Update to Diffusers

.gitignore

@@ -1,5 +1,6 @@
 training_data/
-results/
+experiments/
+wandb/
 
 
 # Byte-compiled / optimized / DLL files

.gitmodules

@@ -1,3 +0,0 @@
-[submodule "lora"]
-	path = lora
-	url = https://github.com/cloneofsimo/lora

.pre-commit-config.yaml

@@ -1,3 +1,4 @@
+exclude: train_dreambooth_lora.py
 repos:
 - repo: https://github.com/pre-commit/pre-commit-hooks
   rev: v4.2.0
@@ -28,6 +29,7 @@ repos:
   hooks:
     - id: mypy
       args: ['--ignore-missing-imports']
+      additional_dependencies: ['types-python-slugify']
 - repo: https://github.com/google/yapf
   rev: v0.32.0
   hooks:

README.md

@@ -1,10 +1,11 @@
 ---
-title: LoRA + SD Training
-emoji: 🏢
+title: LoRA DreamBooth Training UI
+emoji: ⚡
 colorFrom: red
 colorTo: purple
 sdk: gradio
-sdk_version: 3.12.0
+sdk_version: 3.16.2
+python_version: 3.10.9
 app_file: app.py
 pinned: false
 license: mit

app.py

@@ -1,27 +1,21 @@
 #!/usr/bin/env python
-"""Unofficial demo app for https://github.com/cloneofsimo/lora.
-
-The code in this repo is partly adapted from the following repository:
-https://huggingface.co/spaces/multimodalart/dreambooth-training/tree/a00184917aa273c6d8adab08d5deb9b39b997938
-The license of the original code is MIT, which is specified in the README.md.
-"""
 
 from __future__ import annotations
 
 import os
-import pathlib
 
 import gradio as gr
 import torch
 
+from app_inference import create_inference_demo
+from app_training import create_training_demo
+from app_upload import create_upload_demo
 from inference import InferencePipeline
 from trainer import Trainer
-from uploader import upload
 
-TITLE = '# LoRA + StableDiffusion Training UI'
-DESCRIPTION = 'This is an unofficial demo for [https://github.com/cloneofsimo/lora](https://github.com/cloneofsimo/lora).'
+TITLE = '# LoRA DreamBooth Training UI'
 
-ORIGINAL_SPACE_ID = 'hysts/LoRA-SD-training'
+ORIGINAL_SPACE_ID = 'hysts/LoRA-DreamBooth-Training-UI'
 SPACE_ID = os.getenv('SPACE_ID', ORIGINAL_SPACE_ID)
 SHARED_UI_WARNING = f'''# Attention - This Space doesn't work in this shared UI. You can duplicate and use it with a paid private T4 GPU.
 
@@ -29,7 +23,6 @@ SHARED_UI_WARNING = f'''# Attention - This Space doesn't work in this shared UI.
 '''
 if os.getenv('SYSTEM') == 'spaces' and SPACE_ID != ORIGINAL_SPACE_ID:
     SETTINGS = f'<a href="https://huggingface.co/spaces/{SPACE_ID}/settings">Settings</a>'
-
 else:
     SETTINGS = 'Settings'
 CUDA_NOT_AVAILABLE_WARNING = f'''# Attention - Running on CPU.
@@ -39,6 +32,8 @@ You can assign a GPU in the {SETTINGS} tab if you are running this on HF Spaces.
 </center>
 '''
 
+HF_TOKEN = os.getenv('HF_TOKEN')
+
 
 def show_warning(warning_text: str) -> gr.Blocks:
     with gr.Blocks() as demo:
@@ -47,217 +42,7 @@ def show_warning(warning_text: str) -> gr.Blocks:
     return demo
 
 
-def update_output_files() -> dict:
-    paths = sorted(pathlib.Path('results').glob('*.pt'))
-    paths = [path.as_posix() for path in paths]  # type: ignore
-    return gr.update(value=paths or None)
-
-
-def create_training_demo(trainer: Trainer,
-                         pipe: InferencePipeline) -> gr.Blocks:
-    with gr.Blocks() as demo:
-        base_model = gr.Dropdown(
-            choices=['stabilityai/stable-diffusion-2-1-base'],
-            value='stabilityai/stable-diffusion-2-1-base',
-            label='Base Model',
-            visible=False)
-        resolution = gr.Dropdown(choices=['512'],
-                                 value='512',
-                                 label='Resolution',
-                                 visible=False)
-
-        with gr.Row():
-            with gr.Box():
-                gr.Markdown('Training Data')
-                concept_images = gr.Files(label='Images for your concept')
-                concept_prompt = gr.Textbox(label='Concept Prompt',
-                                            max_lines=1)
-                gr.Markdown('''
-                    - Upload images of the style you are planning on training on.
-                    - For a concept prompt, use a unique, made up word to avoid collisions.
-                    ''')
-            with gr.Box():
-                gr.Markdown('Training Parameters')
-                num_training_steps = gr.Number(
-                    label='Number of Training Steps', value=1000, precision=0)
-                learning_rate = gr.Number(label='Learning Rate', value=0.0001)
-                train_text_encoder = gr.Checkbox(label='Train Text Encoder',
-                                                 value=True)
-                learning_rate_text = gr.Number(
-                    label='Learning Rate for Text Encoder', value=0.00005)
-                gradient_accumulation = gr.Number(
-                    label='Number of Gradient Accumulation',
-                    value=1,
-                    precision=0)
-                fp16 = gr.Checkbox(label='FP16', value=True)
-                use_8bit_adam = gr.Checkbox(label='Use 8bit Adam', value=True)
-                gr.Markdown('''
-                    - It will take about 8 minutes to train for 1000 steps with a T4 GPU.
-                    - You may want to try a small number of steps first, like 1, to see if everything works fine in your environment.
-                    - Note that your trained models will be deleted when the second training is started. You can upload your trained model in the "Upload" tab.
-                    ''')
-
-        run_button = gr.Button('Start Training')
-        with gr.Box():
-            with gr.Row():
-                check_status_button = gr.Button('Check Training Status')
-                with gr.Column():
-                    with gr.Box():
-                        gr.Markdown('Message')
-                        training_status = gr.Markdown()
-                    output_files = gr.Files(label='Trained Weight Files')
-
-        run_button.click(fn=pipe.clear)
-        run_button.click(fn=trainer.run,
-                         inputs=[
-                             base_model,
-                             resolution,
-                             concept_images,
-                             concept_prompt,
-                             num_training_steps,
-                             learning_rate,
-                             train_text_encoder,
-                             learning_rate_text,
-                             gradient_accumulation,
-                             fp16,
-                             use_8bit_adam,
-                         ],
-                         outputs=[
-                             training_status,
-                             output_files,
-                         ],
-                         queue=False)
-        check_status_button.click(fn=trainer.check_if_running,
-                                  inputs=None,
-                                  outputs=training_status,
-                                  queue=False)
-        check_status_button.click(fn=update_output_files,
-                                  inputs=None,
-                                  outputs=output_files,
-                                  queue=False)
-    return demo
-
-
-def find_weight_files() -> list[str]:
-    curr_dir = pathlib.Path(__file__).parent
-    paths = sorted(curr_dir.rglob('*.pt'))
-    paths = [path for path in paths if not path.stem.endswith('.text_encoder')]
-    return [path.relative_to(curr_dir).as_posix() for path in paths]
-
-
-def reload_lora_weight_list() -> dict:
-    return gr.update(choices=find_weight_files())
-
-
-def create_inference_demo(pipe: InferencePipeline) -> gr.Blocks:
-    with gr.Blocks() as demo:
-        with gr.Row():
-            with gr.Column():
-                base_model = gr.Dropdown(
-                    choices=['stabilityai/stable-diffusion-2-1-base'],
-                    value='stabilityai/stable-diffusion-2-1-base',
-                    label='Base Model',
-                    visible=False)
-                reload_button = gr.Button('Reload Weight List')
-                lora_weight_name = gr.Dropdown(choices=find_weight_files(),
-                                               value='lora/lora_disney.pt',
-                                               label='LoRA Weight File')
-                prompt = gr.Textbox(
-                    label='Prompt',
-                    max_lines=1,
-                    placeholder='Example: "style of sks, baby lion"')
-                alpha = gr.Slider(label='Alpha',
-                                  minimum=0,
-                                  maximum=2,
-                                  step=0.05,
-                                  value=1)
-                alpha_for_text = gr.Slider(label='Alpha for Text Encoder',
-                                           minimum=0,
-                                           maximum=2,
-                                           step=0.05,
-                                           value=1)
-                seed = gr.Slider(label='Seed',
-                                 minimum=0,
-                                 maximum=100000,
-                                 step=1,
-                                 value=1)
-                with gr.Accordion('Other Parameters', open=False):
-                    num_steps = gr.Slider(label='Number of Steps',
-                                          minimum=0,
-                                          maximum=100,
-                                          step=1,
-                                          value=50)
-                    guidance_scale = gr.Slider(label='CFG Scale',
-                                               minimum=0,
-                                               maximum=50,
-                                               step=0.1,
-                                               value=7)
-
-                run_button = gr.Button('Generate')
-
-                gr.Markdown('''
-                - Models with names starting with "lora/" are the pretrained models provided in the [original repo](https://github.com/cloneofsimo/lora), and the ones with names starting with "results/" are your trained models.
-                - After training, you can press "Reload Weight List" button to load your trained model names.
-                - The pretrained models for "disney", "illust" and "pop" are trained with the concept prompt "style of sks".
-                - The pretrained model for "kiriko" is trained with the concept prompt "game character bnha". For this model, the text encoder is also trained.
-                ''')
-            with gr.Column():
-                result = gr.Image(label='Result')
-
-        reload_button.click(fn=reload_lora_weight_list,
-                            inputs=None,
-                            outputs=lora_weight_name)
-        prompt.submit(fn=pipe.run,
-                      inputs=[
-                          base_model,
-                          lora_weight_name,
-                          prompt,
-                          alpha,
-                          alpha_for_text,
-                          seed,
-                          num_steps,
-                          guidance_scale,
-                      ],
-                      outputs=result,
-                      queue=False)
-        run_button.click(fn=pipe.run,
-                         inputs=[
-                             base_model,
-                             lora_weight_name,
-                             prompt,
-                             alpha,
-                             alpha_for_text,
-                             seed,
-                             num_steps,
-                             guidance_scale,
-                         ],
-                         outputs=result,
-                         queue=False)
-    return demo
-
-
-def create_upload_demo() -> gr.Blocks:
-    with gr.Blocks() as demo:
-        model_name = gr.Textbox(label='Model Name')
-        hf_token = gr.Textbox(
-            label='Hugging Face Token (with write permission)')
-        upload_button = gr.Button('Upload')
-        with gr.Box():
-            gr.Markdown('Message')
-            result = gr.Markdown()
-        gr.Markdown('''
-            - You can upload your trained model to your private Model repo (i.e. https://huggingface.co/{your_username}/{model_name}).
-            - You can find your Hugging Face token [here](https://huggingface.co/settings/tokens).
-            ''')
-
-    upload_button.click(fn=upload,
-                        inputs=[model_name, hf_token],
-                        outputs=result)
-
-    return demo
-
-
-pipe = InferencePipeline()
+pipe = InferencePipeline(HF_TOKEN)
 trainer = Trainer()
 
 with gr.Blocks(css='style.css') as demo:
@@ -267,14 +52,12 @@ with gr.Blocks(css='style.css') as demo:
         show_warning(CUDA_NOT_AVAILABLE_WARNING)
 
     gr.Markdown(TITLE)
-    gr.Markdown(DESCRIPTION)
-
     with gr.Tabs():
         with gr.TabItem('Train'):
             create_training_demo(trainer, pipe)
         with gr.TabItem('Test'):
-            create_inference_demo(pipe)
+            create_inference_demo(pipe, HF_TOKEN)
         with gr.TabItem('Upload'):
-            create_upload_demo()
+            create_upload_demo(HF_TOKEN)
 
-demo.queue(default_enabled=False).launch(share=False)
+demo.queue(max_size=1).launch(share=False)

app_inference.py

@@ -0,0 +1,150 @@
+#!/usr/bin/env python
+
+from __future__ import annotations
+
+import enum
+
+import gradio as gr
+from huggingface_hub import HfApi
+
+from inference import InferencePipeline
+from utils import find_exp_dirs
+
+SAMPLE_MODEL_IDS = ['patrickvonplaten/lora_dreambooth_dog_example']
+
+
+class ModelSource(enum.Enum):
+    SAMPLE = 'Sample'
+    HUB_LIB = 'Hub (lora-library)'
+    LOCAL = 'Local'
+
+
+class InferenceUtil:
+    def __init__(self, hf_token: str | None):
+        self.hf_token = hf_token
+
+    @staticmethod
+    def load_sample_lora_model_list():
+        return gr.update(choices=SAMPLE_MODEL_IDS, value=SAMPLE_MODEL_IDS[0])
+
+    def load_hub_lora_model_list(self) -> dict:
+        api = HfApi(token=self.hf_token)
+        choices = [
+            info.modelId for info in api.list_models(author='lora-library')
+        ]
+        return gr.update(choices=choices,
+                         value=choices[0] if choices else None)
+
+    @staticmethod
+    def load_local_lora_model_list() -> dict:
+        choices = find_exp_dirs()
+        return gr.update(choices=choices,
+                         value=choices[0] if choices else None)
+
+    def reload_lora_model_list(self, model_source: str) -> dict:
+        if model_source == ModelSource.SAMPLE.value:
+            return self.load_sample_lora_model_list()
+        elif model_source == ModelSource.HUB_LIB.value:
+            return self.load_hub_lora_model_list()
+        elif model_source == ModelSource.LOCAL.value:
+            return self.load_local_lora_model_list()
+        else:
+            raise ValueError
+
+    def load_model_info(self, lora_model_id: str) -> tuple[str, str]:
+        try:
+            card = InferencePipeline.get_model_card(lora_model_id,
+                                                    self.hf_token)
+        except Exception:
+            return '', ''
+        base_model = getattr(card.data, 'base_model', '')
+        instance_prompt = getattr(card.data, 'instance_prompt', '')
+        return base_model, instance_prompt
+
+
+def create_inference_demo(pipe: InferencePipeline,
+                          hf_token: str | None = None) -> gr.Blocks:
+    app = InferenceUtil(hf_token)
+
+    with gr.Blocks() as demo:
+        with gr.Row():
+            with gr.Column():
+                with gr.Box():
+                    model_source = gr.Radio(
+                        label='Model Source',
+                        choices=[_.value for _ in ModelSource],
+                        value=ModelSource.SAMPLE.value)
+                    reload_button = gr.Button('Reload Model List')
+                    lora_model_id = gr.Dropdown(label='LoRA Model ID',
+                                                choices=SAMPLE_MODEL_IDS,
+                                                value=SAMPLE_MODEL_IDS[0])
+                    with gr.Accordion(
+                            label=
+                            'Model info (Base model and instance prompt used for training)',
+                            open=False):
+                        with gr.Row():
+                            base_model_used_for_training = gr.Text(
+                                label='Base model', interactive=False)
+                            instance_prompt_used_for_training = gr.Text(
+                                label='Instance prompt', interactive=False)
+                prompt = gr.Textbox(
+                    label='Prompt',
+                    max_lines=1,
+                    placeholder='Example: "A picture of a sks dog in a bucket"'
+                )
+                seed = gr.Slider(label='Seed',
+                                 minimum=0,
+                                 maximum=100000,
+                                 step=1,
+                                 value=0)
+                with gr.Accordion('Other Parameters', open=False):
+                    num_steps = gr.Slider(label='Number of Steps',
+                                          minimum=0,
+                                          maximum=100,
+                                          step=1,
+                                          value=25)
+                    guidance_scale = gr.Slider(label='CFG Scale',
+                                               minimum=0,
+                                               maximum=50,
+                                               step=0.1,
+                                               value=7.5)
+
+                run_button = gr.Button('Generate')
+
+                gr.Markdown('''
+                - After training, you can press "Reload Model List" button to load your trained model names.
+                ''')
+            with gr.Column():
+                result = gr.Image(label='Result')
+
+        model_source.change(fn=app.reload_lora_model_list,
+                            inputs=model_source,
+                            outputs=lora_model_id)
+        reload_button.click(fn=app.reload_lora_model_list,
+                            inputs=model_source,
+                            outputs=lora_model_id)
+        lora_model_id.change(fn=app.load_model_info,
+                             inputs=lora_model_id,
+                             outputs=[
+                                 base_model_used_for_training,
+                                 instance_prompt_used_for_training,
+                             ])
+        inputs = [
+            lora_model_id,
+            prompt,
+            seed,
+            num_steps,
+            guidance_scale,
+        ]
+        prompt.submit(fn=pipe.run, inputs=inputs, outputs=result)
+        run_button.click(fn=pipe.run, inputs=inputs, outputs=result)
+    return demo
+
+
+if __name__ == '__main__':
+    import os
+
+    hf_token = os.getenv('HF_TOKEN')
+    pipe = InferencePipeline(hf_token)
+    demo = create_inference_demo(pipe, hf_token)
+    demo.queue(max_size=10).launch(share=False)

app_training.py

@@ -0,0 +1,128 @@
+#!/usr/bin/env python
+
+from __future__ import annotations
+
+import gradio as gr
+
+from constants import UploadTarget
+from inference import InferencePipeline
+from trainer import Trainer
+
+
+def create_training_demo(trainer: Trainer,
+                         pipe: InferencePipeline | None = None) -> gr.Blocks:
+    with gr.Blocks() as demo:
+        with gr.Row():
+            with gr.Column():
+                with gr.Box():
+                    gr.Markdown('Training Data')
+                    instance_images = gr.Files(label='Instance images')
+                    instance_prompt = gr.Textbox(label='Instance prompt',
+                                                 max_lines=1)
+                    gr.Markdown('''
+                        - Upload images of the style you are planning on training on.
+                        - For an instance prompt, use a unique, made up word to avoid collisions.
+                        ''')
+                with gr.Box():
+                    gr.Markdown('Output Model')
+                    output_model_name = gr.Text(label='Name of your model',
+                                                max_lines=1)
+                    delete_existing_model = gr.Checkbox(
+                        label='Delete existing model of the same name',
+                        value=False)
+                    validation_prompt = gr.Text(label='Validation Prompt')
+                with gr.Box():
+                    gr.Markdown('Upload Settings')
+                    with gr.Row():
+                        upload_to_hub = gr.Checkbox(
+                            label='Upload model to Hub', value=False)
+                        use_private_repo = gr.Checkbox(label='Private',
+                                                       value=False)
+                        delete_existing_repo = gr.Checkbox(
+                            label='Delete existing repo of the same name',
+                            value=False)
+                    upload_to = gr.Radio(
+                        label='Upload to',
+                        choices=[_.value for _ in UploadTarget],
+                        value=UploadTarget.PERSONAL_PROFILE.value)
+
+            with gr.Box():
+                gr.Markdown('Training Parameters')
+                with gr.Row():
+                    base_model = gr.Text(
+                        label='Base Model',
+                        value='stabilityai/stable-diffusion-2-1-base',
+                        max_lines=1)
+                    resolution = gr.Dropdown(choices=['512', '768'],
+                                             value='512',
+                                             label='Resolution')
+                num_training_steps = gr.Number(
+                    label='Number of Training Steps', value=1000, precision=0)
+                learning_rate = gr.Number(label='Learning Rate', value=0.0001)
+                gradient_accumulation = gr.Number(
+                    label='Number of Gradient Accumulation',
+                    value=1,
+                    precision=0)
+                seed = gr.Slider(label='Seed',
+                                 minimum=0,
+                                 maximum=100000,
+                                 step=1,
+                                 value=0)
+                fp16 = gr.Checkbox(label='FP16', value=True)
+                use_8bit_adam = gr.Checkbox(label='Use 8bit Adam', value=True)
+                checkpointing_steps = gr.Number(label='Checkpointing Steps',
+                                                value=100,
+                                                precision=0)
+                use_wandb = gr.Checkbox(label='Use W&B', value=False)
+                validation_epochs = gr.Number(label='Validation Epochs',
+                                              value=100,
+                                              precision=0)
+                gr.Markdown('''
+                    - It will take about 8 minutes to train for 1000 steps with a T4 GPU.
+                    - You may want to try a small number of steps first, like 1, to see if everything works fine in your environment.
+                    - You need to set the environment variable `WANDB_API_KEY` if you'd like to use W&B. See [W&B documentation](https://docs.wandb.ai/guides/track/advanced/environment-variables).
+                    - **Note:** Due to [this issue](https://github.com/huggingface/accelerate/issues/944), currently, training will not terminate properly if you use W&B.
+                    ''')
+
+        # TODO currently disabled
+        remove_gpu_after_training = gr.Checkbox(
+            label='Remove GPU after training', value=False, interactive=False)
+        run_button = gr.Button('Start Training')
+
+        with gr.Box():
+            gr.Markdown('Message')
+            message = gr.Markdown()
+
+        if pipe is not None:
+            run_button.click(fn=pipe.clear)
+        run_button.click(fn=trainer.run,
+                         inputs=[
+                             instance_images,
+                             instance_prompt,
+                             output_model_name,
+                             delete_existing_model,
+                             validation_prompt,
+                             base_model,
+                             resolution,
+                             num_training_steps,
+                             learning_rate,
+                             gradient_accumulation,
+                             seed,
+                             fp16,
+                             use_8bit_adam,
+                             checkpointing_steps,
+                             use_wandb,
+                             validation_epochs,
+                             upload_to_hub,
+                             use_private_repo,
+                             delete_existing_repo,
+                             upload_to,
+                         ],
+                         outputs=message)
+    return demo
+
+
+if __name__ == '__main__':
+    trainer = Trainer()
+    demo = create_training_demo(trainer)
+    demo.queue(max_size=1).launch(share=False)

app_upload.py

@@ -0,0 +1,95 @@
+#!/usr/bin/env python
+
+from __future__ import annotations
+
+import pathlib
+
+import gradio as gr
+import slugify
+
+from constants import UploadTarget
+from uploader import Uploader
+from utils import find_exp_dirs
+
+
+class LoRAModelUploader(Uploader):
+    def upload_lora_model(self, folder_path: str, repo_name: str,
+                          upload_to: str, private: bool,
+                          delete_existing_repo: bool) -> str:
+        if not repo_name:
+            repo_name = pathlib.Path(folder_path).name
+        repo_name = slugify.slugify(repo_name)
+
+        if upload_to == UploadTarget.PERSONAL_PROFILE.value:
+            organization = ''
+        elif upload_to == UploadTarget.LORA_LIBRARY.value:
+            organization = 'lora-library'
+        else:
+            raise ValueError
+
+        return self.upload(folder_path,
+                           repo_name,
+                           organization=organization,
+                           private=private,
+                           delete_existing_repo=delete_existing_repo)
+
+
+def load_local_lora_model_list() -> dict:
+    choices = find_exp_dirs(ignore_repo=True)
+    return gr.update(choices=choices, value=choices[0] if choices else None)
+
+
+def create_upload_demo(hf_token: str | None) -> gr.Blocks:
+    uploader = LoRAModelUploader(hf_token)
+    model_dirs = find_exp_dirs(ignore_repo=True)
+
+    with gr.Blocks() as demo:
+        with gr.Box():
+            gr.Markdown('Local Models')
+            reload_button = gr.Button('Reload Model List')
+            model_dir = gr.Dropdown(
+                label='LoRA Model ID',
+                choices=model_dirs,
+                value=model_dirs[0] if model_dirs else None)
+            gr.Markdown(
+                '- Models uploaded in training time will not be shown here.')
+        with gr.Box():
+            gr.Markdown('Upload Settings')
+            with gr.Row():
+                use_private_repo = gr.Checkbox(label='Private', value=False)
+                delete_existing_repo = gr.Checkbox(
+                    label='Delete existing repo of the same name', value=False)
+            upload_to = gr.Radio(label='Upload to',
+                                 choices=[_.value for _ in UploadTarget],
+                                 value=UploadTarget.PERSONAL_PROFILE.value)
+            model_name = gr.Textbox(label='Model Name')
+        upload_button = gr.Button('Upload')
+        gr.Markdown('''
+            - You can upload your trained model to your personal profile (i.e. https://huggingface.co/{your_username}/{model_name}) or to the public [LoRA Concepts Library](https://huggingface.co/lora-library) (i.e. https://huggingface.co/lora-library/{model_name}).
+            ''')
+        with gr.Box():
+            gr.Markdown('Message')
+            message = gr.Markdown()
+
+        reload_button.click(fn=load_local_lora_model_list,
+                            inputs=None,
+                            outputs=model_dir)
+        upload_button.click(fn=uploader.upload_lora_model,
+                            inputs=[
+                                model_dir,
+                                model_name,
+                                upload_to,
+                                use_private_repo,
+                                delete_existing_repo,
+                            ],
+                            outputs=message)
+
+    return demo
+
+
+if __name__ == '__main__':
+    import os
+
+    hf_token = os.getenv('HF_TOKEN')
+    demo = create_upload_demo(hf_token)
+    demo.queue(max_size=1).launch(share=False)

constants.py

@@ -0,0 +1,6 @@
+import enum
+
+
+class UploadTarget(enum.Enum):
+    PERSONAL_PROFILE = 'Personal Profile'
+    LORA_LIBRARY = 'LoRA Library'

inference.py

@@ -2,78 +2,77 @@ from __future__ import annotations
 
 import gc
 import pathlib
-import sys
 
 import gradio as gr
 import PIL.Image
 import torch
-from diffusers import StableDiffusionPipeline
-
-sys.path.insert(0, 'lora')
-from lora_diffusion import monkeypatch_lora, tune_lora_scale
+from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
+from huggingface_hub import ModelCard
 
 
 class InferencePipeline:
-    def __init__(self):
+    def __init__(self, hf_token: str | None = None):
+        self.hf_token = hf_token
         self.pipe = None
         self.device = torch.device(
             'cuda:0' if torch.cuda.is_available() else 'cpu')
-        self.weight_path = None
+        self.lora_model_id = None
+        self.base_model_id = None
 
     def clear(self) -> None:
-        self.weight_path = None
+        self.lora_model_id = None
+        self.base_model_id = None
         del self.pipe
         self.pipe = None
         torch.cuda.empty_cache()
         gc.collect()
 
     @staticmethod
-    def get_lora_weight_path(name: str) -> pathlib.Path:
-        curr_dir = pathlib.Path(__file__).parent
-        return curr_dir / name
+    def check_if_model_is_local(lora_model_id: str) -> bool:
+        return pathlib.Path(lora_model_id).exists()
 
