Update Hidiffusion_node.py
add new
This commit is contained in:
smthemex
+256
-91
@@ -15,6 +15,8 @@ from diffusers import (StableDiffusionXLPipeline, DiffusionPipeline, DDIMSchedul
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EulerAncestralDiscreteScheduler, UniPCMultistepScheduler, AutoencoderKL,
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StableDiffusionXLControlNetPipeline, DDPMScheduler, TCDScheduler, LCMScheduler,
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StableDiffusionPipeline, StableDiffusionControlNetPipeline)
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from huggingface_hub import hf_hub_download
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from .hidiffusion.hidiffusion import apply_hidiffusion
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import folder_paths
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from safetensors.torch import load_file, load
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@@ -30,17 +32,30 @@ else:
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from diffusers.models.unet_2d_condition import UNet2DConditionModel
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from comfy.utils import common_upscale
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from .guided_filter import FastGuidedFilter
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from .ip_adapter import IPAdapterXL,IPAdapter
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dir_path = os.path.dirname(os.path.abspath(__file__))
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path_dir = os.path.dirname(dir_path)
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file_path = os.path.dirname(path_dir)
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paths = []
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paths_a = []
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for search_path in folder_paths.get_folder_paths("diffusers"):
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if os.path.exists(search_path):
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for root, subdir, files in os.walk(search_path, followlinks=True):
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if "model_index.json" in files:
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paths.append(os.path.relpath(root, start=search_path))
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if paths:
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paths = ["none"] + [x for x in paths if x]
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else:
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paths = ["none", ]
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paths_a = []
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paths_b= []
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for search_path in folder_paths.get_folder_paths("diffusers"):
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if os.path.exists(search_path):
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for root, subdir, files in os.walk(search_path, followlinks=True):
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if "config.json" in files:
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paths_a.append(os.path.relpath(root, start=search_path))
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paths_a = ([z for z in paths_a if "controlnet-canny-sdxl-1.0" in z]
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@@ -49,11 +64,14 @@ for search_path in folder_paths.get_folder_paths("diffusers"):
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+ [Q for Q in paths_a if "controlnet-openpose-sdxl-1.0" in Q]
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+ [Z for Z in paths_a if "controlnet-scribble-sdxl-1.0" in Z]
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+[x for x in paths_a if "controlnet-tile-sdxl-1.0" in x])
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if paths != [] or paths_a != []:
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paths = ["none"] + [x for x in paths if x] + [y for y in paths_a if y]
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if "model_index.json" in files:
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paths_b.append(os.path.relpath(root, start=search_path))
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if paths_a:
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paths_a = ["none"] + [y for y in paths_a if y]+[x for x in paths_b if x.split("\\")[-1]=="stable-diffusion-xl-1.0-inpainting-0.1"]
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else:
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paths = ["none", ]
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paths_a = ["none", ]
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scheduler_list = ["DDIM",
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"Euler",
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@@ -211,14 +229,15 @@ class HI_Diffusers_Model_Loader:
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"local_model_path": (paths,),
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"repo_id": ("STRING", {"default": "stabilityai/stable-diffusion-xl-base-1.0"}),
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"unet_model": (["none"] + folder_paths.get_filename_list("unet"),),
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"controlnet_local_model": (paths,),
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"controlnet_local_model": (paths_a,),
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"controlnet_repo_id": ("STRING", {"default": "diffusers/controlnet-canny-sdxl-1.0"}),
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"function_choice": (["txt2img", "img2img", ],),
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"scheduler": (scheduler_list,),
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"lora": (["none"] + folder_paths.get_filename_list("loras"),),
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"lora_scale": ("FLOAT", {"default": 0.8, "min": 0.1, "max": 1.0, "step": 0.1}),
