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

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comfyanonymous 2025-11-25 11:58:39 -08:00 committed by GitHub
parent 18b79acba9
commit d196a905bb
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3 changed files with 15 additions and 8 deletions
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7
comfy/sd1_clip.py
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@ -90,7 +90,6 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
special_tokens={"start": 49406, "end": 49407, "pad": 49407}, layer_norm_hidden_state=True, enable_attention_masks=False, zero_out_masked=False, special_tokens={"start": 49406, "end": 49407, "pad": 49407}, layer_norm_hidden_state=True, enable_attention_masks=False, zero_out_masked=False,
return_projected_pooled=True, return_attention_masks=False, model_options={}): # clip-vit-base-patch32 return_projected_pooled=True, return_attention_masks=False, model_options={}): # clip-vit-base-patch32
super().__init__() super().__init__()
assert layer in self.LAYERS
if textmodel_json_config is None: if textmodel_json_config is None:
textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_clip_config.json") textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_clip_config.json")
@ -164,7 +163,7 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
def set_clip_options(self, options): def set_clip_options(self, options):
layer_idx = options.get("layer", self.layer_idx) layer_idx = options.get("layer", self.layer_idx)
self.return_projected_pooled = options.get("projected_pooled", self.return_projected_pooled) self.return_projected_pooled = options.get("projected_pooled", self.return_projected_pooled)
if self.layer == "all": if isinstance(self.layer, list) or self.layer == "all":
pass pass
elif layer_idx is None or abs(layer_idx) > self.num_layers: elif layer_idx is None or abs(layer_idx) > self.num_layers:
self.layer = "last" self.layer = "last"
@ -266,7 +265,9 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
if self.enable_attention_masks: if self.enable_attention_masks:
attention_mask_model = attention_mask attention_mask_model = attention_mask
if self.layer == "all": if isinstance(self.layer, list):
intermediate_output = self.layer
elif self.layer == "all":
intermediate_output = "all" intermediate_output = "all"
else: else:
intermediate_output = self.layer_idx intermediate_output = self.layer_idx

4
comfy/text_encoders/flux.py
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@ -138,7 +138,7 @@ class Flux2Tokenizer(sd1_clip.SD1Tokenizer):
return tokens return tokens
class Mistral3_24BModel(sd1_clip.SDClipModel): 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 = {} textmodel_json_config = {}
num_layers = model_options.get("num_layers", None) num_layers = model_options.get("num_layers", None)
if num_layers is not None: if num_layers is not None:
@ -154,7 +154,7 @@ class Flux2TEModel(sd1_clip.SD1ClipModel):
def encode_token_weights(self, token_weight_pairs): def encode_token_weights(self, token_weight_pairs):
out, pooled, extra = super().encode_token_weights(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.movedim(1, 2)
out = out.reshape(out.shape[0], out.shape[1], -1) out = out.reshape(out.shape[0], out.shape[1], -1)
return out, pooled, extra return out, pooled, extra

12
comfy/text_encoders/llama.py
View File

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