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indent fix

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Dr.Lt.Data
2023-08-31 13:20:04 +09:00
parent 1a0a59060d
commit 583da4999e
1 changed files with 36 additions and 38 deletions
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efficiency_nodes.py
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@@ -1406,32 +1406,30 @@ class TSC_KSampler:
sampler_type, latent_list=[], image_tensor_list=[], image_pil_list=[], xy_capsule=None):
capsule_result = None
if xy_capsule is not None:
capsule_result = xy_capsule.get_result(model, clip, vae)
if capsule_result is not None:
image, latent = capsule_result
latent_list.append(latent['samples'])
if xy_capsule is not None:
capsule_result = xy_capsule.get_result(model, clip, vae)
if capsule_result is not None:
image, latent = capsule_result
latent_list.append(latent['samples'])
if capsule_result is None:
if preview_method != "none":
send_command_to_frontend(startListening=True, maxCount=steps - 1, sendBlob=False)
if capsule_result is None:
if preview_method != "none":
send_command_to_frontend(startListening=True, maxCount=steps - 1, sendBlob=False)
samples = sample_latent_image(model, seed, steps, cfg, sampler_name, scheduler, positive, negative,
latent_image, denoise, sampler_type, add_noise, start_at_step,
end_at_step,
return_with_leftover_noise, refiner_model, refiner_positive,
refiner_negative)
samples = sample_latent_image(model, seed, steps, cfg, sampler_name, scheduler,
positive, negative, latent_image, denoise, sampler_type, add_noise, start_at_step, end_at_step,
return_with_leftover_noise, refiner_model, refiner_positive, refiner_negative)
# Add the latent tensor to the tensors list
latent_list.append(samples)
# Add the latent tensor to the tensors list
latent_list.append(samples)
# Decode the latent tensor
image = vae_decode_latent(vae, samples, vae_decode)
# Decode the latent tensor
image = vae_decode_latent(vae, samples, vae_decode)
if xy_capsule is not None:
xy_capsule.set_result(image, latent)
# Add the resulting image tensor to image_tensor_list
image_tensor_list.append(image)
# Add the resulting image tensor to image_tensor_list
image_tensor_list.append(image)
# Convert the image from tensor to PIL Image and add it to the image_pil_list
image_pil_list.append(tensor2pil(image))
@@ -1463,12 +1461,12 @@ class TSC_KSampler:
set_preview_method(preview_method)
original_model = model.clone()
original_clip = clip.clone()
original_clip = clip.clone()
# Fill Plot Rows (X)
for X_index, X in enumerate(X_value):
model = original_model.clone()
clip = original_clip.clone()
# Fill Plot Rows (X)
for X_index, X in enumerate(X_value):
model = original_model.clone()
clip = original_clip.clone()
# Define X parameters and generate labels
add_noise, seed, steps, start_at_step, end_at_step, return_with_leftover_noise, cfg,\
@@ -1481,9 +1479,9 @@ class TSC_KSampler:
negative_prompt, ascore, lora_stack, cnet_stack, X_label, len(X_value))
if X_type != "Nothing" and Y_type == "Nothing":
if X_type == "XY_Capsule":
model, clip, vae = X.pre_define_model(model, clip, vae)
if X_type != "Nothing" and Y_type == "Nothing":
if X_type == "XY_Capsule":
model, clip, vae = X.pre_define_model(model, clip, vae)
# Models & Conditionings
model, positive, negative, refiner_model, refiner_positive, refiner_negative, vae = \
@@ -1505,11 +1503,11 @@ class TSC_KSampler:
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()
model = original_model.clone()
clip = original_clip.clone()
if Y_type == "XY_Capsule" and X_type == "XY_Capsule":
Y.set_x_capsule(X)
if Y_type == "XY_Capsule" and X_type == "XY_Capsule":
Y.set_x_capsule(X)
# Define Y parameters and generate labels
add_noise, seed, steps, start_at_step, end_at_step, return_with_leftover_noise, cfg,\
@@ -1522,12 +1520,12 @@ class TSC_KSampler:
negative_prompt, ascore, lora_stack, cnet_stack, Y_label, len(Y_value))
if Y_type == "XY_Capsule":
model, clip, vae = Y.pre_define_model(model, clip, vae)
elif X_type == "XY_Capsule":
model, clip, vae = X.pre_define_model(model, clip, vae)
model, clip, vae = Y.pre_define_model(model, clip, vae)
elif X_type == "XY_Capsule":
model, clip, vae = X.pre_define_model(model, clip, vae)
# Models & Conditionings
model, positive, negative, refiner_model, refiner_positive, refiner_negative, vae = \
# Models & Conditionings
model, positive, negative, refiner_model, refiner_positive, refiner_negative, vae = \
define_model(model, clip, clip_skip[0], refiner_model, refiner_clip, refiner_clip_skip[0],
ckpt_name, refiner_name, positive, negative, refiner_positive, refiner_negative,
positive_prompt[0], negative_prompt[0], ascore, vae, vae_name, lora_stack, cnet_stack[0],