18 KiB
18 KiB
Documentation
- Class name: ttN pipeLoaderSDXL_v2
- Category: ttN/pipe
- Output node: False
此节点用于加载和初始化Stable Diffusion XL模型以进行图像生成任务，提供了针对大规模操作优化的增强版本。
Input types
Required
- ckpt_name
- 指定Stable Diffusion XL模型的检查点名称，对于加载正确的模型版本进行图像生成至关重要。
- Comfy dtype: COMBO[STRING]
- Python dtype: str
- config_name
- 定义配置名称，对于使用适当的参数和优化设置模型至关重要。
- Comfy dtype: COMBO[STRING]
- Python dtype: str
- vae_name
- 命名与Stable Diffusion XL模型一起使用的VAE，对图像生成过程和质量很重要。
- Comfy dtype: COMBO[STRING]
- Python dtype: str
- clip_skip
- 确定要在CLIP模型中跳过的层数，影响文本指导的整合。
- Comfy dtype: INT
- Python dtype: int
- loras
- 指定要应用的LoRA修改，通过额外参数增强模型能力。
- Comfy dtype: STRING
- Python dtype: str
- refiner_ckpt_name
- 命名精炼器模型的检查点，用于精炼或改变生成的图像。
- Comfy dtype: COMBO[STRING]
- Python dtype: str
- refiner_config_name
- Comfy dtype: COMBO[STRING]
- Python dtype: unknown
- positive_g
- Comfy dtype: STRING
- Python dtype: unknown
- positive_l
- Comfy dtype: STRING
- Python dtype: unknown
- negative_g
- Comfy dtype: STRING
- Python dtype: unknown
- negative_l
- Comfy dtype: STRING
- Python dtype: unknown
- conditioning_aspect
- Comfy dtype: COMBO[STRING]
- Python dtype: unknown
- conditioning_width
- Comfy dtype: INT
- Python dtype: unknown
- conditioning_height
- Comfy dtype: INT
- Python dtype: unknown
- crop_width
- Comfy dtype: INT
- Python dtype: unknown
- crop_height
- Comfy dtype: INT
- Python dtype: unknown
- target_aspect
- Comfy dtype: COMBO[STRING]
- Python dtype: unknown
- target_width
- Comfy dtype: INT
- Python dtype: unknown
- target_height
- Comfy dtype: INT
- Python dtype: unknown
- positive_ascore
- Comfy dtype: INT
- Python dtype: unknown
- negative_ascore
- Comfy dtype: INT
- Python dtype: unknown
- empty_latent_aspect
- Comfy dtype: COMBO[STRING]
- Python dtype: unknown
- empty_latent_width
- Comfy dtype: INT
- Python dtype: unknown
- empty_latent_height
- Comfy dtype: INT
- Python dtype: unknown
- seed
- Comfy dtype: INT
- Python dtype: unknown
Optional
- model_override
- Comfy dtype: MODEL
- Python dtype: unknown
- clip_override
- Comfy dtype: CLIP
- Python dtype: unknown
- optional_lora_stack
- Comfy dtype: LORA_STACK
- Python dtype: unknown
- optional_controlnet_stack
- Comfy dtype: CONTROLNET_STACK
- Python dtype: unknown
- refiner_model_override
- Comfy dtype: MODEL
- Python dtype: unknown
- refiner_clip_override
- Comfy dtype: CLIP
- Python dtype: unknown
- prepend_positive_g
- Comfy dtype: STRING
- Python dtype: unknown
- prepend_positive_l
- Comfy dtype: STRING
- Python dtype: unknown
- prepend_negative_g
- Comfy dtype: STRING
- Python dtype: unknown
- prepend_negative_l
- Comfy dtype: STRING
- Python dtype: unknown
Output types
- sdxl_pipe
- 提供初始化的Stable Diffusion XL管道，可用于图像生成任务。
- Comfy dtype: PIPE_LINE_SDXL
- Python dtype: dict
- model
- 返回Stable Diffusion XL管道的加载模型组件。
- Comfy dtype: MODEL
- Python dtype: torch.nn.Module
- positive
- 输出正面调节组件，引导图像生成朝向所需主题。
- Comfy dtype: CONDITIONING
- Python dtype: str
- negative
- 输出负面调节组件，引导图像生成远离不需要的主题。
- Comfy dtype: CONDITIONING
- Python dtype: str
- vae
- 返回加载的VAE组件，对图像编码和解码过程至关重要。
- Comfy dtype: VAE
- Python dtype: torch.nn.Module
- clip
- 输出加载的CLIP模型组件，用于图像生成中的文本引导。
- Comfy dtype: CLIP
- Python dtype: torch.nn.Module
- refiner_model
- 提供加载的精炼器模型组件，用于精炼或改变生成的图像。
- Comfy dtype: MODEL
- Python dtype: torch.nn.Module
- refiner_positive
- Comfy dtype: CONDITIONING
- Python dtype: unknown
- refiner_negative
- Comfy dtype: CONDITIONING
- Python dtype: unknown
- refiner_clip
- Comfy dtype: CLIP
- Python dtype: unknown
- latent
- Comfy dtype: LATENT
- Python dtype: unknown
- seed
- Comfy dtype: INT
- Python dtype: unknown
- width
- Comfy dtype: INT
- Python dtype: unknown
- height
- Comfy dtype: INT
- Python dtype: unknown
- pos_string
- Comfy dtype: STRING
- Python dtype: unknown
- neg_string
- Comfy dtype: STRING
- Python dtype: unknown
Usage tips
- Infra type:
GPU - Common nodes: unknown
Source code
class ttN_pipeLoaderSDXL_v2:
version = '2.0.0'
@classmethod
def INPUT_TYPES(cls):
aspect_ratios = ["width x height [custom]",
"1024 x 1024 [S] 1:1",
"640 x 1536 [P] 9:21",
"704 x 1472 [P] 9:19",
"768 x 1344 [P] 9:16",
"768 x 1216 [P] 5:8",
"832 x 1216 [P] 2:3",
"896 x 1152 [P] 3:4",
"1536 x 640 [L] 21:9",
"1472 x 704 [L] 19:9",
"1344 x 768 [L] 16:9",
"1216 x 768 [L] 8:5",
"1216 x 832 [L] 3:2",
"1152 x 896 [L] 4:3",
]
relative_ratios = ["width x height [custom]",
"1x Empty Latent Aspect",
"2x Empty Latent Aspect",
"3x Empty Latent Aspect",
"4x Empty Latent Aspect",
"5x Empty Latent Aspect",
"6x Empty Latent ASpect",
"7x Empty Latent Aspect",
"8x Empty Latent Aspect",
]
return {"required": {
"ckpt_name": (folder_paths.get_filename_list("checkpoints"), ),
"config_name": (["Default",] + folder_paths.get_filename_list("configs"), {"default": "Default"} ),
"vae_name": (["Baked VAE"] + folder_paths.get_filename_list("vae"),),
"clip_skip": ("INT", {"default": -2, "min": -24, "max": 0, "step": 1}),
"loras": ("STRING", {"placeholder": "Loras - <lora:loraName:weight:optClipWeight>", "multiline": True}),
"refiner_ckpt_name": (["None"] + folder_paths.get_filename_list("checkpoints"), ),
"refiner_config_name": (["Default",] + folder_paths.get_filename_list("configs"), {"default": "Default"} ),
"positive_g": ("STRING", {"placeholder": "Linguistic Positive (positive_g)","multiline": True, "dynamicPrompts": True}),
"positive_l": ("STRING", {"placeholder": "Supporting Terms (positive_l)", "multiline": True, "dynamicPrompts": True}),
"negative_g": ("STRING", {"placeholder": "negative_g", "multiline": True, "dynamicPrompts": True}),
"negative_l": ("STRING", {"placeholder": "negative_l", "multiline": True, "dynamicPrompts": True}),
"conditioning_aspect": (relative_ratios, {"default": "2x Empty Latent Aspect"}),
"conditioning_width": ("INT", {"default": 2048.0, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
"conditioning_height": ("INT", {"default": 2048.0, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
"crop_width": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION}),
"crop_height": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION}),
"target_aspect": (relative_ratios, {"default": "1x Empty Latent Aspect"}),
"target_width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}),
