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Merge pull request #67 from LucianoCirino/KSampler-Adv.-(Efficient)-Node

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KSampler Adv. (Efficient) Node
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TSC
2023-07-30 20:51:37 -05:00
committed by GitHub
parent ad3a073fdb c7574186b7
commit acfecf6ce8
1 changed files with 60 additions and 8 deletions
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68
efficiency_nodes.py
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@@ -2,7 +2,7 @@
# by Luciano Cirino (Discord: TSC#9184) - April 2023
from comfy.sd import ModelPatcher, CLIP, VAE
from nodes import common_ksampler, CLIPSetLastLayer, CLIPTextEncode
from nodes import KSampler, KSamplerAdvanced, CLIPSetLastLayer, CLIPTextEncode
from torch import Tensor
from PIL import Image, ImageOps, ImageDraw, ImageFont
@@ -244,7 +244,8 @@ class TSC_KSampler:
def sample(self, sampler_state, model, seed, steps, cfg, sampler_name, scheduler, positive, negative,
latent_image, preview_image, denoise=1.0, prompt=None, extra_pnginfo=None, my_unique_id=None,
optional_vae=(None,), script=None):
optional_vae=(None,), script=None, add_noise=None, start_at_step=None, end_at_step=None,
return_with_leftover_noise=None):
# Extract node_settings from json
def get_settings():
@@ -376,9 +377,12 @@ class TSC_KSampler:
# Check the current sampler state
if sampler_state == "Sample":
# Sample using the common KSampler function and store the samples
samples = common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative,
latent_image, denoise=denoise)
# Sample using the Comfy KSampler nodes
if add_noise==None:
samples = KSampler().sample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise)
else:
samples = KSamplerAdvanced().sample(model, add_noise, seed, steps, cfg, sampler_name, scheduler, positive, negative,
latent_image, start_at_step, end_at_step, return_with_leftover_noise, denoise=1.0)
# Extract the latent samples from the returned samples dictionary
latent = samples[0]["samples"]
@@ -801,9 +805,14 @@ class TSC_KSampler:
def process_values(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
denoise, vae, latent_list=[], image_tensor_list=[], image_pil_list=[]):
# Sample
samples = common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative,
latent_image, denoise=denoise)
# Sample using the Comfy KSampler nodes
if add_noise == None:
samples = KSampler().sample(model, seed, steps, cfg, sampler_name, scheduler,
positive, negative, latent_image, denoise=denoise)
else:
samples = KSamplerAdvanced().sample(model, add_noise, seed, steps, cfg, sampler_name, scheduler,
positive, negative, latent_image, start_at_step, end_at_step,
return_with_leftover_noise, denoise=1.0)
# Decode images and store
latent = samples[0]["samples"]
@@ -1264,6 +1273,48 @@ class TSC_KSampler:
"result": (model, positive, negative, {"samples": latent_list}, vae, image_tensor_list,)
}
# TSC KSampler Adv (Efficient)
class TSC_KSamplerAdvanced(TSC_KSampler):
@classmethod
def INPUT_TYPES(cls):
return {"required":
{"sampler_state": (["Sample", "Hold", "Script"],),
"model": ("MODEL",),
"add_noise": (["enable", "disable"],),
"noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
"cfg": ("FLOAT", {"default": 7.0, "min": 0.0, "max": 100.0}),
"sampler_name": (comfy.samplers.KSampler.SAMPLERS,),
"scheduler": (comfy.samplers.KSampler.SCHEDULERS,),
"positive": ("CONDITIONING",),
"negative": ("CONDITIONING",),
"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": (["disable", "enable"],),
"preview_image": (["Disabled", "Enabled", "Output Only"],),
},
"optional": {"optional_vae": ("VAE",),
"script": ("SCRIPT",), },
"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "my_unique_id": "UNIQUE_ID", },
}
RETURN_TYPES = ("MODEL", "CONDITIONING", "CONDITIONING", "LATENT", "VAE", "IMAGE",)
RETURN_NAMES = ("MODEL", "CONDITIONING+", "CONDITIONING-", "LATENT", "VAE", "IMAGE",)
OUTPUT_NODE = True
FUNCTION = "sampleadv"
CATEGORY = "Efficiency Nodes/Sampling"
def sampleadv(self, sampler_state, model, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative,
latent_image, start_at_step, end_at_step, return_with_leftover_noise, preview_image,
prompt=None, extra_pnginfo=None, my_unique_id=None, optional_vae=(None,), script=None):
return super().sample(sampler_state, model, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative,
latent_image, preview_image, denoise=1.0, prompt=prompt, extra_pnginfo=extra_pnginfo, my_unique_id=my_unique_id,
optional_vae=optional_vae, script=script, add_noise=add_noise, start_at_step=start_at_step,end_at_step=end_at_step,
return_with_leftover_noise=return_with_leftover_noise)
########################################################################################################################
# TSC XY Plot
class TSC_XYplot:
@@ -2451,6 +2502,7 @@ class TSC_EvalExamples:
# NODE MAPPING
NODE_CLASS_MAPPINGS = {
"KSampler (Efficient)": TSC_KSampler,
"KSampler Adv. (Efficient)":TSC_KSamplerAdvanced,
"Efficient Loader": TSC_EfficientLoader,
"LoRA Stacker": TSC_LoRA_Stacker,
"LoRA Stacker Adv.": TSC_LoRA_Stacker_Adv,