wangbo/ComfyUI
1
0
Fork 0
mirror of https://github.com/comfyanonymous/ComfyUI.git synced 2026-01-09 13:50:49 +08:00
Code Issues Actions 9 Packages Projects Releases Wiki Activity

Add memory estimation function to ltxav text encoder. (#11716)

Browse Source
This commit is contained in:
comfyanonymous 2026-01-07 17:11:22 -08:00 committed by GitHub
parent 3cd19e99c1
commit 25bc1b5b57
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
2 changed files with 15 additions and 4 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

11
comfy/sd.py
View File

@ -218,7 +218,7 @@ class CLIP:
if unprojected:
self.cond_stage_model.set_clip_options({"projected_pooled": False})
self.load_model()
self.load_model(tokens)
self.cond_stage_model.set_clip_options({"execution_device": self.patcher.load_device})
all_hooks.reset()
self.patcher.patch_hooks(None)
@ -266,7 +266,7 @@ class CLIP:
if return_pooled == "unprojected":
self.cond_stage_model.set_clip_options({"projected_pooled": False})
self.load_model()
self.load_model(tokens)
self.cond_stage_model.set_clip_options({"execution_device": self.patcher.load_device})
o = self.cond_stage_model.encode_token_weights(tokens)
cond, pooled = o[:2]
@ -299,8 +299,11 @@ class CLIP:
sd_clip[k] = sd_tokenizer[k]
return sd_clip
def load_model(self):
model_management.load_model_gpu(self.patcher)
def load_model(self, tokens={}):
memory_used = 0
if hasattr(self.cond_stage_model, "memory_estimation_function"):
memory_used = self.cond_stage_model.memory_estimation_function(tokens, device=self.patcher.load_device)
model_management.load_models_gpu([self.patcher], memory_required=memory_used)
return self.patcher
def get_key_patches(self):

8
comfy/text_encoders/lt.py
View File

@ -121,6 +121,14 @@ class LTXAVTEModel(torch.nn.Module):
return self.load_state_dict(sdo, strict=False)
def memory_estimation_function(self, token_weight_pairs, device=None):
constant = 6.0
if comfy.model_management.should_use_bf16(device):
constant /= 2.0
token_weight_pairs = token_weight_pairs.get("gemma3_12b", [])
num_tokens = sum(map(lambda a: len(a), token_weight_pairs))
return num_tokens * constant * 1024 * 1024
def ltxav_te(dtype_llama=None, llama_quantization_metadata=None):
class LTXAVTEModel_(LTXAVTEModel):