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[Community, Enhancement] Add reference tricks in README (huggingface#3589)
add reference tricks
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‎examples/community/README.md‎

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@@ -1326,6 +1326,8 @@ image.save('tensorrt_img2img_new_zealand_hills.png')
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This pipeline uses the Reference Control. Refer to the [sd-webui-controlnet discussion: Reference-only Control](https://github.com/Mikubill/sd-webui-controlnet/discussions/1236)[sd-webui-controlnet discussion: Reference-adain Control](https://github.com/Mikubill/sd-webui-controlnet/discussions/1280).
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Based on [this issue](https://github.com/huggingface/diffusers/issues/3566),
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- `EulerAncestralDiscreteScheduler` got poor results.
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```py
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import torch
@@ -1369,6 +1371,9 @@ Output Image of `reference_attn=True` and `reference_adain=True`
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This pipeline uses the Reference Control with ControlNet. Refer to the [sd-webui-controlnet discussion: Reference-only Control](https://github.com/Mikubill/sd-webui-controlnet/discussions/1236)[sd-webui-controlnet discussion: Reference-adain Control](https://github.com/Mikubill/sd-webui-controlnet/discussions/1280).
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Based on [this issue](https://github.com/huggingface/diffusers/issues/3566),
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- `EulerAncestralDiscreteScheduler` got poor results.
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- `guess_mode=True` works well for ControlNet v1.1
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```py
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import cv2

‎examples/community/stable_diffusion_controlnet_reference.py‎

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@@ -505,8 +505,8 @@ def hack_CrossAttnDownBlock2D_forward(
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if MODE == "write":
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if gn_auto_machine_weight >= self.gn_weight:
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var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
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self.mean_bank.append(mean)
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self.var_bank.append(var)
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self.mean_bank.append([mean])
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self.var_bank.append([var])
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if MODE == "read":
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if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
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var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
@@ -545,8 +545,8 @@ def hacked_DownBlock2D_forward(self, hidden_states, temb=None):
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if MODE == "write":
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if gn_auto_machine_weight >= self.gn_weight:
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var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
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self.mean_bank.append(mean)
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self.var_bank.append(var)
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self.mean_bank.append([mean])
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self.var_bank.append([var])
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if MODE == "read":
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if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
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var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
@@ -605,8 +605,8 @@ def hacked_CrossAttnUpBlock2D_forward(
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if MODE == "write":
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if gn_auto_machine_weight >= self.gn_weight:
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var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
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self.mean_bank.append(mean)
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self.var_bank.append(var)
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self.mean_bank.append([mean])
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self.var_bank.append([var])
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if MODE == "read":
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if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
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var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
@@ -642,8 +642,8 @@ def hacked_UpBlock2D_forward(self, hidden_states, res_hidden_states_tuple, temb=
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if MODE == "write":
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if gn_auto_machine_weight >= self.gn_weight:
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var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
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self.mean_bank.append(mean)
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self.var_bank.append(var)
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self.mean_bank.append([mean])
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self.var_bank.append([var])
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if MODE == "read":
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if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
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var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)

‎examples/community/stable_diffusion_reference.py‎

Lines changed: 8 additions & 8 deletions
Original file line numberDiff line numberDiff line change
@@ -499,8 +499,8 @@ def hack_CrossAttnDownBlock2D_forward(
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if MODE == "write":
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if gn_auto_machine_weight >= self.gn_weight:
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var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
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self.mean_bank.append(mean)
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self.var_bank.append(var)
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self.mean_bank.append([mean])
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self.var_bank.append([var])
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if MODE == "read":
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if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
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var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
@@ -539,8 +539,8 @@ def hacked_DownBlock2D_forward(self, hidden_states, temb=None):
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if MODE == "write":
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if gn_auto_machine_weight >= self.gn_weight:
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var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
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self.mean_bank.append(mean)
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self.var_bank.append(var)
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self.mean_bank.append([mean])
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self.var_bank.append([var])
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if MODE == "read":
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if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
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var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
@@ -599,8 +599,8 @@ def hacked_CrossAttnUpBlock2D_forward(
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if MODE == "write":
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if gn_auto_machine_weight >= self.gn_weight:
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var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
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self.mean_bank.append(mean)
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self.var_bank.append(var)
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self.mean_bank.append([mean])
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self.var_bank.append([var])
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if MODE == "read":
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if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
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var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
@@ -636,8 +636,8 @@ def hacked_UpBlock2D_forward(self, hidden_states, res_hidden_states_tuple, temb=
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if MODE == "write":
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if gn_auto_machine_weight >= self.gn_weight:
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var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
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self.mean_bank.append(mean)
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self.var_bank.append(var)
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self.mean_bank.append([mean])
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self.var_bank.append([var])
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if MODE == "read":
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if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
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var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)

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