Merge fa87f263ce into 2307ff6746

comfy/sample.py

@@ -5,12 +5,15 @@ import comfy.utils
 import numpy as np
 import logging
 
-def prepare_noise(latent_image, seed, noise_inds=None):
+def prepare_noise(latent_image, seed, noise_inds=None, disable_noise=False):
     """
     creates random noise given a latent image and a seed.
     optional arg skip can be used to skip and discard x number of noise generations for a given seed
     """
     generator = torch.manual_seed(seed)
+    if disable_noise:
+        return torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
+
     if noise_inds is None:
         return torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu")
 

comfy/sd.py

@@ -111,7 +111,7 @@ class CLIP:
             model_management.load_models_gpu([self.patcher], force_full_load=True)
         self.layer_idx = None
         self.use_clip_schedule = False
-        logging.info("CLIP model load device: {}, offload device: {}, current: {}, dtype: {}".format(load_device, offload_device, params['device'], dtype))
+        logging.info("CLIP/text encoder model load device: {}, offload device: {}, current: {}, dtype: {}".format(load_device, offload_device, params['device'], dtype))
 
     def clone(self):
         n = CLIP(no_init=True)
@@ -898,7 +898,7 @@ def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_c
     if output_model:
         model_patcher = comfy.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=model_management.unet_offload_device())
         if inital_load_device != torch.device("cpu"):
-            logging.info("loaded straight to GPU")
+            logging.info("loaded diffusion model directly to GPU")
             model_management.load_models_gpu([model_patcher], force_full_load=True)
 
     return (model_patcher, clip, vae, clipvision)

comfy_extras/nodes_custom_sampler.py

@@ -418,7 +418,7 @@ class Noise_EmptyNoise:
 
     def generate_noise(self, input_latent):
         latent_image = input_latent["samples"]
-        return torch.zeros(latent_image.shape, dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
+        return comfy.sample.prepare_noise(latent_image, self.seed, disable_noise=True)
 
 
 class Noise_RandomNoise:

nodes.py

@@ -1485,11 +1485,8 @@ def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive,
     latent_image = latent["samples"]
     latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image)
 
-    if disable_noise:
-        noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
-    else:
-        batch_inds = latent["batch_index"] if "batch_index" in latent else None
-        noise = comfy.sample.prepare_noise(latent_image, seed, batch_inds)
+    batch_inds = latent["batch_index"] if "batch_index" in latent else None
+    noise = comfy.sample.prepare_noise(latent_image, seed, batch_inds, disable_noise)
 
     noise_mask = None
     if "noise_mask" in latent:

ruff.toml

@@ -4,7 +4,8 @@ lint.ignore = ["ALL"]
 # Enable specific rules
 lint.select = [
     "S307",  # suspicious-eval-usage
-    "T201",  # print-usage
+    "S102", # exec
+    "T",  # print-usage
     "W",
     # The "F" series in Ruff stands for "Pyflakes" rules, which catch various Python syntax errors and undefined names.
     # See all rules here: https://docs.astral.sh/ruff/rules/#pyflakes-f