File size: 4,563 Bytes
0163a2c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 | import gradio as gr
from modules import script_callbacks
from modules import sd_models, sd_vae
from modules.ui import create_refresh_button
from scripts import convert
def gr_show(visible=True):
return {"visible": visible, "__type__": "update"}
def add_tab():
with gr.Blocks(analytics_enabled=False) as ui:
with gr.Row(equal_height=True):
with gr.Column(variant='panel'):
gr.HTML(value="<p>Converted checkpoints will be saved in your <b>checkpoint</b> directory.</p>")
with gr.Tabs():
with gr.TabItem(label='Single process'):
with gr.Row():
model_name = gr.Dropdown(sd_models.checkpoint_tiles(),
elem_id="model_converter_model_name",
label="Model")
create_refresh_button(model_name, sd_models.list_models,
lambda: {"choices": sd_models.checkpoint_tiles()},
"refresh_checkpoint_Z")
custom_name = gr.Textbox(label="Custom Name (Optional)")
with gr.TabItem(label='Input file path'):
with gr.Row():
model_path = gr.Textbox(label="model path")
with gr.TabItem(label='Batch from directory'):
with gr.Row():
input_directory = gr.Textbox(label="Input Directory")
with gr.Row():
precision = gr.Radio(choices=["fp32", "fp16", "bf16"], value="fp16", label="Precision")
m_type = gr.Radio(choices=["disabled", "no-ema", "ema-only"], value="disabled", label="Pruning Methods")
with gr.Row():
checkpoint_formats = gr.CheckboxGroup(choices=["ckpt", "safetensors"], value=["safetensors"], label="Checkpoint Format")
show_extra_options = gr.Checkbox(label="Show extra options", value=False)
with gr.Row():
bake_in_vae = gr.Dropdown(choices=["None"] + list(sd_vae.vae_dict), value="None", label="Bake in VAE")
create_refresh_button(bake_in_vae, sd_vae.refresh_vae_list, lambda: {"choices": ["None"] + list(sd_vae.vae_dict)}, "model_converter_refresh_bake_in_vae")
with gr.Row():
force_position_id = gr.Checkbox(label="Force CLIP position_id to int64 before convert", value=True)
fix_clip = gr.Checkbox(label="Fix clip", value=False)
delete_known_junk_data = gr.Checkbox(label="Delete known junk data", value=False)
with gr.Row(visible=False) as extra_options:
specific_part_conv = ["copy", "convert", "delete"]
unet_conv = gr.Dropdown(specific_part_conv, value="convert", label="unet")
text_encoder_conv = gr.Dropdown(specific_part_conv, value="convert", label="text encoder")
vae_conv = gr.Dropdown(specific_part_conv, value="convert", label="vae")
others_conv = gr.Dropdown(specific_part_conv, value="convert", label="others")
model_converter_convert = gr.Button(elem_id="model_converter_convert", label="Convert",
variant='primary')
with gr.Column(variant='panel'):
submit_result = gr.Textbox(elem_id="model_converter_result", show_label=False)
show_extra_options.change(
fn=lambda x: gr_show(x),
inputs=[show_extra_options],
outputs=[extra_options],
)
model_converter_convert.click(
fn=convert.convert_warp,
inputs=[
model_name,
model_path,
input_directory,
checkpoint_formats,
precision, m_type, custom_name,
bake_in_vae,
unet_conv,
text_encoder_conv,
vae_conv,
others_conv,
fix_clip,
force_position_id,
delete_known_junk_data
],
outputs=[submit_result]
)
return [(ui, "Model Converter", "model_converter")]
script_callbacks.on_ui_tabs(add_tab)
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