improving negative prompting if we can

app.py

@@ -105,13 +105,16 @@ def generate_stable_audio_loop(prompt, loop_type, bpm, bars, seed=-1):
         seconds_per_bar = seconds_per_beat * 4  # 4/4 time
         target_loop_duration = seconds_per_bar * bars
         
-        # Enhance prompt based on loop type and BPM
+        
+        # Enhance prompt based on loop type and BPM - minimal modification
         if loop_type == "drums":
-            enhanced_prompt = f"{prompt} drum loop {bpm}bpm"
-            negative_prompt = "melody, harmony, pitched instruments, vocals, singing"
+            enhanced_prompt = f"{prompt} {bpm}bpm"
+            # Comprehensive negative prompting for drums - exclude all melodic/harmonic content
+            negative_prompt = "melody, harmony, pitched instruments, vocals, singing, piano, guitar, bass, synth, strings, horns, woodwinds, flute, saxophone, violin, cello, organ, keyboard, chords, notes, musical scale, tonal, melodic, harmonic"
         else:  # instruments
-            enhanced_prompt = f"{prompt} instrumental loop {bpm}bpm"
-            negative_prompt = "drums, percussion, kick, snare, hi-hat"
+            enhanced_prompt = f"{prompt} {bpm}bpm"  
+            # Comprehensive negative prompting for instruments - exclude all percussive content
+            negative_prompt = "drums, percussion, kick, snare, hi-hat, cymbals, tom, drum kit, rhythm section, beats, drumming, percussive, drum machine, 808, trap drums, boom bap drums, breakbeat, drum breaks, kick drum, snare drum"
         
         # Set seed
         if seed == -1:
@@ -144,8 +147,8 @@ def generate_stable_audio_loop(prompt, loop_type, bpm, bars, seed=-1):
         
         # Removed aggressive resource cleanup wrapper
         # Clear GPU cache once before generation (not after)
-        if device == "cuda":
-            torch.cuda.empty_cache()
+        # if device == "cuda":
+        #     torch.cuda.empty_cache()
         
         with torch.cuda.amp.autocast(enabled=(device == "cuda")):
             output = generate_diffusion_cond(
@@ -370,7 +373,7 @@ with gr.Blocks(title="stable-melodyflow") as iface:
             -  [our melodyflow api](https://github.com/betweentwomidnights/melodyflow)
             """)
         
-        with gr.Accordion("gary4live Project", open=False):
+        with gr.Accordion("gary4live project", open=False):
             gr.Markdown("""
             **gary4live** is a free/open source project that uses these models, along with musicGen, inside of ableton live to iterate on your projects with you. i run a backend myself so that we can all experiment with it, but you can also spin the backend up locally using docker-compose with our repo.