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Merge branch 'comfyanonymous:master' into master

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patientx 2025-10-19 15:20:17 +03:00 committed by GitHub
commit 657a7872ab
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5 changed files with 20 additions and 24 deletions
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23
comfy/ldm/chroma_radiance/model.py
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@ -189,15 +189,15 @@ class ChromaRadiance(Chroma):
nerf_pixels = nn.functional.unfold(img_orig, kernel_size=patch_size, stride=patch_size)
nerf_pixels = nerf_pixels.transpose(1, 2) # -> [B, NumPatches, C * P * P]
# Reshape for per-patch processing
nerf_hidden = img_out.reshape(B * num_patches, params.hidden_size)
nerf_pixels = nerf_pixels.reshape(B * num_patches, C, patch_size**2).transpose(1, 2)
if params.nerf_tile_size > 0 and num_patches > params.nerf_tile_size:
# Enable tiling if nerf_tile_size isn't 0 and we actually have more patches than
# the tile size.
img_dct = self.forward_tiled_nerf(img_out, nerf_pixels, B, C, num_patches, patch_size, params)
img_dct = self.forward_tiled_nerf(nerf_hidden, nerf_pixels, B, C, num_patches, patch_size, params)
else:
# Reshape for per-patch processing
nerf_hidden = img_out.reshape(B * num_patches, params.hidden_size)
nerf_pixels = nerf_pixels.reshape(B * num_patches, C, patch_size**2).transpose(1, 2)
# Get DCT-encoded pixel embeddings [pixel-dct]
img_dct = self.nerf_image_embedder(nerf_pixels)
@ -240,17 +240,8 @@ class ChromaRadiance(Chroma):
end = min(i + tile_size, num_patches)
# Slice the current tile from the input tensors
nerf_hidden_tile = nerf_hidden[:, i:end, :]
nerf_pixels_tile = nerf_pixels[:, i:end, :]
# Get the actual number of patches in this tile (can be smaller for the last tile)
num_patches_tile = nerf_hidden_tile.shape[1]
# Reshape the tile for per-patch processing
# [B, NumPatches_tile, D] -> [B * NumPatches_tile, D]
nerf_hidden_tile = nerf_hidden_tile.reshape(batch * num_patches_tile, params.hidden_size)
# [B, NumPatches_tile, C*P*P] -> [B*NumPatches_tile, C, P*P] -> [B*NumPatches_tile, P*P, C]
nerf_pixels_tile = nerf_pixels_tile.reshape(batch * num_patches_tile, channels, patch_size**2).transpose(1, 2)
nerf_hidden_tile = nerf_hidden[i * batch:end * batch]
nerf_pixels_tile = nerf_pixels[i * batch:end * batch]
# get DCT-encoded pixel embeddings [pixel-dct]
img_dct_tile = self.nerf_image_embedder(nerf_pixels_tile)

2
comfy/model_detection.py
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@ -213,7 +213,7 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
dit_config["nerf_mlp_ratio"] = 4
dit_config["nerf_depth"] = 4
dit_config["nerf_max_freqs"] = 8
dit_config["nerf_tile_size"] = 32
dit_config["nerf_tile_size"] = 512
dit_config["nerf_final_head_type"] = "conv" if f"{key_prefix}nerf_final_layer_conv.norm.scale" in state_dict_keys else "linear"
dit_config["nerf_embedder_dtype"] = torch.float32
else:

3
comfy/model_management.py
View File

@ -372,6 +372,9 @@ try:
except:
pass
if torch.cuda.is_available() and torch.backends.cudnn.is_available() and PerformanceFeature.AutoTune in args.fast:
torch.backends.cudnn.benchmark = True
try:
if torch_version_numeric >= (2, 5):
torch.backends.cuda.allow_fp16_bf16_reduction_math_sdp(True)

9
comfy/ops.py
View File

@ -25,6 +25,9 @@ import comfy.rmsnorm
import contextlib
def run_every_op():
if torch.compiler.is_compiling():
return
comfy.model_management.throw_exception_if_processing_interrupted()
def scaled_dot_product_attention(q, k, v, *args, **kwargs):
@ -55,7 +58,7 @@ except (ModuleNotFoundError, TypeError):
NVIDIA_MEMORY_CONV_BUG_WORKAROUND = False
try:
if comfy.model_management.is_nvidia():
if torch.backends.cudnn.version() >= 91200 and comfy.model_management.torch_version_numeric >= (2, 9) and comfy.model_management.torch_version_numeric <= (2, 10):
if torch.backends.cudnn.version() >= 91002 and comfy.model_management.torch_version_numeric >= (2, 9) and comfy.model_management.torch_version_numeric <= (2, 10):
#TODO: change upper bound version once it's fixed'
NVIDIA_MEMORY_CONV_BUG_WORKAROUND = True
logging.info("working around nvidia conv3d memory bug.")
@ -64,12 +67,10 @@ except:
cast_to = comfy.model_management.cast_to #TODO: remove once no more references
if torch.cuda.is_available() and torch.backends.cudnn.is_available() and PerformanceFeature.AutoTune in args.fast:
torch.backends.cudnn.benchmark = True
def cast_to_input(weight, input, non_blocking=False, copy=True):
return comfy.model_management.cast_to(weight, input.dtype, input.device, non_blocking=non_blocking, copy=copy)
@torch.compiler.disable()
def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None):
if input is not None:
if dtype is None:

7
cuda_malloc.py
View File

@ -1,6 +1,6 @@
import os
import importlib.util
from comfy.cli_args import args
from comfy.cli_args import args, PerformanceFeature
import subprocess
#Can't use pytorch to get the GPU names because the cuda malloc has to be set before the first import.
@ -78,8 +78,9 @@ if not args.cuda_malloc:
spec.loader.exec_module(module)
version = module.__version__
if int(version[0]) >= 2 and "+cu" in version: #enable by default for torch version 2.0 and up only on cuda torch
args.cuda_malloc = cuda_malloc_supported()
if int(version[0]) >= 2 and "+cu" in version: # enable by default for torch version 2.0 and up only on cuda torch
if PerformanceFeature.AutoTune not in args.fast: # Autotune has issues with cuda malloc
args.cuda_malloc = cuda_malloc_supported()
except:
pass