Add arguments to run the VAE in fp16 or bf16 for testing.

.github/workflows/windows_release_nightly_pytorch.yml

@@ -54,7 +54,7 @@ jobs:
 
             cd ..
 
-            "C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma -mx=8 -mfb=64 -md=32m -ms=on ComfyUI_windows_portable_nightly_pytorch.7z ComfyUI_windows_portable_nightly_pytorch
+            "C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma -mx=8 -mfb=64 -md=32m -ms=on -mf=BCJ2 ComfyUI_windows_portable_nightly_pytorch.7z ComfyUI_windows_portable_nightly_pytorch
             mv ComfyUI_windows_portable_nightly_pytorch.7z ComfyUI/ComfyUI_windows_portable_nvidia_or_cpu_nightly_pytorch.7z
 
             cd ComfyUI_windows_portable_nightly_pytorch

comfy/cli_args.py

@@ -46,6 +46,10 @@ fp_group = parser.add_mutually_exclusive_group()
 fp_group.add_argument("--force-fp32", action="store_true", help="Force fp32 (If this makes your GPU work better please report it).")
 fp_group.add_argument("--force-fp16", action="store_true", help="Force fp16.")
 
+fpvae_group = parser.add_mutually_exclusive_group()
+fpvae_group.add_argument("--fp16-vae", action="store_true", help="Run the VAE in fp16, might cause black images.")
+fpvae_group.add_argument("--bf16-vae", action="store_true", help="Run the VAE in bf16, might lower quality.")
+
 parser.add_argument("--directml", type=int, nargs="?", metavar="DIRECTML_DEVICE", const=-1, help="Use torch-directml.")
 
 class LatentPreviewMethod(enum.Enum):

comfy/model_management.py

@@ -366,6 +366,14 @@ def vae_offload_device():
     else:
         return torch.device("cpu")
 
+def vae_dtype():
+    if args.fp16_vae:
+        return torch.float16
+    elif args.bf16_vae:
+        return torch.bfloat16
+    else:
+        return torch.float32
+
 def get_autocast_device(dev):
     if hasattr(dev, 'type'):
         return dev.type

comfy/sd.py

@@ -505,6 +505,8 @@ class VAE:
             device = model_management.vae_device()
         self.device = device
         self.offload_device = model_management.vae_offload_device()
+        self.vae_dtype = model_management.vae_dtype()
+        self.first_stage_model.to(self.vae_dtype)
 
     def decode_tiled_(self, samples, tile_x=64, tile_y=64, overlap = 16):
         steps = samples.shape[0] * utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x, tile_y, overlap)
@@ -512,7 +514,7 @@ class VAE:
         steps += samples.shape[0] * utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x * 2, tile_y // 2, overlap)
         pbar = utils.ProgressBar(steps)
 
-        decode_fn = lambda a: (self.first_stage_model.decode(a.to(self.device)) + 1.0)
+        decode_fn = lambda a: (self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)) + 1.0).float()
         output = torch.clamp((
             (utils.tiled_scale(samples, decode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = 8, pbar = pbar) +
             utils.tiled_scale(samples, decode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = 8, pbar = pbar) +
@@ -526,7 +528,7 @@ class VAE:
         steps += pixel_samples.shape[0] * utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x * 2, tile_y // 2, overlap)
         pbar = utils.ProgressBar(steps)
 
-        encode_fn = lambda a: self.first_stage_model.encode(2. * a.to(self.device) - 1.).sample()
+        encode_fn = lambda a: self.first_stage_model.encode(2. * a.to(self.vae_dtype).to(self.device) - 1.).sample().float()
         samples = utils.tiled_scale(pixel_samples, encode_fn, tile_x, tile_y, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar)
         samples += utils.tiled_scale(pixel_samples, encode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar)
         samples += utils.tiled_scale(pixel_samples, encode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar)
@@ -543,8 +545,8 @@ class VAE:
 
             pixel_samples = torch.empty((samples_in.shape[0], 3, round(samples_in.shape[2] * 8), round(samples_in.shape[3] * 8)), device="cpu")
             for x in range(0, samples_in.shape[0], batch_number):
-                samples = samples_in[x:x+batch_number].to(self.device)
-                pixel_samples[x:x+batch_number] = torch.clamp((self.first_stage_model.decode(samples) + 1.0) / 2.0, min=0.0, max=1.0).cpu()
+                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) + 1.0) / 2.0, min=0.0, max=1.0).cpu().float()
         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)
@@ -570,8 +572,8 @@ class VAE:
             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")
             for x in range(0, pixel_samples.shape[0], batch_number):
-                pixels_in = (2. * pixel_samples[x:x+batch_number] - 1.).to(self.device)
-                samples[x:x+batch_number] = self.first_stage_model.encode(pixels_in).sample().cpu()
+                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).sample().cpu().float()
 
         except model_management.OOM_EXCEPTION as e:
             print("Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.")