kamp/IOPaint
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add forward_post_process function

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Qing 2022-11-25 09:29:20 +08:00
parent f7d7e89197
commit af87cca643
3 changed files with 25 additions and 22 deletions
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30
lama_cleaner/model/base.py
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@ -56,17 +56,15 @@ class InpaintModel:
result = self.forward(pad_image, pad_mask, config)
result = result[0:origin_height, 0:origin_width, :]
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)
result, image, mask = self.forward_post_process(result, image, mask, config)
mask = mask[:, :, np.newaxis]
result = result * (mask / 255) + image[:, :, ::-1] * (1 - (mask / 255))
return result
def forward_post_process(self, result, image, mask, config):
return result, image, mask
@torch.no_grad()
def __call__(self, image, mask, config: Config):
"""
@ -179,7 +177,7 @@ class InpaintModel:
cdf = histogram.cumsum()
normalized_cdf = cdf / float(cdf.max())
return normalized_cdf
def _calculate_lookup(self, source_cdf, reference_cdf):
lookup_table = np.zeros(256)
lookup_val = 0
@ -190,27 +188,27 @@ class InpaintModel:
break
lookup_table[source_index] = lookup_val
return lookup_table
def _match_histograms(self, source, reference, mask):
transformed_channels = []
for channel in range(source.shape[-1]):
source_channel = source[:, :, channel]
reference_channel = reference[:, :, channel]
# only calculate histograms for non-masked parts
source_histogram, _ = np.histogram(source_channel[mask == 0], 256, [0,256])
reference_histogram, _ = np.histogram(reference_channel[mask == 0], 256, [0,256])
source_histogram, _ = np.histogram(source_channel[mask == 0], 256, [0, 256])
reference_histogram, _ = np.histogram(reference_channel[mask == 0], 256, [0, 256])
source_cdf = self._calculate_cdf(source_histogram)
reference_cdf = self._calculate_cdf(reference_histogram)
lookup = self._calculate_lookup(source_cdf, reference_cdf)
transformed_channels.append(cv2.LUT(source_channel, lookup))
result = cv2.merge(transformed_channels)
result = cv2.convertScaleAbs(result)
return result
def _run_box(self, image, mask, box, config: Config):

14
lama_cleaner/model/sd.py
View File

@ -200,17 +200,21 @@ class SD(InpaintModel):
return inpaint_result
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
return True
class SD14(SD):
model_id_or_path = "CompVis/stable-diffusion-v1-4"
image_key = "init_image"
class SD15(SD):
model_id_or_path = "runwayml/stable-diffusion-inpainting"
image_key = "image"

3
lama_cleaner/tests/test_sd_model.py
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@ -109,7 +109,8 @@ def test_runway_sd_1_5_negative_prompt(sd_device, strategy, sampler):
sd_steps=sd_steps,
prompt='Face of a fox, high resolution, sitting on a park bench',
negative_prompt='orange, yellow, small',
sd_sampler=sampler
sd_sampler=sampler,
sd_match_histograms=True
)
name = f"{sampler}_negative_prompt"