Merge branch 'comfyanonymous:master' into improve/maskeditor

comfy/cldm/cldm.py

@@ -283,7 +283,7 @@ class ControlNet(nn.Module):
         return TimestepEmbedSequential(zero_module(operations.conv_nd(self.dims, channels, channels, 1, padding=0)))
 
     def forward(self, x, hint, timesteps, context, y=None, **kwargs):
-        t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(self.dtype)
+        t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype)
         emb = self.time_embed(t_emb)
 
         guided_hint = self.input_hint_block(hint, emb, context)
@@ -295,7 +295,7 @@ class ControlNet(nn.Module):
             assert y.shape[0] == x.shape[0]
             emb = emb + self.label_emb(y)
 
-        h = x.type(self.dtype)
+        h = x
         for module, zero_conv in zip(self.input_blocks, self.zero_convs):
             if guided_hint is not None:
                 h = module(h, emb, context)

comfy/cli_args.py

@@ -55,7 +55,10 @@ 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.")
 
-parser.add_argument("--bf16-unet", action="store_true", help="Run the UNET in bf16. This should only be used for testing stuff.")
+fpunet_group = parser.add_mutually_exclusive_group()
+fpunet_group.add_argument("--bf16-unet", action="store_true", help="Run the UNET in bf16. This should only be used for testing stuff.")
+fpunet_group.add_argument("--fp8_e4m3fn-unet", action="store_true", help="Store unet weights in fp8_e4m3fn.")
+fpunet_group.add_argument("--fp8_e5m2-unet", action="store_true", help="Store unet weights in fp8_e5m2.")
 
 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.")

comfy/controlnet.py

@@ -1,10 +1,12 @@
 import torch
 import math
 import os
+import contextlib
 import comfy.utils
 import comfy.model_management
 import comfy.model_detection
 import comfy.model_patcher
+import comfy.ops
 
 import comfy.cldm.cldm
 import comfy.t2i_adapter.adapter
@@ -146,24 +148,31 @@ class ControlNet(ControlBase):
                 else:
                     return None
 
+        dtype = self.control_model.dtype
+        if comfy.model_management.supports_dtype(self.device, dtype):
+            precision_scope = lambda a: contextlib.nullcontext(a)
+        else:
+            precision_scope = torch.autocast
+            dtype = torch.float32
+
         output_dtype = x_noisy.dtype
         if self.cond_hint is None or x_noisy.shape[2] * 8 != self.cond_hint.shape[2] or x_noisy.shape[3] * 8 != self.cond_hint.shape[3]:
             if self.cond_hint is not None:
                 del self.cond_hint
             self.cond_hint = None
-            self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * 8, x_noisy.shape[2] * 8, 'nearest-exact', "center").to(self.control_model.dtype).to(self.device)
+            self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * 8, x_noisy.shape[2] * 8, 'nearest-exact', "center").to(dtype).to(self.device)
         if x_noisy.shape[0] != self.cond_hint.shape[0]:
             self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number)
 
-
         context = cond['c_crossattn']
         y = cond.get('y', None)
         if y is not None:
-            y = y.to(self.control_model.dtype)
+            y = y.to(dtype)
         timestep = self.model_sampling_current.timestep(t)
         x_noisy = self.model_sampling_current.calculate_input(t, x_noisy)
 
-        control = self.control_model(x=x_noisy.to(self.control_model.dtype), hint=self.cond_hint, timesteps=timestep.float(), context=context.to(self.control_model.dtype), y=y)
+        with precision_scope(comfy.model_management.get_autocast_device(self.device)):
+            control = self.control_model(x=x_noisy.to(dtype), hint=self.cond_hint, timesteps=timestep.float(), context=context.to(dtype), y=y)
         return self.control_merge(None, control, control_prev, output_dtype)
 
     def copy(self):
@@ -199,7 +208,7 @@ class ControlLoraOps:
 
         def forward(self, input):
             if self.up is not None:
-                return torch.nn.functional.linear(input, self.weight.to(input.device) + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), self.bias)
+                return torch.nn.functional.linear(input, self.weight.to(input.dtype).to(input.device) + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), self.bias)
             else:
                 return torch.nn.functional.linear(input, self.weight.to(input.device), self.bias)
 
@@ -238,7 +247,7 @@ class ControlLoraOps:
 
         def forward(self, input):
             if self.up is not None:
-                return torch.nn.functional.conv2d(input, self.weight.to(input.device) + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), self.bias, self.stride, self.padding, self.dilation, self.groups)
+                return torch.nn.functional.conv2d(input, self.weight.to(input.dtype).to(input.device) + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), self.bias, self.stride, self.padding, self.dilation, self.groups)
             else:
                 return torch.nn.functional.conv2d(input, self.weight.to(input.device), self.bias, self.stride, self.padding, self.dilation, self.groups)
 
@@ -248,6 +257,15 @@ class ControlLoraOps:
         else:
             raise ValueError(f"unsupported dimensions: {dims}")
 
