Refactor more code to sample.py

comfy/sample.py

 import torch
 import comfy.model_management
+import comfy.samplers
 
 
 def prepare_noise(latent_image, seed, skip=0):
@@ -13,24 +14,22 @@ def prepare_noise(latent_image, seed, skip=0):
     noise = torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu")
     return noise
 
-def prepare_mask(noise_mask, noise):
+def prepare_mask(noise_mask, shape, device):
     """ensures noise mask is of proper dimensions"""
-    device = comfy.model_management.get_torch_device()
-    noise_mask = torch.nn.functional.interpolate(noise_mask[None,None,], size=(noise.shape[2], noise.shape[3]), mode="bilinear")
+    noise_mask = torch.nn.functional.interpolate(noise_mask[None,None,], size=(shape[2], shape[3]), mode="bilinear")
     noise_mask = noise_mask.round()
-    noise_mask = torch.cat([noise_mask] * noise.shape[1], dim=1)
-    noise_mask = torch.cat([noise_mask] * noise.shape[0])
+    noise_mask = torch.cat([noise_mask] * shape[1], dim=1)
+    noise_mask = torch.cat([noise_mask] * shape[0])
     noise_mask = noise_mask.to(device)
     return noise_mask
 
-def broadcast_cond(cond, noise):
-    """broadcasts conditioning to the noise batch size"""
-    device = comfy.model_management.get_torch_device()
+def broadcast_cond(cond, batch, device):
+    """broadcasts conditioning to the batch size"""
     copy = []
     for p in cond:
         t = p[0]
-        if t.shape[0] < noise.shape[0]:
-            t = torch.cat([t] * noise.shape[0])
+        if t.shape[0] < batch:
+            t = torch.cat([t] * batch)
         t = t.to(device)
         copy += [[t] + p[1:]]
     return copy
@@ -54,4 +53,30 @@ def load_additional_models(positive, negative):
 def cleanup_additional_models(models):
     """cleanup additional models that were loaded"""
     for m in models:
-        m.cleanup()
+        m.cleanup()
\ No newline at end of file
+
+def sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False, noise_mask=None, sigmas=None):
+    device = comfy.model_management.get_torch_device()
+
+    if noise_mask is not None:
+        noise_mask = prepare_mask(noise_mask, noise.shape, device)
+
+    real_model = None
+    comfy.model_management.load_model_gpu(model)
+    real_model = model.model
+
+    noise = noise.to(device)
+    latent_image = latent_image.to(device)
+
+    positive_copy = broadcast_cond(positive, noise.shape[0], device)
+    negative_copy = broadcast_cond(negative, noise.shape[0], device)
+
+    models = load_additional_models(positive, negative)
+
+    sampler = comfy.samplers.KSampler(real_model, steps=steps, device=device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options)
+
+    samples = sampler.sample(noise, positive_copy, negative_copy, cfg=cfg, latent_image=latent_image, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, denoise_mask=noise_mask, sigmas=sigmas)
+    samples = samples.cpu()
+
+    cleanup_additional_models(models)
+    return samples

nodes.py

@@ -752,31 +752,11 @@ def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive,
 
     noise_mask = None
     if "noise_mask" in latent:
-        noise_mask = comfy.sample.prepare_mask(latent["noise_mask"], noise)
-
-    real_model = None
-    comfy.model_management.load_model_gpu(model)
-    real_model = model.model
-
-    noise = noise.to(device)
-    latent_image = latent_image.to(device)
-
-    positive_copy = comfy.sample.broadcast_cond(positive, noise)
-    negative_copy = comfy.sample.broadcast_cond(negative, noise)
-
-    models = comfy.sample.load_additional_models(positive, negative)
-
-    if sampler_name in comfy.samplers.KSampler.SAMPLERS:
-        sampler = comfy.samplers.KSampler(real_model, steps=steps, device=device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options)
-    else:
-        #other samplers
-        pass
-
-    samples = sampler.sample(noise, positive_copy, negative_copy, cfg=cfg, latent_image=latent_image, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, denoise_mask=noise_mask)
-    samples = samples.cpu()
-    
-    comfy.sample.cleanup_additional_models(models)
+        noise_mask = latent["noise_mask"]
 
+    samples = comfy.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
+                                  denoise=denoise, disable_noise=disable_noise, start_step=start_step, last_step=last_step,
+                                  force_full_denoise=force_full_denoise, noise_mask=noise_mask)
     out = latent.copy()
     out["samples"] = samples
     return (out, )