     @staticmethod
-    def get_lora_text_encoder_weight_path(path: pathlib.Path) -> str:
-        parent_dir = path.parent
-        stem = path.stem
-        text_encoder_filename = f'{stem}.text_encoder.pt'
-        path = parent_dir / text_encoder_filename
-        return path.as_posix() if path.exists() else ''
-
-    def load_pipe(self, model_id: str, lora_filename: str) -> None:
-        weight_path = self.get_lora_weight_path(lora_filename)
-        if weight_path == self.weight_path:
-            return
-        self.weight_path = weight_path
-        lora_weight = torch.load(self.weight_path, map_location=self.device)
-
-        if self.device.type == 'cpu':
-            pipe = StableDiffusionPipeline.from_pretrained(model_id)
+    def get_model_card(model_id: str,
+                       hf_token: str | None = None) -> ModelCard:
+        if InferencePipeline.check_if_model_is_local(model_id):
+            card_path = (pathlib.Path(model_id) / 'README.md').as_posix()
         else:
-            pipe = StableDiffusionPipeline.from_pretrained(
-                model_id, torch_dtype=torch.float16)
-            pipe = pipe.to(self.device)
+            card_path = model_id
+        return ModelCard.load(card_path, token=hf_token)
 
-        monkeypatch_lora(pipe.unet, lora_weight)
-
-        lora_text_encoder_weight_path = self.get_lora_text_encoder_weight_path(
-            weight_path)
-        if lora_text_encoder_weight_path:
-            lora_text_encoder_weight = torch.load(
-                lora_text_encoder_weight_path, map_location=self.device)
-            monkeypatch_lora(pipe.text_encoder,
-                             lora_text_encoder_weight,
-                             target_replace_module=['CLIPAttention'])
+    @staticmethod
+    def get_base_model_info(lora_model_id: str,
+                            hf_token: str | None = None) -> str:
+        card = InferencePipeline.get_model_card(lora_model_id, hf_token)
+        return card.data.base_model
 
-        self.pipe = pipe
+    def load_pipe(self, lora_model_id: str) -> None:
+        if lora_model_id == self.lora_model_id:
+            return
+        base_model_id = self.get_base_model_info(lora_model_id, self.hf_token)
+        if base_model_id != self.base_model_id:
+            if self.device.type == 'cpu':
+                pipe = DiffusionPipeline.from_pretrained(
+                    base_model_id, use_auth_token=self.hf_token)
+            else:
+                pipe = DiffusionPipeline.from_pretrained(
+                    base_model_id,
+                    torch_dtype=torch.float16,
+                    use_auth_token=self.hf_token)
+                pipe = pipe.to(self.device)
+            pipe.scheduler = DPMSolverMultistepScheduler.from_config(
+                pipe.scheduler.config)
+            self.pipe = pipe
+        self.pipe.unet.load_attn_procs(  # type: ignore
+            lora_model_id, use_auth_token=self.hf_token)
+
+        self.lora_model_id = lora_model_id  # type: ignore
+        self.base_model_id = base_model_id  # type: ignore
 
     def run(
         self,
-        base_model: str,
-        lora_weight_name: str,
+        lora_model_id: str,
         prompt: str,
-        alpha: float,
-        alpha_for_text: float,
         seed: int,
         n_steps: int,
         guidance_scale: float,
@@ -81,11 +80,9 @@ class InferencePipeline:
         if not torch.cuda.is_available():
             raise gr.Error('CUDA is not available.')
 
-        self.load_pipe(base_model, lora_weight_name)
+        self.load_pipe(lora_model_id)
 
         generator = torch.Generator(device=self.device).manual_seed(seed)
-        tune_lora_scale(self.pipe.unet, alpha)  # type: ignore
-        tune_lora_scale(self.pipe.text_encoder, alpha_for_text)  # type: ignore
         out = self.pipe(prompt,
                         num_inference_steps=n_steps,
                         guidance_scale=guidance_scale,

lora

@@ -1 +0,0 @@
-Subproject commit 26787a09bff4ebcb08f0ad4e848b67bce4389a7a

requirements.txt

@@ -1,10 +1,12 @@
 accelerate==0.15.0
-bitsandbytes==0.35.4
-diffusers==0.10.2
+bitsandbytes==0.36.0.post2
+datasets==2.8.0
+git+https://github.com/huggingface/diffusers@a66f2baeb782e091dde4e1e6394e46f169e5ba58#egg=diffusers
 ftfy==6.1.1
-Pillow==9.3.0
-torch==1.13.0
-torchvision==0.14.0
+gradio==3.14.0
+Pillow==9.4.0
+python-slugify==7.0.0
+torch==1.13.1
+torchvision==0.14.1
 transformers==4.25.1
-triton==2.0.0.dev20220701
-xformers==0.0.13
+wandb==0.13.9

