Streamlit fixes and batch text to audio with multiple models

Topic: batch_text_with_multiple_models

riffusion/streamlit/tasks/audio_to_audio.py

@@ -43,7 +43,9 @@ def render() -> None:
 
     device = streamlit_util.select_device(st.sidebar)
     extension = streamlit_util.select_audio_extension(st.sidebar)
+    checkpoint = streamlit_util.select_checkpoint(st.sidebar)
 
+    use_20k = st.sidebar.checkbox("Use 20kHz", value=False)
     use_magic_mix = st.sidebar.checkbox("Use Magic Mix", False)
 
     with st.sidebar:
@@ -183,7 +185,19 @@ def render() -> None:
 
     st.write(f"## Counter: {counter.value}")
 
-    params = SpectrogramParams()
+    if use_20k:
+        params = SpectrogramParams(
+            min_frequency=10,
+            max_frequency=20000,
+            sample_rate=44100,
+            stereo=True,
+        )
+    else:
+        params = SpectrogramParams(
+            min_frequency=0,
+            max_frequency=10000,
+            stereo=False,
+        )
 
     if interpolate:
         # TODO(hayk): Make not linspace
@@ -237,6 +251,7 @@ def render() -> None:
                 inputs=inputs,
                 init_image=init_image_resized,
                 device=device,
+                checkpoint=checkpoint,
             )
         elif use_magic_mix:
             assert not prompt_input_a.negative_prompt, "No negative prompt with magic mix"
@@ -251,6 +266,7 @@ def render() -> None:
                 mix_factor=magic_mix_mix_factor,
                 device=device,
                 scheduler=scheduler,
+                checkpoint=checkpoint,
             )
         else:
             image = streamlit_util.run_img2img(
@@ -264,6 +280,7 @@ def render() -> None:
                 progress_callback=progress_callback,
                 device=device,
                 scheduler=scheduler,
+                checkpoint=checkpoint,
             )
 
         # Resize back to original size

riffusion/streamlit/tasks/interpolation.py

@@ -241,13 +241,18 @@ def get_prompt_inputs(
 
 @st.cache_data
 def run_interpolation(
-    inputs: InferenceInput, init_image: Image.Image, device: str = "cuda", extension: str = "mp3"
+    inputs: InferenceInput,
+    init_image: Image.Image,
+    checkpoint: str = streamlit_util.DEFAULT_CHECKPOINT,
+    device: str = "cuda",
+    extension: str = "mp3",
 ) -> T.Tuple[Image.Image, io.BytesIO]:
     """
     Cached function for riffusion interpolation.
     """
     pipeline = streamlit_util.load_riffusion_checkpoint(
         device=device,
+        checkpoint=checkpoint,
         # No trace so we can have variable width
         no_traced_unet=True,
     )

riffusion/streamlit/tasks/text_to_audio.py

@@ -55,7 +55,7 @@ def render() -> None:
         st.form_submit_button("Riff", type="primary")
 
     with st.sidebar:
-        num_inference_steps = T.cast(int, st.number_input("Inference steps", value=25))
+        num_inference_steps = T.cast(int, st.number_input("Inference steps", value=30))
         width = T.cast(int, st.number_input("Width", value=512))
         guidance = st.number_input(
             "Guidance", value=7.0, help="How much the model listens to the text prompt"

riffusion/streamlit/tasks/text_to_audio_batch.py

@@ -11,21 +11,25 @@ from riffusion.streamlit import util as streamlit_util
 EXAMPLE_INPUT = """
 {
   "params": {
-    "seed": 42,
+    "checkpoint": "riffusion/riffusion-model-v1",
+    "scheduler": "DPMSolverMultistepScheduler",
     "num_inference_steps": 50,
     "guidance": 7.0,
-    "width": 512
+    "width": 512,
   },
   "entries": [
     {
-      "prompt": "Church bells"
+      "prompt": "Church bells",
+      "seed": 42
     },
     {
       "prompt": "electronic beats",
-      "negative_prompt": "drums"
+      "negative_prompt": "drums",
+      "seed": 100
     },
     {
-      "prompt": "classical violin concerto"
+      "prompt": "classical violin concerto",
+      "seed": 4
     }
   ]
 }
@@ -71,10 +75,22 @@ def render() -> None:
     with st.expander("Input Data", expanded=False):
         st.json(data)
 
-    params = data["params"]
+    # Params can either be a list or a single entry
+    if isinstance(data["params"], list):
+        param_sets = data["params"]
+    else:
+        param_sets = [data["params"]]
+
     entries = data["entries"]
 
-    show_images = st.sidebar.checkbox("Show Images", False)
+    show_images = st.sidebar.checkbox("Show Images", True)
+    num_seeds = st.sidebar.number_input(
+        "Num Seeds",
+        value=1,
+        min_value=1,
+        max_value=10,
+        help="When > 1, increments the seed and runs multiple for each entry",
+    )
 
     # Optionally specify an output directory
     output_dir = st.sidebar.text_input("Output Directory", "")
@@ -83,55 +99,83 @@ def render() -> None:
         output_path = Path(output_dir)
         output_path.mkdir(parents=True, exist_ok=True)
 
