import os
import json
from datetime import datetime
import gradio as gr
from modules import sd_models, sd_vae, extras
from modules.ui_components import FormRow, ToolButton
from modules.ui_common import create_refresh_button
from modules.call_queue import wrap_gradio_gpu_call
from modules.shared import opts, log
import modules.errors
def create_ui():
dummy_component = gr.Label(visible=False)
with gr.Row(id="models_tab", elem_id="models_tab"):
with gr.Column(elem_id='models_output_container', scale=1):
# models_output = gr.Text(elem_id="models_output", value="", show_label=False)
gr.HTML(elem_id="models_progress", value="")
models_outcome = gr.HTML(elem_id="models_error", value="")
with gr.Column(elem_id='models_input_container', scale=3):
def gr_show(visible=True):
return {"visible": visible, "__type__": "update"}
with gr.Tab(label="Convert"):
with gr.Row():
model_name = gr.Dropdown(sd_models.checkpoint_tiles(), label="Original model")
create_refresh_button(model_name, sd_models.list_models, lambda: {"choices": sd_models.checkpoint_tiles()}, "refresh_checkpoint_Z")
with gr.Row():
custom_name = gr.Textbox(label="New model name")
with gr.Row():
precision = gr.Radio(choices=["fp32", "fp16", "bf16"], value="fp32", label="Model precision")
m_type = gr.Radio(choices=["disabled", "no-ema", "ema-only"], value="disabled", label="Model pruning methods")
with gr.Row():
checkpoint_formats = gr.CheckboxGroup(choices=["ckpt", "safetensors"], value=["safetensors"], label="Model Format")
with gr.Row():
show_extra_options = gr.Checkbox(label="Show extra options", value=False)
fix_clip = gr.Checkbox(label="Fix clip", 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")
show_extra_options.change(fn=lambda x: gr_show(x), inputs=[show_extra_options], outputs=[extra_options])
model_converter_convert = gr.Button(label="Convert", variant='primary')
model_converter_convert.click(
fn=extras.run_modelconvert,
inputs=[
model_name,
checkpoint_formats,
precision, m_type, custom_name,
unet_conv,
text_encoder_conv,
vae_conv,
others_conv,
fix_clip
],
outputs=[models_outcome]
)
with gr.Tab(label="Merge"):
with gr.Row().style(equal_height=False):
with gr.Column(variant='compact'):
with FormRow():
custom_name = gr.Textbox(label="New model name")
with FormRow():
def sd_model_choices():
return ['None'] + sd_models.checkpoint_tiles()
primary_model_name = gr.Dropdown(sd_model_choices(), label="Primary model", value="None")
create_refresh_button(primary_model_name, sd_models.list_models, lambda: {"choices": sd_model_choices()}, "refresh_checkpoint_A")
secondary_model_name = gr.Dropdown(sd_model_choices(), label="Secondary model", value="None")
create_refresh_button(secondary_model_name, sd_models.list_models, lambda: {"choices": sd_model_choices()}, "refresh_checkpoint_B")
tertiary_model_name = gr.Dropdown(sd_model_choices(), label="Tertiary model", value="None")
create_refresh_button(tertiary_model_name, sd_models.list_models, lambda: {"choices": sd_model_choices()}, "refresh_checkpoint_C")
with FormRow():
interp_method = gr.Radio(choices=["No interpolation", "Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method")
interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Interpolation ratio from Primary to Secondary', value=0.5)
with FormRow():
checkpoint_format = gr.Radio(choices=["ckpt", "safetensors"], value="safetensors", label="Model format")
with gr.Box():
save_as_half = gr.Radio(choices=["fp16", "fp32"], value="fp16", label="Model precision", type="index")
with FormRow():
config_source = gr.Radio(choices=["Primary", "Secondary", "Tertiary", "None"], value="Primary", label="Model configuration", type="index")
with FormRow():
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)}, "modelmerger_refresh_bake_in_vae")
with FormRow():
discard_weights = gr.Textbox(value="", label="Discard weights with matching name")
with FormRow():
save_metadata = gr.Checkbox(value=True, label="Save metadata")
with gr.Row():
modelmerger_merge = gr.Button(value="Merge", variant='primary')
def modelmerger(*args):
try:
results = extras.run_modelmerger(*args)
except Exception as e:
modules.errors.display(e, 'model merge')
sd_models.list_models() # to remove the potentially missing models from the list
return [*[gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)], f"Error merging checkpoints: {e}"]
return results
modelmerger_merge.click(
fn=wrap_gradio_gpu_call(modelmerger, extra_outputs=lambda: [gr.update() for _ in range(4)]),
_js='modelmerger',
inputs=[
dummy_component,
primary_model_name,
secondary_model_name,
tertiary_model_name,
interp_method,
interp_amount,
save_as_half,
custom_name,
checkpoint_format,
config_source,
bake_in_vae,
discard_weights,
save_metadata,
],
outputs=[
primary_model_name,
secondary_model_name,