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"trigger_words": ("STRING", {"default": "best quality"}),
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"apply_window_attn":("BOOLEAN", {"default": False},)
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"apply_window_attn":("BOOLEAN", {"default": False},),
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"ip_adapter": ("BOOLEAN", {"default": False},),
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}
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}
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@@ -228,7 +247,7 @@ class HI_Diffusers_Model_Loader:
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CATEGORY = "Hidiffusion_Pro"
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def loader_models(self, local_model_path, repo_id, unet_model, controlnet_local_model, controlnet_repo_id,
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function_choice,scheduler,lora,lora_scale,trigger_words,apply_window_attn):
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function_choice,scheduler,lora,lora_scale,trigger_words,apply_window_attn,ip_adapter):
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repo_id = instance_path(local_model_path, repo_id)
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controlnet_repo_id = instance_path(controlnet_local_model, controlnet_repo_id)
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scheduler_used = get_sheduler(scheduler)
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@@ -239,6 +258,7 @@ class HI_Diffusers_Model_Loader:
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control_model_type="none"
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else:
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control_model_type = controlnet_repo_id.rsplit("/")[-1]
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if control_model_type == "none":
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if model_type in xl_model_support:
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if unet_model in sdxl_lightning_list:
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@@ -337,6 +357,7 @@ class HI_Diffusers_Model_Loader:
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else:
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raise "only support SDXL controlnet"
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model.scheduler = scheduler_used.from_config(model.scheduler.config, timestep_spacing="trailing")
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if lora!="none":
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lora_path = folder_paths.get_full_path("loras", lora)
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lora_path = get_instance_path(lora_path)
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@@ -345,16 +366,80 @@ class HI_Diffusers_Model_Loader:
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# model.fuse_lora()
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model.load_lora_weights(lora_path, adapter_name=trigger_words)
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model.fuse_lora(lora_scale=lora_scale, adapter_names=[trigger_words,])
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# Optional. enable_xformers_memory_efficient_attention can save memory usage and increase inference
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# speed. enable_model_cpu_offload and enable_vae_tiling can save memory usage.
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# Apply hidiffusion with a single line of code.
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apply_hidiffusion(model,apply_window_attn=apply_window_attn)
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model.enable_xformers_memory_efficient_attention()
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model.enable_model_cpu_offload()
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model.enable_vae_tiling()
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model_info = str(";".join([model_type, unet_model, control_model_type, function_choice,lora,trigger_words]))
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#apply_hidiffusion(model)
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apply_hidiffusion(model,apply_window_attn=apply_window_attn)
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#model.enable_model_cpu_offload() # need below apply_hidiffusion(model)
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adapter_info = "none"
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if ip_adapter:
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ip_path = os.path.join(dir_path, "weights")
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ip_model_dir_origin=get_instance_path(ip_path)
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device = "cuda"
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if model_type in xl_model_support: # #ip-adapter_sdxl.bin
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ip_model_path_xl = os.path.join(ip_path, "sdxl_models", "ip-adapter_sdxl.bin")
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if not os.path.exists(ip_model_path_xl):
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hf_hub_download(
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repo_id="h94/IP-Adapter",
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subfolder="sdxl_models",
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filename="ip-adapter_sdxl.bin",
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repo_type="model",
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local_dir=ip_model_dir_origin,
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)
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adapter_path_xl = get_instance_path(ip_model_path_xl)
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else:
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adapter_path_xl = get_instance_path(ip_model_path_xl)
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image_encoder_path_xl = get_instance_path(
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os.path.join(ip_path, "sdxl_models", "image_encoder", "model.safetensors"))
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ip_model_path_dir = get_instance_path(os.path.join(ip_path, "sdxl_models", "image_encoder"))
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if not os.path.exists(image_encoder_path_xl):
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hf_hub_download(
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repo_id="h94/IP-Adapter",
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subfolder="sdxl_models/image_encoder",
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repo_type="model",
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local_dir=ip_model_dir_origin,
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)
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adapter_encoder_xl = get_instance_path(ip_model_path_dir)
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else:
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adapter_encoder_xl = get_instance_path(ip_model_path_dir)
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model = IPAdapterXL(model, adapter_encoder_xl, adapter_path_xl, device,
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target_blocks=["up_blocks.0.attentions.1"])
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elif model_type == "stable-diffusion-v1-5": # SD1.5 ip-adapter_sd15.bin
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# sd1.5
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ip_model_path = os.path.join(ip_path, "models", "ip-adapter_sd15.bin")
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if not os.path.exists(ip_model_path):
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hf_hub_download(
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repo_id="h94/IP-Adapter",
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subfolder="models",
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filename="ip-adapter_sd15.bin",
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repo_type="model",
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local_dir=ip_model_dir_origin,
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)
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adapter_path_sd = get_instance_path(ip_model_path)
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else:
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adapter_path_sd = get_instance_path(ip_model_path)
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image_encoder_path = get_instance_path(
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os.path.join(ip_path, "models", "image_encoder", "model.safetensors"))
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ip_model_encoder_dir = get_instance_path(os.path.join(ip_path, "models", "image_encoder"))
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if not os.path.exists(image_encoder_path):
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hf_hub_download(
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repo_id="h94/IP-Adapter",
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subfolder="models/image_encoder",
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filename="model.safetensors",
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repo_type="model",
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local_dir=ip_model_dir_origin,
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)
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adapter_encoder = get_instance_path(ip_model_encoder_dir)
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else:
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adapter_encoder = get_instance_path(ip_model_encoder_dir)
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model = IPAdapter(model, adapter_encoder, adapter_path_sd, device, target_blocks=["block"])
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adapter_info="ture"
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model_info = str(";".join([model_type, unet_model, control_model_type, function_choice,lora,trigger_words, adapter_info]))
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return (model, model_info,)
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@@ -372,9 +457,7 @@ class Hi_Sampler:
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"prompt": ("STRING", {"multiline": True,
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"default": "a girl,8k,smile,best quality"}),
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"negative_prompt": ("STRING", {"multiline": True,
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"default": "blurry, ugly, duplicate, poorly drawn face, deformed, "
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"mosaic, artifacts, bad limbs"}),
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"default": "text, watermark, lowres, low quality, worst quality, deformed, glitch, low contrast, noisy, saturation, blurry"}),
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"controlnet_scale": ("FLOAT", {"default": 0.5, "min": 0.1, "max": 1.0, "step": 0.1}),
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"clip_skip": ("INT", {"default": 1, "min": -5, "max": 100,"step": 1}),
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"pre_input": ("INT", {"default": 512, "min": 256, "max": 1024, "step": 64}),
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@@ -383,9 +466,11 @@ class Hi_Sampler:
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"cfg": ("FLOAT", {"default": 7.5, "min": 0.0, "max": 100.0, "step": 0.1, "round": 0.01}),
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"width": ("INT", {"default": 2048, "min": 64, "max": 8192, "step": 64, "display": "number"}),
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"height": ("INT", {"default": 2048, "min": 64, "max": 8192, "step": 64, "display": "number"}),
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"adapter_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.1,}),