train_dreambooth_lora.py

@@ -0,0 +1,1018 @@
+#!/usr/bin/env python
+# This file is adapted from https://github.com/huggingface/diffusers/blob/a66f2baeb782e091dde4e1e6394e46f169e5ba58/examples/dreambooth/train_dreambooth_lora.py
+# The original license is as below.
+#
+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+
+import argparse
+import hashlib
+import logging
+import math
+import os
+import warnings
+from pathlib import Path
+from typing import Optional
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch.utils.data import Dataset
+
+import datasets
+import diffusers
+import transformers
+from accelerate import Accelerator
+from accelerate.logging import get_logger
+from accelerate.utils import set_seed
+from diffusers import (
+    AutoencoderKL,
+    DDPMScheduler,
+    DiffusionPipeline,
+    DPMSolverMultistepScheduler,
+    UNet2DConditionModel,
+)
+from diffusers.loaders import AttnProcsLayers
+from diffusers.models.cross_attention import LoRACrossAttnProcessor
+from diffusers.optimization import get_scheduler
+from diffusers.utils import check_min_version, is_wandb_available
+from diffusers.utils.import_utils import is_xformers_available
+from huggingface_hub import HfFolder, Repository, create_repo, delete_repo, whoami
+from PIL import Image
+from torchvision import transforms
+from tqdm.auto import tqdm
+from transformers import AutoTokenizer, PretrainedConfig
+
+
+# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
+check_min_version("0.12.0.dev0")
+
+logger = get_logger(__name__)
+
+
+def save_model_card(repo_name, base_model, instance_prompt, test_prompt="", images=None, repo_folder=""):
+    img_str = f"Test prompt: {test_prompt}\n" if test_prompt else ""
+    for i, image in enumerate(images or []):
+        image.save(os.path.join(repo_folder, f"image_{i}.png"))
+        img_str += f"![img_{i}](./image_{i}.png)\n"
+
+    yaml = f"""
+---
+license: creativeml-openrail-m
+base_model: {base_model}
+instance_prompt: {instance_prompt}
+tags:
+- stable-diffusion
+- stable-diffusion-diffusers
+- text-to-image
+- diffusers
+inference: true
+---
+"""
+    model_card = f"""
+# LoRA DreamBooth - {repo_name}
+
+These are LoRA adaption weights for [{base_model}](https://huggingface.co/{base_model}). The weights were trained on the instance prompt "{instance_prompt}" using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.\n
+{img_str}
+"""
+    with open(os.path.join(repo_folder, "README.md"), "w") as f:
+        f.write(yaml + model_card)
+
+
+def import_model_class_from_model_name_or_path(pretrained_model_name_or_path: str, revision: str):
+    text_encoder_config = PretrainedConfig.from_pretrained(
+        pretrained_model_name_or_path,
+        subfolder="text_encoder",
+        revision=revision,
+    )
+    model_class = text_encoder_config.architectures[0]
+
+    if model_class == "CLIPTextModel":
+        from transformers import CLIPTextModel
+
+        return CLIPTextModel
+    elif model_class == "RobertaSeriesModelWithTransformation":
+        from diffusers.pipelines.alt_diffusion.modeling_roberta_series import RobertaSeriesModelWithTransformation
+
+        return RobertaSeriesModelWithTransformation
+    else:
+        raise ValueError(f"{model_class} is not supported.")
+
+
+def parse_args(input_args=None):
+    parser = argparse.ArgumentParser(description="Simple example of a training script.")
+    parser.add_argument(
+        "--pretrained_model_name_or_path",
+        type=str,
+        default=None,
+        required=True,
+        help="Path to pretrained model or model identifier from huggingface.co/models.",
+    )
+    parser.add_argument(
+        "--revision",
+        type=str,
+        default=None,
+        required=False,
+        help="Revision of pretrained model identifier from huggingface.co/models.",
+    )
+    parser.add_argument(
+        "--tokenizer_name",
+        type=str,
+        default=None,
+        help="Pretrained tokenizer name or path if not the same as model_name",
+    )
+    parser.add_argument(
+        "--instance_data_dir",
+        type=str,
+        default=None,
+        required=True,
+        help="A folder containing the training data of instance images.",
+    )
+    parser.add_argument(
+        "--class_data_dir",
+        type=str,
+        default=None,
+        required=False,
+        help="A folder containing the training data of class images.",
+    )
+    parser.add_argument(
+        "--instance_prompt",
+        type=str,
+        default=None,
+        required=True,
+        help="The prompt with identifier specifying the instance",
+    )
+    parser.add_argument(
+        "--class_prompt",
+        type=str,
+        default=None,
+        help="The prompt to specify images in the same class as provided instance images.",
+    )
+    parser.add_argument(
+        "--validation_prompt",
+        type=str,
+        default=None,
+        help="A prompt that is used during validation to verify that the model is learning.",
+    )
+    parser.add_argument(
+        "--num_validation_images",
+        type=int,
+        default=4,
+        help="Number of images that should be generated during validation with `validation_prompt`.",
+    )
+    parser.add_argument(
+        "--validation_epochs",
+        type=int,
+        default=50,
+        help=(
+            "Run dreambooth validation every X epochs. Dreambooth validation consists of running the prompt"
+            " `args.validation_prompt` multiple times: `args.num_validation_images`."
+        ),
+    )
+    parser.add_argument(
+        "--with_prior_preservation",
+        default=False,
+        action="store_true",
+        help="Flag to add prior preservation loss.",
+    )
+    parser.add_argument("--prior_loss_weight", type=float, default=1.0, help="The weight of prior preservation loss.")
+    parser.add_argument(
+        "--num_class_images",
+        type=int,
+        default=100,
+        help=(
+            "Minimal class images for prior preservation loss. If there are not enough images already present in"
+            " class_data_dir, additional images will be sampled with class_prompt."
+        ),
+    )
+    parser.add_argument(
+        "--output_dir",
+        type=str,
+        default="lora-dreambooth-model",
+        help="The output directory where the model predictions and checkpoints will be written.",
+    )
+    parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
+    parser.add_argument(
+        "--resolution",
+        type=int,
+        default=512,
+        help=(
+            "The resolution for input images, all the images in the train/validation dataset will be resized to this"
+            " resolution"
+        ),
+    )
+    parser.add_argument(
+        "--center_crop", action="store_true", help="Whether to center crop images before resizing to resolution"
+    )
+    parser.add_argument(
+        "--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
+    )
+    parser.add_argument(
+        "--sample_batch_size", type=int, default=4, help="Batch size (per device) for sampling images."
+    )
+    parser.add_argument("--num_train_epochs", type=int, default=1)
+    parser.add_argument(
+        "--max_train_steps",
+        type=int,
+        default=None,
+        help="Total number of training steps to perform.  If provided, overrides num_train_epochs.",
+    )
+    parser.add_argument(
+        "--checkpointing_steps",
+        type=int,
+        default=500,
+        help=(
+            "Save a checkpoint of the training state every X updates. These checkpoints can be used both as final"
+            " checkpoints in case they are better than the last checkpoint, and are also suitable for resuming"
+            " training using `--resume_from_checkpoint`."
+        ),
+    )
+    parser.add_argument(
+        "--resume_from_checkpoint",
+        type=str,
+        default=None,
+        help=(
+            "Whether training should be resumed from a previous checkpoint. Use a path saved by"
+            ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
+        ),
+    )
+    parser.add_argument(
+        "--gradient_accumulation_steps",
+        type=int,
+        default=1,
+        help="Number of updates steps to accumulate before performing a backward/update pass.",
+    )
+    parser.add_argument(
+        "--gradient_checkpointing",
+        action="store_true",
+        help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
+    )
+    parser.add_argument(
+        "--learning_rate",
+        type=float,
+        default=5e-4,
+        help="Initial learning rate (after the potential warmup period) to use.",
+    )
+    parser.add_argument(
+        "--scale_lr",
+        action="store_true",
+        default=False,
+        help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
+    )
+    parser.add_argument(
+        "--lr_scheduler",
+        type=str,
+        default="constant",
+        help=(
+            'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
+            ' "constant", "constant_with_warmup"]'
+        ),
+    )
+    parser.add_argument(
+        "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
+    )
+    parser.add_argument(
+        "--lr_num_cycles",
+        type=int,
+        default=1,
+        help="Number of hard resets of the lr in cosine_with_restarts scheduler.",
+    )
+    parser.add_argument("--lr_power", type=float, default=1.0, help="Power factor of the polynomial scheduler.")
+    parser.add_argument(
+        "--dataloader_num_workers",
+        type=int,
+        default=0,
+        help=(
+            "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
+        ),
+    )
+    parser.add_argument(
+        "--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
+    )
+    parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
+    parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
+    parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
+    parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
+    parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
+    parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
+    parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
+    parser.add_argument(
+        "--hub_model_id",
+        type=str,
+        default=None,
+        help="The name of the repository to keep in sync with the local `output_dir`.",
+    )
+    parser.add_argument(
+        "--logging_dir",
+        type=str,
+        default="logs",
+        help=(
+            "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
+            " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
+        ),
+    )
+    parser.add_argument(
+        "--allow_tf32",
+        action="store_true",
+        help=(
+            "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
+            " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
+        ),
+    )
+    parser.add_argument(
+        "--report_to",
+        type=str,
+        default="tensorboard",
+        help=(
+            'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
+            ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
+        ),
+    )
+    parser.add_argument(
+        "--mixed_precision",
+        type=str,
+        default=None,
+        choices=["no", "fp16", "bf16"],
+        help=(
+            "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
+            " 1.10.and an Nvidia Ampere GPU.  Default to the value of accelerate config of the current system or the"
+            " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
+        ),
+    )
+    parser.add_argument(
+        "--prior_generation_precision",
+        type=str,
+        default=None,
+        choices=["no", "fp32", "fp16", "bf16"],
+        help=(
+            "Choose prior generation precision between fp32, fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
+            " 1.10.and an Nvidia Ampere GPU.  Default to  fp16 if a GPU is available else fp32."
+        ),
+    )
+    parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
+    parser.add_argument(
+        "--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
+    )
+    parser.add_argument("--private_repo", action="store_true")
+    parser.add_argument("--delete_existing_repo", action="store_true")
+    parser.add_argument("--upload_to_lora_library", action="store_true")
+
+    if input_args is not None:
+        args = parser.parse_args(input_args)
+    else:
+        args = parser.parse_args()
+
+    env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
+    if env_local_rank != -1 and env_local_rank != args.local_rank:
+        args.local_rank = env_local_rank
+
+    if args.with_prior_preservation:
+        if args.class_data_dir is None:
+            raise ValueError("You must specify a data directory for class images.")
+        if args.class_prompt is None:
+            raise ValueError("You must specify prompt for class images.")
+    else:
+        # logger is not available yet
+        if args.class_data_dir is not None:
