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Small Fixes & Updates

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1) Reintroduced Manual XY Entry Node
2) Fixed issue with KSampler outputs when in script mode
3) Unpack SDXL Tuple was fixed
This commit is contained in:
TSC
2023-09-03 13:53:07 -05:00
committed by GitHub
parent 01bb6c810a
commit 6b5589794a
1 changed files with 71 additions and 27 deletions
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98
efficiency_nodes.py
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@@ -234,9 +234,9 @@ class TSC_Unpack_SDXL_Tuple:
def INPUT_TYPES(cls):
return {"required": {"sdxl_tuple": ("SDXL_TUPLE",)},}
RETURN_TYPES = ("MODEL", "CLIP", "MODEL", "CLIP", "CONDITIONING","CONDITIONING", "CONDITIONING", "CONDITIONING",)
RETURN_NAMES = ("BASE_MODEL", "BASE_CLIP", "REFINER_MODEL", "REFINER_CLIP",
"BASE_CONDITIONING+", "BASE_CONDITIONING-","REFINER_CONDITIONING+","REFINER_CONDITIONING-",)
RETURN_TYPES = ("MODEL", "CLIP", "CONDITIONING","CONDITIONING", "MODEL", "CLIP", "CONDITIONING", "CONDITIONING",)
RETURN_NAMES = ("BASE_MODEL", "BASE_CLIP", "BASE_CONDITIONING+", "BASE_CONDITIONING-",
"REFINER_MODEL", "REFINER_CLIP","REFINER_CONDITIONING+","REFINER_CONDITIONING-",)
FUNCTION = "unpack_sdxl_tuple"
CATEGORY = "Efficiency Nodes/Misc"
@@ -1240,15 +1240,15 @@ class TSC_KSampler:
elif var_type == "LoRA Wt":
lora_model_wt = format(float(lora_model_wt), ".2f").rstrip('0').rstrip('.')
text = f"LoRA[Wt]: {lora_model_wt}"
text = f"LoRA Wt: {lora_model_wt}"
elif var_type == "LoRA MStr":
lora_model_wt = format(float(lora_model_wt), ".2f").rstrip('0').rstrip('.')
text = f"LoRA[M]: {lora_model_wt}"
text = f"LoRA Mstr: {lora_model_wt}"
elif var_type == "LoRA CStr":
lora_clip_wt = format(float(lora_clip_wt), ".2f").rstrip('0').rstrip('.')
text = f"LoRA[C]: {lora_clip_wt}"
text = f"LoRA Cstr: {lora_clip_wt}"
elif var_type in ["ControlNetStrength", "ControlNetStart%", "ControlNetEnd%"]:
if "Strength" in var_type:
@@ -1463,13 +1463,25 @@ class TSC_KSampler:
# Change the global preview method temporarily during this node's execution
set_preview_method(preview_method)
original_model = model.clone()
original_clip = clip.clone()
# Clone original model parameters
def clone_or_none(*originals):
cloned_items = []
for original in originals:
try:
cloned_items.append(original.clone())
except (AttributeError, TypeError):
# If not clonable, just append the original item
cloned_items.append(original)
return cloned_items
original_model, original_clip, original_positive, original_negative,\
original_refiner_model, original_refiner_clip, original_refiner_positive, original_refiner_negative =\
clone_or_none(model, clip, positive, negative, refiner_model, refiner_clip, refiner_positive, refiner_negative)
# Fill Plot Rows (X)
for X_index, X in enumerate(X_value):
model = original_model.clone()
clip = original_clip.clone()
# Reset model parameters to their originals
model, clip, refiner_model, refiner_clip = \
clone_or_none(original_model, original_clip, original_refiner_model, original_refiner_clip)
# Define X parameters and generate labels
add_noise, seed, steps, start_at_step, end_at_step, return_with_leftover_noise, cfg,\
@@ -1504,10 +1516,11 @@ class TSC_KSampler:
