POC using hard-coded wheel

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.whl filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,6 +1,6 @@
 ---
 title: Rerun Streaming Poc
-emoji: 🖼
+emoji: 🏞️
 colorFrom: purple
 colorTo: red
 sdk: gradio

app.py

@@ -1,146 +1,54 @@
+import rerun as rr
+import rerun.blueprint as rrb
 import gradio as gr
-import numpy as np
-import random
-from diffusers import DiffusionPipeline
-import torch
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-if torch.cuda.is_available():
-    torch.cuda.max_memory_allocated(device=device)
-    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
-    pipe.enable_xformers_memory_efficient_attention()
-    pipe = pipe.to(device)
-else: 
-    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True)
-    pipe = pipe.to(device)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
-
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-        
-    generator = torch.Generator().manual_seed(seed)
-    
-    image = pipe(
-        prompt = prompt, 
-        negative_prompt = negative_prompt,
-        guidance_scale = guidance_scale, 
-        num_inference_steps = num_inference_steps, 
-        width = width, 
-        height = height,
-        generator = generator
-    ).images[0] 
-    
-    return image
-
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
-
-css="""
-#col-container {
-    margin: 0 auto;
-    max-width: 520px;
-}
-"""
-
-if torch.cuda.is_available():
-    power_device = "GPU"
-else:
-    power_device = "CPU"
-
-with gr.Blocks(css=css) as demo:
-    
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(f"""
-        # Text-to-Image Gradio Template
-        Currently running on {power_device}.
-        """)
-        
-        with gr.Row():
-            
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-            
-            run_button = gr.Button("Run", scale=0)
-        
-        result = gr.Image(label="Result", show_label=False)
-
-        with gr.Accordion("Advanced Settings", open=False):
-            
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
-            
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-            
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-            
-            with gr.Row():
-                
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=512,
-                )
-                
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=512,
-                )
-            
-            with gr.Row():
-                
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,
-                )
-                
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=12,
-                    step=1,
-                    value=2,
-                )
-        
-        gr.Examples(
-            examples = examples,
-            inputs = [prompt]
-        )
-
-    run_button.click(
-        fn = infer,
-        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        outputs = [result]
+from gradio_rerun import Rerun
+import time
+import cv2
+
+
+@rr.thread_local_stream("rerun_example_streaming_blur")
+def repeated_blur(img):
+    stream = rr.binary_stream()
+
+    blueprint = rrb.Blueprint(
+        rrb.Horizontal(
+            rrb.Spatial2DView(origin="image/original"),
+            rrb.Spatial2DView(origin="image/blurred"),
+        ),
+        collapse_panels=True,
     )
 
-demo.queue().launch()
+    rr.send_blueprint(blueprint)
+
+    rr.set_time_sequence("iteration", 0)
+
+    rr.log("image/original", rr.Image(img))
+    yield stream.read()
+
+    blur = img
+
+    for i in range(100):
+        rr.set_time_sequence("iteration", i)
+
+        # Pretend blurring takes a while
+        time.sleep(0.1)
+        blur = cv2.GaussianBlur(blur, (5, 5), 0)
+
+        rr.log("image/blurred", rr.Image(blur))
+
+        yield stream.read()
+
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        img = gr.Image(interactive=True, label="Image")
+        with gr.Column():
+            blur = gr.Button("Repeated Blur")
+    with gr.Row():
+        viewer = Rerun(streaming=True)
+
+    blur.click(repeated_blur, inputs=[img], outputs=[viewer])
+
+
+if __name__ == "__main__":
+    demo.launch()

gradio_rerun-0.0.2-py3-none-any.whl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dc6d4f7e92e167b97d6be7d613c3427a975c9a0553f19fe2535e3fd9fc7a9a71
+size 10247004

requirements.txt

@@ -1,6 +1,3 @@
-accelerate
-diffusers
-invisible_watermark
-torch
-transformers
-xformers
+opencv-python
+rerun-sdk==0.16
+./gradio_rerun-0.0.2-py3-none-any.whl