feat: RegionalSamplerAdvanced
improve: RegionalSampler
This commit is contained in:
Dr.Lt.Data
@@ -146,8 +146,14 @@ This takes latent as input and outputs latent as the result.
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* If the `Inspire Pack` is installed, you can use **Lora Block Weight** in the form of `LBW=lbw spec;`
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* `<lora:chunli:1.0:1.0:LBW=B11:0,0,0,0,0,0,0,0,0,0,A,0,0,0,0,0,0;A=0.;>`, `<lora:chunli:1.0:1.0:LBW=0,0,0,0,0,0,0,0,0,0,A,B,0,0,0,0,0;A=0.5;B=0.2;>`, `<lora:chunli:1.0:1.0:LBW=SD-MIDD;>`
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* RegionalSampler, CombineRegionalPrompts, RegionalPrompt - experimental feature
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- multiple region version of TwoAdvancedSamplersForMask
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* Regional Sampling - These nodes offer the capability to divide regions and perform partial sampling using a mask. Unlike TwoSamplersForMask, sampling for each region is applied during each step.
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* RegionalPrompt - This node combines a **mask** for specifying regions and the **sampler** to apply to each region to create `REGIONAL_PROMPTS`.
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* CombineRegionalPrompts - Combine multiple `REGIONAL_PROMPTS` to create a single `REGIONAL_PROMPTS`.
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* RegionalSampler - This node performs sampling using a base sampler and regional prompts. Sampling by the base sampler is executed at each step, while sampling for each region is performed through the sampler bound to each region.
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* overlap_factor - Specifies the amount of overlap for each region to blend well with the area outside the mask.
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* latent_restore - When sampling each region, restore the areas outside the mask to the base latent, preventing additional noise from being introduced outside the mask during region sampling.
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* RegionalSamplerAdvanced - This is the Advanced version of the RegionalSampler. You can control it using `step` instead of `denoise`.
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* NOTE: The `sde` sampler and `uni_pc` sampler introduce additional noise during each step of the sampling process. To mitigate this, when sampling each region, the `uni_pc` sampler applies additional `dpmpp_fast`, and the sde sampler applies the `dpmpp_2m` sampler as an additional measure.
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* KSampler (pipe), KSampler (advanced/pipe)
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@@ -225,6 +225,7 @@ NODE_CLASS_MAPPINGS = {
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"ImpactMakeImageList": MakeImageList,
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"RegionalSampler": RegionalSampler,
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"RegionalSamplerAdvanced": RegionalSamplerAdvanced,
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"CombineRegionalPrompts": CombineRegionalPrompts,
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"RegionalPrompt": RegionalPrompt,
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+2
-1
@@ -187,7 +187,8 @@ app.registerExtension({
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},
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async beforeRegisterNodeDef(nodeType, nodeData, app) {
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if (nodeData.name == "IterativeLatentUpscale" || nodeData.name == "IterativeImageUpscale" || nodeData.name == "RegionalSampler") {
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if (nodeData.name == "IterativeLatentUpscale" || nodeData.name == "IterativeImageUpscale"
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|| nodeData.name == "RegionalSampler"|| nodeData.name == "RegionalSamplerAdvanced") {
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impactProgressBadge.addStatusHandler(nodeType);
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}
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@@ -2,7 +2,7 @@ import configparser
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import os
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version = "V4.8.5"
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version = "V4.9"
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dependency_version = 11
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+36
-15
@@ -53,9 +53,9 @@ def ksampler_wrapper(model, seed, steps, cfg, sampler_name, scheduler, positive,
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print(f"pre: {start_at_step} .. {end_at_step} / {advanced_steps}")
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temp_latent = \
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nodes.KSamplerAdvanced().sample(model, "enable", seed, advanced_steps, cfg, sampler_name, scheduler,
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positive, negative, latent_image, start_at_step, end_at_step,
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"enable")[0]
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nodes.KSamplerAdvanced().sample(model, "enable", seed, advanced_steps, cfg, sampler_name, scheduler,
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positive, negative, latent_image, start_at_step, end_at_step,
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"enable")[0]
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if 'noise_mask' in latent_image:
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# noise_latent = \
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@@ -65,14 +65,14 @@ def ksampler_wrapper(model, seed, steps, cfg, sampler_name, scheduler, positive,
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latent_compositor = nodes.NODE_CLASS_MAPPINGS['LatentCompositeMasked']()
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temp_latent = \
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latent_compositor.composite(latent_image, temp_latent, 0, 0, False, latent_image['noise_mask'])[0]
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latent_compositor.composite(latent_image, temp_latent, 0, 0, False, latent_image['noise_mask'])[0]
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print(f"post: {end_at_step} .. {advanced_steps + 1} / {advanced_steps}")
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refined_latent = \
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nodes.KSamplerAdvanced().sample(refiner_model, "disable", seed, advanced_steps, cfg, sampler_name, scheduler,
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refiner_positive, refiner_negative, temp_latent, end_at_step,
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advanced_steps + 1,
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"disable")[0]
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nodes.KSamplerAdvanced().sample(refiner_model, "disable", seed, advanced_steps, cfg, sampler_name, scheduler,
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refiner_positive, refiner_negative, temp_latent, end_at_step,
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advanced_steps + 1,
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"disable")[0]
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return refined_latent
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@@ -251,9 +251,6 @@ def composite_to(dest_latent, crop_region, src_latent):
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# composite to original latent
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lc = nodes.LatentComposite()
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# 현재 mask 를 고려한 composite 가 없음... 이거 처리 필요.
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orig_image = lc.composite(dest_latent, src_latent, x1, y1)
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return orig_image[0]
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@@ -925,7 +922,7 @@ class KSamplerAdvancedWrapper:
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self.params = model, cfg, sampler_name, scheduler, positive, negative
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def sample_advanced(self, add_noise, seed, steps, latent_image, start_at_step, end_at_step,
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return_with_leftover_noise, hook=None):
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return_with_leftover_noise, hook=None, recover_special_sampler=False):
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model, cfg, sampler_name, scheduler, positive, negative = self.params
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if hook is not None:
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@@ -934,9 +931,33 @@ class KSamplerAdvancedWrapper:
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positive, negative, latent_image, start_at_step, end_at_step,
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return_with_leftover_noise)
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return nodes.KSamplerAdvanced().sample(model, add_noise, seed, steps, cfg, sampler_name, scheduler,
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positive, negative, latent_image, start_at_step, end_at_step,
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return_with_leftover_noise)[0]
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if recover_special_sampler and sampler_name in ['uni_pc', 'uni_pc_bh2', 'dpmpp_sde', 'dpmpp_sde_gpu', 'dpmpp_2m_sde', 'dpmpp_2m_sde_gpu', 'dpmpp_3m_sde', 'dpmpp_3m_sde_gpu']:
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base_image = latent_image.copy()
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else:
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base_image = None
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latent_image = nodes.KSamplerAdvanced().sample(model, add_noise, seed, steps, cfg, sampler_name, scheduler,
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positive, negative, latent_image, start_at_step, end_at_step,
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return_with_leftover_noise)[0]
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if recover_special_sampler and sampler_name in ['uni_pc', 'uni_pc_bh2', 'dpmpp_sde', 'dpmpp_sde_gpu', 'dpmpp_2m_sde', 'dpmpp_2m_sde_gpu', 'dpmpp_3m_sde', 'dpmpp_3m_sde_gpu']:
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compensate = 0 if sampler_name in ['uni_pc', 'uni_pc_bh2'] else 2
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sampler_name = 'dpmpp_fast' if sampler_name in ['uni_pc', 'uni_pc_bh2', 'dpmpp_sde', 'dpmpp_sde_gpu'] else 'dpmpp_2m'
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print(f"recover latent!!: {sampler_name} ->")
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latent_compositor = nodes.NODE_CLASS_MAPPINGS['LatentCompositeMasked']()
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noise_mask = latent_image['noise_mask']
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if len(noise_mask.shape) == 4:
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noise_mask = noise_mask.squeeze(0).squeeze(0)
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latent_image = \
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latent_compositor.composite(base_image, latent_image, 0, 0, False, noise_mask)[0]
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latent_image = nodes.KSamplerAdvanced().sample(model, add_noise, seed, steps, cfg, sampler_name, scheduler,
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positive, negative, latent_image, start_at_step-compensate, end_at_step,
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return_with_leftover_noise)[0]
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return latent_image
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class PixelKSampleHook:
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@@ -1,3 +1,5 @@
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import time
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import comfy
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import math
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import impact.core as core
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@@ -165,10 +167,10 @@ class TwoAdvancedSamplersForMask:
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return_with_leftover_noise = "enable" if i+1 != adv_steps else "disable"
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new_latent_image['noise_mask'] = inv_mask
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new_latent_image = base_sampler.sample_advanced(add_noise, seed, adv_steps, new_latent_image, i, i + 1, "enable")
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new_latent_image = base_sampler.sample_advanced(add_noise, seed, adv_steps, new_latent_image, i, i + 1, "enable", recover_special_sampler=True)
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new_latent_image['noise_mask'] = mask_erosion
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new_latent_image = mask_sampler.sample_advanced("disable", seed, adv_steps, new_latent_image, i, i + 1, return_with_leftover_noise)
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new_latent_image = mask_sampler.sample_advanced("disable", seed, adv_steps, new_latent_image, i, i + 1, return_with_leftover_noise, recover_special_sampler=True)
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del new_latent_image['noise_mask']
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@@ -225,7 +227,8 @@ class RegionalSampler:
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"samples": ("LATENT", ),
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"base_sampler": ("KSAMPLER_ADVANCED", ),
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"regional_prompts": ("REGIONAL_PROMPTS", ),
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"overlap_factor": ("INT", {"default": 10, "min": 0, "max": 10000})
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"overlap_factor": ("INT", {"default": 10, "min": 0, "max": 10000}),
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"latent_restore": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled"})
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},
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"hidden": {"unique_id": "UNIQUE_ID"},
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}
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@@ -253,7 +256,11 @@ class RegionalSampler:
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return mask_erosion[:, :, :w, :h].round()
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def doit(self, seed, steps, denoise, samples, base_sampler, regional_prompts, overlap_factor, unique_id):
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def doit(self, seed, steps, denoise, samples, base_sampler, regional_prompts, overlap_factor, latent_restore, unique_id=None):
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if latent_restore:
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latent_compositor = nodes.NODE_CLASS_MAPPINGS['LatentCompositeMasked']()
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else:
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latent_compositor = None
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masks = [regional_prompt.mask.numpy() for regional_prompt in regional_prompts]
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masks = [np.ceil(mask).astype(np.int32) for mask in masks]
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@@ -268,31 +275,159 @@ class RegionalSampler:
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total = steps*region_len
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new_latent_image = samples.copy()
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base_latent_image = None
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for i in range(start_at_step, adv_steps):
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core.update_node_status(unique_id, f"{i}/{steps} steps | ", (i*region_len)/total)
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add_noise = "enable" if i == start_at_step else "disable"
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return_with_leftover_noise = "enable" if i+1 != adv_steps else "disable"
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new_latent_image['noise_mask'] = inv_mask
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new_latent_image = base_sampler.sample_advanced(add_noise, seed, adv_steps, new_latent_image, i, i + 1, "enable")
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new_latent_image = base_sampler.sample_advanced(add_noise, seed, adv_steps, new_latent_image, i, i + 1, "enable", recover_special_sampler=True)
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if latent_restore:
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del new_latent_image['noise_mask']
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base_latent_image = new_latent_image.copy()
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j = 1
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for regional_prompt in regional_prompts:
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if latent_restore:
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new_latent_image = base_latent_image.copy()
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core.update_node_status(unique_id, f"{i}/{steps} steps | {j}/{region_len}", (i*region_len + j)/total)
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new_latent_image['noise_mask'] = regional_prompt.get_mask_erosion(overlap_factor)
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region_mask = regional_prompt.get_mask_erosion(overlap_factor).squeeze(0).squeeze(0)
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new_latent_image['noise_mask'] = region_mask
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new_latent_image = regional_prompt.sampler.sample_advanced("disable", seed, adv_steps, new_latent_image,
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i, i + 1, return_with_leftover_noise)
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i, i + 1, "enable", recover_special_sampler=True)
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if latent_restore:
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del new_latent_image['noise_mask']
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base_latent_image = latent_compositor.composite(base_latent_image, new_latent_image, 0, 0, False, region_mask)[0]
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new_latent_image = base_latent_image
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j += 1
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# finalize
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core.update_node_status(unique_id, f"finalize")
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if base_latent_image is not None:
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new_latent_image = base_latent_image
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new_latent_image['noise_mask'] = inv_mask
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new_latent_image = base_sampler.sample_advanced("disable", seed, adv_steps, new_latent_image, adv_steps, adv_steps+1, "disable", recover_special_sampler=False)
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core.update_node_status(unique_id, f"{steps}/{steps} steps", total)
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core.update_node_status(unique_id, "", None)
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del new_latent_image['noise_mask']
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if latent_restore:
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new_latent_image = base_latent_image
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if 'noise_mask' in new_latent_image:
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del new_latent_image['noise_mask']
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return (new_latent_image, )
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class RegionalSamplerAdvanced:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"add_noise": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled"}),
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"noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
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"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
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"start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}),
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"end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000}),
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"overlap_factor": ("INT", {"default": 10, "min": 0, "max": 10000}),
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"latent_restore": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled"}),
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"return_with_leftover_noise": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
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"latent_image": ("LATENT", ),
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"base_sampler": ("KSAMPLER_ADVANCED", ),
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"regional_prompts": ("REGIONAL_PROMPTS", ),
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},
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"hidden": {"unique_id": "UNIQUE_ID"},
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}
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RETURN_TYPES = ("LATENT", )
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FUNCTION = "doit"
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CATEGORY = "ImpactPack/Regional"
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def doit(self, add_noise, noise_seed, steps, start_at_step, end_at_step, overlap_factor, latent_restore,
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return_with_leftover_noise, latent_image, base_sampler, regional_prompts, unique_id):
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if latent_restore:
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latent_compositor = nodes.NODE_CLASS_MAPPINGS['LatentCompositeMasked']()
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else:
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latent_compositor = None
