diff --git a/Nodes/Outputs.py b/Nodes/Outputs.py
index 8da17ff..029fa76 100644
--- a/Nodes/Outputs.py
+++ b/Nodes/Outputs.py
@@ -15,6 +15,7 @@ import random
import nodes
import comfy_extras.nodes_custom_sampler as nodes_custom_sampler
import comfy_extras.nodes_stable_cascade as nodes_stable_cascade
+import comfy_extras.nodes_align_your_steps as nodes_align_your_steps
import torch
from ..components import utility
from ..components import latentnoise
@@ -433,11 +434,11 @@ class PrimereKSampler:
return {
"required": {
"model": ("MODEL", {"forceInput": True}),
- "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_name": (comfy.samplers.KSampler.SCHEDULERS, ),
+ "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "forceInput": True}),
+ "steps": ("INT", {"default": 20, "min": 1, "max": 10000, "forceInput": True}),
+ "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "forceInput": True}),
+ "sampler_name": (comfy.samplers.KSampler.SAMPLERS, {"forceInput": True}),
+ "scheduler_name": (comfy.samplers.KSampler.SCHEDULERS, {"forceInput": True}),
"positive": ("CONDITIONING", ),
"negative": ("CONDITIONING", ),
"latent_image": ("LATENT", ),
@@ -445,6 +446,7 @@ class PrimereKSampler:
"variation_extender": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"variation_batch_step": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 0.5, "step": 0.01}),
"device": (["DEFAULT", "GPU", "CPU"], {"default": 'DEFAULT'}),
+ "align_your_steps": ("BOOLEAN", {"default": False, "label_on": "Use AlignYourSteps", "label_off": "Ignore AlignYourSteps"}),
},
"optional": {
"model_concept": ("STRING", {"default": "Normal", "forceInput": True}),
@@ -455,7 +457,7 @@ class PrimereKSampler:
}
}
- def pk_sampler(self, model, seed, steps, cfg, sampler_name, scheduler_name, positive, negative, latent_image, extra_pnginfo, prompt, model_concept = "Normal", denoise=1.0, variation_extender = 0, variation_batch_step = 0, device = 'DEFAULT'):
+ def pk_sampler(self, model, seed, steps, cfg, sampler_name, scheduler_name, positive, negative, latent_image, extra_pnginfo, prompt, model_concept = "Normal", denoise=1.0, variation_extender = 0, variation_batch_step = 0, device = 'DEFAULT', align_your_steps = False):
samples = latent_image
variation_extender_original = variation_extender
variation_batch_step_original = variation_batch_step
@@ -471,55 +473,67 @@ class PrimereKSampler:
batch_counter = int(check_state(self, extra_pnginfo, prompt)) + 1
- match model_concept:
- case "Turbo":
- sigmas = nodes_custom_sampler.SDTurboScheduler().get_sigmas(model, steps, denoise)
- sampler = comfy.samplers.sampler_object(sampler_name)
- turbo_samples = nodes_custom_sampler.SamplerCustom().sample(model, True, seed, cfg, positive, negative, sampler, sigmas[0], latent_image)
- samples = (turbo_samples[0],)
- # return samples
+ if align_your_steps == True:
+ modelname_only = model
+ model_version = utility.get_value_from_cache('model_version', modelname_only)
+ match model_version:
+ case 'SDXL_2048':
+ model_type = 'SDXL'
+ case _:
+ model_type = 'SD1'
- case "Cascade":
- if type(model).__name__ == 'list':
- latent_size = utility.getLatentSize(latent_image)
