mirror of https://github.com/vladmandic/automatic
593 lines
30 KiB
Python
593 lines
30 KiB
Python
import copy
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import time
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import logging
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import torch
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from modules import shared, devices, sd_models, model_quant
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from installer import install, setup_logging
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#Used by OpenVINO, can be used with TensorRT or Olive
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class CompiledModelState:
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def __init__(self):
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self.is_compiled = False
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self.model_hash_str = ""
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self.first_pass = True
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self.first_pass_refiner = True
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self.first_pass_vae = True
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self.height = 512
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self.width = 512
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self.batch_size = 1
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self.partition_id = 0
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self.cn_model = []
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self.lora_model = []
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self.compiled_cache = {}
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self.req_cache = {}
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self.partitioned_modules = {}
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quant_last_model_name = None
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quant_last_model_device = None
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deepcache_worker = None
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def apply_compile_to_model(sd_model, function, options, op=None):
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if "Model" in options:
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if hasattr(sd_model, 'unet') and hasattr(sd_model.unet, 'config'):
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sd_model.unet = function(sd_model.unet, op="unet", sd_model=sd_model)
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if hasattr(sd_model, 'transformer') and hasattr(sd_model.transformer, 'config'):
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sd_model.transformer = function(sd_model.transformer, op="transformer", sd_model=sd_model)
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if hasattr(sd_model, 'decoder_pipe') and hasattr(sd_model, 'decoder'):
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sd_model.decoder = None
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sd_model.decoder = sd_model.decoder_pipe.decoder = function(sd_model.decoder_pipe.decoder, op="decoder_pipe.decoder", sd_model=sd_model)
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if hasattr(sd_model, 'prior_pipe') and hasattr(sd_model.prior_pipe, 'prior'):
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if op == "nncf" and "StableCascade" in sd_model.__class__.__name__: # fixes dtype errors
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backup_clip_txt_pooled_mapper = copy.deepcopy(sd_model.prior_pipe.prior.clip_txt_pooled_mapper)
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sd_model.prior_pipe.prior = function(sd_model.prior_pipe.prior, op="prior_pipe.prior", sd_model=sd_model)
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if op == "nncf" and "StableCascade" in sd_model.__class__.__name__:
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sd_model.prior_pipe.prior.clip_txt_pooled_mapper = backup_clip_txt_pooled_mapper
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if "Text Encoder" in options:
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if hasattr(sd_model, 'text_encoder') and hasattr(sd_model.text_encoder, 'config'):
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if hasattr(sd_model, 'decoder_pipe') and hasattr(sd_model.decoder_pipe, 'text_encoder'):
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sd_model.decoder_pipe.text_encoder = function(sd_model.decoder_pipe.text_encoder, op="decoder_pipe.text_encoder", sd_model=sd_model)
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else:
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if op == "nncf" and sd_model.text_encoder.__class__.__name__ in {"T5EncoderModel", "UMT5EncoderModel"}:
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from modules.sd_hijack import NNCF_T5DenseGatedActDense # T5DenseGatedActDense uses fp32
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for i in range(len(sd_model.text_encoder.encoder.block)):
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sd_model.text_encoder.encoder.block[i].layer[1].DenseReluDense = NNCF_T5DenseGatedActDense(
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sd_model.text_encoder.encoder.block[i].layer[1].DenseReluDense,
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dtype=torch.float32 if devices.dtype != torch.bfloat16 else torch.bfloat16
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)
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sd_model.text_encoder = function(sd_model.text_encoder, op="text_encoder", sd_model=sd_model)
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if hasattr(sd_model, 'text_encoder_2') and hasattr(sd_model.text_encoder_2, 'config'):
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if op == "nncf" and sd_model.text_encoder_2.__class__.__name__ in {"T5EncoderModel", "UMT5EncoderModel"}:
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from modules.sd_hijack import NNCF_T5DenseGatedActDense # T5DenseGatedActDense uses fp32
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for i in range(len(sd_model.text_encoder_2.encoder.block)):
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sd_model.text_encoder_2.encoder.block[i].layer[1].DenseReluDense = NNCF_T5DenseGatedActDense(
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sd_model.text_encoder_2.encoder.block[i].layer[1].DenseReluDense,
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dtype=torch.float32 if devices.dtype != torch.bfloat16 else torch.bfloat16
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)
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sd_model.text_encoder_2 = function(sd_model.text_encoder_2, op="text_encoder_2", sd_model=sd_model)
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if hasattr(sd_model, 'text_encoder_3') and hasattr(sd_model.text_encoder_3, 'config'):
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if op == "nncf" and sd_model.text_encoder_3.__class__.__name__ in {"T5EncoderModel", "UMT5EncoderModel"}:
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from modules.sd_hijack import NNCF_T5DenseGatedActDense # T5DenseGatedActDense uses fp32
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for i in range(len(sd_model.text_encoder_3.encoder.block)):
