nodes/yolain-ComfyUI-Easy-Use
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fix:compatibility with comfyui-brushnet new commit

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yolain
2024-05-05 11:30:41 +08:00
parent b3b0a961c5
commit dbe2cd6569
4 changed files with 22 additions and 32 deletions
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README.md
+1 -1
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@@ -44,7 +44,7 @@
- 增加 brushnet模型加载的支持 - [ComfyUI-BrushNet](https://github.com/nullquant/ComfyUI-BrushNet)
- 增加 `easy applyFooocusInpaint` - Fooocus内补节点 替代原有的 FooocusInpaintLoader
- 移除 `easy fooocusInpaintLoader` - 容易bug，不再使用
- 修改 easy kSampler等采样器中并联的model 不再替换pipe中model
- 修改 easy kSampler等采样器中并联的model 不再替换输出中pipe里的model
**v1.1.6**
py/easyNodes.py
+18 -21
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@@ -9,7 +9,7 @@ except:
from comfy.sd import CLIP, VAE
from comfy.model_patcher import ModelPatcher
from comfy_extras.chainner_models import model_loading
from comfy_extras.nodes_mask import LatentCompositeMasked
from comfy_extras.nodes_mask import LatentCompositeMasked, GrowMask
from comfy.clip_vision import load as load_clip_vision
from urllib.request import urlopen
from PIL import Image
@@ -4090,20 +4090,12 @@ class samplerFull(LayerDiffuse):
FUNCTION = "run"
CATEGORY = "EasyUse/Sampler"
def clear_model_config(self, samp_model):
if hasattr(samp_model.model.diffusion_model, 'original_forward'):
samp_model.model.diffusion_model.forward = samp_model.model.diffusion_model.original_forward
samp_model.model_options['transformer_options'] = {}
del samp_model.model.diffusion_model.original_forward
if hasattr(ModelPatcher, "original_calculate_weight"):
ModelPatcher.calculate_weight = ModelPatcher.original_calculate_weight
def run(self, pipe, steps, cfg, sampler_name, scheduler, denoise, image_output, link_id, save_prefix, seed=None, model=None, positive=None, negative=None, latent=None, vae=None, clip=None, xyPlot=None, tile_size=None, prompt=None, extra_pnginfo=None, my_unique_id=None, force_full_denoise=False, disable_noise=False, downscale_options=None):
# Clean loaded_objects
easyCache.update_loaded_objects(prompt)
samp_model = model if model is not None else pipe["model"]
samp_model = model.clone() if model is not None else pipe["model"].clone()
samp_positive = positive if positive is not None else pipe["positive"]
samp_negative = negative if negative is not None else pipe["negative"]
samp_samples = latent if latent is not None else pipe["samples"]
@@ -4287,8 +4279,8 @@ class samplerFull(LayerDiffuse):
if image_output in ("Sender", "Sender&Save"):
PromptServer.instance.send_sync("img-send", {"link_id": link_id, "images": results})
self.clear_model_config(samp_model)
if hasattr(ModelPatcher, "original_calculate_weight"):
ModelPatcher.calculate_weight = ModelPatcher.original_calculate_weight
return {"ui": {"images": results},
"result": sampler.get_output(new_pipe,)}
@@ -4403,7 +4395,8 @@ class samplerFull(LayerDiffuse):
del pipe
self.clear_model_config(samp_model)
if hasattr(ModelPatcher, "original_calculate_weight"):
ModelPatcher.calculate_weight = ModelPatcher.original_calculate_weight
if image_output in ("Hide", "Hide&Save"):
return sampler.get_output(new_pipe)
@@ -4671,8 +4664,8 @@ class samplerSimpleInpainting:
cls = ALL_NODE_CLASS_MAPPINGS['BrushNetLoader']
brushnet, = cls().brushnet_loading(brushname)
cls = ALL_NODE_CLASS_MAPPINGS['BrushNet']
m, latent = cls().model_update(model=model, vae=vae, image=image, mask=mask, brushnet=brushnet, positive=positive, negative=negative, scale=scale, start_at=start_at, end_at=end_at)
return m, latent
m, positive, negative, latent = cls().model_update(model=model, vae=vae, image=image, mask=mask, brushnet=brushnet, positive=positive, negative=negative, scale=scale, start_at=start_at, end_at=end_at)
return m, positive, negative, latent
def run(self, pipe, grow_mask_by, image_output, link_id, save_prefix, additional, model=None, mask=None, tile_size=None, prompt=None, extra_pnginfo=None, my_unique_id=None, force_full_denoise=False, disable_noise=False):
_model = model if model is not None else pipe['model']
@@ -4694,8 +4687,7 @@ class samplerSimpleInpainting:
positive, negative, latent, _model = self.dd(_model, positive, negative, images, vae, mask)
case 'InpaintModelCond':
if mask is not None:
latent, = VAEEncodeForInpaint().encode(vae, images, mask, grow_mask_by)
mask = latent['noise_mask']
mask, = GrowMask().expand_mask(mask, grow_mask_by, False)
positive, negative, latent = InpaintModelConditioning().encode(positive, negative, images, vae, mask)
case 'Fooocus Inpaint':
head = list(FOOOCUS_INPAINT_HEAD.keys())[0]
@@ -4706,21 +4698,26 @@ class samplerSimpleInpainting:
case 'Fooocus Inpaint + DD':
head = list(FOOOCUS_INPAINT_HEAD.keys())[0]
patch = list(FOOOCUS_INPAINT_PATCH.keys())[0]
mask, = GrowMask().expand_mask(mask, grow_mask_by, False)
positive, negative, latent, _model = self.dd(_model, positive, negative, images, vae, mask)
_model, = applyFooocusInpaint().apply(_model, latent, head, patch)
case 'Brushnet Random':
mask, = GrowMask().expand_mask(mask, grow_mask_by, False)
brush_name = self.get_brushnet_model('random', _model)
_model, latent = self.apply_brushnet(brush_name, _model, vae, images, mask, positive, negative)
_model, positive, negative, latent = self.apply_brushnet(brush_name, _model, vae, images, mask, positive, negative)
case 'Brushnet Random + DD':
mask, = GrowMask().expand_mask(mask, grow_mask_by, False)
brush_name = self.get_brushnet_model('random', _model)
_model, latent = self.apply_brushnet(brush_name, _model, vae, images, mask, positive, negative)
_model, positive, negative, latent = self.apply_brushnet(brush_name, _model, vae, images, mask, positive, negative)
positive, negative, latent, _model = self.dd(_model, positive, negative, images, vae, mask)
case 'Brushnet Segmentation':
mask, = GrowMask().expand_mask(mask, grow_mask_by, False)
brush_name = self.get_brushnet_model('segmentation', _model)
_model, latent = self.apply_brushnet(brush_name, _model, vae, images, mask, positive, negative)
_model, positive, negative, latent = self.apply_brushnet(brush_name, _model, vae, images, mask, positive, negative)
case 'Brushnet Segmentation + DD':
mask, = GrowMask().expand_mask(mask, grow_mask_by, False)
brush_name = self.get_brushnet_model('segmentation', _model)
_model, latent = self.apply_brushnet(brush_name, _model, vae, images, mask, positive, negative)
_model, positive, negative, latent = self.apply_brushnet(brush_name, _model, vae, images, mask, positive, negative)
positive, negative, latent, _model = self.dd(_model, positive, negative, images, vae, mask)
case _:
latent, = VAEEncodeForInpaint().encode(vae, images, mask, grow_mask_by)
py/libs/sampler.py
+2 -1
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@@ -106,6 +106,7 @@ class easySampler:
to = self.add_model_patch_option(model)
to['model_patch']['step'] = 0
to['model_patch']['total_steps'] = steps
to['model_patch']['cfg'] = cfg
def callback(step, x0, x, total_steps):
if to is not None and "model_patch" in to:
@@ -163,7 +164,7 @@ class easySampler:
to = self.add_model_patch_option(model)
to['model_patch']['step'] = 0
to['model_patch']['total_steps'] = steps
to['model_patch']['cfg'] = cfg
#
#######################################################################################
web/js/easy/easyDynamicWidgets.js
+1 -9
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@@ -307,14 +307,6 @@ function widgetLogic(node, widget) {
}
updateNodeHeight(node)
}
if(widget.name == 'additional'){
if(['None','Fooocus Inpaint','InpaintModelCond'].includes(widget.value)){
toggleWidget(node, findWidgetByName(node,'grow_mask_by'), true)
}else{
toggleWidget(node, findWidgetByName(node,'grow_mask_by'))
}
}
}
function widgetLogic2(node, widget) {
@@ -1114,7 +1106,7 @@ const getSetWidgets = ['rescale_after_model', 'rescale',
'num_loras', 'mode', 'toggle', 'resolution', 'target_parameter',
'input_count', 'replace_count', 'downscale_mode', 'range_mode','text_combine_mode', 'input_mode',
'lora_count','ckpt_count', 'conditioning_mode', 'preset', 'use_tiled', 'use_batch', 'num_embeds',
"easing_mode", "guider", "scheduler", "additional"
"easing_mode", "guider", "scheduler"
]
function getSetters(node) {