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},
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"optional": {"image": ("IMAGE",),
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"control_image": ("IMAGE",)}
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"control_image": ("IMAGE",),
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"ip_image": ("IMAGE",)}
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}
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RETURN_TYPES = ("IMAGE",)
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@@ -395,85 +480,165 @@ class Hi_Sampler:
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def hi_sampler(self, model, model_info, prompt, negative_prompt, controlnet_scale,clip_skip,pre_input,
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seed,steps, cfg, width,height,**kwargs):
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model_type, unet_model, control_net, function_choice, lora, trigger_words = model_info.split(";")
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seed,steps, cfg, width,height,adapter_scale,**kwargs):
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model_type, unet_model, control_net, function_choice, lora, trigger_words, adapter_info = model_info.split(";")
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# control_image = np.transpose(control_image, (
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# # 0, 3, 1, 2)) # 将comfy输入的（batch_size,channels,rows,cols）改成条件需求的（3通道，16输出，3，1 ，pading=1）
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if lora!="none":
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prompt = prompt + " " + trigger_words
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#print(model_type, unet_model, control_net, function_choice)
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if control_net == "none":
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if function_choice == "img2img":
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image = kwargs["image"]
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image = input_size_adaptation_output(image, pre_input, width, height)
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image = \
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model(prompt, negative_prompt=negative_prompt, image=image, num_inference_steps=steps,
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guidance_scale=cfg, clip_skip=clip_skip,
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height=height, width=width, seed=seed, ).images[0]
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else:
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image = \
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model(prompt, negative_prompt=negative_prompt, num_inference_steps=steps,
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guidance_scale=cfg, clip_skip=clip_skip,
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height=height, width=width, seed=seed, ).images[0]
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else:
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control_image = kwargs["control_image"]
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if control_net == "controlnet-tile-sdxl-1.0":
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control_image = input_size_adaptation_output(control_image, pre_input, width, height)
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controlnet_img = cv2.cvtColor(np.asarray(control_image), cv2.COLOR_RGB2BGR)
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new_height, new_width, _ = controlnet_img.shape
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ratio = np.sqrt(1024. * 1024. / (new_width * new_height))
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W, H = int(new_width * ratio), int(new_height * ratio)
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crop_w, crop_h = 0, 0
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controlnet_img = cv2.resize(controlnet_img, (W, H))
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blur_strength = random.sample([i / 10. for i in range(10, 201, 2)], k=1)[0]
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radius = random.sample([i for i in range(1, 40, 2)], k=1)[0]
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eps = random.sample([i / 1000. for i in range(1, 101, 2)], k=1)[0]
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scale_factor = random.sample([i / 10. for i in range(10, 181, 5)], k=1)[0]
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if random.random() > 0.5:
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controlnet_img = apply_gaussian_blur(controlnet_img, ksize=int(blur_strength),
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sigmaX=blur_strength / 2)
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if random.random() > 0.5:
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# Apply Guided Filter
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controlnet_img = apply_guided_filter(controlnet_img, radius, eps, scale_factor)
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# Resize image
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controlnet_img = cv2.resize(controlnet_img, (int(W / scale_factor), int(H / scale_factor)),
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interpolation=cv2.INTER_AREA)
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controlnet_img = cv2.resize(controlnet_img, (W, H), interpolation=cv2.INTER_CUBIC)
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controlnet_img = cv2.cvtColor(controlnet_img, cv2.COLOR_BGR2RGB)
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control_image = Image.fromarray(controlnet_img)
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else:
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control_image=input_size_adaptation_output(control_image, pre_input, width, height)
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if function_choice == "img2img":