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sd_model.text_encoder_3.encoder.block[i].layer[1].DenseReluDense = NNCF_T5DenseGatedActDense(
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sd_model.text_encoder_3.encoder.block[i].layer[1].DenseReluDense,
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dtype=torch.float32 if devices.dtype != torch.bfloat16 else torch.bfloat16
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)
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sd_model.text_encoder_3 = function(sd_model.text_encoder_3, op="text_encoder_3", sd_model=sd_model)
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if hasattr(sd_model, 'prior_pipe') and hasattr(sd_model.prior_pipe, 'text_encoder'):
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sd_model.prior_pipe.text_encoder = function(sd_model.prior_pipe.text_encoder, op="prior_pipe.text_encoder", sd_model=sd_model)
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if "VAE" in options:
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if hasattr(sd_model, 'vae') and hasattr(sd_model.vae, 'decode'):
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if op == "compile":
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sd_model.vae.decode = function(sd_model.vae.decode, op="vae_decode", sd_model=sd_model)
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sd_model.vae.encode = function(sd_model.vae.encode, op="vae_encode", sd_model=sd_model)
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else:
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sd_model.vae = function(sd_model.vae, op="vae", sd_model=sd_model)
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if hasattr(sd_model, 'movq') and hasattr(sd_model.movq, 'decode'):
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if op == "compile":
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sd_model.movq.decode = function(sd_model.movq.decode, op="movq_decode", sd_model=sd_model)
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sd_model.movq.encode = function(sd_model.movq.encode, op="movq_encode", sd_model=sd_model)
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else:
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sd_model.movq = function(sd_model.movq, op="movq", sd_model=sd_model)
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if hasattr(sd_model, 'vqgan') and hasattr(sd_model.vqgan, 'decode'):
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if op == "compile":
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sd_model.vqgan.decode = function(sd_model.vqgan.decode, op="vqgan_decode", sd_model=sd_model)
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sd_model.vqgan.encode = function(sd_model.vqgan.encode, op="vqgan_encode", sd_model=sd_model)
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else:
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sd_model.vqgan = function(sd_model.vqgan, op="vqgan", sd_model=sd_model)
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if hasattr(sd_model, 'decoder_pipe') and hasattr(sd_model.decoder_pipe, 'vqgan'):
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if op == "compile":
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sd_model.decoder_pipe.vqgan.decode = function(sd_model.decoder_pipe.vqgan.decode, op="vqgan_decode", sd_model=sd_model)
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sd_model.decoder_pipe.vqgan.encode = function(sd_model.decoder_pipe.vqgan.encode, op="vqgan_encode", sd_model=sd_model)
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else:
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sd_model.decoder_pipe.vqgan = sd_model.vqgan
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if hasattr(sd_model, 'image_encoder') and hasattr(sd_model.image_encoder, 'config'):
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sd_model.image_encoder = function(sd_model.image_encoder, op="image_encoder", sd_model=sd_model)
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return sd_model
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def ipex_optimize(sd_model):
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try:
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t0 = time.time()
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def ipex_optimize_model(model, op=None, sd_model=None): # pylint: disable=unused-argument
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import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import
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model.eval()
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model.training = False
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if model.device.type != "meta":
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return_device = model.device
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model = ipex.optimize(model.to(devices.device),
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dtype=devices.dtype,
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inplace=True,
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weights_prepack=False
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).to(return_device) # pylint: disable=attribute-defined-outside-init
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else:
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model = ipex.optimize(model,
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dtype=devices.dtype,
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inplace=True,
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weights_prepack=False
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) # pylint: disable=attribute-defined-outside-init
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devices.torch_gc()
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return model
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sd_model = apply_compile_to_model(sd_model, ipex_optimize_model, shared.opts.ipex_optimize, op="ipex")
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t1 = time.time()
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shared.log.info(f"IPEX Optimize: time={t1-t0:.2f}")
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except Exception as e:
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shared.log.warning(f"IPEX Optimize: error: {e}")
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return sd_model
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def nncf_send_to_device(model):
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for child in model.children():
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if child.__class__.__name__ == "WeightsDecompressor":
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child.scale = child.scale.to(devices.device)
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child.zero_point = child.zero_point.to(devices.device)
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nncf_send_to_device(child)
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def nncf_compress_model(model, op=None, sd_model=None):
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import nncf
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global quant_last_model_name, quant_last_model_device # pylint: disable=global-statement
