kamp/IOPaint
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clean code

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Qing 2023-11-15 08:58:52 +08:00
parent 58b931fdb2
commit f13bfb9085
4 changed files with 9 additions and 26 deletions
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9
lama_cleaner/model/base.py
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@ -390,3 +390,12 @@ class DiffusionInpaintModel(InpaintModel):
sd_sampler = SDSampler.lcm
scheduler = get_scheduler(sd_sampler, scheduler_config)
self.model.scheduler = scheduler
def forward_post_process(self, result, image, mask, config):
if config.sd_match_histograms:
result = self._match_histograms(result, image[:, :, ::-1], mask)
if config.sd_mask_blur != 0:
k = 2 * config.sd_mask_blur + 1
mask = cv2.GaussianBlur(mask, (k, k), 0)
return result, image, mask

9
lama_cleaner/model/kandinsky.py
View File

@ -66,15 +66,6 @@ class Kandinsky(DiffusionInpaintModel):
output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
return output
def forward_post_process(self, result, image, mask, config):
if config.sd_match_histograms:
result = self._match_histograms(result, image[:, :, ::-1], mask)
if config.sd_mask_blur != 0:
k = 2 * config.sd_mask_blur + 1
mask = cv2.GaussianBlur(mask, (k, k), 0)
return result, image, mask
@staticmethod
def is_downloaded() -> bool:
# model will be downloaded when app start, and can't switch in frontend settings

9
lama_cleaner/model/sd.py
View File

@ -157,15 +157,6 @@ class SD(DiffusionInpaintModel):
output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
return output
def forward_post_process(self, result, image, mask, config):
if config.sd_match_histograms:
result = self._match_histograms(result, image[:, :, ::-1], mask)
if config.sd_mask_blur != 0:
k = 2 * config.sd_mask_blur + 1
mask = cv2.GaussianBlur(mask, (k, k), 0)
return result, image, mask
@staticmethod
def is_downloaded() -> bool:
# model will be downloaded when app start, and can't switch in frontend settings

8
lama_cleaner/model/sdxl.py
View File

@ -85,14 +85,6 @@ class SDXL(DiffusionInpaintModel):
output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
return output
def forward_post_process(self, result, image, mask, config):
if config.sd_match_histograms:
result = self._match_histograms(result, image[:, :, ::-1], mask)
if config.sd_mask_blur != 0:
k = 2 * config.sd_mask_blur + 1
mask = cv2.GaussianBlur(mask, (k, k), 0)
return result, image, mask
@staticmethod
def is_downloaded() -> bool: