multimodalart HF staff commited on
Commit
44ac339
1 Parent(s): 8a48df3

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +15 -6
app.py CHANGED
@@ -34,11 +34,11 @@ model = Blip2ForConditionalGeneration.from_pretrained(
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  training_option_settings = {
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  "face": {
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- "rank": 64,
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  "lr_scheduler": "constant",
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  "with_prior_preservation": True,
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  "class_prompt": "a photo of a person",
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- "train_steps_multiplier": 100,
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  "file_count": 150,
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  "dataset_path": FACES_DATASET_PATH
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  },
@@ -49,12 +49,19 @@ training_option_settings = {
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  "class_prompt": "",
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  "train_steps_multiplier": 150
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  },
 
 
 
 
 
 
 
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  "object": {
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- "rank": 8,
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  "lr_scheduler": "constant",
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  "with_prior_preservation": False,
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  "class_prompt": "",
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- "train_steps_multiplier": 150
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  },
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  "custom": {
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  "rank": 32,
@@ -69,7 +76,7 @@ num_images_settings = {
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  #>24 images, 1 repeat; 10<x<24 images 2 repeats; <10 images 3 repeats
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  "repeats": [(24, 1), (10, 2), (0, 3)],
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  "train_steps_min": 500,
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- "train_steps_max": 2400
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  }
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  def load_captioning(uploaded_images, option):
@@ -106,6 +113,8 @@ def make_options_visible(option):
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  sentence = "A photo of TOK"
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  elif option == "style":
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  sentence = "in the style of TOK"
 
 
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  elif option == "custom":
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  sentence = "TOK"
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  return (
@@ -522,7 +531,7 @@ with gr.Blocks(css=css, theme=theme) as demo:
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  with gr.Column(elem_classes=["main_unlogged"]) as main_ui:
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  lora_name = gr.Textbox(label="The name of your LoRA", info="This has to be a unique name", placeholder="e.g.: Persian Miniature Painting style, Cat Toy")
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  training_option = gr.Radio(
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- label="What are you training?", choices=["object", "style", "face", "custom"]
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  )
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  concept_sentence = gr.Textbox(
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  label="Concept sentence",
 
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  training_option_settings = {
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  "face": {
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+ "rank": 32,
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  "lr_scheduler": "constant",
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  "with_prior_preservation": True,
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  "class_prompt": "a photo of a person",
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+ "train_steps_multiplier": 150,
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  "file_count": 150,
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  "dataset_path": FACES_DATASET_PATH
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  },
 
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  "class_prompt": "",
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  "train_steps_multiplier": 150
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  },
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+ "character": {
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+ "rank": 32,
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+ "lr_scheduler": "constant",
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+ "with_prior_preservation": False,
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+ "class_prompt": "",
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+ "train_steps_multiplier": 200
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+ },
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  "object": {
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+ "rank": 16,
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  "lr_scheduler": "constant",
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  "with_prior_preservation": False,
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  "class_prompt": "",
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+ "train_steps_multiplier": 50
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  },
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  "custom": {
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  "rank": 32,
 
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  #>24 images, 1 repeat; 10<x<24 images 2 repeats; <10 images 3 repeats
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  "repeats": [(24, 1), (10, 2), (0, 3)],
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  "train_steps_min": 500,
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+ "train_steps_max": 1500
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  }
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  def load_captioning(uploaded_images, option):
 
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  sentence = "A photo of TOK"
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  elif option == "style":
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  sentence = "in the style of TOK"
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+ elif option == "character":
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+ sentence = "A TOK character"
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  elif option == "custom":
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  sentence = "TOK"
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  return (
 
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  with gr.Column(elem_classes=["main_unlogged"]) as main_ui:
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  lora_name = gr.Textbox(label="The name of your LoRA", info="This has to be a unique name", placeholder="e.g.: Persian Miniature Painting style, Cat Toy")
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  training_option = gr.Radio(
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+ label="What are you training?", choices=["object", "style", "character", "face", "custom"]
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  )
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  concept_sentence = gr.Textbox(
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  label="Concept sentence",