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import torch
import comfy.utils
class PatchModelAddDownscale:
upscale_methods = ["bicubic", "nearest-exact", "bilinear", "area", "bislerp"]
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"block_number": ("INT", {"default": 3, "min": 1, "max": 32, "step": 1}),
"downscale_factor": ("FLOAT", {"default": 2.0, "min": 0.1, "max": 9.0, "step": 0.001}),
"start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
"end_percent": ("FLOAT", {"default": 0.35, "min": 0.0, "max": 1.0, "step": 0.001}),
"downscale_after_skip": ("BOOLEAN", {"default": True}),
"downscale_method": (s.upscale_methods,),
"upscale_method": (s.upscale_methods,),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
CATEGORY = "_for_testing"
def patch(self, model, block_number, downscale_factor, start_percent, end_percent, downscale_after_skip, downscale_method, upscale_method):
model_sampling = model.get_model_object("model_sampling")
sigma_start = model_sampling.percent_to_sigma(start_percent)
sigma_end = model_sampling.percent_to_sigma(end_percent)
def input_block_patch(h, transformer_options):
if transformer_options["block"][1] == block_number:
sigma = transformer_options["sigmas"][0].item()
if sigma <= sigma_start and sigma >= sigma_end:
h = comfy.utils.common_upscale(h, round(h.shape[-1] * (1.0 / downscale_factor)), round(h.shape[-2] * (1.0 / downscale_factor)), downscale_method, "disabled")
return h
def output_block_patch(h, hsp, transformer_options):
if h.shape[2] != hsp.shape[2]:
h = comfy.utils.common_upscale(h, hsp.shape[-1], hsp.shape[-2], upscale_method, "disabled")
return h, hsp
m = model.clone()
if downscale_after_skip:
m.set_model_input_block_patch_after_skip(input_block_patch)
else:
m.set_model_input_block_patch(input_block_patch)
m.set_model_output_block_patch(output_block_patch)
return (m, )
NODE_CLASS_MAPPINGS = {
"PatchModelAddDownscale": PatchModelAddDownscale,
}
NODE_DISPLAY_NAME_MAPPINGS = {
# Sampling
"PatchModelAddDownscale": "PatchModelAddDownscale (Kohya Deep Shrink)",
}
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