Reduce num_steps in inference pipeline to 5

.ipynb_checkpoints/app-checkpoint.py

@@ -24,7 +24,7 @@ def inference(prompt, model, n_images, seed):
     print(f"Inferencing '{prompt}' for {n_images} images.")
 
     for i in range(n_images):
-        result = pipe(prompt, generator=generators[i], num_inference_steps=25).images[0]
+        result = pipe(prompt, generator=generators[i], num_inference_steps=5).images[0]
         result_images.append(result)
 
         # Start with empty UI elements
@@ -50,9 +50,6 @@ def inference(prompt, model, n_images, seed):
                     st.image(result_images[i], caption=f"Image - {i+3}")
 
 
-def main():
-    pass
-
 if __name__ == "__main__":
     # --- START UI ---
     st.title("Finetune LoRA inference")

.ipynb_checkpoints/requirements-checkpoint.txt

@@ -1,5 +1,6 @@
 transformers
 diffusers
+accelerate
 huggingface_hub
 streamlit
 torch

app.py

@@ -24,7 +24,7 @@ def inference(prompt, model, n_images, seed):
     print(f"Inferencing '{prompt}' for {n_images} images.")
 
     for i in range(n_images):
-        result = pipe(prompt, generator=generators[i], num_inference_steps=25).images[0]
+        result = pipe(prompt, generator=generators[i], num_inference_steps=5).images[0]
         result_images.append(result)
 
         # Start with empty UI elements
@@ -50,9 +50,6 @@ def inference(prompt, model, n_images, seed):
                     st.image(result_images[i], caption=f"Image - {i+3}")
 
 
-def main():
-    pass
-
 if __name__ == "__main__":
     # --- START UI ---
     st.title("Finetune LoRA inference")

requirements.txt

@@ -1,5 +1,6 @@
 transformers
 diffusers
+accelerate
 huggingface_hub
 streamlit
 torch