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Runtime error
Runtime error
1lint
commited on
Commit
•
7833185
1
Parent(s):
f94c315
add textual inversion functionality
Browse files- .gitignore +3 -1
- app.py +126 -7
- inpaint_noise_issue.ipynb +0 -0
- textual_inversion.py +778 -0
.gitignore
CHANGED
@@ -1,2 +1,4 @@
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__pycache__/
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-
WIP/
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__pycache__/
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WIP/
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concept_images/
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output_model/
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app.py
CHANGED
@@ -5,7 +5,6 @@ from inpaint_pipeline import SDInpaintPipeline as StableDiffusionInpaintPipeline
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from diffusers import (
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StableDiffusionPipeline,
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StableDiffusionImg2ImgPipeline,
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-
#StableDiffusionInpaintPipelineLegacy # uncomment this line to use original inpaint pipeline
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)
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import diffusers.schedulers
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@@ -14,9 +13,29 @@ import torch
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import random
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from multiprocessing import cpu_count
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import json
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import importlib
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_xformers_available = importlib.util.find_spec("xformers") is not None
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device = "cuda" if torch.cuda.is_available() else "cpu"
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low_vram_mode = False
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@@ -56,8 +75,7 @@ def load_pipe(
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if model_id != loaded_model_id:
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pipe = pipe_class.from_pretrained(
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model_id,
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-
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# revision='fp16',
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safety_checker=None,
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requires_safety_checker=False,
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scheduler=scheduler.from_pretrained(model_id, subfolder="scheduler"),
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@@ -87,7 +105,8 @@ def load_pipe(
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pipe = None
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pipe = load_pipe(model_ids[0], default_scheduler)
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-
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def generate(
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model_name,
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scheduler_name,
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@@ -177,9 +196,75 @@ def generate(
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)
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else:
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None, f"Unhandled pipeline class: {pipe_class}"
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-
return
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default_img_size = 512
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@@ -260,6 +345,32 @@ with gr.Blocks(css="style.css") as demo:
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inpaint_options = ["preserve non-masked portions of image", "output entire inpainted image"]
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inpaint_radio = gr.Radio(inpaint_options, value=inpaint_options[0], show_label=False, interactive=True)
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with gr.Row():
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batch_size = gr.Slider(
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label="Batch Size", value=1, minimum=1, maximum=8, step=1
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@@ -325,10 +436,18 @@ with gr.Blocks(css="style.css") as demo:
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pipe_state,
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pipe_kwargs,
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]
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-
outputs = [gallery, generation_details]
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prompt.submit(generate, inputs=inputs, outputs=outputs)
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generate_button.click(generate, inputs=inputs, outputs=outputs)
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demo.queue(concurrency_count=cpu_count())
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demo.launch()
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from diffusers import (
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StableDiffusionPipeline,
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StableDiffusionImg2ImgPipeline,
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)
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import diffusers.schedulers
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import random
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from multiprocessing import cpu_count
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import json
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from PIL import Image
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import os
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import argparse
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import shutil
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import gc
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import importlib
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from textual_inversion import main as run_textual_inversion
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def pad_image(image):
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w, h = image.size
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if w == h:
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return image
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elif w > h:
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new_image = Image.new(image.mode, (w, w), (0, 0, 0))
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new_image.paste(image, (0, (w - h) // 2))
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return new_image
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else:
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new_image = Image.new(image.mode, (h, h), (0, 0, 0))
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new_image.paste(image, ((h - w) // 2, 0))
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return new_image
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_xformers_available = importlib.util.find_spec("xformers") is not None
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device = "cuda" if torch.cuda.is_available() else "cpu"
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low_vram_mode = False
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if model_id != loaded_model_id:
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pipe = pipe_class.from_pretrained(
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model_id,
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torch_dtype=torch.float16,
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safety_checker=None,
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requires_safety_checker=False,
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scheduler=scheduler.from_pretrained(model_id, subfolder="scheduler"),
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pipe = None
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pipe = load_pipe(model_ids[0], default_scheduler)
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@torch.autocast(device)
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@torch.no_grad()
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def generate(
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model_name,
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scheduler_name,
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)
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else:
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return None, f"Unhandled pipeline class: {pipe_class}", -1
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return result.images, status_message, seed
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# based on lvkaokao/textual-inversion-training
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def train_textual_inversion(model_name, scheduler_name, type_of_thing, files, concept_word, init_word, text_train_steps, text_train_bsz, text_learning_rate, progress=gr.Progress(track_tqdm=True)):
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pipe = load_pipe(
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model_id=model_name,
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scheduler_name=scheduler_name,
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pipe_class=StableDiffusionPipeline,
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)
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pipe.disable_xformers_memory_efficient_attention() # xformers handled by textual inversion script
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concept_dir = 'concept_images'
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output_dir = 'output_model'
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training_resolution = 512
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if os.path.exists(output_dir): shutil.rmtree('output_model')
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if os.path.exists(concept_dir): shutil.rmtree('concept_images')
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os.makedirs(concept_dir, exist_ok=True)
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os.makedirs(output_dir, exist_ok=True)
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gc.collect()
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torch.cuda.empty_cache()
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if(prompt == "" or prompt == None):
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raise gr.Error("You forgot to define your concept prompt")
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for j, file_temp in enumerate(files):
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file = Image.open(file_temp.name)
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image = pad_image(file)
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image = image.resize((training_resolution, training_resolution))
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extension = file_temp.name.split(".")[1]
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image = image.convert('RGB')
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image.save(f'{concept_dir}/{j+1}.{extension}', quality=100)
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args_general = argparse.Namespace(
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train_data_dir=concept_dir,
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learnable_property=type_of_thing,
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placeholder_token=concept_word,
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initializer_token=init_word,
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resolution=training_resolution,
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train_batch_size=text_train_bsz,
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gradient_accumulation_steps=1,
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gradient_checkpointing=True,
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mixed_precision='fp16',
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use_bf16=False,
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max_train_steps=int(text_train_steps),
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learning_rate=text_learning_rate,
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scale_lr=True,
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lr_scheduler="constant",
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lr_warmup_steps=0,
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output_dir=output_dir,
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)
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try:
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final_result = run_textual_inversion(pipe, args_general)
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except Exception as e:
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raise gr.Error(e)
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gc.collect()
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torch.cuda.empty_cache()
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return f'Finished training! Check the {output_dir} directory for saved model weights'
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default_img_size = 512
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inpaint_options = ["preserve non-masked portions of image", "output entire inpainted image"]
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inpaint_radio = gr.Radio(inpaint_options, value=inpaint_options[0], show_label=False, interactive=True)
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with gr.Tab("Textual Inversion") as tab:
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tab.select(lambda: StableDiffusionPipeline, [], pipe_state)
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type_of_thing = gr.Dropdown(label="What would you like to train?", choices=["object", "person", "style"], value="object", interactive=True)
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text_train_bsz = gr.Slider(
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label="Training Batch Size",
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minimum=1,
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maximum=8,
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step=1,
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value=1,
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)
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files = gr.File(label=f'''Upload the images for your concept''', file_count="multiple", interactive=True, visible=True)
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+
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text_train_steps = gr.Number(label="How many steps", value=1000)
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text_learning_rate = gr.Number(label="Learning Rate", value=5.e-4)
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+
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concept_word = gr.Textbox(label=f'''concept word - use a unique, made up word to avoid collisions''')
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init_word = gr.Textbox(label=f'''initial word - to init the concept embedding''')
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+
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textual_inversion_button = gr.Button(value="Train Textual Inversion")
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+
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training_status = gr.Text(label="Training Status")
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with gr.Row():
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batch_size = gr.Slider(
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label="Batch Size", value=1, minimum=1, maximum=8, step=1
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pipe_state,
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pipe_kwargs,
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]
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outputs = [gallery, generation_details, seed]
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prompt.submit(generate, inputs=inputs, outputs=outputs)
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generate_button.click(generate, inputs=inputs, outputs=outputs)
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textual_inversion_inputs = [model_name, scheduler_name, type_of_thing, files, concept_word, init_word, text_train_steps, text_train_bsz, text_learning_rate]
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+
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textual_inversion_button.click(train_textual_inversion, inputs=textual_inversion_inputs, outputs=[training_status])
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+
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#demo = gr.TabbedInterface([demo, dreambooth_tab], ["Main", "Dreambooth"])
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demo.queue(concurrency_count=cpu_count())
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demo.launch()
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inpaint_noise_issue.ipynb
DELETED
The diff for this file is too large to render.
See raw diff
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textual_inversion.py
ADDED
@@ -0,0 +1,778 @@
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|
1 |
+
#!/usr/bin/env python
|
2 |
+
# coding=utf-8
|
3 |
+
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
|
4 |
+
#
|
5 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
6 |
+
# you may not use this file except in compliance with the License.
|
7 |
+
# You may obtain a copy of the License at
|
8 |
+
#
|
9 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
10 |
+
#
|
11 |
+
# Unless required by applicable law or agreed to in writing, software
|
12 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
13 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
14 |
+
# See the License for the specific language governing permissions and
|
15 |
+
|
16 |
+
import argparse
|
17 |
+
import logging
|
18 |
+
import math
|
19 |
+
import os
|
20 |
+
import random
|
21 |
+
from pathlib import Path
|
22 |
+
from typing import Optional
|
23 |
+
|
24 |
+
import numpy as np
|
25 |
+
import torch
|
26 |
+
import torch.nn.functional as F
|
27 |
+
import torch.utils.checkpoint
|
28 |
+
from torch.utils.data import Dataset
|
29 |
+
|
30 |
+
import datasets
|
31 |
+
import diffusers
|
32 |
+
import PIL
|
33 |
+
import transformers
|
34 |
+
from accelerate import Accelerator
|
35 |
+
from accelerate.logging import get_logger
|
36 |
+
from accelerate.utils import set_seed
|
37 |
+
from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel
|
38 |
+
from diffusers.optimization import get_scheduler
|
39 |
+
from diffusers.utils import check_min_version
|
40 |
+
from diffusers.utils.import_utils import is_xformers_available
|
41 |
+
from huggingface_hub import HfFolder, Repository, create_repo, whoami
|
42 |
+
|
43 |
+
# TODO: remove and import from diffusers.utils when the new version of diffusers is released
|
44 |
+
from packaging import version
|
45 |
+
from PIL import Image
|
46 |
+
from torchvision import transforms
|
47 |
+
from tqdm.auto import tqdm
|
48 |
+
from transformers import CLIPTextModel, CLIPTokenizer
|
49 |
+
|
50 |
+
|
51 |
+
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
|
52 |
+
PIL_INTERPOLATION = {
|
53 |
+
"linear": PIL.Image.Resampling.BILINEAR,
|
54 |
+
"bilinear": PIL.Image.Resampling.BILINEAR,
|
55 |
+
"bicubic": PIL.Image.Resampling.BICUBIC,
|
56 |
+
"lanczos": PIL.Image.Resampling.LANCZOS,
|
57 |
+
"nearest": PIL.Image.Resampling.NEAREST,
|
58 |
+
}
|
59 |
+
else:
|
60 |
+
PIL_INTERPOLATION = {
|
61 |
+
"linear": PIL.Image.LINEAR,
|
62 |
+
"bilinear": PIL.Image.BILINEAR,
|
63 |
+
"bicubic": PIL.Image.BICUBIC,
|
64 |
+
"lanczos": PIL.Image.LANCZOS,
|
65 |
+
"nearest": PIL.Image.NEAREST,
|
66 |
+
}
|
67 |
+
# ------------------------------------------------------------------------------
|
68 |
+
|
69 |
+
|
70 |
+
# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
|
71 |
+
check_min_version("0.10.0.dev0")
|
72 |
+
|
73 |
+
|
74 |
+
logger = get_logger(__name__)
|
75 |
+
|
76 |
+
|
77 |
+
def save_progress(text_encoder, placeholder_token_id, accelerator, args, save_path):
|
78 |
+
logger.info("Saving embeddings")
|
79 |
+
learned_embeds = accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[placeholder_token_id]
|
80 |
+
learned_embeds_dict = {args.placeholder_token: learned_embeds.detach().cpu()}
|
81 |
+
torch.save(learned_embeds_dict, save_path)
|
82 |
+
|
83 |
+
|
84 |
+
def parse_args():
|
85 |
+
parser = argparse.ArgumentParser(description="Simple example of a training script.")