- if (latent_size[0] < latent_size[1]):
- orientation = 'Vertical'
+ sigmas = nodes_align_your_steps.AlignYourStepsScheduler.get_sigmas(self, model_type, steps, denoise)
+ sampler = comfy.samplers.sampler_object(sampler_name)
+ AYS_samples = nodes_custom_sampler.SamplerCustom().sample(model, True, seed, cfg, positive, negative, sampler, sigmas[0], latent_image)
+ samples = (AYS_samples[0],)
+ else:
+ match model_concept:
+ case "Turbo":
+ sigmas = nodes_custom_sampler.SDTurboScheduler().get_sigmas(model, steps, denoise)
+ sampler = comfy.samplers.sampler_object(sampler_name)
+ turbo_samples = nodes_custom_sampler.SamplerCustom().sample(model, True, seed, cfg, positive, negative, sampler, sigmas[0], latent_image)
+ samples = (turbo_samples[0],)
+
+ case "Cascade":
+ if type(model).__name__ == 'list':
+ latent_size = utility.getLatentSize(latent_image)
+ if (latent_size[0] < latent_size[1]):
+ orientation = 'Vertical'
+ else:
+ orientation = 'Horizontal'
+
+ dimensions = utility.get_dimensions_by_shape(self, 'Square [1:1]', 1024, orientation, True, True, latent_size[0], latent_size[1], 'CASCADE')
+ dimension_x = dimensions[0]
+ dimension_y = dimensions[1]
+
+ height = dimension_y
+ width = dimension_x
+ compression = 42
+ if type(model[0]).__name__ == 'ModelPatcher' and type(model[1]).__name__ == 'ModelPatcher':
+ c_latent = {"samples": torch.zeros([1, 16, height // compression, width // compression])}
+ b_latent = {"samples": torch.zeros([1, 4, height // 4, width // 4])}
+ samples_c = nodes.KSampler.sample(self, model[1], seed, steps, cfg, sampler_name, scheduler_name, positive, negative, c_latent, denoise=denoise)[0]
+ conditining_c = nodes_stable_cascade.StableCascade_StageB_Conditioning.set_prior(self, positive, samples_c)[0]
+ samples = nodes.KSampler.sample(self, model[0], seed, 10, 1.00, sampler_name, scheduler_name, conditining_c, negative, b_latent, denoise=denoise)
+ # return samples
+ case _:
+ if variation_batch_step_original > 0:
+ if batch_counter > 0:
+ variation_batch_step = variation_batch_step_original * batch_counter
+
+ variation_extender = round(variation_extender_original + variation_batch_step, 2)
+
+ if variation_extender_original > 0 or device != 'DEFAULT' or variation_batch_step_original > 0:
+ if (variation_extender > 1):
+ random.seed(batch_counter)
+ variation_extender = round(random.uniform(0.01, 1.00), 2)
+ if variation_batch_step == 0:
+ variation_seed = batch_counter + seed
+ else:
+ variation_seed = seed
+ samples = latentnoise.noisy_samples(model, device, steps, cfg, sampler_name, scheduler_name, positive, negative, latent_image, denoise, variation_seed, variation_extender)
else:
- orientation = 'Horizontal'
-
- dimensions = utility.get_dimensions_by_shape(self, 'Square [1:1]', 1024, orientation, True, True, latent_size[0], latent_size[1], 'CASCADE')
- dimension_x = dimensions[0]
- dimension_y = dimensions[1]
-
- height = dimension_y
- width = dimension_x
- compression = 42
- if type(model[0]).__name__ == 'ModelPatcher' and type(model[1]).__name__ == 'ModelPatcher':
- c_latent = {"samples": torch.zeros([1, 16, height // compression, width // compression])}
