update

app.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python
 """Demo app for https://github.com/ziqihuangg/ReVersion.
 The code in this repo is partly adapted from the following repository:
-https://huggingface.co/spaces/nupurkmr9/custom-diffusion
+https://github.com/ziqihuangg/ReVersion
 
 S-Lab License 1.0
 
@@ -39,7 +39,14 @@ from inference import inference_fn
 
 
 TITLE = '# ReVersion'
-DESCRIPTION = '''This is a demo for [https://github.com/ziqihuangg/ReVersion](https://github.com/ziqihuangg/ReVersion).
+DESCRIPTION = '''
+This is a demo for **ReVersion: Diffusion-Based Relation Inversion from Images**
+<br>
+[[Paper](https://arxiv.org/abs/2303.13495)] |
+[[Project Page](https://ziqihuangg.github.io/projects/reversion.html)] |
+[[GitHub Code](https://github.com/ziqihuangg/ReVersion)] |
+[[Video](https://www.youtube.com/watch?v=pkal3yjyyKQ)]
+<br>
 It is recommended to upgrade to GPU in Settings after duplicating this space to use it.
 <a href="https://huggingface.co/spaces/Ziqi/ReVersion?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
 '''
@@ -47,25 +54,17 @@ DETAILDESCRIPTION='''
 ReVersion
 '''
 DETAILDESCRIPTION='''
-ReVersion: \<R\> represents the learned text token for a relation. Use \<R\> in your prompt for relation-specific generation.
+We propose a new task, **Relation Inversion**: Given a few exemplar images, where a relation co-exists in every image, we aim to find a relation prompt **\<R>** to capture this interaction, and apply the relation to new entities to synthesize new scenes.
 <center>
-<img src="https://huggingface.co/spaces/Ziqi/ReVersion/teaser.jpg" width="800" align="center" >
+<img src="https://huggingface.co/spaces/Ziqi/ReVersion/resolve/main/teaser.jpg" width="800" align="center" >
 </center>
 '''
-# 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 = 'Ziqi/ReVersion'
 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>'
 
@@ -88,22 +87,6 @@ def show_warning(warning_text: str) -> gr.Blocks:
     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(func: inference_fn) -> gr.Blocks:
     with gr.Blocks() as demo:
         with gr.Row():
@@ -141,17 +124,10 @@ def create_inference_demo(func: inference_fn) -> gr.Blocks:
                                                value=50)
                 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=func,
                       inputs=[
                           model_id,
@@ -185,8 +161,6 @@ else:
     print('*** Now using %s.'%('cpu'))
 
 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)