Delete multi_image_loader.py
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CGlide
@@ -1,182 +0,0 @@
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import torch
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import torch.nn.functional as F
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import numpy as np
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from PIL import Image, ImageOps
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import os
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import folder_paths
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import io
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import comfy.utils
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class MultiImageLoader:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"image_paths": ("STRING", {"default": "", "multiline": True}),
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"width": ("INT", {"default": 0, "min": 0, "max": 8192, "step": 1}),
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"height": ("INT", {"default": 0, "min": 0, "max": 8192, "step": 1}),
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"interpolation": (["lanczos", "nearest", "bilinear", "bicubic", "area", "nearest-exact"],),
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"resize_method": (["keep proportion", "stretch", "pad", "crop"],),
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"multiple_of": ("INT", {"default": 32, "min": 0, "max": 512, "step": 1}),
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"img_compression": ("INT", {"default": 18, "min": 0, "max": 100, "step": 1}),
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},
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}
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# Added "IMAGE" at the beginning for multi_output + 50 individual outputs = 51 outputs
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RETURN_TYPES = ("IMAGE",) * 51
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RETURN_NAMES = ("multi_output",) + tuple(f"image_{i+1}" for i in range(50))
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FUNCTION = "load_images"
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CATEGORY = "WhatDreamsCost"
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def resize_image(self, image, width, height, resize_method="keep proportion", interpolation="nearest", multiple_of=0):
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MAX_RESOLUTION = 8192
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_, oh, ow, _ = image.shape
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x = y = x2 = y2 = 0
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pad_left = pad_right = pad_top = pad_bottom = 0
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if multiple_of > 1:
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width = width - (width % multiple_of)
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height = height - (height % multiple_of)
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if resize_method == 'keep proportion' or resize_method == 'pad':
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if width == 0 and oh < height:
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width = MAX_RESOLUTION
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elif width == 0 and oh >= height:
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width = ow
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if height == 0 and ow < width:
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height = MAX_RESOLUTION
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elif height == 0 and ow >= width:
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height = oh
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ratio = min(width / ow, height / oh)
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new_width = round(ow * ratio)
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new_height = round(oh * ratio)
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if resize_method == 'pad':
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pad_left = (width - new_width) // 2
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pad_right = width - new_width - pad_left
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pad_top = (height - new_height) // 2
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pad_bottom = height - new_height - pad_top
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width = new_width
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height = new_height
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elif resize_method == 'crop':
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width = width if width > 0 else ow
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height = height if height > 0 else oh
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ratio = max(width / ow, height / oh)
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new_width = round(ow * ratio)
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new_height = round(oh * ratio)
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x = (new_width - width) // 2
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y = (new_height - height) // 2
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x2 = x + width
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y2 = y + height
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if x2 > new_width:
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x -= (x2 - new_width)
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if x < 0:
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x = 0
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if y2 > new_height:
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y -= (y2 - new_height)
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if y < 0:
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y = 0
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width = new_width
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height = new_height
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else:
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width = width if width > 0 else ow
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height = height if height > 0 else oh
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# Always apply resize logic
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outputs = image.permute(0, 3, 1, 2)
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if interpolation == "lanczos":
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outputs = comfy.utils.lanczos(outputs, width, height)
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else:
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outputs = F.interpolate(outputs, size=(height, width), mode=interpolation)
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if resize_method == 'pad':
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if pad_left > 0 or pad_right > 0 or pad_top > 0 or pad_bottom > 0:
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outputs = F.pad(outputs, (pad_left, pad_right, pad_top, pad_bottom), value=0)
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outputs = outputs.permute(0, 2, 3, 1)
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if resize_method == 'crop':
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if x > 0 or y > 0 or x2 > 0 or y2 > 0:
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outputs = outputs[:, y:y2, x:x2, :]
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if multiple_of > 1 and (outputs.shape[2] % multiple_of != 0 or outputs.shape[1] % multiple_of != 0):
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width = outputs.shape[2]
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height = outputs.shape[1]
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x = (width % multiple_of) // 2
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y = (height % multiple_of) // 2
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x2 = width - ((width % multiple_of) - x)
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y2 = height - ((height % multiple_of) - y)
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outputs = outputs[:, y:y2, x:x2, :]
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outputs = torch.clamp(outputs, 0, 1)
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return outputs
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def load_images(self, image_paths, width, height, interpolation, resize_method, multiple_of, img_compression):
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results = []
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valid_paths = [p.strip() for p in image_paths.split("\n") if p.strip()]
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for path in valid_paths:
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try:
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# Resolve full path
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full_path = path
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if not os.path.exists(full_path):
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full_path = os.path.join(folder_paths.get_input_directory(), path)
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if not os.path.exists(full_path):
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print(f"Warning: Image path not found: {path}")
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continue
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# Load image
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image = Image.open(full_path)
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image = ImageOps.exif_transpose(image)
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image = image.convert("RGB")
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# Convert to Torch Tensor to prepare for Advanced Resize Logic
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image_np = np.array(image).astype(np.float32) / 255.0
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image_tensor = torch.from_numpy(image_np)[None,]
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# Apply Advanced Resize
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image_tensor = self.resize_image(image_tensor, width, height, resize_method, interpolation, multiple_of)
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# Compression (Applied after resize to accurately maintain the effect)
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if img_compression > 0:
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img_np = (image_tensor[0].numpy() * 255).clip(0, 255).astype(np.uint8)
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img_pil = Image.fromarray(img_np)
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img_byte_arr = io.BytesIO()
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img_pil.save(img_byte_arr, format="JPEG", quality=max(1, 100 - img_compression))
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img_pil = Image.open(img_byte_arr)
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image_tensor = torch.from_numpy(np.array(img_pil).astype(np.float32) / 255.0)[None,]
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results.append(image_tensor)
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except Exception as e:
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print(f"Error loading {path}: {e}")
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# Combine all successfully loaded images into a single batched tensor for multi_output
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if len(results) > 0:
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# Safety Check: Advanced resize methods might output differently sized tensors (e.g., 'keep proportion')
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first_shape = results[0].shape
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all_same_shape = all(r.shape == first_shape for r in results)
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if all_same_shape:
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multi_output = torch.cat(results, dim=0)
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else:
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print("MultiImageLoader Warning: Images have different dimensions due to resize settings. Cannot batch into multi_output. Outputting zero tensor for the batch, but individual output nodes will still work fine.")
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multi_output = torch.zeros((1, 64, 64, 3))
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else:
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# Fallback empty tensor if no valid paths
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multi_output = torch.zeros((1, 64, 64, 3))
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results = [multi_output]
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# Pad individual outputs exactly to length 50 as defined in RETURN_TYPES
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padded_results = results + [torch.zeros((1, 64, 64, 3))] * (50 - len(results))
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# Return the multi batch output first, followed by the individual padded items
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return (multi_output, *padded_results[:50])
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