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wip: xy_capsule support

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Dr.Lt.Data
2023-08-30 17:55:48 +09:00
parent 26c81dba51
commit 97aa014e76
1 changed files with 38 additions and 18 deletions
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56
efficiency_nodes.py
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@@ -828,6 +828,9 @@ class TSC_KSampler:
cnet_stack = var
text = f'ControlNetStr: {round(cnet_stack[0][2], 3)}'
elif var_type == "XY_Capsule":
text = var.getLabel()
else: # No matching type found
text=""
@@ -917,28 +920,36 @@ class TSC_KSampler:
# The below function is used to generate the results based on all the processed variables
def process_values(model, add_noise, seed, steps, start_at_step, end_at_step, return_with_leftover_noise,
cfg, sampler_name, scheduler, positive, negative, latent_image, denoise, vae, vae_decode,
ksampler_adv_flag, latent_list=[], image_tensor_list=[], image_pil_list=[]):
ksampler_adv_flag, latent_list=[], image_tensor_list=[], image_pil_list=[], xy_capsule=None):
if preview_method != "none":
send_command_to_frontend(startListening=True, maxCount=steps - 1, sendBlob=False)
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)
# Sample using the Comfy KSampler nodes
if ksampler_adv_flag == False:
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)
if capsule_result is None:
if preview_method != "none":
send_command_to_frontend(startListening=True, maxCount=steps - 1, sendBlob=False)
# Decode images and store
latent = samples[0]["samples"]
# Sample using the Comfy KSampler nodes
if ksampler_adv_flag == False:
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)
# Add the latent tensor to the tensors list
latent_list.append(latent)
# Decode images and store
latent = samples[0]["samples"]
# Decode the latent tensor
image = vae_decode_latent(latent, vae_decode)
# Add the latent tensor to the tensors list
latent_list.append(latent)
# Decode the latent tensor
image = vae_decode_latent(latent, vae_decode)
# Add the resulting image tensor to image_tensor_list
image_tensor_list.append(image)
@@ -1006,6 +1017,8 @@ 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):
if Y_type == "XY_Capsule" and X_type == "XY_Capsule":
Y.set_another_capsule(X)
if Y_type == "Seeds++ Batch":
# Update seed based on the inner loop index
@@ -1019,16 +1032,23 @@ class TSC_KSampler:
return_with_leftover_noise, cfg, sampler_name, scheduler, denoise, vae_name, ckpt_name,
clip_skip, positive_prompt, negative_prompt, lora_stack, cnet_stack, Y_label, len(Y_value))
if Y_type == "XY_Capsule":
model, clip, vae = Y.pre_define_model(model, clip, vae)
# Models & Conditionings
model, positive, negative, vae = \
define_model(model, clip, positive, negative, positive_prompt, negative_prompt, clip_skip[0], vae,
vae_name, ckpt_name, lora_stack, cnet_stack, Y_index, types, script_node_id, cache)
# Generate Results
xy_capsule = None
if Y_type == "XY_Capsule":
xy_capsule = Y
latent_list, image_tensor_list, image_pil_list = \
process_values(model, add_noise, seed_updated, steps, start_at_step, end_at_step,
return_with_leftover_noise, cfg, sampler_name, scheduler[0],
positive, negative, latent_image, denoise, vae, vae_decode, ksampler_adv_flag)
positive, negative, latent_image, denoise, vae, vae_decode, ksampler_adv_flag, xy_capsule)
# Clean up cache
if cache_models == "False":