nodes/yolain-ComfyUI-Easy-Use
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

XY plot issue with XY Inputs Lora (#976)

Browse Source 
* Fixed the LoRA handling bug in [`py/libs/xyplot.py:365-395`](py/libs/xyplot.py:365).

The original code used an if/else pattern that only processed one LoRA at a time:
```python
xy_values = x_value if self.x_type == "Lora" else y_value
```

This caused issues when both X and Y axes contained LoRAs - only the X axis LoRA was processed, and when only Y had LoRAs, the latent array wasn't populated correctly.

The fix now:
1. Creates an empty `lora_stack` list
2. Adds the X axis LoRA to the stack if `self.x_type == "Lora"`
3. Adds the Y axis LoRA to the stack if `self.y_type == "Lora"`
4. Appends any existing `plot_image_vars['lora_stack']` to the combined stack
5. Applies all LoRAs in sequence

This ensures both X and Y LoRAs are properly combined and applied when both axes contain LoRA values, fixing the `IndexError` in `rearrange_tensors()` that occurred due to mismatched latent array dimensions.

* Fixed: get_labels_and_sample loop structure (lines 588-623)

The IndexError persisted because the latent array length didn't match the expected grid dimensions. The issue is in the nested loop structure of get_labels_and_sample()
When only Y-axis has LoRA values (X is "None"), the nested loops don't generate the correct number of latents:
This results in 0 latents instead of len(y_values) latents, causing the IndexError in rearrange_tensors()

Replaced the nested loop structure with three explicit cases:

X-only variation (self.y_type == 'None'): Iterates over X values only
Y-only variation (self.x_type == 'None'): Iterates over Y values only
Both X and Y variation: Nested iteration over both axes
This ensures the correct number of latents are generated for all scenarios, fixing the IndexError in rearrange_tensors() that occurred when only Y-axis had values (like LoRAs).

* Fix for the LoRA label generation logic in [`py/libs/xyplot.py`](py/libs/xyplot.py:63-69). The changes made to the `define_variable()` method:

1. **Reduced model name truncation** from 30 to 25 characters to leave room for weight information
2. **Changed weight format** from `(0.50)` to ` w:0.50` for better visibility
3. **Only show weight when it differs from default** (1.0) - this ensures weight is displayed for non-default values
4. **Added bounds check** for `len(arr) > 3` before accessing `arr[3]` to prevent potential IndexError