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model.eval()
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backup_embeddings = None
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if hasattr(model, "get_input_embeddings"):
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backup_embeddings = copy.deepcopy(model.get_input_embeddings())
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model = nncf.compress_weights(model)
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nncf_send_to_device(model)
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if hasattr(model, "set_input_embeddings") and backup_embeddings is not None:
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model.set_input_embeddings(backup_embeddings)
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if op is not None and shared.opts.quant_shuffle_weights:
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if quant_last_model_name is not None:
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if "." in quant_last_model_name:
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last_model_names = quant_last_model_name.split(".")
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getattr(getattr(sd_model, last_model_names[0]), last_model_names[1]).to(quant_last_model_device)
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else:
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getattr(sd_model, quant_last_model_name).to(quant_last_model_device)
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devices.torch_gc(force=True)
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if shared.cmd_opts.medvram or shared.cmd_opts.lowvram or shared.opts.diffusers_offload_mode != "none":
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quant_last_model_name = op
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quant_last_model_device = model.device
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else:
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quant_last_model_name = None
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quant_last_model_device = None
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model.to(devices.device)
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devices.torch_gc(force=True)
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return model
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def nncf_compress_weights(sd_model):
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try:
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t0 = time.time()
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shared.log.info(f"Quantization: type=NNCF modules={shared.opts.nncf_compress_weights}")
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global quant_last_model_name, quant_last_model_device # pylint: disable=global-statement
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install('nncf==2.7.0', quiet=True)
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sd_model = apply_compile_to_model(sd_model, nncf_compress_model, shared.opts.nncf_compress_weights, op="nncf")
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if quant_last_model_name is not None:
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if "." in quant_last_model_name:
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last_model_names = quant_last_model_name.split(".")
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getattr(getattr(sd_model, last_model_names[0]), last_model_names[1]).to(quant_last_model_device)
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else:
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getattr(sd_model, quant_last_model_name).to(quant_last_model_device)
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devices.torch_gc(force=True)
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quant_last_model_name = None
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quant_last_model_device = None
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t1 = time.time()
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shared.log.info(f"Quantization: type=NNCF time={t1-t0:.2f}")
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except Exception as e:
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shared.log.warning(f"Quantization: type=NNCF {e}")
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return sd_model
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def optimum_quanto_model(model, op=None, sd_model=None, weights=None, activations=None):
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quanto = model_quant.load_quanto('Compile model: type=Optimum Quanto')
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global quant_last_model_name, quant_last_model_device # pylint: disable=global-statement
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if sd_model is not None and "Flux" in sd_model.__class__.__name__: # LayerNorm is not supported
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exclude_list = ["transformer_blocks.*.norm1.norm", "transformer_blocks.*.norm2", "transformer_blocks.*.norm1_context.norm", "transformer_blocks.*.norm2_context", "single_transformer_blocks.*.norm.norm", "norm_out.norm"]
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else:
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exclude_list = None
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weights = getattr(quanto, weights) if weights is not None else getattr(quanto, shared.opts.optimum_quanto_weights_type)
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if activations is not None:
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activations = getattr(quanto, activations) if activations != 'none' else None
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elif shared.opts.optimum_quanto_activations_type != 'none':
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activations = getattr(quanto, shared.opts.optimum_quanto_activations_type)
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else:
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activations = None
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model.eval()
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backup_embeddings = None
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if hasattr(model, "get_input_embeddings"):
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backup_embeddings = copy.deepcopy(model.get_input_embeddings())
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quanto.quantize(model, weights=weights, activations=activations, exclude=exclude_list)
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quanto.freeze(model)
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if hasattr(model, "set_input_embeddings") and backup_embeddings is not None:
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model.set_input_embeddings(backup_embeddings)
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if op is not None and shared.opts.quant_shuffle_weights:
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if quant_last_model_name is not None:
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if "." in quant_last_model_name:
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last_model_names = quant_last_model_name.split(".")
