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import os
import logging
from spandrel import ModelLoader, ImageModelDescriptor
from comfy import model_management
import torch
import comfy.utils
import folder_paths
try:
from spandrel_extra_arches import EXTRA_REGISTRY
from spandrel import MAIN_REGISTRY
MAIN_REGISTRY.add(*EXTRA_REGISTRY)
logging.info("Successfully imported spandrel_extra_arches: support for non commercial upscale models.")
except:
pass
class UpscaleModelLoader:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model_name": (folder_paths.get_filename_list("upscale_models"), ),
}}
RETURN_TYPES = ("UPSCALE_MODEL",)
FUNCTION = "load_model"
CATEGORY = "loaders"
def load_model(self, model_name):
model_path = folder_paths.get_full_path_or_raise("upscale_models", model_name)
sd = comfy.utils.load_torch_file(model_path, safe_load=True)
if "module.layers.0.residual_group.blocks.0.norm1.weight" in sd:
sd = comfy.utils.state_dict_prefix_replace(sd, {"module.":""})
out = ModelLoader().load_from_state_dict(sd).eval()
if not isinstance(out, ImageModelDescriptor):
raise Exception("Upscale model must be a single-image model.")
return (out, )
class ImageUpscaleWithModel:
@classmethod
def INPUT_TYPES(s):
return {"required": { "upscale_model": ("UPSCALE_MODEL",),
"image": ("IMAGE",),
}}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "upscale"
CATEGORY = "image/upscaling"
def upscale(self, upscale_model, image):
device = model_management.get_torch_device()
memory_required = model_management.module_size(upscale_model.model)
memory_required += (512 * 512 * 3) * image.element_size() * max(upscale_model.scale, 1.0) * 384.0 #The 384.0 is an estimate of how much some of these models take, TODO: make it more accurate
memory_required += image.nelement() * image.element_size()
model_management.free_memory(memory_required, device)
upscale_model.to(device)
in_img = image.movedim(-1,-3).to(device)
tile = 512
overlap = 32
oom = True
while oom:
try:
steps = in_img.shape[0] * comfy.utils.get_tiled_scale_steps(in_img.shape[3], in_img.shape[2], tile_x=tile, tile_y=tile, overlap=overlap)
pbar = comfy.utils.ProgressBar(steps)
s = comfy.utils.tiled_scale(in_img, lambda a: upscale_model(a), tile_x=tile, tile_y=tile, overlap=overlap, upscale_amount=upscale_model.scale, pbar=pbar)
oom = False
except model_management.OOM_EXCEPTION as e:
tile //= 2
if tile < 128:
raise e
upscale_model.to("cpu")
s = torch.clamp(s.movedim(-3,-1), min=0, max=1.0)
return (s,)
NODE_CLASS_MAPPINGS = {
"UpscaleModelLoader": UpscaleModelLoader,
"ImageUpscaleWithModel": ImageUpscaleWithModel
}
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