nodes/ltdrdata-ComfyUI-Impact-Pack
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fix: TwoAdvancedSamplersForMask - doesn't work properly

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Dr.Lt.Data
2024-08-03 02:46:38 +09:00
parent 83ad10cdde
commit f8723ab7ac
1 changed files with 9 additions and 42 deletions
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modules/impact/special_samplers.py
+9 -42
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@@ -212,49 +212,15 @@ class TwoAdvancedSamplersForMask:
CATEGORY = "ImpactPack/Sampler"
@staticmethod
def mask_erosion(samples, mask, grow_mask_by):
mask = mask.clone()
w = samples['samples'].shape[3]
h = samples['samples'].shape[2]
mask2 = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(w, h), mode="bilinear")
if grow_mask_by == 0:
mask_erosion = mask2
else:
kernel_tensor = torch.ones((1, 1, grow_mask_by, grow_mask_by))
padding = math.ceil((grow_mask_by - 1) / 2)
mask_erosion = torch.clamp(torch.nn.functional.conv2d(mask2.round(), kernel_tensor, padding=padding), 0, 1)
return mask_erosion[:, :, :w, :h].round()
@staticmethod
def doit(seed, steps, denoise, samples, base_sampler, mask_sampler, mask, overlap_factor):
regional_prompts = RegionalPrompt().doit(mask=mask, advanced_sampler=mask_sampler)[0]
inv_mask = torch.where(mask != 1.0, torch.tensor(1.0), torch.tensor(0.0))
adv_steps = int(steps / denoise)
start_at_step = adv_steps - steps
new_latent_image = samples.copy()
mask_erosion = TwoAdvancedSamplersForMask.mask_erosion(samples, mask, overlap_factor)
for i in range(start_at_step, adv_steps):
add_noise = "enable" if i == start_at_step else "disable"
return_with_leftover_noise = "enable" if i+1 != adv_steps else "disable"
new_latent_image['noise_mask'] = inv_mask
new_latent_image = base_sampler.sample_advanced(add_noise, seed, adv_steps, new_latent_image, i, i + 1, "enable", recovery_mode="ratio additional")
new_latent_image['noise_mask'] = mask_erosion
new_latent_image = mask_sampler.sample_advanced("disable", seed, adv_steps, new_latent_image, i, i + 1, return_with_leftover_noise, recovery_mode="ratio additional")
del new_latent_image['noise_mask']
return (new_latent_image, )
return RegionalSampler().doit(seed=seed, seed_2nd=0, seed_2nd_mode="ignore", steps=steps, base_only_steps=1,
denoise=denoise, samples=samples, base_sampler=base_sampler,
regional_prompts=regional_prompts, overlap_factor=overlap_factor,
restore_latent=True, additional_mode="ratio between",
additional_sampler="AUTO", additional_sigma_ratio=0.3)
class RegionalPrompt:
@@ -409,7 +375,7 @@ class RegionalSampler:
"additional_sampler": (["AUTO", "euler", "heun", "heunpp2", "dpm_2", "dpm_fast", "dpmpp_2m", "ddpm"],),
"additional_sigma_ratio": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 1.0, "step": 0.01}),
},
"hidden": {"unique_id": "UNIQUE_ID"},
"hidden": {"unique_id": "UNIQUE_ID"},
}
TOOLTIPS = {
@@ -519,7 +485,8 @@ class RegionalSampler:
core.update_node_status(unique_id, f"{i}/{steps} steps | ", ((i-start_at_step)*region_len)/total)
new_latent_image['noise_mask'] = inv_mask
new_latent_image = base_sampler.sample_advanced(add_noise, seed, adv_steps, new_latent_image, i, i + 1, True,
new_latent_image = base_sampler.sample_advanced(add_noise, seed, adv_steps, new_latent_image,
start_at_step=i, end_at_step=i + 1, return_with_leftover_noise=True,
recovery_mode=additional_mode, recovery_sampler=additional_sampler, recovery_sigma_ratio=additional_sigma_ratio, noise=noise)
if restore_latent: