import os from fcbh_extras.chainner_models import model_loading from fcbh import model_management import torch import fcbh.utils import folder_paths 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("upscale_models", model_name) sd = fcbh.utils.load_torch_file(model_path, safe_load=True) if "module.layers.0.residual_group.blocks.0.norm1.weight" in sd: sd = fcbh.utils.state_dict_prefix_replace(sd, {"module.":""}) out = model_loading.load_state_dict(sd).eval() 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() upscale_model.to(device) in_img = image.movedim(-1,-3).to(device) free_memory = model_management.get_free_memory(device) tile = 512 overlap = 32 oom = True while oom: try: steps = in_img.shape[0] * fcbh.utils.get_tiled_scale_steps(in_img.shape[3], in_img.shape[2], tile_x=tile, tile_y=tile, overlap=overlap) pbar = fcbh.utils.ProgressBar(steps) s = fcbh.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.cpu() s = torch.clamp(s.movedim(-3,-1), min=0, max=1.0) return (s,) NODE_CLASS_MAPPINGS = { "UpscaleModelLoader": UpscaleModelLoader, "ImageUpscaleWithModel": ImageUpscaleWithModel }