- b_latent = {"samples": torch.zeros([1, 4, height // 4, width // 4])}
- samples_c = nodes.KSampler.sample(self, model[1], seed, steps, cfg, sampler_name, scheduler_name, positive, negative, c_latent, denoise=denoise)[0]
- conditining_c = nodes_stable_cascade.StableCascade_StageB_Conditioning.set_prior(self, positive, samples_c)[0]
- samples = nodes.KSampler.sample(self, model[0], seed, 10, 1.00, sampler_name, scheduler_name, conditining_c, negative, b_latent, denoise=denoise)
- # return samples
- case _:
- if variation_batch_step_original > 0:
- if batch_counter > 0:
- variation_batch_step = variation_batch_step_original * batch_counter
-
- variation_extender = round(variation_extender_original + variation_batch_step, 2)
-
- if variation_extender_original > 0 or device != 'DEFAULT' or variation_batch_step_original > 0:
- if (variation_extender > 1):
- random.seed(batch_counter)
- variation_extender = round(random.uniform(0.01, 1.00), 2)
- if variation_batch_step == 0:
- variation_seed = batch_counter + seed
- else:
- variation_seed = seed
- samples = latentnoise.noisy_samples(model, device, steps, cfg, sampler_name, scheduler_name, positive, negative, latent_image, denoise, variation_seed, variation_extender)
- else:
- samples = nodes.KSampler.sample(self, model, seed, steps, cfg, sampler_name, scheduler_name, positive, negative, latent_image, denoise=denoise)
- # return samples
+ samples = nodes.KSampler.sample(self, model, seed, steps, cfg, sampler_name, scheduler_name, positive, negative, latent_image, denoise=denoise)
return samples
diff --git a/README.md b/README.md
index bd6e96e..31dbb8f 100644
--- a/README.md
+++ b/README.md
@@ -55,6 +55,7 @@ Git link: https://github.com/CosmicLaca/ComfyUI_Primere_Nodes
## Last changes:
#### Usually after node changes have to reload/re-wire nodes within existing workflow, or open the latest workflows from the nodepack's **Workflow** folder.
+- Nvidia AlignYourSteps support on sampler: https://research.nvidia.com/labs/toronto-ai/AlignYourSteps/
- Image recycler node read images without meta, using Pic2Story model to generate prompt from picture only
- Some nodes moved to **deprecated** subtree. Nodes can be used but not developed in the future.
- **Segmented refiners** will mesure the aesthetic score of results, and if the original segment is better, changes will be ignored. Only in **Primere_full_workflow.json** workflow. Feature can switch off.
@@ -518,6 +519,7 @@ Get the aesthetic score of your generated image.
### Primere KSampler:
Sampler using the 'model_concept' input this node automatically handle Turbo and Cascade modes, no need another workflow or extra node. You can select device (CPU or GPU), and use 'variation_extender' input for new image with very less (adjustable) difference from previous one (if seed and other details freezed). This settings can be used in queued workflow.
+- One button support of Nvidia AlignYourStpes: https://research.nvidia.com/labs/toronto-ai/AlignYourSteps/
diff --git a/Workflow/Primere_advanced_workflow.json b/Workflow/Primere_advanced_workflow.json
index 7050320..098ece1 100644
--- a/Workflow/Primere_advanced_workflow.json
