Merge branch 'comfyanonymous:master' into refactor/execution

cuda_malloc.py

@@ -0,0 +1,77 @@
+import os
+import importlib.util
+from comfy.cli_args import args
+
+#Can't use pytorch to get the GPU names because the cuda malloc has to be set before the first import.
+def get_gpu_names():
+    if os.name == 'nt':
+        import ctypes
+
+        # Define necessary C structures and types
+        class DISPLAY_DEVICEA(ctypes.Structure):
+            _fields_ = [
+                ('cb', ctypes.c_ulong),
+                ('DeviceName', ctypes.c_char * 32),
+                ('DeviceString', ctypes.c_char * 128),
+                ('StateFlags', ctypes.c_ulong),
+                ('DeviceID', ctypes.c_char * 128),
+                ('DeviceKey', ctypes.c_char * 128)
+            ]
+
+        # Load user32.dll
+        user32 = ctypes.windll.user32
+
+        # Call EnumDisplayDevicesA
+        def enum_display_devices():
+            device_info = DISPLAY_DEVICEA()
+            device_info.cb = ctypes.sizeof(device_info)
+            device_index = 0
+            gpu_names = set()
+
+            while user32.EnumDisplayDevicesA(None, device_index, ctypes.byref(device_info), 0):
+                device_index += 1
+                gpu_names.add(device_info.DeviceString.decode('utf-8'))
+            return gpu_names
+        return enum_display_devices()
+    else:
+        return set()
+
+def cuda_malloc_supported():
+    blacklist = {"GeForce GTX 960M", "GeForce GTX 950M", "GeForce 945M", "GeForce 940M", "GeForce 930M", "GeForce 920M", "GeForce 910M", "GeForce GTX 750", "GeForce GTX 745"}
+    try:
+        names = get_gpu_names()
+    except:
+        names = set()
+    for x in names:
+        if "NVIDIA" in x:
+            for b in blacklist:
+                if b in x:
+                    return False
+    return True
+
+
+if not args.cuda_malloc:
+    try:
+        version = ""
+        torch_spec = importlib.util.find_spec("torch")
+        for folder in torch_spec.submodule_search_locations:
+            ver_file = os.path.join(folder, "version.py")
+            if os.path.isfile(ver_file):
+                spec = importlib.util.spec_from_file_location("torch_version_import", ver_file)
+                module = importlib.util.module_from_spec(spec)
+                spec.loader.exec_module(module)
+                version = module.__version__
+        if int(version[0]) >= 2: #enable by default for torch version 2.0 and up
+            args.cuda_malloc = cuda_malloc_supported()
+    except:
+        pass
+
+
+if args.cuda_malloc and not args.disable_cuda_malloc:
+    env_var = os.environ.get('PYTORCH_CUDA_ALLOC_CONF', None)
+    if env_var is None:
+        env_var = "backend:cudaMallocAsync"
+    else:
+        env_var += ",backend:cudaMallocAsync"
+
+    os.environ['PYTORCH_CUDA_ALLOC_CONF'] = env_var

latent_preview.py

@@ -1,6 +1,5 @@
 import torch
-from PIL import Image, ImageOps
-from io import BytesIO
+from PIL import Image
 import struct
 import numpy as np
 from comfy.cli_args import args, LatentPreviewMethod
@@ -15,26 +14,7 @@ class LatentPreviewer:
 
     def decode_latent_to_preview_image(self, preview_format, x0):
         preview_image = self.decode_latent_to_preview(x0)
-
-        if hasattr(Image, 'Resampling'):
-            resampling = Image.Resampling.BILINEAR
-        else:
-            resampling = Image.ANTIALIAS
-
-        preview_image = ImageOps.contain(preview_image, (MAX_PREVIEW_RESOLUTION, MAX_PREVIEW_RESOLUTION), resampling)
-
-        preview_type = 1
-        if preview_format == "JPEG":
-            preview_type = 1
-        elif preview_format == "PNG":
-            preview_type = 2
-
-        bytesIO = BytesIO()
-        header = struct.pack(">I", preview_type)
-        bytesIO.write(header)
-        preview_image.save(bytesIO, format=preview_format, quality=95)
-        preview_bytes = bytesIO.getvalue()
-        return preview_bytes
+        return ("JPEG", preview_image, MAX_PREVIEW_RESOLUTION)
 
 class TAESDPreviewerImpl(LatentPreviewer):
     def __init__(self, taesd):

main.py

@@ -62,30 +62,7 @@ if __name__ == "__main__":
         os.environ['CUDA_VISIBLE_DEVICES'] = str(args.cuda_device)
         print("Set cuda device to:", args.cuda_device)
 