|
86 |
+
parser.add_argument(
|
87 |
+
"--save_steps",
|
88 |
+
type=int,
|
89 |
+
default=500,
|
90 |
+
help="Save learned_embeds.bin every X updates steps.",
|
91 |
+
)
|
92 |
+
parser.add_argument(
|
93 |
+
"--only_save_embeds",
|
94 |
+
action="store_true",
|
95 |
+
default=False,
|
96 |
+
help="Save only the embeddings for the new concept.",
|
97 |
+
)
|
98 |
+
parser.add_argument(
|
99 |
+
"--pretrained_model_name_or_path",
|
100 |
+
type=str,
|
101 |
+
default=None,
|
102 |
+
help="Path to pretrained model or model identifier from huggingface.co/models.",
|
103 |
+
)
|
104 |
+
parser.add_argument(
|
105 |
+
"--revision",
|
106 |
+
type=str,
|
107 |
+
default=None,
|
108 |
+
help="Revision of pretrained model identifier from huggingface.co/models.",
|
109 |
+
)
|
110 |
+
parser.add_argument(
|
111 |
+
"--tokenizer_name",
|
112 |
+
type=str,
|
113 |
+
default=None,
|
114 |
+
help="Pretrained tokenizer name or path if not the same as model_name",
|
115 |
+
)
|
116 |
+
parser.add_argument(
|
117 |
+
"--train_data_dir", type=str, default=None, help="A folder containing the training data."
|
118 |
+
)
|
119 |
+
parser.add_argument(
|
120 |
+
"--placeholder_token",
|
121 |
+
type=str,
|
122 |
+
default=None,
|
123 |
+
help="A token to use as a placeholder for the concept.",
|
124 |
+
)
|
125 |
+
parser.add_argument(
|
126 |
+
"--initializer_token", type=str, default=None, help="A token to use as initializer word."
|
127 |
+
)
|
128 |
+
|
129 |
+
parser.add_argument("--learnable_property", type=str, default="object", help="Choose between 'object' and 'style'")
|
130 |
+
parser.add_argument("--repeats", type=int, default=100, help="How many times to repeat the training data.")
|
131 |
+
parser.add_argument(
|
132 |
+
"--output_dir",
|
133 |
+
type=str,
|
134 |
+
default="text-inversion-model",
|
135 |
+
help="The output directory where the model predictions and checkpoints will be written.",
|
136 |
+
)
|
137 |
+
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
|
138 |
+
parser.add_argument(
|
139 |
+
"--resolution",
|
140 |
+
type=int,
|
141 |
+
default=512,
|
142 |
+
help=(
|
143 |
+
"The resolution for input images, all the images in the train/validation dataset will be resized to this"
|
144 |
+
" resolution"
|
145 |
+
),
|
146 |
+
)
|
147 |
+
parser.add_argument(
|
148 |
+
"--center_crop", action="store_true", help="Whether to center crop images before resizing to resolution"
|
149 |
+
)
|
150 |
+
parser.add_argument(
|
151 |
+
"--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
|
152 |
+
)
|
153 |
+
parser.add_argument("--num_train_epochs", type=int, default=100)
|
154 |
+
parser.add_argument(
|
155 |
+
"--max_train_steps",
|
156 |
+
type=int,
|
157 |
+
default=5000,
|
158 |
+
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
|
159 |
+
)
|
160 |
+
parser.add_argument(
|
161 |
+
"--gradient_accumulation_steps",
|
162 |
+
type=int,
|
163 |
+
default=1,
|
164 |
+
help="Number of updates steps to accumulate before performing a backward/update pass.",
|
165 |
+
)
|
166 |
+
parser.add_argument(
|
167 |
+
"--gradient_checkpointing",
|
168 |
+
action="store_true",
|
169 |
+
help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
|
170 |
+
)
|
171 |
+
parser.add_argument(
|
172 |
+
"--learning_rate",
|
173 |
+
type=float,
|
174 |
+
default=1e-4,
|
175 |
+
help="Initial learning rate (after the potential warmup period) to use.",
|
176 |
+
)
|
177 |
+
parser.add_argument(
|
178 |
+
"--scale_lr",
|
179 |
+
action="store_true",
|
180 |
+
default=False,
|
181 |
+
help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
|
182 |
+
)
|
183 |
+
parser.add_argument(
|
184 |
+
"--lr_scheduler",
|
185 |
+
type=str,
|
186 |
+
default="constant",
|
187 |
+
help=(
|
188 |
+
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
|
189 |
+
' "constant", "constant_with_warmup"]'
|
190 |
+
),
|
191 |
+
)
|
192 |
+
parser.add_argument(
|
193 |
+
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
|
194 |
+
)
|
195 |
+
parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
|
196 |
+
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
|
197 |
+
parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
|
198 |
+
parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
|
199 |
+
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
|
200 |
+
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
|
201 |
+
parser.add_argument(
|
202 |
+
"--hub_model_id",
|
203 |
+
type=str,
|
204 |
+
default=None,
|
205 |
+
help="The name of the repository to keep in sync with the local `output_dir`.",
|
206 |
+
)
|
207 |
+
parser.add_argument(
|
208 |
+
"--logging_dir",
|
209 |
+
type=str,
|
210 |
+
default="logs",
|
211 |
+
help=(
|
212 |
+
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
|
213 |
+
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
|
214 |
+
),
|
215 |
+
)
|
216 |
+
parser.add_argument(
|
217 |
+
"--mixed_precision",
|
218 |
+
type=str,
|
219 |
+
default="no",
|
220 |
+
choices=["no", "fp16", "bf16"],
|
221 |
+
help=(
|
222 |
+
"Whether to use mixed precision. Choose"
|
223 |
+
"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
|
224 |
+
"and an Nvidia Ampere GPU."
