Make --gpu-only put intermediate values in GPU memory instead of cpu.

comfy/clip_vision.py

@@ -54,10 +54,10 @@ class ClipVisionModel():
             t = outputs[k]
             if t is not None:
                 if k == 'hidden_states':
-                    outputs["penultimate_hidden_states"] = t[-2].cpu()
+                    outputs["penultimate_hidden_states"] = t[-2].to(comfy.model_management.intermediate_device())
                     outputs["hidden_states"] = None
                 else:
-                    outputs[k] = t.cpu()
+                    outputs[k] = t.to(comfy.model_management.intermediate_device())
 
         return outputs
 

comfy/model_management.py

@@ -508,6 +508,12 @@ def text_encoder_dtype(device=None):
     else:
         return torch.float32
 
+def intermediate_device():
+    if args.gpu_only:
+        return get_torch_device()
+    else:
+        return torch.device("cpu")
+
 def vae_device():
     return get_torch_device()
 

comfy/sample.py

@@ -98,7 +98,7 @@ def sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative
     sampler = comfy.samplers.KSampler(real_model, steps=steps, device=model.load_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, callback=callback, disable_pbar=disable_pbar, seed=seed)
-    samples = samples.cpu()
+    samples = samples.to(comfy.model_management.intermediate_device())
 
     cleanup_additional_models(models)
     cleanup_additional_models(set(get_models_from_cond(positive_copy, "control") + get_models_from_cond(negative_copy, "control")))
@@ -111,7 +111,7 @@ def sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent
     sigmas = sigmas.to(model.load_device)
 
     samples = comfy.samplers.sample(real_model, noise, positive_copy, negative_copy, cfg, model.load_device, sampler, sigmas, model_options=model.model_options, latent_image=latent_image, denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)
-    samples = samples.cpu()
+    samples = samples.to(comfy.model_management.intermediate_device())
     cleanup_additional_models(models)
     cleanup_additional_models(set(get_models_from_cond(positive_copy, "control") + get_models_from_cond(negative_copy, "control")))
     return samples

comfy/sd.py

@@ -190,6 +190,7 @@ class VAE:
         offload_device = model_management.vae_offload_device()
         self.vae_dtype = model_management.vae_dtype()
         self.first_stage_model.to(self.vae_dtype)
+        self.output_device = model_management.intermediate_device()
 
         self.patcher = comfy.model_patcher.ModelPatcher(self.first_stage_model, load_device=self.device, offload_device=offload_device)
 
@@ -201,9 +202,9 @@ class VAE:
 
         decode_fn = lambda a: (self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)) + 1.0).float()
         output = torch.clamp((
-            (comfy.utils.tiled_scale(samples, decode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = 8, pbar = pbar) +
-            comfy.utils.tiled_scale(samples, decode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = 8, pbar = pbar) +
-             comfy.utils.tiled_scale(samples, decode_fn, tile_x, tile_y, overlap, upscale_amount = 8, pbar = pbar))
+            (comfy.utils.tiled_scale(samples, decode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = 8, output_device=self.output_device, pbar = pbar) +
+            comfy.utils.tiled_scale(samples, decode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = 8, output_device=self.output_device, pbar = pbar) +
+             comfy.utils.tiled_scale(samples, decode_fn, tile_x, tile_y, overlap, upscale_amount = 8, output_device=self.output_device, pbar = pbar))
             / 3.0) / 2.0, min=0.0, max=1.0)
         return output
 
@@ -214,9 +215,9 @@ class VAE:
         pbar = comfy.utils.ProgressBar(steps)
 
         encode_fn = lambda a: self.first_stage_model.encode((2. * a - 1.).to(self.vae_dtype).to(self.device)).float()
-        samples = comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x, tile_y, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar)
-        samples += comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar)
-        samples += comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar)
+        samples = comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x, tile_y, overlap, upscale_amount = (1/8), out_channels=4, output_device=self.output_device, pbar=pbar)
+        samples += comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = (1/8), out_channels=4, output_device=self.output_device, pbar=pbar)
+        samples += comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = (1/8), out_channels=4, output_device=self.output_device, pbar=pbar)
         samples /= 3.0
         return samples
 
@@ -228,15 +229,15 @@ class VAE:
             batch_number = int(free_memory / memory_used)
             batch_number = max(1, batch_number)
 
-            pixel_samples = torch.empty((samples_in.shape[0], 3, round(samples_in.shape[2] * 8), round(samples_in.shape[3] * 8)), device="cpu")
+            pixel_samples = torch.empty((samples_in.shape[0], 3, round(samples_in.shape[2] * 8), round(samples_in.shape[3] * 8)), device=self.output_device)
             for x in range(0, samples_in.shape[0], batch_number):
                 samples = samples_in[x:x+batch_number].to(self.vae_dtype).to(self.device)
-                pixel_samples[x:x+batch_number] = torch.clamp((self.first_stage_model.decode(samples).cpu().float() + 1.0) / 2.0, min=0.0, max=1.0)
+                pixel_samples[x:x+batch_number] = torch.clamp((self.first_stage_model.decode(samples).to(self.output_device).float() + 1.0) / 2.0, min=0.0, max=1.0)
         except model_management.OOM_EXCEPTION as e:
             print("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.")
             pixel_samples = self.decode_tiled_(samples_in)
 
