /dev/null +++ b/sd/npuengine.py @@ -0,0 +1,50 @@ +# from tpu_perf.infer import SGInfer +import numpy as np +import time +import torch +import os +import sophon.sail as sail + + +class EngineOV: + + def __init__(self, model_path="",output_names="",device_id=0) : + # 如果环境变量中没有设置device_id,则使用默认值 + if "DEVICE_ID" in os.environ: + device_id = int(os.environ["DEVICE_ID"]) + print(">>>> device_id is in os.environ. and device_id = ",device_id) + self.model_path = model_path + self.device_id = device_id + try: + self.model = sail.Engine(model_path, device_id, sail.IOMode.SYSIO) + except Exception as e: + print("load model error; please check model path and device status;") + print(">>>> model_path: ",model_path) + print(">>>> device_id: ",device_id) + print(">>>> sail.Engine error: ",e) + raise e + sail.set_print_flag(True) + self.graph_name = self.model.get_graph_names()[0] + self.input_name = self.model.get_input_names(self.graph_name) + self.output_name= self.model.get_output_names(self.graph_name) + + def __str__(self): + return "EngineOV: model_path={}, device_id={}".format(self.model_path,self.device_id) + + def __call__(self, args): + if isinstance(args, list): + values = args + elif isinstance(args, dict): + values = list(args.values()) + else: + raise TypeError("args is not list or dict") + args = {} + for i in range(len(values)): + args[self.input_name[i]] = values[i] + # import pdb; pdb.set_trace() + output = self.model.process(self.graph_name, args) + res = [] + + for name in self.output_name: + res.append(output[name]) + return res diff --git a/sd/npuengine2.md b/sd/npuengine2.md new file mode 100755 index 0000000..10fcbef --- /dev/null +++ b/sd/npuengine2.md @@ -0,0 +1,63 @@ +# from tpu_perf.infer import SGInfer +import numpy as np +import time +import torch +import os +import sophon.sail as sail + +step = 1 + +def input_output_dump(inputs, outputs, name): + global step + name = "./test/{}_{}.npz".format(name,step) + input_name = "input" + output_name= "output" + res = {} + for i in range(len(inputs)): + input_name += "_{}".format(i) + res[input_name] = inputs[i] + for i in range(len(outputs)): + output_name += "_{}".format(i) + res[output_name] = outputs[i] + step += 1 + np.savez(name,**res) + + +class EngineOV: + + def __init__(self, model_path="",output_names="",device_id=0) : + # 如果环境变量中没有设置device_id,则使用默认值 + if "DEVICE_ID" in os.environ: + device_id = int(os.environ["DEVICE_ID"]) + print(">>>> device_id is in os.environ. and device_id = ",device_id) + self.model_path = model_path + self.device_id = device_id + try: + self.model = sail.Engine(model_path, device_id, sail.IOMode.SYSIO) + except Exception as e: + raise e + self.graph_name = self.model.get_graph_names()[0] + self.input_name = self.model.get_input_names(self.graph_name) + self.output_name= self.model.get_output_names(self.graph_name) + + def __str__(self): + return "EngineOV: model_path={}, device_id={}".format(self.model_path,self.device_id) + + def __call__(self, args): + if isinstance(args, list): + values = args + elif isinstance(args, dict): + values = list(args.values()) + else: + raise TypeError("args is not list or dict") + args = {} + for i in range(len(values)): + args[self.input_name[i]] = values[i] + output = self.model.process(self.graph_name, args) + res = [] + + for name in self.output_name: + res.append(output[name]) + + input_output_dump(values, res, self.model_path.split("/")[-1]) + return res diff --git a/sd/pipeline152device.py b/sd/pipeline152device.py new file mode 100644 index 0000000..61c592c --- /dev/null +++ b/sd/pipeline152device.py @@ -0,0 +1,923 @@ +import inspect + +import cv2 +from .npuengine import EngineOV +from .scheduler import diffusers_scheduler_config +import numpy as np +import torch +from transformers import CLIPTokenizer +from tqdm import tqdm +import threading +from diffusers.schedulers import EulerDiscreteScheduler +from diffusers.image_processor import VaeImageProcessor +from diffusers.loaders import LoraLoaderMixin, TextualInversionLoaderMixin +from diffusers.utils import ( + logging +) +import random +import time +import os +from multiprocessing import Process,Pool +import multiprocessing +def seed_torch(seed=1029): + seed=seed%4294967296 + random.seed(seed) + os.environ['PYTHONHASHSEED'] = str(seed) # 为了禁止hash随机化,使得实验可复现 + np.random.seed(seed) + torch.manual_seed(seed) + print("set seed to:", seed) + +#seed_torch() +randn_tensor = lambda *x, **kwargs: torch.randn(*x, **kwargs) +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +def parallel_manager(pipe,model_path, device_id): + model = EngineOV(model_path, device_id=device_id) + while True: + message = pipe.recv() + if message['state'] == 1: + data = message['data'] + # print("inner start ",time.time()) + res = model(data) + # print("inner end ",time.time()) + pipe.send({"from_sub":res}) + elif message['state'] == 2: + break + del model + +def parallel_maneger_prompt_embed(pipe,text_encoder_path,tokenizer_path, device_id): + tokenizer = CLIPTokenizer.from_pretrained(tokenizer_path) + text_encoder = EngineOV(text_encoder_path, device_id=device_id) + while True: + message = pipe.recv() + if message['state'] == 1: + prompt = message['prompt'] + # =======================处理============================ + text_inputs = tokenizer( + prompt, + padding="max_length", + max_length=tokenizer.model_max_length, + truncation=True, + return_tensors="pt", + ) + text_input_ids = text_inputs.input_ids + untruncated_ids = tokenizer(prompt, padding="longest", return_tensors="pt").input_ids + + if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( + text_input_ids, untruncated_ids + ): + removed_text = tokenizer.batch_decode( + untruncated_ids[:, tokenizer.model_max_length - 1 : -1] + ) + logger.warning( + "The following part of your input was truncated because CLIP can only handle sequences up to" + f" {tokenizer.model_max_length} tokens: {removed_text}" + ) + + prompt_embeds = text_encoder({"tokens": np.array(text_input_ids)}) + prompt_embeds = prompt_embeds[0] + # =======================结束============================ + pipe.send({"from_sub":prompt_embeds}) + elif message['state'] == 2: + negative_prompt = message['negative_prompt'] + prompt = message['prompt'] + batch_size = message['batch_size'] + # =======================处理============================ + if negative_prompt is None: + uncond_tokens = [""] * batch_size + elif prompt is not None and type(prompt) is not type(negative_prompt): + raise TypeError( + f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" + f" {type(prompt)}." + ) + elif isinstance(negative_prompt, str): + uncond_tokens = [negative_prompt] + elif batch_size != len(negative_prompt): + raise ValueError( + f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" + f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" + " the batch size of `prompt`." + ) + else: + uncond_tokens = negative_prompt + + max_length = tokenizer.model_max_length + uncond_input = tokenizer( + uncond_tokens, + padding="max_length", + max_length=max_length, + truncation=True, + return_tensors="pt", + ) + + uncond_input_text_input_ids = uncond_input.input_ids + negative_prompt_embeds = text_encoder({"tokens": np.array(uncond_input_text_input_ids)}) + + negative_prompt_embeds = negative_prompt_embeds[0] + # =========================结束============================ + pipe.send({"from_sub":negative_prompt_embeds}) + + elif message['state'] == 3: + break + del text_encoder + + +class SD15Parallel(): + def __init__( + self, + vae_encoder_path, + vae_decoder_path, + text_encoder_path, + tokenizer_path, + unet_path, + device_id=0, + parallel=False, + is_tiny_vae = True + ): + # Tokenizer load + self.tokenizer = CLIPTokenizer.from_pretrained(tokenizer_path) + self.device_id = device_id + self.parallel = parallel + + # Unet load + # self.unet = EngineOV(unet_path, device_id=self.device_id) + if self.parallel == True: + parent_pipe, child_pipe = multiprocessing.Pipe() + self.child_pipe = child_pipe + self.parent_pipe= parent_pipe + process = multiprocessing.Process(target=parallel_manager, args=(child_pipe, unet_path, 1,)) + process.start() + self.process = process + # warmup + self.parent_pipe.send({"state":10,"data":np.random.rand(50,60,70)}) + + parent_pipe2, child_pipe2 = multiprocessing.Pipe() + self.child_pipe2 = child_pipe2 + self.parent_pipe2= parent_pipe2 + process2 = multiprocessing.Process(target=parallel_manager, args=(child_pipe2, unet_path,0,)) + process2.start() + self.process2 = process2 + self.parent_pipe2.send({"state":10,"data":np.random.rand(40,50,60,7)}) + + # text部分多进程处理 + self.text_parent_pipe, self.text_child_pipe = multiprocessing.Pipe() + text_process = multiprocessing.Process(target=parallel_maneger_prompt_embed, args=(self.text_child_pipe,text_encoder_path,tokenizer_path, 1,)) + text_process.start() + self.text_process = text_process + self.text_parent_pipe.send({"state":10,"data":np.random.rand(40,50,60,7)}) + + + self.text_parent_pipe2, self.text_child_pipe2 = multiprocessing.Pipe() + text_process2 = multiprocessing.Process(target=parallel_maneger_prompt_embed, args=(self.text_child_pipe2,text_encoder_path,tokenizer_path, 0,)) + text_process2.start() + self.text_process2 = text_process2 + self.text_parent_pipe2.send({"state":10,"data":np.random.rand(40,50,60,7)}) + + + # VAE load + self.vae_decoder = EngineOV(vae_decoder_path, device_id=self.device_id) + self.vae_encoder = EngineOV(vae_encoder_path, device_id=self.device_id) + + # Text_Encoder load + self.text_encoder_path = text_encoder_path + self.text_encoder = EngineOV(text_encoder_path, device_id=self.device_id) + + # Scheculer + self.scheduler = EulerDiscreteScheduler(**(diffusers_scheduler_config['Euler D'])) + + # other config + self.config_force_zeros_for_empty_prompt = False + self.prompt_embeds_dtype = torch.float16 + self.vae_config_scaling_factor = 0.18215 + if is_tiny_vae: + self.vae_config_scaling_factor = 1.0 + + self.unet_config_in_channels = 4 + self.vae_scale_factor = 8 + self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) + self._execution_device = 'cpu' + + def set_height_width(self, height, width): + pass + + def _preprocess_image(self, image): + if image.mode != "RGB": + image = image.convert("RGB") # RGBA or other -> RGB + image = np.array(image) + h, w = image.shape[:-1] + if h != 512 or w != 512: + image = cv2.resize( + image, + (512,512), + interpolation=cv2.INTER_LANCZOS4 + ) + # normalize + image = image.astype(np.float32) / 255.0 + image = 2.0 * image - 1.0 + # to batch + image = image[None].transpose(0, 3, 1, 2) + return image + + def _encode_image(self, init_image): + moments = self.vae_encoder({ + "input.1": self._preprocess_image(init_image) + })[0] + mean, logvar = np.split(moments, 2, axis=1) + logvar = np.clip(logvar, -30.0, 20.0) + std = np.exp(logvar * 0.5) + latent = (mean + std * np.random.randn(*mean.shape)) * self.vae_config_scaling_factor + return torch.from_numpy(latent) + + # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.encode_prompt + def encode_prompt( + self, + prompt, + prompt_2 = None, + device = None, + num_images_per_prompt = 1, + do_classifier_free_guidance = True, + negative_prompt = None, + negative_prompt_2 = None, + prompt_embeds = None, + negative_prompt_embeds = None, + pooled_prompt_embeds = None, + negative_pooled_prompt_embeds = None, + lora_scale = None, + ): + device = device or self._execution_device + + # set lora scale so that monkey patched LoRA + # function of text encoder can correctly access it + if lora_scale is not None and isinstance(self, LoraLoaderMixin): + self._lora_scale = lora_scale + + if prompt is not None and isinstance(prompt, str): + batch_size = 1 + elif prompt is not None and isinstance(prompt, list): + batch_size = len(prompt) + else: + batch_size = prompt_embeds.shape[0] + + # Define tokenizers and text encoders + tokenizers = self.tokenizer + text_encoders = self.text_encoder + + if prompt_embeds is None: + # textual inversion: procecss multi-vector tokens if necessary + prompt_embeds_list = [] + prompts = prompt + + for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders): + if isinstance(self, TextualInversionLoaderMixin): + prompt = self.maybe_convert_prompt(prompt, tokenizer) + text_inputs = tokenizer( + prompt, + padding="max_length", + max_length=tokenizer.model_max_length, + truncation=True, + return_tensors="pt", + ) + + text_input_ids = text_inputs.input_ids + untruncated_ids = tokenizer(prompt, padding="longest", return_tensors="pt").input_ids + + if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( + text_input_ids, untruncated_ids + ): + removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1]) + logger.warning( + "The following part of your input was truncated because CLIP can only handle sequences up to" + f" {tokenizer.model_max_length} tokens: {removed_text}" + ) + + prompt_embeds_data = text_encoder({"prompt_tokens":text_input_ids.numpy()}) + + # We are only ALWAYS interested in the pooled output of the final text encoder + + pooled_prompt_embeds = torch.tensor(prompt_embeds_data[0]) + prompt_embeds = prompt_embeds_data[1] + + prompt_embeds_list.append(torch.tensor(prompt_embeds)) + prompt_embeds = torch.concat(prompt_embeds_list, dim=-1) + + # get unconditional embeddings for classifier free guidance + zero_out_negative_prompt = negative_prompt is None and self.config_force_zeros_for_empty_prompt + if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt: + negative_prompt_embeds = torch.zeros_like(prompt_embeds) + negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds) + elif do_classifier_free_guidance and negative_prompt_embeds is None: + negative_prompt = negative_prompt or "" + negative_prompt_2 = negative_prompt_2 or negative_prompt + + # uncond_tokens: List[str] + if prompt is not None and type(prompt) is not type(negative_prompt): + raise TypeError( + f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" + f" {type(prompt)}." + ) + elif isinstance(negative_prompt, str): + uncond_tokens = [negative_prompt, negative_prompt_2] + elif batch_size != len(negative_prompt): + raise ValueError( + f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" + f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" + " the batch size of `prompt`." + ) + else: + uncond_tokens = [negative_prompt, negative_prompt_2] + + negative_prompt_embeds_list = [] + + for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders): + if isinstance(self, TextualInversionLoaderMixin): + negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer) + + max_length = prompt_embeds.shape[1] + uncond_input = tokenizer( + negative_prompt, + padding="max_length", + max_length=max_length, + truncation=True, + return_tensors="pt", + ) + + negative_prompt_embeds = text_encoder({"prompt_tokens":uncond_input.input_ids.numpy()}) #text_encoder.run(input_node, {"prompt_tokens":uncond_input.input_ids.numpy()}) + # We are only ALWAYS interested in the pooled output of the final text encoder + negative_pooled_prompt_embeds = torch.tensor(negative_prompt_embeds[0]) + negative_prompt_embeds = negative_prompt_embeds[1] + + negative_prompt_embeds_list.append(torch.tensor(negative_prompt_embeds)) + + negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1) + + prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2_dtype, device=device) + bs_embed, seq_len, _ = prompt_embeds.shape + + # duplicate text embeddings for each generation per prompt, using mps friendly method + prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) + prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) + + if do_classifier_free_guidance: + # duplicate unconditional embeddings for each generation per prompt, using mps friendly method + seq_len = negative_prompt_embeds.shape[1] + negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2_dtype, device=device) + negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) + negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) + + pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view( + bs_embed * num_images_per_prompt, -1 + ) + if do_classifier_free_guidance: + negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view( + bs_embed * num_images_per_prompt, -1 + ) + + return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds + + def _encode_prompt( + self, + prompt, + num_images_per_prompt, + do_classifier_free_guidance, + negative_prompt=None, + prompt_embeds = None, + negative_prompt_embeds = None, + lora_scale = None, + ): + # set lora scale so that monkey patched LoRA + # function of text encoder can correctly access it + if prompt is not None and isinstance(prompt, str): + batch_size = 1 + elif prompt is not None and isinstance(prompt, list): + batch_size = len(prompt) + else: + batch_size = prompt_embeds.shape[0] + + if prompt_embeds is None: + # textual inversion: procecss multi-vector tokens if necessary + text_inputs = self.tokenizer( + prompt, + padding="max_length", + max_length=self.tokenizer.model_max_length, + truncation=True, + return_tensors="pt", + ) + text_input_ids = text_inputs.input_ids + untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids + + if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( + text_input_ids, untruncated_ids + ): + removed_text = self.tokenizer.batch_decode( + untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] + ) + logger.warning( + "The following part of your input was truncated because CLIP can only handle sequences up to" + f" {self.tokenizer.model_max_length} tokens: {removed_text}" + ) + + prompt_embeds = self.text_encoder({"tokens": np.array(text_input_ids)}) + prompt_embeds = prompt_embeds[0] + + # get unconditional embeddings for classifier free guidance + if do_classifier_free_guidance and negative_prompt_embeds is None: + #uncond_tokens: List[str] + if negative_prompt is None: + uncond_tokens = [""] * batch_size + elif prompt is not None and type(prompt) is not type(negative_prompt): + raise TypeError( + f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" + f" {type(prompt)}." + ) + elif isinstance(negative_prompt, str): + uncond_tokens = [negative_prompt] + elif batch_size != len(negative_prompt): + raise ValueError( + f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" + f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" + " the batch size of `prompt`." + ) + else: + uncond_tokens = negative_prompt + + max_length = prompt_embeds.shape[1] + uncond_input = self.tokenizer( + uncond_tokens, + padding="max_length", + max_length=max_length, + truncation=True, + return_tensors="pt", + ) + + uncond_input_text_input_ids = uncond_input.input_ids + negative_prompt_embeds = self.text_encoder({"tokens": np.array(uncond_input_text_input_ids)}) + + negative_prompt_embeds = negative_prompt_embeds[0] + + if do_classifier_free_guidance: + prompt_embeds = np.concatenate([negative_prompt_embeds, prompt_embeds], axis=0) + + return prompt_embeds + + def _encode_prompt_parallel( + self, + prompt, + num_images_per_prompt, + do_classifier_free_guidance, + negative_prompt=None, + prompt_embeds = None, + negative_prompt_embeds = None, + lora_scale = None, + ): + # set lora scale so that monkey patched LoRA + # function of text encoder can correctly access it + if prompt is not None and isinstance(prompt, str): + batch_size = 1 + elif prompt is not None and isinstance(prompt, list): + batch_size = len(prompt) + else: + batch_size = prompt_embeds.shape[0] + + + if prompt_embeds is None: + # textual inversion: procecss multi-vector tokens if necessary + self.text_parent_pipe.send({"state":1,"prompt":prompt}) + + # get unconditional embeddings for classifier free guidance + if do_classifier_free_guidance and negative_prompt_embeds is None: + self.text_parent_pipe2.send({"state":2,"negative_prompt":negative_prompt, + "prompt":prompt, + "batch_size":batch_size}) + negative_prompt_embeds = self.text_parent_pipe2.recv() + negative_prompt_embeds = negative_prompt_embeds["from_sub"] + + if prompt_embeds is None: + prompt_embeds = self.text_parent_pipe.recv() + prompt_embeds = prompt_embeds["from_sub"] + + + if do_classifier_free_guidance: + # For classifier free guidance, we need to do two forward passes. + # Here we concatenate the unconditional and text embeddings into a single batch + # to avoid doing two forward passes + prompt_embeds = np.concatenate([negative_prompt_embeds, prompt_embeds], axis=0) + + return prompt_embeds + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs + def prepare_extra_step_kwargs(self, generator, eta): + # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature + # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. + # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 + # and should be between [0, 1] + + accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) + extra_step_kwargs = {} + if accepts_eta: + extra_step_kwargs["eta"] = eta + + # check if the scheduler accepts generator + accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) + if accepts_generator: + extra_step_kwargs["generator"] = generator + return extra_step_kwargs + + def check_inputs( + self, + prompt, + prompt_2, + strength, + num_inference_steps, + callback_steps, + negative_prompt=None, + negative_prompt_2=None, + prompt_embeds=None, + negative_prompt_embeds=None, + ): + if strength < 0 or strength > 1: + raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}") + if num_inference_steps is None: + raise ValueError("`num_inference_steps` cannot be None.") + elif not isinstance(num_inference_steps, int) or num_inference_steps <= 0: + raise ValueError( + f"`num_inference_steps` has to be a positive integer but is {num_inference_steps} of type" + f" {type(num_inference_steps)}." + ) + if (callback_steps is None) or ( + callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) + ): + raise ValueError( + f"`callback_steps` has to be a positive integer but is {callback_steps} of type" + f" {type(callback_steps)}." + ) + + if prompt is not None and prompt_embeds is not None: + raise ValueError( + f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" + " only forward one of the two." + ) + elif prompt_2 is not None and prompt_embeds is not None: + raise ValueError( + f"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to" + " only forward one of the two." + ) + elif prompt is None and prompt_embeds is None: + raise ValueError( + "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." + ) + elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): + raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") + elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)): + raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}") + + if negative_prompt is not None and negative_prompt_embeds is not None: + raise ValueError( + f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" + f" {negative_prompt_embeds}. Please make sure to only forward one of the two." + ) + elif negative_prompt_2 is not None and negative_prompt_embeds is not None: + raise ValueError( + f"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:" + f" {negative_prompt_embeds}. Please make sure to only forward one of the two." + ) + + if prompt_embeds is not None and negative_prompt_embeds is not None: + if prompt_embeds.shape != negative_prompt_embeds.shape: + raise ValueError( + "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" + f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" + f" {negative_prompt_embeds.shape}." + ) + + def get_timesteps(self, num_inference_steps, strength, device, denoising_start=None): + # get the original timestep using init_timestep + if denoising_start is None: + init_timestep = min(int(num_inference_steps * strength), num_inference_steps) + t_start = max(num_inference_steps - init_timestep, 0) + else: + t_start = 0 + + timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :] + + # Strength is irrelevant if we directly request a timestep to start at; + # that is, strength is determined by the denoising_start instead. + if denoising_start is not None: + discrete_timestep_cutoff = int( + round( + self.scheduler.config.num_train_timesteps + - (denoising_start * self.scheduler.config.num_train_timesteps) + ) + ) + timesteps = list(filter(lambda ts: ts < discrete_timestep_cutoff, timesteps)) + return torch.tensor(timesteps), len(timesteps) + + return timesteps, num_inference_steps - t_start + + def prepare_rand_latents(self, batch_size, num_channels_latents, height, width, dtype, generator): + shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor) + if isinstance(generator, list) and len(generator) != batch_size: + raise ValueError( + f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" + f" size of {batch_size}. Make sure the batch size matches the length of the generators." + ) + latents = randn_tensor(shape, generator=generator, dtype=dtype) + # scale the initial noise by the standard deviation required by the scheduler + latents = latents * self.scheduler.init_noise_sigma + return latents + + def prepare_latents( + self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None, add_noise=True + ): + + batch_size = batch_size * num_images_per_prompt + init_latents = self._encode_image(image) + init_latents = init_latents.to(dtype) + + if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0: + # expand init_latents for batch_size + additional_image_per_prompt = batch_size // init_latents.shape[0] + init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0) + elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0: + raise ValueError( + f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts." + ) + else: + init_latents = torch.cat([init_latents], dim=0) + + if add_noise: + shape = init_latents.shape + noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype) + # get latents + init_latents = self.scheduler.add_noise(init_latents, noise, timestep) + + latents = init_latents + + return latents + + def _get_add_time_ids( + self, original_size, crops_coords_top_left, target_size, aesthetic_score, negative_aesthetic_score, dtype + ): + if self.config_requires_aesthetics_score: + add_time_ids = list(original_size + crops_coords_top_left + (aesthetic_score,)) + add_neg_time_ids = list(original_size + crops_coords_top_left + (negative_aesthetic_score,)) + else: + add_time_ids = list(original_size + crops_coords_top_left + target_size) + add_neg_time_ids = list(original_size + crops_coords_top_left + target_size) + + passed_add_embed_dim = ( + self.unet_config_addition_time_embed_dim * len(add_time_ids) + self.text_encoder_2_config_projection_dim + ) + expected_add_embed_dim = self.unet_add_embedding_linear_1_in_features + + if ( + expected_add_embed_dim > passed_add_embed_dim + and (expected_add_embed_dim - passed_add_embed_dim) == self.unet_config_addition_time_embed_dim + ): + raise ValueError( + f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. Please make sure to enable `requires_aesthetics_score` with `pipe.register_to_config(requires_aesthetics_score=True)` to make sure `aesthetic_score` {aesthetic_score} and `negative_aesthetic_score` {negative_aesthetic_score} is correctly used by the model." + ) + elif ( + expected_add_embed_dim < passed_add_embed_dim + and (passed_add_embed_dim - expected_add_embed_dim) == self.unet_config_addition_time_embed_dim + ): + raise ValueError( + f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. Please make sure to disable `requires_aesthetics_score` with `pipe.register_to_config(requires_aesthetics_score=False)` to make sure `target_size` {target_size} is correctly used by the model." + ) + elif expected_add_embed_dim != passed_add_embed_dim: + raise ValueError( + f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`." + ) + + add_time_ids = torch.tensor([add_time_ids], dtype=dtype) + add_neg_time_ids = torch.tensor([add_neg_time_ids], dtype=dtype) + + return add_time_ids, add_neg_time_ids + + def run_unet(self, inputs): + inputss = [] + for i in inputs: + inputss.append(torch.tensor(i)) + inputs1 = [ inputss[0][0].unsqueeze(0), inputss[1], inputss[2][0].unsqueeze(0) ] + inputs2 = [ inputss[0][1].unsqueeze(0), inputss[1], inputss[2][1].unsqueeze(0) ] + self.parent_pipe.send({"state":1, "data":inputs2}) + self.parent_pipe2.send({"state":1, "data":inputs1}) + res1 = self.parent_pipe2.recv() + res2 = self.parent_pipe.recv() + res2 = res2["from_sub"] + res1 = res1["from_sub"] + res = np.concatenate([res1[0], res2[0]], axis=0) + return [res] + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae + @torch.no_grad() + def __call__( + self, + prompt = None, + prompt_2 = None, + init_image = None, + strength = 0.7, + height = 512, + width = 512, + num_inference_steps = 50, + denoising_start = None, + denoising_end = None, + guidance_scale = 5.0, + negative_prompt = None, + negative_prompt_2 = None, + num_images_per_prompt = 1, + eta = 0.0, + generator = None, + latents = None, + prompt_embeds = None, + negative_prompt_embeds = None, + pooled_prompt_embeds = None, + negative_pooled_prompt_embeds = None, + output_type = "pil", + callback = None, + callback_steps = 1, + cross_attention_kwargs = None, + original_size = None, + crops_coords_top_left = (0, 0), + target_size = None, + aesthetic_score = 6.0, + negative_aesthetic_score = 2.5, + **kwargs, + ): + # 1. Check inputs. Raise error if not correct + # 2. Define call parameters + if prompt is not None and isinstance(prompt, str): + batch_size = 1 + elif prompt is not None and isinstance(prompt, list): + batch_size = len(prompt) + else: + batch_size = prompt_embeds.shape[0] + + device = self._execution_device + + + do_classifier_free_guidance = guidance_scale > 1.0 + # 3. Encode input prompt + text_encoder_lora_scale = ( + cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None + ) + start_time = time.time() + prompt_embeds = self._encode_prompt( + prompt=prompt, + num_images_per_prompt=num_images_per_prompt, + do_classifier_free_guidance=do_classifier_free_guidance, + negative_prompt=negative_prompt, + prompt_embeds=prompt_embeds, + negative_prompt_embeds=negative_prompt_embeds, + lora_scale=text_encoder_lora_scale, + ) + # 4. Preprocess images + # image = self._preprocess_image(image) + + # 5. Prepare timesteps and latent variables + def denoising_value_valid(dnv): + return type(denoising_end) == float and 0 < dnv < 1 + + self.scheduler.set_timesteps(num_inference_steps, device=device) + if init_image is None: + timesteps = self.scheduler.timesteps + num_channels_latents = self.unet_config_in_channels + latents = self.prepare_rand_latents( + batch_size * num_images_per_prompt, + num_channels_latents, + height, + width, + self.prompt_embeds_dtype, + generator, + ) + else: + timesteps, num_inference_steps = self.get_timesteps( + num_inference_steps, strength, device, denoising_start=denoising_start if denoising_value_valid else None) + + latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt) + add_noise = True if denoising_start is None else False + latents = self.prepare_latents( + init_image, + latent_timestep, + batch_size, + num_images_per_prompt, + self.prompt_embeds_dtype, + device, + generator, + add_noise, + ) + # 7. Prepare extra step kwargs. + extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) + + original_size = (height, width) + target_size = (height, width) + + # 9. Denoising loop + + num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0) + # 9.1 Apply denoising_end + if ( + denoising_end is not None + and denoising_start is not None + and denoising_value_valid(denoising_end) + and denoising_value_valid(denoising_start) + and denoising_start >= denoising_end + ): + raise ValueError( + f"`denoising_start`: {denoising_start} cannot be larger than or equal to `denoising_end`: " + + f" {denoising_end} when using type float." + ) + elif denoising_end is not None and denoising_value_valid(denoising_end): + discrete_timestep_cutoff = int( + round( + self.scheduler.config.num_train_timesteps + - (denoising_end * self.scheduler.config.num_train_timesteps) + ) + ) + num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps))) + timesteps = timesteps[:num_inference_steps] + + for i, t in tqdm(enumerate(timesteps)): + # expand the latents if we are doing classifier free guidance + latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents + + latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) + timestamp = np.array([t]) + # predict the noise residual + # import time + # tm = time.time() + if self.parallel: + noise_pred = self.run_unet([ + latent_model_input, + timestamp, + prompt_embeds] + )[0] + else: + noise_pred = self.unet([ + latent_model_input, + timestamp, + prompt_embeds] + )[0] + # print(time.time() - tm, noise_pred.shape) + + if do_classifier_free_guidance: + noise_pred_uncond, noise_pred_text = np.array_split(noise_pred, 2) + noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) + + # compute the previous noisy sample x_t -> x_t-1 + latents = self.scheduler.step(torch.from_numpy(noise_pred), t, latents, **extra_step_kwargs, return_dict=False)[0] + + # call the callback, if provided + if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): + if callback is not None and i % callback_steps == 0: + callback(i, t, latents) + + + latents = latents.to(torch.float32) + if not output_type == "latent": + init_image = torch.from_numpy(self.vae_decoder([latents / self.vae_config_scaling_factor])[0][0]) + else: + return latents + if len(init_image.shape) == 3: + init_image = init_image.unsqueeze(0) + + # if self.parallel: + # self.parent_pipe.send({"state":2}) + # self.process.join() + # self.parent_pipe2.send({"state":2}) + # self.process2.join() + # self.text_parent_pipe.send({"state":3}) + # self.text_process.join() + # self.text_parent_pipe2.send({"state":3}) + # self.text_process2.join() + init_image = self.image_processor.postprocess(init_image, output_type=output_type) + return init_image[0] + + +if __name__ == "__main__": + + pipe = StableDiffusionPipeline( + vae_encoder_path='/workspace/demos/onebatch/sdv1.5/models/vae_encoder_1684x_f16.bmodel', # vae_encoder_1684x_bf16.bmodel', + vae_decoder_path='/workspace/demos/onebatch/sdv1.5/models/tae1_5.bmodel', + text_encoder_path='/workspace/demos/onebatch/sdv1.5/models/text_encoder_1684x_f32.bmodel', + tokenizer_path='/workspace/demos/onebatch/sdv1.5/models/tokenizer', + unet_path='/workspace/demos/onebatch/sdv1.5/models/unet_1684x_1bs_f16.bmodel', + parallel=True + # unet_path="/workspace/aa/sdxl/models/unet.bmodel" + ) + + prompt = "fuzzy cat" + negative = "strip" + + time1 = time.time() + image0 = pipe(prompt, + negative_prompt = negative, + num_inference_steps = 20) + print('Totoal time: {:4f}s '.format(time.time() - time1)) + image0.save('test_xinhong.png') + + + # 销毁 + # image = pipe(prompt + ", blue hair", + # image=Image.open('my_eyes.png'), + # num_inference_steps = 20) + # image.save('girl_img2img.png') diff --git a/sd/pipeline2.py b/sd/pipeline2.py index 4d4a3d6..e5246d1 100644 --- a/sd/pipeline2.py +++ b/sd/pipeline2.py @@ -8,6 +8,7 @@ from .scheduler import create_random_tensors, sample from PIL import Image, ImageFilter, ImageOps import PIL +from .npuengine import EngineOV import time from . import masking from .utils import PromptChunk, resize_image, flatten, apply_overlay, WrapOutput @@ -17,7 +18,6 @@ from . import ultimate import random import os -from .newtool import UntoolEngineOV opt_C = 4 @@ -58,28 +58,30 @@ def __init__( self.scheduler = scheduler self.basemodel_name = basic_model st_time = time.time() - self.text_encoder = UntoolEngineOV("./models/basic/{}/babes_te_f32.bmodel".format( # encoder_1684x_f32.bmodel - basic_model), device_id=self.device_id, pre_malloc=True, output_list=[0], sg=False) + self.text_encoder = EngineOV("./models/basic/{}/encoder_1684x_f32.bmodel".format( + basic_model), device_id=self.device_id) print("====================== Load TE in ", time.time()-st_time) st_time = time.time() - # unet_multize.bmodel - self.unet_pure = UntoolEngineOV("./models/basic/{}/unet_1_1684x_f16_attention.bmodel".format( - basic_model, extra), device_id=self.device_id, pre_malloc=True, output_list=[0], sg=False) - self.unet_pure.default_input() + self.unet_pure = EngineOV("./models/basic/{}/unet_multize.bmodel".format( + basic_model, extra), device_id=self.device_id) print("====================== Load UNET in ", time.time()-st_time) self.unet_lora = None + if os.path.exists("./models/basic/{}/unet_multize_lora.bmodel"): + st_time = time.time() + self.unet_lora = EngineOV("./models/basic/{}/unet_multize_lora.bmodel".format( + basic_model, extra), device_id=self.device_id) + print("====================== Load UNET-lora in ", time.time()-st_time) st_time = time.time() - # self.vae_decoder = UntoolEngineOV("./models/basic/{}/{}vae_decoder_f16_512.bmodel".format(#vae_decoder_multize.bmodel".format( - self.vae_decoder = UntoolEngineOV("./models/basic/{}/{}vae_decoder_multize.bmodel".format( - basic_model, extra), device_id=self.device_id, pre_malloc=True, output_list=[0], sg=False) + self.vae_decoder = EngineOV("./models/basic/{}/{}vae_decoder_multize.bmodel".format( + basic_model, extra), device_id=self.device_id) print("====================== Load VAE DE in ", time.time()-st_time) st_time = time.time() - self.vae_encoder = UntoolEngineOV("./models/basic/{}/{}vae_encoder_multize.bmodel".format( - basic_model, extra), device_id=self.device_id, pre_malloc=True, output_list=[0], sg=False) + self.vae_encoder = EngineOV("./models/basic/{}/{}vae_encoder_multize.bmodel".format( + basic_model, extra), device_id=self.device_id) print("====================== Load VAE EN in ", time.time()-st_time) if controlnet_name: @@ -695,7 +697,7 @@ def __call__( controlnet_weight=1.0, use_controlnet=True, init_latents=None, - enable_prompt_weight=False, + enable_prompt_weight=True, scheduler=None, generator=None ): @@ -730,10 +732,9 @@ def __call__( max_length=self.tokenizer.model_max_length, truncation=True ).input_ids # 换tokenizer后 此处一致 - # import pdb; pdb.set_trace() # text_embedding use npu engine to inference text_embeddings = self.text_encoder( - {"tokens": np.array([tokens]).astype(np.int32)})[0] + {"tokens": np.array([tokens]).astype(np.int32)})[0] # 一致 # do classifier free guidance if guidance_scale > 1.0 or negative_prompt is not None: @@ -775,14 +776,14 @@ def __call__( # handle latents shape = self.latent_shape - # 这里是torch manual seed = seeds[0] rand_latents = create_random_tensors(shape, seeds, subseeds=subseeds, subseed_strength=subseed_strength, seed_resize_from_h=seed_resize_from_h, seed_resize_from_w=seed_resize_from_w) + if init_image is not None and mask is not None: mask = self._preprocess_mask(mask) else: mask = None - if scheduler!="LCM" and not self.is_v2 or (self.is_v2 and scheduler in ['Euler', None]): + if scheduler!="LCM" and not self.is_v2 or (self.is_v2 and scheduler in ['Euler', None]): # run scheduler if scheduler is not None: self.scheduler = scheduler @@ -819,11 +820,9 @@ def get_timesteps(scheduler, num_inference_steps, strength): t_start = max(num_inference_steps - init_timestep, 0) timesteps = scheduler.timesteps[t_start * scheduler.order :] return timesteps, num_inference_steps - t_start - # import pdb;pdb.set_trace() + self.scheduler.set_timesteps(num_inference_steps) - print("scheduler.timesteps: ", self.scheduler.timesteps) timesteps, num_inference_steps = get_timesteps(self.scheduler, num_inference_steps, strength) - # print(timesteps) latent_timestep = timesteps[:1] # Prepare latent variables @@ -838,55 +837,24 @@ def get_timesteps(scheduler, num_inference_steps, strength): # Denoising loop # self.scheduler.set_timesteps(num_inference_steps) - # self.scheduler.timesteps[-2] = 399 - # self.scheduler.timesteps[-1] = 109 timesteps = self.scheduler.timesteps do_classifier_free_guidance = guidance_scale > 1.0 # num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order extra_step_kwargs = {} - start_time = time.time() - - print(timesteps) - print(start_time) for i, t in tqdm(enumerate(timesteps)): # expand the latents if we are doing classifier free guidance - latent_model_input = torch.cat([latents]*2) if do_classifier_free_guidance else latents + latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) + controlnet_res = self.generate_zero_controlnet_data() + down_block_additional_residuals = controlnet_res[:-1] + mid_block_additional_residual = controlnet_res[-1] latent_model_input = latent_model_input.numpy() timestamp = np.array([t]) - if i == 0: - default_input_map = { - 0:{ - "data": latent_model_input, - "flag": 0 - }, - 1:{ - "data": timestamp, - "flag": 0 - }, - 2: { - "data": text_embeddings[-1], # prompt embedding, no negative - "flag": 0 - } - } - else: - default_input_map = { - 0:{ - "data": latent_model_input, - "flag": 0 - }, - 1:{ - "data": timestamp, - "flag": 0 - } - } - #noise_pred = self.unet([latent_model_input,timestamp,text_embeddings,mid_block_additional_residual,*down_block_additional_residuals])[0] + noise_pred = self.unet([latent_model_input,timestamp,text_embeddings,mid_block_additional_residual,*down_block_additional_residuals])[0] # perform guidance - noise_pred = self.unet.run_with_np(default_input_map)[0] if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = np.split(noise_pred, 2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) - # compute the previous noisy sample x_t -> x_t-1 noise_pred = torch.from_numpy(noise_pred) latents = self.scheduler.step(noise_pred, @@ -894,11 +862,9 @@ def get_timesteps(scheduler, num_inference_steps, strength): **extra_step_kwargs, return_dict=False, generator=generator)[0] - end_time = time.time() - print("time cost: ", end_time - start_time) latents = latents.numpy() - latents = latents / 0.18215 + latents = 1 / 0.18215 * latents image = self.vae_decoder({"input.1": latents.astype(np.float32)})[0] image = (image / 2 + 0.5).clip(0, 1) image = (image[0].transpose(1, 2, 0)* 255).astype(np.uint8) # RGB diff --git a/sd/pipelineXL.py b/sd/pipelineXL.py new file mode 100644 index 0000000..e059d78 --- /dev/null +++ b/sd/pipelineXL.py @@ -0,0 +1,706 @@ +import inspect + +import cv2 +from .npuengine import EngineOV +from .scheduler import diffusers_scheduler_config +import numpy as np +import torch +from transformers import CLIPTokenizer +from tqdm import tqdm + +from diffusers.schedulers import EulerDiscreteScheduler +from diffusers.image_processor import VaeImageProcessor +from diffusers.loaders import LoraLoaderMixin, TextualInversionLoaderMixin +from diffusers.utils import ( + logging +) +randn_tensor = lambda *x, **kwargs: torch.randn(*x, **kwargs) +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + +class StableDiffusionXLPipeline(): + def __init__( + self, + vae_encoder_path, + vae_decoder_path, + text_encoder_path, + text_encoder_2_path, + tokenizer_path, + tokenizer_2_path, + unet_path, + is_refine=False, + device_id=0 + ): + # Tokenizer1, 2 load + self.tokenizer = CLIPTokenizer.from_pretrained(tokenizer_path) + self.tokenizer_2 = CLIPTokenizer.from_pretrained(tokenizer_2_path) + self.device_id = device_id + # Unet load + self.unet = EngineOV(unet_path, device_id=self.device_id) + + # VAE load + self.vae_decoder = EngineOV(vae_decoder_path, device_id=self.device_id) + self.vae_encoder = EngineOV(vae_encoder_path, device_id=self.device_id) + + # Text_Encoder1, 2, load (no need to use encoder1 in refine net) + self.text_encoder = None if is_refine else EngineOV(text_encoder_path, device_id=self.device_id) + self.text_encoder_2 = EngineOV(text_encoder_2_path, device_id=self.device_id) + + # Scheculer + self.scheduler = EulerDiscreteScheduler(**(diffusers_scheduler_config['Euler D'])) + + # other config + self.is_refiner = is_refine + self.config_force_zeros_for_empty_prompt = False + self.config_requires_aesthetics_score = is_refine + self.unet_add_embedding_linear_1_in_features = 2560 if is_refine else 2816 + self.text_encoder_2_dtype = torch.float16 + self.prompt_embeds_dtype = torch.float16 + self.vae_config_scaling_factor = 0.13025 + self.unet_config_in_channels = 4 + self.unet_config_addition_time_embed_dim = 256 + self.text_encoder_2_config_projection_dim = 1280 + self.vae_scale_factor = 8 + self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) + self._execution_device = 'cpu' + + + def ch_unet(self, unet_path, te1_path='./xl_models/text_encoder_1684x_f32.bmodel'): + if self.is_refiner == (unet_path.find('refine') != -1): + return + self.is_refiner = (unet_path.find('refine') != -1) + # Unet load + del self.unet + self.unet = EngineOV(unet_path, device_id=self.device_id) + self.config_requires_aesthetics_score = self.is_refiner + self.unet_add_embedding_linear_1_in_features = 2560 if self.is_refiner else 2816 + if self.is_refiner: + del self.text_encoder + self.text_encoder = None + else: + self.text_encoder = EngineOV(te1_path, device_id=self.device_id) + + + def _preprocess_image(self, image): + if image.mode != "RGB": + image = image.convert("RGB") # RGBA or other -> RGB + image = np.array(image) + h, w = image.shape[:-1] + if h != 1024 or w != 1024: + image = cv2.resize( + image, + (1024,1024), + interpolation=cv2.INTER_LANCZOS4 + ) + # normalize + image = image.astype(np.float32) / 255.0 + image = 2.0 * image - 1.0 + # to batch + image = image[None].transpose(0, 3, 1, 2) + return image + + def _encode_image(self, init_image): + moments = self.vae_encoder({ + "input.1": self._preprocess_image(init_image) + })[0] + mean, logvar = np.split(moments, 2, axis=1) + logvar = np.clip(logvar, -30.0, 20.0) + std = np.exp(logvar * 0.5) + latent = (mean + std * np.random.randn(*mean.shape)) * self.vae_config_scaling_factor + return torch.from_numpy(latent) + + # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.encode_prompt + def encode_prompt( + self, + prompt, + prompt_2 = None, + device = None, + num_images_per_prompt = 1, + do_classifier_free_guidance = True, + negative_prompt = None, + negative_prompt_2 = None, + prompt_embeds = None, + negative_prompt_embeds = None, + pooled_prompt_embeds = None, + negative_pooled_prompt_embeds = None, + lora_scale = None, + ): + device = device or self._execution_device + + # set lora scale so that monkey patched LoRA + # function of text encoder can correctly access it + if lora_scale is not None and isinstance(self, LoraLoaderMixin): + self._lora_scale = lora_scale + + if prompt is not None and isinstance(prompt, str): + batch_size = 1 + elif prompt is not None and isinstance(prompt, list): + batch_size = len(prompt) + else: + batch_size = prompt_embeds.shape[0] + + # Define tokenizers and text encoders + tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2] + text_encoders = ( + [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2] + ) + + if prompt_embeds is None: + prompt_2 = prompt_2 or prompt + # textual inversion: procecss multi-vector tokens if necessary + prompt_embeds_list = [] + prompts = [prompt, prompt_2] + + for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders): + if isinstance(self, TextualInversionLoaderMixin): + prompt = self.maybe_convert_prompt(prompt, tokenizer) + text_inputs = tokenizer( + prompt, + padding="max_length", + max_length=tokenizer.model_max_length, + truncation=True, + return_tensors="pt", + ) + + text_input_ids = text_inputs.input_ids + untruncated_ids = tokenizer(prompt, padding="longest", return_tensors="pt").input_ids + + if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( + text_input_ids, untruncated_ids + ): + removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1]) + logger.warning( + "The following part of your input was truncated because CLIP can only handle sequences up to" + f" {tokenizer.model_max_length} tokens: {removed_text}" + ) + + prompt_embeds_data = text_encoder({"prompt_tokens":text_input_ids.numpy()}) + + # We are only ALWAYS interested in the pooled output of the final text encoder + + pooled_prompt_embeds = torch.tensor(prompt_embeds_data[0]) + prompt_embeds = prompt_embeds_data[1] + + prompt_embeds_list.append(torch.tensor(prompt_embeds)) + prompt_embeds = torch.concat(prompt_embeds_list, dim=-1) + + # get unconditional embeddings for classifier free guidance + zero_out_negative_prompt = negative_prompt is None and self.config_force_zeros_for_empty_prompt + if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt: + negative_prompt_embeds = torch.zeros_like(prompt_embeds) + negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds) + elif do_classifier_free_guidance and negative_prompt_embeds is None: + negative_prompt = negative_prompt or "" + negative_prompt_2 = negative_prompt_2 or negative_prompt + + # uncond_tokens: List[str] + if prompt is not None and type(prompt) is not type(negative_prompt): + raise TypeError( + f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" + f" {type(prompt)}." + ) + elif isinstance(negative_prompt, str): + uncond_tokens = [negative_prompt, negative_prompt_2] + elif batch_size != len(negative_prompt): + raise ValueError( + f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" + f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" + " the batch size of `prompt`." + ) + else: + uncond_tokens = [negative_prompt, negative_prompt_2] + + negative_prompt_embeds_list = [] + + for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders): + if isinstance(self, TextualInversionLoaderMixin): + negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer) + + max_length = prompt_embeds.shape[1] + uncond_input = tokenizer( + negative_prompt, + padding="max_length", + max_length=max_length, + truncation=True, + return_tensors="pt", + ) + + negative_prompt_embeds = text_encoder({"prompt_tokens":uncond_input.input_ids.numpy()}) #text_encoder.run(input_node, {"prompt_tokens":uncond_input.input_ids.numpy()}) + # We are only ALWAYS interested in the pooled output of the final text encoder + negative_pooled_prompt_embeds = torch.tensor(negative_prompt_embeds[0]) + negative_prompt_embeds = negative_prompt_embeds[1] + + negative_prompt_embeds_list.append(torch.tensor(negative_prompt_embeds)) + + negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1) + + prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2_dtype, device=device) + bs_embed, seq_len, _ = prompt_embeds.shape + + # duplicate text embeddings for each generation per prompt, using mps friendly method + prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) + prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) + + if do_classifier_free_guidance: + # duplicate unconditional embeddings for each generation per prompt, using mps friendly method + seq_len = negative_prompt_embeds.shape[1] + negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2_dtype, device=device) + negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) + negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) + + pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view( + bs_embed * num_images_per_prompt, -1 + ) + if do_classifier_free_guidance: + negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view( + bs_embed * num_images_per_prompt, -1 + ) + + return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds + + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs + def prepare_extra_step_kwargs(self, generator, eta): + # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature + # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. + # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 + # and should be between [0, 1] + + accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) + extra_step_kwargs = {} + if accepts_eta: + extra_step_kwargs["eta"] = eta + + # check if the scheduler accepts generator + accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) + if accepts_generator: + extra_step_kwargs["generator"] = generator + return extra_step_kwargs + + def check_inputs( + self, + prompt, + prompt_2, + strength, + num_inference_steps, + callback_steps, + negative_prompt=None, + negative_prompt_2=None, + prompt_embeds=None, + negative_prompt_embeds=None, + ): + if strength < 0 or strength > 1: + raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}") + if num_inference_steps is None: + raise ValueError("`num_inference_steps` cannot be None.") + elif not isinstance(num_inference_steps, int) or num_inference_steps <= 0: + raise ValueError( + f"`num_inference_steps` has to be a positive integer but is {num_inference_steps} of type" + f" {type(num_inference_steps)}." + ) + if (callback_steps is None) or ( + callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) + ): + raise ValueError( + f"`callback_steps` has to be a positive integer but is {callback_steps} of type" + f" {type(callback_steps)}." + ) + + if prompt is not None and prompt_embeds is not None: + raise ValueError( + f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" + " only forward one of the two." + ) + elif prompt_2 is not None and prompt_embeds is not None: + raise ValueError( + f"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to" + " only forward one of the two." + ) + elif prompt is None and prompt_embeds is None: + raise ValueError( + "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." + ) + elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): + raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") + elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)): + raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}") + + if negative_prompt is not None and negative_prompt_embeds is not None: + raise ValueError( + f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" + f" {negative_prompt_embeds}. Please make sure to only forward one of the two." + ) + elif negative_prompt_2 is not None and negative_prompt_embeds is not None: + raise ValueError( + f"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:" + f" {negative_prompt_embeds}. Please make sure to only forward one of the two." + ) + + if prompt_embeds is not None and negative_prompt_embeds is not None: + if prompt_embeds.shape != negative_prompt_embeds.shape: + raise ValueError( + "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" + f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" + f" {negative_prompt_embeds.shape}." + ) + + def get_timesteps(self, num_inference_steps, strength, device, denoising_start=None): + # get the original timestep using init_timestep + if denoising_start is None: + init_timestep = min(int(num_inference_steps * strength), num_inference_steps) + t_start = max(num_inference_steps - init_timestep, 0) + else: + t_start = 0 + + timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :] + + # Strength is irrelevant if we directly request a timestep to start at; + # that is, strength is determined by the denoising_start instead. + if denoising_start is not None: + discrete_timestep_cutoff = int( + round( + self.scheduler.config.num_train_timesteps + - (denoising_start * self.scheduler.config.num_train_timesteps) + ) + ) + timesteps = list(filter(lambda ts: ts < discrete_timestep_cutoff, timesteps)) + return torch.tensor(timesteps), len(timesteps) + + return timesteps, num_inference_steps - t_start + + def prepare_rand_latents(self, batch_size, num_channels_latents, height, width, dtype, generator): + shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor) + if isinstance(generator, list) and len(generator) != batch_size: + raise ValueError( + f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" + f" size of {batch_size}. Make sure the batch size matches the length of the generators." + ) + latents = randn_tensor(shape, generator=generator, dtype=dtype) + # scale the initial noise by the standard deviation required by the scheduler + latents = latents * self.scheduler.init_noise_sigma + return latents + + def prepare_latents( + self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None, add_noise=True + ): + + batch_size = batch_size * num_images_per_prompt + init_latents = self._encode_image(image) + init_latents = init_latents.to(dtype) + + if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0: + # expand init_latents for batch_size + additional_image_per_prompt = batch_size // init_latents.shape[0] + init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0) + elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0: + raise ValueError( + f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts." + ) + else: + init_latents = torch.cat([init_latents], dim=0) + + if add_noise: + shape = init_latents.shape + noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype) + # get latents + init_latents = self.scheduler.add_noise(init_latents, noise, timestep) + + latents = init_latents + + return latents + + def _get_add_time_ids( + self, original_size, crops_coords_top_left, target_size, aesthetic_score, negative_aesthetic_score, dtype + ): + if self.config_requires_aesthetics_score: + add_time_ids = list(original_size + crops_coords_top_left + (aesthetic_score,)) + add_neg_time_ids = list(original_size + crops_coords_top_left + (negative_aesthetic_score,)) + else: + add_time_ids = list(original_size + crops_coords_top_left + target_size) + add_neg_time_ids = list(original_size + crops_coords_top_left + target_size) + + passed_add_embed_dim = ( + self.unet_config_addition_time_embed_dim * len(add_time_ids) + self.text_encoder_2_config_projection_dim + ) + expected_add_embed_dim = self.unet_add_embedding_linear_1_in_features + + if ( + expected_add_embed_dim > passed_add_embed_dim + and (expected_add_embed_dim - passed_add_embed_dim) == self.unet_config_addition_time_embed_dim + ): + raise ValueError( + f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. Please make sure to enable `requires_aesthetics_score` with `pipe.register_to_config(requires_aesthetics_score=True)` to make sure `aesthetic_score` {aesthetic_score} and `negative_aesthetic_score` {negative_aesthetic_score} is correctly used by the model." + ) + elif ( + expected_add_embed_dim < passed_add_embed_dim + and (passed_add_embed_dim - expected_add_embed_dim) == self.unet_config_addition_time_embed_dim + ): + raise ValueError( + f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. Please make sure to disable `requires_aesthetics_score` with `pipe.register_to_config(requires_aesthetics_score=False)` to make sure `target_size` {target_size} is correctly used by the model." + ) + elif expected_add_embed_dim != passed_add_embed_dim: + raise ValueError( + f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`." + ) + + add_time_ids = torch.tensor([add_time_ids], dtype=dtype) + add_neg_time_ids = torch.tensor([add_neg_time_ids], dtype=dtype) + + return add_time_ids, add_neg_time_ids + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae + @torch.no_grad() + def __call__( + self, + prompt = None, + prompt_2 = None, + init_image = None, + strength = 0.7, + height = 1024, + width = 1024, + num_inference_steps = 50, + denoising_start = None, + denoising_end = None, + guidance_scale = 5.0, + negative_prompt = None, + negative_prompt_2 = None, + num_images_per_prompt = 1, + eta = 0.0, + generator = None, + latents = None, + prompt_embeds = None, + negative_prompt_embeds = None, + pooled_prompt_embeds = None, + negative_pooled_prompt_embeds = None, + output_type = "pil", + callback = None, + callback_steps = 1, + cross_attention_kwargs = None, + original_size = None, + crops_coords_top_left = (0, 0), + target_size = None, + aesthetic_score = 6.0, + negative_aesthetic_score = 2.5, + ): + # 1. Check inputs. Raise error if not correct + self.check_inputs( + prompt, + prompt_2, + strength, + num_inference_steps, + callback_steps, + negative_prompt, + negative_prompt_2, + prompt_embeds, + negative_prompt_embeds, + ) + # 2. Define call parameters + if prompt is not None and isinstance(prompt, str): + batch_size = 1 + elif prompt is not None and isinstance(prompt, list): + batch_size = len(prompt) + else: + batch_size = prompt_embeds.shape[0] + + device = self._execution_device + + # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) + # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` + # corresponds to doing no classifier free guidance. + do_classifier_free_guidance = guidance_scale > 1.0 + # 3. Encode input prompt + text_encoder_lora_scale = ( + cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None + ) + ( + prompt_embeds, + negative_prompt_embeds, + pooled_prompt_embeds, + negative_pooled_prompt_embeds, + ) = self.encode_prompt( + prompt=prompt, + prompt_2=prompt_2, + device=device, + num_images_per_prompt=num_images_per_prompt, + do_classifier_free_guidance=do_classifier_free_guidance, + negative_prompt=negative_prompt, + negative_prompt_2=negative_prompt_2, + prompt_embeds=prompt_embeds, + negative_prompt_embeds=negative_prompt_embeds, + pooled_prompt_embeds=pooled_prompt_embeds, + negative_pooled_prompt_embeds=negative_pooled_prompt_embeds, + lora_scale=text_encoder_lora_scale, + ) + + # 4. Preprocess images + # image = self._preprocess_image(image) + + # 5. Prepare timesteps and latent variables + def denoising_value_valid(dnv): + return type(denoising_end) == float and 0 < dnv < 1 + + self.scheduler.set_timesteps(num_inference_steps, device=device) + + if init_image is None: + timesteps = self.scheduler.timesteps + num_channels_latents = self.unet_config_in_channels + latents = self.prepare_rand_latents( + batch_size * num_images_per_prompt, + num_channels_latents, + height, + width, + prompt_embeds.dtype, + generator, + ) + else: + timesteps, num_inference_steps = self.get_timesteps( + num_inference_steps, strength, device, denoising_start=denoising_start if denoising_value_valid else None) + + latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt) + add_noise = True if denoising_start is None else False + latents = self.prepare_latents( + init_image, + latent_timestep, + batch_size, + num_images_per_prompt, + prompt_embeds.dtype, + device, + generator, + add_noise, + ) + # 7. Prepare extra step kwargs. + extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) + + # height, width = latents.shape[-2:] + # height = height * self.vae_scale_factor + # width = width * self.vae_scale_factor + # original_size = original_size or (height, width) + # target_size = target_size or (height, width) + original_size = (height, width) + target_size = (height, width) + + # 8. Prepare added time ids & embeddings + add_text_embeds = pooled_prompt_embeds + add_time_ids, add_neg_time_ids = self._get_add_time_ids( + original_size, + crops_coords_top_left, + target_size, + aesthetic_score, + negative_aesthetic_score, + dtype=prompt_embeds.dtype, + ) + add_time_ids = add_time_ids.repeat(batch_size * num_images_per_prompt, 1) + + if do_classifier_free_guidance: + prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0) + add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0) + if init_image is not None: + add_neg_time_ids = add_neg_time_ids.repeat(batch_size * num_images_per_prompt, 1) + add_time_ids = torch.cat([add_neg_time_ids, add_time_ids], dim=0) + else: + add_time_ids = torch.cat([add_time_ids, add_time_ids], dim=0) + + prompt_embeds = prompt_embeds.to(device) + add_text_embeds = add_text_embeds.to(device) + add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1) + # 9. Denoising loop + + num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0) + + # 9.1 Apply denoising_end + if ( + denoising_end is not None + and denoising_start is not None + and denoising_value_valid(denoising_end) + and denoising_value_valid(denoising_start) + and denoising_start >= denoising_end + ): + raise ValueError( + f"`denoising_start`: {denoising_start} cannot be larger than or equal to `denoising_end`: " + + f" {denoising_end} when using type float." + ) + elif denoising_end is not None and denoising_value_valid(denoising_end): + discrete_timestep_cutoff = int( + round( + self.scheduler.config.num_train_timesteps + - (denoising_end * self.scheduler.config.num_train_timesteps) + ) + ) + num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps))) + timesteps = timesteps[:num_inference_steps] + + for