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Browse files- README.md +1 -1
- modules/__pycache__/__init__.cpython-310.pyc +0 -0
- modules/__pycache__/attention.cpython-310.pyc +0 -0
- modules/__pycache__/autoencoder.cpython-310.pyc +0 -0
- modules/__pycache__/conditioner.cpython-310.pyc +0 -0
- modules/__pycache__/connector_edit.cpython-310.pyc +0 -0
- modules/__pycache__/layers.cpython-310.pyc +0 -0
- modules/__pycache__/model_edit.cpython-310.pyc +0 -0
- modules/attention.py +1 -1
- modules/conditioner.py +1 -1
- modules/layers.py +14 -13
- requirements.txt +8 -4
README.md
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@@ -3,7 +3,7 @@ title: Test
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emoji: π
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colorFrom: indigo
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colorTo: pink
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sdk:
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app_file: app.py
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pinned: false
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license: mit
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emoji: π
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colorFrom: indigo
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colorTo: pink
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sdk: gradio
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app_file: app.py
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pinned: false
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license: mit
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modules/__pycache__/__init__.cpython-310.pyc
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modules/__pycache__/attention.cpython-310.pyc
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modules/__pycache__/autoencoder.cpython-310.pyc
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modules/__pycache__/conditioner.cpython-310.pyc
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modules/__pycache__/connector_edit.cpython-310.pyc
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modules/__pycache__/layers.cpython-310.pyc
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modules/__pycache__/model_edit.cpython-310.pyc
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modules/attention.py
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q,
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k,
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v,
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mode="
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drop_rate=0,
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attn_mask=None,
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causal=False,
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q,
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k,
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v,
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mode="torch",
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drop_rate=0,
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attn_mask=None,
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causal=False,
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modules/conditioner.py
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@@ -73,7 +73,7 @@ class Qwen25VL_7b_Embedder(torch.nn.Module):
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self.model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
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model_path,
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torch_dtype=dtype,
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attn_implementation="
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).to(torch.cuda.current_device())
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self.model.requires_grad_(False)
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self.model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
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model_path,
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torch_dtype=dtype,
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attn_implementation="eager",
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).to(torch.cuda.current_device())
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self.model.requires_grad_(False)
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modules/layers.py
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@@ -24,7 +24,7 @@ from functools import partial
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import torch
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import torch.nn.functional as F
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from einops import rearrange
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from liger_kernel.ops.rms_norm import LigerRMSNormFunction
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from torch import Tensor, nn
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q,
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k,
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v,
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mode="
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drop_rate=0,
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attn_mask=None,
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causal=False,
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super().__init__()
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self.scale = nn.Parameter(torch.ones(dim))
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@staticmethod
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def rms_norm_fast(x, weight, eps):
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@staticmethod
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def rms_norm(x, weight, eps):
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return (x * rrms).to(dtype=x_dtype) * weight
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def forward(self, x: Tensor):
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return self.rms_norm_fast(x, self.scale, 1e-6)
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class QKNorm(torch.nn.Module):
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import torch
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import torch.nn.functional as F
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from einops import rearrange
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# from liger_kernel.ops.rms_norm import LigerRMSNormFunction
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from torch import Tensor, nn
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q,
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k,
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v,
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mode="torch",
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drop_rate=0,
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attn_mask=None,
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causal=False,
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super().__init__()
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self.scale = nn.Parameter(torch.ones(dim))
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# @staticmethod
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# def rms_norm_fast(x, weight, eps):
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# return LigerRMSNormFunction.apply(
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# x,
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# weight,
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# eps,
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# 0.0,
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# "gemma",
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# True,
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# )
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@staticmethod
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def rms_norm(x, weight, eps):
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return (x * rrms).to(dtype=x_dtype) * weight
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def forward(self, x: Tensor):
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# return self.rms_norm_fast(x, self.scale, 1e-6)
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return self.rms_norm(x, self.scale, 1e-6)
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class QKNorm(torch.nn.Module):
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requirements.txt
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liger_kernel==0.5.4
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einops==0.8.1
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transformers==4.49.0
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qwen_vl_utils==0.0.10
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safetensors==0.4.5
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pillow==11.1.0
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huggingface_hub
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einops
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transformers==4.49.0
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qwen_vl_utils==0.0.10
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safetensors==0.4.5
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pillow==11.1.0
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huggingface_hub
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transformers
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diffusers
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peft
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opencv-python
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sentencepiece
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boto3
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