Update Hidiffusion_node.py
update
This commit is contained in:
smthemex
+251
-564
@@ -9,16 +9,25 @@ import sys
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from diffusers import (StableDiffusionXLPipeline, DiffusionPipeline, DDIMScheduler, ControlNetModel,
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KDPM2AncestralDiscreteScheduler, LMSDiscreteScheduler,
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AutoPipelineForInpainting, DPMSolverMultistepScheduler, DPMSolverSinglestepScheduler,
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EulerDiscreteScheduler, HeunDiscreteScheduler, UNet2DConditionModel,StableDiffusionXLImg2ImgPipeline,
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EulerDiscreteScheduler, HeunDiscreteScheduler, UNet2DConditionModel,
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StableDiffusionXLImg2ImgPipeline,
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AutoPipelineForText2Image, StableDiffusionXLControlNetImg2ImgPipeline, KDPM2DiscreteScheduler,
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EulerAncestralDiscreteScheduler, UniPCMultistepScheduler, AutoencoderKL,
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StableDiffusionXLControlNetPipeline, DDPMScheduler, TCDScheduler, LCMScheduler)
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StableDiffusionXLControlNetPipeline, DDPMScheduler, TCDScheduler, LCMScheduler,
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StableDiffusionPipeline, StableDiffusionControlNetPipeline)
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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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import yaml
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import diffusers
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dif_version = str(diffusers.__version__)
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dif_version_int = int(dif_version.split(".")[1])
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if dif_version_int >= 28:
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from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
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else:
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from diffusers.models.unet_2d_condition import UNet2DConditionModel
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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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@@ -64,11 +73,10 @@ scheduler_list = [
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"LMS",
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"LMS Karras",
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"UniPC",
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"TCD"
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]
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fs = open(os.path.join(dir_path, "model.yaml"), encoding="UTF-8")
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datas = yaml.load(fs, Loader=yaml.FullLoader) # 添加后就不警告了
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datas = yaml.load(fs, Loader=yaml.FullLoader)
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normal_model_list = datas["surport_model"]
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sdxl_lightning_list = datas["lightning_unet"]
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@@ -79,13 +87,6 @@ lcm_unet = ["dmd2_sdxl_4step_unet_fp16.bin", "dmd2_sdxl_1step_unet_fp16.bin", "l
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"Hyper-SDXL-1step-Unet.safetensors"]
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def crop_center(img, cropx, cropy):
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y, x, _ = img.shape
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startx = x // 2 - (cropx // 2)
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starty = y // 2 - (cropy // 2)
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return img[starty:starty + cropy, startx:startx + cropx]
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def get_sheduler(name):
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scheduler = False
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if name == "Euler":
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@@ -126,36 +127,9 @@ def get_sheduler(name):
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scheduler = LMSDiscreteScheduler(use_karras_sigmas=True)
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elif name == "UniPC":
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scheduler = UniPCMultistepScheduler()
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elif name == "TCD":
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scheduler = TCDScheduler()
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return scheduler
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def img_resize(img, width, height):
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height_img, width_img, _ = img.shape
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if height / width != 1:
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long_side_ratio_out = max(width, height)
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if height_img / width_img != 1:
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short_side_ratio_in = min(height_img, height_img)
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if height / width == 1:
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if height_img / width_img == 1:
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ratio = np.sqrt(1024. * 1024. / (width_img * height_img))
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else:
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ratio = height / short_side_ratio_in
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else:
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if height_img / width_img == 1:
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ratio = long_side_ratio_out / height_img
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else:
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width_factor = width / width_img
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height_factor = height / height_img
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ratio = max(width_factor, height_factor)
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new_width, new_height = int(width_img * ratio), int(height_img * ratio)
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controlnet_img = cv2.resize(img, (new_width, new_height), interpolation=cv2.INTER_LANCZOS4)
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controlnet_img = crop_center(controlnet_img, width, height)
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img = Image.fromarray(controlnet_img)
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return img
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def tensor_to_image(tensor):
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tensor = tensor.cpu()
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image_np = tensor.squeeze().mul(255).clamp(0, 255).byte().numpy()
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@@ -163,11 +137,6 @@ def tensor_to_image(tensor):
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return image
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def pil2cv(img):
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img_cv = cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR)
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return img_cv
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def get_local_path(file_path, model_path):
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path = os.path.join(file_path, "models", "diffusers", model_path)
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model_path = os.path.normpath(path)
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@@ -183,7 +152,17 @@ def get_instance_path(path):
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return instance_path
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class HI_Diffusers_Or_Repo:
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def instance_path(path, repo):
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if repo == "":
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if path == "none":
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repo = "none"
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else:
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model_path = get_local_path(file_path, path)
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repo = get_instance_path(model_path)
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return repo
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class HI_Diffusers_Model_Loader:
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def __init__(self):
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pass
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@@ -192,30 +171,155 @@ class HI_Diffusers_Or_Repo:
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return {
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"required": {
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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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"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_repo_id": ("STRING", {"default": "diffusers/controlnet-canny-sdxl-1.0"}),
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"function_choice": (["text2img", "img2img", ],),
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"scheduler": (scheduler_list,),
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"lora": (["none"] + folder_paths.get_filename_list("loras"),),
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"trigger_words": ("STRING", {"default": "best quality"}),
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}
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}
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RETURN_TYPES = ("STRING",)
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RETURN_NAMES = ("repo_id",)
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FUNCTION = "repo_choice"
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RETURN_TYPES = ("MODEL", "STRING")
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RETURN_NAMES = ("model", "model_info")
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FUNCTION = "loader_models"
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CATEGORY = "Hidiffusion_Pro"
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def repo_choice(self, local_model_path, repo_id):
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if repo_id == "":
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if local_model_path == "none":
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repo_id = "none"
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elif local_model_path != "none":
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model_path = get_local_path(file_path, local_model_path)
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repo_id = get_instance_path(model_path)
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elif repo_id != "" and repo_id.find("/") == -1:
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raise "Incorrect repo_id format"
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elif repo_id != "" and repo_id.find("\\") != -1:
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repo_id = get_instance_path(repo_id)
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return (repo_id,)
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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,trigger_words):
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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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if repo_id == "none":
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raise "need repo_id or model path"
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model_type = repo_id.rsplit("/")[-1]
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if controlnet_repo_id=="none":
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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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light_path = os.path.join(file_path, "models", "unet", unet_model)
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ckpt = get_instance_path(light_path)
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if unet_model.rsplit('.', 1)[-1] == "bin":
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unet = UNet2DConditionModel.from_config(repo_id, subfolder="unet").to("cuda", torch.float16)
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unet.load_state_dict(torch.load(ckpt))
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else:
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unet = UNet2DConditionModel.from_pretrained(repo_id, subfolder="unet").to("cuda", torch.float16)
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unet.load_state_dict(load_file(ckpt, device="cuda"), strict=False, )
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if function_choice == "img2img":
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model = StableDiffusionXLControlNetImg2ImgPipeline.from_pretrained(repo_id, unet=unet, torch_dtype=torch.float16,
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variant="fp16").to("cuda")
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else:
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model = StableDiffusionXLPipeline.from_pretrained(repo_id, unet=unet, torch_dtype=torch.float16,
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variant="fp16").to("cuda")
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else:
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if function_choice == "img2img":
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model = StableDiffusionXLControlNetImg2ImgPipeline.from_pretrained(repo_id,
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torch_dtype=torch.float16, variant="fp16").to(
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"cuda")
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else:
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model = StableDiffusionXLPipeline.from_pretrained(repo_id,
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torch_dtype=torch.float16, variant="fp16").to(
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"cuda")
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elif model_type == "sdxl-turbo":
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model = AutoPipelineForText2Image.from_pretrained(repo_id, torch_dtype=torch.float16,
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variant="fp16").to('cuda')
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elif model_type in normal_model_list:
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model = DiffusionPipeline.from_pretrained(repo_id,
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torch_dtype=torch.float16, variant="fp16").to("cuda")
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else:
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raise "Unsupported model_path or repo_id"
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else:
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if control_model_type != "stable-diffusion-xl-1.0-inpainting-0.1":
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controlnet = ControlNetModel.from_pretrained(controlnet_repo_id, torch_dtype=torch.float16,
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variant="fp16").to("cuda")
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else:
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controlnet=""
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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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light_path = os.path.join(file_path, "models", "unet", unet_model)
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ckpt = get_instance_path(light_path)
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if unet_model.rsplit('.', 1)[-1] == "bin":
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unet = UNet2DConditionModel.from_config(repo_id, subfolder="unet").to("cuda", torch.float16)
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unet.load_state_dict(torch.load(ckpt))
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else:
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unet = UNet2DConditionModel.from_pretrained(repo_id, subfolder="unet").to("cuda", torch.float16)
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unet.load_state_dict(load_file(ckpt, device="cuda"), strict=False, )
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if control_model_type == "stable-diffusion-xl-1.0-inpainting-0.1":
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model = AutoPipelineForInpainting.from_pretrained(repo_id, unet=unet,
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torch_dtype=torch.float16,
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variant="fp16").to("cuda")
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elif function_choice == "img2img":
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model = StableDiffusionXLControlNetImg2ImgPipeline.from_pretrained(repo_id, unet=unet,
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controlnet=controlnet,
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torch_dtype=torch.float16,
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variant="fp16").to("cuda")
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else:
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model = StableDiffusionXLControlNetPipeline.from_pretrained(repo_id, unet=unet,
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controlnet=controlnet,
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torch_dtype=torch.float16,
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variant="fp16").to("cuda")
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else:
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if control_model_type == "stable-diffusion-xl-1.0-inpainting-0.1":
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model = AutoPipelineForInpainting.from_pretrained(repo_id,
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torch_dtype=torch.float16,
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variant="fp16").to("cuda")
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elif function_choice == "img2img":
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model = StableDiffusionXLControlNetImg2ImgPipeline.from_pretrained(repo_id,
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controlnet=controlnet,
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torch_dtype=torch.float16,
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variant="fp16").to("cuda")
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else:
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model = StableDiffusionXLControlNetPipeline.from_pretrained(repo_id,
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controlnet=controlnet,
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torch_dtype=torch.float16,
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variant="fp16").to("cuda")
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elif model_type == "sdxl-turbo":
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model = StableDiffusionXLControlNetPipeline.from_pretrained(repo_id, controlnet=controlnet,
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torch_dtype=torch.float16,
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variant="fp16").to('cuda')
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else:
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raise "only support SDXL controlnet"
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scheduler_used = get_sheduler(scheduler)
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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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if unet_model in lcm_unet:
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model.scheduler = LCMScheduler.from_config(model.scheduler.config, timestep_spacing="trailing")
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else:
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model.scheduler = scheduler_used.from_config(model.scheduler.config,
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timestep_spacing="trailing")
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else:
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model.scheduler = scheduler_used.from_config(model.scheduler.config, timestep_spacing="trailing")
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elif model_type == "sdxl-turbo":
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pass
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elif model_type in normal_model_list:
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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 = get_instance_path(lora_path)
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model.load_lora_weights(lora, adapter_name=trigger_words)
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model.set_adapters(trigger_words)
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model.fuse_lora()
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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)
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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]))
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return (model, model_info)
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class Hi_Text2Img:
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class Hi_Sampler:
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def __init__(self):
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pass
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@@ -223,533 +327,116 @@ class Hi_Text2Img:
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def INPUT_TYPES(cls):
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return {
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"required": {
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"image": ("IMAGE",),
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"control_image": ("IMAGE",),
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"model": ("MODEL",),
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"model_info": ("STRING", {"forceInput": True}),
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"prompt": ("STRING", {"multiline": True,
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"default": "Standing tall amidst the ruins, a stone golem awakens, vines and "
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"flowers sprouting from the crevices in its body"}),
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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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"repo_id": ("STRING", {"forceInput": True}),
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"scheduler": (scheduler_list,),
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"unet_model": (["none"] + folder_paths.get_filename_list("unet"),),
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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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"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
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"steps": ("INT", {"default": 50, "min": 1, "max": 10000}),
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"steps": ("INT", {"default": 30, "min": 1, "max": 10000}),
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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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"eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.1, "round": 0.01}),
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"timesteps": ("INT", {"default": 0, "min": 0, "max": 999, "step": 1, "display": "number"}),
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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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"width": ("INT", {"default": 2048, "min": 64, "max": 8192, "step": 64, "display": "number"})
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}
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("output_image",)
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FUNCTION = "text2image"
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RETURN_NAMES = ("image",)
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FUNCTION = "hi_sampler"
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CATEGORY = "Hidiffusion_Pro"
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def text2image(self, prompt, negative_prompt, repo_id, scheduler, unet_model, seed,
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steps, cfg, eta, timesteps, height, width):
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timesteps = [timesteps]
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model_type = repo_id.rsplit("/")[-1]
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scheduler_used = get_sheduler(scheduler)
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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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light_path = os.path.join(file_path, "models", "unet", unet_model)
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ckpt = get_instance_path(light_path)
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if unet_model.rsplit('.', 1)[-1] == "bin":
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unet = UNet2DConditionModel.from_config(repo_id, subfolder="unet").to("cuda", torch.float16)
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unet.load_state_dict(torch.load(ckpt))
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else:
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unet = UNet2DConditionModel.from_pretrained(repo_id, subfolder="unet").to("cuda", torch.float16)
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unet.load_state_dict(load_file(ckpt, device="cuda"), strict=False, )
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pipe = StableDiffusionXLPipeline.from_pretrained(repo_id, unet=unet, torch_dtype=torch.float16,
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variant="fp16").to("cuda")
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# Ensure sampler uses "trailing" timesteps.
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if unet_model in lcm_unet:
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pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
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else:
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pipe.scheduler = scheduler_used.from_config(pipe.scheduler.config, timestep_spacing="trailing")
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else:
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scheduler = scheduler_used.from_pretrained(repo_id, subfolder="scheduler")
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pipe = StableDiffusionXLPipeline.from_pretrained(repo_id, scheduler=scheduler,
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torch_dtype=torch.float16, variant="fp16").to("cuda")
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elif model_type == "sdxl-turbo":
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pipe = AutoPipelineForText2Image.from_pretrained(repo_id, torch_dtype=torch.float16,
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variant="fp16").to('cuda')
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elif model_type in normal_model_list:
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scheduler = scheduler_used.from_pretrained(repo_id, subfolder="scheduler")
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pipe = DiffusionPipeline.from_pretrained(repo_id, scheduler=scheduler,
|
||||
torch_dtype=torch.float16, variant="fp16").to("cuda")
|
||||
else:
|
||||
raise "Unsupported model_path or repo_id"
|
||||
|
||||
# Optional. enable_xformers_memory_efficient_attention can save memory usage and increase inference
|
||||
# speed. enable_model_cpu_offload and enable_vae_tiling can save memory usage.
|
||||
pipe.enable_xformers_memory_efficient_attention()
|
||||
pipe.enable_model_cpu_offload()
|
||||
pipe.enable_vae_tiling()
|
||||
# Apply hidiffusion with a single line of code.
|
||||
apply_hidiffusion(pipe)
|
||||
if unet_model in lcm_unet:
|
||||
image = pipe(prompt, num_inference_steps=steps, guidance_scale=cfg, timesteps=timesteps, height=height,
|
||||
width=width,
|
||||
eta=eta,
|
||||
seed=seed, negative_prompt=negative_prompt).images[0]
|
||||
else:
|
||||
image = pipe(prompt, num_inference_steps=steps, guidance_scale=cfg, height=height, width=width, eta=eta,
|
||||
seed=seed, negative_prompt=negative_prompt).images[0]
|
||||
output_image = torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
|
||||
del pipe
|
||||
return (output_image,)
|
||||
|
||||
|
||||
class Hi_SDXL_Control2Img:
|
||||
def __init__(self):
|
||||
self.class_a = Hi_Text2Img()
|
||||
|
||||
def inpainting_gener(self, image, repo_id, controlnet_repo_id, prompt, negative_prompt, scheduler,
|
||||
unet_model, model_type, seed, steps, cfg, eta, controlnet_strength, timesteps, height, width,
|
||||
control_image):
|
||||
scheduler_used = get_sheduler(scheduler)
|
||||
scheduler = scheduler_used.from_pretrained(repo_id, subfolder="scheduler")
|
||||
if unet_model in sdxl_lightning_list:
|
||||
light_path = os.path.join(file_path, "models", "unet", unet_model)
|
||||
ckpt = get_instance_path(light_path)
|
||||
if unet_model.rsplit('.', 1)[-1] == "bin":
|
||||
unet = UNet2DConditionModel.from_config(repo_id, subfolder="unet").to("cuda", torch.float16)
|
||||
unet.load_state_dict(torch.load(ckpt))
|
||||
else:
|
||||
unet = UNet2DConditionModel.from_pretrained(repo_id, subfolder="unet").to("cuda", torch.float16)
|
||||
unet.load_state_dict(load_file(ckpt, device="cuda"), strict=False, )
|
||||
pipe = AutoPipelineForInpainting.from_pretrained(controlnet_repo_id, unet=unet, torch_dtype=torch.float16,
|
||||
variant="fp16").to("cuda")
|
||||
# Ensure sampler uses "trailing" timesteps.
|
||||
if unet_model in lcm_unet:
|
||||
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
|
||||
else:
|
||||
pipe.scheduler = scheduler_used.from_config(pipe.scheduler.config, timestep_spacing="trailing")
|
||||
else:
|
||||
pipe = AutoPipelineForInpainting.from_pretrained(
|
||||
controlnet_repo_id, torch_dtype=torch.float16, variant="fp16",
|
||||
scheduler=scheduler
|
||||
)
|
||||
apply_hidiffusion(pipe)
|
||||
pipe.enable_xformers_memory_efficient_attention()
|
||||
pipe.enable_model_cpu_offload()
|
||||
pipe.enable_vae_tiling()
|
||||
if unet_model in lcm_unet:
|
||||
output_img = pipe(prompt=prompt, image=image, mask_image=control_image, height=height, width=width,
|
||||
strength=controlnet_strength, num_inference_steps=steps, seed=seed, timesteps=timesteps,
|
||||
guidance_scale=cfg, negative_prompt=negative_prompt, eta=eta).images[0]
|
||||
else:
|
||||
output_img = pipe(prompt=prompt, image=image, mask_image=control_image, height=height, width=width,
|
||||
strength=controlnet_strength, num_inference_steps=steps, seed=seed,
|
||||
guidance_scale=cfg, negative_prompt=negative_prompt, eta=eta).images[0]
|
||||
del pipe
|
||||
return output_img
|
||||
|
||||
def control_img2img(self, image, prompt, negative_prompt, repo_id, controlnet_repo_id, scheduler,
|
||||
unet_model, model_type, seed, steps, cfg, eta, controlnet_conditioning_scale,
|
||||
controlnet_strength, timesteps, height, width, control_image):
|
||||
scheduler_used = get_sheduler(scheduler)
|
||||
controlnet = ControlNetModel.from_pretrained(controlnet_repo_id, torch_dtype=torch.float16,
|
||||
variant="fp16").to("cuda")
|
||||
if unet_model in sdxl_lightning_list:
|
||||
light_path = os.path.join(file_path, "models", "unet", unet_model)
|
||||
ckpt = get_instance_path(light_path)
|
||||
if unet_model.rsplit('.', 1)[-1] == "bin":
|
||||
unet = UNet2DConditionModel.from_config(repo_id, subfolder="unet").to("cuda", torch.float16)
|
||||
unet.load_state_dict(torch.load(ckpt))
|
||||
else:
|
||||
unet = UNet2DConditionModel.from_pretrained(repo_id, subfolder="unet").to("cuda", torch.float16)
|
||||
unet.load_state_dict(load_file(ckpt, device="cuda"), strict=False, )
|
||||
pipe = StableDiffusionXLControlNetImg2ImgPipeline.from_pretrained(repo_id, unet=unet, controlnet=controlnet,
|
||||
torch_dtype=torch.float16,
|
||||
variant="fp16").to("cuda")
|
||||
# Ensure sampler uses "trailing" timesteps.
|
||||
if unet_model in lcm_unet:
|
||||
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
|
||||
else:
|
||||
pipe.scheduler = scheduler_used.from_config(pipe.scheduler.config, timestep_spacing="trailing")
|
||||
else:
|
||||
scheduler = scheduler_used.from_pretrained(repo_id, subfolder="scheduler")
|
||||
pipe = StableDiffusionXLControlNetImg2ImgPipeline.from_pretrained(repo_id, controlnet=controlnet,
|
||||
scheduler=scheduler,
|
||||
torch_dtype=torch.float16, ).to("cuda")
|
||||
apply_hidiffusion(pipe)
|
||||
pipe.enable_xformers_memory_efficient_attention()
|
||||
pipe.enable_model_cpu_offload()
|
||||
pipe.enable_vae_tiling()
|
||||
if unet_model in lcm_unet:
|
||||
output_img = pipe(prompt=prompt,
|
||||
image=image,
|
||||
control_image=control_image,
|
||||
height=height,
|
||||
width=width,
|
||||
seed=seed,
|
||||
strength=controlnet_strength,
|
||||
num_inference_steps=steps,
|
||||
controlnet_conditioning_scale=controlnet_conditioning_scale,
|
||||
guidance_scale=cfg,
|
||||
negative_prompt=negative_prompt,
|
||||
timesteps=timesteps,
|
||||
eta=eta
|
||||
).images[0]
|
||||
else:
|
||||
output_img = pipe(prompt=prompt,
|
||||
image=image,
|
||||
control_image=control_image,
|
||||
height=height,
|
||||
width=width,
|
||||
seed=seed,
|
||||
strength=controlnet_strength,
|
||||
num_inference_steps=steps,
|
||||
controlnet_conditioning_scale=controlnet_conditioning_scale,
|
||||
guidance_scale=cfg,
|
||||
negative_prompt=negative_prompt,
|
||||
eta=eta
|
||||
).images[0]
|
||||
del pipe
|
||||
return output_img
|
||||
|
||||
def img2img(self, image, prompt, negative_prompt, repo_id, scheduler,
|
||||
unet_model, model_type, seed, steps, cfg, eta, timesteps,
|
||||
height, width):
|
||||
scheduler_used = get_sheduler(scheduler)
|
||||
if unet_model in sdxl_lightning_list:
|
||||
light_path = os.path.join(file_path, "models", "unet", unet_model)
|
||||
ckpt = get_instance_path(light_path)
|
||||
if unet_model.rsplit('.', 1)[-1] == "bin":
|
||||
unet = UNet2DConditionModel.from_config(repo_id, subfolder="unet").to("cuda", torch.float16)
|
||||
unet.load_state_dict(torch.load(ckpt))
|
||||
else:
|
||||
unet = UNet2DConditionModel.from_pretrained(repo_id, subfolder="unet").to("cuda", torch.float16)
|
||||
unet.load_state_dict(load_file(ckpt, device="cuda"), strict=False, )
|
||||
pipe = StableDiffusionXLImg2ImgPipeline.from_pretrained(repo_id, unet=unet, torch_dtype=torch.float16,
|
||||
variant="fp16").to("cuda")
|
||||
# Ensure sampler uses "trailing" timesteps.
|
||||
if unet_model in lcm_unet:
|
||||
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
|
||||
else:
|
||||
pipe.scheduler = scheduler_used.from_config(pipe.scheduler.config, timestep_spacing="trailing")
|
||||
else:
|
||||
scheduler = scheduler_used.from_pretrained(repo_id, subfolder="scheduler")
|
||||
pipe = StableDiffusionXLImg2ImgPipeline.from_pretrained(repo_id,
|
||||
scheduler=scheduler,
|
||||
torch_dtype=torch.float16, ).to("cuda")
|
||||
apply_hidiffusion(pipe)
|
||||
pipe.enable_xformers_memory_efficient_attention()
|
||||
pipe.enable_model_cpu_offload()
|
||||
pipe.enable_vae_tiling()
|
||||
if unet_model in lcm_unet:
|
||||
output_img = pipe(image=image,
|
||||
prompt=prompt,
|
||||
height=height,
|
||||
width=width,
|
||||
seed=seed,
|
||||
num_inference_steps=steps,
|
||||
guidance_scale=cfg,
|
||||
negative_prompt=negative_prompt,
|
||||
timesteps=timesteps,
|
||||
eta=eta
|
||||
).images[0]
|
||||
else:
|
||||
output_img = pipe(image=image,
|
||||
prompt=prompt,
|
||||
height=height,
|
||||
width=width,
|
||||
seed=seed,
|
||||
num_inference_steps=steps,
|
||||
guidance_scale=cfg,
|
||||
negative_prompt=negative_prompt,
|
||||
eta=eta
|
||||
).images[0]
|
||||
del pipe
|
||||
return output_img
|
||||
|
||||
def control_txt2img(self, prompt, negative_prompt, repo_id, controlnet_repo_id, scheduler,
|
||||
unet_model, model_type, seed, steps, cfg, eta, controlnet_conditioning_scale,
|
||||
controlnet_strength, timesteps, height, width, control_image):
|
||||
scheduler_used = get_sheduler(scheduler)
|
||||
controlnet = ControlNetModel.from_pretrained(controlnet_repo_id, torch_dtype=torch.float16,
|
||||
variant="fp16").to("cuda")
|
||||
if unet_model in sdxl_lightning_list:
|
||||
light_path = os.path.join(file_path, "models", "unet", unet_model)
|
||||
ckpt = get_instance_path(light_path)
|
||||
if unet_model.rsplit('.', 1)[-1] == "bin":
|
||||
unet = UNet2DConditionModel.from_config(repo_id, subfolder="unet").to("cuda", torch.float16)
|
||||
unet.load_state_dict(torch.load(ckpt))
|
||||
else:
|
||||
unet = UNet2DConditionModel.from_pretrained(repo_id, subfolder="unet").to("cuda", torch.float16)
|
||||
unet.load_state_dict(load_file(ckpt, device="cuda"), strict=False, )
|
||||
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(repo_id, unet=unet, controlnet=controlnet,
|
||||
torch_dtype=torch.float16,
|
||||
variant="fp16").to("cuda")