-    if not args.cuda_malloc:
-        try: #if there's a better way to check the torch version without importing it let me know
-            version = ""
-            torch_spec = importlib.util.find_spec("torch")
-            for folder in torch_spec.submodule_search_locations:
-                ver_file = os.path.join(folder, "version.py")
-                if os.path.isfile(ver_file):
-                    spec = importlib.util.spec_from_file_location("torch_version_import", ver_file)
-                    module = importlib.util.module_from_spec(spec)
-                    spec.loader.exec_module(module)
-                    version = module.__version__
-            if int(version[0]) >= 2: #enable by default for torch version 2.0 and up
-                args.cuda_malloc = True
-        except:
-            pass
-
-    if args.cuda_malloc and not args.disable_cuda_malloc:
-        env_var = os.environ.get('PYTORCH_CUDA_ALLOC_CONF', None)
-        if env_var is None:
-            env_var = "backend:cudaMallocAsync"
-        else:
-            env_var += ",backend:cudaMallocAsync"
-
-        os.environ['PYTORCH_CUDA_ALLOC_CONF'] = env_var
+    import cuda_malloc
 
 import comfy.utils
 import yaml
@@ -118,10 +95,10 @@ async def run(server, address='', port=8188, verbose=True, call_on_start=None):
 
 
 def hijack_progress(server):
-    def hook(value, total, preview_image_bytes):
+    def hook(value, total, preview_image):
         server.send_sync("progress", {"value": value, "max": total}, server.client_id)
-        if preview_image_bytes is not None:
-            server.send_sync(BinaryEventTypes.PREVIEW_IMAGE, preview_image_bytes, server.client_id)
+        if preview_image is not None:
+            server.send_sync(BinaryEventTypes.UNENCODED_PREVIEW_IMAGE, preview_image, server.client_id)
     comfy.utils.set_progress_bar_global_hook(hook)
 
 

script_examples/basic_api_example.py

@@ -2,8 +2,12 @@ import json
 from urllib import request, parse
 import random
 
-#this is the ComfyUI api prompt format. If you want it for a specific workflow you can copy it from the prompt section
-#of the image metadata of images generated with ComfyUI
+#This is the ComfyUI api prompt format.
+
+#If you want it for a specific workflow you can "enable dev mode options"
+#in the settings of the UI (gear beside the "Queue Size: ") this will enable
+#a button on the UI to save workflows in api format.
+
 #keep in mind ComfyUI is pre alpha software so this format will change a bit.
 
 #this is the one for the default workflow

server.py

@@ -8,7 +8,7 @@ import uuid
 import json
 import glob
 import struct
-from PIL import Image
+from PIL import Image, ImageOps
 from io import BytesIO
 
 try:
@@ -29,6 +29,7 @@ import comfy.model_management
 
 class BinaryEventTypes:
     PREVIEW_IMAGE = 1
+    UNENCODED_PREVIEW_IMAGE = 2
 
 async def send_socket_catch_exception(function, message):
     try:
@@ -498,7 +499,9 @@ class PromptServer():
         return prompt_info
 
     async def send(self, event, data, sid=None):
-        if isinstance(data, (bytes, bytearray)):
+        if event == BinaryEventTypes.UNENCODED_PREVIEW_IMAGE:
+            await self.send_image(data, sid=sid)
+        elif isinstance(data, (bytes, bytearray)):
             await self.send_bytes(event, data, sid)
         else:
             await self.send_json(event, data, sid)
@@ -512,6 +515,30 @@ class PromptServer():
         message.extend(data)
         return message
 
+    async def send_image(self, image_data, sid=None):
+        image_type = image_data[0]
+        image = image_data[1]
+        max_size = image_data[2]
+        if max_size is not None:
+            if hasattr(Image, 'Resampling'):
+                resampling = Image.Resampling.BILINEAR
+            else:
+                resampling = Image.ANTIALIAS
+
+            image = ImageOps.contain(image, (max_size, max_size), resampling)
+        type_num = 1
+        if image_type == "JPEG":
+            type_num = 1
+        elif image_type == "PNG":
+            type_num = 2
+
+        bytesIO = BytesIO()
+        header = struct.pack(">I", type_num)
+        bytesIO.write(header)
+        image.save(bytesIO, format=image_type, quality=95, compress_level=4)
+        preview_bytes = bytesIO.getvalue()
+        await self.send_bytes(BinaryEventTypes.PREVIEW_IMAGE, preview_bytes, sid=sid)
+
     async def send_bytes(self, event, data, sid=None):
         message = self.encode_bytes(event, data)