mirror of https://github.com/vladmandic/automatic
457 lines
29 KiB
Python
457 lines
29 KiB
Python
import os
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import json
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from datetime import datetime
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import gradio as gr
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from modules import sd_models, sd_vae, extras
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from modules.ui_components import FormRow, ToolButton
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from modules.ui_common import create_refresh_button
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from modules.call_queue import wrap_gradio_gpu_call
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from modules.shared import opts, log, req
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import modules.errors
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import modules.hashes
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search_metadata_civit = None
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def create_ui():
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dummy_component = gr.Label(visible=False)
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with gr.Row(elem_id="models_tab"):
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with gr.Column(elem_id='models_output_container', scale=1):
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# models_output = gr.Text(elem_id="models_output", value="", show_label=False)
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gr.HTML(elem_id="models_progress", value="")
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models_image = gr.Image(elem_id="models_image", show_label=False, interactive=False, type='pil')
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models_outcome = gr.HTML(elem_id="models_error", value="")
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with gr.Column(elem_id='models_input_container', scale=3):
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def gr_show(visible=True):
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return {"visible": visible, "__type__": "update"}
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with gr.Tab(label="Convert"):
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with gr.Row():
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model_name = gr.Dropdown(sd_models.checkpoint_tiles(), label="Original model")
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create_refresh_button(model_name, sd_models.list_models, lambda: {"choices": sd_models.checkpoint_tiles()}, "refresh_checkpoint_Z")
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with gr.Row():
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custom_name = gr.Textbox(label="New model name")
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with gr.Row():
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precision = gr.Radio(choices=["fp32", "fp16", "bf16"], value="fp16", label="Model precision")
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m_type = gr.Radio(choices=["disabled", "no-ema", "ema-only"], value="disabled", label="Model pruning methods")
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with gr.Row():
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checkpoint_formats = gr.CheckboxGroup(choices=["ckpt", "safetensors"], value=["safetensors"], label="Model Format")
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with gr.Row():
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show_extra_options = gr.Checkbox(label="Show extra options", value=False)
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fix_clip = gr.Checkbox(label="Fix clip", value=False)
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with gr.Row(visible=False) as extra_options:
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specific_part_conv = ["copy", "convert", "delete"]
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unet_conv = gr.Dropdown(specific_part_conv, value="convert", label="unet")
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text_encoder_conv = gr.Dropdown(specific_part_conv, value="convert", label="text encoder")
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vae_conv = gr.Dropdown(specific_part_conv, value="convert", label="vae")
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others_conv = gr.Dropdown(specific_part_conv, value="convert", label="others")
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show_extra_options.change(fn=lambda x: gr_show(x), inputs=[show_extra_options], outputs=[extra_options])
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model_converter_convert = gr.Button(label="Convert", variant='primary')
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model_converter_convert.click(
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fn=extras.run_modelconvert,
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inputs=[
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model_name,
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checkpoint_formats,
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precision, m_type, custom_name,
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unet_conv,
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text_encoder_conv,
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vae_conv,
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others_conv,
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fix_clip
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],
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outputs=[models_outcome]
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)
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with gr.Tab(label="Merge"):
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with gr.Row(equal_height=False):
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with gr.Column(variant='compact'):
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with FormRow():
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custom_name = gr.Textbox(label="New model name")
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with FormRow():
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def sd_model_choices():
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return ['None'] + sd_models.checkpoint_tiles()
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primary_model_name = gr.Dropdown(sd_model_choices(), label="Primary model", value="None")
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create_refresh_button(primary_model_name, sd_models.list_models, lambda: {"choices": sd_model_choices()}, "refresh_checkpoint_A")
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secondary_model_name = gr.Dropdown(sd_model_choices(), label="Secondary model", value="None")
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create_refresh_button(secondary_model_name, sd_models.list_models, lambda: {"choices": sd_model_choices()}, "refresh_checkpoint_B")
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tertiary_model_name = gr.Dropdown(sd_model_choices(), label="Tertiary model", value="None")
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create_refresh_button(tertiary_model_name, sd_models.list_models, lambda: {"choices": sd_model_choices()}, "refresh_checkpoint_C")
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with FormRow():
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interp_method = gr.Radio(choices=["No interpolation", "Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method")
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interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Interpolation ratio from Primary to Secondary', value=0.5)
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with FormRow():
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checkpoint_format = gr.Radio(choices=["ckpt", "safetensors"], value="safetensors", label="Model format")
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with gr.Box():
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save_as_half = gr.Radio(choices=["fp16", "fp32"], value="fp16", label="Model precision", type="index")
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with FormRow():
