Merge branch 'comfyanonymous:master' into master

README.md

@@ -95,24 +95,23 @@ Put your SD checkpoints (the huge ckpt/safetensors files) in: models/checkpoints
 
 Put your VAE in: models/vae
 
-Note: pytorch stable does not support python 3.12 yet. If you have python 3.12 you will have to use the nightly version of pytorch. If you run into issues you should try python 3.11 instead.
 
 ### AMD GPUs (Linux only)
 AMD users can install rocm and pytorch with pip if you don't have it already installed, this is the command to install the stable version:
 
-```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm5.6```
+```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm5.7```
 
-This is the command to install the nightly with ROCm 5.7 which has a python 3.12 package and might have some performance improvements:
+This is the command to install the nightly with ROCm 6.0 which might have some performance improvements:
 
-```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm5.7```
+```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm6.0```
 
 ### NVIDIA
 
 Nvidia users should install stable pytorch using this command:
 
 ```pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu121```
 
-This is the command to install pytorch nightly instead which has a python 3.12 package and might have performance improvements:
+This is the command to install pytorch nightly instead which might have performance improvements:
 
 ```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu121```
 

comfy/model_management.py

@@ -496,7 +496,7 @@ def unet_dtype(device=None, model_params=0):
         return torch.float8_e4m3fn
     if args.fp8_e5m2_unet:
         return torch.float8_e5m2
-    if should_use_fp16(device=device, model_params=model_params):
+    if should_use_fp16(device=device, model_params=model_params, manual_cast=True):
         return torch.float16
     return torch.float32
 
@@ -546,10 +546,8 @@ def text_encoder_dtype(device=None):
     if is_device_cpu(device):
         return torch.float16
 
-    if should_use_fp16(device, prioritize_performance=False):
-        return torch.float16
-    else:
-        return torch.float32
+    return torch.float16
+
 
 def intermediate_device():
     if args.gpu_only:
@@ -698,7 +696,7 @@ def is_device_mps(device):
             return True
     return False
 
-def should_use_fp16(device=None, model_params=0, prioritize_performance=True):
+def should_use_fp16(device=None, model_params=0, prioritize_performance=True, manual_cast=False):
     global directml_enabled
 
     if device is not None:
@@ -724,10 +722,13 @@ def should_use_fp16(device=None, model_params=0, prioritize_performance=True):
     if is_intel_xpu():
         return True
 
-    if torch.cuda.is_bf16_supported():
+    if torch.version.hip:
         return True
 
     props = torch.cuda.get_device_properties("cuda")
+    if props.major >= 8:
+        return True
+
     if props.major < 6:
         return False
 
@@ -740,7 +741,7 @@ def should_use_fp16(device=None, model_params=0, prioritize_performance=True):
         if x in props.name.lower():
             fp16_works = True
 
-    if fp16_works:
+    if fp16_works or manual_cast:
         free_model_memory = (get_free_memory() * 0.9 - minimum_inference_memory())
         if (not prioritize_performance) or model_params * 4 > free_model_memory:
             return True

comfy/sd.py

@@ -462,7 +462,7 @@ class WeightsLoader(torch.nn.Module):
         model.load_model_weights(sd, "model.diffusion_model.")
 
     if output_vae:
-        vae_sd = comfy.utils.state_dict_prefix_replace(sd, {"first_stage_model.": ""}, filter_keys=True)
+        vae_sd = comfy.utils.state_dict_prefix_replace(sd, {k: "" for k in model_config.vae_key_prefix}, filter_keys=True)
         vae_sd = model_config.process_vae_state_dict(vae_sd)
         vae = VAE(sd=vae_sd)
 

comfy/supported_models_base.py

@@ -21,6 +21,7 @@ class BASE:
     noise_aug_config = None
     sampling_settings = {}
     latent_format = latent_formats.LatentFormat
+    vae_key_prefix = ["first_stage_model."]
 
     manual_cast_dtype = None
 

comfy/utils.py

@@ -413,6 +413,8 @@ def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_am
         out_div = torch.zeros((s.shape[0], out_channels, round(s.shape[2] * upscale_amount), round(s.shape[3] * upscale_amount)), device=output_device)
         for y in range(0, s.shape[2], tile_y - overlap):
             for x in range(0, s.shape[3], tile_x - overlap):
+                x = max(0, min(s.shape[-1] - overlap, x))
+                y = max(0, min(s.shape[-2] - overlap, y))
                 s_in = s[:,:,y:y+tile_y,x:x+tile_x]
 
                 ps = function(s_in).to(output_device)

comfy_extras/nodes_latent.py

@@ -126,7 +126,7 @@ class LatentBatchSeedBehavior:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "samples": ("LATENT",),
-                              "seed_behavior": (["random", "fixed"],),}}
+                              "seed_behavior": (["random", "fixed"],{"default": "fixed"}),}}
 
     RETURN_TYPES = ("LATENT",)
     FUNCTION = "op"

custom_nodes/example_node.py.example

@@ -6,6 +6,8 @@ class Example:
     -------------
     INPUT_TYPES (dict): 
         Tell the main program input parameters of nodes.
+    IS_CHANGED:
+        optional method to control when the node is re executed.
 
     Attributes
     ----------
@@ -89,6 +91,17 @@ class Example:
         image = 1.0 - image
         return (image,)
 
+    """
+        The node will always be re executed if any of the inputs change but
+        this method can be used to force the node to execute again even when the inputs don't change.
+        You can make this node return a number or a string. This value will be compared to the one returned the last time the node was
+        executed, if it is different the node will be executed again.
+        This method is used in the core repo for the LoadImage node where they return the image hash as a string, if the image hash
+        changes between executions the LoadImage node is executed again.
+    """
+    #@classmethod
+    #def IS_CHANGED(s, image, string_field, int_field, float_field, print_to_screen):
+    #    return ""
 
 # A dictionary that contains all nodes you want to export with their names
 # NOTE: names should be globally unique

web/extensions/core/groupNode.js

@@ -910,6 +910,9 @@ export class GroupNodeHandler {
 		const self = this;
 		const onNodeCreated = this.node.onNodeCreated;
 		this.node.onNodeCreated = function () {
+			if (!this.widgets) {
+				return;
+			}
 			const config = self.groupData.nodeData.config;
 			if (config) {
 				for (const n in config) {

web/extensions/core/groupNodeManage.css

@@ -48,7 +48,7 @@
 	list-style: none;
 }
 .comfy-group-manage-list-items {
-	max-height: 70vh;
+	max-height: calc(100% - 40px);
 	overflow-y: scroll;
 	overflow-x: hidden;
 }