|
||||
# Ensure sampler uses "trailing" timesteps.
|
||||
if unet_model in lcm_unet:
|
||||
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
|
||||
else:
|
||||
pipe.scheduler = scheduler_used.from_config(pipe.scheduler.config, timestep_spacing="trailing")
|
||||
else:
|
||||
scheduler = scheduler_used.from_pretrained(repo_id, subfolder="scheduler")
|
||||
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
|
||||
repo_id, controlnet=controlnet, torch_dtype=torch.float16,
|
||||
scheduler=scheduler
|
||||
)
|
||||
apply_hidiffusion(pipe)
|
||||
pipe.enable_xformers_memory_efficient_attention()
|
||||
pipe.enable_model_cpu_offload()
|
||||
pipe.enable_vae_tiling()
|
||||
if unet_model in lcm_unet:
|
||||
output_img = pipe(
|
||||
prompt=prompt, controlnet_conditioning_scale=controlnet_conditioning_scale, image=control_image,
|
||||
height=height, width=width, guidance_scale=cfg, negative_prompt=negative_prompt, seed=seed,
|
||||
strength=controlnet_strength, timesteps=timesteps,
|
||||
num_inference_steps=steps, eta=eta
|
||||
).images[0]
|
||||
else:
|
||||
output_img = pipe(
|
||||
prompt=prompt, controlnet_conditioning_scale=controlnet_conditioning_scale, image=control_image,
|
||||
height=height, width=width, guidance_scale=cfg, negative_prompt=negative_prompt, seed=seed,
|
||||
strength=controlnet_strength,
|
||||
num_inference_steps=steps, eta=eta
|
||||
).images[0]
|
||||
del pipe
|
||||
return output_img
|
||||
|
||||
def openpose_gener(self, prompt, negative_prompt, repo_id, controlnet_repo_id, scheduler,
|
||||
unet_model, model_type, seed, steps, cfg, eta, controlnet_conditioning_scale,
|
||||
controlnet_strength, timesteps, height, width, control_image):
|
||||
scheduler_used = get_sheduler(scheduler)
|
||||
controlnet = ControlNetModel.from_pretrained(controlnet_repo_id, torch_dtype=torch.float16,
|
||||
variant="fp16").to("cuda")
|
||||
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
|
||||
if unet_model in sdxl_lightning_list:
|
||||
light_path = os.path.join(file_path, "models", "unet", unet_model)
|
||||
ckpt = get_instance_path(light_path)
|
||||
if unet_model.rsplit('.', 1)[-1] == "bin":
|
||||
unet = UNet2DConditionModel.from_config(repo_id, subfolder="unet").to("cuda", torch.float16)
|
||||
unet.load_state_dict(torch.load(ckpt))
|
||||
else:
|
||||
unet = UNet2DConditionModel.from_pretrained(repo_id, subfolder="unet").to("cuda", torch.float16)
|
||||
unet.load_state_dict(load_file(ckpt, device="cuda"), strict=False, )
|
||||
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(repo_id, unet=unet, controlnet=controlnet,
|
||||
torch_dtype=torch.float16,
|
||||
variant="fp16").to("cuda")
|
||||
# Ensure sampler uses "trailing" timesteps.
|
||||
if unet_model in lcm_unet:
|
||||
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
|
||||
else:
|
||||
pipe.scheduler = scheduler_used.from_config(pipe.scheduler.config, timestep_spacing="trailing")
|
||||
else:
|
||||
scheduler = scheduler_used.from_pretrained(repo_id, subfolder="scheduler")
|
||||
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
|
||||
repo_id, controlnet=controlnet, vae=vae, torch_dtype=torch.float16,
|
||||
scheduler=scheduler
|
||||
)
|
||||
|
||||
apply_hidiffusion(pipe)
|
||||
pipe.enable_xformers_memory_efficient_attention()
|
||||
pipe.enable_model_cpu_offload()
|
||||
pipe.enable_vae_tiling()
|
||||
|
||||
if unet_model in lcm_unet:
|
||||
output_img = pipe(
|
||||
prompt=prompt, controlnet_conditioning_scale=controlnet_conditioning_scale, image=control_image,
|
||||
height=height, width=width, guidance_scale=cfg, negative_prompt=negative_prompt, seed=seed,
|
||||
strength=controlnet_strength, timesteps=timesteps,
|
||||
num_inference_steps=steps, eta=eta
|
||||
).images[0]
|
||||
else:
|
||||
output_img = pipe(
|
||||
prompt=prompt, controlnet_conditioning_scale=controlnet_conditioning_scale, image=control_image,
|
||||
height=height, width=width, guidance_scale=cfg, negative_prompt=negative_prompt, seed=seed,
|
||||
strength=controlnet_strength,
|
||||
num_inference_steps=steps, eta=eta
|
||||
).images[0]
|
||||
del pipe
|
||||
return output_img
|
||||
|
||||
def scribble_gener(self, prompt, negative_prompt, repo_id, controlnet_repo_id, scheduler,
|
||||
unet_model, model_type, seed, steps, cfg, eta, controlnet_conditioning_scale,
|
||||
controlnet_strength, timesteps, height, width, control_image):
|
||||
scheduler_used = get_sheduler(scheduler)
|
||||
controlnet = ControlNetModel.from_pretrained(controlnet_repo_id, torch_dtype=torch.float16,
|
||||
variant="fp16").to("cuda")
|
||||
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
|
||||
if unet_model in sdxl_lightning_list:
|
||||
light_path = os.path.join(file_path, "models", "unet", unet_model)
|
||||
ckpt = get_instance_path(light_path)
|
||||
if unet_model.rsplit('.', 1)[-1] == "bin":
|
||||
unet = UNet2DConditionModel.from_config(repo_id, subfolder="unet").to("cuda", torch.float16)
|
||||
unet.load_state_dict(torch.load(ckpt))
|
||||
else:
|
||||
unet = UNet2DConditionModel.from_pretrained(repo_id, subfolder="unet").to("cuda", torch.float16)
|
||||
unet.load_state_dict(load_file(ckpt, device="cuda"), strict=False, )
|
||||
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(repo_id, unet=unet, controlnet=controlnet,
|
||||
torch_dtype=torch.float16,
|
||||
variant="fp16").to("cuda")