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config_source = gr.Radio(choices=["Primary", "Secondary", "Tertiary", "None"], value="Primary", label="Model configuration", type="index")
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with FormRow():
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bake_in_vae = gr.Dropdown(choices=["None"] + list(sd_vae.vae_dict), value="None", label="Bake in VAE")
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create_refresh_button(bake_in_vae, sd_vae.refresh_vae_list, lambda: {"choices": ["None"] + list(sd_vae.vae_dict)}, "modelmerger_refresh_bake_in_vae")
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with FormRow():
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discard_weights = gr.Textbox(value="", label="Discard weights with matching name")
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with FormRow():
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save_metadata = gr.Checkbox(value=True, label="Save metadata")
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with gr.Row():
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modelmerger_merge = gr.Button(value="Merge", variant='primary')
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def modelmerger(*args):
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try:
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results = extras.run_modelmerger(*args)
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except Exception as e:
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modules.errors.display(e, 'model merge')
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sd_models.list_models() # to remove the potentially missing models from the list
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return [*[gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)], f"Error merging checkpoints: {e}"]
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return results
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modelmerger_merge.click(
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fn=wrap_gradio_gpu_call(modelmerger, extra_outputs=lambda: [gr.update() for _ in range(4)]),
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_js='modelmerger',
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inputs=[
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dummy_component,
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primary_model_name,
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secondary_model_name,
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tertiary_model_name,
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interp_method,
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interp_amount,
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save_as_half,
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custom_name,
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checkpoint_format,
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config_source,
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bake_in_vae,
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discard_weights,
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save_metadata,
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],
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outputs=[
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primary_model_name,
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secondary_model_name,
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tertiary_model_name,
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dummy_component,
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models_outcome,
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]
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)
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with gr.Tab(label="Validate"):
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model_headers = ['name', 'type', 'filename', 'hash', 'added', 'size', 'metadata']
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model_data = []
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with gr.Row():
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model_list_btn = gr.Button(value="List model details", variant='primary')
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model_checkhash_btn = gr.Button(value="Calculate hash for all models", variant='primary')
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model_checkhash_btn.click(fn=sd_models.update_model_hashes, inputs=[], outputs=[models_outcome])
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with gr.Row():
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model_table = gr.DataFrame(
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value = None,
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headers = model_headers,
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label = 'Model data',
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show_label = True,
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interactive = False,
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wrap = True,
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overflow_row_behaviour = 'paginate',
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max_rows = 50,
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)
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def list_models():
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total_size = 0
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model_data.clear()
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txt = ''
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for m in sd_models.checkpoints_list.values():
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try:
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stat = os.stat(m.filename)
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m_name = m.name.replace('.ckpt', '').replace('.safetensors', '')
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m_type = 'ckpt' if m.name.endswith('.ckpt') else 'safe'
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m_meta = len(json.dumps(m.metadata)) - 2
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m_size = round(stat.st_size / 1024 / 1024 / 1024, 3)
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m_time = datetime.fromtimestamp(stat.st_mtime)
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model_data.append([m_name, m_type, m.filename, m.shorthash, m_time, m_size, m_meta])
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total_size += stat.st_size
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except Exception as e:
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txt += f"Error: {m.name} {e}<br>"
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txt += f"Model list enumerated {len(sd_models.checkpoints_list.keys())} models in {round(total_size / 1024 / 1024 / 1024, 3)} GB<br>"
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return model_data, txt
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model_list_btn.click(fn=list_models, inputs=[], outputs=[model_table, models_outcome])
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with gr.Tab(label="Huggingface"):
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data = []
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os.environ.setdefault('HF_HUB_DISABLE_EXPERIMENTAL_WARNING', '1')
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os.environ.setdefault('HF_HUB_DISABLE_SYMLINKS_WARNING', '1')
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os.environ.setdefault('HF_HUB_DISABLE_IMPLICIT_TOKEN', '1')
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os.environ.setdefault('HUGGINGFACE_HUB_VERBOSITY', 'warning')
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def hf_search(keyword):
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import huggingface_hub as hf
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hf_api = hf.HfApi()
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model_filter = hf.ModelFilter(
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model_name=keyword,
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# task='text-to-image',
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library=['diffusers'],
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)
