import torch import os from PIL import Image, ImageDraw from .utils import get_first_folder_list, tensor2pil, pil2tensor, diffuserOutpaintSamples, get_device_by_name, get_dtype_by_name, clearVram # Get the absolute path of various directories my_dir = os.path.dirname(os.path.abspath(__file__)) def can_expand(source_width, source_height, target_width, target_height, alignment): """Checks if the image can be expanded based on the alignment.""" if alignment in ("Left", "Right") and source_width >= target_width: return False if alignment in ("Top", "Bottom") and source_height >= target_height: return False return True class PadImageForDiffusersOutpaint: _alignment_options = ["Middle", "Left", "Right", "Top", "Bottom"] _resize_option = ["Full", "50%", "33%", "25%", "Custom"] @classmethod def INPUT_TYPES(s): return { "required": { "image": ("IMAGE",), "width": ("INT", {"default": 720, "tooltip": "The width used for the image."}), "height": ("INT", {"default": 1280, "tooltip": "The height used for the image."}), "alignment": (s._alignment_options, {"tooltip": "Where the original image should be in the outpainted one"}), "resize_image": (s._resize_option, {"tooltip": "Resize input image"}), "custom_resize_image_percentage": ("INT", {"min": 1, "default": 50, "max": 100, "step": 1, "tooltip": "Custom resize (%)"}), "mask_overlap_percentage": ("INT", {"min": 1, "default": 10, "max": 50, "step": 1, "tooltip": "Mask overlap (%)"}), "overlap_left": ("BOOLEAN", {"default": True}), "overlap_right": ("BOOLEAN", {"default": True}), "overlap_top": ("BOOLEAN", {"default": True}), "overlap_bottom": ("BOOLEAN", {"default": True}), }, } RETURN_TYPES = ("IMAGE", "MASK", "IMAGE") RETURN_NAMES = ("IMAGE", "MASK", "diffuser_outpaint_cnet_image") FUNCTION = "prepare_image_and_mask" CATEGORY = "DiffusersOutpaint" def prepare_image_and_mask(self, image, width, height, mask_overlap_percentage, resize_image, custom_resize_image_percentage, overlap_left, overlap_right, overlap_top, overlap_bottom, alignment="Middle"): im=tensor2pil(image) source=im.convert('RGB') target_size = (width, height) # Calculate the scaling factor to fit the image within the target size scale_factor = min(target_size[0] / source.width, target_size[1] / source.height) new_width = int(source.width * scale_factor) new_height = int(source.height * scale_factor) # Resize the source image to fit within target size source = source.resize((new_width, new_height), Image.LANCZOS) # Initialize new_width and new_height new_width, new_height = source.width, source.height # Apply resize option using percentages if resize_image == "Full": resize_percentage = 100 elif resize_image == "50%": resize_percentage = 50 elif resize_image == "33%": resize_percentage = 33 elif resize_image == "25%": resize_percentage = 25 else: # Custom resize_percentage = custom_resize_image_percentage # Calculate new dimensions based on percentage resize_factor = resize_percentage / 100 new_width = int(source.width * resize_factor) new_height = int(source.height * resize_factor) # Ensure minimum size of 64 pixels new_width = max(new_width, 64) new_height = max(new_height, 64) # Resize the image source = source.resize((new_width, new_height), Image.LANCZOS) # Calculate the overlap in pixels based on the percentage overlap_x = int(new_width * (mask_overlap_percentage / 100)) overlap_y = int(new_height * (mask_overlap_percentage / 100)) # Ensure minimum overlap of 1 pixel overlap_x = max(overlap_x, 1) overlap_y = max(overlap_y, 1) # Calculate margins based on alignment if alignment == "Middle": margin_x = (target_size[0] - source.width) // 2 margin_y = (target_size[1] - source.height) // 2 elif alignment == "Left": margin_x = 0 margin_y = (target_size[1] - source.height) // 2 elif alignment == "Right": margin_x = target_size[0] - source.width margin_y = (target_size[1] - source.height) // 2 elif alignment == "Top": margin_x = (target_size[0] - source.width) // 2 margin_y = 0 elif alignment == "Bottom": margin_x = (target_size[0] - source.width) // 2 margin_y = target_size[1] - source.height # Adjust margins to eliminate gaps margin_x = max(0, min(margin_x, target_size[0] - new_width)) margin_y = max(0, min(margin_y, target_size[1] - new_height)) # Create a new background image and paste the resized source image background = Image.new('RGB', target_size, (255, 255, 255)) background.paste(source, (margin_x, margin_y)) image=pil2tensor(background) #---------------------------------------------------- # Create the mask d1, d2, d3, d4 = image.size() left, top, bottom, right = 0, 0, 0, 0 # Image new_image = torch.ones( (d1, d2 + top + bottom, d3 + left + right, d4), dtype=torch.float32, ) * 0.5 new_image[:, top:top + d2, left:left + d3, :] = image im=tensor2pil(new_image) pil_new_image=im.convert('RGB') #---------------------------------------------------- # Create the mask mask = Image.new('L', target_size, 255) mask_draw = ImageDraw.Draw(mask) #---------------------------------------------------- # Calculate overlap areas white_gaps_patch = 2 left_overlap = margin_x + overlap_x if overlap_left else margin_x + white_gaps_patch right_overlap = margin_x + new_width - overlap_x if overlap_right else margin_x + new_width - white_gaps_patch top_overlap = margin_y + overlap_y if overlap_top else margin_y + white_gaps_patch bottom_overlap = margin_y + new_height - overlap_y if overlap_bottom else margin_y + new_height - white_gaps_patch #---------------------------------------------------- # Mask coordinates if alignment == "Left": left_overlap = margin_x + overlap_x if overlap_left else margin_x elif alignment == "Right": right_overlap = margin_x + new_width - overlap_x if overlap_right else margin_x + new_width elif alignment == "Top": top_overlap = margin_y + overlap_y if overlap_top else margin_y elif alignment == "Bottom": bottom_overlap = margin_y + new_height - overlap_y if overlap_bottom else margin_y + new_height # Draw the mask mask_draw.rectangle([ (left_overlap, top_overlap), (right_overlap, bottom_overlap) ], fill=0) tensor_mask=pil2tensor(mask) #---------------------------------------------------- if not can_expand(background.width, background.height, width, height, alignment): alignment = "Middle" cnet_image = pil_new_image.copy() # copy background as cnet_image cnet_image.paste(0, (0, 0), mask) # paste mask over cnet_image, cropping it a bit tensor_cnet_image=pil2tensor(cnet_image) return (new_image, tensor_mask, tensor_cnet_image,) class LoadDiffusersOutpaintModels: @classmethod def INPUT_TYPES(s): return { "required": { "model": (get_first_folder_list("diffusion_models"), {"default": "RealVisXL_V5.0_Lightning", "tooltip": "The diffuser model used for denoising the input latent. (Put model files in a folder, in diffusion_models folder)."}), "controlnet_model": (get_first_folder_list("diffusion_models"), {"default": "controlnet-union-sdxl-1.0", "tooltip": "The controlnet model used for denoising the input latent. (Put model files in a folder, in diffusion_models folder)."}), "device": (["auto", "cuda", "cpu", "mps", "xpu", "meta"],{"default": "auto", "tooltip": "Device for inference, default is auto checked by comfyui"}), "dtype": (["auto","fp16","bf16","fp32", "fp8_e4m3fn", "fp8_e4m3fnuz", "fp8_e5m2", "fp8_e5m2fnuz"],{"default":"auto", "tooltip": "Model precision for inference, default is auto checked by comfyui"}), "sequential_cpu_offload": ("BOOLEAN", {"default": False, "tooltip": "Inference by default needs around 8gb vram, if this option is on it will move controlnet and unet back and forth between cpu and vram, to have only one model loaded at a time (around 6 gb vram used), useful for gpus under 8gb but will impact inference speed."}), }, } RETURN_TYPES = ("PIPE",) RETURN_NAMES = ("diffusers_outpaint_pipe",) FUNCTION = "load" CATEGORY = "DiffusersOutpaint" def load(self, model, controlnet_model, device, dtype, sequential_cpu_offload): # Go 2 folders back comfy_dir = os.path.dirname(os.path.dirname(my_dir)) model_path = f"{comfy_dir}/models/diffusion_models/{model}" controlnet_path = f"{comfy_dir}/models/diffusion_models/{controlnet_model}" device = get_device_by_name(device) dtype = get_dtype_by_name(dtype) diffusers_outpaint_pipe = { "model_path": model_path, "controlnet_model": controlnet_model, "controlnet_path": controlnet_path, "device": device, "dtype": dtype, "keep_model_device": sequential_cpu_offload, } return (diffusers_outpaint_pipe,) class EncodeDiffusersOutpaintPrompt: @classmethod def INPUT_TYPES(s): return { "required": { "diffusers_outpaint_pipe": ("PIPE", {"tooltip": "Load the diffusers outpaint models."}), "text": ("STRING", {"multiline": True, "dynamicPrompts": True, "tooltip": "The text to be encoded."}), "clip": ("CLIP", {"tooltip": "The CLIP model used for encoding the text."}) } } RETURN_TYPES = ("PIPE","CONDITIONING",) RETURN_NAMES = ("diffusers_outpaint_pipe","diffusers_conditioning",) OUTPUT_TOOLTIPS = ("A conditioning containing the embedded text used to guide the diffusion model.",) FUNCTION = "encode" CATEGORY = "DiffusersOutpaint" DESCRIPTION = "Encodes a text prompt using a CLIP model into an embedding that can be used to guide the diffusion model towards generating specific images." def encode(self, diffusers_outpaint_pipe, text, clip): dtype = diffusers_outpaint_pipe["dtype"] device = diffusers_outpaint_pipe["device"] text = f"{text}, high quality, 4k" tokens = clip.tokenize(text) output = clip.encode_from_tokens(tokens, return_pooled=True, return_dict=True) prompt_embeds = output.pop("cond") prompt_embeds = prompt_embeds.to(device, dtype=dtype) pooled_prompt_embeds = output["pooled_output"].to(device, dtype=dtype) bs_embed, seq_len, _ = prompt_embeds.shape # duplicate text embeddings for each generation per prompt, using mps friendly method prompt_embeds = prompt_embeds.repeat(1, 1, 1) prompt_embeds = prompt_embeds.view(bs_embed * 1, seq_len, -1) pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, 1).view(bs_embed * 1, -1) diffusers_conditioning = { "prompt_embeds": prompt_embeds, "pooled_prompt_embeds": pooled_prompt_embeds, } return (diffusers_outpaint_pipe,diffusers_conditioning,) class DiffusersImageOutpaint: @classmethod def INPUT_TYPES(s): return { "required": { "diffusers_outpaint_pipe": ("PIPE", {"tooltip": "Load the diffusers outpaint models."}), "positive": ("CONDITIONING", {"tooltip": "The prompt describing what you want."}), "negative": ("CONDITIONING", {"tooltip": "The prompt describing what you don't want."}), "diffuser_outpaint_cnet_image": ("IMAGE", {"tooltip": "The image to outpaint."}), "guidance_scale": ("FLOAT", {"default": 1.50, "min": 1.01, "max": 10, "step": 0.01, "tooltip": "The Classifier-Free Guidance scale balances creativity and adherence to the prompt. Higher values result in images more closely matching the prompt, however too high values will negatively impact quality."}), "controlnet_strength": ("FLOAT", {"default": 1.00, "min": 0.00, "max": 10, "step": 0.01}), "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "Fake seed, workaround used to keep generating different outpaints. Set to -1 to generate different images, or a fixed number to stop that."}), "steps": ("INT", {"default": 8, "min": 4, "max": 20, "tooltip": "The number of steps used in the denoising process."}), } } RETURN_TYPES = ("LATENT",) FUNCTION = "sample" CATEGORY = "DiffusersOutpaint" def sample(self, diffusers_outpaint_pipe, positive, negative, diffuser_outpaint_cnet_image, guidance_scale, controlnet_strength, seed, steps): cnet_image = diffuser_outpaint_cnet_image cnet_image=tensor2pil(cnet_image) cnet_image=cnet_image.convert('RGB') model_path = diffusers_outpaint_pipe["model_path"] controlnet_model = diffusers_outpaint_pipe["controlnet_model"] controlnet_path = diffusers_outpaint_pipe["controlnet_path"] dtype = diffusers_outpaint_pipe["dtype"] device = diffusers_outpaint_pipe["device"] keep_model_device = diffusers_outpaint_pipe["keep_model_device"] prompt_embeds = positive["prompt_embeds"] pooled_prompt_embeds = positive["pooled_prompt_embeds"] negative_prompt_embeds = negative["prompt_embeds"] negative_pooled_prompt_embeds = negative["pooled_prompt_embeds"] last_rgb_latent = diffuserOutpaintSamples(model_path, controlnet_model, diffuser_outpaint_cnet_image, dtype, controlnet_path, prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds, device, steps, controlnet_strength, guidance_scale, keep_model_device) del prompt_embeds, pooled_prompt_embeds, negative_prompt_embeds, negative_pooled_prompt_embeds clearVram(device) return ({"samples":last_rgb_latent},)