diff options
| -rw-r--r-- | modules/devices.py | 4 | ||||
| -rw-r--r-- | modules/interrogate.py | 5 | ||||
| -rw-r--r-- | modules/sd_models_xl.py | 4 | ||||
| -rw-r--r-- | modules/upscaler_utils.py | 5 | ||||
| -rw-r--r-- | modules/xlmr.py | 4 | ||||
| -rw-r--r-- | modules/xlmr_m18.py | 5 | ||||
| -rw-r--r-- | test/test_torch_utils.py | 4 |
7 files changed, 14 insertions, 17 deletions
diff --git a/modules/devices.py b/modules/devices.py index bd6bd579..ff279ac5 100644 --- a/modules/devices.py +++ b/modules/devices.py @@ -4,7 +4,7 @@ from functools import lru_cache import torch from modules import errors, shared -from modules.torch_utils import get_param +from modules import torch_utils if sys.platform == "darwin": from modules import mac_specific @@ -132,7 +132,7 @@ patch_module_list = [ def manual_cast_forward(self, *args, **kwargs): - org_dtype = get_param(self).dtype + org_dtype = torch_utils.get_param(self).dtype self.to(dtype) args = [arg.to(dtype) if isinstance(arg, torch.Tensor) else arg for arg in args] kwargs = {k: v.to(dtype) if isinstance(v, torch.Tensor) else v for k, v in kwargs.items()} diff --git a/modules/interrogate.py b/modules/interrogate.py index 5be5a10f..35a627ca 100644 --- a/modules/interrogate.py +++ b/modules/interrogate.py @@ -10,8 +10,7 @@ import torch.hub from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode
-from modules import devices, paths, shared, lowvram, modelloader, errors
-from modules.torch_utils import get_param
+from modules import devices, paths, shared, lowvram, modelloader, errors, torch_utils
blip_image_eval_size = 384
clip_model_name = 'ViT-L/14'
@@ -132,7 +131,7 @@ class InterrogateModels: self.clip_model = self.clip_model.to(devices.device_interrogate)
- self.dtype = get_param(self.clip_model).dtype
+ self.dtype = torch_utils.get_param(self.clip_model).dtype
def send_clip_to_ram(self):
if not shared.opts.interrogate_keep_models_in_memory:
diff --git a/modules/sd_models_xl.py b/modules/sd_models_xl.py index c3602a7e..0de17af3 100644 --- a/modules/sd_models_xl.py +++ b/modules/sd_models_xl.py @@ -6,7 +6,7 @@ import sgm.models.diffusion import sgm.modules.diffusionmodules.denoiser_scaling
import sgm.modules.diffusionmodules.discretizer
from modules import devices, shared, prompt_parser
-from modules.torch_utils import get_param
+from modules import torch_utils
def get_learned_conditioning(self: sgm.models.diffusion.DiffusionEngine, batch: prompt_parser.SdConditioning | list[str]):
@@ -91,7 +91,7 @@ sgm.modules.GeneralConditioner.get_target_prompt_token_count = get_target_prompt def extend_sdxl(model):
"""this adds a bunch of parameters to make SDXL model look a bit more like SD1.5 to the rest of the codebase."""
- dtype = get_param(model.model.diffusion_model).dtype
+ dtype = torch_utils.get_param(model.model.diffusion_model).dtype
model.model.diffusion_model.dtype = dtype
model.model.conditioning_key = 'crossattn'
model.cond_stage_key = 'txt'
diff --git a/modules/upscaler_utils.py b/modules/upscaler_utils.py index c60e3beb..f5cb92d5 100644 --- a/modules/upscaler_utils.py +++ b/modules/upscaler_utils.py @@ -6,8 +6,7 @@ import torch import tqdm from PIL import Image -from modules import images, shared -from modules.torch_utils import get_param +from modules import images, shared, torch_utils logger = logging.getLogger(__name__) @@ -18,7 +17,7 @@ def upscale_without_tiling(model, img: Image.Image): img = np.ascontiguousarray(np.transpose(img, (2, 0, 1))) / 255 img = torch.from_numpy(img).float() - param = get_param(model) + param = torch_utils.get_param(model) img = img.unsqueeze(0).to(device=param.device, dtype=param.dtype) with torch.no_grad(): diff --git a/modules/xlmr.py b/modules/xlmr.py index 6e000a56..319771b7 100644 --- a/modules/xlmr.py +++ b/modules/xlmr.py @@ -5,7 +5,7 @@ from transformers.models.xlm_roberta.configuration_xlm_roberta import XLMRoberta from transformers import XLMRobertaModel,XLMRobertaTokenizer from typing import Optional -from modules.torch_utils import get_param +from modules import torch_utils class BertSeriesConfig(BertConfig): @@ -65,7 +65,7 @@ class BertSeriesModelWithTransformation(BertPreTrainedModel): self.post_init() def encode(self,c): - device = get_param(self).device + device = torch_utils.get_param(self).device text = self.tokenizer(c, truncation=True, max_length=77, diff --git a/modules/xlmr_m18.py b/modules/xlmr_m18.py index e3e81961..f6055504 100644 --- a/modules/xlmr_m18.py +++ b/modules/xlmr_m18.py @@ -4,8 +4,7 @@ import torch from transformers.models.xlm_roberta.configuration_xlm_roberta import XLMRobertaConfig from transformers import XLMRobertaModel,XLMRobertaTokenizer from typing import Optional - -from modules.torch_utils import get_param +from modules import torch_utils class BertSeriesConfig(BertConfig): @@ -71,7 +70,7 @@ class BertSeriesModelWithTransformation(BertPreTrainedModel): self.post_init() def encode(self,c): - device = get_param(self).device + device = torch_utils.get_param(self).device text = self.tokenizer(c, truncation=True, max_length=77, diff --git a/test/test_torch_utils.py b/test/test_torch_utils.py index f1aec832..23ccb93a 100644 --- a/test/test_torch_utils.py +++ b/test/test_torch_utils.py @@ -3,7 +3,7 @@ import types import pytest import torch -from modules.torch_utils import get_param +from modules import torch_utils @pytest.mark.parametrize("wrapped", [True, False]) @@ -14,6 +14,6 @@ def test_get_param(wrapped): if wrapped: # more or less how spandrel wraps a thing mod = types.SimpleNamespace(model=mod) - p = get_param(mod) + p = torch_utils.get_param(mod) assert p.dtype == torch.float16 assert p.device == cpu |