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Merge pull request #140 from LucianoCirino/Small-Fixes-&-Updates

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Small Fixes & Updates
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TSC
2023-09-03 13:57:29 -05:00
committed by GitHub
parent 01bb6c810a 6b5589794a
commit 3569f8705d
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): def INPUT_TYPES(cls):
return {"required": {"sdxl_tuple": ("SDXL_TUPLE",)},} return {"required": {"sdxl_tuple": ("SDXL_TUPLE",)},}
RETURN_TYPES = ("MODEL", "CLIP", "MODEL", "CLIP", "CONDITIONING","CONDITIONING", "CONDITIONING", "CONDITIONING",) RETURN_TYPES = ("MODEL", "CLIP", "CONDITIONING","CONDITIONING", "MODEL", "CLIP", "CONDITIONING", "CONDITIONING",)
RETURN_NAMES = ("BASE_MODEL", "BASE_CLIP", "REFINER_MODEL", "REFINER_CLIP", RETURN_NAMES = ("BASE_MODEL", "BASE_CLIP", "BASE_CONDITIONING+", "BASE_CONDITIONING-",
"BASE_CONDITIONING+", "BASE_CONDITIONING-","REFINER_CONDITIONING+","REFINER_CONDITIONING-",) "REFINER_MODEL", "REFINER_CLIP","REFINER_CONDITIONING+","REFINER_CONDITIONING-",)
FUNCTION = "unpack_sdxl_tuple" FUNCTION = "unpack_sdxl_tuple"
CATEGORY = "Efficiency Nodes/Misc" CATEGORY = "Efficiency Nodes/Misc"
@@ -1240,15 +1240,15 @@ class TSC_KSampler:
elif var_type == "LoRA Wt": elif var_type == "LoRA Wt":
lora_model_wt = format(float(lora_model_wt), ".2f").rstrip('0').rstrip('.') 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": elif var_type == "LoRA MStr":
lora_model_wt = format(float(lora_model_wt), ".2f").rstrip('0').rstrip('.') 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": elif var_type == "LoRA CStr":
lora_clip_wt = format(float(lora_clip_wt), ".2f").rstrip('0').rstrip('.') 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%"]: elif var_type in ["ControlNetStrength", "ControlNetStart%", "ControlNetEnd%"]:
if "Strength" in var_type: if "Strength" in var_type:
@@ -1463,13 +1463,25 @@ class TSC_KSampler:
# Change the global preview method temporarily during this node's execution # Change the global preview method temporarily during this node's execution
set_preview_method(preview_method) set_preview_method(preview_method)
original_model = model.clone() # Clone original model parameters
original_clip = clip.clone() 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) # Fill Plot Rows (X)
for X_index, X in enumerate(X_value): for X_index, X in enumerate(X_value):
model = original_model.clone() # Reset model parameters to their originals
clip = original_clip.clone() 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 # Define X parameters and generate labels
add_noise, seed, steps, start_at_step, end_at_step, return_with_leftover_noise, cfg,\ 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) refiner_positive, refiner_negative, latent_image, denoise, vae, vae_decode, sampler_type, xy_capsule=xy_capsule)
elif X_type != "Nothing" and Y_type != "Nothing": elif X_type != "Nothing" and Y_type != "Nothing":
# Seed control based on loop index during Batch
for Y_index, Y in enumerate(Y_value): for Y_index, Y in enumerate(Y_value):
model = original_model.clone() # Reset model parameters to their originals
clip = original_clip.clone() 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": if Y_type == "XY_Capsule" and X_type == "XY_Capsule":
Y.set_x_capsule(X) 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): 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 # Define types based on mode
primary_type = "Checkpoint" if mode == "ckpt" else "Refiner" primary_type = "Checkpoint" if mode == "ckpt" else "Refiner"
clip_type = "Clip Skip" if mode == "ckpt" else "Clip Skip (Refiner)" clip_type = "Clip Skip" if mode == "ckpt" else "Clip Skip (Refiner)"
@@ -1609,11 +1626,10 @@ class TSC_KSampler:
if vae_name: if vae_name:
vae_name = get_vae_name(X_type, Y_type, X_value, Y_value, 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) clip_skip = get_clip_skip(X_type, Y_type, X_value, Y_value, clip_skip)
ckpt_name = os.path.basename(ckpt_name) return ckpt_name, clip_skip, vae_name
return os.path.basename(ckpt_name), clip_skip, vae_name
else: else:
# For refn mode # For refn mode
return os.path.basename(ckpt_name), clip_skip return ckpt_name, clip_skip
