Add support for more options, rework UI
This commit is contained in:
bmaltais
parent
379ab73496
commit
0d52ff4842
149
finetune_gui.py
149
finetune_gui.py
@ -30,7 +30,11 @@ def save_variables(
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seed,
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num_cpu_threads_per_process,
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convert_to_safetensors,
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convert_to_ckpt
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convert_to_ckpt,
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cache_latent,
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caption_extention,
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use_safetensors,
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enable_bucket
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):
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# Return the values of the variables as a dictionary
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variables = {
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@ -54,7 +58,11 @@ def save_variables(
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"seed": seed,
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"num_cpu_threads_per_process": num_cpu_threads_per_process,
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"convert_to_safetensors": convert_to_safetensors,
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"convert_to_ckpt": convert_to_ckpt
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"convert_to_ckpt": convert_to_ckpt,
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"cache_latent": cache_latent,
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"caption_extention": caption_extention,
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"use_safetensors": use_safetensors,
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"enable_bucket": enable_bucket
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}
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# Save the data to the selected file
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@ -73,7 +81,6 @@ def load_variables(file_path):
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my_data.get("v2", None),
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my_data.get("v_model", None),
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my_data.get("logging_dir", None),
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# my_data.get("model_list", None),
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my_data.get("train_data_dir", None),
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my_data.get("reg_data_dir", None),
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my_data.get("output_dir", None),
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@ -89,7 +96,11 @@ def load_variables(file_path):
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my_data.get("seed", None),
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my_data.get("num_cpu_threads_per_process", None),
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my_data.get("convert_to_safetensors", None),
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my_data.get("convert_to_ckpt", None)
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my_data.get("convert_to_ckpt", None),
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my_data.get("cache_latent", None),
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my_data.get("caption_extention", None),
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my_data.get("use_safetensors", None),
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my_data.get("enable_bucket", None),
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)
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@ -114,7 +125,10 @@ def train_model(
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num_cpu_threads_per_process,
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convert_to_safetensors,
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convert_to_ckpt,
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cache_latent_input
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cache_latent,
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caption_extention,
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use_safetensors,
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enable_bucket
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):
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def save_inference_file(output_dir, v2, v_model):
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# Copy inference model for v2 if required
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@ -170,8 +184,12 @@ def train_model(
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run_cmd += " --v2"
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if v_model:
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run_cmd += " --v_parameterization"
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if cache_latent_input:
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if cache_latent:
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run_cmd += " --cache_latents"
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if use_safetensors:
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run_cmd += " --use_safetensors"
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if enable_bucket:
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run_cmd += " --enable_bucket"
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run_cmd += f" --pretrained_model_name_or_path={pretrained_model_name_or_path}"
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run_cmd += f" --train_data_dir={train_data_dir}"
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run_cmd += f" --reg_data_dir={reg_data_dir}"
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@ -189,6 +207,7 @@ def train_model(
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run_cmd += f" --seed={seed}"
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run_cmd += f" --save_precision={save_precision}"
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run_cmd += f" --logging_dir={logging_dir}"
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run_cmd += f" --caption_extention={caption_extention}"
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print(run_cmd)
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# Run the command
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@ -207,7 +226,8 @@ def train_model(
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save_inference_file(output_dir, v2, v_model)
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if convert_to_safetensors:
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print(f"Converting diffuser model {last_dir} to {last_dir}.safetensors")
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print(
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f"Converting diffuser model {last_dir} to {last_dir}.safetensors")
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os.system(
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f"python ./tools/convert_diffusers20_original_sd.py {last_dir} {last_dir}.safetensors --{save_precision}"
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)
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@ -223,26 +243,33 @@ def train_model(
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def set_pretrained_model_name_or_path_input(value, v2, v_model):
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# define a list of substrings to search for
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substrings_v2 = ["stable-diffusion-2-1-base", "stable-diffusion-2-base"]
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substrings_v2 = ["stabilityai/stable-diffusion-2-1-base", "stabilityai/stable-diffusion-2-base"]
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# check if $v2 and $v_model are empty and if $pretrained_model_name_or_path contains any of the substrings in the v2 list
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if str(value) in substrings_v2:
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print("SD v2 model detected. Setting --v2 parameter")
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v2 = True
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v_model = False
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value = "stabilityai/{}".format(value)
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return value, v2, v_model
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# define a list of substrings to search for v-objective
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substrings_v_model = ["stable-diffusion-2-1", "stable-diffusion-2"]
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substrings_v_model = ["stabilityai/stable-diffusion-2-1", "stabilityai/stable-diffusion-2"]
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# check if $v2 and $v_model are empty and if $pretrained_model_name_or_path contains any of the substrings in the v_model list
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if str(value) in substrings_v_model:
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print("SD v2 v_model detected. Setting --v2 parameter and --v_parameterization")
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v2 = True
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v_model = True
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value = "stabilityai/{}".format(value)
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return value, v2, v_model
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# define a list of substrings to v1.x
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substrings_v1_model = ["CompVis/stable-diffusion-v1-4", "runwayml/stable-diffusion-v1-5"]
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if str(value) in substrings_v1_model:
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v2 = False
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v_model = False
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return value, v2, v_model
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@ -263,10 +290,11 @@ with interface:
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gr.Markdown("Enter kohya finetuner parameter using this interface.")