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masks = [regional_prompt.mask.numpy() for regional_prompt in regional_prompts]
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masks = [np.ceil(mask).astype(np.int32) for mask in masks]
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combined_mask = torch.from_numpy(np.bitwise_or.reduce(masks))
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inv_mask = torch.where(combined_mask == 0, torch.tensor(1.0), torch.tensor(0.0))
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region_len = len(regional_prompts)
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end_at_step = min(steps, end_at_step)
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total = (end_at_step - start_at_step) * region_len
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new_latent_image = latent_image.copy()
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base_latent_image = None
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region_masks = {}
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for i in range(start_at_step, end_at_step):
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core.update_node_status(unique_id, f"{start_at_step+i}/{end_at_step} steps | ", ((i-start_at_step)*region_len)/total)
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cur_add_noise = "enable" if i == start_at_step and add_noise else "disable"
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new_latent_image['noise_mask'] = inv_mask
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new_latent_image = base_sampler.sample_advanced(cur_add_noise, noise_seed, steps, new_latent_image, i, i + 1, "enable", recover_special_sampler=True)
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if latent_restore:
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del new_latent_image['noise_mask']
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base_latent_image = new_latent_image.copy()
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j = 1
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for regional_prompt in regional_prompts:
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if latent_restore:
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new_latent_image = base_latent_image.copy()
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core.update_node_status(unique_id, f"{start_at_step+i}/{end_at_step} steps | {j}/{region_len}", ((i-start_at_step)*region_len + j)/total)
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if j not in region_masks:
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region_mask = regional_prompt.get_mask_erosion(overlap_factor).squeeze(0).squeeze(0)
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region_masks[j] = region_mask
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else:
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region_mask = region_masks[j]
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new_latent_image['noise_mask'] = region_mask
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new_latent_image = regional_prompt.sampler.sample_advanced("disable", noise_seed, steps, new_latent_image,
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i, i + 1, "enable", recover_special_sampler=True)
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if latent_restore:
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del new_latent_image['noise_mask']
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base_latent_image = latent_compositor.composite(base_latent_image, new_latent_image, 0, 0, False, region_mask)[0]
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new_latent_image = base_latent_image
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j += 1
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# finalize
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core.update_node_status(unique_id, f"finalize")
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if base_latent_image is not None:
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new_latent_image = base_latent_image
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new_latent_image['noise_mask'] = inv_mask
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new_latent_image = base_sampler.sample_advanced("disable", noise_seed, steps, new_latent_image, end_at_step, end_at_step+1, "disable", recover_special_sampler=False)
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core.update_node_status(unique_id, f"{end_at_step}/{end_at_step} steps", total)
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core.update_node_status(unique_id, "", None)
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if latent_restore:
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new_latent_image = base_latent_image
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if 'noise_mask' in new_latent_image:
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del new_latent_image['noise_mask']
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return (new_latent_image, )
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class KSamplerBasicPipe:
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@classmethod
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def INPUT_TYPES(s):
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@@ -357,3 +492,43 @@ class KSamplerAdvancedBasicPipe:
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latent = nodes.KSamplerAdvanced().sample(model, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step, return_with_leftover_noise, denoise)[0]
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return (basic_pipe, latent, vae)
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class KSamplerAdvancedBasicPipe:
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@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required":
|
||||
{"basic_pipe": ("BASIC_PIPE",),
|
||||
"add_noise": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled"}),
|
||||
"noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
|
||||
"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
|
||||
"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
|
||||
"sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
|
||||
"scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
|
||||
"latent_image": ("LATENT", ),
|
||||
"start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}),
|
||||
"end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000}),
|
||||
"return_with_leftover_noise": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("BASIC_PIPE", "LATENT", "VAE")
|
||||
FUNCTION = "sample"
|
||||
|
||||
CATEGORY = "sampling"
|
||||
|
||||
def sample(self, basic_pipe, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, latent_image, start_at_step, end_at_step, return_with_leftover_noise, denoise=1.0):
|
||||
model, clip, vae, positive, negative = basic_pipe
|
||||
|
||||
if add_noise:
|
||||
add_noise = "enabled"
|
||||
else:
|
||||
add_noise = "disabled"
|
||||
|
||||
if return_with_leftover_noise:
|
||||
return_with_leftover_noise = "enabled"
|
||||
else:
|
||||
return_with_leftover_noise = "disabled"
|
||||
|
||||
latent = nodes.KSamplerAdvanced().sample(model, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step, return_with_leftover_noise, denoise)[0]
|
||||
return (basic_pipe, latent, vae)
|
||||
|
||||
Reference in New Issue
Block a user