tertiary_model_name,
dummy_component,
models_outcome,
]
)
with gr.Tab(label="Validate"):
model_headers = ['name', 'type', 'filename', 'hash', 'added', 'size', 'metadata']
model_data = []
with gr.Row():
model_list_btn = gr.Button(value="List model details", variant='primary')
model_checkhash_btn = gr.Button(value="Calculate hash for all models (may take a long time)", variant='primary')
model_checkhash_btn.click(fn=sd_models.update_model_hashes, inputs=[], outputs=[models_outcome])
with gr.Row():
model_table = gr.DataFrame(model_data, label = 'Model data', show_label = True, interactive = False, wrap = True, overflow_row_behaviour = 'paginate', max_rows = 10, headers = model_headers)
def list_models():
total_size = 0
for m in sd_models.checkpoints_list.values():
txt = ''
try:
stat = os.stat(m.filename)
m_name = m.name.replace('.ckpt', '').replace('.safetensors', '')
m_type = 'ckpt' if m.name.endswith('.ckpt') else 'safe'
m_meta = len(json.dumps(m.metadata)) - 2
m_size = round(stat.st_size / 1024 / 1024 / 1024, 3)
m_time = datetime.fromtimestamp(stat.st_mtime)
model_data.append([m_name, m_type, m.filename, m.hash, m_time, m_size, m_meta])
total_size += stat.st_size
except Exception as e:
txt += f"Error: {m.name} {e}
"
txt += f"Model list enumerated {len(sd_models.checkpoints_list.keys())} models in {round(total_size / 1024 / 1024 / 1024, 3)} GB
"
return model_data, txt
model_list_btn.click(fn=list_models, inputs=[], outputs=[model_table, models_outcome])
with gr.Tab(label="Huggingface"):
data = []
os.environ.setdefault('HF_HUB_DISABLE_EXPERIMENTAL_WARNING', '1')
os.environ.setdefault('HF_HUB_DISABLE_SYMLINKS_WARNING', '1')
os.environ.setdefault('HF_HUB_DISABLE_IMPLICIT_TOKEN', '1')
os.environ.setdefault('HUGGINGFACE_HUB_VERBOSITY', 'warning')
def hf_search(keyword):
import huggingface_hub as hf
hf_api = hf.HfApi()
model_filter = hf.ModelFilter(
model_name=keyword,
task='text-to-image',
library=['diffusers'],
)
models = hf_api.list_models(filter=model_filter, full=True, limit=50, sort="downloads", direction=-1)
data.clear()
for model in models:
tags = [t for t in model.tags if not t.startswith('diffusers') and not t.startswith('license') and not t.startswith('arxiv') and len(t) > 2]
data.append([model.modelId, model.pipeline_tag, tags, model.downloads, model.lastModified, f'https://huggingface.co/{model.modelId}'])
return data
def hf_select(evt: gr.SelectData, data):
return data[evt.index[0]][0]
def hf_download_model(hub_id: str, token, variant, revision, mirror):
from modules.modelloader import download_diffusers_model
try:
download_diffusers_model(hub_id, cache_dir=opts.diffusers_dir, token=token, variant=variant, revision=revision, mirror=mirror)
except Exception as e:
log.error(f"Diffuser model downloaded error: model={hub_id} {e}")
return f"Diffuser model downloaded error: model={hub_id} {e}"
from modules.sd_models import list_models # pylint: disable=W0621
list_models()
log.info(f"Diffuser model downloaded: model={hub_id}")
return f'Diffuser model downloaded: model={hub_id}'
with gr.Column(scale=6):
with gr.Row():
hf_search_text = gr.Textbox('', label = 'Seach models', placeholder='search huggingface models')
hf_search_btn = ToolButton(value="🔍", label="Search")
with gr.Row():
with gr.Column(scale=2):
with gr.Row():
hf_selected = gr.Textbox('', label = 'Select model', placeholder='select model from search results or enter model name manually')
with gr.Column(scale=1):
with gr.Row():
hf_variant = gr.Textbox(opts.cuda_dtype.lower(), label = 'Specify model variant', placeholder='')
hf_revision = gr.Textbox('', label = 'Specify model revision', placeholder='')
with gr.Row():
hf_token = gr.Textbox('', label = 'Huggingface token', placeholder='optional access token for private or gated models')
hf_mirror = gr.Textbox('', label = 'Huggingface mirror', placeholder='optional mirror site for downloads')
with gr.Column(scale=1):
hf_download_model_btn = gr.Button(value="Download model", variant='primary')
with gr.Row():
hf_headers = ['Name', 'Pipeline', 'Tags', 'Downloads', 'Updated', 'URL']
hf_types = ['str', 'str', 'str', 'number', 'date', 'markdown']
hf_results = gr.DataFrame([], label = 'Search results', show_label = True, interactive = False, wrap = True, overflow_row_behaviour = 'paginate', max_rows = 10, headers = hf_headers, datatype = hf_types, type='array')
hf_search_text.submit(fn=hf_search, inputs=[hf_search_text], outputs=[hf_results])
hf_search_btn.click(fn=hf_search, inputs=[hf_search_text], outputs=[hf_results])
hf_results.select(fn=hf_select, inputs=[hf_results], outputs=[hf_selected])
hf_download_model_btn.click(fn=hf_download_model, inputs=[hf_selected, hf_token, hf_variant, hf_revision, hf_mirror], outputs=[models_outcome])
# with gr.Tab(label="CivitAI"):
# pass