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getattr(getattr(sd_model, last_model_names[0]), last_model_names[1]).to(quant_last_model_device)
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else:
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getattr(sd_model, quant_last_model_name).to(quant_last_model_device)
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devices.torch_gc(force=True)
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if shared.cmd_opts.medvram or shared.cmd_opts.lowvram or shared.opts.diffusers_offload_mode != "none":
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quant_last_model_name = op
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quant_last_model_device = model.device
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else:
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quant_last_model_name = None
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quant_last_model_device = None
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model.to(devices.device)
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devices.torch_gc(force=True)
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return model
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def optimum_quanto_weights(sd_model):
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try:
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if shared.opts.diffusers_offload_mode in {"balanced", "sequential"}:
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shared.log.warning(f"Quantization: type=Optimum.quanto offload={shared.opts.diffusers_offload_mode} not compatible")
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return sd_model
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t0 = time.time()
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shared.log.info(f"Quantization: type=Optimum.quanto: modules={shared.opts.optimum_quanto_weights}")
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global quant_last_model_name, quant_last_model_device # pylint: disable=global-statement
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quanto = model_quant.load_quanto()
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quanto.tensor.qbits.QBitsTensor.create = lambda *args, **kwargs: quanto.tensor.qbits.QBitsTensor(*args, **kwargs)
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sd_model = apply_compile_to_model(sd_model, optimum_quanto_model, shared.opts.optimum_quanto_weights, op="optimum-quanto")
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if quant_last_model_name is not None:
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if "." in quant_last_model_name:
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last_model_names = quant_last_model_name.split(".")
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getattr(getattr(sd_model, last_model_names[0]), last_model_names[1]).to(quant_last_model_device)
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else:
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getattr(sd_model, quant_last_model_name).to(quant_last_model_device)
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devices.torch_gc(force=True)
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quant_last_model_name = None
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quant_last_model_device = None
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if shared.opts.optimum_quanto_activations_type != 'none':
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activations = getattr(quanto, shared.opts.optimum_quanto_activations_type)
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else:
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activations = None
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if activations is not None:
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def optimum_quanto_freeze(model, op=None, sd_model=None): # pylint: disable=unused-argument
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quanto.freeze(model)
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return model
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if shared.opts.diffusers_offload_mode == "model":
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sd_model.enable_model_cpu_offload(device=devices.device)
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if hasattr(sd_model, "encode_prompt"):
