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Small fixes Kandinsky5

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This commit is contained in:
Mihail Karaev 2026-02-02 09:26:09 +00:00
parent c55a6fb271
commit 94d1df4b83
2 changed files with 8 additions and 16 deletions
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12
comfy/model_base.py
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@ -1791,17 +1791,9 @@ class Kandinsky5Image(Kandinsky5):
def concat_cond(self, **kwargs): def concat_cond(self, **kwargs):
return None return None
class Kandinsky5ImageToImage(BaseModel): class Kandinsky5ImageToImage(Kandinsky5):
def __init__(self, model_config, model_type=ModelType.FLOW, device=None): def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
super().__init__( super().__init__(model_config, model_type, device=device)
model_config,
model_type,
device=device,
unet_model=comfy.ldm.kandinsky5.model.Kandinsky5
)
def encode_adm(self, **kwargs):
return kwargs["pooled_output"]
def concat_cond(self, **kwargs): def concat_cond(self, **kwargs):
noise = kwargs["noise"] noise = kwargs["noise"]

12
comfy_extras/nodes_kandinsky5.py
View File

@ -62,15 +62,15 @@ class Kandinsky5ImageToImage(io.ComfyNode):
def define_schema(cls): def define_schema(cls):
return io.Schema( return io.Schema(
node_id="Kandinsky5ImageToImage", node_id="Kandinsky5ImageToImage",
category="image", category="advanced/conditioning/kandinsky5",
inputs=[ inputs=[
io.Vae.Input("vae"), io.Vae.Input("vae"),
io.Int.Input("batch_size", default=1, min=1, max=4096), io.Int.Input("batch_size", default=1, min=1, max=4096),
io.Image.Input("start_image"), io.Image.Input("start_image"),
], ],
outputs=[ outputs=[
io.Latent.Output(display_name="latent", tooltip="Empty video latent"), io.Latent.Output(display_name="latent", tooltip="Latent of resized source image"),
io.Image.Output("resized_image"), io.Image.Output("resized_image", tooltip="Resized source image"),
], ],
) )
@ -78,8 +78,8 @@ class Kandinsky5ImageToImage(io.ComfyNode):
def execute(cls, vae, batch_size, start_image) -> io.NodeOutput: def execute(cls, vae, batch_size, start_image) -> io.NodeOutput:
height, width = start_image.shape[1:-1] height, width = start_image.shape[1:-1]
available_res = [(1024, 1024), (640, 1408), (1408, 640), (768, 1280), (1280, 768), (896, 1152), (1152, 896)] available_res = [(1024, 1024), (640, 1408), (1408, 640), (768, 1280), (1280, 768), (896, 1152), (1152, 896)]
nearest_index = torch.argmin(torch.Tensor([abs((w / h) - (width / height))for (w, h) in available_res])) nearest_index = torch.argmin(torch.Tensor([abs((h / w) - (height / width))for (h, w) in available_res]))
nw, nh = available_res[nearest_index] nh, nw = available_res[nearest_index]
scale_factor = min(height / nh, width / nw) scale_factor = min(height / nh, width / nw)
start_image = start_image.permute(0,3,1,2) start_image = start_image.permute(0,3,1,2)
start_image = F.resize(start_image, (int(height / scale_factor), int(width / scale_factor))) start_image = F.resize(start_image, (int(height / scale_factor), int(width / scale_factor)))
@ -150,7 +150,7 @@ class CLIPTextEncodeKandinsky5(io.ComfyNode):
return io.Schema( return io.Schema(
node_id="CLIPTextEncodeKandinsky5", node_id="CLIPTextEncodeKandinsky5",
search_aliases=["kandinsky prompt"], search_aliases=["kandinsky prompt"],
category="advanced/conditioning", category="advanced/conditioning/kandinsky5",
inputs=[ inputs=[
io.Clip.Input("clip"), io.Clip.Input("clip"),
io.String.Input("prompt", multiline=True, dynamic_prompts=True), io.String.Input("prompt", multiline=True, dynamic_prompts=True),