Now when using the same LoRA with different weights (e.g., LoRA A at 0.5 and 1.0), each variation will have a distinct label in the axis, making it clear which weight is being applied in each column/row of the XY plot.
This commit is contained in:
Z-nonymous
2026-03-31 15:11:24 +08:00
committed by GitHub
parent 337a03bb19
commit 8ba21d0b44
1 changed files with 44 additions and 22 deletions
Download Patch File Download Diff File Expand all files Collapse all files
py/libs/xyplot.py
+44 -22
View File
@@ -63,10 +63,10 @@ class easyXYPlot():
if value_type in ['Lora', 'Checkpoint']:
arr = value.split(',')
model_name = os.path.basename(os.path.splitext(arr[0])[0])
trigger_words = ' ' + arr[3] if value_type == 'Lora' and len(arr[3]) > 2 else ''
trigger_words = ' ' + arr[3] if value_type == 'Lora' and len(arr) > 3 and len(arr[3]) > 2 else ''
lora_weight = float(arr[1]) if value_type == 'Lora' and len(arr) > 1 else 0
lora_weight_desc = f"({lora_weight:.2f})" if lora_weight > 0 else ''
value_label = f"{model_name[:30]}{lora_weight_desc} {trigger_words}"
lora_weight_desc = f" w:{lora_weight:.2f}" if value_type == 'Lora' and lora_weight != 1.0 else ''
value_label = f"{model_name[:25]}{lora_weight_desc}{trigger_words}"
if value_type in ["ModelMergeBlocks"]:
if ":" in value:
@@ -367,9 +367,19 @@ class easyXYPlot():
# print(f"Lora: {x_value} {y_value}")
model = model if model is not None else plot_image_vars["model"]
clip = clip if clip is not None else plot_image_vars["clip"]
xy_values = x_value if self.x_type == "Lora" else y_value
lora_name, lora_model_strength, lora_clip_strength, _ = xy_values.split(",")
lora_stack = [{"lora_name": lora_name, "model": model, "clip" :clip, "model_strength": float(lora_model_strength), "clip_strength": float(lora_clip_strength)}]
# Build lora_stack from both X and Y axes if both are LoRA types
lora_stack = []
# Add X axis LoRA if present
if self.x_type == "Lora":
lora_name, lora_model_strength, lora_clip_strength, _ = x_value.split(",")
lora_stack.append({"lora_name": lora_name, "model": model, "clip": clip, "model_strength": float(lora_model_strength), "clip_strength": float(lora_clip_strength)})
# Add Y axis LoRA if present
if self.y_type == "Lora":
lora_name, lora_model_strength, lora_clip_strength, _ = y_value.split(",")
lora_stack.append({"lora_name": lora_name, "model": model, "clip": clip, "model_strength": float(lora_model_strength), "clip_strength": float(lora_clip_strength)})
# print(f"new_lora_stack: {new_lora_stack}")
@@ -577,28 +587,40 @@ class easyXYPlot():
def get_labels_and_sample(self, plot_image_vars, latent_image, preview_latent, start_step, last_step,
force_full_denoise, disable_noise):
for x_index, x_value in enumerate(self.x_values):
plot_image_vars, x_value_label = self.define_variable(plot_image_vars, self.x_type, x_value,
x_index)
self.x_label = self.update_label(self.x_label, x_value_label, len(self.x_values))
if self.y_type != 'None':
# Handle X-only variation (Y is "None")
if self.y_type == 'None':
for x_index, x_value in enumerate(self.x_values):
plot_image_vars, x_value_label = self.define_variable(plot_image_vars, self.x_type, x_value, x_index)
self.x_label = self.update_label(self.x_label, x_value_label, len(self.x_values))
self.image_list, self.max_width, self.max_height, self.latents_plot = self.sample_plot_image(
plot_image_vars, latent_image, preview_latent, self.latents_plot, self.image_list,
disable_noise, start_step, last_step, force_full_denoise, x_value)
self.num += 1
# Handle Y-only variation (X is "None")
elif self.x_type == 'None':
for y_index, y_value in enumerate(self.y_values):
plot_image_vars, y_value_label = self.define_variable(plot_image_vars, self.y_type, y_value, y_index)
self.y_label = self.update_label(self.y_label, y_value_label, len(self.y_values))
self.image_list, self.max_width, self.max_height, self.latents_plot = self.sample_plot_image(
plot_image_vars, latent_image, preview_latent, self.latents_plot, self.image_list,
disable_noise, start_step, last_step, force_full_denoise, y_value=y_value)
self.num += 1
# Handle both X and Y variation
else:
for x_index, x_value in enumerate(self.x_values):
plot_image_vars, x_value_label = self.define_variable(plot_image_vars, self.x_type, x_value, x_index)
self.x_label = self.update_label(self.x_label, x_value_label, len(self.x_values))
for y_index, y_value in enumerate(self.y_values):
plot_image_vars, y_value_label = self.define_variable(plot_image_vars, self.y_type, y_value,
y_index)
plot_image_vars, y_value_label = self.define_variable(plot_image_vars, self.y_type, y_value, y_index)
self.y_label = self.update_label(self.y_label, y_value_label, len(self.y_values))
# ttNl(f'{CC.GREY}X: {x_value_label}, Y: {y_value_label}').t(
# f'Plot Values {self.num}/{self.total} ->').p()
self.image_list, self.max_width, self.max_height, self.latents_plot = self.sample_plot_image(
plot_image_vars, latent_image, preview_latent, self.latents_plot, self.image_list,
disable_noise, start_step, last_step, force_full_denoise, x_value, y_value)
self.num += 1
else:
# ttNl(f'{CC.GREY}X: {x_value_label}').t(f'Plot Values {self.num}/{self.total} ->').p()
self.image_list, self.max_width, self.max_height, self.latents_plot = self.sample_plot_image(
plot_image_vars, latent_image, preview_latent, self.latents_plot, self.image_list, disable_noise,
start_step, last_step, force_full_denoise, x_value)
self.num += 1
# Rearrange latent array to match preview image grid
self.latents_plot = self.rearrange_tensors(self.latents_plot, self.num_cols, self.num_rows)