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Zero
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import torch
import folder_paths
import comfy.utils
import comfy.ops
import comfy.model_management
import comfy.ldm.common_dit
import comfy.latent_formats
class BlockWiseControlBlock(torch.nn.Module):
# [linear, gelu, linear]
def __init__(self, dim: int = 3072, device=None, dtype=None, operations=None):
super().__init__()
self.x_rms = operations.RMSNorm(dim, eps=1e-6)
self.y_rms = operations.RMSNorm(dim, eps=1e-6)
self.input_proj = operations.Linear(dim, dim)
self.act = torch.nn.GELU()
self.output_proj = operations.Linear(dim, dim)
def forward(self, x, y):
x, y = self.x_rms(x), self.y_rms(y)
x = self.input_proj(x + y)
x = self.act(x)
x = self.output_proj(x)
return x
class QwenImageBlockWiseControlNet(torch.nn.Module):
def __init__(
self,
num_layers: int = 60,
in_dim: int = 64,
additional_in_dim: int = 0,
dim: int = 3072,
device=None, dtype=None, operations=None
):
super().__init__()
self.additional_in_dim = additional_in_dim
self.img_in = operations.Linear(in_dim + additional_in_dim, dim, device=device, dtype=dtype)
self.controlnet_blocks = torch.nn.ModuleList(
[
BlockWiseControlBlock(dim, device=device, dtype=dtype, operations=operations)
for _ in range(num_layers)
]
)
def process_input_latent_image(self, latent_image):
latent_image[:, :16] = comfy.latent_formats.Wan21().process_in(latent_image[:, :16])
patch_size = 2
hidden_states = comfy.ldm.common_dit.pad_to_patch_size(latent_image, (1, patch_size, patch_size))
orig_shape = hidden_states.shape
hidden_states = hidden_states.view(orig_shape[0], orig_shape[1], orig_shape[-2] // 2, 2, orig_shape[-1] // 2, 2)
hidden_states = hidden_states.permute(0, 2, 4, 1, 3, 5)
hidden_states = hidden_states.reshape(orig_shape[0], (orig_shape[-2] // 2) * (orig_shape[-1] // 2), orig_shape[1] * 4)
return self.img_in(hidden_states)
def control_block(self, img, controlnet_conditioning, block_id):
return self.controlnet_blocks[block_id](img, controlnet_conditioning)
class ModelPatchLoader:
@classmethod
def INPUT_TYPES(s):
return {"required": { "name": (folder_paths.get_filename_list("model_patches"), ),
}}
RETURN_TYPES = ("MODEL_PATCH",)
FUNCTION = "load_model_patch"
EXPERIMENTAL = True
CATEGORY = "advanced/loaders"
def load_model_patch(self, name):
model_patch_path = folder_paths.get_full_path_or_raise("model_patches", name)
sd = comfy.utils.load_torch_file(model_patch_path, safe_load=True)
dtype = comfy.utils.weight_dtype(sd)
# TODO: this node will work with more types of model patches
additional_in_dim = sd["img_in.weight"].shape[1] - 64
model = QwenImageBlockWiseControlNet(additional_in_dim=additional_in_dim, device=comfy.model_management.unet_offload_device(), dtype=dtype, operations=comfy.ops.manual_cast)
model.load_state_dict(sd)
model = comfy.model_patcher.ModelPatcher(model, load_device=comfy.model_management.get_torch_device(), offload_device=comfy.model_management.unet_offload_device())
return (model,)
class DiffSynthCnetPatch:
def __init__(self, model_patch, vae, image, strength, mask=None):
self.model_patch = model_patch
self.vae = vae
self.image = image
self.strength = strength
self.mask = mask
self.encoded_image = model_patch.model.process_input_latent_image(self.encode_latent_cond(image))
self.encoded_image_size = (image.shape[1], image.shape[2])
def encode_latent_cond(self, image):
latent_image = self.vae.encode(image)
if self.model_patch.model.additional_in_dim > 0:
if self.mask is None:
mask_ = torch.ones_like(latent_image)[:, :self.model_patch.model.additional_in_dim // 4]
else:
mask_ = comfy.utils.common_upscale(self.mask.mean(dim=1, keepdim=True), latent_image.shape[-1], latent_image.shape[-2], "bilinear", "none")
return torch.cat([latent_image, mask_], dim=1)
else:
return latent_image
def __call__(self, kwargs):
x = kwargs.get("x")
img = kwargs.get("img")
block_index = kwargs.get("block_index")
spacial_compression = self.vae.spacial_compression_encode()
if self.encoded_image is None or self.encoded_image_size != (x.shape[-2] * spacial_compression, x.shape[-1] * spacial_compression):
image_scaled = comfy.utils.common_upscale(self.image.movedim(-1, 1), x.shape[-1] * spacial_compression, x.shape[-2] * spacial_compression, "area", "center")
loaded_models = comfy.model_management.loaded_models(only_currently_used=True)
self.encoded_image = self.model_patch.model.process_input_latent_image(self.encode_latent_cond(image_scaled.movedim(1, -1)))
self.encoded_image_size = (image_scaled.shape[-2], image_scaled.shape[-1])
comfy.model_management.load_models_gpu(loaded_models)
img[:, :self.encoded_image.shape[1]] += (self.model_patch.model.control_block(img[:, :self.encoded_image.shape[1]], self.encoded_image.to(img.dtype), block_index) * self.strength)
kwargs['img'] = img
return kwargs
def to(self, device_or_dtype):
if isinstance(device_or_dtype, torch.device):
self.encoded_image = self.encoded_image.to(device_or_dtype)
return self
def models(self):
return [self.model_patch]
class QwenImageDiffsynthControlnet:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"model_patch": ("MODEL_PATCH",),
"vae": ("VAE",),
"image": ("IMAGE",),
"strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
},
"optional": {"mask": ("MASK",)}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "diffsynth_controlnet"
EXPERIMENTAL = True
CATEGORY = "advanced/loaders/qwen"
def diffsynth_controlnet(self, model, model_patch, vae, image, strength, mask=None):
model_patched = model.clone()
image = image[:, :, :, :3]
if mask is not None:
if mask.ndim == 3:
mask = mask.unsqueeze(1)
if mask.ndim == 4:
mask = mask.unsqueeze(2)
mask = 1.0 - mask
model_patched.set_model_double_block_patch(DiffSynthCnetPatch(model_patch, vae, image, strength, mask))
return (model_patched,)
NODE_CLASS_MAPPINGS = {
"ModelPatchLoader": ModelPatchLoader,
"QwenImageDiffsynthControlnet": QwenImageDiffsynthControlnet,
}
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