Add some low vram modes: --lowvram and --novram

comfy/k_diffusion/external.py

@@ -66,7 +66,7 @@ class DiscreteSchedule(nn.Module):
     def sigma_to_t(self, sigma, quantize=None):
         quantize = self.quantize if quantize is None else quantize
         log_sigma = sigma.log()
-        dists = log_sigma - self.log_sigmas[:, None]
+        dists = log_sigma.to(self.log_sigmas.device) - self.log_sigmas[:, None]
         if quantize:
             return dists.abs().argmin(dim=0).view(sigma.shape)
         low_idx = dists.ge(0).cumsum(dim=0).argmax(dim=0).clamp(max=self.log_sigmas.shape[0] - 2)

comfy/model_management.py

@@ -1,11 +1,48 @@
 
+CPU = 0
+NO_VRAM = 1
+LOW_VRAM = 2
+NORMAL_VRAM = 3
+
+accelerate_enabled = False
+vram_state = NORMAL_VRAM
+
+import sys
+
+set_vram_to = NORMAL_VRAM
+if "--lowvram" in sys.argv:
+    set_vram_to = LOW_VRAM
+if "--novram" in sys.argv:
+    set_vram_to = NO_VRAM
+
+if set_vram_to != NORMAL_VRAM:
+    try:
+        import accelerate
+        accelerate_enabled = True
+        vram_state = set_vram_to
+    except Exception as e:
+        import traceback
+        print(traceback.format_exc())
+        print("ERROR: COULD NOT ENABLE LOW VRAM MODE.")
+
+
+print("Set vram state to:", ["CPU", "NO VRAM", "LOW VRAM", "NORMAL VRAM"][vram_state])
+
 
 current_loaded_model = None
 
 
+model_accelerated = False
+
+
 def unload_model():
     global current_loaded_model
+    global model_accelerated
     if current_loaded_model is not None:
+        if model_accelerated:
+            accelerate.hooks.remove_hook_from_submodules(current_loaded_model.model)
+            model_accelerated = False
+
         current_loaded_model.model.cpu()
         current_loaded_model.unpatch_model()
         current_loaded_model = None
@@ -13,6 +50,9 @@ def unload_model():
 
 def load_model_gpu(model):
     global current_loaded_model
+    global vram_state
+    global model_accelerated
+
     if model is current_loaded_model:
         return
     unload_model()
@@ -22,5 +62,16 @@ def load_model_gpu(model):
         model.unpatch_model()
         raise e
     current_loaded_model = model
-    real_model.cuda()
+    if vram_state == CPU:
+        pass
+    elif vram_state == NORMAL_VRAM:
+        model_accelerated = False
+        real_model.cuda()
+    else:
+        if vram_state == NO_VRAM:
+            device_map = accelerate.infer_auto_device_map(real_model, max_memory={0: "256MiB", "cpu": "16GiB"})
+        elif vram_state == LOW_VRAM:
+            device_map = accelerate.infer_auto_device_map(real_model, max_memory={0: "1GiB", "cpu": "16GiB"})
+        accelerate.dispatch_model(real_model, device_map=device_map, main_device="cuda")
+        model_accelerated = True
     return current_loaded_model

main.py

@@ -14,6 +14,9 @@ if __name__ == "__main__":
         print("\t--dont-upcast-attention\t\tDisable upcasting of attention \n\t\t\t\t\tcan boost speed but increase the chances of black images.\n")
         print("\t--use-split-cross-attention\tUse the split cross attention optimization instead of the sub-quadratic one.\n\t\t\t\t\tIgnored when xformers is used.")
         print()
+        print("\t--lowvram\t\t\tSplit the unet in parts to use less vram.")
+        print("\t--novram\t\t\tWhen lowvram isn't enough.")
+        print()
         exit()
 
 if '--dont-upcast-attention' in sys.argv:

requirements.txt

@@ -8,3 +8,5 @@ transformers
 safetensors
 pytorch_lightning
 
+accelerate
+