+++ b/Workflow/Primere_advanced_workflow.json
@@ -765,2243 +765,6 @@
"color": "#945414",
"bgcolor": "#804000"
},
- {
- "id": 126,
- "type": "PrimereAestheticCKPTScorer",
- "pos": [
- 3189,
- 383
- ],
- "size": {
- "0": 304.99700927734375,
- "1": 168
- },
- "flags": {},
- "order": 42,
- "mode": 0,
- "inputs": [
- {
- "name": "image",
- "type": "IMAGE",
- "link": 769
- },
- {
- "name": "workflow_data",
- "type": "TUPLE",
- "link": null
- }
- ],
- "outputs": [
- {
- "name": "SCORE",
- "type": "INT",
- "links": null,
- "shape": 3
- }
- ],
- "properties": {
- "Node name for S&R": "PrimereAestheticCKPTScorer"
- },
- "widgets_values": [
- true,
- false,
- false,
- "717"
- ],
- "color": "#540054",
- "bgcolor": "#680068"
- },
- {
- "id": 105,
- "type": "PrimerePromptSwitch",
- "pos": [
- 356,
- 92
- ],
- "size": {
- "0": 276.6921691894531,
- "1": 278
- },
- "flags": {},
- "order": 17,
- "mode": 0,
- "inputs": [
- {
- "name": "prompt_pos_1",
- "type": "STRING",
- "link": 494
- },
- {
- "name": "prompt_neg_1",
- "type": "STRING",
- "link": 495
- },
- {
- "name": "preferred_1",
- "type": "STRING",
- "link": 496
- },
- {
- "name": "prompt_pos_2",
- "type": "STRING",
- "link": 497
- },
- {
- "name": "prompt_neg_2",
- "type": "STRING",
- "link": 498
- },
- {
- "name": "preferred_2",
- "type": "STRING",
- "link": 499
- },
- {
- "name": "prompt_pos_3",
- "type": "STRING",
- "link": 500
- },
- {
- "name": "prompt_neg_3",
- "type": "STRING",
- "link": 501
- },
- {
- "name": "preferred_3",
- "type": "STRING",
- "link": 502
- },
- {
- "name": "prompt_pos_4",
- "type": "STRING",
- "link": null
- },
- {
- "name": "prompt_neg_4",
- "type": "STRING",
- "link": null
- },
- {
- "name": "preferred_4",
- "type": "STRING",
- "link": null
- }
- ],
- "outputs": [
- {
- "name": "PROMPT+",
- "type": "STRING",
- "links": [
- 503
- ],
- "shape": 3,
- "slot_index": 0
- },
- {
- "name": "PROMPT-",
- "type": "STRING",
- "links": [
- 504
- ],
- "shape": 3,
- "slot_index": 1
- },
- {
- "name": "SELECTED_INDEX",
- "type": "STRING",
- "links": null,
- "shape": 3
- },
- {
- "name": "PREFERRED",
- "type": "TUPLE",
- "links": [
- 559
- ],
- "shape": 3,
- "slot_index": 3
- }
- ],
- "properties": {
- "Node name for S&R": "PrimerePromptSwitch"
- },
- "widgets_values": [
- 2
- ],
- "color": "#145414",
- "bgcolor": "#004000"
- },
- {
- "id": 41,
- "type": "PrimereModelKeyword",
- "pos": [
- 660.8593785309592,
- 255.63418691496116
- ],
- "size": {
- "0": 468.95831298828125,
- "1": 186.38485717773438
- },
- "flags": {},
- "order": 18,
- "mode": 0,
- "inputs": [
- {
- "name": "model_name",
- "type": "CHECKPOINT_NAME",
- "link": 146
- }
- ],
- "outputs": [
- {
- "name": "MODEL_KEYWORD",
- "type": "MODEL_KEYWORD",
- "links": [
- 752
- ],
- "shape": 3,
- "slot_index": 0
- }
- ],
- "properties": {
- "Node name for S&R": "PrimereModelKeyword"
- },
- "widgets_values": [
- true,
- "Last",
- 1,
- 1,
- "design"
- ],
- "color": "#1414ff",
- "bgcolor": "#0000ff"
- },
- {
- "id": 121,
- "type": "PrimereConceptDataTuple",
- "pos": [
- 1432,
- 461
- ],
- "size": {
- "0": 359.9978942871094,
- "1": 134
- },
- "flags": {},
- "order": 19,
- "mode": 0,
- "inputs": [
- {
- "name": "lightning_selector",
