Add [batch_number] and [generation_number] filename patterns
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@ -352,6 +352,8 @@ class FilenameGenerator:
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'prompt_no_styles': lambda self: self.prompt_no_style(),
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'prompt_no_styles': lambda self: self.prompt_no_style(),
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'prompt_spaces': lambda self: sanitize_filename_part(self.prompt, replace_spaces=False),
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'prompt_spaces': lambda self: sanitize_filename_part(self.prompt, replace_spaces=False),
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'prompt_words': lambda self: self.prompt_words(),
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'prompt_words': lambda self: self.prompt_words(),
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'batch_number': lambda self: self.p.batch_index + 1,
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'generation_number': lambda self: self.p.iteration * self.p.batch_size + self.p.batch_index + 1,
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}
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}
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default_time_format = '%Y%m%d%H%M%S'
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default_time_format = '%Y%m%d%H%M%S'
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@ -403,6 +405,10 @@ class FilenameGenerator:
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for m in re_pattern.finditer(x):
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for m in re_pattern.finditer(x):
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text, pattern = m.groups()
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text, pattern = m.groups()
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if pattern is not None and (pattern.lower() == 'batch_number' and self.p.batch_size == 1 or pattern.lower() == 'generation_number' and self.p.n_iter == 1 and self.p.batch_size == 1):
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continue
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res += text
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res += text
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if pattern is None:
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if pattern is None:
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@ -670,6 +670,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n)
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p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n)
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for i, x_sample in enumerate(x_samples_ddim):
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for i, x_sample in enumerate(x_samples_ddim):
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p.batch_index = i
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x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
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x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
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x_sample = x_sample.astype(np.uint8)
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x_sample = x_sample.astype(np.uint8)
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