refiner_positive, refiner_negative, latent_image, denoise, vae, vae_decode, sampler_type, xy_capsule=xy_capsule)
elif X_type != "Nothing" and Y_type != "Nothing":
# Seed control based on loop index during Batch
for Y_index, Y in enumerate(Y_value):
model = original_model.clone()
clip = original_clip.clone()
# Reset model parameters to their originals
model, clip, refiner_model, refiner_clip = \
clone_or_none(original_model, original_clip, original_refiner_model, original_refiner_clip)
if Y_type == "XY_Capsule" and X_type == "XY_Capsule":
Y.set_x_capsule(X)
@@ -1592,6 +1605,10 @@ class TSC_KSampler:
def get_checkpoint_name(X_type, Y_type, X_value, Y_value, ckpt_name, clip_skip, mode, vae_name=None):
# If ckpt_name is None, return it as is
if ckpt_name is not None:
ckpt_name = os.path.basename(ckpt_name)
# Define types based on mode
primary_type = "Checkpoint" if mode == "ckpt" else "Refiner"
clip_type = "Clip Skip" if mode == "ckpt" else "Clip Skip (Refiner)"
@@ -1609,11 +1626,10 @@ class TSC_KSampler:
if vae_name:
vae_name = get_vae_name(X_type, Y_type, X_value, Y_value, vae_name)
clip_skip = get_clip_skip(X_type, Y_type, X_value, Y_value, clip_skip)
ckpt_name = os.path.basename(ckpt_name)
return os.path.basename(ckpt_name), clip_skip, vae_name
return ckpt_name, clip_skip, vae_name
else:
# For refn mode
return os.path.basename(ckpt_name), clip_skip
return ckpt_name, clip_skip
# Process clip skip based on mode
if othr_type == clip_type:
@@ -1664,24 +1680,46 @@ class TSC_KSampler:
else:
if X_type in lora_types:
value = get_lora_sublist_name(X_type, X_value)
if X_type == "LoRA" or X_type == "LoRA Batch":
if X_type == "LoRA":
lora_name = value
lora_model_str = None
lora_clip_str = None
if X_type == "LoRA Batch":
lora_name = value
lora_model_str = X_value[0][0][1] if lora_model_str is None else lora_model_str
lora_clip_str = X_value[0][0][2] if lora_clip_str is None else lora_clip_str
elif X_type == "LoRA MStr":
lora_name = os.path.basename(X_value[0][0][0]) if lora_name is None else lora_name
lora_model_str = value
lora_clip_str = X_value[0][0][2] if lora_clip_str is None else lora_clip_str
elif X_type == "LoRA CStr":
lora_name = os.path.basename(X_value[0][0][0]) if lora_name is None else lora_name
lora_model_str = X_value[0][0][1] if lora_model_str is None else lora_model_str
lora_clip_str = value
elif X_type == "LoRA Wt":
lora_name = os.path.basename(X_value[0][0][0]) if lora_name is None else lora_name
lora_wt = value
if Y_type in lora_types:
value = get_lora_sublist_name(Y_type, Y_value)
if Y_type == "LoRA" or Y_type == "LoRA Batch":
if Y_type == "LoRA":
lora_name = value
lora_model_str = None
lora_clip_str = None
if Y_type == "LoRA Batch":
lora_name = value
lora_model_str = Y_value[0][0][1] if lora_model_str is None else lora_model_str
lora_clip_str = Y_value[0][0][2] if lora_clip_str is None else lora_clip_str
elif Y_type == "LoRA MStr":
lora_name = os.path.basename(Y_value[0][0][0]) if lora_name is None else lora_name
lora_model_str = value
lora_clip_str = Y_value[0][0][2] if lora_clip_str is None else lora_clip_str
elif Y_type == "LoRA CStr":
lora_name = os.path.basename(Y_value[0][0][0]) if lora_name is None else lora_name
lora_model_str = Y_value[0][0][1] if lora_model_str is None else lora_model_str
lora_clip_str = value
elif Y_type == "LoRA Wt":
lora_name = os.path.basename(Y_value[0][0][0]) if lora_name is None else lora_name
lora_wt = value
return lora_name, lora_wt, lora_model_str, lora_clip_str
@@ -1791,13 +1829,13 @@ class TSC_KSampler:
print(f"+ascore: {pos_ascore if pos_ascore is not None else ''}")
print(f"-ascore: {neg_ascore if neg_ascore is not None else ''}")
if lora_name:
print(f"lora: {lora_name if lora_name is not None else ''}")
print(f"lora: {lora_name}")
if lora_wt:
print(f"lora_wt: {lora_wt}")
if lora_model_str:
print(f"lora_model_str: {lora_model_str}")
print(f"lora_mstr: {lora_model_str}")
if lora_clip_str:
print(f"lora_clip_str: {lora_clip_str}")
print(f"lora_cstr: {lora_clip_str}")
if vae_name:
print(f"vae: {vae_name}")
if sampler_type == "advanced":
@@ -2119,9 +2157,11 @@ class TSC_KSampler:
send_command_to_frontend(startListening=False)
if sampler_type == "sdxl":
result = (sdxl_tuple, {"samples": latent_list}, vae, output_images,)
sdxl_tuple = original_model, original_clip, original_positive, original_negative,\
original_refiner_model, original_refiner_clip, original_refiner_positive, original_refiner_negative
result = (sdxl_tuple, latent_list, optional_vae, output_images,)
else:
result = (model, positive, negative, {"samples": latent_list}, vae, output_images,)
result = (original_model, original_positive, original_negative, latent_list, optional_vae, output_images,)
return {"ui": {"images": preview_images}, "result": result}
#=======================================================================================================================
@@ -3680,7 +3720,7 @@ class TSC_XYplot_Manual_XY_Entry:
if checkpoint is None or clip_skip is None:
return None
else:
return checkpoint, clip_skip
return checkpoint, clip_skip, None
else:
print(
f"\033[31mXY Plot Error:\033[0m '{value}' is not a valid checkpoint.'")
@@ -3692,7 +3732,7 @@ class TSC_XYplot_Manual_XY_Entry:
f"\033[31mXY Plot Error:\033[0m '{value}' is not a valid checkpoint. Valid checkpoints are:\n{valid_checkpoints}")
return None
else:
return value, None
return value, None, None
# __________________________________________________________________________________________________________
# Clip Skip
elif value_type == "Clip Skip":
@@ -3787,6 +3827,10 @@ class TSC_XYplot_Manual_XY_Entry:
if plot_value[i] == None:
return (None,)
# Set up Seeds++ Batch structure
if plot_type == "Seeds++ Batch":
plot_value = list(range(plot_value[0]))
# Nest LoRA value in another array to reflect LoRA stack changes
if plot_type == "LoRA":
plot_value = [[x] for x in plot_value]
@@ -3964,8 +4008,8 @@ NODE_CLASS_MAPPINGS = {
"XY Input: LoRA Stacks": TSC_XYplot_LoRA_Stacks,
"XY Input: Control Net": TSC_XYplot_Control_Net,
"XY Input: Control Net Plot": TSC_XYplot_Control_Net_Plot,
#"XY Input: Manual XY Entry": TSC_XYplot_Manual_XY_Entry, # DISABLED, NEEDS UPDATE
#"Manual XY Entry Info": TSC_XYplot_Manual_XY_Entry_Info, # DISABLED, NEEDS UPDATE
"XY Input: Manual XY Entry": TSC_XYplot_Manual_XY_Entry, # DISABLED, NEEDS UPDATE
"Manual XY Entry Info": TSC_XYplot_Manual_XY_Entry_Info, # DISABLED, NEEDS UPDATE
"Join XY Inputs of Same Type": TSC_XYplot_JoinInputs,
"Image Overlay": TSC_ImageOverlay
}