+            warnings.warn("You need not use --class_data_dir without --with_prior_preservation.")
+        if args.class_prompt is not None:
+            warnings.warn("You need not use --class_prompt without --with_prior_preservation.")
+
+    return args
+
+
+class DreamBoothDataset(Dataset):
+    """
+    A dataset to prepare the instance and class images with the prompts for fine-tuning the model.
+    It pre-processes the images and the tokenizes prompts.
+    """
+
+    def __init__(
+        self,
+        instance_data_root,
+        instance_prompt,
+        tokenizer,
+        class_data_root=None,
+        class_prompt=None,
+        size=512,
+        center_crop=False,
+    ):
+        self.size = size
+        self.center_crop = center_crop
+        self.tokenizer = tokenizer
+
+        self.instance_data_root = Path(instance_data_root)
+        if not self.instance_data_root.exists():
+            raise ValueError("Instance images root doesn't exists.")
+
+        self.instance_images_path = list(Path(instance_data_root).iterdir())
+        self.num_instance_images = len(self.instance_images_path)
+        self.instance_prompt = instance_prompt
+        self._length = self.num_instance_images
+
+        if class_data_root is not None:
+            self.class_data_root = Path(class_data_root)
+            self.class_data_root.mkdir(parents=True, exist_ok=True)
+            self.class_images_path = list(self.class_data_root.iterdir())
+            self.num_class_images = len(self.class_images_path)
+            self._length = max(self.num_class_images, self.num_instance_images)
+            self.class_prompt = class_prompt
+        else:
+            self.class_data_root = None
+
+        self.image_transforms = transforms.Compose(
+            [
+                transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),
+                transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),
+                transforms.ToTensor(),
+                transforms.Normalize([0.5], [0.5]),
+            ]
+        )
+
+    def __len__(self):
+        return self._length
+
+    def __getitem__(self, index):
+        example = {}
+        instance_image = Image.open(self.instance_images_path[index % self.num_instance_images])
+        if not instance_image.mode == "RGB":
+            instance_image = instance_image.convert("RGB")
+        example["instance_images"] = self.image_transforms(instance_image)
+        example["instance_prompt_ids"] = self.tokenizer(
+            self.instance_prompt,
+            truncation=True,
+            padding="max_length",
+            max_length=self.tokenizer.model_max_length,
+            return_tensors="pt",
+        ).input_ids
+
+        if self.class_data_root:
+            class_image = Image.open(self.class_images_path[index % self.num_class_images])
+            if not class_image.mode == "RGB":
+                class_image = class_image.convert("RGB")
+            example["class_images"] = self.image_transforms(class_image)
+            example["class_prompt_ids"] = self.tokenizer(
+                self.class_prompt,
+                truncation=True,
+                padding="max_length",
+                max_length=self.tokenizer.model_max_length,
+                return_tensors="pt",
+            ).input_ids
+
+        return example
+
+
+def collate_fn(examples, with_prior_preservation=False):
+    input_ids = [example["instance_prompt_ids"] for example in examples]
+    pixel_values = [example["instance_images"] for example in examples]
+
+    # Concat class and instance examples for prior preservation.
+    # We do this to avoid doing two forward passes.
+    if with_prior_preservation:
+        input_ids += [example["class_prompt_ids"] for example in examples]
+        pixel_values += [example["class_images"] for example in examples]
+
+    pixel_values = torch.stack(pixel_values)
+    pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
+
+    input_ids = torch.cat(input_ids, dim=0)
+
+    batch = {
+        "input_ids": input_ids,
+        "pixel_values": pixel_values,
+    }
+    return batch
+
+
+class PromptDataset(Dataset):
+    "A simple dataset to prepare the prompts to generate class images on multiple GPUs."
+
+    def __init__(self, prompt, num_samples):
+        self.prompt = prompt
+        self.num_samples = num_samples
+
+    def __len__(self):
+        return self.num_samples
+
+    def __getitem__(self, index):
+        example = {}
+        example["prompt"] = self.prompt
+        example["index"] = index
+        return example
+
+
+def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):
+    if token is None:
+        token = HfFolder.get_token()
+    if organization is None:
+        username = whoami(token)["name"]
+        return f"{username}/{model_id}"
+    else:
+        return f"{organization}/{model_id}"
+
+
+def main(args):
+    logging_dir = Path(args.output_dir, args.logging_dir)
+
+    accelerator = Accelerator(
+        gradient_accumulation_steps=args.gradient_accumulation_steps,
+        mixed_precision=args.mixed_precision,
+        log_with=args.report_to,
+        logging_dir=logging_dir,
+    )
+
+    if args.report_to == "wandb":
+        if not is_wandb_available():
+            raise ImportError("Make sure to install wandb if you want to use it for logging during training.")
+        import wandb
+
+    # Currently, it's not possible to do gradient accumulation when training two models with accelerate.accumulate
+    # This will be enabled soon in accelerate. For now, we don't allow gradient accumulation when training two models.
+    # TODO (patil-suraj): Remove this check when gradient accumulation with two models is enabled in accelerate.
+    # Make one log on every process with the configuration for debugging.
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        level=logging.INFO,
+    )
+    logger.info(accelerator.state, main_process_only=False)
+    if accelerator.is_local_main_process:
+        datasets.utils.logging.set_verbosity_warning()
+        transformers.utils.logging.set_verbosity_warning()
+        diffusers.utils.logging.set_verbosity_info()
+    else:
+        datasets.utils.logging.set_verbosity_error()
+        transformers.utils.logging.set_verbosity_error()
+        diffusers.utils.logging.set_verbosity_error()
+
+    # If passed along, set the training seed now.
+    if args.seed is not None:
+        set_seed(args.seed)
+
+    # Generate class images if prior preservation is enabled.
+    if args.with_prior_preservation:
+        class_images_dir = Path(args.class_data_dir)
+        if not class_images_dir.exists():
+            class_images_dir.mkdir(parents=True)
+        cur_class_images = len(list(class_images_dir.iterdir()))
+
+        if cur_class_images < args.num_class_images:
+            torch_dtype = torch.float16 if accelerator.device.type == "cuda" else torch.float32
+            if args.prior_generation_precision == "fp32":
+                torch_dtype = torch.float32
+            elif args.prior_generation_precision == "fp16":
+                torch_dtype = torch.float16
+            elif args.prior_generation_precision == "bf16":
+                torch_dtype = torch.bfloat16
+            pipeline = DiffusionPipeline.from_pretrained(
+                args.pretrained_model_name_or_path,
+                torch_dtype=torch_dtype,
+                safety_checker=None,
+                revision=args.revision,
+            )
+            pipeline.set_progress_bar_config(disable=True)
+
+            num_new_images = args.num_class_images - cur_class_images
+            logger.info(f"Number of class images to sample: {num_new_images}.")
+
+            sample_dataset = PromptDataset(args.class_prompt, num_new_images)
+            sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size)
+
+            sample_dataloader = accelerator.prepare(sample_dataloader)
+            pipeline.to(accelerator.device)
+
+            for example in tqdm(
+                sample_dataloader, desc="Generating class images", disable=not accelerator.is_local_main_process
+            ):
+                images = pipeline(example["prompt"]).images
+
+                for i, image in enumerate(images):
+                    hash_image = hashlib.sha1(image.tobytes()).hexdigest()
+                    image_filename = class_images_dir / f"{example['index'][i] + cur_class_images}-{hash_image}.jpg"
+                    image.save(image_filename)
+
+            del pipeline
+            if torch.cuda.is_available():
+                torch.cuda.empty_cache()
+
+    # Handle the repository creation
+    if accelerator.is_main_process:
+        if args.push_to_hub:
+            if args.hub_model_id is None:
+                organization = 'lora-library' if args.upload_to_lora_library else None
+                repo_name = get_full_repo_name(Path(args.output_dir).name, organization=organization, token=args.hub_token)
+            else:
+                repo_name = args.hub_model_id
+
+            if args.delete_existing_repo:
+                try:
+                    delete_repo(repo_name, token=args.hub_token)
+                except Exception:
+                    pass
+            create_repo(repo_name, token=args.hub_token, private=args.private_repo)
+            repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token)
+
+            with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
+                if "step_*" not in gitignore:
+                    gitignore.write("step_*\n")
+                if "epoch_*" not in gitignore:
+                    gitignore.write("epoch_*\n")
+        elif args.output_dir is not None:
+            os.makedirs(args.output_dir, exist_ok=True)
+
+    # Load the tokenizer
+    if args.tokenizer_name:
+        tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, revision=args.revision, use_fast=False)
+    elif args.pretrained_model_name_or_path:
+        tokenizer = AutoTokenizer.from_pretrained(
+            args.pretrained_model_name_or_path,
+            subfolder="tokenizer",
+            revision=args.revision,
+            use_fast=False,
+        )
+
+    # import correct text encoder class
+    text_encoder_cls = import_model_class_from_model_name_or_path(args.pretrained_model_name_or_path, args.revision)
+
+    # Load scheduler and models
+    noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
+    text_encoder = text_encoder_cls.from_pretrained(
+        args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
+    )
+    vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision)
+    unet = UNet2DConditionModel.from_pretrained(
+        args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision
+    )
+
+    # We only train the additional adapter LoRA layers
+    vae.requires_grad_(False)
+    text_encoder.requires_grad_(False)
+    unet.requires_grad_(False)
+
+    # For mixed precision training we cast the text_encoder and vae weights to half-precision
+    # as these models are only used for inference, keeping weights in full precision is not required.
+    weight_dtype = torch.float32
+    if accelerator.mixed_precision == "fp16":
+        weight_dtype = torch.float16
+    elif accelerator.mixed_precision == "bf16":
+        weight_dtype = torch.bfloat16
+
+    # Move unet, vae and text_encoder to device and cast to weight_dtype
+    unet.to(accelerator.device, dtype=weight_dtype)
+    vae.to(accelerator.device, dtype=weight_dtype)
+    text_encoder.to(accelerator.device, dtype=weight_dtype)
+
+    if args.enable_xformers_memory_efficient_attention:
+        if is_xformers_available():
+            unet.enable_xformers_memory_efficient_attention()
+        else:
+            raise ValueError("xformers is not available. Make sure it is installed correctly")
+
+    # now we will add new LoRA weights to the attention layers
+    # It's important to realize here how many attention weights will be added and of which sizes
+    # The sizes of the attention layers consist only of two different variables:
+    # 1) - the "hidden_size", which is increased according to `unet.config.block_out_channels`.
+    # 2) - the "cross attention size", which is set to `unet.config.cross_attention_dim`.
+
+    # Let's first see how many attention processors we will have to set.
+    # For Stable Diffusion, it should be equal to:
+    # - down blocks (2x attention layers) * (2x transformer layers) * (3x down blocks) = 12
+    # - mid blocks (2x attention layers) * (1x transformer layers) * (1x mid blocks) = 2
+    # - up blocks (2x attention layers) * (3x transformer layers) * (3x down blocks) = 18
+    # => 32 layers
+
+    # Set correct lora layers
+    lora_attn_procs = {}
+    for name in unet.attn_processors.keys():
+        cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
+        if name.startswith("mid_block"):
+            hidden_size = unet.config.block_out_channels[-1]
+        elif name.startswith("up_blocks"):
+            block_id = int(name[len("up_blocks.")])
+            hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
+        elif name.startswith("down_blocks"):
+            block_id = int(name[len("down_blocks.")])
+            hidden_size = unet.config.block_out_channels[block_id]
+
+        lora_attn_procs[name] = LoRACrossAttnProcessor(
+            hidden_size=hidden_size, cross_attention_dim=cross_attention_dim
+        )
+
+    unet.set_attn_processor(lora_attn_procs)
+    lora_layers = AttnProcsLayers(unet.attn_processors)
+
+    accelerator.register_for_checkpointing(lora_layers)
+
+    if args.scale_lr:
+        args.learning_rate = (
+            args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
+        )
+
+    # Enable TF32 for faster training on Ampere GPUs,
+    # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
+    if args.allow_tf32:
+        torch.backends.cuda.matmul.allow_tf32 = True
+
+    if args.scale_lr:
+        args.learning_rate = (
+            args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
+        )
+
+    # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs
+    if args.use_8bit_adam:
+        try:
+            import bitsandbytes as bnb
+        except ImportError:
+            raise ImportError(
+                "To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`."
+            )
+
+        optimizer_class = bnb.optim.AdamW8bit
+    else:
+        optimizer_class = torch.optim.AdamW
+
+    # Optimizer creation
+    optimizer = optimizer_class(
+        lora_layers.parameters(),
+        lr=args.learning_rate,
+        betas=(args.adam_beta1, args.adam_beta2),
+        weight_decay=args.adam_weight_decay,
+        eps=args.adam_epsilon,
+    )
+
+    # Dataset and DataLoaders creation:
+    train_dataset = DreamBoothDataset(
+        instance_data_root=args.instance_data_dir,
+        instance_prompt=args.instance_prompt,
+        class_data_root=args.class_data_dir if args.with_prior_preservation else None,
+        class_prompt=args.class_prompt,
+        tokenizer=tokenizer,
+        size=args.resolution,
+        center_crop=args.center_crop,
+    )
+
+    train_dataloader = torch.utils.data.DataLoader(
+        train_dataset,
+        batch_size=args.train_batch_size,
+        shuffle=True,
+        collate_fn=lambda examples: collate_fn(examples, args.with_prior_preservation),
+        num_workers=args.dataloader_num_workers,
+    )
+
+    # Scheduler and math around the number of training steps.
+    overrode_max_train_steps = False
+    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
+    if args.max_train_steps is None:
+        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
+        overrode_max_train_steps = True
+
+    lr_scheduler = get_scheduler(
+        args.lr_scheduler,
+        optimizer=optimizer,
+        num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
+        num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
+        num_cycles=args.lr_num_cycles,
+        power=args.lr_power,
+    )
+
+    # Prepare everything with our `accelerator`.
+    lora_layers, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
+        lora_layers, optimizer, train_dataloader, lr_scheduler
+    )
+
+    # We need to recalculate our total training steps as the size of the training dataloader may have changed.
+    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
+    if overrode_max_train_steps:
+        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
+    # Afterwards we recalculate our number of training epochs
+    args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
+
+    # We need to initialize the trackers we use, and also store our configuration.
+    # The trackers initializes automatically on the main process.
+    if accelerator.is_main_process:
+        accelerator.init_trackers("dreambooth-lora", config=vars(args))
+
+    # Train!
+    total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
+
+    logger.info("***** Running training *****")
+    logger.info(f"  Num examples = {len(train_dataset)}")
+    logger.info(f"  Num batches each epoch = {len(train_dataloader)}")
+    logger.info(f"  Num Epochs = {args.num_train_epochs}")
+    logger.info(f"  Instantaneous batch size per device = {args.train_batch_size}")
+    logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
+    logger.info(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
+    logger.info(f"  Total optimization steps = {args.max_train_steps}")
+    global_step = 0
+    first_epoch = 0
+
+    # Potentially load in the weights and states from a previous save
+    if args.resume_from_checkpoint:
+        if args.resume_from_checkpoint != "latest":
+            path = os.path.basename(args.resume_from_checkpoint)
+        else:
+            # Get the mos recent checkpoint
+            dirs = os.listdir(args.output_dir)
+            dirs = [d for d in dirs if d.startswith("checkpoint")]
+            dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
+            path = dirs[-1]
+        accelerator.print(f"Resuming from checkpoint {path}")
+        accelerator.load_state(os.path.join(args.output_dir, path))
+        global_step = int(path.split("-")[1])
+
+        resume_global_step = global_step * args.gradient_accumulation_steps
+        first_epoch = resume_global_step // num_update_steps_per_epoch
+        resume_step = resume_global_step % num_update_steps_per_epoch
+
+    # Only show the progress bar once on each machine.
+    progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process)
+    progress_bar.set_description("Steps")
+
+    for epoch in range(first_epoch, args.num_train_epochs):
+        unet.train()
+        for step, batch in enumerate(train_dataloader):
+            # Skip steps until we reach the resumed step
+            if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:
+                if step % args.gradient_accumulation_steps == 0:
+                    progress_bar.update(1)
+                continue
+
+            with accelerator.accumulate(unet):
+                # Convert images to latent space
+                latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample()
+                latents = latents * 0.18215
+
+                # Sample noise that we'll add to the latents
+                noise = torch.randn_like(latents)
+                bsz = latents.shape[0]
+                # Sample a random timestep for each image
+                timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
+                timesteps = timesteps.long()
+
+                # Add noise to the latents according to the noise magnitude at each timestep
+                # (this is the forward diffusion process)
+                noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
+
+                # Get the text embedding for conditioning
+                encoder_hidden_states = text_encoder(batch["input_ids"])[0]
+
+                # Predict the noise residual
+                model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
+
+                # Get the target for loss depending on the prediction type
+                if noise_scheduler.config.prediction_type == "epsilon":
+                    target = noise
+                elif noise_scheduler.config.prediction_type == "v_prediction":
+                    target = noise_scheduler.get_velocity(latents, noise, timesteps)
+                else:
+                    raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
+
+                if args.with_prior_preservation:
+                    # Chunk the noise and model_pred into two parts and compute the loss on each part separately.
+                    model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0)
+                    target, target_prior = torch.chunk(target, 2, dim=0)
+
+                    # Compute instance loss
+                    loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
+
+                    # Compute prior loss
+                    prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction="mean")
+
+                    # Add the prior loss to the instance loss.
+                    loss = loss + args.prior_loss_weight * prior_loss
+                else:
+                    loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
+
+                accelerator.backward(loss)
+                if accelerator.sync_gradients:
+                    params_to_clip = lora_layers.parameters()
+                    accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
+                optimizer.step()
+                lr_scheduler.step()
+                optimizer.zero_grad()
+
+            # Checks if the accelerator has performed an optimization step behind the scenes
+            if accelerator.sync_gradients:
+                progress_bar.update(1)
+                global_step += 1
+
+                if global_step % args.checkpointing_steps == 0:
+                    if accelerator.is_main_process:
+                        save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
+                        accelerator.save_state(save_path)
+                        logger.info(f"Saved state to {save_path}")
+
+            logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
+            progress_bar.set_postfix(**logs)
+            accelerator.log(logs, step=global_step)
+
+            if global_step >= args.max_train_steps:
+                break
+
+        if args.validation_prompt is not None and epoch % args.validation_epochs == 0:
+            logger.info(
+                f"Running validation... \n Generating {args.num_validation_images} images with prompt:"
+                f" {args.validation_prompt}."
+            )
+            # create pipeline
+            pipeline = DiffusionPipeline.from_pretrained(
+                args.pretrained_model_name_or_path,
+                unet=accelerator.unwrap_model(unet),
+                text_encoder=accelerator.unwrap_model(text_encoder),
+                revision=args.revision,
+                torch_dtype=weight_dtype,
+            )
+            pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
+            pipeline = pipeline.to(accelerator.device)
+            pipeline.set_progress_bar_config(disable=True)
+
+            # run inference
+            generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)
+            prompt = args.num_validation_images * [args.validation_prompt]
+            images = pipeline(prompt, num_inference_steps=25, generator=generator).images
+
+            for tracker in accelerator.trackers:
+                if tracker.name == "wandb":
+                    tracker.log(
+                        {
+                            "validation": [
+                                wandb.Image(image, caption=f"{i}: {args.validation_prompt}")
+                                for i, image in enumerate(images)
+                            ]
+                        }
+                    )
+
+            del pipeline
+            torch.cuda.empty_cache()
+
+    # Save the lora layers
+    accelerator.wait_for_everyone()
+    if accelerator.is_main_process:
+        unet = unet.to(torch.float32)
+        unet.save_attn_procs(args.output_dir)
+
+        # Final inference
+        # Load previous pipeline
+        pipeline = DiffusionPipeline.from_pretrained(
+            args.pretrained_model_name_or_path, revision=args.revision, torch_dtype=weight_dtype
+        )
+        pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
+        pipeline = pipeline.to(accelerator.device)
+
+        # load attention processors
+        pipeline.unet.load_attn_procs(args.output_dir)
+
+        # run inference
+        generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)
+        prompt = args.num_validation_images * [args.validation_prompt]
+        images = pipeline(prompt, num_inference_steps=25, generator=generator).images
+
+        for tracker in accelerator.trackers:
+            if tracker.name == "wandb":
+                tracker.log(
+                    {
+                        "test": [
+                            wandb.Image(image, caption=f"{i}: {args.validation_prompt}")
+                            for i, image in enumerate(images)
+                        ]
+                    }
+                )
+
+        if args.push_to_hub:
+            save_model_card(
+                repo_name,
+                base_model=args.pretrained_model_name_or_path,
+                instance_prompt=args.instance_prompt,
+                test_prompt=args.validation_prompt,
+                images=images,
+                repo_folder=args.output_dir,
+            )
+            repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True)
+        else:
+            repo_name = Path(args.output_dir).name
+            save_model_card(
+                repo_name,
+                base_model=args.pretrained_model_name_or_path,
+                instance_prompt=args.instance_prompt,
+                test_prompt=args.validation_prompt,
+                images=images,
+                repo_folder=args.output_dir,
+            )
+
+    accelerator.end_training()
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    main(args)