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models = hf_api.list_models(filter=model_filter, full=True, limit=50, sort="downloads", direction=-1)
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data.clear()
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for model in models:
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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]
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data.append([model.modelId, model.pipeline_tag, tags, model.downloads, model.lastModified, f'https://huggingface.co/{model.modelId}'])
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return data
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def hf_select(evt: gr.SelectData, data):
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return data[evt.index[0]][0]
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def hf_download_model(hub_id: str, token, variant, revision, mirror, custom_pipeline):
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from modules.modelloader import download_diffusers_model
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download_diffusers_model(hub_id, cache_dir=opts.diffusers_dir, token=token, variant=variant, revision=revision, mirror=mirror, custom_pipeline=custom_pipeline)
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from modules.sd_models import list_models # pylint: disable=W0621
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list_models()
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log.info(f'Diffuser model downloaded: model="{hub_id}"')
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return f'Diffuser model downloaded: model="{hub_id}"'
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with gr.Column(scale=6):
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gr.HTML('<h2>Search for models</h2>Select a model from the search results to download<br><br>')
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with gr.Row():
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hf_search_text = gr.Textbox('', label = 'Search models', placeholder='search huggingface models')
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hf_search_btn = ToolButton(value="🔍", label="Search")
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with gr.Row():
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with gr.Column(scale=2):
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with gr.Row():
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hf_selected = gr.Textbox('', label = 'Select model', placeholder='select model from search results or enter model name manually')
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with gr.Column(scale=1):
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with gr.Row():
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hf_variant = gr.Textbox(opts.cuda_dtype.lower(), label = 'Specify model variant', placeholder='')
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hf_revision = gr.Textbox('', label = 'Specify model revision', placeholder='')
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with gr.Row():
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hf_token = gr.Textbox('', label = 'Huggingface token', placeholder='optional access token for private or gated models')
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hf_mirror = gr.Textbox('', label = 'Huggingface mirror', placeholder='optional mirror site for downloads')
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hf_custom_pipeline = gr.Textbox('', label = 'Custom pipeline', placeholder='optional pipeline for downloads')
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with gr.Column(scale=1):
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gr.HTML('<br>')
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hf_download_model_btn = gr.Button(value="Download model", variant='primary')
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with gr.Row():
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hf_headers = ['Name', 'Pipeline', 'Tags', 'Downloads', 'Updated', 'URL']
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hf_types = ['str', 'str', 'str', 'number', 'date', 'markdown']
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hf_results = gr.DataFrame(None, 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')
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hf_search_text.submit(fn=hf_search, inputs=[hf_search_text], outputs=[hf_results])
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hf_search_btn.click(fn=hf_search, inputs=[hf_search_text], outputs=[hf_results])
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hf_results.select(fn=hf_select, inputs=[hf_results], outputs=[hf_selected])
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hf_download_model_btn.click(fn=hf_download_model, inputs=[hf_selected, hf_token, hf_variant, hf_revision, hf_mirror, hf_custom_pipeline], outputs=[models_outcome])
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with gr.Tab(label="CivitAI"):
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data = []
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def civit_search_model(name, tag, model_type):
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# types = 'LORA' if model_type == 'LoRA' else 'Checkpoint'
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url = 'https://civitai.com/api/v1/models?limit=25&&Sort=Newest'
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if name is not None and len(name) > 0:
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url += f'&query={name}'
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if tag is not None and len(tag) > 0:
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url += f'&tag={tag}'
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r = req(url)
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log.debug(f'CivitAI search: name="{name}" tag={tag or "none"} status={r.status_code}')
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if r.status_code != 200:
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return [], [], []
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body = r.json()
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nonlocal data
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data = body.get('items', [])
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data1 = []
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for model in data:
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found = 0
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if model_type == 'LoRA' and model['type'] in ['LORA', 'LoCon']:
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found += 1
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for variant in model['modelVersions']:
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if model_type == 'SD 1.5':
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if 'SD 1.' in variant['baseModel']:
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found += 1
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if model_type == 'SD XL':
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if 'SDXL' in variant['baseModel']:
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found += 1
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else:
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if 'SD 1.' not in variant['baseModel'] and 'SDXL' not in variant['baseModel']:
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found += 1
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if found > 0:
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data1.append([
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model['id'],
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model['name'],
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', '.join(model['tags']),
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model['stats']['downloadCount'],
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model['stats']['rating']
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])
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res = f'Search result: name={name} tag={tag or "none"} type={model_type} models={len(data1)}'
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return res, gr.update(visible=len(data1) > 0, value=data1 if len(data1) > 0 else []), gr.update(visible=False, value=None), gr.update(visible=False, value=None)