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image = kwargs["image"]
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image=input_size_adaptation_output(image, pre_input, width, height)
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if control_net == "stable-diffusion-xl-1.0-inpainting-0.1":
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image = \
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model(prompt, negative_prompt=negative_prompt, image=image, mask_image=control_image,
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num_inference_steps=steps, guidance_scale=cfg, height=height, clip_skip=clip_skip,
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width=width, controlnet_conditioning_scale=controlnet_scale,
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seed=seed, ).images[0]
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if adapter_info!="none":
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ip_image = kwargs["ip_image"]
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ip_image = input_size_adaptation_output(ip_image, pre_input, width, height)
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if lora != "none":
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prompt = prompt + " " + trigger_words
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# print(model_type, unet_model, control_net, function_choice)
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if control_net == "none":
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if function_choice == "img2img":
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image = kwargs["image"]
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image = input_size_adaptation_output(image, pre_input, width, height)
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images = \
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model.generate(prompt=prompt, negative_prompt=negative_prompt,pil_image=ip_image, image=image, scale=adapter_scale,num_inference_steps=steps,
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guidance_scale=cfg, clip_skip=clip_skip,
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height=height, width=width, seed=seed, )
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else:
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image = model(prompt, negative_prompt=negative_prompt, image=image, control_image=control_image,
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num_inference_steps=steps, guidance_scale=cfg, height=height, width=width,
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clip_skip=clip_skip,
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controlnet_conditioning_scale=controlnet_scale,
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seed=seed, ).images[0]
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images = \
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model.generate(pil_image=ip_image,prompt=prompt, negative_prompt=negative_prompt,scale=adapter_scale, num_inference_steps=steps,
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guidance_scale=cfg, clip_skip=clip_skip,
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height=height, width=width, seed=seed, )
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else:
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image = model(prompt, negative_prompt=negative_prompt, image=control_image,
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num_inference_steps=steps, guidance_scale=cfg, height=height, width=width,
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clip_skip=clip_skip,
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controlnet_conditioning_scale=controlnet_scale,
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seed=seed, ).images[0]
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control_image = kwargs["control_image"]
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if control_net == "controlnet-tile-sdxl-1.0":
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control_image = input_size_adaptation_output(control_image, pre_input, width, height)
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controlnet_img = cv2.cvtColor(np.asarray(control_image), cv2.COLOR_RGB2BGR)
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new_height, new_width, _ = controlnet_img.shape
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ratio = np.sqrt(1024. * 1024. / (new_width * new_height))
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W, H = int(new_width * ratio), int(new_height * ratio)
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crop_w, crop_h = 0, 0
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controlnet_img = cv2.resize(controlnet_img, (W, H))
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blur_strength = random.sample([i / 10. for i in range(10, 201, 2)], k=1)[0]
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radius = random.sample([i for i in range(1, 40, 2)], k=1)[0]
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eps = random.sample([i / 1000. for i in range(1, 101, 2)], k=1)[0]
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scale_factor = random.sample([i / 10. for i in range(10, 181, 5)], k=1)[0]
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if random.random() > 0.5:
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controlnet_img = apply_gaussian_blur(controlnet_img, ksize=int(blur_strength),
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sigmaX=blur_strength / 2)
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if random.random() > 0.5:
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# Apply Guided Filter
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controlnet_img = apply_guided_filter(controlnet_img, radius, eps, scale_factor)
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# Resize image
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controlnet_img = cv2.resize(controlnet_img, (int(W / scale_factor), int(H / scale_factor)),
