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Lower vram usage for flux 2 text encoder. (#10887)

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This commit is contained in:
comfyanonymous
2025-11-25 11:58:39 -08:00
committed by GitHub
parent 18b79acba9
commit d196a905bb
3 changed files with 15 additions and 8 deletions
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4
comfy/text_encoders/flux.py
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@@ -138,7 +138,7 @@ class Flux2Tokenizer(sd1_clip.SD1Tokenizer):
return tokens
class Mistral3_24BModel(sd1_clip.SDClipModel):
def __init__(self, device="cpu", layer="all", layer_idx=None, dtype=None, attention_mask=True, model_options={}):
def __init__(self, device="cpu", layer=[10, 20, 30], layer_idx=None, dtype=None, attention_mask=True, model_options={}):
textmodel_json_config = {}
num_layers = model_options.get("num_layers", None)
if num_layers is not None:
@@ -154,7 +154,7 @@ class Flux2TEModel(sd1_clip.SD1ClipModel):
def encode_token_weights(self, token_weight_pairs):
out, pooled, extra = super().encode_token_weights(token_weight_pairs)
out = torch.stack((out[:, 10], out[:, 20], out[:, 30]), dim=1)
out = torch.stack((out[:, 0], out[:, 1], out[:, 2]), dim=1)
out = out.movedim(1, 2)
out = out.reshape(out.shape[0], out.shape[1], -1)
return out, pooled, extra

12
comfy/text_encoders/llama.py
View File

@@ -434,8 +434,12 @@ class Llama2_(nn.Module):
intermediate = None
all_intermediate = None
only_layers = None
if intermediate_output is not None:
if intermediate_output == "all":
if isinstance(intermediate_output, list):
all_intermediate = []
only_layers = set(intermediate_output)
elif intermediate_output == "all":
all_intermediate = []
intermediate_output = None
elif intermediate_output < 0:
@@ -443,7 +447,8 @@ class Llama2_(nn.Module):
for i, layer in enumerate(self.layers):
if all_intermediate is not None:
all_intermediate.append(x.unsqueeze(1).clone())
if only_layers is None or (i in only_layers):
all_intermediate.append(x.unsqueeze(1).clone())
x = layer(
x=x,
attention_mask=mask,
@@ -457,7 +462,8 @@ class Llama2_(nn.Module):
x = self.norm(x)
if all_intermediate is not None:
all_intermediate.append(x.unsqueeze(1).clone())
if only_layers is None or ((i + 1) in only_layers):
all_intermediate.append(x.unsqueeze(1).clone())
if all_intermediate is not None:
intermediate = torch.cat(all_intermediate, dim=1)