|
225 |
+
),
|
226 |
+
)
|
227 |
+
parser.add_argument(
|
228 |
+
"--allow_tf32",
|
229 |
+
action="store_true",
|
230 |
+
help=(
|
231 |
+
"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
|
232 |
+
" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
|
233 |
+
),
|
234 |
+
)
|
235 |
+
parser.add_argument(
|
236 |
+
"--report_to",
|
237 |
+
type=str,
|
238 |
+
default="tensorboard",
|
239 |
+
help=(
|
240 |
+
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
|
241 |
+
' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
|
242 |
+
),
|
243 |
+
)
|
244 |
+
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
|
245 |
+
parser.add_argument(
|
246 |
+
"--checkpointing_steps",
|
247 |
+
type=int,
|
248 |
+
default=500,
|
249 |
+
help=(
|
250 |
+
"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming"
|
251 |
+
" training using `--resume_from_checkpoint`."
|
252 |
+
),
|
253 |
+
)
|
254 |
+
parser.add_argument(
|
255 |
+
"--resume_from_checkpoint",
|
256 |
+
type=str,
|
257 |
+
default=None,
|
258 |
+
help=(
|
259 |
+
"Whether training should be resumed from a previous checkpoint. Use a path saved by"
|
260 |
+
' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
|
261 |
+
),
|
262 |
+
)
|
263 |
+
parser.add_argument(
|
264 |
+
"--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
|
265 |
+
)
|
266 |
+
|
267 |
+
args = parser.parse_args()
|
268 |
+
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
|
269 |
+
if env_local_rank != -1 and env_local_rank != args.local_rank:
|
270 |
+
args.local_rank = env_local_rank
|
271 |
+
|
272 |
+
#if args.train_data_dir is None:
|
273 |
+
# raise ValueError("You must specify a train data directory.")
|
274 |
+
|
275 |
+
return args
|
276 |
+
|
277 |
+
|
278 |
+
imagenet_templates_small = [
|
279 |
+
"a photo of a {}",
|
280 |
+
"a rendering of a {}",
|
281 |
+
"a cropped photo of the {}",
|
282 |
+
"the photo of a {}",
|
283 |
+
"a photo of a clean {}",
|
284 |
+
"a photo of a dirty {}",
|
285 |
+
"a dark photo of the {}",
|
286 |
+
"a photo of my {}",
|
287 |
+
"a photo of the cool {}",
|
288 |
+
"a close-up photo of a {}",
|
289 |
+
"a bright photo of the {}",
|
290 |
+
"a cropped photo of a {}",
|
291 |
+
"a photo of the {}",
|
292 |
+
"a good photo of the {}",
|
293 |
+
"a photo of one {}",
|
294 |
+
"a close-up photo of the {}",
|
295 |
+
"a rendition of the {}",
|
296 |
+
"a photo of the clean {}",
|
297 |
+
"a rendition of a {}",
|
298 |
+
"a photo of a nice {}",
|
299 |
+
"a good photo of a {}",
|
300 |
+
"a photo of the nice {}",
|
301 |
+
"a photo of the small {}",
|
302 |
+
"a photo of the weird {}",
|
303 |
+
"a photo of the large {}",
|
304 |
+
"a photo of a cool {}",
|
305 |
+
"a photo of a small {}",
|
306 |
+
]
|
307 |
+
|
308 |
+
imagenet_style_templates_small = [
|
309 |
+
"a painting in the style of {}",
|
310 |
+
"a rendering in the style of {}",
|
311 |
+
"a cropped painting in the style of {}",
|
312 |
+
"the painting in the style of {}",
|
313 |
+
"a clean painting in the style of {}",
|
314 |
+
"a dirty painting in the style of {}",
|
315 |
+
"a dark painting in the style of {}",
|
316 |
+
"a picture in the style of {}",
|
317 |
+
"a cool painting in the style of {}",
|
318 |
+
"a close-up painting in the style of {}",
|
319 |
+
"a bright painting in the style of {}",
|
320 |
+
"a cropped painting in the style of {}",
|
321 |
+
"a good painting in the style of {}",
|
322 |
+
"a close-up painting in the style of {}",
|
323 |
+
"a rendition in the style of {}",
|
324 |
+
"a nice painting in the style of {}",
|
325 |
+
"a small painting in the style of {}",
|
326 |
+
"a weird painting in the style of {}",
|
327 |
+
"a large painting in the style of {}",
|
328 |
+
]
|
329 |
+
|
330 |
+
|
331 |
+
class TextualInversionDataset(Dataset):
