update

.gitignore

@@ -1 +1,2 @@
-experiments/*
+experiments/*
+trash/*

app.py

@@ -81,7 +81,7 @@ def reload_custom_diffusion_weight_list() -> dict:
     return gr.update(choices=find_weight_files())
 
 
-def create_inference_demo(pipe: InferencePipeline) -> gr.Blocks:
+def create_inference_demo(func: inference_fn) -> gr.Blocks:
     with gr.Blocks() as demo:
         with gr.Row():
             with gr.Column():
@@ -125,7 +125,7 @@ def create_inference_demo(pipe: InferencePipeline) -> gr.Blocks:
         reload_button.click(fn=reload_custom_diffusion_weight_list,
                             inputs=None,
                             outputs=weight_name)
-        prompt.submit(fn=inference_fn,
+        prompt.submit(fn=func,
                       inputs=[
                           model_id,
                           prompt,
@@ -134,7 +134,7 @@ def create_inference_demo(pipe: InferencePipeline) -> gr.Blocks:
                       ],
                       outputs=result,
                       queue=False)
-        run_button.click(fn=inference_fn,
+        run_button.click(fn=func,
                         inputs=[
                             model_id,
                             prompt,
@@ -159,7 +159,7 @@ with gr.Blocks(css='style.css') as demo:
     with gr.Tabs():
 
         with gr.TabItem('Test'):
-            create_inference_demo(pipe)
+            create_inference_demo(inference_fn)
 
 
 demo.queue(default_enabled=False).launch(share=False)

app_000.py

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

app_001.py

@@ -1,199 +0,0 @@
-#!/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")
-
-
-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()
-
-demo.queue(default_enabled=False).launch(share=False)
-

inference.py

@@ -21,7 +21,7 @@ from PIL import Image
 
 # from diffusers import StableDiffusionPipeline
 # sys.path.insert(0, './ReVersion')
-from templates.templates import inference_templates
+# from templates.templates import inference_templates
 
 import math