+    class Conv3d(comfy.ops.Conv3d):
+        pass
+
+    class GroupNorm(comfy.ops.GroupNorm):
+        pass
+
+    class LayerNorm(comfy.ops.LayerNorm):
+        pass
+
 
 class ControlLora(ControlNet):
     def __init__(self, control_weights, global_average_pooling=False, device=None):

comfy/ldm/modules/attention.py

@@ -83,16 +83,6 @@ class FeedForward(nn.Module):
     def forward(self, x):
         return self.net(x)
 
-
-def zero_module(module):
-    """
-    Zero out the parameters of a module and return it.
-    """
-    for p in module.parameters():
-        p.detach().zero_()
-    return module
-
-
 def Normalize(in_channels, dtype=None, device=None):
     return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype, device=device)
 
@@ -414,10 +404,10 @@ class BasicTransformerBlock(nn.Module):
 
             self.attn2 = CrossAttention(query_dim=inner_dim, context_dim=context_dim_attn2,
                                 heads=n_heads, dim_head=d_head, dropout=dropout, dtype=dtype, device=device, operations=operations)  # is self-attn if context is none
-            self.norm2 = nn.LayerNorm(inner_dim, dtype=dtype, device=device)
+            self.norm2 = operations.LayerNorm(inner_dim, dtype=dtype, device=device)
 
-        self.norm1 = nn.LayerNorm(inner_dim, dtype=dtype, device=device)
+        self.norm1 = operations.LayerNorm(inner_dim, dtype=dtype, device=device)
-        self.norm3 = nn.LayerNorm(inner_dim, dtype=dtype, device=device)
+        self.norm3 = operations.LayerNorm(inner_dim, dtype=dtype, device=device)
         self.checkpoint = checkpoint
         self.n_heads = n_heads
         self.d_head = d_head
@@ -559,7 +549,7 @@ class SpatialTransformer(nn.Module):
             context_dim = [context_dim] * depth
         self.in_channels = in_channels
         inner_dim = n_heads * d_head
-        self.norm = Normalize(in_channels, dtype=dtype, device=device)
+        self.norm = operations.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype, device=device)
         if not use_linear:
             self.proj_in = operations.Conv2d(in_channels,
                                      inner_dim,

comfy/ldm/modules/diffusionmodules/openaimodel.py

@@ -177,7 +177,7 @@ class ResBlock(TimestepBlock):
             padding = kernel_size // 2
 
         self.in_layers = nn.Sequential(
-            nn.GroupNorm(32, channels, dtype=dtype, device=device),
+            operations.GroupNorm(32, channels, dtype=dtype, device=device),
             nn.SiLU(),
             operations.conv_nd(dims, channels, self.out_channels, kernel_size, padding=padding, dtype=dtype, device=device),
         )
@@ -206,12 +206,11 @@ class ResBlock(TimestepBlock):
                 ),
             )
         self.out_layers = nn.Sequential(
-            nn.GroupNorm(32, self.out_channels, dtype=dtype, device=device),
+            operations.GroupNorm(32, self.out_channels, dtype=dtype, device=device),
             nn.SiLU(),
             nn.Dropout(p=dropout),
-            zero_module(
+            operations.conv_nd(dims, self.out_channels, self.out_channels, kernel_size, padding=padding, dtype=dtype, device=device)
-                operations.conv_nd(dims, self.out_channels, self.out_channels, kernel_size, padding=padding, dtype=dtype, device=device)
+            ,
-            ),
         )
 
         if self.out_channels == channels:
@@ -810,13 +809,13 @@ class UNetModel(nn.Module):
                 self._feature_size += ch
 
         self.out = nn.Sequential(
-            nn.GroupNorm(32, ch, dtype=self.dtype, device=device),
+            operations.GroupNorm(32, ch, dtype=self.dtype, device=device),
             nn.SiLU(),
             zero_module(operations.conv_nd(dims, model_channels, out_channels, 3, padding=1, dtype=self.dtype, device=device)),
         )
         if self.predict_codebook_ids:
             self.id_predictor = nn.Sequential(
-            nn.GroupNorm(32, ch, dtype=self.dtype, device=device),
+            operations.GroupNorm(32, ch, dtype=self.dtype, device=device),
             operations.conv_nd(dims, model_channels, n_embed, 1, dtype=self.dtype, device=device),
             #nn.LogSoftmax(dim=1)  # change to cross_entropy and produce non-normalized logits
         )
@@ -842,14 +841,14 @@ class UNetModel(nn.Module):
             self.num_classes is not None
         ), "must specify y if and only if the model is class-conditional"
         hs = []
-        t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(self.dtype)
+        t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype)
         emb = self.time_embed(t_emb)
 
         if self.num_classes is not None:
             assert y.shape[0] == x.shape[0]
             emb = emb + self.label_emb(y)
 
