Add FG/BG blending (#8)
* Add cond support * hijack calculate_weight * Add examples
This commit is contained in:
Chenlei Hu
@@ -0,0 +1,668 @@
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51,
|
||||
33,
|
||||
0,
|
||||
29,
|
||||
0,
|
||||
"IMAGE"
|
||||
],
|
||||
[
|
||||
52,
|
||||
33,
|
||||
0,
|
||||
34,
|
||||
0,
|
||||
"IMAGE"
|
||||
]
|
||||
],
|
||||
"groups": [],
|
||||
"config": {},
|
||||
"extra": {},
|
||||
"version": 0.4
|
||||
}
|
||||
+183
-20
@@ -1,12 +1,14 @@
|
||||
import os
|
||||
from enum import Enum
|
||||
import torch
|
||||
import functools
|
||||
|
||||
import folder_paths
|
||||
import comfy.model_management
|
||||
from comfy.model_patcher import ModelPatcher
|
||||
from comfy.utils import load_torch_file
|
||||
from comfy_extras.nodes_compositing import JoinImageWithAlpha
|
||||
from comfy.conds import CONDRegular
|
||||
from .lib_layerdiffusion.utils import (
|
||||
rgba2rgbfp32,
|
||||
load_file_from_url,
|
||||
@@ -19,6 +21,65 @@ layer_model_root = os.path.join(folder_paths.models_dir, "layer_model")
|
||||
load_layer_model_state_dict = load_torch_file
|
||||
|
||||
|
||||
def calculate_weight_adjust_channel(func):
|
||||
@functools.wraps(func)
|
||||
def calculate_weight(
|
||||
self: ModelPatcher, patches, weight: torch.Tensor, key: str
|
||||
) -> torch.Tensor:
|
||||
weight = func(self, patches, weight, key)
|
||||
|
||||
for p in patches:
|
||||
alpha = p[0]
|
||||
v = p[1]
|
||||
|
||||
if isinstance(v, list):
|
||||
v = (func(v[1:], v[0].clone(), key),)
|
||||
|
||||
if len(v) == 1:
|
||||
patch_type = "diff"
|
||||
elif len(v) == 2:
|
||||
patch_type = v[0]
|
||||
v = v[1]
|
||||
|
||||
if patch_type == "diff":
|
||||
w1 = v[0]
|
||||
if all(
|
||||
(
|
||||
alpha != 0.0,
|
||||
w1.shape != weight.shape,
|
||||
w1.ndim == weight.ndim == 4,
|
||||
)
|
||||
):
|
||||
new_shape = [max(n, m) for n, m in zip(weight.shape, w1.shape)]
|
||||
print(f"Merged with {key} channel changed from {weight.shape} to {new_shape}")
|
||||
new_diff = alpha * comfy.model_management.cast_to_device(
|
||||
w1, weight.device, weight.dtype
|
||||
)
|
||||
new_weight = torch.zeros(size=new_shape).to(weight)
|
||||
new_weight[
|
||||
: weight.shape[0],
|
||||
: weight.shape[1],
|
||||
: weight.shape[2],
|
||||
: weight.shape[3],
|
||||
] = weight
|
||||
new_weight[
|
||||
: new_diff.shape[0],
|
||||
: new_diff.shape[1],
|
||||
: new_diff.shape[2],
|
||||
: new_diff.shape[3],
|
||||
] += new_diff
|
||||
new_weight = new_weight.contiguous().clone()
|
||||
weight = new_weight
|
||||
return weight
|
||||
|
||||
return calculate_weight
|
||||
|
||||
|
||||
ModelPatcher.calculate_weight = calculate_weight_adjust_channel(
|
||||
ModelPatcher.calculate_weight
|
||||
)
|
||||
|
||||
|
||||
class RGBA2RBGfp32:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
@@ -100,7 +161,52 @@ class LayerMethod(Enum):
|
||||
CONV = "Conv Injection"
|
||||
|
||||
|
||||
class LayeredDiffusionApply:
|
||||
class LayerType(Enum):
|
||||
FG = "Foreground"
|
||||
BG = "Background"
|
||||
|
||||
|
||||
class LayeredDiffusionBase:
|
||||
def __init__(self, model_file_name: str, model_url: str) -> None:
|
||||
self.model_file_name = model_file_name
|
||||
self.model_url = model_url
|
||||
|
||||
def apply_c_concat(self, cond, uncond, c_concat):
|
||||
"""Set foreground/background concat condition."""
|
||||
|
||||
def write_c_concat(cond):
|
||||
new_cond = []
|
||||
for t in cond:
|
||||
n = [t[0], t[1].copy()]
|
||||
if "model_conds" not in n[1]:
|
||||
n[1]["model_conds"] = {}
|
||||
n[1]["model_conds"]["c_concat"] = CONDRegular(c_concat)
|
||||
new_cond.append(n)
|
||||
return new_cond
|
||||
|
||||
return (write_c_concat(cond), write_c_concat(uncond))
|
||||
|
||||
def apply_layered_diffusion(
|
||||
self,
|
||||
model: ModelPatcher,
|
||||
weight: float,
|
||||
):
|
||||
"""Patch model"""
|
||||
model_path = load_file_from_url(
|
||||
url=self.model_url,
|
||||
model_dir=layer_model_root,
|
||||
file_name=self.model_file_name,
|
||||
)
|
||||
layer_lora_state_dict = load_layer_model_state_dict(model_path)
|
||||
layer_lora_patch_dict = to_lora_patch_dict(layer_lora_state_dict)
|
||||
work_model = model.clone()
|
||||
work_model.add_patches(layer_lora_patch_dict, weight)
|
||||
return (work_model,)
|
||||
|
||||
|
||||
class LayeredDiffusionFG:
|
||||
"""Generate foreground with transparent background."""
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
@@ -126,44 +232,101 @@ class LayeredDiffusionApply:
|
||||
FUNCTION = "apply_layered_diffusion"
|
||||
CATEGORY = "layered_diffusion"
|
||||
|
||||
def __init__(self) -> None:
|
||||
self.fg_attn = LayeredDiffusionBase(
|
||||
model_file_name="layer_xl_transparent_attn.safetensors",
|
||||
model_url="https://huggingface.co/LayerDiffusion/layerdiffusion-v1/resolve/main/layer_xl_transparent_attn.safetensors",
|
||||
)
|
||||
self.fg_conv = LayeredDiffusionBase(
|
||||
model_file_name="layer_xl_transparent_conv.safetensors",
|
||||
model_url="https://huggingface.co/LayerDiffusion/layerdiffusion-v1/resolve/main/layer_xl_transparent_conv.safetensors",
|
||||
)
|
||||
|
||||
def apply_layered_diffusion(
|
||||
self,
|
||||
model: ModelPatcher,
|
||||
method: str,
|
||||
weight: float,
|
||||
):
|
||||
"""Patch model"""
|
||||
method = LayerMethod(method)
|
||||
|
||||
# Patch unet
|
||||
if method == LayerMethod.ATTN:
|
||||
model_path = load_file_from_url(
|
||||
url="https://huggingface.co/LayerDiffusion/layerdiffusion-v1/resolve/main/layer_xl_transparent_attn.safetensors",
|
||||
model_dir=layer_model_root,
|
||||
file_name="layer_xl_transparent_attn.safetensors",
|
||||
)
|
||||
return self.fg_attn.apply_layered_diffusion(model, weight)
|
||||
if method == LayerMethod.CONV:
|
||||
model_path = load_file_from_url(
|
||||
url="https://huggingface.co/LayerDiffusion/layerdiffusion-v1/resolve/main/layer_xl_transparent_conv.safetensors",
|
||||
model_dir=layer_model_root,
|
||||
file_name="layer_xl_transparent_conv.safetensors",
|
||||
)
|
||||
return self.fg_conv.apply_layered_diffusion(model, weight)
|
||||
|
||||
layer_lora_state_dict = load_layer_model_state_dict(model_path)
|
||||
layer_lora_patch_dict = to_lora_patch_dict(layer_lora_state_dict)
|
||||
work_model = model.clone()
|
||||
work_model.add_patches(layer_lora_patch_dict, weight)
|
||||
return (work_model,)
|
||||
|
||||
class LayeredDiffusionCond:
|
||||
"""Generate foreground + background given background / foreground."""
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"model": ("MODEL",),
|
||||
"cond": ("CONDITIONING",),
|
||||
"uncond": ("CONDITIONING",),
|
||||
"latent": ("LATENT",),
|
||||
"layer_type": (
|
||||
[
|
||||
LayerType.FG.value,
|
||||
LayerType.BG.value,
|
||||
],
|
||||
{
|
||||
"default": LayerType.BG.value,
|
||||
},
|
||||
),
|
||||
"weight": (
|
||||
"FLOAT",
|
||||
{"default": 1.0, "min": -1, "max": 3, "step": 0.05},
|
||||
),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("MODEL", "CONDITIONING", "CONDITIONING")
|
||||
FUNCTION = "apply_layered_diffusion"
|
||||
CATEGORY = "layered_diffusion"
|
||||
|
||||
def __init__(self) -> None:
|
||||
self.fg_cond = LayeredDiffusionBase(
|
||||
model_file_name="layer_xl_fg2ble.safetensors",
|
||||
model_url="https://huggingface.co/LayerDiffusion/layerdiffusion-v1/resolve/main/layer_xl_fg2ble.safetensors",
|
||||
)
|
||||
self.bg_cond = LayeredDiffusionBase(
|
||||
model_file_name="layer_xl_bg2ble.safetensors",
|
||||
model_url="https://huggingface.co/LayerDiffusion/layerdiffusion-v1/resolve/main/layer_xl_bg2ble.safetensors",
|
||||
)
|
||||
|
||||
def apply_layered_diffusion(
|
||||
self,
|
||||
model: ModelPatcher,
|
||||
cond,
|
||||
uncond,
|
||||
latent,
|
||||
layer_type,
|
||||
weight: float,
|
||||
):
|
||||
layer_type = LayerType(layer_type)
|
||||
if layer_type == LayerType.FG:
|
||||
ld = self.fg_cond
|
||||
elif layer_type == LayerType.BG:
|
||||
ld = self.bg_cond
|
||||
|
||||
c_concat = model.model.latent_format.process_in(latent["samples"])
|
||||
return ld.apply_layered_diffusion(model, weight) + ld.apply_c_concat(
|
||||
cond, uncond, c_concat
|
||||
)
|
||||
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"LayeredDiffusionApply": LayeredDiffusionApply,
|
||||
"LayeredDiffusionApply": LayeredDiffusionFG,
|
||||
"LayeredDiffusionCondApply": LayeredDiffusionCond,
|
||||
"LayeredDiffusionDecode": LayeredDiffusionDecode,
|
||||
"LayeredDiffusionDecodeRGBA": LayeredDiffusionDecodeRGBA,
|
||||
}
|
||||
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"LayeredDiffusionApply": "Layer Diffusion Apply",
|
||||
"LayeredDiffusionCondApply": "Layer Diffusion Cond Apply",
|
||||
"LayeredDiffusionDecode": "Layer Diffusion Decode",
|
||||
"LayeredDiffusionDecodeRGBA": "Layer Diffusion Decode (RGBA)",
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user