update

app.py

@@ -1,7 +1,200 @@
+#!/usr/bin/env python
+"""Demo app for https://github.com/adobe-research/custom-diffusion.
+The code in this repo is partly adapted from the following repository:
+https://huggingface.co/spaces/hysts/LoRA-SD-training
+MIT License
+Copyright (c) 2022 hysts
+==========================================================================================
+Adobe’s modifications are Copyright 2022 Adobe Research. All rights reserved.
+Adobe’s modifications are licensed under the Adobe Research License. To view a copy of the license, visit
+LICENSE.
+==========================================================================================
+"""
+
+from __future__ import annotations
+import sys
+import os
+import pathlib
+
 import gradio as gr
+import torch
+
+from inference import InferencePipeline
+from trainer import Trainer
+from uploader import upload
+
+TITLE = '# Custom Diffusion + StableDiffusion Training UI'
+DESCRIPTION = '''This is a demo for [https://github.com/adobe-research/custom-diffusion](https://github.com/adobe-research/custom-diffusion).
+It is recommended to upgrade to GPU in Settings after duplicating this space to use it.
+<a href="https://huggingface.co/spaces/nupurkmr9/custom-diffusion?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
+'''
+DETAILDESCRIPTION='''
+Custom Diffusion allows you to fine-tune text-to-image diffusion models, such as Stable Diffusion, given a few images of a new concept (~4-20).
+We fine-tune only a subset of model parameters, namely key and value projection matrices, in the cross-attention layers and the modifier token used to represent the object.
+This also reduces the extra storage for each additional concept to 75MB. Our method also allows you to use a combination of concepts. There's still limitations on which compositions work. For more analysis please refer to our [website](https://www.cs.cmu.edu/~custom-diffusion/).
+<center>
+<img src="https://huggingface.co/spaces/nupurkmr9/custom-diffusion/resolve/main/method.jpg" width="600" align="center" >
+</center>
+'''
+
+ORIGINAL_SPACE_ID = 'nupurkmr9/custom-diffusion'
+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.
+<center><a class="duplicate-button" style="display:inline-block" target="_blank" href="https://huggingface.co/spaces/{SPACE_ID}?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a></center>
+'''
+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.
+<center>
+You can assign a GPU in the {SETTINGS} tab if you are running this on HF Spaces.
+"T4 small" is sufficient to run this demo.
+</center>
+'''
+
+os.system("git clone https://github.com/ziqihuangg/ReVersion")
+sys.path.append("ReVersion")
+
+from ReVersion.inference import *
+
+def show_warning(warning_text: str) -> gr.Blocks:
+    with gr.Blocks() as demo:
+        with gr.Box():
+            gr.Markdown(warning_text)
+    return demo
+
+
+def update_output_files() -> dict:
+    paths = sorted(pathlib.Path('results').glob('*.bin'))
+    paths = [path.as_posix() for path in paths]  # type: ignore
+    return gr.update(value=paths or None)
+
+
+def find_weight_files() -> list[str]:
+    curr_dir = pathlib.Path(__file__).parent
+    paths = sorted(curr_dir.rglob('*.bin'))
+    paths = [path for path in paths if '.lfs' not in str(path)]
+    return [path.relative_to(curr_dir).as_posix() for path in paths]
+
+
+def reload_custom_diffusion_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=['ReVersion/experiments/painted_on'],
+                    value='ReVersion/experiments/painted_on',
+                    label='Base Model',
+                    visible=True)
+                resolution = gr.Dropdown(choices=[512, 768],
+                                 value=512,
+                                 label='Resolution',
+                                 visible=True)
+                reload_button = gr.Button('Reload Weight List')
+                weight_name = gr.Dropdown(choices=find_weight_files(),
+                                               value='ReVersion/experiments/painted_on',
+                                               label='ReVersion/experiments/painted_on')
+                prompt = gr.Textbox(
+                    label='Prompt',
+                    max_lines=1,
+                    placeholder='Example: "cat <R> stone"')
+                seed = gr.Slider(label='Seed',
+                                 minimum=0,
+                                 maximum=100000,
+                                 step=1,
+                                 value=42)
+                with gr.Accordion('Other Parameters', open=False):
+                    num_steps = gr.Slider(label='Number of Steps',
+                                          minimum=0,
+                                          maximum=500,
+                                          step=1,
+                                          value=100)
+                    guidance_scale = gr.Slider(label='CFG Scale',
+                                               minimum=0,
+                                               maximum=50,
+                                               step=0.1,
+                                               value=6)
+                    eta = gr.Slider(label='DDIM eta',
+                                               minimum=0,
+                                               maximum=1.,
+                                               step=0.1,
+                                               value=1.)
+                    batch_size = gr.Slider(label='Batch Size',
+                                               minimum=0,
+                                               maximum=10.,
+                                               step=1,
+                                               value=1)
+
+                run_button = gr.Button('Generate')
+
+                gr.Markdown('''
+                - Models with names starting with "custom-diffusion-models/" are the pretrained models provided in the [original repo](https://github.com/adobe-research/custom-diffusion), and the ones with names starting with "results/delta.bin" are your trained models.
+                - After training, you can press "Reload Weight List" button to load your trained model names.
+                - Increase number of steps in Other parameters for better samples qualitatively.
+                ''')
+            with gr.Column():
+                result = gr.Image(label='Result')
+
+        reload_button.click(fn=reload_custom_diffusion_weight_list,
+                            inputs=None,
+                            outputs=weight_name)
+        prompt.submit(fn=pipe.run,
+                      inputs=[
+                          base_model,
+                          weight_name,
+                          prompt,
+                          seed,
+                          num_steps,
+                          guidance_scale,
+                          eta,
+                          batch_size,
+                          resolution
+                      ],
+                      outputs=result,
+                      queue=False)
+        run_button.click(fn=pipe.run,
+                         inputs=[
+                             base_model,
+                             weight_name,
+                             prompt,
+                             seed,
+                             num_steps,
+                             guidance_scale,
+                             eta,
+                             batch_size,
+                             resolution
+                         ],
+                         outputs=result,
+                         queue=False)
+    return demo
+
+
+pipe = InferencePipeline()
+trainer = Trainer()
+
+with gr.Blocks(css='style.css') as demo:
+    if os.getenv('IS_SHARED_UI'):
+        show_warning(SHARED_UI_WARNING)
+    if not torch.cuda.is_available():
+        show_warning(CUDA_NOT_AVAILABLE_WARNING)
+
+    gr.Markdown(TITLE)
+    gr.Markdown(DESCRIPTION)
+    gr.Markdown(DETAILDESCRIPTION)
+
+    with gr.Tabs():
+        # with gr.TabItem('Train'):
+        #     create_training_demo(trainer, pipe)
+        with gr.TabItem('Inference'):
+            create_inference_demo(pipe)
+        # with gr.TabItem('Upload'):
+        #     create_upload_demo()
 
-def greet(name):
-    return "Hello " + name + "!!"
+demo.queue(default_enabled=False).launch(share=False)
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()

app_000.py

@@ -0,0 +1,7 @@
+import gradio as gr
+
+def greet(name):
+    return "Hello " + name + "!!"
+
+iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+iface.launch()