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V 0.6.0 - Align your steps to Sampler

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DESKTOP-CNFQ7PM\Primere
2024-04-30 23:41:17 +02:00
parent 5e40fa7b56
commit fad4aa8f2c
6 changed files with 3288 additions and 3258 deletions
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Nodes/Outputs.py
+67 -53
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@@ -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
README.md
+2
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@@ -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/
<a href="./Workflow/readme_images/pksampler.jpg" target="_blank"><img src="./Workflow/readme_images/pksampler.jpg" height="220px"></a>
<hr>
Workflow/Primere_advanced_workflow.json
+2246 -2241
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Workflow/Primere_basic_workflow.json
+9 -8
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@@ -1306,7 +1306,7 @@
},
"widgets_values": [
"",
712453657043459,
211084894698341,
"randomize"
],
"color": "#145414",
@@ -1361,7 +1361,7 @@
},
"widgets_values": [
"",
946952457448339,
239872422581783,
"randomize"
],
"color": "#145414",
@@ -1825,7 +1825,7 @@
false,
1.6,
2.8,
711756667041831,
1095399425108326,
"randomize",
"BaseModel_1024",
"Normal"
@@ -1998,7 +1998,7 @@
1,
0.8,
1.4,
503831462214994,
1071756380805453,
"randomize",
false,
"cpu",
@@ -2267,7 +2267,7 @@
],
"size": {
"0": 322.92181396484375,
"1": 310
"1": 334
},
"flags": {},
"order": 26,
@@ -2357,7 +2357,7 @@
"Node name for S&R": "PrimereKSampler"
},
"widgets_values": [
311963377597273,
349545987479056,
"randomize",
20,
8,
@@ -2367,6 +2367,7 @@
0,
0,
"DEFAULT",
false,
"Normal"
],
"color": "#941414",
@@ -2458,8 +2459,8 @@
"id": 92,
"type": "PrimereAestheticCKPTScorer",
"pos": [
2609,
523
2611,
546
],
"size": {
"0": 323.20916748046875,
Workflow/Primere_full_workflow.json
+953 -946
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File diff suppressed because it is too large Load Diff
Workflow/Primere_minimal_workflow.json
+11 -10
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@@ -1,5 +1,5 @@
{
"last_node_id": 66,
"last_node_id": 69,
"last_link_id": 284,
"nodes": [
{
@@ -724,7 +724,7 @@
},
"widgets_values": [
"",
636402502684314,
1093923594010474,
"randomize"
],
"color": "#145414",
@@ -779,7 +779,7 @@
},
"widgets_values": [
"",
798381124197252,
744357966969803,
"randomize"
],
"color": "#145414",
@@ -873,7 +873,7 @@
false,
1.6,
2.8,
90995882162185,
663109167090432,
"randomize",
"BaseModel_1024",
"Normal"
@@ -1023,7 +1023,7 @@
1,
0.8,
1.4,
242258049340592,
1113455142479464,
"randomize",
false,
"cpu",
@@ -1494,7 +1494,7 @@
],
"size": {
"0": 341.0091247558594,
"1": 310
"1": 334
},
"flags": {},
"order": 19,
@@ -1584,7 +1584,7 @@
"Node name for S&R": "PrimereKSampler"
},
"widgets_values": [
1089729015963368,
205252127749919,
"randomize",
20,
8,
@@ -1594,6 +1594,7 @@
0,
0,
"DEFAULT",
false,
"Normal"
],
"color": "#941414",
@@ -1685,8 +1686,8 @@
"id": 66,
"type": "PrimereAestheticCKPTScorer",
"pos": [
2504,
522
2508,
550
],
"size": {
"0": 341.3660888671875,
@@ -1722,7 +1723,7 @@
true,
false,
false,
"619"
"620"
],
"color": "#2c002c",
"bgcolor": "#400040"