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with gr.Accordion("Configuration File Load/Save", open=False):
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with gr.Row():
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config_file_name = gr.inputs.Textbox(label="Config file name", default="")
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config_file_name = gr.inputs.Textbox(
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label="Config file name", default="")
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b1 = gr.Button("Load config")
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b2 = gr.Button("Save config")
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with gr.Tab("model"):
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with gr.Tab("Source model"):
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# Define the input elements
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with gr.Row():
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pretrained_model_name_or_path_input = gr.inputs.Textbox(
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@ -277,10 +305,12 @@ with interface:
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label="Model Quick Pick",
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choices=[
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"custom",
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"stable-diffusion-2-1-base",
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"stable-diffusion-2-base",
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"stable-diffusion-2-1",
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"stable-diffusion-2",
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"stabilityai/stable-diffusion-2-1-base",
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"stabilityai/stable-diffusion-2-base",
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"stabilityai/stable-diffusion-2-1",
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"stabilityai/stable-diffusion-2",
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"runwayml/stable-diffusion-v1-5",
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"CompVis/stable-diffusion-v1-4"
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],
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value="custom",
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)
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@ -290,28 +320,30 @@ with interface:
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model_list.change(
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set_pretrained_model_name_or_path_input,
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inputs=[model_list, v2_input, v_model_input],
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outputs=[pretrained_model_name_or_path_input, v2_input, v_model_input],
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outputs=[pretrained_model_name_or_path_input,
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v2_input, v_model_input],
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)
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with gr.Tab("training dataset and output directory"):
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train_data_dir_input = gr.inputs.Textbox(
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label="Image folder", placeholder="directory where the training folders containing the images are located"
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)
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reg_data_dir_input = gr.inputs.Textbox(
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label="Regularisation folder", placeholder="directory where where the regularization folders containing the images are located"
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)
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output_dir_input = gr.inputs.Textbox(
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label="Output directory",
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placeholder="directory to output trained model",
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)
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logging_dir_input = gr.inputs.Textbox(
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label="Logging directory", placeholder="Optional: enable logging and output TensorBoard log to this directory"
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)
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max_resolution_input = gr.inputs.Textbox(
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label="Max resolution", default="512,512"
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)
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with gr.Tab("training parameters"):
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with gr.Tab("Directories"):
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with gr.Row():
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learning_rate_input = gr.inputs.Textbox(label="Learning rate", default=1e-6)
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train_data_dir_input = gr.inputs.Textbox(
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label="Image folder", placeholder="directory where the training folders containing the images are located"
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)
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reg_data_dir_input = gr.inputs.Textbox(
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label="Regularisation folder", placeholder="directory where where the regularization folders containing the images are located"
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)
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with gr.Row():
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output_dir_input = gr.inputs.Textbox(
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label="Output directory",
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placeholder="directory to output trained model",
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)
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logging_dir_input = gr.inputs.Textbox(
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label="Logging directory", placeholder="Optional: enable logging and output TensorBoard log to this directory"
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)
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with gr.Tab("Training parameters"):
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with gr.Row():
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learning_rate_input = gr.inputs.Textbox(
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label="Learning rate", default=1e-6)
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lr_scheduler_input = gr.Dropdown(
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label="LR Scheduler",
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choices=[
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@ -330,10 +362,10 @@ with interface:
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label="Train batch size", default=1
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)
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epoch_input = gr.inputs.Textbox(label="Epoch", default=1)
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with gr.Row():
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save_every_n_epochs_input = gr.inputs.Textbox(
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label="Save every N epochs", default=1
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)
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with gr.Row():
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mixed_precision_input = gr.Dropdown(
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label="Mixed precision",
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choices=[
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@ -352,22 +384,36 @@ with interface:
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],
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value="fp16",
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)
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with gr.Row():
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seed_input = gr.inputs.Textbox(label="Seed", default=1234)
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num_cpu_threads_per_process_input = gr.inputs.Textbox(
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label="Number of CPU threads per process", default=4
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)
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with gr.Row():
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seed_input = gr.inputs.Textbox(label="Seed", default=1234)
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max_resolution_input = gr.inputs.Textbox(
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label="Max resolution", default="512,512"
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)
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caption_extention_input = gr.inputs.Textbox(
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label="Caption Extension", placeholder="(Optional) Extension for caption files. default: .caption")
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with gr.Row():
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use_safetensors_input = gr.inputs.Checkbox(
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label="Use safetensor when saving checkpoint", default=False
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)
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enable_bucket_input = gr.inputs.Checkbox(
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label="Enable buckets", default=False
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)
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cache_latent_input = gr.inputs.Checkbox(
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label="Cache latent", default=True
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)
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with gr.Tab("model conveersion"):
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with gr.Tab("Model conversion"):
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convert_to_safetensors_input = gr.inputs.Checkbox(
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label="Convert to SafeTensors", default=False
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)
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convert_to_ckpt_input = gr.inputs.Checkbox(
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label="Convert to CKPT", default=False
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)
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b3 = gr.Button("Run")
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b1.click(
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@ -393,10 +439,14 @@ with interface:
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seed_input,
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num_cpu_threads_per_process_input,
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convert_to_safetensors_input,
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convert_to_ckpt_input
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convert_to_ckpt_input,
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cache_latent_input,
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caption_extention_input,
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use_safetensors_input,
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enable_bucket_input
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]
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)
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b2.click(
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save_variables,
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inputs=[
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@ -420,7 +470,11 @@ with interface:
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seed_input,
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num_cpu_threads_per_process_input,
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convert_to_safetensors_input,
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convert_to_ckpt_input
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convert_to_ckpt_input,
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cache_latent_input,
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caption_extention_input,
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use_safetensors_input,
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enable_bucket_input
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]
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)
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b3.click(
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@ -446,7 +500,10 @@ with interface:
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num_cpu_threads_per_process_input,
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convert_to_safetensors_input,
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convert_to_ckpt_input,
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cache_latent_input
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cache_latent_input,
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caption_extention_input,
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use_safetensors_input,
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enable_bucket_input
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]
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)
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