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interpolation=cv2.INTER_AREA)
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controlnet_img = cv2.resize(controlnet_img, (W, H), interpolation=cv2.INTER_CUBIC)
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controlnet_img = cv2.cvtColor(controlnet_img, cv2.COLOR_BGR2RGB)
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control_image = Image.fromarray(controlnet_img)
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else:
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control_image = input_size_adaptation_output(control_image, pre_input, width, height)
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if function_choice == "img2img":
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image = kwargs["image"]
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image = input_size_adaptation_output(image, pre_input, width, height)
|
||||
if control_net == "stable-diffusion-xl-1.0-inpainting-0.1":
|
||||
images = model.generate(prompt=prompt, negative_prompt=negative_prompt, image=image,pil_image=ip_image, scale=adapter_scale,mask_image=control_image,
|
||||
num_inference_steps=steps, guidance_scale=cfg, height=height, clip_skip=clip_skip,
|
||||
width=width, controlnet_conditioning_scale=controlnet_scale,
|
||||
seed=seed, )
|
||||
else:
|
||||
images = model.generate(prompt=prompt, negative_prompt=negative_prompt, image=image, pil_image=ip_image,scale=adapter_scale,control_image=control_image,
|
||||
num_inference_steps=steps, guidance_scale=cfg, height=height, width=width,
|
||||
clip_skip=clip_skip,
|
||||
controlnet_conditioning_scale=controlnet_scale,
|
||||
seed=seed, )
|
||||
else:
|
||||
images = model.generate(prompt=prompt, negative_prompt=negative_prompt, pil_image=ip_image,scale=adapter_scale,control_image=control_image,
|
||||
num_inference_steps=steps, guidance_scale=cfg, height=height, width=width,
|
||||
clip_skip=clip_skip,
|
||||
controlnet_conditioning_scale=controlnet_scale,
|
||||
seed=seed, )
|
||||
images = images[0]
|
||||
else:
|
||||
if lora != "none":
|
||||
prompt = prompt + " " + trigger_words
|
||||
# print(model_type, unet_model, control_net, function_choice)
|
||||
if control_net == "none":
|
||||
if function_choice == "img2img":
|
||||
image = kwargs["image"]
|
||||
image = input_size_adaptation_output(image, pre_input, width, height)
|
||||
images = \
|
||||
model(prompt, negative_prompt=negative_prompt, image=image, num_inference_steps=steps,
|
||||
guidance_scale=cfg, clip_skip=clip_skip,
|
||||
height=height, width=width, seed=seed, ).images[0]
|
||||
else:
|
||||
images = \
|
||||
model(prompt, negative_prompt=negative_prompt, num_inference_steps=steps,
|
||||
guidance_scale=cfg, clip_skip=clip_skip,
|
||||
height=height, width=width, seed=seed, ).images[0]
|
||||
else:
|
||||
control_image = kwargs["control_image"]
|
||||
if control_net == "controlnet-tile-sdxl-1.0":
|
||||
control_image = input_size_adaptation_output(control_image, pre_input, width, height)
|
||||
controlnet_img = cv2.cvtColor(np.asarray(control_image), cv2.COLOR_RGB2BGR)
|
||||
new_height, new_width, _ = controlnet_img.shape
|
||||
ratio = np.sqrt(1024. * 1024. / (new_width * new_height))
|
||||
W, H = int(new_width * ratio), int(new_height * ratio)
|
||||
|
||||
crop_w, crop_h = 0, 0
|
||||
controlnet_img = cv2.resize(controlnet_img, (W, H))
|
||||
|
||||
blur_strength = random.sample([i / 10. for i in range(10, 201, 2)], k=1)[0]
|
||||
radius = random.sample([i for i in range(1, 40, 2)], k=1)[0]
|
||||
eps = random.sample([i / 1000. for i in range(1, 101, 2)], k=1)[0]
|
||||
scale_factor = random.sample([i / 10. for i in range(10, 181, 5)], k=1)[0]
|
||||
|
||||
if random.random() > 0.5:
|
||||
controlnet_img = apply_gaussian_blur(controlnet_img, ksize=int(blur_strength),
|
||||
sigmaX=blur_strength / 2)
|
||||
|
||||
if random.random() > 0.5:
|
||||
# Apply Guided Filter
|
||||
controlnet_img = apply_guided_filter(controlnet_img, radius, eps, scale_factor)
|
||||
|
||||
# Resize image
|
||||
controlnet_img = cv2.resize(controlnet_img, (int(W / scale_factor), int(H / scale_factor)),
|
||||
interpolation=cv2.INTER_AREA)
|
||||
controlnet_img = cv2.resize(controlnet_img, (W, H), interpolation=cv2.INTER_CUBIC)
|
||||
|
||||
controlnet_img = cv2.cvtColor(controlnet_img, cv2.COLOR_BGR2RGB)
|
||||
control_image = Image.fromarray(controlnet_img)
|
||||
else:
|
||||
control_image = input_size_adaptation_output(control_image, pre_input, width, height)
|
||||
|
||||
if function_choice == "img2img":
|
||||
image = kwargs["image"]
|
||||
image = input_size_adaptation_output(image, pre_input, width, height)
|
||||
if control_net == "stable-diffusion-xl-1.0-inpainting-0.1":
|
||||
images = \
|
||||
model(prompt, negative_prompt=negative_prompt, image=image, mask_image=control_image,
|
||||
num_inference_steps=steps, guidance_scale=cfg, height=height, clip_skip=clip_skip,
|
||||
width=width, controlnet_conditioning_scale=controlnet_scale,
|
||||
seed=seed, ).images[0]
|
||||
else:
|
||||
images = model(prompt, negative_prompt=negative_prompt, image=image, control_image=control_image,
|
||||
num_inference_steps=steps, guidance_scale=cfg, height=height, width=width,
|
||||
clip_skip=clip_skip,
|
||||
controlnet_conditioning_scale=controlnet_scale,
|
||||
seed=seed, ).images[0]
|
||||
else:
|
||||
images = model(prompt, negative_prompt=negative_prompt, control_image=control_image,
|
||||
num_inference_steps=steps, guidance_scale=cfg, height=height, width=width,
|
||||
clip_skip=clip_skip,
|
||||
controlnet_conditioning_scale=controlnet_scale,
|
||||
seed=seed, ).images[0]
|
||||
|
||||
|
||||
output_image = torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
|
||||
model.unfuse_lora()
|
||||
output_image = torch.from_numpy(np.array(images).astype(np.float32) / 255.0).unsqueeze(0)
|
||||
if lora != "none":
|
||||
if adapter_info=="none":
|
||||
model.unfuse_lora()
|
||||
del model
|
||||
return (output_image,)
|
||||
|
||||
|
||||
Reference in New Issue
Block a user