-        h = x.type(self.dtype)
+        h = x
         for id, module in enumerate(self.input_blocks):
             transformer_options["block"] = ("input", id)
             h = forward_timestep_embed(module, h, emb, context, transformer_options, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator)

comfy/model_base.py

@@ -5,6 +5,7 @@ from comfy.ldm.modules.diffusionmodules.openaimodel import Timestep
 import comfy.model_management
 import comfy.conds
 from enum import Enum
+import contextlib
 from . import utils
 
 class ModelType(Enum):
@@ -61,6 +62,13 @@ class BaseModel(torch.nn.Module):
 
         context = c_crossattn
         dtype = self.get_dtype()
+
+        if comfy.model_management.supports_dtype(xc.device, dtype):
+            precision_scope = lambda a: contextlib.nullcontext(a)
+        else:
+            precision_scope = torch.autocast
+            dtype = torch.float32
+
         xc = xc.to(dtype)
         t = self.model_sampling.timestep(t).float()
         context = context.to(dtype)
@@ -70,7 +78,10 @@ class BaseModel(torch.nn.Module):
             if hasattr(extra, "to"):
                 extra = extra.to(dtype)
             extra_conds[o] = extra
-        model_output = self.diffusion_model(xc, t, context=context, control=control, transformer_options=transformer_options, **extra_conds).float()
+
+        with precision_scope(comfy.model_management.get_autocast_device(xc.device)):
+            model_output = self.diffusion_model(xc, t, context=context, control=control, transformer_options=transformer_options, **extra_conds).float()
+
         return self.model_sampling.calculate_denoised(sigma, model_output, x)
 
     def get_dtype(self):

comfy/model_management.py

@@ -430,6 +430,13 @@ def dtype_size(dtype):
     dtype_size = 4
     if dtype == torch.float16 or dtype == torch.bfloat16:
         dtype_size = 2
+    elif dtype == torch.float32:
+        dtype_size = 4
+    else:
+        try:
+            dtype_size = dtype.itemsize
+        except: #Old pytorch doesn't have .itemsize
+            pass
     return dtype_size
 
 def unet_offload_device():
@@ -459,6 +466,10 @@ def unet_inital_load_device(parameters, dtype):
 def unet_dtype(device=None, model_params=0):
     if args.bf16_unet:
         return torch.bfloat16
+    if args.fp8_e4m3fn_unet:
+        return torch.float8_e4m3fn
+    if args.fp8_e5m2_unet:
+        return torch.float8_e5m2
     if should_use_fp16(device=device, model_params=model_params):
         return torch.float16
     return torch.float32
@@ -515,6 +526,17 @@ def get_autocast_device(dev):
         return dev.type
     return "cuda"
 
+def supports_dtype(device, dtype): #TODO
+    if dtype == torch.float32:
+        return True
+    if torch.device("cpu") == device:
+        return False
+    if dtype == torch.float16:
+        return True
+    if dtype == torch.bfloat16:
+        return True
+    return False
+
 def cast_to_device(tensor, device, dtype, copy=False):
     device_supports_cast = False
     if tensor.dtype == torch.float32 or tensor.dtype == torch.float16:

comfy/ops.py

@@ -13,6 +13,14 @@ class Conv3d(torch.nn.Conv3d):
     def reset_parameters(self):
         return None
 
+class GroupNorm(torch.nn.GroupNorm):
+    def reset_parameters(self):
+        return None
+
+class LayerNorm(torch.nn.LayerNorm):
+    def reset_parameters(self):
+        return None
+
 def conv_nd(dims, *args, **kwargs):
     if dims == 2:
         return Conv2d(*args, **kwargs)

comfy/sample.py

@@ -101,7 +101,7 @@ def sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative
     samples = samples.cpu()
 
     cleanup_additional_models(models)
-    cleanup_additional_models(set(get_models_from_cond(positive, "control") + get_models_from_cond(negative, "control")))
+    cleanup_additional_models(set(get_models_from_cond(positive_copy, "control") + get_models_from_cond(negative_copy, "control")))
     return samples
 
 def sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image, noise_mask=None, callback=None, disable_pbar=False, seed=None):
@@ -113,6 +113,6 @@ def sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent
     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()
     cleanup_additional_models(models)
-    cleanup_additional_models(set(get_models_from_cond(positive, "control") + get_models_from_cond(negative, "control")))
+    cleanup_additional_models(set(get_models_from_cond(positive_copy, "control") + get_models_from_cond(negative_copy, "control")))
     return samples
 

comfy/sd1_clip.py

@@ -84,12 +84,16 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
 
         self.inner_name = inner_name
         if dtype is not None:
-            self.transformer.to(dtype)
             inner_model = getattr(self.transformer, self.inner_name)
             if hasattr(inner_model, "embeddings"):
-                inner_model.embeddings.to(torch.float32)
+                embeddings_bak = inner_model.embeddings.to(torch.float32)
+                inner_model.embeddings = None
+                self.transformer.to(dtype)
+                inner_model.embeddings = embeddings_bak
             else:
-                self.transformer.set_input_embeddings(self.transformer.get_input_embeddings().to(torch.float32))
+                previous_inputs = self.transformer.get_input_embeddings().to(torch.float32, copy=True)
+                self.transformer.to(dtype)
+                self.transformer.set_input_embeddings(previous_inputs)
 
         self.max_length = max_length
         if freeze: