import torch import torch.nn.functional as F import comfy.utils from nodes import MAX_RESOLUTION class RSUpscaleResize: @classmethod def INPUT_TYPES(s): return { "required": { "image": ("IMAGE",), "width": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), "height": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), "upscale_method": (["lanczos", "bicubic", "bilinear", "nearest-exact", "area"], {"default": "lanczos"}), "scale_by": ("FLOAT", {"default": 2.0, "min": 0.01, "max": 8.0, "step": 0.01}), "method": (["stretch", "keep proportion", "fill / crop", "pad"], {"default": "stretch"}), "condition": (["always", "downscale if bigger", "upscale if smaller", "if bigger area", "if smaller area"], {"default": "always"}), "multiple_of": ([8, 16, 32, 64], {"default": 8}), } } RETURN_TYPES = ("IMAGE", "INT", "INT") RETURN_NAMES = ("IMAGE", "width", "height") FUNCTION = "execute" CATEGORY = "🦊 RaykoStudio" DESCRIPTION = "Hybrid upscaler and resizer. Uses 'scale_by' by default. When width/height inputs are connected or set manually (>0), it switches to exact external dimensions." def execute(self, image, width, height, upscale_method, scale_by, method, condition, multiple_of): _, oh, ow, _ = image.shape use_external = (width > 0) or (height > 0) if use_external: if width > 0 and height == 0: target_w = width target_h = round(oh * (width / ow)) elif height > 0 and width == 0: target_h = height target_w = round(ow * (height / oh)) else: target_w = width if width > 0 else ow target_h = height if height > 0 else oh else: target_w = round(ow * scale_by) target_h = round(oh * scale_by) target_w = min(target_w, MAX_RESOLUTION) target_h = min(target_h, MAX_RESOLUTION) x = y = x2 = y2 = 0 pad_left = pad_right = pad_top = pad_bottom = 0 if method == 'keep proportion' or method == 'pad': if target_w == 0 and oh < target_h: target_w = MAX_RESOLUTION elif target_w == 0 and oh >= target_h: target_w = ow if target_h == 0 and ow < target_w: target_h = MAX_RESOLUTION elif target_h == 0 and ow >= target_w: target_h = oh ratio = min(target_w / ow, target_h / oh) new_width = round(ow * ratio) new_height = round(oh * ratio) if method == 'pad': pad_left = (target_w - new_width) // 2 pad_right = target_w - new_width - pad_left pad_top = (target_h - new_height) // 2 pad_bottom = target_h - new_height - pad_top target_w = new_width target_h = new_height elif method.startswith('fill'): target_w = target_w if target_w > 0 else ow target_h = target_h if target_h > 0 else oh ratio = max(target_w / ow, target_h / oh) new_width = round(ow * ratio) new_height = round(oh * ratio) x = (new_width - target_w) // 2 y = (new_height - target_h) // 2 x2 = x + target_w y2 = y + target_h if x2 > new_width: x -= (x2 - new_width) if x < 0: x = 0 if y2 > new_height: y -= (y2 - new_height) if y < 0: y = 0 target_w = new_width target_h = new_height else: target_w = target_w if target_w > 0 else ow target_h = target_h if target_h > 0 else oh should_resize = False if "always" in condition: should_resize = True elif "downscale if bigger" == condition and (oh > target_h or ow > target_w): should_resize = True elif "upscale if smaller" == condition and (oh < target_h or ow < target_w): should_resize = True elif "bigger area" in condition and (oh * ow > target_h * target_w): should_resize = True elif "smaller area" in condition and (oh * ow < target_h * target_w): should_resize = True outputs = image if should_resize: outputs = image.permute(0, 3, 1, 2) if upscale_method == "lanczos": outputs = comfy.utils.lanczos(outputs, target_w, target_h) else: outputs = F.interpolate(outputs, size=(target_h, target_w), mode=upscale_method) if method == 'pad': if pad_left > 0 or pad_right > 0 or pad_top > 0 or pad_bottom > 0: outputs = F.pad(outputs, (pad_left, pad_right, pad_top, pad_bottom), value=0) outputs = outputs.permute(0, 2, 3, 1) if method.startswith('fill'): if x > 0 or y > 0 or x2 > 0 or y2 > 0: outputs = outputs[:, y:y2, x:x2, :] else: outputs = image final_w = outputs.shape[2] final_h = outputs.shape[1] if multiple_of > 1 and (final_w % multiple_of != 0 or final_h % multiple_of != 0): x_crop = (final_w % multiple_of) // 2 y_crop = (final_h % multiple_of) // 2 x2_crop = final_w - ((final_w % multiple_of) - x_crop) y2_crop = final_h - ((final_h % multiple_of) - y_crop) outputs = outputs[:, y_crop:y2_crop, x_crop:x2_crop, :] outputs = torch.clamp(outputs, 0, 1) return (outputs, outputs.shape[2], outputs.shape[1]) NODE_CLASS_MAPPINGS = { "RSUpscaleResize": RSUpscaleResize } NODE_DISPLAY_NAME_MAPPINGS = { "RSUpscaleResize": "🦊 RS Upscale & Resize" }