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original_encode_prompt = sd_model.encode_prompt
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def encode_prompt(*args, **kwargs):
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embeds = original_encode_prompt(*args, **kwargs)
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sd_model.maybe_free_model_hooks() # Diffusers keeps the TE on VRAM
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return embeds
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sd_model.encode_prompt = encode_prompt
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else:
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sd_models.move_model(sd_model, devices.device)
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with quanto.Calibration(momentum=0.9):
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sd_model(prompt="dummy prompt", num_inference_steps=10)
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sd_model = apply_compile_to_model(sd_model, optimum_quanto_freeze, shared.opts.optimum_quanto_weights, op="optimum-quanto-freeze")
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if shared.opts.diffusers_offload_mode == "model":
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sd_models.disable_offload(sd_model)
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sd_models.move_model(sd_model, devices.cpu)
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if hasattr(sd_model, "encode_prompt"):
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sd_model.encode_prompt = original_encode_prompt
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devices.torch_gc(force=True)
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t1 = time.time()
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shared.log.info(f"Quantization: type=Optimum.quanto time={t1-t0:.2f}")
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except Exception as e:
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shared.log.warning(f"Quantization: type=Optimum.quanto {e}")
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return sd_model
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def optimize_openvino(sd_model):
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try:
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from modules.intel.openvino import openvino_fx # pylint: disable=unused-import
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torch._dynamo.eval_frame.check_if_dynamo_supported = lambda: True # pylint: disable=protected-access
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if shared.compiled_model_state is not None:
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shared.compiled_model_state.compiled_cache.clear()
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shared.compiled_model_state.req_cache.clear()
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shared.compiled_model_state.partitioned_modules.clear()
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shared.compiled_model_state = CompiledModelState()
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shared.compiled_model_state.is_compiled = True
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shared.compiled_model_state.first_pass = True if not shared.opts.cuda_compile_precompile else False
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shared.compiled_model_state.first_pass_vae = True if not shared.opts.cuda_compile_precompile else False
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shared.compiled_model_state.first_pass_refiner = True if not shared.opts.cuda_compile_precompile else False
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sd_models.set_accelerate(sd_model)
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except Exception as e:
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shared.log.warning(f"Model compile: task=OpenVINO: {e}")
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return sd_model
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def compile_onediff(sd_model):
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try:
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from onediff.infer_compiler import oneflow_compile
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except Exception as e:
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shared.log.warning(f"Model compile: task=onediff {e}")
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return sd_model
