ComfyUI pipeline WIP

runner/app/live/pipelines/comfyui.py

@@ -3,34 +3,61 @@
 import os
 import json
 import torch
+import torch.nn as nn
+from torchvision import transforms
 from PIL import Image
+import asyncio
 
+from .interface import Pipeline
 
-class ComfyPipeline(Pipeline):
+
+class ComfyUI(Pipeline):
   def __init__(self, **params):
+    super().__init__(**params)
     # configure as is needed
     comfy_ui_workspace = os.getenv("COMFY_UI_WORKSPACE")
-    default_workflow = os.genenv("COMFY_UI_DEFAULT_WORKFLOW")
+    default_workflow = os.getenv("COMFY_UI_DEFAULT_WORKFLOW")
 
-    self.client = ComfyStreamClient(comfy_ui_workspace)
+    self.client = ComfyStreamClient(cwd=comfy_ui_workspace)
     with open(default_workflow, "r") as f:
       prompt = json.load(f)
     self.client.set_prompt(prompt)
 
     # Comfy will cache nodes that only need to be run once (i.e. a node that loads model weights)
     # We can run the prompt once before actual inputs come in to "warmup"
-    input = torch.randn(1, 512, 512, 3)
-    self.client.queue_prompt(input)
-
-    self.update_params(**params)
-
-  async def process_frame(self, image: Image.Image) -> Image.Image:
+    # input = torch.randn(1, 512, 512, 3)
+    # self.client.queue_prompt(input)
+
+    # self.update_params(**params)
+
+  def process_frame(self, image: Image.Image) -> Image.Image:
+    # transform = transforms.ToTensor()
+    transform = transforms.Compose([
+      transforms.Resize((512, 512)),  # Resize the image to 512x512
+      transforms.ToTensor(),          # Convert the image to a tensor
+      transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])  # Normalize the image
+    ])
+
+    # Apply the transformation to convert to torch.Tensor
+    if image.mode != 'RGB':
+      image = image.convert('RGB')
+    tensor = transform(image)
+    if tensor.dim() == 3:  # If there's no batch dimension
+      tensor = tensor.unsqueeze(0)
+    conv_layer = nn.Conv2d(3, 512, kernel_size=1)
+    extended_tensor = conv_layer(tensor) 
+    loop = asyncio.get_event_loop()
+    result = loop.run_until_complete(self.client.queue_prompt(extended_tensor))
+
+    # to_pil = transforms.ToPILImage()
+    # result_image = to_pil(result)
     # preprocess image into a torch.Tensor
-    output = self.client.queue_prompt(input)
+    # output = self.client.queue_prompt(input)
     # postprocess output into a Image.Image
-    return output
+    # return output
+    return image.convert("RGB")
 
   def update_params(self, **params):
     # Convert params into a Prompt type which describes the workflow
     # self.client.set_prompt(params["config"])
-
+    return

runner/app/live/pipelines/loader.py

@@ -7,4 +7,7 @@ def load_pipeline(name: str, **params) -> Pipeline:
     elif name == "liveportrait":
         from .liveportrait import LivePortrait
         return LivePortrait(**params)
+    elif name == "comfyui":
+        from .comfyui import ComfyUI
+        return ComfyUI(**params)
     raise ValueError(f"Unknown pipeline: {name}")