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mirror of https://github.com/vladmandic/automatic synced 2026-09-19 09:14:35 +02:00
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fix hires and corrections with batch processing

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Signed-off-by: Vladimir Mandic <mandic00@live.com>
This commit is contained in:
Vladimir Mandic
2024-12-30 21:31:35 -05:00
parent 86ac38d94f
commit 05d5ac03be
6 changed files with 42 additions and 49 deletions
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modules/processing_correction.py
+18 -30
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@@ -16,7 +16,7 @@ skip_correction = False
def sharpen_tensor(tensor, ratio=0):
if ratio == 0:
debug("Sharpen: Early exit")
# debug("Sharpen: Early exit")
return tensor
kernel = torch.ones((3, 3), dtype=tensor.dtype, device=tensor.device)
kernel[1, 1] = 5.0
@@ -42,18 +42,18 @@ def soft_clamp_tensor(tensor, threshold=0.8, boundary=4):
min_replace = ((tensor + threshold) / (min_vals + threshold)) * (-boundary + threshold) - threshold
under_mask = tensor < -threshold
tensor = torch.where(over_mask, max_replace, torch.where(under_mask, min_replace, tensor))
debug(f'HDR soft clamp: threshold={threshold} boundary={boundary} shape={tensor.shape}')
# debug(f'HDR soft clamp: threshold={threshold} boundary={boundary} shape={tensor.shape}')
return tensor
def center_tensor(tensor, channel_shift=0.0, full_shift=0.0, offset=0.0):
if channel_shift == 0 and full_shift == 0 and offset == 0:
return tensor
debug(f'HDR center: Before Adjustment: Full mean={tensor.mean().item()} Channel means={tensor.mean(dim=(-1, -2)).float().cpu().numpy()}')
# debug(f'HDR center: Before Adjustment: Full mean={tensor.mean().item()} Channel means={tensor.mean(dim=(-1, -2)).float().cpu().numpy()}')
tensor -= tensor.mean(dim=(-1, -2), keepdim=True) * channel_shift
tensor -= tensor.mean() * full_shift - offset
debug(f'HDR center: channel-shift={channel_shift} full-shift={full_shift}')
debug(f'HDR center: After Adjustment: Full mean={tensor.mean().item()} Channel means={tensor.mean(dim=(-1, -2)).float().cpu().numpy()}')
# debug(f'HDR center: channel-shift={channel_shift} full-shift={full_shift}')
# debug(f'HDR center: After Adjustment: Full mean={tensor.mean().item()} Channel means={tensor.mean(dim=(-1, -2)).float().cpu().numpy()}')
return tensor
@@ -65,7 +65,7 @@ def maximize_tensor(tensor, boundary=1.0):
max_val = tensor.max()
normalization_factor = boundary / max(abs(min_val), abs(max_val))
tensor *= normalization_factor
debug(f'HDR maximize: boundary={boundary} min={min_val} max={max_val} factor={normalization_factor}')
# debug(f'HDR maximize: boundary={boundary} min={min_val} max={max_val} factor={normalization_factor}')
return tensor
@@ -78,7 +78,7 @@ def get_color(colorstr):
def color_adjust(tensor, colorstr, ratio):
color = get_color(colorstr)
debug(f'HDR tint: str={colorstr} color={color} ratio={ratio}')
# debug(f'HDR tint: str={colorstr} color={color} ratio={ratio}')
for i in range(3):
tensor[i] = center_tensor(tensor[i], full_shift=1, offset=color[i]*(ratio/2))
return tensor
@@ -86,35 +86,26 @@ def color_adjust(tensor, colorstr, ratio):
def correction(p, timestep, latent):
if timestep > 950 and p.hdr_clamp:
p.extra_generation_params["HDR clamp"] = f'{p.hdr_threshold}/{p.hdr_boundary}'
latent = soft_clamp_tensor(latent, threshold=p.hdr_threshold, boundary=p.hdr_boundary)
if 600 < timestep < 900 and (p.hdr_color != 0 or p.hdr_tint_ratio != 0):
if p.hdr_brightness != 0:
latent[0:1] = center_tensor(latent[0:1], full_shift=float(p.hdr_mode), offset=2*p.hdr_brightness) # Brightness
p.extra_generation_params["HDR brightness"] = f'{p.hdr_brightness}'
p.hdr_brightness = 0
if p.hdr_color != 0:
latent[1:] = center_tensor(latent[1:], channel_shift=p.hdr_color, full_shift=float(p.hdr_mode)) # Color
p.extra_generation_params["HDR color"] = f'{p.hdr_color}'
p.hdr_color = 0
if p.hdr_tint_ratio != 0:
latent = color_adjust(latent, p.hdr_color_picker, p.hdr_tint_ratio)
p.hdr_tint_ratio = 0
p.extra_generation_params["HDR clamp"] = f'{p.hdr_threshold}/{p.hdr_boundary}'
if 600 < timestep < 900 and p.hdr_color != 0:
latent[1:] = center_tensor(latent[1:], channel_shift=p.hdr_color, full_shift=float(p.hdr_mode)) # Color
p.extra_generation_params["HDR color"] = f'{p.hdr_color}'
if 600 < timestep < 900 and p.hdr_tint_ratio != 0:
latent = color_adjust(latent, p.hdr_color_picker, p.hdr_tint_ratio)
p.extra_generation_params["HDR tint"] = f'{p.hdr_tint_ratio}'
if timestep < 200 and (p.hdr_brightness != 0): # do it late so it doesn't change the composition
if p.hdr_brightness != 0:
latent[0:1] = center_tensor(latent[0:1], full_shift=float(p.hdr_mode), offset=2*p.hdr_brightness) # Brightness
p.extra_generation_params["HDR brightness"] = f'{p.hdr_brightness}'
p.hdr_brightness = 0
latent[0:1] = center_tensor(latent[0:1], full_shift=float(p.hdr_mode), offset=p.hdr_brightness) # Brightness
p.extra_generation_params["HDR brightness"] = f'{p.hdr_brightness}'
if timestep < 350 and p.hdr_sharpen != 0:
p.extra_generation_params["HDR sharpen"] = f'{p.hdr_sharpen}'
per_step_ratio = 2 ** (timestep / 250) * p.hdr_sharpen / 16
if abs(per_step_ratio) > 0.01:
debug(f"HDR Sharpen: timestep={timestep} ratio={p.hdr_sharpen} val={per_step_ratio}")
latent = sharpen_tensor(latent, ratio=per_step_ratio)
p.extra_generation_params["HDR sharpen"] = f'{p.hdr_sharpen}'
if 1 < timestep < 100 and p.hdr_maximize:
p.extra_generation_params["HDR max"] = f'{p.hdr_max_center}/{p.hdr_max_boundry}'
latent = center_tensor(latent, channel_shift=p.hdr_max_center, full_shift=1.0)
latent = maximize_tensor(latent, boundary=p.hdr_max_boundry)
p.extra_generation_params["HDR max"] = f'{p.hdr_max_center}/{p.hdr_max_boundry}'
return latent
@@ -129,9 +120,6 @@ def correction_callback(p, timestep, kwargs, initial: bool = False):
elif skip_correction:
return kwargs
latents = kwargs["latents"]
if debug_enabled:
debug('')
debug(f' Timestep: {timestep}')
# debug(f'HDR correction: latents={latents.shape}')
if len(latents.shape) == 4: # standard batched latent
for i in range(latents.shape[0]):