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try:
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t0 = time.time()
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# For some reason compiling the text_encoder, when it is used by
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# the 'compel' package which sdnext uses, it becomes 100 times
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# slower as if it is recompiling every time.
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#sd_model.text_encoder = oneflow_compile(sd_model.text_encoder)
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#if hasattr(sd_model, 'text_endcoder_2'):
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# sd_model.text_encoder_2 = oneflow_compile(sd_model.text_encoder_2)
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sd_model.unet = oneflow_compile(sd_model.unet)
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sd_model.vae.encoder = oneflow_compile(sd_model.vae.encoder)
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sd_model.vae.decoder = oneflow_compile(sd_model.vae.decoder)
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# How are Loras, Adaptors, and other things compiled
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# DW: I'm unclear whether this is also a problem with onediff
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# as it was for sfast.
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setup_logging() # compile messes with logging so reset is needed
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if shared.opts.cuda_compile_precompile:
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sd_model("dummy prompt")
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t1 = time.time()
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shared.log.info(f"Model compile: task=onediff time={t1-t0:.2f}")
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except Exception as e:
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shared.log.info(f"Model compile: task=onediff {e}")
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return sd_model
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def compile_stablefast(sd_model):
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try:
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import sfast.compilers.stable_diffusion_pipeline_compiler as sf
|
|
except Exception as e:
|
|
shared.log.warning(f'Model compile: task=stablefast: {e}')
|
|
return sd_model
|
|
config = sf.CompilationConfig.Default()
|
|
try:
|
|
import xformers # pylint: disable=unused-import
|
|
config.enable_xformers = True
|
|
except Exception:
|
|
pass
|
|
try:
|
|
import triton # pylint: disable=unused-import
|
|
config.enable_triton = True
|
|
except Exception:
|
|
pass
|
|
import warnings
|
|
warnings.filterwarnings("ignore", category=torch.jit.TracerWarning)
|
|
config.enable_cuda_graph = shared.opts.cuda_compile_fullgraph
|
|
config.enable_jit_freeze = shared.opts.diffusers_eval
|
|
config.memory_format = torch.channels_last if shared.opts.opt_channelslast else torch.contiguous_format
|
|
# config.trace_scheduler = False
|
|
# config.enable_cnn_optimization
|
|
# config.prefer_lowp_gemm
|
|
try:
|
|
t0 = time.time()
|
|
sd_model = sf.compile(sd_model, config)
|
|
sd_model.sfast = True
|
|
setup_logging() # compile messes with logging so reset is needed
|
|
if shared.opts.cuda_compile_precompile:
|
|
sd_model("dummy prompt")
|
|
t1 = time.time()
|
|
shared.log.info(f"Model compile: task=stablefast config={config.__dict__} time={t1-t0:.2f}")
|
|
except Exception as e:
|
|
shared.log.info(f"Model compile: task=stablefast {e}")
|
|
return sd_model
|
|
|
|
|
|
def compile_torch(sd_model):
|
|
try:
|
|
t0 = time.time()
|
|
import torch._dynamo # pylint: disable=unused-import,redefined-outer-name
|
|
torch._dynamo.reset() # pylint: disable=protected-access
|
|
shared.log.debug(f"Model compile: task=torch backends={torch._dynamo.list_backends()}") # pylint: disable=protected-access
|
|
|
|
def torch_compile_model(model, op=None, sd_model=None): # pylint: disable=unused-argument
|
|
if hasattr(model, "device") and model.device.type != "meta":
|
|
return_device = model.device
|
|
model = torch.compile(model.to(devices.device),
|
|
mode=shared.opts.cuda_compile_mode,
|
|
backend=shared.opts.cuda_compile_backend,
|
|
fullgraph=shared.opts.cuda_compile_fullgraph
|
|
).to(return_device)
|
|
else:
|
|
model = torch.compile(model,
|
|
mode=shared.opts.cuda_compile_mode,
|
|
backend=shared.opts.cuda_compile_backend,
|
|
fullgraph=shared.opts.cuda_compile_fullgraph
|
|
)
|
|
devices.torch_gc()
|
|
return model
|
|
|
|
if shared.opts.cuda_compile_backend == "openvino_fx":
|
|
sd_model = optimize_openvino(sd_model)
|
|
elif shared.opts.cuda_compile_backend == "olive-ai":
|
|
if shared.compiled_model_state is None:
|
|
shared.compiled_model_state = CompiledModelState()
|
|
return sd_model
|
|
elif shared.opts.cuda_compile_backend == "migraphx":
|
|
import torch_migraphx # pylint: disable=unused-import
|
|
log_level = logging.WARNING if shared.opts.cuda_compile_verbose else logging.CRITICAL # pylint: disable=protected-access
|
|
if hasattr(torch, '_logging'):
|
|
torch._logging.set_logs(dynamo=log_level, aot=log_level, inductor=log_level) # pylint: disable=protected-access
|
|
torch._dynamo.config.verbose = shared.opts.cuda_compile_verbose # pylint: disable=protected-access
|
|
torch._dynamo.config.suppress_errors = shared.opts.cuda_compile_errors # pylint: disable=protected-access
|
|
|
|
try:
|
|
torch._inductor.config.conv_1x1_as_mm = True # pylint: disable=protected-access
|
|
torch._inductor.config.coordinate_descent_tuning = True # pylint: disable=protected-access
|
|
torch._inductor.config.epilogue_fusion = False # pylint: disable=protected-access
|
|
torch._inductor.config.coordinate_descent_check_all_directions = True # pylint: disable=protected-access
|
|
torch._inductor.config.use_mixed_mm = True # pylint: disable=protected-access
|
|
# torch._inductor.config.force_fuse_int_mm_with_mul = True # pylint: disable=protected-access
|
|
except Exception as e:
|
|
shared.log.error(f"Model compile: torch inductor config error: {e}")
|
|
|
|
sd_model = apply_compile_to_model(sd_model, function=torch_compile_model, options=shared.opts.cuda_compile, op="compile")
|
|
|
|
setup_logging() # compile messes with logging so reset is needed
|
|
if shared.opts.cuda_compile_precompile:
|
|
sd_model("dummy prompt")
|
|
t1 = time.time()
|
|
shared.log.info(f"Model compile: task=torch time={t1-t0:.2f}")
|
|
except Exception as e:
|
|
shared.log.warning(f"Model compile: task=torch {e}")
|
|
return sd_model
|
|
|
|
|
|
def check_deepcache(enable: bool):
|
|
if deepcache_worker is not None:
|
|
if enable:
|
|
deepcache_worker.enable()
|
|
else:
|
|
deepcache_worker.disable()
|
|
|
|
|
|
def compile_deepcache(sd_model):
|
|
global deepcache_worker # pylint: disable=global-statement
|
|
if not hasattr(sd_model, 'unet'):
|
|
shared.log.warning(f'Model compile: task=deepcache pipeline={sd_model.__class__} not supported')
|
|
return sd_model
|
|
try:
|
|
from DeepCache import DeepCacheSDHelper
|
|
except Exception as e:
|
|
shared.log.warning(f'Model compile: task=deepcache {e}')
|
|
return sd_model
|
|
t0 = time.time()
|
|
check_deepcache(False)
|
|
deepcache_worker = DeepCacheSDHelper(pipe=sd_model)
|
|
deepcache_worker.set_params(cache_interval=shared.opts.deep_cache_interval, cache_branch_id=0)
|
|
t1 = time.time()
|
|
shared.log.info(f"Model compile: task=deepcache config={deepcache_worker.params} time={t1-t0:.2f}")
|
|
# config={'cache_interval': 3, 'cache_layer_id': 0, 'cache_block_id': 0, 'skip_mode': 'uniform'} time=0.00
|
|
return sd_model
|
|
|
|
|
|
def compile_diffusers(sd_model):
|
|
if 'Model' not in shared.opts.cuda_compile:
|
|
return sd_model
|
|
if shared.opts.cuda_compile_backend == 'none':
|
|
shared.log.warning('Model compile enabled but no backend specified')
|
|
return sd_model
|
|
shared.log.info(f"Model compile: pipeline={sd_model.__class__.__name__} mode={shared.opts.cuda_compile_mode} backend={shared.opts.cuda_compile_backend} fullgraph={shared.opts.cuda_compile_fullgraph} compile={shared.opts.cuda_compile}")
|
|
if shared.opts.cuda_compile_backend == 'onediff':
|
|
sd_model = compile_onediff(sd_model)
|
|
elif shared.opts.cuda_compile_backend == 'stable-fast':
|
|
sd_model = compile_stablefast(sd_model)
|
|
elif shared.opts.cuda_compile_backend == 'deep-cache':
|
|
sd_model = compile_deepcache(sd_model)
|
|
else:
|
|
check_deepcache(False)
|
|
sd_model = compile_torch(sd_model)
|
|
return sd_model
|
|
|
|
|
|
def torchao_quantization(sd_model):
|
|
try:
|
|
install('torchao', quiet=True)
|
|
from torchao import quantization as q
|
|
except Exception as e:
|
|