|
332 |
+
def __init__(
|
333 |
+
self,
|
334 |
+
data_root,
|
335 |
+
tokenizer,
|
336 |
+
learnable_property="object", # [object, style]
|
337 |
+
size=512,
|
338 |
+
repeats=100,
|
339 |
+
interpolation="bicubic",
|
340 |
+
flip_p=0.5,
|
341 |
+
set="train",
|
342 |
+
placeholder_token="*",
|
343 |
+
center_crop=False,
|
344 |
+
):
|
345 |
+
self.data_root = data_root
|
346 |
+
self.tokenizer = tokenizer
|
347 |
+
self.learnable_property = learnable_property
|
348 |
+
self.size = size
|
349 |
+
self.placeholder_token = placeholder_token
|
350 |
+
self.center_crop = center_crop
|
351 |
+
self.flip_p = flip_p
|
352 |
+
|
353 |
+
self.image_paths = [os.path.join(self.data_root, file_path) for file_path in os.listdir(self.data_root)]
|
354 |
+
|
355 |
+
self.num_images = len(self.image_paths)
|
356 |
+
self._length = self.num_images
|
357 |
+
|
358 |
+
if set == "train":
|
359 |
+
self._length = self.num_images * repeats
|
360 |
+
|
361 |
+
self.interpolation = {
|
362 |
+
"linear": PIL_INTERPOLATION["linear"],
|
363 |
+
"bilinear": PIL_INTERPOLATION["bilinear"],
|
364 |
+
"bicubic": PIL_INTERPOLATION["bicubic"],
|
365 |
+
"lanczos": PIL_INTERPOLATION["lanczos"],
|
366 |
+
}[interpolation]
|
367 |
+
|
368 |
+
self.templates = imagenet_style_templates_small if learnable_property == "style" else imagenet_templates_small
|
369 |
+
self.flip_transform = transforms.RandomHorizontalFlip(p=self.flip_p)
|
370 |
+
|
371 |
+
def __len__(self):
|
372 |
+
return self._length
|
373 |
+
|
374 |
+
def __getitem__(self, i):
|
375 |
+
example = {}
|
376 |
+
image = Image.open(self.image_paths[i % self.num_images])
|
377 |
+
|
378 |
+
if not image.mode == "RGB":
|
379 |
+
image = image.convert("RGB")
|
380 |
+
|
381 |
+
placeholder_string = self.placeholder_token
|
382 |
+
text = random.choice(self.templates).format(placeholder_string)
|
383 |
+
|
384 |
+
example["input_ids"] = self.tokenizer(
|
385 |
+
text,
|
386 |
+
padding="max_length",
|
387 |
+
truncation=True,
|
388 |
+
max_length=self.tokenizer.model_max_length,
|
389 |
+
return_tensors="pt",
|
390 |
+
).input_ids[0]
|
391 |
+
|
392 |
+
# default to score-sde preprocessing
|
393 |
+
img = np.array(image).astype(np.uint8)
|
394 |
+
|
395 |
+
if self.center_crop:
|
396 |
+
crop = min(img.shape[0], img.shape[1])
|
397 |
+
(
|
398 |
+
h,
|
399 |
+
w,
|
400 |
+
) = (
|
401 |
+
img.shape[0],
|
402 |
+
img.shape[1],
|
403 |
+
)
|
404 |
+
img = img[(h - crop) // 2 : (h + crop) // 2, (w - crop) // 2 : (w + crop) // 2]
|
405 |
+
|
406 |
+
image = Image.fromarray(img)
|
407 |
+
image = image.resize((self.size, self.size), resample=self.interpolation)
|
408 |
+
|
409 |
+
image = self.flip_transform(image)
|
410 |
+
image = np.array(image).astype(np.uint8)
|
411 |
+
image = (image / 127.5 - 1.0).astype(np.float32)
|
412 |
+
|
413 |
+
example["pixel_values"] = torch.from_numpy(image).permute(2, 0, 1)
|
414 |
+
return example
|
415 |
+
|
416 |
+
|
417 |
+
def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):
|
418 |
+
if token is None:
|
419 |
+
token = HfFolder.get_token()
|
420 |
+
if organization is None:
|
421 |
+
username = whoami(token)["name"]
|
422 |
+
return f"{username}/{model_id}"
|
423 |
+
else:
|
424 |
+
return f"{organization}/{model_id}"
|
425 |
+
|
426 |
+
|
427 |
+
|
428 |
+
def main(pipe, args_imported):
|
429 |
+
|
430 |
+
args = parse_args()
|
431 |
+
vars(args).update(vars(args_imported))
|
432 |
+
|
433 |
+
print(args)
|
434 |
+
logging_dir = os.path.join(args.output_dir, args.logging_dir)
|
435 |
+
|
436 |
+
accelerator = Accelerator(
|
437 |
+
gradient_accumulation_steps=args.gradient_accumulation_steps,
|
438 |
+
mixed_precision=args.mixed_precision,
|
439 |
+
log_with=args.report_to,
|
440 |
+
logging_dir=logging_dir,
|
441 |
+
)