trainer.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import datetime
 import os
 import pathlib
 import shlex
@@ -8,9 +9,10 @@ import subprocess
 
 import gradio as gr
 import PIL.Image
+import slugify
 import torch
 
-os.environ['PYTHONPATH'] = f'lora:{os.getenv("PYTHONPATH", "")}'
+from constants import UploadTarget
 
 
 def pad_image(image: PIL.Image.Image) -> PIL.Image.Image:
@@ -28,94 +30,105 @@ def pad_image(image: PIL.Image.Image) -> PIL.Image.Image:
 
 
 class Trainer:
-    def __init__(self):
-        self.is_running = False
-        self.is_running_message = 'Another training is in progress.'
-
-        self.output_dir = pathlib.Path('results')
-        self.instance_data_dir = self.output_dir / 'training_data'
-
-    def check_if_running(self) -> dict:
-        if self.is_running:
-            return gr.update(value=self.is_running_message)
-        else:
-            return gr.update(value='No training is running.')
-
-    def cleanup_dirs(self) -> None:
-        shutil.rmtree(self.output_dir, ignore_errors=True)
-
-    def prepare_dataset(self, concept_images: list, resolution: int) -> None:
-        self.instance_data_dir.mkdir(parents=True)
-        for i, temp_path in enumerate(concept_images):
+    def prepare_dataset(self, instance_images: list, resolution: int,
+                        instance_data_dir: pathlib.Path) -> None:
+        shutil.rmtree(instance_data_dir, ignore_errors=True)
+        instance_data_dir.mkdir(parents=True)
+        for i, temp_path in enumerate(instance_images):
             image = PIL.Image.open(temp_path.name)
             image = pad_image(image)
             image = image.resize((resolution, resolution))
             image = image.convert('RGB')
-            out_path = self.instance_data_dir / f'{i:03d}.jpg'
+            out_path = instance_data_dir / f'{i:03d}.jpg'
             image.save(out_path, format='JPEG', quality=100)
 
     def run(
         self,
+        instance_images: list | None,
+        instance_prompt: str,
+        output_model_name: str,
+        overwrite_existing_model: bool,
+        validation_prompt: str,
         base_model: str,
         resolution_s: str,
-        concept_images: list | None,
-        concept_prompt: str,
         n_steps: int,
         learning_rate: float,
-        train_text_encoder: bool,
-        learning_rate_text: float,
         gradient_accumulation: int,
+        seed: int,
         fp16: bool,
         use_8bit_adam: bool,
-    ) -> tuple[dict, list[pathlib.Path]]:
+        checkpointing_steps: int,
+        use_wandb: bool,
+        validation_epochs: int,
+        upload_to_hub: bool,
+        use_private_repo: bool,
+        delete_existing_repo: bool,
+        upload_to: str,
+    ) -> str:
         if not torch.cuda.is_available():
             raise gr.Error('CUDA is not available.')
-
-        if self.is_running:
-            return gr.update(value=self.is_running_message), []
-
-        if concept_images is None:
+        if instance_images is None:
             raise gr.Error('You need to upload images.')
-        if not concept_prompt:
-            raise gr.Error('The concept prompt is missing.')
+        if not instance_prompt:
+            raise gr.Error('The instance prompt is missing.')
+        if not validation_prompt:
+            raise gr.Error('The validation prompt is missing.')
 
         resolution = int(resolution_s)
 
-        self.cleanup_dirs()
-        self.prepare_dataset(concept_images, resolution)
+        if not output_model_name:
+            output_model_name = datetime.datetime.now().strftime(
+                '%Y-%m-%d-%H-%M-%S')
+        output_model_name = slugify.slugify(output_model_name)
+
+        repo_dir = pathlib.Path(__file__).parent
+        output_dir = repo_dir / 'experiments' / output_model_name
+        if overwrite_existing_model or upload_to_hub:
+            shutil.rmtree(output_dir, ignore_errors=True)
+        if not upload_to_hub:
+            output_dir.mkdir(parents=True)
+
+        instance_data_dir = repo_dir / 'training_data' / output_model_name
+        self.prepare_dataset(instance_images, resolution, instance_data_dir)
 
         command = f'''
-        accelerate launch lora/train_lora_dreambooth.py \
+        accelerate launch train_dreambooth_lora.py \
           --pretrained_model_name_or_path={base_model}  \
-          --instance_data_dir={self.instance_data_dir} \
-          --output_dir={self.output_dir} \
-          --instance_prompt="{concept_prompt}" \
+          --instance_data_dir={instance_data_dir} \
+          --output_dir={output_dir} \
+          --instance_prompt="{instance_prompt}" \
           --resolution={resolution} \
           --train_batch_size=1 \
           --gradient_accumulation_steps={gradient_accumulation} \
           --learning_rate={learning_rate} \
           --lr_scheduler=constant \
           --lr_warmup_steps=0 \
-          --max_train_steps={n_steps}
+          --max_train_steps={n_steps} \
+          --checkpointing_steps={checkpointing_steps} \
+          --validation_prompt="{validation_prompt}" \
+          --validation_epochs={validation_epochs} \
+          --seed={seed}
         '''
         if fp16:
             command += ' --mixed_precision fp16'
         if use_8bit_adam:
             command += ' --use_8bit_adam'
-        if train_text_encoder:
-            command += f' --train_text_encoder --learning_rate_text={learning_rate_text} --color_jitter'
-
-        with open(self.output_dir / 'train.sh', 'w') as f:
+        if use_wandb:
+            command += ' --report_to wandb'
+        if upload_to_hub:
+            hf_token = os.getenv('HF_TOKEN')
+            command += f' --push_to_hub --hub_token {hf_token}'
+            if use_private_repo:
+                command += ' --private_repo'
+            if delete_existing_repo:
+                command += ' --delete_existing_repo'
+            if upload_to == UploadTarget.LORA_LIBRARY.value:
+                command += ' --upload_to_lora_library'
+
+        subprocess.run(shlex.split(command))
+
+        with open(output_dir / 'train.sh', 'w') as f:
             command_s = ' '.join(command.split())
             f.write(command_s)
 
-        self.is_running = True
-        res = subprocess.run(shlex.split(command))
-        self.is_running = False
-
-        if res.returncode == 0:
-            result_message = 'Training Completed!'
-        else:
-            result_message = 'Training Failed!'
-        weight_paths = sorted(self.output_dir.glob('*.pt'))
-        return gr.update(value=result_message), weight_paths
+        return 'Training completed!'

uploader.py

@@ -1,20 +1,39 @@
-import gradio as gr
+from __future__ import annotations
+
 from huggingface_hub import HfApi
 
 
-def upload(model_name: str, hf_token: str) -> None:
-    api = HfApi(token=hf_token)
-    user_name = api.whoami()['name']
-    model_id = f'{user_name}/{model_name}'
-    try:
-        api.create_repo(model_id, repo_type='model', private=True)
-        api.upload_folder(repo_id=model_id,
-                          folder_path='results',
-                          path_in_repo='results',
-                          repo_type='model')
-        url = f'https://huggingface.co/{model_id}'
-        message = f'Your model was successfully uploaded to [{url}]({url}).'
-    except Exception as e:
-        message = str(e)
+class Uploader:
+    def __init__(self, hf_token: str | None):
+        self.api = HfApi(token=hf_token)
+
+    def get_username(self) -> str:
+        return self.api.whoami()['name']
 
-    return gr.update(value=message, visible=True)
+    def upload(self,
+               folder_path: str,
+               repo_name: str,
+               organization: str = '',
+               repo_type: str = 'model',
+               private: bool = True,
+               delete_existing_repo: bool = False) -> str:
+        if not organization:
+            organization = self.get_username()
+        repo_id = f'{organization}/{repo_name}'
+        if delete_existing_repo:
+            try:
+                self.api.delete_repo(repo_id, repo_type=repo_type)
+            except Exception:
+                pass
+        try:
+            self.api.create_repo(repo_id, repo_type=repo_type, private=private)
+            self.api.upload_folder(repo_id=repo_id,
+                                   folder_path=folder_path,
+                                   path_in_repo='.',
+                                   repo_type=repo_type)
+            url = f'https://huggingface.co/{repo_id}'
+            message = f'Your model was successfully uploaded to <a href="{url}" target="_blank">{url}</a>.'
+        except Exception as e:
+            message = str(e)
+        print(message)
+        return message

utils.py

@@ -0,0 +1,18 @@
+import pathlib
+
+
+def find_exp_dirs(ignore_repo: bool = False) -> list[str]:
+    repo_dir = pathlib.Path(__file__).parent
+    exp_root_dir = repo_dir / 'experiments'
+    if not exp_root_dir.exists():
+        return []
+    exp_dirs = sorted(exp_root_dir.glob('*'))
+    exp_dirs = [
+        exp_dir for exp_dir in exp_dirs
+        if (exp_dir / 'pytorch_lora_weights.bin').exists()
+    ]
+    if ignore_repo:
+        exp_dirs = [
+            exp_dir for exp_dir in exp_dirs if not (exp_dir / '.git').exists()
+        ]
+    return [path.relative_to(repo_dir).as_posix() for path in exp_dirs]