i, t in enumerate(timesteps): + # expand the latents if we are doing classifier free guidance + latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents + + latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) + timestamp = np.array([t]) + # predict the noise residual + noise_pred = self.unet([ + latent_model_input, + timestamp, + prompt_embeds, + add_text_embeds, + add_time_ids] + )[0] + + # perform guidance + if do_classifier_free_guidance: + noise_pred_uncond, noise_pred_text = np.array_split(noise_pred, 2) + noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) + + # compute the previous noisy sample x_t -> x_t-1 + latents = self.scheduler.step(torch.from_numpy(noise_pred), t, latents, **extra_step_kwargs, return_dict=False)[0] + + # call the callback, if provided + if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): + if callback is not None and i % callback_steps == 0: + callback(i, t, latents) + # del self.unet + # del self.vae_encoder + + latents = latents.to(torch.float32) + if not output_type == "latent": + init_image = torch.from_numpy(self.vae_decoder([latents / self.vae_config_scaling_factor])[0][0]) + else: + return latents + if len(init_image.shape) == 3: + init_image = init_image.unsqueeze(0) + init_image = self.image_processor.postprocess(init_image, output_type=output_type) + return init_image[0] + + +if __name__ == "__main__": + + from PIL import Image + scheduler_dict = { + "_class_name": "EulerDiscreteScheduler", + "_diffusers_version": "0.20.0.dev0", + "beta_end": 0.012, + "beta_schedule": "scaled_linear", + "beta_start": 0.00085, + "interpolation_type": "linear", + "num_train_timesteps": 1000, + "prediction_type": "epsilon", + "steps_offset": 1, + "timestep_spacing": "leading" + } + scheduler = EulerDiscreteScheduler(**scheduler_dict) + + pipe = StableDiffusionXLPipeline( + vae_encoder_path='./xl_models/vae_encoder_fp32_1684x_bf16.bmodel', # vae_encoder_1684x_bf16.bmodel', + vae_decoder_path='./xl_models/vae_decoder_1684x_bf16_2.bmodel', + text_encoder_path='./xl_models/text_encoder_1684x_f32.bmodel', + text_encoder_2_path='./xl_models/text_encoder_2_1684x_f32.bmodel', + tokenizer_path='./xl_models/tokenizer', + tokenizer_2_path='./xl_models/tokenizer_2', + unet_path='./xl_models/unet.bmodel') + + prompt = "happy young girl, cute, 8K, HD" + image0 = pipe(prompt, + num_inference_steps = 20) + image0.save('girl_txt2img.png') + + image = pipe(prompt + ", blue hair", + image=Image.open('my_eyes.png'), + num_inference_steps = 20) + image.save('girl_img2img.png') diff --git a/sd/pipelinexl2device.py b/sd/pipelinexl2device.py new file mode 100644 index 0000000..a991887 --- /dev/null +++ b/sd/pipelinexl2device.py @@ -0,0 +1,773 @@ +import inspect + +import cv2 +from .npuengine import EngineOV +from .scheduler import diffusers_scheduler_config +import numpy as np +import torch +from transformers import CLIPTokenizer +from tqdm import tqdm +import threading +from diffusers.schedulers import EulerDiscreteScheduler +from diffusers.image_processor import VaeImageProcessor +from diffusers.loaders import LoraLoaderMixin, TextualInversionLoaderMixin +from diffusers.utils import ( + logging +) +import random +import time +import os +from multiprocessing import Process +import multiprocessing +def seed_torch(seed=1029): + seed=seed%4294967296 + random.seed(seed) + os.environ['PYTHONHASHSEED'] = str(seed) # 为了禁止hash随机化,使得实验可复现 + np.random.seed(seed) + torch.manual_seed(seed) + print("set seed to:", seed) + +#seed_torch() +randn_tensor = lambda *x, **kwargs: torch.randn(*x, **kwargs) +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +def parallel_manager(pipe,model_path, device_id): + model = EngineOV(model_path, device_id=device_id) + while True: + message = pipe.recv() + if message['state'] == 1: + data = message['data'] + print("inner start ",time.time()) + res = model(data) + print("inner end ",time.time()) + pipe.send({"from_sub":res}) + elif message['state'] == 2: + break + del model + + +class SDXL2Parallel(): + def __init__( + self, + vae_encoder_path, + vae_decoder_path, + text_encoder_path, + text_encoder_2_path, + tokenizer_path, + tokenizer_2_path, + unet_path, + is_refine=False, + device_id=0, + parallel=False + ): + # Tokenizer1, 2 load + self.tokenizer = CLIPTokenizer.from_pretrained(tokenizer_path) + self.tokenizer_2 = CLIPTokenizer.from_pretrained(tokenizer_2_path) + self.device_id = device_id + self.parallel = parallel + # Unet load + if self.parallel == True: + parent_pipe, child_pipe = multiprocessing.Pipe() + self.child_pipe = child_pipe + self.parent_pipe= parent_pipe + process = multiprocessing.Process(target=parallel_manager, args=(child_pipe, unet_path, device_id,)) + process.start() + self.process = process + # warmup + self.parent_pipe.send({"state":10,"data":np.random.rand(50,60,70)}) + + parent_pipe2, child_pipe2 = multiprocessing.Pipe() + self.child_pipe2 = child_pipe2 + self.parent_pipe2= parent_pipe2 + process2 = multiprocessing.Process(target=parallel_manager, args=(child_pipe2, unet_path,device_id+1,)) + process2.start() + self.process2 = process2 + self.parent_pipe2.send({"state":10,"data":np.random.rand(40,50,60,7)}) + + # VAE load + self.vae_decoder = EngineOV(vae_decoder_path, device_id=self.device_id) + self.vae_encoder = EngineOV(vae_encoder_path, device_id=self.device_id) + + # Text_Encoder1, 2, load (no need to use encoder1 in refine net) + self.text_encoder = None if is_refine else EngineOV(text_encoder_path, device_id=self.device_id) + self.text_encoder_2 = EngineOV(text_encoder_2_path, device_id=self.device_id) + + # Scheculer + self.scheduler = EulerDiscreteScheduler(**(diffusers_scheduler_config['Euler D'])) + + # other config + self.is_refiner = is_refine + self.config_force_zeros_for_empty_prompt = False + self.config_requires_aesthetics_score = is_refine + self.unet_add_embedding_linear_1_in_features = 2560 if is_refine else 2816 + self.text_encoder_2_dtype = torch.float16 + self.prompt_embeds_dtype = torch.float16 + self.vae_config_scaling_factor = 0.13025 + self.unet_config_in_channels = 4 + self.unet_config_addition_time_embed_dim = 256 + self.text_encoder_2_config_projection_dim = 1280 + self.vae_scale_factor = 8 + self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) + self._execution_device = 'cpu' + + def set_height_width(self, height, width): + pass + + def _preprocess_image(self, image): + if image.mode != "RGB": + image = image.convert("RGB") # RGBA or other -> RGB + image = np.array(image) + h, w = image.shape[:-1] + if h != 1024 or w != 1024: + image = cv2.resize( + image, + (1024,1024), + interpolation=cv2.INTER_LANCZOS4 + ) + # normalize + image = image.astype(np.float32) / 255.0 + image = 2.0 * image - 1.0 + # to batch + image = image[None].transpose(0, 3, 1, 2) + return image + + def _encode_image(self, init_image): + moments = self.vae_encoder({ + "input.1": self._preprocess_image(init_image) + })[0] + mean, logvar = np.split(moments, 2, axis=1) + logvar = np.clip(logvar, -30.0, 20.0) + std = np.exp(logvar * 0.5) + latent = (mean + std * np.random.randn(*mean.shape)) * self.vae_config_scaling_factor + return torch.from_numpy(latent) + + # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.encode_prompt + def encode_prompt( + self, + prompt, + prompt_2 = None, + device = None, + num_images_per_prompt = 1, + do_classifier_free_guidance = True, + negative_prompt = None, + negative_prompt_2 = None, + prompt_embeds = None, + negative_prompt_embeds = None, + pooled_prompt_embeds = None, + negative_pooled_prompt_embeds = None, + lora_scale = None, + ): + device = device or self._execution_device + + # set lora scale so that monkey patched LoRA + # function of text encoder can correctly access it + if lora_scale is not None and isinstance(self, LoraLoaderMixin): + self._lora_scale = lora_scale + + if prompt is not None and isinstance(prompt, str): + batch_size = 1 + elif prompt is not None and isinstance(prompt, list): + batch_size = len(prompt) + else: + batch_size = prompt_embeds.shape[0] + + # Define tokenizers and text encoders + tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2] + text_encoders = ( + [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2] + ) + + if prompt_embeds is None: + prompt_2 = prompt_2 or prompt + # textual inversion: procecss multi-vector tokens if necessary + prompt_embeds_list = [] + prompts = [prompt, prompt_2] + + for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders): + if isinstance(self, TextualInversionLoaderMixin): + prompt = self.maybe_convert_prompt(prompt, tokenizer) + text_inputs = tokenizer( + prompt, + padding="max_length", + max_length=tokenizer.model_max_length, + truncation=True, + return_tensors="pt", + ) + + text_input_ids = text_inputs.input_ids + untruncated_ids = tokenizer(prompt, padding="longest", return_tensors="pt").input_ids + + if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( + text_input_ids, untruncated_ids + ): + removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1]) + logger.warning( + "The following part of your input was truncated because CLIP can only handle sequences up to" + f" {tokenizer.model_max_length} tokens: {removed_text}" + ) + + prompt_embeds_data = text_encoder({"prompt_tokens":text_input_ids.numpy()}) + + # We are only ALWAYS interested in the pooled output of the final text encoder + + pooled_prompt_embeds = torch.tensor(prompt_embeds_data[0]) + prompt_embeds = prompt_embeds_data[1] + + prompt_embeds_list.append(torch.tensor(prompt_embeds)) + prompt_embeds = torch.concat(prompt_embeds_list, dim=-1) + + # get unconditional embeddings for classifier free guidance + zero_out_negative_prompt = negative_prompt is None and self.config_force_zeros_for_empty_prompt + if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt: + negative_prompt_embeds = torch.zeros_like(prompt_embeds) + negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds) + elif do_classifier_free_guidance and negative_prompt_embeds is None: + negative_prompt = negative_prompt or "" + negative_prompt_2 = negative_prompt_2 or negative_prompt + + # uncond_tokens: List[str] + if prompt is not None and type(prompt) is not type(negative_prompt): + raise TypeError( + f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" + f" {type(prompt)}." + ) + elif isinstance(negative_prompt, str): + uncond_tokens = [negative_prompt, negative_prompt_2] + elif batch_size != len(negative_prompt): + raise ValueError( + f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" + f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" + " the batch size of `prompt`." + ) + else: + uncond_tokens = [negative_prompt, negative_prompt_2] + + negative_prompt_embeds_list = [] + + for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders): + if isinstance(self, TextualInversionLoaderMixin): + negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer) + + max_length = prompt_embeds.shape[1] + uncond_input = tokenizer( + negative_prompt, + padding="max_length", + max_length=max_length, + truncation=True, + return_tensors="pt", + ) + + negative_prompt_embeds = text_encoder({"prompt_tokens":uncond_input.input_ids.numpy()}) #text_encoder.run(input_node, {"prompt_tokens":uncond_input.input_ids.numpy()}) + # We are only ALWAYS interested in the pooled output of the final text encoder + negative_pooled_prompt_embeds = torch.tensor(negative_prompt_embeds[0]) + negative_prompt_embeds = negative_prompt_embeds[1] + + negative_prompt_embeds_list.append(torch.tensor(negative_prompt_embeds)) + + negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1) + + prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2_dtype, device=device) + bs_embed, seq_len, _ = prompt_embeds.shape + + # duplicate text embeddings for each generation per prompt, using mps friendly method + prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) + prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) + + if do_classifier_free_guidance: + # duplicate unconditional embeddings for each generation per prompt, using mps friendly method + seq_len = negative_prompt_embeds.shape[1] + negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2_dtype, device=device) + negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) + negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) + + pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view( + bs_embed * num_images_per_prompt, -1 + ) + if do_classifier_free_guidance: + negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view( + bs_embed * num_images_per_prompt, -1 + ) + + return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds + + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs + def prepare_extra_step_kwargs(self, generator, eta): + # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature + # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. + # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 + # and should be between [0, 1] + + accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) + extra_step_kwargs = {} + if accepts_eta: + extra_step_kwargs["eta"] = eta + + # check if the scheduler accepts generator + accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) + if accepts_generator: + extra_step_kwargs["generator"] = generator + return extra_step_kwargs + + def check_inputs( + self, + prompt, + prompt_2, + strength, + num_inference_steps, + callback_steps, + negative_prompt=None, + negative_prompt_2=None, + prompt_embeds=None, + negative_prompt_embeds=None, + ): + if strength < 0 or strength > 1: + raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}") + if num_inference_steps is None: + raise ValueError("`num_inference_steps` cannot be None.") + elif not isinstance(num_inference_steps, int) or num_inference_steps <= 0: + raise ValueError( + f"`num_inference_steps` has to be a positive integer but is {num_inference_steps} of type" + f" {type(num_inference_steps)}." + ) + if (callback_steps is None) or ( + callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) + ): + raise ValueError( + f"`callback_steps` has to be a positive integer but is {callback_steps} of type" + f" {type(callback_steps)}." + ) + + if prompt is not None and prompt_embeds is not None: + raise ValueError( + f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" + " only forward one of the two." + ) + elif prompt_2 is not None and prompt_embeds is not None: + raise ValueError( + f"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to" + " only forward one of the two." + ) + elif prompt is None and prompt_embeds is None: + raise ValueError( + "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." + ) + elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): + raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") + elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)): + raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}") + + if negative_prompt is not None and negative_prompt_embeds is not None: + raise ValueError( + f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" + f" {negative_prompt_embeds}. Please make sure to only forward one of the two." + ) + elif negative_prompt_2 is not None and negative_prompt_embeds is not None: + raise ValueError( + f"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:" + f" {negative_prompt_embeds}. Please make sure to only forward one of the two." + ) + + if prompt_embeds is not None and negative_prompt_embeds is not None: + if prompt_embeds.shape != negative_prompt_embeds.shape: + raise ValueError( + "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" + f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" + f" {negative_prompt_embeds.shape}." + ) + + def get_timesteps(self, num_inference_steps, strength, device, denoising_start=None): + # get the original timestep using init_timestep + if denoising_start is None: + init_timestep = min(int(num_inference_steps * strength), num_inference_steps) + t_start = max(num_inference_steps - init_timestep, 