|
442 |
+
|
443 |
+
# Make one log on every process with the configuration for debugging.
|
444 |
+
logging.basicConfig(
|
445 |
+
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
|
446 |
+
datefmt="%m/%d/%Y %H:%M:%S",
|
447 |
+
level=logging.INFO,
|
448 |
+
)
|
449 |
+
logger.info(accelerator.state, main_process_only=False)
|
450 |
+
if accelerator.is_local_main_process:
|
451 |
+
datasets.utils.logging.set_verbosity_warning()
|
452 |
+
transformers.utils.logging.set_verbosity_warning()
|
453 |
+
diffusers.utils.logging.set_verbosity_info()
|
454 |
+
else:
|
455 |
+
datasets.utils.logging.set_verbosity_error()
|
456 |
+
transformers.utils.logging.set_verbosity_error()
|
457 |
+
diffusers.utils.logging.set_verbosity_error()
|
458 |
+
|
459 |
+
# If passed along, set the training seed now.
|
460 |
+
if args.seed is not None:
|
461 |
+
set_seed(args.seed)
|
462 |
+
|
463 |
+
# Handle the repository creation
|
464 |
+
if accelerator.is_main_process:
|
465 |
+
if args.push_to_hub:
|
466 |
+
if args.hub_model_id is None:
|
467 |
+
repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
|
468 |
+
else:
|
469 |
+
repo_name = args.hub_model_id
|
470 |
+
create_repo(repo_name, exist_ok=True, token=args.hub_token)
|
471 |
+
repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token)
|
472 |
+
|
473 |
+
with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
|
474 |
+
if "step_*" not in gitignore:
|
475 |
+
gitignore.write("step_*\n")
|
476 |
+
if "epoch_*" not in gitignore:
|
477 |
+
gitignore.write("epoch_*\n")
|
478 |
+
elif args.output_dir is not None:
|
479 |
+
os.makedirs(args.output_dir, exist_ok=True)
|
480 |
+
|
481 |
+
# Load tokenizer
|
482 |
+
tokenizer = pipe.tokenizer
|
483 |
+
|
484 |
+
# Load scheduler and models
|
485 |
+
noise_scheduler = pipe.scheduler
|
486 |
+
text_encoder = pipe.text_encoder
|
487 |
+
vae = pipe.vae
|
488 |
+
unet = pipe.unet
|
489 |
+
|
490 |
+
# Add the placeholder token in tokenizer
|
491 |
+
num_added_tokens = tokenizer.add_tokens(args.placeholder_token)
|
492 |
+
if num_added_tokens == 0:
|
493 |
+
raise ValueError(
|
494 |
+
f"The tokenizer already contains the token {args.placeholder_token}. Please pass a different"
|
495 |
+
" `placeholder_token` that is not already in the tokenizer."
|
496 |
+
)
|
497 |
+
|
498 |
+
# Convert the initializer_token, placeholder_token to ids
|
499 |
+
token_ids = tokenizer.encode(args.initializer_token, add_special_tokens=False)
|
500 |
+
# Check if initializer_token is a single token or a sequence of tokens
|
501 |
+
if len(token_ids) > 1:
|
502 |
+
raise ValueError("The initializer token must be a single token.")
|
503 |
+
|
504 |
+
initializer_token_id = token_ids[0]
|
505 |
+
placeholder_token_id = tokenizer.convert_tokens_to_ids(args.placeholder_token)
|
506 |
+
|
507 |
+
# Resize the token embeddings as we are adding new special tokens to the tokenizer
|
508 |
+
text_encoder.resize_token_embeddings(len(tokenizer))
|
509 |
+
|
510 |
+
# Initialise the newly added placeholder token with the embeddings of the initializer token
|
511 |
+
token_embeds = text_encoder.get_input_embeddings().weight.data
|
512 |
+
token_embeds[placeholder_token_id] = token_embeds[initializer_token_id]
|
513 |
+
|
514 |
+
# Freeze vae and unet
|
515 |
+
vae.requires_grad_(False)
|
516 |
+
unet.requires_grad_(False)
|
517 |
+
# Freeze all parameters except for the token embeddings in text encoder
|
518 |
+
text_encoder.text_model.encoder.requires_grad_(False)
|
519 |
+
text_encoder.text_model.final_layer_norm.requires_grad_(False)
|
520 |
+
text_encoder.text_model.embeddings.position_embedding.requires_grad_(False)
|
521 |
+
|
522 |
+
if args.gradient_checkpointing:
|
523 |
+
# Keep unet in train mode if we are using gradient checkpointing to save memory.
|
524 |
+
# The dropout cannot be != 0 so it doesn't matter if we are in eval or train mode.
|
525 |
+
unet.train()
|
526 |
+
text_encoder.gradient_checkpointing_enable()
|
527 |
+
unet.enable_gradient_checkpointing()
|
528 |
+
|
529 |
+
if args.enable_xformers_memory_efficient_attention:
|
530 |
+
if is_xformers_available():
|
531 |
+
unet.enable_xformers_memory_efficient_attention()
|
532 |
+
else:
|
533 |
+
raise ValueError("xformers is not available. Make sure it is installed correctly")
|
534 |
+
|
535 |
+
# Enable TF32 for faster training on Ampere GPUs,
|
536 |
+
# cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
|
537 |
+
if args.allow_tf32:
|
538 |
+
torch.backends.cuda.matmul.allow_tf32 = True
|
539 |
+
|
540 |
+
if args.scale_lr:
|
541 |
+
args.learning_rate = (
|
542 |
+
args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
|
543 |
+
)
|
544 |
+
|
545 |
+
# Initialize the optimizer
|
546 |
+
optimizer = torch.optim.AdamW(
|
547 |
+
text_encoder.get_input_embeddings().parameters(), # only optimize the embeddings
|
548 |
+
lr=args.learning_rate,
|
549 |
+
betas=(args.adam_beta1, args.adam_beta2),
|
550 |
+
weight_decay=args.adam_weight_decay,
|
551 |
+
eps=args.adam_epsilon,
|
552 |
+
)
|
553 |
+
|
554 |
+
# Dataset and DataLoaders creation:
|
555 |
+
train_dataset = TextualInversionDataset(
|
556 |
+
data_root=args.train_data_dir,
|
557 |
+
tokenizer=tokenizer,
|
558 |
+
size=args.resolution,
|
559 |
+
placeholder_token=args.placeholder_token,
|
560 |
+
repeats=args.repeats,
|
561 |
+
learnable_property=args.learnable_property,
|
562 |
+
center_crop=args.center_crop,
|
563 |
+
set="train",
|
564 |
+
)
|
565 |
+
train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=args.train_batch_size, shuffle=True)
