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Bugfix encode step for XY_Capsule (inspire pack) (#187)

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* bugfix: in case of XY_Capsule, prompt needs to be encoded by the analysed LoRA's clip

* bugfix: when processing LoRAs you need to reload the LoRA every index on the y scale (not only the first one) to maintain the LoRA-patched clip.

* add align your step schedulers via monkey patching

---------

Co-authored-by: larsupb <larsupb@gmx.net>
This commit is contained in:
larsupb
2024-06-17 09:19:59 +02:00
committed by GitHub
parent 08a06700fa
commit 5c5a8b7382
1 changed files with 19 additions and 4 deletions
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23
efficiency_nodes.py
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@@ -18,6 +18,8 @@ import subprocess
import json
import psutil
from comfy_extras.nodes_align_your_steps import AlignYourStepsScheduler
# Get the absolute path of various directories
my_dir = os.path.dirname(os.path.abspath(__file__))
custom_nodes_dir = os.path.abspath(os.path.join(my_dir, '..'))
@@ -52,12 +54,17 @@ from .py import bnk_tiled_samplers
from .py import bnk_adv_encode
sys.path.remove(my_dir)
from comfy import samplers
# Append custom_nodes_dir to sys.path
sys.path.append(custom_nodes_dir)
# GLOBALS
REFINER_CFG_OFFSET = 0 #Refiner CFG Offset
# Monkey patch schedulers
samplers.SCHEDULER_NAMES = samplers.SCHEDULER_NAMES + ["AYS SD1", "AYS SDXL", "AYS SVD"]
samplers.KSampler.SCHEDULERS = samplers.KSampler.SCHEDULERS + ["AYS SD1", "AYS SDXL", "AYS SVD"]
########################################################################################################################
# Common function for encoding prompts
def encode_prompts(positive_prompt, negative_prompt, token_normalization, weight_interpretation, clip, clip_skip,
@@ -394,7 +401,6 @@ class TSC_Apply_ControlNet_Stack:
########################################################################################################################
# TSC KSampler (Efficient)
class TSC_KSampler:
empty_image = pil2tensor(Image.new('RGBA', (1, 1), (0, 0, 0, 0)))
@classmethod
@@ -429,6 +435,15 @@ class TSC_KSampler:
optional_vae=(None,), script=None, add_noise=None, start_at_step=None, end_at_step=None,
return_with_leftover_noise=None, sampler_type="regular"):
# monkey patch the sample function
original_calculation = comfy.samplers.calculate_sigmas
def calculate_sigmas(model_sampling, scheduler_name: str, steps):
if scheduler_name.startswith("AYS"):
return AlignYourStepsScheduler().get_sigmas(scheduler_name.split(" ")[1], steps, denoise=1.0)[0]
return original_calculation(model_sampling, scheduler_name, steps)
comfy.samplers.SCHEDULER_NAMES = comfy.samplers.SCHEDULER_NAMES + ["AYS SD1", "AYS SDXL", "AYS SVD"]
comfy.samplers.calculate_sigmas = calculate_sigmas
# Rename the vae variable
vae = optional_vae
@@ -1363,7 +1378,7 @@ class TSC_KSampler:
encode = True
# Load LoRA if required
elif (X_type == "LoRA" and index == 0):
elif (X_type == "LoRA"):
# Don't cache Checkpoints
model, clip = load_lora(lora_stack, ckpt_name, xyplot_id, cache=cache[2])
encode = True
@@ -1383,13 +1398,13 @@ class TSC_KSampler:
# Encode base prompt if required
encode_types = ["Positive Prompt S/R", "Negative Prompt S/R", "Clip Skip", "ControlNetStrength",
"ControlNetStart%", "ControlNetEnd%"]
"ControlNetStart%", "ControlNetEnd%", "XY_Capsule"]
if (X_type in encode_types and index == 0) or Y_type in encode_types:
encode = True
# Encode refiner prompt if required
encode_refiner_types = ["Positive Prompt S/R", "Negative Prompt S/R", "AScore+", "AScore-",
"Clip Skip (Refiner)"]
"Clip Skip (Refiner)", "XY_Capsule"]
if (X_type in encode_refiner_types and index == 0) or Y_type in encode_refiner_types:
encode_refiner = True