update

img2img.py

@@ -78,7 +78,8 @@ class Pipeline:
             use_tiny_vae=True,
             device=device,
             dtype=torch_dtype,
-            t_index_list=[35, 45],
+            # t_index_list=[35, 45],
+            t_index_list=[1],
             frame_buffer_size=1,
             width=params.width,
             height=params.height,
@@ -101,12 +102,12 @@ class Pipeline:
             prompt=default_prompt,
             negative_prompt=default_negative_prompt,
             num_inference_steps=50,
-            guidance_scale=1.2,
+            guidance_scale=1.0,
         )
 
     def predict(self, image: Image.Image, params: "Pipeline.InputParams") -> Image.Image:
-        image_tensor = self.stream.preprocess_image(image)
+        # image_tensor = self.stream.preprocess_image(image)
         # output_image = self.stream(image=image_tensor, prompt=params.prompt)
-        output_image = self.stream(image=image_tensor, prompt=params.prompt)
+        output_image = self.stream(image=image, prompt=params.prompt)
 
         return output_image

main.py

@@ -8,6 +8,7 @@ import SpoutGL
 from OpenGL.GL import GL_RGBA
 import time
 import img2img
+from multiprocessing import Queue
 
 def main():
     TARGET_FPS = 60
@@ -19,6 +20,8 @@ def main():
     timestamp = datetime.datetime.now()
     fps = 30.0
 
+    prompt_queue = Queue()
+
     print("Initializing StreamDiffusion pipeline...")
     global pipeline
     try:
@@ -39,6 +42,7 @@ def main():
         async def update_prompt(update: PromptUpdate):
             global PROMPT
             PROMPT = update.prompt
+            prompt_queue.put(PROMPT)
             print(f"Prompt updated to: {PROMPT}")
             return {"message": "Prompt updated successfully", "new_prompt": PROMPT}
 
@@ -77,11 +81,18 @@ def main():
                 if spout_receiver.isConnected() and SpoutGL.helpers.isBufferEmpty(image_bgra):
                     continue
 
-                image_rgb_array = image_bgra[:, :, [2,1,0]]
-                input_image = Image.fromarray(image_rgb_array, 'RGB')
+                image_rgb_array = image_bgra[:, :, [2,1,0]]                
+                # image_rgb_array = image_rgb_array.astype(np.float32) / 255.0 
+                input_image = Image.fromarray(image_rgb_array, 'RGB')                
                 # input_image.save("debug_input.png")
                 
+                if not prompt_queue.empty():
+                    new_prompt = prompt_queue.get(block=False)
+                    if new_prompt:
+                        print(f"Received new prompt from queue: {new_prompt}")
+                        PROMPT = new_prompt
 
+                # print(f"current prompt: {PROMPT}")
                 params = img2img.Pipeline.InputParams(prompt=PROMPT)
                 output_image = pipeline.predict(image=input_image, params=params)                
                 # output_image.save("debug_output.png")