|
566 |
+
|
567 |
+
# Scheduler and math around the number of training steps.
|
568 |
+
overrode_max_train_steps = False
|
569 |
+
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
|
570 |
+
if args.max_train_steps is None:
|
571 |
+
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
|
572 |
+
overrode_max_train_steps = True
|
573 |
+
|
574 |
+
lr_scheduler = get_scheduler(
|
575 |
+
args.lr_scheduler,
|
576 |
+
optimizer=optimizer,
|
577 |
+
num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
|
578 |
+
num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
|
579 |
+
)
|
580 |
+
|
581 |
+
# Prepare everything with our `accelerator`.
|
582 |
+
text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
|
583 |
+
text_encoder, optimizer, train_dataloader, lr_scheduler
|
584 |
+
)
|
585 |
+
|
586 |
+
# For mixed precision training we cast the text_encoder and vae weights to half-precision
|
587 |
+
# as these models are only used for inference, keeping weights in full precision is not required.
|
588 |
+
weight_dtype = torch.float32
|
589 |
+
if accelerator.mixed_precision == "fp16":
|
590 |
+
weight_dtype = torch.float16
|
591 |
+
elif accelerator.mixed_precision == "bf16":
|
592 |
+
weight_dtype = torch.bfloat16
|
593 |
+
|
594 |
+
# Move vae and unet to device and cast to weight_dtype
|
595 |
+
unet.to(accelerator.device, dtype=weight_dtype)
|
596 |
+
vae.to(accelerator.device, dtype=weight_dtype)
|
597 |
+
text_encoder.to(accelerator.device, dtype=torch.float32)
|
598 |
+
|
599 |
+
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
|
600 |
+
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
|
601 |
+
if overrode_max_train_steps:
|
602 |
+
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
|
603 |
+
# Afterwards we recalculate our number of training epochs
|
604 |
+
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
|
605 |
+
|
606 |
+
# We need to initialize the trackers we use, and also store our configuration.
|
607 |
+
# The trackers initializes automatically on the main process.
|
608 |
+
if accelerator.is_main_process:
|
609 |
+
accelerator.init_trackers("textual_inversion", config=vars(args))
|
610 |
+
|
611 |
+
# Train!
|
612 |
+
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
|
613 |
+
|
614 |
+
logger.info("***** Running training *****")
|
615 |
+
logger.info(f" Num examples = {len(train_dataset)}")
|
616 |
+
logger.info(f" Num Epochs = {args.num_train_epochs}")
|
617 |
+
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
|
618 |
+
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
|
619 |
+
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
|
620 |
+
logger.info(f" Total optimization steps = {args.max_train_steps}")
|
621 |
+
global_step = 0
|
622 |
+
first_epoch = 0
|
623 |
+
|
624 |
+
# Potentially load in the weights and states from a previous save
|
625 |
+
if args.resume_from_checkpoint:
|
626 |
+
if args.resume_from_checkpoint != "latest":
|
627 |
+
path = os.path.basename(args.resume_from_checkpoint)
|
628 |
+
else:
|
629 |
+
# Get the most recent checkpoint
|
630 |
+
dirs = os.listdir(args.output_dir)
|
631 |
+
dirs = [d for d in dirs if d.startswith("checkpoint")]
|
632 |
+
dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
|
633 |
+
path = dirs[-1]
|
634 |
+
accelerator.print(f"Resuming from checkpoint {path}")
|
635 |
+
accelerator.load_state(os.path.join(args.output_dir, path))
|
636 |
+
global_step = int(path.split("-")[1])