- "type": "STRING",
- "link": 721,
- "widget": {
- "name": "lightning_selector"
- }
- },
- {
- "name": "lightning_model_step",
- "type": "INT",
- "link": 722,
- "widget": {
- "name": "lightning_model_step"
- }
- },
- {
- "name": "cascade_stage_a",
- "type": "STRING",
- "link": 723,
- "widget": {
- "name": "cascade_stage_a"
- }
- },
- {
- "name": "cascade_stage_b",
- "type": "STRING",
- "link": 724,
- "widget": {
- "name": "cascade_stage_b"
- }
- },
- {
- "name": "cascade_stage_c",
- "type": "STRING",
- "link": 725,
- "widget": {
- "name": "cascade_stage_c"
- }
- },
- {
- "name": "cascade_clip",
- "type": "STRING",
- "link": 726,
- "widget": {
- "name": "cascade_clip"
- }
- }
- ],
- "outputs": [
- {
- "name": "CONCEPT_DATA",
- "type": "TUPLE",
- "links": [
- 709,
- 728
- ],
- "shape": 3,
- "slot_index": 0
- }
- ],
- "properties": {
- "Node name for S&R": "PrimereConceptDataTuple"
- },
- "widgets_values": [
- "SAFETENSOR",
- 8,
- "",
- "",
- "",
- ""
- ],
- "color": "#1414ff",
- "bgcolor": "#0000ff"
- },
- {
- "id": 6,
- "type": "PrimereDynamicParser",
- "pos": [
- -73,
- 1417
- ],
- "size": {
- "0": 274.1334533691406,
- "1": 54
- },
- "flags": {},
- "order": 20,
- "mode": 0,
- "inputs": [
- {
- "name": "dyn_prompt",
- "type": "STRING",
- "link": 503,
- "widget": {
- "name": "dyn_prompt"
- },
- "slot_index": 0
- },
- {
- "name": "seed",
- "type": "INT",
- "link": 17,
- "widget": {
- "name": "seed"
- }
- }
- ],
- "outputs": [
- {
- "name": "PROMPT",
- "type": "STRING",
- "links": [
- 13
- ],
- "shape": 3,
- "slot_index": 0
- }
- ],
- "properties": {
- "Node name for S&R": "PrimereDynamicParser"
- },
- "widgets_values": [
- "",
- 801174634269412,
- "randomize"
- ],
- "color": "#145414",
- "bgcolor": "#004000"
- },
- {
- "id": 7,
- "type": "PrimereDynamicParser",
- "pos": [
- -356,
- 1416
- ],
- "size": {
- "0": 275.1334533691406,
- "1": 54
- },
- "flags": {},
- "order": 21,
- "mode": 0,
- "inputs": [
- {
- "name": "dyn_prompt",
- "type": "STRING",
- "link": 504,
- "widget": {
- "name": "dyn_prompt"
- },
- "slot_index": 0
- },
- {
- "name": "seed",
- "type": "INT",
- "link": 18,
- "widget": {
- "name": "seed"
- }
- }
- ],
- "outputs": [
- {
- "name": "PROMPT",
- "type": "STRING",
- "links": [
- 14
- ],
- "shape": 3,
- "slot_index": 0
- }
- ],
- "properties": {
- "Node name for S&R": "PrimereDynamicParser"
- },
- "widgets_values": [
- "",
- 945808882105283,
- "randomize"
- ],
- "color": "#541414",
- "bgcolor": "#400000"
- },
- {
- "id": 98,
- "type": "PrimereCKPTLoader",
- "pos": [
- 1139,
- 258
- ],
- "size": {
- "0": 283.30560302734375,
- "1": 206
- },
- "flags": {},
- "order": 22,
- "mode": 0,
- "inputs": [
- {
- "name": "ckpt_name",
- "type": "CHECKPOINT_NAME",
- "link": 456
- },
- {
- "name": "concept_data",
- "type": "TUPLE",
- "link": 709
- },
- {
- "name": "loaded_model",
- "type": "MODEL",
- "link": null
- },
- {
- "name": "loaded_clip",
- "type": "CLIP",
- "link": null
- },
- {
- "name": "loaded_vae",
- "type": "VAE",
- "link": null
- },
- {
- "name": "model_concept",
- "type": "STRING",
- "link": 720,
- "widget": {
- "name": "model_concept"
- }
- }
- ],
- "outputs": [
- {
- "name": "MODEL",