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def civit_select1(evt: gr.SelectData, in_data):
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model_id = in_data[evt.index[0]][0]
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data2 = []
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preview_img = None
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for model in data:
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if model['id'] == model_id:
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for d in model['modelVersions']:
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if d.get('images') is not None and len(d['images']) > 0 and len(d['images'][0]['url']) > 0:
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preview_img = d['images'][0]['url']
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data2.append([
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d['id'],
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d['modelId'],
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d['name'],
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d['baseModel'],
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d['createdAt'],
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])
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log.debug(f'CivitAI select: model="{in_data[evt.index[0]]}" versions={len(data2)}')
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return data2, None, preview_img
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def civit_select2(evt: gr.SelectData, in_data):
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variant_id = in_data[evt.index[0]][0]
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model_id = in_data[evt.index[0]][1]
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data3 = []
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for model in data:
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if model['id'] == model_id:
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for variant in model['modelVersions']:
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if variant['id'] == variant_id:
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for f in variant['files']:
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try:
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if os.path.splitext(f['name'])[1].lower() in ['.safetensors', '.ckpt', '.pt', '.pth', '.bin']:
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data3.append([
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f['name'],
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round(f['sizeKB']),
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json.dumps(f['metadata']),
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f['downloadUrl'],
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])
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except Exception:
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pass
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log.debug(f'CivitAI select: model="{in_data[evt.index[0]]}" files={len(data3)}')
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return data3
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def civit_select3(evt: gr.SelectData, in_data):
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log.debug(f'CivitAI select: variant={in_data[evt.index[0]]}')
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return in_data[evt.index[0]][3], in_data[evt.index[0]][0], gr.update(interactive=True)
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def civit_download_model(model_url: str, model_name: str, model_path: str, model_type: str, image_url: str):
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if model_url is None or len(model_url) == 0:
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return 'No model selected'
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try:
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from modules.modelloader import download_civit_model
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res = download_civit_model(model_url, model_name, model_path, model_type, image_url)
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except Exception as e:
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res = f"CivitAI model downloaded error: model={model_url} {e}"
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log.error(res)
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return res
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from modules.sd_models import list_models # pylint: disable=W0621
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list_models()
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return res
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def civit_search_metadata(civit_previews_rehash, title):
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log.debug(f'CivitAI search metadata: {title if type(title) == str else "all"}')
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from modules.ui_extra_networks import get_pages
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from modules.modelloader import download_civit_preview, download_civit_meta
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res = []
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for page in get_pages():
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if type(title) == str:
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if page.title != title:
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continue
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if page.name == 'style':
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continue
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for item in page.list_items():
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meta = os.path.splitext(item['filename'])[0] + '.json'
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if ('card-no-preview.png' in item['preview'] or not os.path.isfile(meta)) and os.path.isfile(item['filename']):
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sha = item.get('hash', None)
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found = False
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if sha is not None and len(sha) > 0:
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r = req(f'https://civitai.com/api/v1/model-versions/by-hash/{sha}')
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log.debug(f'CivitAI search: name="{item["name"]}" hash={sha} status={r.status_code}')
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if r.status_code == 200:
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d = r.json()
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res.append(download_civit_meta(item['filename'], d['modelId']))
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if d.get('images') is not None:
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for i in d['images']:
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preview_url = i['url']
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img_res = download_civit_preview(item['filename'], preview_url)
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res.append(img_res)
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if 'error' not in img_res:
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found = True
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break
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if not found and civit_previews_rehash and os.stat(item['filename']).st_size < (1024 * 1024 * 1024):
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sha = modules.hashes.calculate_sha256(item['filename'], quiet=True)[:10]
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r = req(f'https://civitai.com/api/v1/model-versions/by-hash/{sha}')
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log.debug(f'CivitAI search: name="{item["name"]}" hash={sha} status={r.status_code}')
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if r.status_code == 200:
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d = r.json()
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res.append(download_civit_meta(item['filename'], d['modelId']))
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if d.get('images') is not None:
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for i in d['images']:
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preview_url = i['url']
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img_res = download_civit_preview(item['filename'], preview_url)
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res.append(img_res)
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if 'error' not in img_res:
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found = True
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break
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txt = '<br>'.join([r for r in res if len(r) > 0])
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return txt
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global search_metadata_civit # pylint: disable=global-statement
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search_metadata_civit = civit_search_metadata
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with gr.Row():
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gr.HTML('<h2>Fetch information</h2>Fetches preview and metadata information for all models with missing information<br>Models with existing previews and information are not updated<br>')
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with gr.Row():
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civit_previews_btn = gr.Button(value="Start", variant='primary')
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with gr.Row():
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civit_previews_rehash = gr.Checkbox(value=True, label="Check alternative hash")
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with gr.Row():
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gr.HTML('<h2>Search for models</h2>')
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with gr.Row():
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with gr.Column(scale=1):
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civit_model_type = gr.Dropdown(label='Model type', choices=['SD 1.5', 'SD XL', 'LoRA', 'Other'], value='LoRA')
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with gr.Column(scale=15):
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with gr.Row():
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civit_search_text = gr.Textbox('', label = 'Search models', placeholder='keyword')
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civit_search_tag = gr.Textbox('', label = '', placeholder='tags')
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civit_search_btn = ToolButton(value="🔍", label="Search", interactive=False)
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with gr.Row():
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civit_search_res = gr.HTML('')
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with gr.Row():
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gr.HTML('<h2>Download model</h2>')
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with gr.Row():
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civit_download_model_btn = gr.Button(value="Download", variant='primary')
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gr.HTML('<span style="line-height: 2em">Select a model, model version and and model variant from the search results to download or enter model URL manually</span><br>')
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with gr.Row():
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civit_name = gr.Textbox('', label = 'Model name', placeholder='select model from search results', visible=True)
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civit_selected = gr.Textbox('', label = 'Model URL', placeholder='select model from search results', visible=True)
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civit_path = gr.Textbox('', label = 'Download path', placeholder='optional subfolder path where to save model', visible=True)
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with gr.Row():
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gr.HTML('<h2>Search results</h2>')
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with gr.Row():
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civit_headers1 = ['ID', 'Name', 'Tags', 'Downloads', 'Rating']
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civit_types1 = ['number', 'str', 'str', 'number', 'number']
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civit_results1 = gr.DataFrame(value = None, label = None, show_label = False, interactive = False, wrap = True, overflow_row_behaviour = 'paginate', max_rows = 10, headers = civit_headers1, datatype = civit_types1, type='array', visible=False)
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with gr.Row():
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with gr.Column():
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civit_headers2 = ['ID', 'ModelID', 'Name', 'Base', 'Created', 'Preview']
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civit_types2 = ['number', 'number', 'str', 'str', 'date', 'str']
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civit_results2 = gr.DataFrame(value = None, label = 'Model versions', show_label = True, interactive = False, wrap = True, overflow_row_behaviour = 'paginate', max_rows = 10, headers = civit_headers2, datatype = civit_types2, type='array', visible=False)
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with gr.Column():
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civit_headers3 = ['Name', 'Size', 'Metadata', 'URL']
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civit_types3 = ['str', 'number', 'str', 'str']
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civit_results3 = gr.DataFrame(value = None, label = 'Model variants', show_label = True, interactive = False, wrap = True, overflow_row_behaviour = 'paginate', max_rows = 10, headers = civit_headers3, datatype = civit_types3, type='array', visible=False)
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def is_visible(component):
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visible = len(component) > 0 if component is not None else False
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return gr.update(visible=visible)
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civit_search_text.submit(fn=civit_search_model, inputs=[civit_search_text, civit_search_tag, civit_model_type], outputs=[civit_search_res, civit_results1, civit_results2, civit_results3])
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civit_search_tag.submit(fn=civit_search_model, inputs=[civit_search_text, civit_search_tag, civit_model_type], outputs=[civit_search_res, civit_results1, civit_results2, civit_results3])
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civit_search_btn.click(fn=civit_search_model, inputs=[civit_search_text, civit_search_tag, civit_model_type], outputs=[civit_search_res, civit_results1, civit_results2, civit_results3])
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civit_results1.select(fn=civit_select1, inputs=[civit_results1], outputs=[civit_results2, civit_results3, models_image])
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civit_results2.select(fn=civit_select2, inputs=[civit_results2], outputs=[civit_results3])
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civit_results3.select(fn=civit_select3, inputs=[civit_results3], outputs=[civit_selected, civit_name, civit_search_btn])
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civit_results1.change(fn=is_visible, inputs=[civit_results1], outputs=[civit_results1])
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civit_results2.change(fn=is_visible, inputs=[civit_results2], outputs=[civit_results2])
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civit_results3.change(fn=is_visible, inputs=[civit_results3], outputs=[civit_results3])
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civit_download_model_btn.click(fn=civit_download_model, inputs=[civit_selected, civit_name, civit_path, civit_model_type, models_image], outputs=[models_outcome])
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civit_previews_btn.click(fn=civit_search_metadata, inputs=[civit_previews_rehash, civit_previews_rehash], outputs=[models_outcome])
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