|
||||
# Ensure sampler uses "trailing" timesteps.
|
||||
if unet_model in lcm_unet:
|
||||
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
|
||||
else:
|
||||
pipe.scheduler = scheduler_used.from_config(pipe.scheduler.config, timestep_spacing="trailing")
|
||||
else:
|
||||
scheduler = scheduler_used.from_pretrained(repo_id, subfolder="scheduler")
|
||||
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
|
||||
repo_id, controlnet=controlnet, vae=vae, torch_dtype=torch.float16,
|
||||
scheduler=scheduler
|
||||
)
|
||||
apply_hidiffusion(pipe)
|
||||
pipe.enable_xformers_memory_efficient_attention()
|
||||
pipe.enable_model_cpu_offload()
|
||||
pipe.enable_vae_tiling()
|
||||
if unet_model in lcm_unet:
|
||||
output_img = pipe(
|
||||
prompt=prompt, controlnet_conditioning_scale=controlnet_conditioning_scale, image=control_image,
|
||||
height=height, width=width, guidance_scale=cfg, negative_prompt=negative_prompt, seed=seed,
|
||||
strength=controlnet_strength,timesteps=timesteps,
|
||||
num_inference_steps=steps, eta=eta
|
||||
).images[0]
|
||||
else:
|
||||
output_img = pipe(
|
||||
prompt=prompt, controlnet_conditioning_scale=controlnet_conditioning_scale, image=control_image,
|
||||
height=height, width=width, guidance_scale=cfg, negative_prompt=negative_prompt, seed=seed,
|
||||
strength=controlnet_strength,
|
||||
num_inference_steps=steps, eta=eta
|
||||
).images[0]
|
||||
del pipe
|
||||
return output_img
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"image": ("IMAGE",),
|
||||
"repo_id": ("STRING", {"forceInput": True}),
|
||||
"controlnet_repo_id": ("STRING", {"forceInput": True}),
|
||||
"prompt": ("STRING", {"multiline": True,
|
||||
"default": "Lara Croft with brown hair, and is wearing a tank top, a brown "
|
||||
"backpack."
|
||||
"The room is dark and has an old-fashioned decor with a patterned "
|
||||
"floor and a wall"
|
||||
"featuring a design with arches and a dark area on the right side, "
|
||||
"muted color,"
|
||||
" high detail, 8k high definition award winning"}),
|
||||
"negative_prompt": ("STRING", {"multiline": True,
|
||||
"default": "underexposed, poorly drawn hands, duplicate hands, "
|
||||
"overexposed,"
|
||||
"bad art, beginner, amateur, abstract, disfigured, "
|
||||
"deformed, close up, weird colors, watermark"}),
|
||||
|
||||
"scheduler": (scheduler_list,),
|
||||
"unet_model": (["none"] + folder_paths.get_filename_list("unet"),),
|
||||
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
|
||||
"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
|
||||
"cfg": ("FLOAT", {"default": 12.5, "min": 0.0, "max": 100.0, "step": 0.1, "round": 0.01}),
|
||||
"eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.1, "round": 0.01}),
|
||||
"controlnet_choice": (["text2img", "img2img", ],),
|
||||
"controlnet_conditioning_scale": (
|
||||
"FLOAT", {"default": 0.5, "min": 0.1, "max": 1.0, "step": 0.1, "round": 0.01}),
|
||||
"controlnet_strength": (
|
||||
"FLOAT", {"default": 0.99, "min": 0.1, "max": 1.0, "step": 0.1, "round": 0.01}),
|
||||
"timesteps": ("INT", {"default": 0, "min": 0, "max": 999, "step": 1, "display": "number"}),
|
||||
"height": ("INT", {"default": 1536, "min": 64, "max": 8192, "step": 4, "display": "number"}),
|
||||
"width": ("INT", {"default": 2048, "min": 64, "max": 8192, "step": 4, "display": "number"})
|
||||
},
|
||||
"optional": {
|
||||
"control_image": ("IMAGE",),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("IMAGE",)
|
||||
RETURN_NAMES = ("output_image",)
|
||||
FUNCTION = "xl_controlnet_image"
|
||||
CATEGORY = "Hidiffusion_Pro"
|
||||
|
||||
def xl_controlnet_image(self, image, repo_id, controlnet_repo_id, prompt, negative_prompt, scheduler,
|
||||
unet_model, seed, steps, cfg,
|
||||
eta, controlnet_choice,
|
||||
controlnet_conditioning_scale, controlnet_strength, timesteps, height, width, control_image,
|
||||
):
|
||||
timesteps = [timesteps]
|
||||
def hi_sampler(self, image, control_image, model, model_info, prompt, negative_prompt, controlnet_scale,clip_skip,
|
||||
seed,
|
||||
steps, cfg, width,height,):
|
||||
image = tensor_to_image(image)
|
||||
image = pil2cv(image)
|
||||
image = img_resize(image, width, height) # 预处理图片 尤其是非1024*1024的尺度
|
||||
|
||||
control_image = tensor_to_image(control_image)
|
||||
control_image = pil2cv(control_image)
|
||||
control_image = img_resize(control_image, width, height) # 预处理图片 尤其是非1024*1024的尺度
|
||||
|
||||
# control_image = np.transpose(control_image, (
|
||||
# 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）
|
||||
if repo_id == "none":
|
||||
raise "need repo"
|
||||
model_type = repo_id.rsplit("/")[-1]
|
||||
if controlnet_repo_id == "none":
|
||||
if controlnet_choice == "text2img":
|
||||
output_image = self.class_a.text2image(prompt, negative_prompt, repo_id, scheduler, unet_model, seed,
|
||||
steps, cfg, eta, timesteps, height, width)
