'update'

README.md

@@ -1 +1 @@
-# ComfyUI-StylizeFilters
+# ComfyUI-StylizePhoto

__init__.py

@@ -0,0 +1,97 @@
+import os
+import sys
+from .mz_stylize_photo_utils import Utils
+from nodes import MAX_RESOLUTION
+import comfy.utils
+import shutil
+import folder_paths
+import comfy.samplers
+
+
+AUTHOR_NAME = u"[MinusZone]"
+CATEGORY_NAME = f"{AUTHOR_NAME} StylizePhoto"
+
+sys.path.append(os.path.join(os.path.dirname(__file__)))
+
+import importlib
+
+
+NODE_CLASS_MAPPINGS = {
+}
+
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+}
+
+
+
+import importlib
+import mz_stylize_photo_core
+class MZ_StylizePhotoKSamplerNode:
+ @classmethod
+ def INPUT_TYPES(s):
+ STYLE_TYPE = [
+ "clay.v1"
+ ] 
+ return {
+ "required":{
+ "xl_ckpt_name": (["none"] + folder_paths.get_filename_list("checkpoints"),),
+ "image": ("IMAGE",),
+ "resolution": ("INT", {"default": -1, "min": -1, "max": MAX_RESOLUTION}),
+ "style_type": (STYLE_TYPE,),
+ "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+ "steps": ("INT", {"default": 25, "min": 1, "max": 10000}),
+ "cfg": ("FLOAT", {"default": 5.5, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}),
+ "denoise": ("FLOAT", {"default": 0.75, "min": 0.0, "max": 1.0, "step": 0.01}),
+ "positive_prompt": ("STRING", {"default": ""}),
+ "negative_prompt": ("STRING", {"default": ""}),
+ },
+ "optional":{
+ "model": ("MODEL",),
+ "clip": ("CLIP",),
+ "vae": ("VAE",),
+ "control_net": ("StylizePhoto_CONTROL_NET",),
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ FUNCTION = "ksampler"
+
+ CATEGORY = CATEGORY_NAME
+
+
+ def ksampler(self, **kwargs):
+ importlib.reload(mz_stylize_photo_core)
+ return mz_stylize_photo_core.ksampler(kwargs)
+
+
+
+NODE_CLASS_MAPPINGS["MZ_StylizePhotoKSamplerNode"] = MZ_StylizePhotoKSamplerNode
+NODE_DISPLAY_NAME_MAPPINGS["MZ_StylizePhotoKSamplerNode"] = f"{AUTHOR_NAME} - StylizePhotoKSampler"
+
+
+class MZ_StylizePhotoControlNetApply:
+ @classmethod
+ def INPUT_TYPES(s):
+ importlib.reload(mz_stylize_photo_core)
+ required = {}
+ for key, _ in mz_stylize_photo_core.CONTROLNET_TYPES.items():
+ required[
+ f"{key}_control_net_name"
+ ] = (["none"] + folder_paths.get_filename_list("controlnet"),)
+
+
+ return {"required": required}
+
+ RETURN_TYPES = ("StylizePhoto_CONTROL_NET",)
+ FUNCTION = "load_controlnet"
+ CATEGORY = CATEGORY_NAME
+
+ def load_controlnet(self, **kwargs):
+ importlib.reload(mz_stylize_photo_core)
+ return mz_stylize_photo_core.load_controlnet(kwargs)
+
+
+
+NODE_CLASS_MAPPINGS["MZ_StylizePhotoControlNetApply"] = MZ_StylizePhotoControlNetApply
+NODE_DISPLAY_NAME_MAPPINGS["MZ_StylizePhotoControlNetApply"] = f"{AUTHOR_NAME} - StylizePhotoControlNetApply"