-        pixel_samples = pixel_samples.cpu().movedim(1,-1)
+        pixel_samples = pixel_samples.to(self.output_device).movedim(1,-1)
         return pixel_samples
 
     def decode_tiled(self, samples, tile_x=64, tile_y=64, overlap = 16):
@@ -252,10 +253,10 @@ class VAE:
             free_memory = model_management.get_free_memory(self.device)
             batch_number = int(free_memory / memory_used)
             batch_number = max(1, batch_number)
-            samples = torch.empty((pixel_samples.shape[0], 4, round(pixel_samples.shape[2] // 8), round(pixel_samples.shape[3] // 8)), device="cpu")
+            samples = torch.empty((pixel_samples.shape[0], 4, round(pixel_samples.shape[2] // 8), round(pixel_samples.shape[3] // 8)), device=self.output_device)
             for x in range(0, pixel_samples.shape[0], batch_number):
                 pixels_in = (2. * pixel_samples[x:x+batch_number] - 1.).to(self.vae_dtype).to(self.device)
-                samples[x:x+batch_number] = self.first_stage_model.encode(pixels_in).cpu().float()
+                samples[x:x+batch_number] = self.first_stage_model.encode(pixels_in).to(self.output_device).float()
 
         except model_management.OOM_EXCEPTION as e:
             print("Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.")

comfy/sd1_clip.py

@@ -39,7 +39,7 @@ class ClipTokenWeightEncoder:
 
         out, pooled = self.encode(to_encode)
         if pooled is not None:
-            first_pooled = pooled[0:1].cpu()
+            first_pooled = pooled[0:1].to(model_management.intermediate_device())
         else:
             first_pooled = pooled
 
@@ -56,8 +56,8 @@ class ClipTokenWeightEncoder:
             output.append(z)
 
         if (len(output) == 0):
-            return out[-1:].cpu(), first_pooled
-        return torch.cat(output, dim=-2).cpu(), first_pooled
+            return out[-1:].to(model_management.intermediate_device()), first_pooled
+        return torch.cat(output, dim=-2).to(model_management.intermediate_device()), first_pooled
 
 class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
     """Uses the CLIP transformer encoder for text (from huggingface)"""

comfy/utils.py

@@ -376,7 +376,7 @@ def lanczos(samples, width, height):
     images = [image.resize((width, height), resample=Image.Resampling.LANCZOS) for image in images]
     images = [torch.from_numpy(np.array(image).astype(np.float32) / 255.0).movedim(-1, 0) for image in images]
     result = torch.stack(images)
-    return result
+    return result.to(samples.device, samples.dtype)
 
 def common_upscale(samples, width, height, upscale_method, crop):
         if crop == "center":
@@ -405,17 +405,17 @@ def get_tiled_scale_steps(width, height, tile_x, tile_y, overlap):
     return math.ceil((height / (tile_y - overlap))) * math.ceil((width / (tile_x - overlap)))
 
 @torch.inference_mode()
-def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_amount = 4, out_channels = 3, pbar = None):
-    output = torch.empty((samples.shape[0], out_channels, round(samples.shape[2] * upscale_amount), round(samples.shape[3] * upscale_amount)), device="cpu")
+def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_amount = 4, out_channels = 3, output_device="cpu", pbar = None):
+    output = torch.empty((samples.shape[0], out_channels, round(samples.shape[2] * upscale_amount), round(samples.shape[3] * upscale_amount)), device=output_device)
     for b in range(samples.shape[0]):
         s = samples[b:b+1]
-        out = torch.zeros((s.shape[0], out_channels, round(s.shape[2] * upscale_amount), round(s.shape[3] * upscale_amount)), device="cpu")
-        out_div = torch.zeros((s.shape[0], out_channels, round(s.shape[2] * upscale_amount), round(s.shape[3] * upscale_amount)), device="cpu")
+        out = torch.zeros((s.shape[0], out_channels, round(s.shape[2] * upscale_amount), round(s.shape[3] * upscale_amount)), device=output_device)
+        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):
                 s_in = s[:,:,y:y+tile_y,x:x+tile_x]
 
-                ps = function(s_in).cpu()
+                ps = function(s_in).to(output_device)
                 mask = torch.ones_like(ps)
                 feather = round(overlap * upscale_amount)
                 for t in range(feather):

comfy_extras/nodes_canny.py

@@ -291,7 +291,7 @@ class Canny:
 
     def detect_edge(self, image, low_threshold, high_threshold):
         output = canny(image.to(comfy.model_management.get_torch_device()).movedim(-1, 1), low_threshold, high_threshold)
-        img_out = output[1].cpu().repeat(1, 3, 1, 1).movedim(1, -1)
+        img_out = output[1].to(comfy.model_management.intermediate_device()).repeat(1, 3, 1, 1).movedim(1, -1)
         return (img_out,)
 
 NODE_CLASS_MAPPINGS = {

comfy_extras/nodes_post_processing.py

@@ -226,7 +226,7 @@ class Sharpen:
         batch_size, height, width, channels = image.shape
 
         kernel_size = sharpen_radius * 2 + 1
-        kernel = gaussian_kernel(kernel_size, sigma) * -(alpha*10)
+        kernel = gaussian_kernel(kernel_size, sigma, device=image.device) * -(alpha*10)
         center = kernel_size // 2
         kernel[center, center] = kernel[center, center] - kernel.sum() + 1.0
         kernel = kernel.repeat(channels, 1, 1).unsqueeze(1)

nodes.py

@@ -947,8 +947,8 @@ class GLIGENTextBoxApply:
         return (c, )
 
 class EmptyLatentImage:
-    def __init__(self, device="cpu"):
-        self.device = device
+    def __init__(self):
+        self.device = comfy.model_management.intermediate_device()
 
     @classmethod
     def INPUT_TYPES(s):
@@ -961,7 +961,7 @@ class EmptyLatentImage:
     CATEGORY = "latent"
 
     def generate(self, width, height, batch_size=1):
-        latent = torch.zeros([batch_size, 4, height // 8, width // 8])
+        latent = torch.zeros([batch_size, 4, height // 8, width // 8], device=self.device)
         return ({"samples":latent}, )