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Refactor: move clip_preprocess to comfy.clip_model (#11586)

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comfyanonymous 2025-12-31 14:38:36 -08:00 committed by GitHub
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commit d622a61874
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2 changed files with 21 additions and 20 deletions
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19
comfy/clip_model.py
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@ -2,6 +2,25 @@ import torch
from comfy.ldm.modules.attention import optimized_attention_for_device from comfy.ldm.modules.attention import optimized_attention_for_device
import comfy.ops import comfy.ops
def clip_preprocess(image, size=224, mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711], crop=True):
image = image[:, :, :, :3] if image.shape[3] > 3 else image
mean = torch.tensor(mean, device=image.device, dtype=image.dtype)
std = torch.tensor(std, device=image.device, dtype=image.dtype)
image = image.movedim(-1, 1)
if not (image.shape[2] == size and image.shape[3] == size):
if crop:
scale = (size / min(image.shape[2], image.shape[3]))
scale_size = (round(scale * image.shape[2]), round(scale * image.shape[3]))
else:
scale_size = (size, size)
image = torch.nn.functional.interpolate(image, size=scale_size, mode="bicubic", antialias=True)
h = (image.shape[2] - size)//2
w = (image.shape[3] - size)//2
image = image[:,:,h:h+size,w:w+size]
image = torch.clip((255. * image), 0, 255).round() / 255.0
return (image - mean.view([3,1,1])) / std.view([3,1,1])
class CLIPAttention(torch.nn.Module): class CLIPAttention(torch.nn.Module):
def __init__(self, embed_dim, heads, dtype, device, operations): def __init__(self, embed_dim, heads, dtype, device, operations):
super().__init__() super().__init__()

22
comfy/clip_vision.py
View File

@ -1,6 +1,5 @@
from .utils import load_torch_file, transformers_convert, state_dict_prefix_replace from .utils import load_torch_file, transformers_convert, state_dict_prefix_replace
import os import os
import torch
import json import json
import logging import logging
@ -17,24 +16,7 @@ class Output:
def __setitem__(self, key, item): def __setitem__(self, key, item):
setattr(self, key, item) setattr(self, key, item)
def clip_preprocess(image, size=224, mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711], crop=True): clip_preprocess = comfy.clip_model.clip_preprocess # Prevent some stuff from breaking, TODO: remove eventually
image = image[:, :, :, :3] if image.shape[3] > 3 else image
mean = torch.tensor(mean, device=image.device, dtype=image.dtype)
std = torch.tensor(std, device=image.device, dtype=image.dtype)
image = image.movedim(-1, 1)
if not (image.shape[2] == size and image.shape[3] == size):
if crop:
scale = (size / min(image.shape[2], image.shape[3]))
scale_size = (round(scale * image.shape[2]), round(scale * image.shape[3]))
else:
scale_size = (size, size)
image = torch.nn.functional.interpolate(image, size=scale_size, mode="bicubic", antialias=True)
h = (image.shape[2] - size)//2
w = (image.shape[3] - size)//2
image = image[:,:,h:h+size,w:w+size]
image = torch.clip((255. * image), 0, 255).round() / 255.0
return (image - mean.view([3,1,1])) / std.view([3,1,1])
IMAGE_ENCODERS = { IMAGE_ENCODERS = {
"clip_vision_model": comfy.clip_model.CLIPVisionModelProjection, "clip_vision_model": comfy.clip_model.CLIPVisionModelProjection,
@ -73,7 +55,7 @@ class ClipVisionModel():
def encode_image(self, image, crop=True): def encode_image(self, image, crop=True):
comfy.model_management.load_model_gpu(self.patcher) comfy.model_management.load_model_gpu(self.patcher)
pixel_values = clip_preprocess(image.to(self.load_device), size=self.image_size, mean=self.image_mean, std=self.image_std, crop=crop).float() pixel_values = comfy.clip_model.clip_preprocess(image.to(self.load_device), size=self.image_size, mean=self.image_mean, std=self.image_std, crop=crop).float()
out = self.model(pixel_values=pixel_values, intermediate_output='all' if self.return_all_hidden_states else -2) out = self.model(pixel_values=pixel_values, intermediate_output='all' if self.return_all_hidden_states else -2)
outputs = Output() outputs = Output()