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Merge branch 'comfyanonymous:master' into master

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patientx 2024-08-23 11:04:56 +03:00 committed by GitHub
commit 9f87d61bfe
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2 changed files with 16 additions and 8 deletions
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8
comfy/model_detection.py
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@ -472,9 +472,15 @@ def unet_config_from_diffusers_unet(state_dict, dtype=None):
'transformer_depth': [0, 1, 1], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -2, 'use_linear_in_transformer': False,
'context_dim': 768, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 1, 1, 1, 1],
'use_temporal_attention': False, 'use_temporal_resblock': False}
SD15_diffusers_inpaint = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'adm_in_channels': None,
'dtype': dtype, 'in_channels': 9, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0],
'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': False, 'context_dim': 768, 'num_heads': 8,
'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
'use_temporal_attention': False, 'use_temporal_resblock': False}
supported_models = [SDXL, SDXL_refiner, SD21, SD15, SD21_uncliph, SD21_unclipl, SDXL_mid_cnet, SDXL_small_cnet, SDXL_diffusers_inpaint, SSD_1B, Segmind_Vega, KOALA_700M, KOALA_1B, SD09_XS, SD_XS, SDXL_diffusers_ip2p]
supported_models = [SDXL, SDXL_refiner, SD21, SD15, SD21_uncliph, SD21_unclipl, SDXL_mid_cnet, SDXL_small_cnet, SDXL_diffusers_inpaint, SSD_1B, Segmind_Vega, KOALA_700M, KOALA_1B, SD09_XS, SD_XS, SDXL_diffusers_ip2p, SD15_diffusers_inpaint]
for unet_config in supported_models:
matches = True

16
comfy/model_management.py
View File

@ -44,8 +44,10 @@ cpu_state = CPUState.GPU
total_vram = 0
torch_version = torch.version.__version__
lowvram_available = True
xpu_available = False
xpu_available = int(torch_version[0]) < 2 or (int(torch_version[0]) == 2 and int(torch_version[2]) <= 4)
if args.deterministic:
logging.info("Using deterministic algorithms for pytorch")
@ -66,10 +68,10 @@ if args.directml is not None:
try:
import intel_extension_for_pytorch as ipex
if torch.xpu.is_available():
xpu_available = True
_ = torch.xpu.device_count()
xpu_available = torch.xpu.is_available()
except:
pass
xpu_available = xpu_available or (hasattr(torch, "xpu") and torch.xpu.is_available())
try:
if torch.backends.mps.is_available():
@ -189,7 +191,6 @@ VAE_DTYPES = [torch.float32]
try:
if is_nvidia():
torch_version = torch.version.__version__
if int(torch_version[0]) >= 2:
if ENABLE_PYTORCH_ATTENTION == False and args.use_split_cross_attention == False and args.use_quad_cross_attention == False:
ENABLE_PYTORCH_ATTENTION = True
@ -335,8 +336,9 @@ class LoadedModel:
self.model_unload()
raise e
if is_intel_xpu() and not args.disable_ipex_optimize:
self.real_model = ipex.optimize(self.real_model.eval(), graph_mode=True, concat_linear=True)
if is_intel_xpu() and not args.disable_ipex_optimize and self.real_model is not None:
with torch.no_grad():
self.real_model = ipex.optimize(self.real_model.eval(), inplace=True, graph_mode=True, concat_linear=True)
self.weights_loaded = True
return self.real_model