|
637 |
+
|
638 |
+
resume_global_step = global_step * args.gradient_accumulation_steps
|
639 |
+
first_epoch = resume_global_step // num_update_steps_per_epoch
|
640 |
+
resume_step = resume_global_step % num_update_steps_per_epoch
|
641 |
+
|
642 |
+
# Only show the progress bar once on each machine.
|
643 |
+
progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process)
|
644 |
+
progress_bar.set_description("Steps")
|
645 |
+
|
646 |
+
# keep original embeddings as reference
|
647 |
+
orig_embeds_params = accelerator.unwrap_model(text_encoder).get_input_embeddings().weight.data.clone()
|
648 |
+
|
649 |
+
for epoch in (range(first_epoch, args.num_train_epochs)):
|
650 |
+
text_encoder.train()
|
651 |
+
for step, batch in enumerate(train_dataloader):
|
652 |
+
# Skip steps until we reach the resumed step
|
653 |
+
if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:
|
654 |
+
if step % args.gradient_accumulation_steps == 0:
|
655 |
+
progress_bar.update(1)
|
656 |
+
continue
|
657 |
+
|
658 |
+
with accelerator.accumulate(text_encoder):
|
659 |
+
# Convert images to latent space
|
660 |
+
latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample().detach()
|
661 |
+
latents = latents * 0.18215
|
662 |
+
|
663 |
+
# Sample noise that we'll add to the latents
|
664 |
+
noise = torch.randn_like(latents)
|
665 |
+
bsz = latents.shape[0]
|
666 |
+
# Sample a random timestep for each image
|
667 |
+
timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
|
668 |
+
timesteps = timesteps.long()
|
669 |
+
|
670 |
+
# Add noise to the latents according to the noise magnitude at each timestep
|
671 |
+
# (this is the forward diffusion process)
|
672 |
+
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
|
673 |
+
|
674 |
+
# Get the text embedding for conditioning
|
675 |
+
encoder_hidden_states = text_encoder(batch["input_ids"])[0].to(dtype=weight_dtype)
|
676 |
+
|
677 |
+
# Predict the noise residual
|
678 |
+
model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
|
679 |
+
|
680 |
+
# Get the target for loss depending on the prediction type
|
681 |
+
if noise_scheduler.config.prediction_type == "epsilon":
|
682 |
+
target = noise
|
683 |
+
elif noise_scheduler.config.prediction_type == "v_prediction":
|
684 |
+
target = noise_scheduler.get_velocity(latents, noise, timesteps)
|
685 |
+
else:
|
686 |
+
raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
|
687 |
+
|
688 |
+
loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
|
689 |
+
|
690 |
+
accelerator.backward(loss)
|
691 |
+
|
692 |
+
if accelerator.num_processes > 1:
|
693 |
+
grads = text_encoder.module.get_input_embeddings().weight.grad
|
694 |
+
else:
|
695 |
+
grads = text_encoder.get_input_embeddings().weight.grad
|
696 |
+
# Get the index for tokens that we want to zero the grads for
|
697 |
+
index_grads_to_zero = torch.arange(len(tokenizer)) != placeholder_token_id
|
698 |
+
grads.data[index_grads_to_zero, :] = grads.data[index_grads_to_zero, :].fill_(0)
|
699 |
+
|
700 |
+
optimizer.step()
|
701 |
+
lr_scheduler.step()
|
702 |
+
optimizer.zero_grad()
|
703 |
+
|
704 |
+
# Let's make sure we don't update any embedding weights besides the newly added token
|
705 |
+
index_no_updates = torch.arange(len(tokenizer)) != placeholder_token_id
|
706 |
+
with torch.no_grad():
|
707 |
+
accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[
|
708 |
+
index_no_updates
|
709 |
+
] = orig_embeds_params[index_no_updates]
|
710 |
+
|
711 |
+
# Checks if the accelerator has performed an optimization step behind the scenes
|
712 |
+
if accelerator.sync_gradients:
|
713 |
+
progress_bar.update(1)
|
714 |
+
global_step += 1
|
715 |
+
if global_step % args.save_steps == 0:
|
716 |
+
save_path = os.path.join(args.output_dir, f"{args.placeholder_token}-{global_step}.bin")
|
717 |
+
save_progress(text_encoder, placeholder_token_id, accelerator, args, save_path)
|
718 |
+
|
719 |
+
if global_step % args.checkpointing_steps == 0:
|
720 |
+
if accelerator.is_main_process:
|
721 |
+
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
|
722 |
+
accelerator.save_state(save_path)
|
723 |
+
logger.info(f"Saved state to {save_path}")
|
724 |
+
|
725 |
+
logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
|
726 |
+
progress_bar.set_postfix(**logs)
|
727 |
+
accelerator.log(logs, step=global_step)
|
728 |
+
|
729 |
+
if global_step >= args.max_train_steps:
|
730 |
+
break
|
731 |
+
|
732 |
+
# Create the pipeline using using the trained modules and save it.
|
733 |
+
accelerator.wait_for_everyone()
|
734 |
+
if accelerator.is_main_process:
|
735 |
+
if args.push_to_hub and args.only_save_embeds:
|
736 |
+
logger.warn("Enabling full model saving because --push_to_hub=True was specified.")
|
737 |
+
save_full_model = True
|
738 |
+
else:
|
739 |
+
save_full_model = not args.only_save_embeds
|
740 |
+
if save_full_model:
|
741 |
+
pipe.save_pretrained(args.output_dir)
|
742 |
+
# Save the newly trained embeddings
|
743 |
+
save_path = os.path.join(args.output_dir, "learned_embeds.bin")
|
744 |
+
save_progress(text_encoder, placeholder_token_id, accelerator, args, save_path)
|
745 |
+
|
746 |
+
if args.push_to_hub:
|
747 |
+
repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True)
|
748 |
+
|
749 |
+
accelerator.end_training()
|
750 |
+
text_encoder.eval()
|
751 |
+
unet.eval()
|
752 |
+
vae.eval()
|
753 |
+
|
754 |
+
|
755 |
+
if __name__ == "__main__":
|
756 |
+
pipeline = StableDiffusionPipeline.from_pretrained('andite/anything-v4.0', torch_dtype=torch.float16)
|
757 |
+
|
758 |
+
imported_args = argparse.Namespace(
|
759 |
+
train_data_dir="concept_images",
|
760 |
+
learnable_property='object',
|
761 |
+
placeholder_token='redeyegirl',
|
762 |
+
initializer_token='girl',
|
763 |
+
resolution=512,
|
764 |
+
train_batch_size=1,
|
765 |
+
gradient_accumulation_steps=1,
|
766 |
+
gradient_checkpointing=True,
|
767 |
+
mixed_precision='fp16',
|
768 |
+
use_bf16=False,
|
769 |
+
max_train_steps=1000,
|
770 |
+
learning_rate=5.0e-4,
|
771 |
+
scale_lr=False,
|
772 |
+
lr_scheduler="constant",
|
773 |
+
lr_warmup_steps=0,
|
774 |
+
output_dir="output_model",
|
775 |
+
)
|
776 |
+
|
777 |
+
|
778 |
+
main(pipeline, imported_args)
|