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fix width height error #20

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fix width height error #20

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4 changes: 3 additions & 1 deletion model/adapter.py
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Original file line number Diff line number Diff line change
Expand Up @@ -281,7 +281,9 @@ def forward(self, x, t=None):
t = self.time_proj(t) # b, 320
t = t.to(dtype=x[0].dtype)
t = self.time_embedding(t) # b, 1280
output_size = (b, 640, 128, 128) # last CA layer output
# output_size = (b, 640, 128, 128) # last CA layer output
output_size = (b, 640, (x[0].shape)[2] * 4 , (x[0].shape)[3] * 4) # last CA layer output should suit to the input size CSR

for i in range(len(self.channels)):
for j in range(self.nums_rb):
idx = i * self.nums_rb + j
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2 changes: 1 addition & 1 deletion pipeline/pipeline_sd_xl_adapter.py
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Expand Up @@ -1341,7 +1341,7 @@ def prepare_xl_latents_from_sd_1_5(
image = self.vae_sd1_5.decode(latent / self.vae_sd1_5.config.scaling_factor, return_dict=False)[0]
do_denormalize = [True] * image.shape[0]
image = self.image_processor_sd1_5.postprocess(image, output_type='pil', do_denormalize=do_denormalize)[0]
image = image.resize((height, width))
image = image.resize((width, height))
# image.save('./test_img/image_sd1_5.jpg')
# input()

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4 changes: 2 additions & 2 deletions pipeline/pipeline_sd_xl_adapter_controlnet.py
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Expand Up @@ -1727,8 +1727,8 @@ def prepare_xl_latents_from_sd_1_5(
image = self.vae_sd1_5.decode(latent / self.vae_sd1_5.config.scaling_factor, return_dict=False)[0]
do_denormalize = [True] * image.shape[0]
image = self.image_processor_sd1_5.postprocess(image, output_type='pil', do_denormalize=do_denormalize)[0]
image = image.resize((height, width))

image = image.resize((width, height))
if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
raise ValueError(
f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
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2 changes: 1 addition & 1 deletion pipeline/pipeline_sd_xl_adapter_controlnet_img2img.py
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Expand Up @@ -1818,7 +1818,7 @@ def prepare_xl_latents_from_sd_1_5(
image = self.vae_sd1_5.decode(latent / self.vae_sd1_5.config.scaling_factor, return_dict=False)[0]
do_denormalize = [True] * image.shape[0]
image = self.image_processor_sd1_5.postprocess(image, output_type='pil', do_denormalize=do_denormalize)[0]
image = image.resize((height, width))
image = image.resize((width, height))
# image.save('./test_img/image_sd1_5.jpg')
# input()

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