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huchenlei 2024-12-09 18:49:03 -08:00
parent a66fcf31d8
commit 9e2fadd408
5 changed files with 8 additions and 8 deletions
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4
comfy/ldm/modules/diffusionmodules/model.py
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@ -218,7 +218,7 @@ def xformers_attention(q, k, v):
try:
out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None)
out = out.transpose(1, 2).reshape(B, C, H, W)
except NotImplementedError as e:
except NotImplementedError:
out = slice_attention(q.view(B, -1, C), k.view(B, -1, C).transpose(1, 2), v.view(B, -1, C).transpose(1, 2)).reshape(B, C, H, W)
return out
@ -233,7 +233,7 @@ def pytorch_attention(q, k, v):
try:
out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False)
out = out.transpose(2, 3).reshape(B, C, H, W)
except model_management.OOM_EXCEPTION as e:
except model_management.OOM_EXCEPTION:
logging.warning("scaled_dot_product_attention OOMed: switched to slice attention")
out = slice_attention(q.view(B, -1, C), k.view(B, -1, C).transpose(1, 2), v.view(B, -1, C).transpose(1, 2)).reshape(B, C, H, W)
return out

4
comfy/sd.py
View File

@ -435,7 +435,7 @@ class VAE:
if pixel_samples is None:
pixel_samples = torch.empty((samples_in.shape[0],) + tuple(out.shape[1:]), device=self.output_device)
pixel_samples[x:x+batch_number] = out
except model_management.OOM_EXCEPTION as e:
except model_management.OOM_EXCEPTION:
logging.warning("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.")
dims = samples_in.ndim - 2
if dims == 1:
@ -490,7 +490,7 @@ class VAE:
samples = torch.empty((pixel_samples.shape[0],) + tuple(out.shape[1:]), device=self.output_device)
samples[x:x + batch_number] = out
except model_management.OOM_EXCEPTION as e:
except model_management.OOM_EXCEPTION:
logging.warning("Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.")
if len(pixel_samples.shape) == 3:
samples = self.encode_tiled_1d(pixel_samples)

2
comfy/sd1_clip.py
View File

@ -382,7 +382,7 @@ def load_embed(embedding_name, embedding_directory, embedding_size, embed_key=No
embed_out = safe_load_embed_zip(embed_path)
else:
embed = torch.load(embed_path, map_location="cpu")
except Exception as e:
except Exception:
logging.warning("{}\n\nerror loading embedding, skipping loading: {}".format(traceback.format_exc(), embedding_name))
return None

2
fix_torch.py
View File

@ -23,6 +23,6 @@ def fix_pytorch_libomp():
break
try:
mydll = ctypes.cdll.LoadLibrary(test_file)
except FileNotFoundError as e:
except FileNotFoundError:
logging.warning("Detected pytorch version with libomp issue, patching.")
shutil.copyfile(os.path.join(lib_folder, "libiomp5md.dll"), dest)

4
server.py
View File

@ -561,7 +561,7 @@ class PromptServer():
for x in nodes.NODE_CLASS_MAPPINGS:
try:
out[x] = node_info(x)
except Exception as e:
except Exception:
logging.error(f"[ERROR] An error occurred while retrieving information for the '{x}' node.")
logging.error(traceback.format_exc())
return web.json_response(out)
@ -829,7 +829,7 @@ class PromptServer():
for handler in self.on_prompt_handlers:
try:
json_data = handler(json_data)
except Exception as e:
except Exception:
logging.warning(f"[ERROR] An error occurred during the on_prompt_handler processing")
logging.warning(traceback.format_exc())