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Merge pull request #111 from ltdrdata/feat/xy_capsule

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feat:Providing an interface for utilizing the XY Plot externally.
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TSC
2023-09-02 10:31:49 -05:00
committed by GitHub
parent 59b500e80f 51932c7fc8
commit a0fcee716f
2 changed files with 79 additions and 20 deletions
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80
efficiency_nodes.py
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@@ -165,7 +165,7 @@ class TSC_EfficientLoader:
positive_encoded, negative_encoded, refiner_positive_encoded, refiner_negative_encoded = \
encode_prompts(positive, negative, clip, clip_skip, refiner_clip, refiner_clip_skip, ascore,
loader_type == "sdxl", empty_latent_width, empty_latent_height)
# Apply ControlNet Stack if given
if cnet_stack:
positive_encoded = TSC_Apply_ControlNet_Stack().apply_cnet_stack(positive_encoded,cnet_stack)[0]
@@ -173,7 +173,7 @@ class TSC_EfficientLoader:
# Check for custom VAE
if vae_name != "Baked VAE":
vae = load_vae(vae_name, my_unique_id, cache=vae_cache, cache_overwrite=True)
# Data for XY Plot
dependencies = (vae_name, ckpt_name, clip, clip_skip, refiner_name, refiner_clip, refiner_clip_skip,
positive, negative, ascore, empty_latent_width, empty_latent_height, lora_params, cnet_stack)
@@ -181,7 +181,7 @@ class TSC_EfficientLoader:
### Debugging
###print_loaded_objects_entries()
print_loaded_objects_entries(my_unique_id, prompt)
if loader_type == "regular":
return (model, positive_encoded, negative_encoded, {"samples":latent}, vae, clip, dependencies,)
elif loader_type == "sdxl":
@@ -842,7 +842,7 @@ class TSC_KSampler:
elif type_ == "VAE" and isinstance(value, list):
# For each string in the list, extract the filename from the path
return [os.path.basename(v) for v in value]
elif (type_ == "Checkpoint" or type_ == "Refiner") and isinstance(value, list):
# For each tuple in the list, return only the first value if the second or third value is None
return [(os.path.basename(v[0]),) + v[1:] if v[1] is None or v[2] is None
@@ -1288,6 +1288,9 @@ class TSC_KSampler:
# Print the desired value
text = f'{var_type}: {round(cnet_stack[0][0][entry_index], 3)}'
elif var_type == "XY_Capsule":
text = var.getLabel()
else: # No matching type found
text=""
@@ -1400,20 +1403,33 @@ class TSC_KSampler:
def process_values(model, refiner_model, add_noise, seed, steps, start_at_step, end_at_step,
return_with_leftover_noise, cfg, sampler_name, scheduler, positive, negative,
refiner_positive, refiner_negative, latent_image, denoise, vae, vae_decode,
sampler_type, latent_list=[], image_tensor_list=[], image_pil_list=[]):
sampler_type, 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)
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)
if capsule_result is None:
if preview_method != "none":
send_command_to_frontend(startListening=True, maxCount=steps - 1, sendBlob=False)
# Add the latent tensor to the tensors list
latent_list.append(samples)
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)
# Decode the latent tensor
image = vae_decode_latent(vae, samples, vae_decode)
# Add the latent tensor to the tensors list
latent_list.append(samples)
# Decode the latent tensor
image = vae_decode_latent(vae, samples, vae_decode)
if xy_capsule is not None:
xy_capsule.set_result(image, samples)
# Add the resulting image tensor to image_tensor_list
image_tensor_list.append(image)
@@ -1447,8 +1463,13 @@ 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()
# 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,\
@@ -1460,7 +1481,10 @@ class TSC_KSampler:
ckpt_name, clip_skip, refiner_name, refiner_clip_skip, positive_prompt,
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)
# Models & Conditionings
model, positive, negative, refiner_model, refiner_positive, refiner_negative, vae = \
@@ -1469,15 +1493,24 @@ class TSC_KSampler:
positive_prompt[0], negative_prompt[0], ascore, vae, vae_name, lora_stack, cnet_stack[0],
0, types, xyplot_id, cache, sampler_type, empty_latent_width, empty_latent_height)
xy_capsule = None
if X_type == "XY_Capsule":
xy_capsule = X
# Generate Results
latent_list, image_tensor_list, image_pil_list = \
process_values(model, refiner_model, add_noise, seed, steps, start_at_step, end_at_step,
return_with_leftover_noise, cfg, sampler_name, scheduler[0], positive, negative,
refiner_positive, refiner_negative, latent_image, denoise, vae, vae_decode, sampler_type)
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()
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,\
@@ -1489,6 +1522,11 @@ class TSC_KSampler:
ckpt_name, clip_skip, refiner_name, refiner_clip_skip, positive_prompt,
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)
# 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],
@@ -1498,11 +1536,15 @@ class TSC_KSampler:
empty_latent_height)
# Generate Results
xy_capsule = None
if Y_type == "XY_Capsule":
xy_capsule = Y
latent_list, image_tensor_list, image_pil_list = \
process_values(model, refiner_model, add_noise, seed, steps, start_at_step, end_at_step,
return_with_leftover_noise, cfg, sampler_name, scheduler[0],
positive, negative, refiner_positive, refiner_negative, latent_image,
denoise, vae, vae_decode, sampler_type)
denoise, vae, vae_decode, sampler_type, xy_capsule=xy_capsule)
# Clean up cache
if cache_models == "False":
@@ -2284,7 +2326,7 @@ class TSC_XYplot:
Y_value = [""]
# If types are the same exit. If one isn't "Nothing", print error
if (X_type == Y_type):
if X_type != "XY_Capsule" and (X_type == Y_type):
if X_type != "Nothing":
print(f"{error('XY Plot Error:')} X and Y input types must be different.")
return (None,)
@@ -2596,7 +2638,7 @@ class TSC_XYplot_VAE:
# Construct the xy_value using the obtained vaes
xy_value = [vae for vae in vaes]
if batch_max != -1: # If there's a limit
xy_value = xy_value[:batch_max]
@@ -3200,7 +3242,7 @@ class TSC_XYplot_Control_Net_End:
if batch_count == 0:
return (None,)
xy_type = "ControlNetEnd%"
percent_increment = (last_end_percent - first_end_percent) / (batch_count - 1) if batch_count > 1 else 0

19
tsc_utils.py
View File

@@ -626,4 +626,21 @@ def send_command_to_frontend(startListening=False, maxCount=0, sendBlob=False):
if websocket_status == True:
server_thread = threading.Thread(target=run_server)
server_thread.daemon = True
server_thread.start()
server_thread.start()
class XY_Capsule:
def pre_define_model(self, model, clip, vae):
return model, clip, vae
def set_result(self, image, latent):
pass
def get_result(self, model, clip, vae):
return None
def set_x_capsule(self, capsule):
return None
def getLabel(self):
return "Unknown"