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import gradio as gr
import matplotlib.pyplot as plt
from matplotlib.patches import Patch
import io
import json
from PIL import Image
from typing import List
from scripts.enums import StableDiffusionVersion
from scripts.global_state import get_sd_version
from scripts.ipadapter.weight import calc_weights
INPUT_BLOCK_COLOR = "#61bdee"
MIDDLE_BLOCK_COLOR = "#e2e2e2"
OUTPUT_BLOCK_COLOR = "#dc6e55"
def get_bar_colors(
sd_version: StableDiffusionVersion, input_color, middle_color, output_color
):
middle_block_idx = 4 if sd_version == StableDiffusionVersion.SDXL else 6
def get_color(idx):
if idx < middle_block_idx:
return input_color
elif idx == middle_block_idx:
return middle_color
else:
return output_color
return [get_color(i) for i in range(sd_version.transformer_block_num)]
def plot_weights(
numbers: List[float],
colors: List[str],
):
# Create a bar chart
plt.figure(figsize=(8, 4))
plt.bar(range(len(numbers)), numbers, color=colors)
plt.xlabel("Transformer Index")
plt.ylabel("Weight")
plt.legend(
handles=[
Patch(color=color, label=label)
for color, label in (
(INPUT_BLOCK_COLOR, "Input Block"),
(MIDDLE_BLOCK_COLOR, "Middle Block"),
(OUTPUT_BLOCK_COLOR, "Output Block"),
)
],
loc="best",
)
# Save the plot to a BytesIO buffer
buffer = io.BytesIO()
plt.savefig(buffer, format="png")
plt.close()
buffer.seek(0)
# Convert the buffer to a PIL image and return it
image = Image.open(buffer)
return image
class AdvancedWeightControl:
def __init__(self):
self.group = None
self.weight_type = None
self.weight_plot = None
self.weight_editor = None
self.weight_composition = None
def render(self):
with gr.Group(visible=False) as self.group:
with gr.Row():
self.weight_type = gr.Dropdown(
choices=[
"normal",
"ease in",
"ease out",
"ease in-out",
"reverse in-out",
"weak input",
"weak output",
"weak middle",
"strong middle",
"style transfer",
"composition",
"strong style transfer",
"style and composition",
"strong style and composition",
],
label="Weight Type",
value="normal",
)
self.weight_composition = gr.Slider(
label="Composition Weight",
minimum=0,
maximum=2.0,
value=1.0,
step=0.01,
visible=False,
)
self.weight_editor = gr.Textbox(label="Weights", visible=False)
self.weight_plot = gr.Image(
value=None,
label="Weight Plot",
interactive=False,
visible=False,
)
def register_callbacks(
self,
weight_input: gr.Slider,
advanced_weighting: gr.State,
control_type: gr.Radio,
update_unit_counter: gr.Number,
):
def advanced_weighting_supported(control_type: str) -> bool:
return control_type in ("IP-Adapter", "Instant-ID")
self.weight_type.change(
fn=lambda weight_type: gr.update(
visible=weight_type
in ("style and composition", "strong style and composition")
),
inputs=[self.weight_type],
outputs=[self.weight_composition],
)
def update_weight_textbox(
control_type: str,
weight_type: str,
weight: float,
weight_composition: float,
):
if not advanced_weighting_supported(control_type):
return gr.update()
sd_version = get_sd_version()
weights = calc_weights(weight_type, weight, sd_version, weight_composition)
return gr.update(value=str([round(w, 2) for w in weights]), visible=True)
trigger_inputs = [self.weight_type, weight_input, self.weight_composition]
for trigger_input in trigger_inputs:
trigger_input.change(
fn=update_weight_textbox,
inputs=[
control_type,
self.weight_type,
weight_input,
self.weight_composition,
],
outputs=[self.weight_editor],
)
def update_plot(weights_string: str):
try:
weights = json.loads(weights_string)
assert isinstance(weights, list)
except Exception:
return gr.update(visible=False)
sd_version = get_sd_version()
weight_plot = plot_weights(
weights,
get_bar_colors(
sd_version,
input_color=INPUT_BLOCK_COLOR,
middle_color=MIDDLE_BLOCK_COLOR,
output_color=OUTPUT_BLOCK_COLOR,
),
)
return gr.update(value=weight_plot, visible=True)
def update_advanced_weighting(weights_string: str):
try:
weights = json.loads(weights_string)
assert isinstance(weights, list)
except Exception:
return None
return weights
self.weight_editor.change(
fn=update_plot,
inputs=[self.weight_editor],
outputs=[self.weight_plot],
)
self.weight_editor.change(
fn=update_advanced_weighting,
inputs=[self.weight_editor],
outputs=[advanced_weighting],
).then(
fn=lambda x: gr.update(value=x + 1),
inputs=[update_unit_counter],
outputs=[update_unit_counter],
) # Necessary to flush gr.State change to unit state.
# TODO: Expose advanced weighting control for other control types.
def control_type_change(control_type: str, old_weights):
supported = advanced_weighting_supported(control_type)
if supported:
return (
gr.update(visible=supported),
old_weights,
gr.update(),
gr.update(),
)
else:
return (
gr.update(visible=supported),
None,
gr.update(visible=False),
gr.update(visible=False),
)
control_type.change(
fn=control_type_change,
inputs=[control_type, advanced_weighting],
outputs=[
self.group,
advanced_weighting,
self.weight_editor,
self.weight_plot,
],
)
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