custom-diffusion-space

.gitmodules

@@ -1,3 +1,3 @@
-[submodule "customdiffusion"]
-	path = customdiffusion
+[submodule "custom-diffusion"]
+	path = custom-diffusion
 	url = https://github.com/adobe-research/custom-diffusion

app.py

@@ -38,6 +38,8 @@ You can assign a GPU in the {SETTINGS} tab if you are running this on HF Spaces.
 </center>
 '''
 
+os.system("git clone https://github.com/adobe-research/custom-diffusion")
+sys.path.append("custom-diffusion")
 
 def show_warning(warning_text: str) -> gr.Blocks:
     with gr.Blocks() as demo:

inference.py

@@ -10,7 +10,7 @@ import numpy as np
 
 import torch
 from diffusers import StableDiffusionPipeline
-sys.path.insert(0, 'customdiffusion')
+sys.path.insert(0, 'custom-diffusion')
 
 
 def load_model(text_encoder, tokenizer, unet, save_path, modifier_token, freeze_model='crossattn_kv'):

trainer.py

@@ -10,7 +10,7 @@ import gradio as gr
 import PIL.Image
 import torch
 
-os.environ['PYTHONPATH'] = f'customdiffusion:{os.getenv("PYTHONPATH", "")}'
+os.environ['PYTHONPATH'] = f'custom-diffusion:{os.getenv("PYTHONPATH", "")}'
 
 
 def pad_image(image: PIL.Image.Image) -> PIL.Image.Image:
@@ -87,7 +87,7 @@ class Trainer:
         self.prepare_dataset(concept_images, resolution)
 
         command = f'''
-        accelerate launch customdiffusion/src/diffuser_training.py \
+        accelerate launch custom-diffusion/src/diffuser_training.py \
           --pretrained_model_name_or_path={base_model}   \
           --instance_data_dir={self.instance_data_dir}  \
           --output_dir={self.output_dir} \