- "type": "MODEL",
- "links": [
- 538
- ],
- "shape": 3,
- "slot_index": 0
- },
- {
- "name": "CLIP",
- "type": "CLIP",
- "links": [
- 539
- ],
- "shape": 3,
- "slot_index": 1
- },
- {
- "name": "VAE",
- "type": "VAE",
- "links": [
- 488,
- 490,
- 491,
- 492,
- 742
- ],
- "shape": 3,
- "slot_index": 2
- },
- {
- "name": "MODEL_VERSION",
- "type": "STRING",
- "links": [
- 466,
- 468,
- 483,
- 550,
- 564,
- 753
- ],
- "shape": 3,
- "slot_index": 3
- }
- ],
- "properties": {
- "Node name for S&R": "PrimereCKPTLoader"
- },
- "widgets_values": [
- false,
- 1,
- 1,
- "Normal"
- ],
- "color": "#1414ff",
- "bgcolor": "#0000ff"
- },
- {
- "id": 8,
- "type": "PrimereEmbeddingHandler",
- "pos": [
- 211,
- 1417
- ],
- "size": {
- "0": 417.9468688964844,
- "1": 54
- },
- "flags": {},
- "order": 23,
- "mode": 0,
- "inputs": [
- {
- "name": "positive_prompt",
- "type": "STRING",
- "link": 13,
- "widget": {
- "name": "positive_prompt"
- }
- },
- {
- "name": "negative_prompt",
- "type": "STRING",
- "link": 14,
- "widget": {
- "name": "negative_prompt"
- }
- }
- ],
- "outputs": [
- {
- "name": "PROMPT+",
- "type": "STRING",
- "links": [
- 544,
- 546
- ],
- "shape": 3,
- "slot_index": 0
- },
- {
- "name": "PROMPT-",
- "type": "STRING",
- "links": [
- 547
- ],
- "shape": 3,
- "slot_index": 1
- }
- ],
- "properties": {
- "Node name for S&R": "PrimereEmbeddingHandler"
- },
- "widgets_values": [
- "",
- ""
- ],
- "color": "#145414",
- "bgcolor": "#004000"
- },
- {
- "id": 104,
- "type": "PrimereVAELoader",
- "pos": [
- 1045,
- 651
- ],
- "size": {
- "0": 166.3192901611328,
- "1": 46
- },
- "flags": {},
- "order": 24,
- "mode": 0,
- "inputs": [
- {
- "name": "vae_name",
- "type": "VAE_NAME",
- "link": 670
- },
- {
- "name": "baked_vae",
- "type": "VAE",
- "link": 490
- }
- ],
- "outputs": [
- {
- "name": "VAE",
- "type": "VAE",
- "links": [
- 669
- ],
- "shape": 3,
- "slot_index": 0
- }
- ],
- "properties": {
- "Node name for S&R": "PrimereVAELoader"
- },
- "color": "#145494",
- "bgcolor": "#004080"
- },
- {
- "id": 59,
- "type": "PrimereVAELoader",
- "pos": [
- 1218,
- 653
- ],
- "size": {
- "0": 167.17726135253906,
- "1": 46
- },
- "flags": {},
- "order": 25,
- "mode": 0,
- "inputs": [
- {
- "name": "vae_name",
- "type": "VAE_NAME",
- "link": 203
- },
- {
- "name": "baked_vae",
- "type": "VAE",
- "link": 491
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index ed49ebc..c62638d 100644
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index fe3e8ce..437de64 100644
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"type": "PrimereMidjourneyStyles",
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false,
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"Node name for S&R": "PrimereAnyDetailer"
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"color": "#7e461a",
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"color": "#236121",
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diff --git a/Workflow/Primere_minimal_workflow.json b/Workflow/Primere_minimal_workflow.json
index 70c9697..85c7ee4 100644
--- a/Workflow/Primere_minimal_workflow.json
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