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