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fix typos

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Kohya S
2024-08-09 23:02:51 +09:00
parent 36b2e6fc28
commit 808d2d1f48
3 changed files with 4 additions and 4 deletions
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2
flux_train_network.py
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@@ -250,7 +250,7 @@ class FluxNetworkTrainer(train_network.NetworkTrainer):
# ) # )
with accelerator.autocast(): with accelerator.autocast():
# YiYi notes: divide it by 1000 for now because we scale it by 1000 in the transforme rmodel (we should not keep it but I want to keep the inputs same for the model for testing) # YiYi notes: divide it by 1000 for now because we scale it by 1000 in the transformer model (we should not keep it but I want to keep the inputs same for the model for testing)
model_pred = unet( model_pred = unet(
img=packed_noisy_model_input, img=packed_noisy_model_input,
img_ids=img_ids, img_ids=img_ids,

4
library/flux_models.py
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@@ -685,11 +685,11 @@ class DoubleStreamBlock(nn.Module):
attn = attention(q, k, v, pe=pe) attn = attention(q, k, v, pe=pe)
txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :] txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :]
# calculate the img bloks # calculate the img blocks
img = img + img_mod1.gate * self.img_attn.proj(img_attn) img = img + img_mod1.gate * self.img_attn.proj(img_attn)
img = img + img_mod2.gate * self.img_mlp((1 + img_mod2.scale) * self.img_norm2(img) + img_mod2.shift) img = img + img_mod2.gate * self.img_mlp((1 + img_mod2.scale) * self.img_norm2(img) + img_mod2.shift)
# calculate the txt bloks # calculate the txt blocks
txt = txt + txt_mod1.gate * self.txt_attn.proj(txt_attn) txt = txt + txt_mod1.gate * self.txt_attn.proj(txt_attn)
txt = txt + txt_mod2.gate * self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift) txt = txt + txt_mod2.gate * self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift)
return img, txt return img, txt

2
library/flux_utils.py
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@@ -20,7 +20,7 @@ MODEL_VERSION_FLUX_V1 = "flux1"
def load_flow_model(name: str, ckpt_path: str, dtype: torch.dtype, device: Union[str, torch.device]) -> flux_models.Flux: def load_flow_model(name: str, ckpt_path: str, dtype: torch.dtype, device: Union[str, torch.device]) -> flux_models.Flux:
logger.info(f"Bulding Flux model {name}") logger.info(f"Building Flux model {name}")
with torch.device("meta"): with torch.device("meta"):
model = flux_models.Flux(flux_models.configs[name].params).to(dtype) model = flux_models.Flux(flux_models.configs[name].params).to(dtype)