shared.log.error(f"Quantization: type=TorchAO quantization not supported: {e}")
|
|
return sd_model
|
|
if shared.opts.torchao_quantization_type == "int8+act":
|
|
fn = q.int8_dynamic_activation_int8_weight
|
|
elif shared.opts.torchao_quantization_type == "int8":
|
|
fn = q.int8_weight_only
|
|
elif shared.opts.torchao_quantization_type == "int4":
|
|
fn = q.int4_weight_only
|
|
elif shared.opts.torchao_quantization_type == "fp8+act":
|
|
fn = q.float8_dynamic_activation_float8_weight
|
|
elif shared.opts.torchao_quantization_type == "fp8":
|
|
fn = q.float8_weight_only
|
|
elif shared.opts.torchao_quantization_type == "fpx":
|
|
fn = q.fpx_weight_only
|
|
else:
|
|
shared.log.error(f"Quantization: type=TorchAO type={shared.opts.torchao_quantization_type} not supported")
|
|
return sd_model
|
|
shared.log.info(f"Quantization: type=TorchAO pipe={sd_model.__class__.__name__} quant={shared.opts.torchao_quantization_type} fn={fn} targets={shared.opts.torchao_quantization}")
|
|
try:
|
|
t0 = time.time()
|
|
modules = []
|
|
if hasattr(sd_model, 'unet') and 'Model' in shared.opts.torchao_quantization:
|
|
modules.append('unet')
|
|
q.quantize_(sd_model.unet, fn(), device=devices.device)
|
|
if hasattr(sd_model, 'transformer') and 'Model' in shared.opts.torchao_quantization:
|
|
modules.append('transformer')
|
|
q.quantize_(sd_model.transformer, fn(), device=devices.device)
|
|
if hasattr(sd_model, 'vae') and 'VAE' in shared.opts.torchao_quantization:
|
|
modules.append('vae')
|
|
q.quantize_(sd_model.vae, fn(), device=devices.device)
|
|
if hasattr(sd_model, 'text_encoder') and 'Text Encoder' in shared.opts.torchao_quantization:
|
|
modules.append('te1')
|
|
q.quantize_(sd_model.text_encoder, fn(), device=devices.device)
|
|
if hasattr(sd_model, 'text_encoder_2') and 'Text Encoder' in shared.opts.torchao_quantization:
|
|
modules.append('te2')
|
|
q.quantize_(sd_model.text_encoder_2, fn(), device=devices.device)
|
|
if hasattr(sd_model, 'text_encoder_3') and 'Text Encoder' in shared.opts.torchao_quantization:
|
|
modules.append('te3')
|
|
q.quantize_(sd_model.text_encoder_3, fn(), device=devices.device)
|
|
t1 = time.time()
|
|
shared.log.info(f"Quantization: type=TorchAO modules={modules} time={t1-t0:.2f}")
|
|
except Exception as e:
|
|
shared.log.error(f"Quantization: type=TorchAO {e}")
|
|
setup_logging() # torchao uses dynamo which messes with logging so reset is needed
|
|
return sd_model
|
|
|
|
|
|
def openvino_recompile_model(p, hires=False, refiner=False): # recompile if a parameter changes
|
|
if 'Model' in shared.opts.cuda_compile and shared.opts.cuda_compile_backend != 'none':
|
|
if shared.opts.cuda_compile_backend == "openvino_fx":
|
|
compile_height = p.height if not hires and hasattr(p, 'height') else p.hr_upscale_to_y
|
|
compile_width = p.width if not hires and hasattr(p, 'width') else p.hr_upscale_to_x
|
|
if (shared.compiled_model_state is None or
|
|
(not shared.compiled_model_state.first_pass
|
|
and (shared.compiled_model_state.height != compile_height
|
|
or shared.compiled_model_state.width != compile_width
|
|
or shared.compiled_model_state.batch_size != p.batch_size))):
|
|
if refiner:
|
|
shared.log.info("OpenVINO: Recompiling refiner")
|
|
sd_models.unload_model_weights(op='refiner')
|
|
sd_models.reload_model_weights(op='refiner')
|
|
else:
|
|
shared.log.info("OpenVINO: Recompiling base model")
|
|
sd_models.unload_model_weights(op='model')
|
|
sd_models.reload_model_weights(op='model')
|
|
shared.compiled_model_state.height = compile_height
|
|
shared.compiled_model_state.width = compile_width
|
|
shared.compiled_model_state.batch_size = p.batch_size
|
|
|
|
|
|
def openvino_post_compile(op="base"): # delete unet after OpenVINO compile
|
|
if 'Model' in shared.opts.cuda_compile and shared.opts.cuda_compile_backend == "openvino_fx":
|
|
if shared.compiled_model_state.first_pass and op == "base":
|
|
shared.compiled_model_state.first_pass = False
|
|
if not shared.opts.openvino_disable_memory_cleanup and hasattr(shared.sd_model, "unet"):
|
|
shared.sd_model.unet.apply(sd_models.convert_to_faketensors)
|
|
devices.torch_gc(force=True)
|
|
if shared.compiled_model_state.first_pass_refiner and op == "refiner":
|
|
shared.compiled_model_state.first_pass_refiner = False
|
|
if not shared.opts.openvino_disable_memory_cleanup and hasattr(shared.sd_refiner, "unet"):
|
|
shared.sd_refiner.unet.apply(sd_models.convert_to_faketensors)
|
|
devices.torch_gc(force=True)
|