# Process clip skip based on mode # Process clip skip based on mode
if othr_type == clip_type: if othr_type == clip_type:
@@ -1664,24 +1680,46 @@ class TSC_KSampler:
else: else:
if X_type in lora_types: if X_type in lora_types:
value = get_lora_sublist_name(X_type, X_value) 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_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": 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_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": 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 lora_clip_str = value
elif X_type == "LoRA Wt": 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 lora_wt = value
if Y_type in lora_types: if Y_type in lora_types:
value = get_lora_sublist_name(Y_type, Y_value) 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_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": 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_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": 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 lora_clip_str = value
elif Y_type == "LoRA Wt": 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 lora_wt = value
return lora_name, lora_wt, lora_model_str, lora_clip_str 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: {pos_ascore if pos_ascore is not None else ''}")
print(f"-ascore: {neg_ascore if neg_ascore is not None else ''}") print(f"-ascore: {neg_ascore if neg_ascore is not None else ''}")
if lora_name: if lora_name:
print(f"lora: {lora_name if lora_name is not None else ''}") print(f"lora: {lora_name}")
if lora_wt: if lora_wt:
print(f"lora_wt: {lora_wt}") print(f"lora_wt: {lora_wt}")
if lora_model_str: if lora_model_str:
print(f"lora_model_str: {lora_model_str}") print(f"lora_mstr: {lora_model_str}")
if lora_clip_str: if lora_clip_str:
print(f"lora_clip_str: {lora_clip_str}") print(f"lora_cstr: {lora_clip_str}")
if vae_name: if vae_name:
print(f"vae: {vae_name}") print(f"vae: {vae_name}")
if sampler_type == "advanced": if sampler_type == "advanced":
@@ -2119,9 +2157,11 @@ class TSC_KSampler:
send_command_to_frontend(startListening=False) send_command_to_frontend(startListening=False)
if sampler_type == "sdxl": 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: 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} 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: if checkpoint is None or clip_skip is None:
return None return None
else: else:
return checkpoint, clip_skip return checkpoint, clip_skip, None
else: else:
print( print(
f"\033[31mXY Plot Error:\033[0m '{value}' is not a valid checkpoint.'") 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}") f"\033[31mXY Plot Error:\033[0m '{value}' is not a valid checkpoint. Valid checkpoints are:\n{valid_checkpoints}")
return None return None
else: else:
return value, None return value, None, None
# __________________________________________________________________________________________________________ # __________________________________________________________________________________________________________
# Clip Skip # Clip Skip
elif value_type == "Clip Skip": elif value_type == "Clip Skip":
@@ -3787,6 +3827,10 @@ class TSC_XYplot_Manual_XY_Entry:
if plot_value[i] == None: if plot_value[i] == None:
return (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 # Nest LoRA value in another array to reflect LoRA stack changes
if plot_type == "LoRA": if plot_type == "LoRA":
plot_value = [[x] for x in plot_value] 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: LoRA Stacks": TSC_XYplot_LoRA_Stacks,
"XY Input: Control Net": TSC_XYplot_Control_Net, "XY Input: Control Net": TSC_XYplot_Control_Net,
"XY Input: Control Net Plot": TSC_XYplot_Control_Net_Plot, "XY Input: Control Net Plot": TSC_XYplot_Control_Net_Plot,
#"XY Input: Manual XY Entry": TSC_XYplot_Manual_XY_Entry, # 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 "Manual XY Entry Info": TSC_XYplot_Manual_XY_Entry_Info, # DISABLED, NEEDS UPDATE
"Join XY Inputs of Same Type": TSC_XYplot_JoinInputs, "Join XY Inputs of Same Type": TSC_XYplot_JoinInputs,
"Image Overlay": TSC_ImageOverlay "Image Overlay": TSC_ImageOverlay
} }