-    for i, entry in enumerate(entries):
+    # Write title cards for each param set
-        st.write(f"#### Entry {i + 1} / {len(entries)}")
+    title_cols = st.columns(len(param_sets))
+    for i, params in enumerate(param_sets):
+        col = title_cols[i]
 
+        if "name" not in params:
+            params["name"] = f"params[{i}]"
+
+        col.write(f"## Param Set {i}")
+        col.json(params)
+
+    for entry_i, entry in enumerate(entries):
+        st.write("---")
+        print(entry)
+        prompt = entry["prompt"]
         negative_prompt = entry.get("negative_prompt", None)
 
-        st.write(f"**Prompt**: {entry['prompt']}  \n" + f"**Negative prompt**: {negative_prompt}")
+        base_seed = entry.get("seed", 42)
 
-        image = streamlit_util.run_txt2img(
+        text = f"##### ({base_seed}) {prompt}"
-            prompt=entry["prompt"],
+        if negative_prompt:
-            negative_prompt=negative_prompt,
+            text += f"  \n**Negative**: {negative_prompt}"
-            seed=params.get("seed", 42),
+        st.write(text)
-            num_inference_steps=params.get("num_inference_steps", 50),
-            guidance=params.get("guidance", 7.0),
-            width=params.get("width", 512),
-            height=512,
-            device=device,
-        )
 
-        if show_images:
+        for seed in range(base_seed, base_seed + num_seeds):
-            st.image(image)
+            cols = st.columns(len(param_sets))
+            for i, params in enumerate(param_sets):
+                col = cols[i]
+                col.write(params["name"])
 
-        # TODO(hayk): Change the frequency range to [20, 20k] once the model is retrained
+                image = streamlit_util.run_txt2img(
-        p_spectrogram = SpectrogramParams(
+                    prompt=prompt,
-            min_frequency=0,
+                    negative_prompt=negative_prompt,
-            max_frequency=10000,
+                    seed=seed,
-        )
+                    num_inference_steps=params.get("num_inference_steps", 50),
+                    guidance=params.get("guidance", 7.0),
+                    width=params.get("width", 512),
+                    checkpoint=params.get("checkpoint", streamlit_util.DEFAULT_CHECKPOINT),
+                    scheduler=params.get("scheduler", streamlit_util.SCHEDULER_OPTIONS[0]),
+                    height=512,
+                    device=device,
+                )
 
-        output_format = "mp3"
+                if show_images:
-        audio_bytes = streamlit_util.audio_bytes_from_spectrogram_image(
+                    col.image(image)
-            image=image,
-            params=p_spectrogram,
-            device=device,
-            output_format=output_format,
-        )
-        st.audio(audio_bytes)
 
-        if output_path:
+                # TODO(hayk): Change the frequency range to [20, 20k] once the model is retrained
-            prompt_slug = entry["prompt"].replace(" ", "_")
+                p_spectrogram = SpectrogramParams(
-            negative_prompt_slug = entry.get("negative_prompt", "").replace(" ", "_")
+                    min_frequency=0,
+                    max_frequency=10000,
+                )
 
-            image_path = output_path / f"image_{i}_{prompt_slug}_neg_{negative_prompt_slug}.jpg"
+                output_format = "mp3"
-            image.save(image_path, format="JPEG")
+                audio_bytes = streamlit_util.audio_bytes_from_spectrogram_image(
-            entry["image_path"] = str(image_path)
+                    image=image,
+                    params=p_spectrogram,
+                    device=device,
+                    output_format=output_format,
+                )
+                col.audio(audio_bytes)
 
-            audio_path = (
+                if output_path:
-                output_path / f"audio_{i}_{prompt_slug}_neg_{negative_prompt_slug}.{output_format}"
+                    prompt_slug = entry["prompt"].replace(" ", "_")
-            )
+                    negative_prompt_slug = entry.get("negative_prompt", "").replace(" ", "_")
-            audio_path.write_bytes(audio_bytes.getbuffer())
+
-            entry["audio_path"] = str(audio_path)
+                    image_path = (
+                        output_path / f"image_{i}_{prompt_slug}_neg_{negative_prompt_slug}.jpg"
+                    )
+                    image.save(image_path, format="JPEG")
+                    entry["image_path"] = str(image_path)
+
+                    audio_path = (
+                        output_path
+                        / f"audio_{i}_{prompt_slug}_neg_{negative_prompt_slug}.{output_format}"
+                    )
+                    audio_path.write_bytes(audio_bytes.getbuffer())
+                    entry["audio_path"] = str(audio_path)
 
     if output_path:
         output_json_path = output_path / "index.json"

riffusion/streamlit/util.py

@@ -145,7 +145,7 @@ def load_stable_diffusion_img2img_pipeline(
     return pipeline
 
 
-@st.cache_data
+@st.cache_data(persist=True)
 def run_txt2img(
     prompt: str,
     num_inference_steps: int,
@@ -281,12 +281,11 @@ def select_checkpoint(container: T.Any = st.sidebar) -> str:
     """
     Provide a custom model checkpoint.
     """
-    custom_checkpoint = container.text_input(
+    return container.text_input(
         "Custom Checkpoint",
-        value="",
+        value=DEFAULT_CHECKPOINT,
         help="Provide a custom model checkpoint",
     )
-    return custom_checkpoint or DEFAULT_CHECKPOINT
 
 
 @st.cache_data