0) + else: + t_start = 0 + + timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :] + + # Strength is irrelevant if we directly request a timestep to start at; + # that is, strength is determined by the denoising_start instead. + if denoising_start is not None: + discrete_timestep_cutoff = int( + round( + self.scheduler.config.num_train_timesteps + - (denoising_start * self.scheduler.config.num_train_timesteps) + ) + ) + timesteps = list(filter(lambda ts: ts < discrete_timestep_cutoff, timesteps)) + return torch.tensor(timesteps), len(timesteps) + + return timesteps, num_inference_steps - t_start + + def prepare_rand_latents(self, batch_size, num_channels_latents, height, width, dtype, generator): + shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor) + if isinstance(generator, list) and len(generator) != batch_size: + raise ValueError( + f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" + f" size of {batch_size}. Make sure the batch size matches the length of the generators." + ) + latents = randn_tensor(shape, generator=generator, dtype=dtype) + # scale the initial noise by the standard deviation required by the scheduler + latents = latents * self.scheduler.init_noise_sigma + return latents + + def prepare_latents( + self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None, add_noise=True + ): + + batch_size = batch_size * num_images_per_prompt + init_latents = self._encode_image(image) + init_latents = init_latents.to(dtype) + + if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0: + # expand init_latents for batch_size + additional_image_per_prompt = batch_size // init_latents.shape[0] + init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0) + elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0: + raise ValueError( + f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts." + ) + else: + init_latents = torch.cat([init_latents], dim=0) + + if add_noise: + shape = init_latents.shape + noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype) + # get latents + init_latents = self.scheduler.add_noise(init_latents, noise, timestep) + + latents = init_latents + + return latents + + def _get_add_time_ids( + self, original_size, crops_coords_top_left, target_size, aesthetic_score, negative_aesthetic_score, dtype + ): + if self.config_requires_aesthetics_score: + add_time_ids = list(original_size + crops_coords_top_left + (aesthetic_score,)) + add_neg_time_ids = list(original_size + crops_coords_top_left + (negative_aesthetic_score,)) + else: + add_time_ids = list(original_size + crops_coords_top_left + target_size) + add_neg_time_ids = list(original_size + crops_coords_top_left + target_size) + + passed_add_embed_dim = ( + self.unet_config_addition_time_embed_dim * len(add_time_ids) + self.text_encoder_2_config_projection_dim + ) + expected_add_embed_dim = self.unet_add_embedding_linear_1_in_features + + if ( + expected_add_embed_dim > passed_add_embed_dim + and (expected_add_embed_dim - passed_add_embed_dim) == self.unet_config_addition_time_embed_dim + ): + raise ValueError( + f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. Please make sure to enable `requires_aesthetics_score` with `pipe.register_to_config(requires_aesthetics_score=True)` to make sure `aesthetic_score` {aesthetic_score} and `negative_aesthetic_score` {negative_aesthetic_score} is correctly used by the model." + ) + elif ( + expected_add_embed_dim < passed_add_embed_dim + and (passed_add_embed_dim - expected_add_embed_dim) == self.unet_config_addition_time_embed_dim + ): + raise ValueError( + f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. Please make sure to disable `requires_aesthetics_score` with `pipe.register_to_config(requires_aesthetics_score=False)` to make sure `target_size` {target_size} is correctly used by the model." + ) + elif expected_add_embed_dim != passed_add_embed_dim: + raise ValueError( + f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`." + ) + + add_time_ids = torch.tensor([add_time_ids], dtype=dtype) + add_neg_time_ids = torch.tensor([add_neg_time_ids], dtype=dtype) + + return add_time_ids, add_neg_time_ids + + def run_unet1(self,inputs1,t:list): + # print time + res = self.unet(inputs1) + t.append(res) + + def run_unet2(self,inputs2, t:list): + res = self.unet2(inputs2) + t.append(res) + + def run_unet(self, inputs): + inputss = [] + for i in inputs: + inputss.append(torch.tensor(i)) + inputs1 = [ inputss[0][0].unsqueeze(0), inputss[1], inputss[2][0].unsqueeze(0), inputss[3][0].unsqueeze(0), inputss[4][0].unsqueeze(0) ] + inputs2 = [ inputss[0][1].unsqueeze(0), inputss[1], inputss[2][1].unsqueeze(0), inputss[3][1].unsqueeze(0), inputss[4][1].unsqueeze(0) ] + self.parent_pipe.send({"state":1, "data":inputs2}) + self.parent_pipe2.send({"state":1, "data":inputs1}) + res1 = self.parent_pipe2.recv() + res2 = self.parent_pipe.recv() + res2 = res2["from_sub"] + res1 = res1["from_sub"] + res = np.concatenate([res1[0], res2[0]], axis=0) + return [res] + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae + @torch.no_grad() + def __call__( + self, + prompt = None, + prompt_2 = None, + init_image = None, + strength = 0.7, + height = 1024, + width = 1024, + num_inference_steps = 50, + denoising_start = None, + denoising_end = None, + guidance_scale = 5.0, + negative_prompt = None, + negative_prompt_2 = None, + num_images_per_prompt = 1, + eta = 0.0, + generator = None, + latents = None, + prompt_embeds = None, + negative_prompt_embeds = None, + pooled_prompt_embeds = None, + negative_pooled_prompt_embeds = None, + output_type = "pil", + callback = None, + callback_steps = 1, + cross_attention_kwargs = None, + original_size = None, + crops_coords_top_left = (0, 0), + target_size = None, + aesthetic_score = 6.0, + negative_aesthetic_score = 2.5, + **kwargs, + ): + # 1. Check inputs. Raise error if not correct + self.check_inputs( + prompt, + prompt_2, + strength, + num_inference_steps, + callback_steps, + negative_prompt, + negative_prompt_2, + prompt_embeds, + negative_prompt_embeds, + ) + # 2. Define call parameters + if prompt is not None and isinstance(prompt, str): + batch_size = 1 + elif prompt is not None and isinstance(prompt, list): + batch_size = len(prompt) + else: + batch_size = prompt_embeds.shape[0] + + device = self._execution_device + + # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) + # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` + # corresponds to doing no classifier free guidance. + do_classifier_free_guidance = guidance_scale > 1.0 + # 3. Encode input prompt + text_encoder_lora_scale = ( + cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None + ) + ( + prompt_embeds, + negative_prompt_embeds, + pooled_prompt_embeds, + negative_pooled_prompt_embeds, + ) = self.encode_prompt( + prompt=prompt, + prompt_2=prompt_2, + device=device, + num_images_per_prompt=num_images_per_prompt, + do_classifier_free_guidance=do_classifier_free_guidance, + negative_prompt=negative_prompt, + negative_prompt_2=negative_prompt_2, + prompt_embeds=prompt_embeds, + negative_prompt_embeds=negative_prompt_embeds, + pooled_prompt_embeds=pooled_prompt_embeds, + negative_pooled_prompt_embeds=negative_pooled_prompt_embeds, + lora_scale=text_encoder_lora_scale, + ) + + # 4. Preprocess images + # image = self._preprocess_image(image) + + # 5. Prepare timesteps and latent variables + def denoising_value_valid(dnv): + return type(denoising_end) == float and 0 < dnv < 1 + + self.scheduler.set_timesteps(num_inference_steps, device=device) + + if init_image is None: + timesteps = self.scheduler.timesteps + num_channels_latents = self.unet_config_in_channels + latents = self.prepare_rand_latents( + batch_size * num_images_per_prompt, + num_channels_latents, + height, + width, + prompt_embeds.dtype, + generator, + ) + else: + timesteps, num_inference_steps = self.get_timesteps( + num_inference_steps, strength, device, denoising_start=denoising_start if denoising_value_valid else None) + + latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt) + add_noise = True if denoising_start is None else False + latents = self.prepare_latents( + init_image, + latent_timestep, + batch_size, + num_images_per_prompt, + prompt_embeds.dtype, + device, + generator, + add_noise, + ) + # 7. Prepare extra step kwargs. + extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) + + # height, width = latents.shape[-2:] + # height = height * self.vae_scale_factor + # width = width * self.vae_scale_factor + # original_size = original_size or (height, width) + # target_size = target_size or (height, width) + original_size = (height, width) + target_size = (height, width) + + # 8. Prepare added time ids & embeddings + add_text_embeds = pooled_prompt_embeds + add_time_ids, add_neg_time_ids = self._get_add_time_ids( + original_size, + crops_coords_top_left, + target_size, + aesthetic_score, + negative_aesthetic_score, + dtype=prompt_embeds.dtype, + ) + add_time_ids = add_time_ids.repeat(batch_size * num_images_per_prompt, 1) + + if do_classifier_free_guidance: + prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0) + add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0) + if init_image is not None: + add_neg_time_ids = add_neg_time_ids.repeat(batch_size * num_images_per_prompt, 1) + add_time_ids = torch.cat([add_neg_time_ids, add_time_ids], dim=0) + else: + add_time_ids = torch.cat([add_time_ids, add_time_ids], dim=0) + + prompt_embeds = prompt_embeds.to(device) + add_text_embeds = add_text_embeds.to(device) + add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1) + # 9. Denoising loop + + num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0) + + # 9.1 Apply denoising_end + if ( + denoising_end is not None + and denoising_start is not None + and denoising_value_valid(denoising_end) + and denoising_value_valid(denoising_start) + and denoising_start >= denoising_end + ): + raise ValueError( + f"`denoising_start`: {denoising_start} cannot be larger than or equal to `denoising_end`: " + + f" {denoising_end} when using type float." + ) + elif denoising_end is not None and denoising_value_valid(denoising_end): + discrete_timestep_cutoff = int( + round( + self.scheduler.config.num_train_timesteps + - (denoising_end * self.scheduler.config.num_train_timesteps) + ) + ) + num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps))) + timesteps = timesteps[:num_inference_steps] + + for i, t in enumerate(timesteps): + # expand the latents if we are doing classifier free guidance + latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents + + latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) + timestamp = np.array([t]) + # predict the noise residual + # import time + tm = time.time() + if self.parallel: + noise_pred = self.run_unet([ + latent_model_input, + timestamp, + prompt_embeds, + add_text_embeds, + add_time_ids] + )[0] + else: + noise_pred = self.unet([ + latent_model_input, + timestamp, + prompt_embeds, + add_text_embeds, + add_time_ids] + )[0] + # save noise + # perform guidance + if do_classifier_free_guidance: + noise_pred_uncond, noise_pred_text = np.array_split(noise_pred, 2) + noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) + + # compute the previous noisy sample x_t -> x_t-1 + latents = self.scheduler.step(torch.from_numpy(noise_pred), t, latents, **extra_step_kwargs, return_dict=False)[0] + + # call the callback, if provided + if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): + if callback is not None and i % callback_steps == 0: + callback(i, t, latents) + # del self.unet + # del self.vae_encoder + + + latents = latents.to(torch.float32) + if not output_type == "latent": + init_image = torch.from_numpy(self.vae_decoder([latents / self.vae_config_scaling_factor])[0][0]) + else: + return latents + if len(init_image.shape) == 3: + init_image = init_image.unsqueeze(0) + + # if self.parallel: + # self.parent_pipe.send({"state":2}) + # self.process.join() + # self.parent_pipe2.send({"state":2}) + # self.process2.join() + init_image = self.image_processor.postprocess(init_image, output_type=output_type) + return init_image[0] + + +if __name__ == "__main__": + + from PIL import Image + + pipe = StableDiffusionXLPipeline( + vae_encoder_path='/workspace/aa/sdxl/models/vae_encoder_fp32_1684x_bf16.bmodel', # vae_encoder_1684x_bf16.bmodel', + # vae_decoder_path='/workspace/aa/sdxl/models/vae_decoder_1684x_bf16_2.bmodel', + vae_decoder_path = "/workspace/zxh_workspace/xl_models/vae_decoder_taesdxl_float16_1024_1024.bmodel", + text_encoder_path='/workspace/aa/sdxl/models/text_encoder_1684x_f32.bmodel', + text_encoder_2_path='/workspace/aa/sdxl/models/text_encoder_2_1684x_f32.bmodel', + tokenizer_path='/workspace/aa/sdxl/models/tokenizer', + tokenizer_2_path='/workspace/aa/sdxl/models/tokenizer_2', + unet_path='/workspace/demos/onebatch/models/basic/unet.bmodel', + parallel=True + ) + + prompt = "happy young girl, cute, 8K, HD" + image0 = pipe(prompt, + num_inference_steps = 20) + image0.save('girl_txt2img2.png') + # 销毁 + # image = pipe(prompt + ", blue hair", + # image=Image.open('my_eyes.png'), + # num_inference_steps = 20) + # image.save('girl_img2img.png') diff --git a/sd/scheduler.py b/sd/scheduler.py index 8e3b126..a9ff2c0 100644 --- a/sd/scheduler.py +++ b/sd/scheduler.py @@ -911,7 +911,7 @@ def setup_img2img_steps(steps,denoising_strength): -def sample(steps, x, +def sample(steps, x, scheduler_name=None, guidance_scale=7, controlnet_weight=1.0 , diff --git a/sd/untool.py b/sd/untool.py index 49e8ac0..711cba9 100644 --- a/sd/untool.py +++ b/sd/untool.py @@ -12,9 +12,8 @@ MAX_CMD_GROUP_NUM = 64 MAX_STAGE_NUM = 64 -#so_path = ctypes.CDLL('/usr/local/untool/lib/libuntpu.so') -so_path = os.path.abspath(os.path.join(os.path.dirname(__file__), 'libuntpu.so')) -lib = ctypes.CDLL(so_path) +lib = ctypes.CDLL('/usr/local/untool/lib/libuntpu.so') + int_point = ctypes.POINTER(ctypes.c_int) int_ = ctypes.c_int ulonglong = ctypes.c_ulonglong @@ -203,16 +202,15 @@ class ModelInfoSC(ctypes.Structure): lib.find_optimal.restype = ctypes.POINTER(UntensorS) lib.find_optimal.argtypes = [ctypes.POINTER(UntensorS)] -def find_optimal(tensor) -> ctypes.POINTER(UntensorS): +def find_optimal(tensor): """ untensor find_optimal(untensor tensor); :param tensor: ctypes.POINTER(UntensorS) """ return lib.find_optimal(ctypes.byref(tensor)) -lib.copy_tensor.restype = None lib.copy_tensor.argtypes = [ctypes.POINTER(UntensorS), ctypes.POINTER(UntensorS)] -def copy_tensor(src, dst) -> None: +def copy_tensor(src, dst): """ void copy_tensor (untensor src, untensor dst); :param src: ctypes.POINTER(UntensorS) @@ -220,9 +218,8 @@ def copy_tensor(src, dst) -> None: """ return lib.copy_tensor(ctypes.byref(src), ctypes.byref(dst)) -lib.copy_tensor_to_device.restype = None lib.copy_tensor_to_device.argtypes = [ctypes.POINTER(UntensorS), ctypes.c_bool] -def copy_tensor_to_device(tensor, force) -> None: +def copy_tensor_to_device(tensor, force): """ void copy_tensor_to_device(untensor tensor, bool force); :param tensor: ctypes.POINTER(UntensorS) @@ -230,9 +227,8 @@ def copy_tensor_to_device(tensor, force) -> None: """ return lib.copy_tensor_to_device(ctypes.byref(tensor), force) -lib.set_data.restype = None lib.set_data.argtypes = [ctypes.POINTER(UntensorS), ctypes.c_void_p, ctypes.c_size_t, ctypes.c_bool] -def set_data(tensor, data, size, is_copy) -> None: +def set_data(tensor, data, size, is_copy): """ void set_data(untensor tensor, void* data, size_t size, bool is_copy); :param tensor: ctypes.POINTER(UntensorS) @@ -242,9 +238,8 @@ def set_data(tensor, data, size, is_copy) -> None: """ return lib.set_data(ctypes.byref(tensor), data, ctypes.c_size_t(size), is_copy) -lib.copy_tensor_to_host.restype = None lib.copy_tensor_to_host.argtypes = [ctypes.POINTER(UntensorS), ctypes.c_bool] -def copy_tensor_to_host(tensor, force) -> None: +def copy_tensor_to_host(tensor, force): """ void copy_tensor_to_host(untensor tensor, bool force); :param tensor: ctypes.POINTER(UntensorS) @@ -252,9 +247,8 @@ def copy_tensor_to_host(tensor, force) -> None: """ return lib.copy_tensor_to_host(ctypes.byref(tensor), force) -lib.show_device_data.restype = None lib.show_device_data.argtypes = [ctypes.POINTER(UntensorS), ctypes.c_int, ctypes.c_int] -def show_device_data(tensor, start, len) -> None: +def show_device_data(tensor, start, len): """ void show_device_data(untensor tensor, int start, int len); :param tensor: ctypes.POINTER(UntensorS) @@ -265,7 +259,7 @@ def show_device_data(tensor, start, len) -> None: lib.tensor_shape_is_same.restype = ctypes.c_bool lib.tensor_shape_is_same.argtypes = [ctypes.POINTER(UntensorS), ctypes.POINTER(UntensorS)] -def tensor_shape_is_same(tensor1, tensor2) -> ctypes.c_bool: +def tensor_shape_is_same(tensor1, tensor2): """ bool tensor_shape_is_same(untensor tensor1, untensor tensor2); :param tensor1: ctypes.POINTER(UntensorS) @@ -275,7 +269,7 @@ def tensor_shape_is_same(tensor1, tensor2) -> ctypes.c_bool: lib.create_tensor.restype = ctypes.POINTER(UntensorS) lib.create_tensor.argtypes = [ctypes.c_int, ctypes.POINTER(ctypes.c_uint64), ctypes.c_int, ctypes.c_bool, ctypes.c_int] -def create_tensor(dims, shape, dtype, is_malloc, device_id) -> ctypes.POINTER(UntensorS): +def create_tensor(dims, shape, dtype, is_malloc, device_id): """ untensor create_tensor(int dims, uint64_t * shape, int dtype, bool is_malloc, int device_id); :param dims: ctypes.c_int @@ -286,9 +280,8 @@ def create_tensor(dims, shape, dtype, is_malloc, device_id) -> ctypes.POINTER(Un """ return lib.create_tensor(ctypes.c_int(dims), make2_c_uint64_list(shape), ctypes.c_int(dtype), is_malloc, ctypes.c_int(device_id)) -lib.free_data.restype = None lib.free_data.argtypes = [ctypes.POINTER(UntensorS)] -def free_data(tensor) -> None: +def free_data(tensor): """ void free_data(untensor tensor); :param tensor: ctypes.POINTER(UntensorS) @@ -297,25 +290,23 @@ def free_data(tensor) -> None: lib.get_tensor_device_address.restype = ctypes.c_uint64 lib.get_tensor_device_address.argtypes = [ctypes.POINTER(UntensorS)] -def get_tensor_device_address(tensor) -> ctypes.c_uint64: +def get_tensor_device_address(tensor): """ u64 get_tensor_device_address(untensor tensor); :param tensor: ctypes.POINTER(UntensorS) """ return lib.get_tensor_device_address(ctypes.byref(tensor)) -lib.malloc_host_data.restype = None lib.malloc_host_data.argtypes = [ctypes.POINTER(UntensorS)] -def malloc_host_data(tensor) -> None: +def malloc_host_data(tensor): """ void malloc_host_data(untensor tensor); :param tensor: ctypes.POINTER(UntensorS) """ return lib.malloc_host_data(ctypes.byref(tensor)) -lib.show_device_mem_data.restype = None lib.show_device_mem_data.argtypes = [ctypes.c_int, ctypes.c_uint64, ctypes.c_size_t, ctypes.c_int, ctypes.c_int, ctypes.c_int] -def show_device_mem_data(device_id, address, size, source_dtype, start, len) -> None: +def show_device_mem_data(device_id, address, size, source_dtype, start, len): """ void show_device_mem_data(int device_id, u64 address, size_t size, int source_dtype, int start, int len); :param device_id: ctypes.c_int @@ -327,42 +318,28 @@ def show_device_mem_data(device_id, address, size, source_dtype, start, len) -> """ return lib.show_device_mem_data(ctypes.c_int(device_id), ctypes.c_uint64(address), ctypes.c_size_t(size), ctypes.c_int(source_dtype), ctypes.c_int(start), ctypes.c_int(len)) -lib.malloc_device.restype = None lib.malloc_device.argtypes = [ctypes.POINTER(UntensorS)] -def malloc_device(tensor) -> None: +def malloc_device(tensor): """ void malloc_device(untensor tensor); :param tensor: ctypes.POINTER(UntensorS) """ return lib.malloc_device(ctypes.byref(tensor)) -lib.copy_data_from_tensor_with_flag.restype = None -lib.copy_data_from_tensor_with_flag.argtypes = [ctypes.POINTER(UntensorS), ctypes.POINTER(UntensorS), ctypes.c_int] -def copy_data_from_tensor_with_flag(src, dst, flag) -> None: - """ - void copy_data_from_tensor_with_flag(untensor src, untensor dst, int flag); - :param src: ctypes.POINTER(UntensorS) - :param dst: ctypes.POINTER(UntensorS) - :param flag: ctypes.c_int - """ - return lib.copy_data_from_tensor_with_flag(ctypes.byref(src), ctypes.byref(dst), ctypes.c_int(flag)) - lib.load_bmodel.restype = ctypes.c_void_p -lib.load_bmodel.argtypes = [ctypes.c_char_p, ctypes.c_bool, ctypes.c_bool, ctypes.c_int] -def load_bmodel(bmodel_path, pre_malloc, default_io_mem, device_id) -> ctypes.c_void_p: +lib.load_bmodel.argtypes = [ctypes.c_char_p, ctypes.c_bool, ctypes.c_int] +def load_bmodel(bmodel_path, pre_malloc, device_id): """ - struct un_runtime_s * load_bmodel(const char* bmodel_path, bool pre_malloc, bool default_io_mem, int device_id); + struct un_runtime_s * load_bmodel(const char* bmodel_path, bool pre_malloc, int device_id); :param bmodel_path: ctypes.c_char_p :param pre_malloc: ctypes.c_bool - :param default_io_mem: ctypes.c_bool :param device_id: ctypes.c_int """ - return lib.load_bmodel(str2char_point(bmodel_path), pre_malloc, default_io_mem, ctypes.c_int(device_id)) + return lib.load_bmodel(str2char_point(bmodel_path), pre_malloc, ctypes.c_int(device_id)) -lib.set_input_tensor.restype = None lib.set_input_tensor.argtypes = [ctypes.c_void_p, ctypes.c_int, ctypes.POINTER(UntensorS)] -def set_input_tensor(runtime, index, tensor) -> None: +def set_input_tensor(runtime, index, tensor): """ void set_input_tensor (struct un_runtime_s * runtime, int index, untensor tensor); :param runtime: ctypes.c_void_p @@ -371,9 +348,8 @@ def set_input_tensor(runtime, index, tensor) -> None: """ return lib.set_input_tensor(runtime, ctypes.c_int(index), ctypes.byref(tensor)) -lib.set_output_tensor.restype = None lib.set_output_tensor.argtypes = [ctypes.c_void_p, ctypes.c_int, ctypes.POINTER(UntensorS)] -def set_output_tensor(runtime, index, tensor) -> None: +def set_output_tensor(runtime, index, tensor): """ void set_output_tensor(struct un_runtime_s * runtime, int index, untensor tensor); :param runtime: ctypes.c_void_p @@ -383,9 +359,8 @@ def set_output_tensor(runtime, index, tensor) -> None: return lib.set_output_tensor(runtime, ctypes.c_int(index), ctypes.byref(tensor)) -lib.init_io_tensor.restype = None lib.init_io_tensor.argtypes = [ctypes.c_void_p, ctypes.c_int] -def init_io_tensor(runtime, stage_dix) -> None: +def init_io_tensor(runtime, stage_dix): """ void init_io_tensor (struct un_runtime_s * runtime, int stage_dix); :param runtime: ctypes.c_void_p @@ -394,9 +369,8 @@ def init_io_tensor(runtime, stage_dix) -> None: return lib.init_io_tensor(runtime, ctypes.c_int(stage_dix)) -lib.fill_io_tensor.restype = None lib.fill_io_tensor.argtypes = [ctypes.c_void_p, ctypes.c_int] -def fill_io_tensor(runtime, stage_idx) -> None: +def fill_io_tensor(runtime, stage_idx): """ void fill_io_tensor (struct un_runtime_s * runtime, int stage_idx); :param runtime: ctypes.c_void_p @@ -405,28 +379,17 @@ def fill_io_tensor(runtime, stage_idx) -> None: return lib.fill_io_tensor(runtime, ctypes.c_int(stage_idx)) -lib.fill_maxio.restype = None -lib.fill_maxio.argtypes = [ctypes.c_void_p] -def fill_maxio(runtime) -> None: - """ - void fill_maxio(struct un_runtime_s * runtime); - :param runtime: ctypes.c_void_p - """ - return lib.fill_maxio(runtime) - - lib.check_all_data.restype = ctypes.c_bool lib.check_all_data.argtypes = [ctypes.c_void_p] -def check_all_data(runtime) -> ctypes.c_bool: +def check_all_data(runtime): """ bool check_all_data(struct un_runtime_s * runtime); :param runtime: ctypes.c_void_p """ return lib.check_all_data(runtime) -lib.copy_input_to_device.restype = None lib.copy_input_to_device.argtypes = [ctypes.c_void_p, ctypes.c_bool] -def copy_input_to_device(runtime, force) -> None: +def copy_input_to_device(runtime, force): """ void copy_input_to_device(struct un_runtime_s * runtime, bool force); :param runtime: ctypes.c_void_p @@ -434,9 +397,8 @@ def copy_input_to_device(runtime, force) -> None: """ return lib.copy_input_to_device(runtime, force) -lib.copy_output_to_host.restype = None lib.copy_output_to_host.argtypes = [ctypes.c_void_p, ctypes.c_bool] -def copy_output_to_host(runtime, force) -> None: +def copy_output_to_host(runtime, force): """ void copy_output_to_host(struct un_runtime_s * runtime, bool force); :param runtime: ctypes.c_void_p @@ -444,9 +406,8 @@ def copy_output_to_host(runtime, force) -> None: """ return lib.copy_output_to_host(runtime, force) -lib.check_move_to_device_fill_api.restype = None lib.check_move_to_device_fill_api.argtypes = [ctypes.c_void_p, ctypes.c_bool] -def check_move_to_device_fill_api(runtime, default_move_coeff) -> None: +def check_move_to_device_fill_api(runtime, default_move_coeff): """ void check_move_to_device_fill_api(struct un_runtime_s * runtime, bool default_move_coeff); :param runtime: ctypes.c_void_p @@ -454,9 +415,8 @@ def check_move_to_device_fill_api(runtime, default_move_coeff) -> None: """ return lib.check_move_to_device_fill_api(runtime, default_move_coeff) -lib.run.restype = None lib.run.argtypes = [ctypes.c_void_p] -def run(runtime) -> None: +def run(runtime): """ void run(struct un_runtime_s * runtime); :param runtime: ctypes.c_void_p @@ -465,7 +425,7 @@ def run(runtime) -> None: lib.stage_match_io.restype = ctypes.c_bool lib.stage_match_io.argtypes = [ctypes.c_void_p, ctypes.c_int] -def stage_match_io(runtime, stage_index) -> ctypes.c_bool: +def stage_match_io(runtime, stage_index): """ bool stage_match_io(struct un_runtime_s * runtime, int stage_index); :param runtime: ctypes.c_void_p @@ -473,116 +433,25 @@ def stage_match_io(runtime, stage_index) -> ctypes.c_bool: """ return lib.stage_match_io(runtime, ctypes.c_int(stage_index)) -lib.free_runtime.restype = None lib.free_runtime.argtypes = [ctypes.c_void_p] -def free_runtime(runtime) -> None: +def free_runtime(runtime): """ void free_runtime(struct un_runtime_s * runtime); :param runtime: ctypes.c_void_p """ return lib.free_runtime(runtime) -lib.delete_runtime.restype = None lib.delete_runtime.argtypes = [ctypes.c_void_p] -def delete_runtime(runtime) -> None: +def delete_runtime(runtime): """ void delete_runtime(struct un_runtime_s * runtime); :param runtime: ctypes.c_void_p """ return lib.delete_runtime(runtime) -lib.load_bmodel_sg.restype = ctypes.c_void_p -lib.load_bmodel_sg.argtypes = [ctypes.c_char_p, ctypes.c_bool, ctypes.c_bool, ctypes.c_int] -def load_bmodel_sg(bmodel_path, pre_malloc, default_io_mem, device_id) -> ctypes.c_void_p: - """ - un_runtime_sg_s* load_bmodel_sg(const char* bmodel_path, bool pre_malloc, bool default_io_mem, int device_id); - :param bmodel_path: ctypes.c_char_p - :param pre_malloc: ctypes.c_bool - :param default_io_mem: ctypes.c_bool - :param device_id: ctypes.c_int - """ - return lib.load_bmodel_sg(str2char_point(bmodel_path), pre_malloc, default_io_mem, ctypes.c_int(device_id)) -lib.run_sg.restype = None -lib.run_sg.argtypes = [ctypes.c_void_p] -def run_sg(runtime) -> None: - """ - void run_sg(struct un_runtime_sg_s * runtime); - :param runtime: ctypes.c_void_p - """ - return lib.run_sg(runtime) - -lib.fill_maxio_sg.restype = None -lib.fill_maxio_sg.argtypes = [ctypes.c_void_p] -def fill_maxio_sg(runtime) -> None: - """ - void fill_maxio_sg(struct un_runtime_sg_s * runtime); - :param runtime: ctypes.c_void_p - """ - return lib.fill_maxio_sg(runtime) - -lib.set_stage_sg.restype = None -lib.set_stage_sg.argtypes = [ctypes.c_void_p, ctypes.c_int] -def set_stage_sg(runtime, stage_idx) -> None: - """ - void set_stage_sg(struct un_runtime_sg_s * runtime, int stage_idx); - :param runtime: ctypes.c_void_p - :param stage_idx: ctypes.c_int - """ - return lib.set_stage_sg(runtime, ctypes.c_int(stage_idx)) - -lib.free_runtime_sg.restype = None -lib.free_runtime_sg.argtypes = [ctypes.c_void_p] -def free_runtime_sg(runtime) -> None: - """ - void free_runtime_sg(struct un_runtime_sg_s * runtime); - :param runtime: ctypes.c_void_p - """ - return lib.free_runtime_sg(runtime) - -lib.init_tensor_sg.restype = None -lib.init_tensor_sg.argtypes = [ctypes.c_void_p, ctypes.c_int] -def init_tensor_sg(runtime, stage_idx) -> None: - """ - void init_tensor_sg(struct un_runtime_sg_s * runtime, int stage_idx); - :param runtime: ctypes.c_void_p - :param stage_idx: ctypes.c_int - """ - return lib.init_tensor_sg(runtime, ctypes.c_int(stage_idx)) - -lib.get_model_sg_info.restype = ctypes.c_void_p -lib.get_model_sg_info.argtypes = [ctypes.c_void_p] -def get_model_sg_info(runtime) -> ctypes.c_void_p: - """ - model_info_sg* get_model_sg_info(struct un_runtime_sg_s * runtime); - :param runtime: ctypes.c_void_p - """ - return lib.get_model_sg_info(runtime) - -lib.get_input_tensor_sg.restype = ctypes.POINTER(UntensorS) -lib.get_input_tensor_sg.argtypes = [ctypes.c_void_p, ctypes.c_int] -def get_input_tensor_sg(runtime, index) -> ctypes.POINTER(UntensorS): - """ - untensor get_input_tensor_sg(struct un_runtime_sg_s * runtime, int index); - :param runtime: ctypes.c_void_p - :param index: ctypes.c_int - """ - return lib.get_input_tensor_sg(runtime, ctypes.c_int(index)) - -lib.get_output_tensor_sg.restype = ctypes.POINTER(UntensorS) -lib.get_output_tensor_sg.argtypes = [ctypes.c_void_p, ctypes.c_int] -def get_output_tensor_sg(runtime, index) -> ctypes.POINTER(UntensorS): - """ - untensor get_output_tensor_sg(struct un_runtime_sg_s * runtime, int index); - :param runtime: ctypes.c_void_p - :param index: ctypes.c_int - """ - return lib.get_output_tensor_sg(runtime, ctypes.c_int(index)) - - -lib.replace_model_weight.restype = None lib.replace_model_weight.argtypes = [ctypes.c_void_p, ctypes.c_void_p, ctypes.c_size_t, ctypes.c_int] -def replace_model_weight(runtime, new_coeff_data, size, stage_index) -> None: +def replace_model_weight(runtime, new_coeff_data, size, stage_index): """ void replace_model_weight(struct un_runtime_s * runtime, void* new_coeff_data, size_t size, int stage_index); :param runtime: ctypes.c_void_p @@ -594,7 +463,7 @@ def replace_model_weight(runtime, new_coeff_data, size, stage_index) -> None: lib.get_model_info.restype = ctypes.c_void_p lib.get_model_info.argtypes = [ctypes.c_void_p] -def get_model_info(runtime) -> ctypes.c_void_p: +def get_model_info(runtime): """ struct model_info_s * get_model_info(struct un_runtime_s * runtime); :param runtime: ctypes.c_void_p @@ -603,7 +472,7 @@ def get_model_info(runtime) -> ctypes.c_void_p: lib.get_input_tensor.restype = ctypes.POINTER(UntensorS) lib.get_input_tensor.argtypes = [ctypes.c_void_p, ctypes.c_int] -def get_input_tensor(runtime, index) -> ctypes.POINTER(UntensorS): +def get_input_tensor(runtime, index): """ untensor get_input_tensor(struct un_runtime_s * runtime, int index); :param runtime: ctypes.c_void_p @@ -613,7 +482,7 @@ def get_input_tensor(runtime, index) -> ctypes.POINTER(UntensorS): lib.get_output_tensor.restype = ctypes.POINTER(UntensorS) lib.get_output_tensor.argtypes = [ctypes.c_void_p, ctypes.c_int] -def get_output_tensor(runtime, index) -> ctypes.POINTER(UntensorS): +def get_output_tensor(runtime, index): """ untensor get_output_tensor(struct un_runtime_s * runtime, int index); :param runtime: ctypes.c_void_p @@ -621,9 +490,8 @@ def get_output_tensor(runtime, index) -> ctypes.POINTER(UntensorS): """ return lib.get_output_tensor(runtime, ctypes.c_int(index)) -lib.set_stage.restype = None lib.set_stage.argtypes = [ctypes.c_void_p, ctypes.c_int] -def set_stage(runtime, stage_idx) -> None: +def set_stage(runtime, stage_idx): """ void set_stage(struct un_runtime_s * runtime, int stage_idx); :param runtime: ctypes.c_void_p @@ -631,9 +499,8 @@ def set_stage(runtime, stage_idx) -> None: """ return lib.set_stage(runtime, ctypes.c_int(stage_idx)) -lib.free_handle.restype = None lib.free_handle.argtypes = [ctypes.c_void_p] -def free_handle(bm_handle) -> None: +def free_handle(bm_handle): """ void free_handle(bm_handle_t bm_handle) :param bm_handle: ctypes.c_void_p @@ -642,7 +509,7 @@ def free_handle(bm_handle) -> None: lib.get_device_status.restype = ctypes.c_int lib.get_device_status.argtypes = [ctypes.c_int] -def get_device_status(device_id) -> ctypes.c_int: +def get_device_status(device_id): """ int get_device_status(int