|
||||
output_image=output_image[0]
|
||||
# # 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）
|
||||
model_type, unet_model, control_net, function_choice,lora= model_info.split(";")
|
||||
print(model_type, unet_model, control_net, function_choice)
|
||||
if control_net == "none":
|
||||
if unet_model in lcm_unet:
|
||||
if function_choice == "img2img":
|
||||
image = \
|
||||
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:
|
||||
image = \
|
||||
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:
|
||||
output_img = self.img2img(image, prompt, negative_prompt, repo_id, scheduler,
|
||||
unet_model, model_type, seed, steps, cfg, eta,
|
||||
timesteps, height, width)
|
||||
output_image = torch.from_numpy(np.array(output_img).astype(np.float32) / 255.0).unsqueeze(0)
|
||||
if function_choice == "img2img":
|
||||
image = \
|
||||
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:
|
||||
image = \
|
||||
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_model_type = controlnet_repo_id.rsplit("/")[-1]
|
||||
if control_model_type in controlnet_suport:
|
||||
if control_model_type == "stable-diffusion-xl-1.0-inpainting-0.1":
|
||||
output_img = self.inpainting_gener(image, repo_id, controlnet_repo_id, prompt, negative_prompt,
|
||||
scheduler,
|
||||
unet_model, model_type, seed, steps, cfg, eta,
|
||||
controlnet_strength, timesteps,
|
||||
height, width, control_image)
|
||||
elif control_model_type == "controlnet-openpose-sdxl-1.0":
|
||||
output_img = self.openpose_gener(prompt, negative_prompt, repo_id, controlnet_repo_id, scheduler,
|
||||
unet_model, model_type, seed, steps, cfg, eta,
|
||||
controlnet_conditioning_scale,
|
||||
controlnet_strength, timesteps, height, width, control_image)
|
||||
elif control_model_type == "controlnet-scribble-sdxl-1.0":
|
||||
output_img = self.scribble_gener(prompt, negative_prompt, repo_id, controlnet_repo_id, scheduler,
|
||||
unet_model, model_type, seed, steps, cfg, eta,
|
||||
controlnet_conditioning_scale,
|
||||
controlnet_strength, timesteps, height, width, control_image)
|
||||
else:
|
||||
if controlnet_choice == "img2img":
|
||||
output_img = self.control_img2img(image, prompt, negative_prompt, repo_id, controlnet_repo_id,
|
||||
scheduler,
|
||||
unet_model, model_type, seed, steps, cfg, eta,
|
||||
controlnet_conditioning_scale,
|
||||
controlnet_strength, timesteps, height, width, control_image)
|
||||
if unet_model in lcm_unet:
|
||||
if function_choice == "img2img":
|
||||
if control_net == "stable-diffusion-xl-1.0-inpainting-0.1":
|
||||
image = \
|
||||
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:
|
||||
output_img = self.control_txt2img(prompt, negative_prompt, repo_id, controlnet_repo_id,
|
||||
scheduler,
|
||||
unet_model, model_type, seed, steps, cfg, eta,
|
||||
controlnet_conditioning_scale,
|
||||
controlnet_strength, timesteps, height, width, control_image)
|
||||
output_image = torch.from_numpy(np.array(output_img).astype(np.float32) / 255.0).unsqueeze(0)
|
||||
else:
|
||||
if controlnet_choice == "text2img":
|
||||
output_image = self.class_a.text2image(prompt, negative_prompt, repo_id, scheduler, unet_model,
|
||||
seed,
|
||||
steps, cfg, eta, timesteps, height, width)
|
||||
output_image = output_image[0]
|
||||
image = \
|
||||
model(prompt, negative_prompt=negative_prompt, image=image, control_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:
|
||||
output_img = self.img2img(image, prompt, negative_prompt, repo_id, scheduler,
|
||||
unet_model, model_type, seed, steps, cfg, eta,timesteps, height, width)
|
||||
output_image = torch.from_numpy(np.array(output_img).astype(np.float32) / 255.0).unsqueeze(0)
|
||||
image = model(prompt, negative_prompt=negative_prompt, 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:
|
||||
if function_choice == "img2img":
|
||||
if control_net == "stable-diffusion-xl-1.0-inpainting-0.1":
|
||||
image = \
|
||||
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:
|
||||
image = \
|
||||
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:
|
||||
image = model(prompt, negative_prompt=negative_prompt, 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)
|
||||
del model
|
||||
return (output_image,)
|
||||
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"HI_Diffusers_Or_Repo": HI_Diffusers_Or_Repo,
|
||||
"Hi_Text2Img": Hi_Text2Img,
|
||||
"Hi_SDXL_Control2Img": Hi_SDXL_Control2Img
|
||||
"HI_Diffusers_Model_Loader": HI_Diffusers_Model_Loader,
|
||||
"Hi_Sampler": Hi_Sampler
|
||||
}
|
||||
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"HI_Diffusers_Or_Repo": "HI_Diffusers_Or_Repo",
|
||||
"Hi_Text2Img": "Hi_Text2Img",
|
||||
"Hi_SDXL_Control2Img": "Hi_SDXL_Control2Img"
|
||||
"HI_Diffusers_Model_Loader": "HI_Diffusers_Model_Loader",
|
||||
"Hi_Hi_Sampler": "Hi_Sampler"
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user