Don't unload/reload model from CPU uselessly.

comfy/model_management.py

+
+
+current_loaded_model = None
+
+
+def unload_model():
+    global current_loaded_model
+    if current_loaded_model is not None:
+        current_loaded_model.model.cpu()
+        current_loaded_model.unpatch_model()
+        current_loaded_model = None
+
+
+def load_model_gpu(model):
+    global current_loaded_model
+    if model is current_loaded_model:
+        return
+    unload_model()
+    try:
+        real_model = model.patch_model()
+    except Exception as e:
+        model.unpatch_model()
+        raise e
+    current_loaded_model = model
+    real_model.cuda()
+    return current_loaded_model

comfy/sd.py

@@ -2,6 +2,7 @@ import torch
 
 import sd1_clip
 import sd2_clip
+import model_management
 from ldm.util import instantiate_from_config
 from ldm.models.autoencoder import AutoencoderKL
 from omegaconf import OmegaConf
@@ -304,6 +305,7 @@ class VAE:
         self.device = device
 
     def decode(self, samples):
+        model_management.unload_model()
         self.first_stage_model = self.first_stage_model.to(self.device)
         samples = samples.to(self.device)
         pixel_samples = self.first_stage_model.decode(1. / self.scale_factor * samples)
@@ -313,6 +315,7 @@ class VAE:
         return pixel_samples
 
     def encode(self, pixel_samples):
+        model_management.unload_model()
         self.first_stage_model = self.first_stage_model.to(self.device)
         pixel_samples = pixel_samples.movedim(-1,1).to(self.device)
         samples = self.first_stage_model.encode(2. * pixel_samples - 1.).sample() * self.scale_factor

nodes.py

@@ -15,6 +15,7 @@ sys.path.append(os.path.join(sys.path[0], "comfy"))
 
 import comfy.samplers
 import comfy.sd
+import model_management
 
 supported_ckpt_extensions = ['.ckpt']
 supported_pt_extensions = ['.ckpt', '.pt', '.bin']
@@ -353,43 +354,39 @@ def common_ksampler(device, model, seed, steps, cfg, sampler_name, scheduler, po
         noise = torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, generator=torch.manual_seed(seed), device="cpu")
 
     real_model = None
-    try:
+    if device != "cpu":
+        model_management.load_model_gpu(model)
+        real_model = model.model
+    else:
+        #TODO: cpu support
         real_model = model.patch_model()
-        real_model.to(device)
-        noise = noise.to(device)
-        latent_image = latent_image.to(device)
-
-        positive_copy = []
-        negative_copy = []
-
-        for p in positive:
-            t = p[0]
-            if t.shape[0] < noise.shape[0]:
-                t = torch.cat([t] * noise.shape[0])
-            t = t.to(device)
-            positive_copy += [[t] + p[1:]]
-        for n in negative:
-            t = n[0]
-            if t.shape[0] < noise.shape[0]:
-                t = torch.cat([t] * noise.shape[0])
-            t = t.to(device)
-            negative_copy += [[t] + n[1:]]
-
-        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)
-        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)
-        samples = samples.cpu()
-        real_model.cpu()
-        model.unpatch_model()
-    except Exception as e:
-        if real_model is not None:
-            real_model.cpu()
-        model.unpatch_model()
-        raise e
+    noise = noise.to(device)
+    latent_image = latent_image.to(device)
+
+    positive_copy = []
+    negative_copy = []
+
+    for p in positive:
+        t = p[0]
+        if t.shape[0] < noise.shape[0]:
+            t = torch.cat([t] * noise.shape[0])
+        t = t.to(device)
+        positive_copy += [[t] + p[1:]]
+    for n in negative:
+        t = n[0]
+        if t.shape[0] < noise.shape[0]:
+            t = torch.cat([t] * noise.shape[0])
+        t = t.to(device)
+        negative_copy += [[t] + n[1:]]
+
+    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)
+    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)
+    samples = samples.cpu()
 
     return (samples, )