device_id) :param device_id: ctypes.c_int @@ -651,7 +518,7 @@ def get_device_status(device_id) -> ctypes.c_int: lib.get_handle.restype = ctypes.c_void_p lib.get_handle.argtypes = [ctypes.c_int] -def get_handle(device_id) -> ctypes.c_void_p: +def get_handle(device_id): """ bm_handle_t get_handle(int device_id) :param device_id: ctypes.c_int @@ -660,25 +527,23 @@ def get_handle(device_id) -> ctypes.c_void_p: lib.read_bmodel_ctx.restype = ctypes.c_void_p lib.read_bmodel_ctx.argtypes = [ctypes.c_char_p] -def read_bmodel_ctx(filename) -> ctypes.c_void_p: +def read_bmodel_ctx(filename): """ ModelCtx* read_bmodel_ctx(const char* filename); :param filename: ctypes.c_char_p """ return lib.read_bmodel_ctx(str2char_point(filename)) -lib.free_bmodel_ctx.restype = None lib.free_bmodel_ctx.argtypes = [ctypes.c_void_p] -def free_bmodel_ctx(model_ctx) -> None: +def free_bmodel_ctx(model_ctx): """ void free_bmodel_ctx(ModelCtx * model_ctx); :param model_ctx: ctypes.c_void_p """ return lib.free_bmodel_ctx(model_ctx) -lib.read_binary_by_address.restype = None lib.read_binary_by_address.argtypes = [ctypes.c_void_p, ctypes.c_uint64, ctypes.c_size_t, ctypes.c_void_p] -def read_binary_by_address(model_ctx, address, size, data) -> None: +def read_binary_by_address(model_ctx, address, size, data): """ void read_binary_by_address(ModelCtx * model_ctx, uint64_t address, size_t size, void* data); :param model_ctx: ctypes.c_void_p @@ -690,7 +555,7 @@ def read_binary_by_address(model_ctx, address, size, data) -> None: lib.read_coeff_binary_address.restype = ctypes.c_uint64 lib.read_coeff_binary_address.argtypes = [ctypes.c_void_p] -def read_coeff_binary_address(model_ctx) -> ctypes.c_uint64: +def read_coeff_binary_address(model_ctx): """ uint64_t read_coeff_binary_address(ModelCtx * model_ctx); :param model_ctx: ctypes.c_void_p @@ -699,7 +564,7 @@ def read_coeff_binary_address(model_ctx) -> ctypes.c_uint64: lib.get_all_coeff.restype = ctypes.c_void_p lib.get_all_coeff.argtypes = [ctypes.c_void_p, ctypes.c_size_t] -def get_all_coeff(model_ctx, stage_id) -> ctypes.c_void_p: +def get_all_coeff(model_ctx, stage_id): """ void * get_all_coeff(ModelCtx * model_ctx, size_t stage_id); :param model_ctx: ctypes.c_void_p @@ -707,9 +572,8 @@ def get_all_coeff(model_ctx, stage_id) -> ctypes.c_void_p: """ return lib.get_all_coeff(model_ctx, ctypes.c_size_t(stage_id)) -lib.read_binary_by_address_with_mem.restype = None lib.read_binary_by_address_with_mem.argtypes = [ctypes.c_void_p, ctypes.c_uint64, ctypes.c_size_t, ctypes.c_void_p] -def read_binary_by_address_with_mem(data, address, size, data2) -> None: +def read_binary_by_address_with_mem(data, address, size, data2): """ void read_binary_by_address_with_mem(void* data, uint64_t address, size_t size, void* data2); :param data: ctypes.c_void_p @@ -719,9 +583,8 @@ def read_binary_by_address_with_mem(data, address, size, data2) -> None: """ return lib.read_binary_by_address_with_mem(data, ctypes.c_uint64(address), ctypes.c_size_t(size), data2) -lib.write_binary_by_address_with_mem.restype = None lib.write_binary_by_address_with_mem.argtypes = [ctypes.c_void_p, ctypes.c_uint64, ctypes.c_size_t, ctypes.c_void_p] -def write_binary_by_address_with_mem(dest_data, address, size, src_data) -> None: +def write_binary_by_address_with_mem(dest_data, address, size, src_data): """ void write_binary_by_address_with_mem(void* dest_data, uint64_t address, size_t size, void* src_data); :param dest_data: ctypes.c_void_p @@ -731,9 +594,8 @@ def write_binary_by_address_with_mem(dest_data, address, size, src_data) -> None """ return lib.write_binary_by_address_with_mem(dest_data, ctypes.c_uint64(address), ctypes.c_size_t(size), src_data) -lib.convert_model_info_into_c.restype = None lib.convert_model_info_into_c.argtypes = [ctypes.c_void_p, ctypes.POINTER(ModelInfoSC)] -def convert_model_info_into_c(model_info, model_info_c) -> None: +def convert_model_info_into_c(model_info, model_info_c): """ void convert_model_info_into_c(struct model_info_s* model_info, struct model_info_s_c * model_info_c); :param model_info: ctypes.c_void_p @@ -741,19 +603,8 @@ def convert_model_info_into_c(model_info, model_info_c) -> None: """ return lib.convert_model_info_into_c(model_info, ctypes.byref(model_info_c)) -lib.convert_sg_model_info_into_c.restype = None -lib.convert_sg_model_info_into_c.argtypes = [ctypes.c_void_p, ctypes.POINTER(ModelInfoSC)] -def convert_sg_model_info_into_c(model_info, model_info_c) -> None: - """ - void convert_sg_model_info_into_c(model_info_sg* model_info, struct model_info_s_c * model_info_c); - :param model_info: ctypes.c_void_p - :param model_info_c: ctypes.POINTER(ModelInfoSC) - """ - return lib.convert_sg_model_info_into_c(model_info, ctypes.byref(model_info_c)) - -lib.free_tensor.restype = None lib.free_tensor.argtypes = [ctypes.POINTER(UntensorS)] -def free_tensor(tensor) -> None: +def free_tensor(tensor): """ void free_tensor(untensor tensor); :param tensor: ctypes.POINTER(UntensorS) @@ -762,16 +613,15 @@ def free_tensor(tensor) -> None: lib.get_model_info.restype = ctypes.c_void_p lib.get_model_info.argtypes = [ctypes.c_void_p] -def get_model_info(runtime) -> ctypes.c_void_p: +def get_model_info(runtime): """ struct model_info_s * get_model_info(struct un_runtime_s * runtime); :param runtime: ctypes.c_void_p """ return lib.get_model_info(runtime) -lib.print_data_by_fp32.restype = None lib.print_data_by_fp32.argtypes = [ctypes.c_void_p, ctypes.c_int, ctypes.c_int, ctypes.c_int, ctypes.c_int] -def print_data_by_fp32(data, size, dtype, start, len) -> None: +def print_data_by_fp32(data, size, dtype, start, len): """ void print_data_by_fp32(void* data, int size, int dtype, int start, int len); :param data: ctypes.c_void_p @@ -782,9 +632,8 @@ def print_data_by_fp32(data, size, dtype, start, len) -> None: """ return lib.print_data_by_fp32(data, ctypes.c_int(size), ctypes.c_int(dtype), ctypes.c_int(start), ctypes.c_int(len)) -lib.data_convert_to_fp32.restype = None lib.data_convert_to_fp32.argtypes = [ctypes.c_void_p, ctypes.c_void_p, ctypes.c_int, ctypes.c_size_t] -def data_convert_to_fp32(src, target, dtype, size) -> None: +def data_convert_to_fp32(src, target, dtype, size): """ void data_convert_to_fp32(void* src, void* target, int dtype, size_t size); :param src: ctypes.c_void_p @@ -796,195 +645,10 @@ def data_convert_to_fp32(src, target, dtype, size) -> None: lib.convert_to_fp32.restype = ctypes.c_float lib.convert_to_fp32.argtypes = [ctypes.c_void_p, ctypes.c_int] -def convert_to_fp32(source, dtype) -> ctypes.c_float: +def convert_to_fp32(source, dtype): """ float convert_to_fp32(void* source, int dtype); :param source: ctypes.c_void_p :param dtype: ctypes.c_int """ return lib.convert_to_fp32(source, ctypes.c_int(dtype)) - - -lib.premalloc_input_output_addr.restype = None -lib.premalloc_input_output_addr.argtypes = [ctypes.c_void_p] -def premalloc_input_output_addr(model_info_p) -> None: - """ - void premalloc_input_output_addr(struct model_info_s * model_info_p); - :param model_info_p: ctypes.c_void_p - """ - return lib.premalloc_input_output_addr(model_info_p) - -lib.set_model_info_user_map.restype = None -lib.set_model_info_user_map.argtypes = [ctypes.c_void_p] -def set_model_info_user_map(model_info_p) -> None: - """ - void set_model_info_user_map(struct model_info_s * model_info_p);// 设置 input output addr - :param model_info_p: ctypes.c_void_p - """ - return lib.set_model_info_user_map(model_info_p) - -lib.set_max_input_output.restype = None -lib.set_max_input_output.argtypes = [ctypes.c_void_p] -def set_max_input_output(model_info_p) -> None: - """ - void set_max_input_output(struct model_info_s * model_info_p); - :param model_info_p: ctypes.c_void_p - """ - return lib.set_max_input_output(model_info_p) - - -lib.move_to_device.restype = None -lib.move_to_device.argtypes = [ctypes.c_void_p, ctypes.c_bool] -def move_to_device(model_info_p, default_move_coeff) -> None: - """ - void move_to_device(struct model_info_s * model_info_p, bool default_move_coeff); - :param model_info_p: ctypes.c_void_p - :param default_move_coeff: ctypes.c_bool - """ - return lib.move_to_device(model_info_p, default_move_coeff) - -lib.convert_bdc_cmd.restype = None -lib.convert_bdc_cmd.argtypes = [ctypes.c_void_p, ctypes.POINTER(ctypes.c_uint32), ctypes.c_bool] -def convert_bdc_cmd(model_info_p, cmd, is_last) -> None: - """ - void convert_bdc_cmd(struct model_info_s * model_info_p, u32* cmd, bool is_last);// 转换 bdc cmd - :param model_info_p: ctypes.c_void_p - :param cmd: ctypes.POINTER(ctypes.c_uint32) - :param is_last: ctypes.c_bool - """ - return lib.convert_bdc_cmd(model_info_p, cmd, is_last) - -lib.get_bdc_cmd_len.restype = ctypes.c_uint32 -lib.get_bdc_cmd_len.argtypes = [ctypes.c_void_p, ctypes.c_void_p, ctypes.c_uint64, ctypes.c_bool] -def get_bdc_cmd_len(model_ctx, binary_cmd, start_offset, last_cmd) -> ctypes.c_uint32: - """ - uint32_t get_bdc_cmd_len(ModelCtx* model_ctx, const Binary* binary_cmd, u64 start_offset, bool last_cmd); - :param model_ctx: ctypes.c_void_p - :param binary_cmd: ctypes.c_void_p - :param start_offset: ctypes.c_uint64 - :param last_cmd: ctypes.c_bool - """ - return lib.get_bdc_cmd_len(model_ctx, binary_cmd, ctypes.c_uint64(start_offset), last_cmd) - -lib.get_gdma_cmd_len.restype = ctypes.c_uint32 -lib.get_gdma_cmd_len.argtypes = [ctypes.c_void_p, ctypes.c_void_p, ctypes.c_uint64, ctypes.c_bool] -def get_gdma_cmd_len(model_ctx, binary_cmd, start_offset, last_cmd) -> ctypes.c_uint32: - """ - uint32_t get_gdma_cmd_len(ModelCtx* model_ctx, const Binary* binary_cmd, u64 start_offset, bool last_cmd); - :param model_ctx: ctypes.c_void_p - :param binary_cmd: ctypes.c_void_p - :param start_offset: ctypes.c_uint64 - :param last_cmd: ctypes.c_bool - """ - return lib.get_gdma_cmd_len(model_ctx, binary_cmd, ctypes.c_uint64(start_offset), last_cmd) - -lib.convert_gdma_cmd.restype = None -lib.convert_gdma_cmd.argtypes = [ctypes.c_void_p, ctypes.POINTER(ctypes.c_uint32), ctypes.c_bool, ctypes.c_size_t] -def convert_gdma_cmd(model_info_p, cmd, is_last, stage_idx) -> None: - """ - void convert_gdma_cmd(struct model_info_s * model_info_p, u32* cmd, bool is_last, size_t stage_idx); - :param model_info_p: ctypes.c_void_p - :param cmd: ctypes.POINTER(ctypes.c_uint32) - :param is_last: ctypes.c_bool - :param stage_idx: ctypes.c_size_t - """ - return lib.convert_gdma_cmd(model_info_p, cmd, is_last, ctypes.c_size_t(stage_idx)) - -lib.bdc_gdma_move_to_device.restype = None -lib.bdc_gdma_move_to_device.argtypes = [ctypes.c_void_p] -def bdc_gdma_move_to_device(model_info_p) -> None: - """ - void bdc_gdma_move_to_device(struct model_info_s * model_info_p); - :param model_info_p: ctypes.c_void_p - """ - return lib.bdc_gdma_move_to_device(model_info_p) - -lib.stage_bdc_gdma_move_to_device.restype = None -lib.stage_bdc_gdma_move_to_device.argtypes = [ctypes.c_void_p, ctypes.c_int] -def stage_bdc_gdma_move_to_device(model_info_p, stage_idx) -> None: - """ - void stage_bdc_gdma_move_to_device(struct model_info_s * model_info_p, int stage_idx); - :param model_info_p: ctypes.c_void_p - :param stage_idx: ctypes.c_int - """ - return lib.stage_bdc_gdma_move_to_device(model_info_p, ctypes.c_int(stage_idx)) - - -lib.malloc_coeff_neuron_on_device_set_offset.restype = None -lib.malloc_coeff_neuron_on_device_set_offset.argtypes = [ctypes.c_void_p] -def malloc_coeff_neuron_on_device_set_offset(model_info_p) -> None: - """ - void malloc_coeff_neuron_on_device_set_offset(struct model_info_s * model_info_p); - :param model_info_p: ctypes.c_void_p - """ - return lib.malloc_coeff_neuron_on_device_set_offset(model_info_p) - -lib.set_coeff_neuron_map.restype = None -lib.set_coeff_neuron_map.argtypes = [ctypes.c_void_p] -def set_coeff_neuron_map(model_info_p) -> None: - """ - void set_coeff_neuron_map(struct model_info_s * model_info_p); - :param model_info_p: ctypes.c_void_p - """ - return lib.set_coeff_neuron_map(model_info_p) - -lib.coeff_to_device.restype = None -lib.coeff_to_device.argtypes = [ctypes.c_void_p] -def coeff_to_device(model_info_p) -> None: - """ - void coeff_to_device(struct model_info_s * model_info_p); - :param model_info_p: ctypes.c_void_p - """ - return lib.coeff_to_device(model_info_p) - - -lib.load_kernel_module.restype = None -lib.load_kernel_module.argtypes = [ctypes.c_void_p] -def load_kernel_module(model_info_p) -> None: - """ - void load_kernel_module(struct model_info_s * model_info_p); - :param model_info_p: ctypes.c_void_p - """ - return lib.load_kernel_module(model_info_p) - - -lib.bm1684x_fill_api_info.restype = None -lib.bm1684x_fill_api_info.argtypes = [ctypes.c_void_p, ctypes.c_void_p, ctypes.c_int] -def bm1684x_fill_api_info(model_info_p, api_info_p, stage_idx) -> None: - """ - void bm1684x_fill_api_info(struct model_info_s * model_info_p, struct api_info_t * api_info_p, int stage_idx); - :param model_info_p: ctypes.c_void_p - :param api_info_p: ctypes.c_void_p - :param stage_idx: ctypes.c_int - """ - return lib.bm1684x_fill_api_info(model_info_p, api_info_p, ctypes.c_int(stage_idx)) - -lib.fill_api_info.restype = None -lib.fill_api_info.argtypes = [ctypes.c_void_p] -def fill_api_info(model_info_p) -> None: - """ - void fill_api_info(struct model_info_s * model_info_p); - :param model_info_p: ctypes.c_void_p - """ - return lib.fill_api_info(model_info_p) - -lib.run_model.restype = None -lib.run_model.argtypes = [ctypes.c_void_p, ctypes.c_size_t] -def run_model(model_info_p, stage_idx) -> None: - """ - void run_model(struct model_info_s * model_info_p, size_t stage_idx); - :param model_info_p: ctypes.c_void_p - :param stage_idx: ctypes.c_size_t - """ - return lib.run_model(model_info_p, ctypes.c_size_t(stage_idx)) - -lib.free_model.restype = None -lib.free_model.argtypes = [ctypes.c_void_p, ctypes.c_bool] -def free_model(model_info_p, free_input_output) -> None: - """ - void free_model(struct model_info_s * model_info_p, bool free_input_output); - :param model_info_p: ctypes.c_void_p - :param free_input_output: ctypes.c_bool - """ - return lib.free_model(model_info_p, free_input_output) -version='2023-12-21-14-32-11' diff --git a/test.py b/test.py new file mode 100644 index 0000000..73f150e --- /dev/null +++ b/test.py @@ -0,0 +1,33 @@ +from sd import StableDiffusionPipeline, StableDiffusionXLPipeline, SD2_1_MODELS, SD_XL_MODELS +from sd import SD15Parallel, SDXL2Parallel +from sd import UpscaleModel +from PIL import Image +import numpy as np +import os +import time + +DEVICE_ID = 0 +BASENAME = "wujielcm" +scheduler = "LCM" +# scheduler = 'Euler a' + +pipe = StableDiffusionPipeline( + basic_model=BASENAME, + scheduler=scheduler +) +pipe.set_height_width(512,512) + +#npz_file = "/data/aigc/demos/tpukern/test/mw/coeffex/lcm_lora_reorder.npz" +#match_file = "/data/aigc/demos/tpukern/test/mw/coeffex/unet_match.csv" +#pipe.unet_pure.load_lora_file(npz_file, match_file) + +img_pil = pipe( + prompt="a chinese girl, 4k", + negative_prompt="low resolution", + num_inference_steps=4, + scheduler=scheduler, + guidance_scale=1.05 +) +img_pil.save("test.png") + +# print(img_pil) diff --git a/tokenizerV21/merges.txt b/tokenizerV21/merges.txt new file mode 100644 index 0000000..76e821f --- /dev/null +++ b/tokenizerV21/merges.txt @@ -0,0 +1,48895 @@ +#version: 0.2 +i n +t h +a n +r e +a r +e r +th e</w> +in g</w> +o u +o n +s t +o r +e n +o n</w> +a l +a t +e r</w> +i t +i n</w> +t o</w> +r o +i s</w> +l e +i c +a t</w> +an d</w> +e d</w> +o f</w> +c h +o r</w> +e s</w> +i l +e l +s t</w> +a c +o m +a m +l o +a n</w> +a y</w> +s h +r i +l i +t i +f or</w> +n e +ð Ł +r a +h a +d e +o l +v e</w> +s i +u r +a l</w> +s e +' s</w> +u n +d i +b e +l a +w h +o o +d ay</w> +e n</w> +m a +n o +l e</w> +t o +ou r</w> +i r +g h +w it +i t</w> +y o +a s +s p +th is</w> +t s</w> +at i +yo u</w> +wit h</w> +a d +i s +a b +l y</w> +w e +th e +t e +a s</w> +a g +v i +p p +s u +h o +m y</w> +. . +b u +c om +s e</w> +er s</w> +m e +m e</w> +al l</w> +c on +m o +k 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