mz_stylize_photo_core.py

@@ -0,0 +1,149 @@
+
+import os
+
+import cv2
+from nodes import common_ksampler,VAEEncode,VAEDecode,CheckpointLoaderSimple,ControlNetLoader,ControlNetApplyAdvanced
+import importlib 
+
+
+import mz_stylize_photo_utils
+
+# importlib.reload(mz_stylize_photo_utils)
+
+
+
+FIXED_QUALITY_PROMPT = "(high quality), (best quality), (masterpiece), (8K resolution), (2k wallpaper)"
+
+STYLE_PROMPTS = {
+ "clay.v1": {
+ # "positive": "clay_world,clay_style,plasticine_style,photography,macro,tilt shift,cute,by Adult Swim,",
+ "positive": "clay,clay_model,photography,macro,cute,by Makoto Shinkai,",
+ "negative": "blurry,noisy,text,watermark"
+ }
+}
+
+STYLE_TYPE_LORA_INFOS = {
+ "clay.v1": {
+ "url": "https://www.modelscope.cn/api/v1/models/wailovet/MinusZoneAIModels/repo?Revision=master&FilePath=stylize_photo_models%2Fclay_v1.pt",
+ "output": "stylize_photo_models/clay_v1.pt",
+ }
+}
+
+Utils = mz_stylize_photo_utils.Utils
+
+def ksampler(kwargs):
+ image = kwargs.get("image")
+ resolution = kwargs.get("resolution")
+ style_type = kwargs.get("style_type")
+
+ ckpt_name = kwargs.get("xl_ckpt_name", None)
+ if ckpt_name is not None and ckpt_name != "none":
+ cache_key = f"checkpoints_{ckpt_name}"
+ mcv = Utils.cache_get(cache_key)
+ if mcv is not None:
+ print("Using cached model, clip, vae")
+ model, clip, vae = mcv
+ else:
+ model, clip, vae = CheckpointLoaderSimple().load_checkpoint(ckpt_name)
+ Utils.cache_set(cache_key, (model, clip, vae))
+ else:
+ model = kwargs.get("model")
+ clip = kwargs.get("clip")
+ vae = kwargs.get("vae")
+
+ seed = kwargs.get("seed")
+ steps = kwargs.get("steps")
+ cfg = kwargs.get("cfg")
+ denoise = kwargs.get("denoise")
+ positive_prompt = kwargs.get("positive_prompt")
+ negative_prompt = kwargs.get("negative_prompt")
+
+ sampler_name = "euler_ancestral"
+ scheduler = "normal"
+
+
+ if resolution > 0:
+ image_pil = Utils.tensor2pil(image)
+ image_pil = Utils.resize_max(image_pil, resolution, resolution)
+ image = Utils.pil2tensor(image_pil)
+ image = Utils.list_tensor2tensor([image])
+
+ latent_image = VAEEncode().encode(vae, image)[0]
+
+ lora_info = STYLE_TYPE_LORA_INFOS[style_type]
+ lora_path = Utils.download_model(lora_info)
+
+
+
+ model = Utils.load_lora(model, lora_path, 0.88)
+
+
+
+ positive = Utils.native_clip_text_encode(clip, f"{FIXED_QUALITY_PROMPT},{STYLE_PROMPTS[style_type]['positive']},{positive_prompt}")
+ negative = Utils.native_clip_text_encode(clip, f"{STYLE_PROMPTS[style_type]['negative']},{negative_prompt}")
+
+
+ control_net = kwargs.get("control_net", {})
+ for key, item in control_net.items():
+ print(f"Applying controlnet {key}")
+ preprocessed_func = CONTROLNET_TYPES[key].get("preprocessing", None)
+ preprocessed_image = Utils.tensor2pil(image)
+ if preprocessed_func is not None:
+ preprocessed_image = preprocessed_func(preprocessed_image)
+ preprocessed_image = Utils.pil2tensor(preprocessed_image)
+ preprocessed_image = Utils.list_tensor2tensor([preprocessed_image])
+
+ strength = CONTROLNET_TYPES[key].get("strength", 1.0)
+ start_percent = CONTROLNET_TYPES[key].get("start_percent", 0.0)
+ end_percent = CONTROLNET_TYPES[key].get("end_percent", 1)
+ positive, negative = ControlNetApplyAdvanced().apply_controlnet(positive, negative, item, preprocessed_image, strength, start_percent, end_percent)
+
+
+ latent = common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise)[0]
+
+ image = VAEDecode().decode(vae, latent)[0]
+ return (image,)
+
+
+
+from PIL import ImageFilter
+
+
+def preprocessing_canny(image): 
+ # image = Utils.resize_max(image, 512, 512)
+ # image = image.filter(ImageFilter.FIND_EDGES)
+
+ image = Utils.pil2cv(image)
+ image = cv2.Canny(image, 100, 200)
+ image = Utils.cv2pil(image)
+
+ # image.save("canny.jpg")
+ return image
+
+
+def preprocessing_tile(image):
+ return Utils.resize_max(image, 512, 512)
+
+CONTROLNET_TYPES = {
+ "tile": {
+ "preprocessing": preprocessing_tile,
+ "strength": 0.35,
+ "start_percent": 0.0,
+ "end_percent": 1,
+ },
+ "canny": {
+ "preprocessing": preprocessing_canny,
+ "strength": 0.45,
+ "start_percent": 0.0,
+ "end_percent": 1,
+ },
+}
+
+def load_controlnet(kwargs):
+ result = {}
+ for key, _ in CONTROLNET_TYPES.items():
+ control_net_name = kwargs.get(f"{key}_control_net_name", None)
+ if control_net_name is not None and control_net_name != "none":
+ result[key] = ControlNetLoader().load_controlnet(control_net_name)[0]
+
+ return (result,)