"target_height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}),
"positive_ascore": ("INT", {"default": 6.0, "min": 0, "step": 0.1}),
"negative_ascore": ("INT", {"default": 2.0, "min": 0, "step": 0.1}),
"empty_latent_aspect": (aspect_ratios, {"default": "1024 x 1024 [S] 1:1"}),
"empty_latent_width": ("INT", {"default": 1024, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
"empty_latent_height": ("INT", {"default": 1024, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
},
"optional": {
"model_override": ("MODEL",),
"clip_override": ("CLIP",),
"optional_lora_stack": ("LORA_STACK",),
"optional_controlnet_stack": ("CONTROLNET_STACK",),
"refiner_model_override": ("MODEL",),
"refiner_clip_override": ("CLIP",),
"prepend_positive_g": ("STRING", {"default": None, "forceInput": True}),
"prepend_positive_l": ("STRING", {"default": None, "forceInput": True}),
"prepend_negative_g": ("STRING", {"default": None, "forceInput": True}),
"prepend_negative_l": ("STRING", {"default": None, "forceInput": True}),
},
"hidden": {"prompt": "PROMPT", "ttNnodeVersion": ttN_pipeLoaderSDXL_v2.version}, "my_unique_id": "UNIQUE_ID",}
RETURN_TYPES = ("PIPE_LINE_SDXL" ,"MODEL", "CONDITIONING", "CONDITIONING", "VAE", "CLIP", "MODEL", "CONDITIONING", "CONDITIONING", "CLIP", "LATENT", "INT", "INT", "INT", "STRING", "STRING")
RETURN_NAMES = ("sdxl_pipe","model", "positive", "negative", "vae", "clip", "refiner_model", "refiner_positive", "refiner_negative", "refiner_clip", "latent", "seed", "width", "height", "pos_string", "neg_string")
FUNCTION = "sdxl_pipeloader"
CATEGORY = "ttN/pipe"
def sdxl_pipeloader(self, ckpt_name, config_name, vae_name, clip_skip, loras,
refiner_ckpt_name, refiner_config_name,
conditioning_aspect, conditioning_width, conditioning_height, crop_width, crop_height, target_aspect, target_width, target_height,
positive_g, positive_l, negative_g, negative_l,
positive_ascore, negative_ascore,
empty_latent_aspect, empty_latent_width, empty_latent_height, seed,
model_override=None, clip_override=None, optional_lora_stack=None, optional_controlnet_stack=None,
refiner_model_override=None, refiner_clip_override=None,
prepend_positive_g=None, prepend_positive_l=None, prepend_negative_g=None, prepend_negative_l=None,
prompt=None, my_unique_id=None):
model: ModelPatcher | None = None
clip: CLIP | None = None
vae: VAE | None = None
# Create Empty Latent
latent = sampler.emptyLatent(empty_latent_aspect, 1, empty_latent_width, empty_latent_height)
samples = {"samples":latent}
# Clean models from loaded_objects
loader.update_loaded_objects(prompt)
model, clip, vae = loader.load_main3(ckpt_name, config_name, vae_name, loras, model_override, clip_override, optional_lora_stack)
if refiner_ckpt_name not in ["None", None]:
refiner_model, refiner_clip, refiner_vae = loader.load_main3(refiner_ckpt_name, refiner_config_name, vae_name, refiner_model_override, refiner_clip_override)
else:
refiner_model, refiner_clip, refiner_vae = None, None, None
if empty_latent_aspect and empty_latent_aspect != "width x height [custom]":
empty_latent_width, empty_latent_height = empty_latent_aspect.replace(' ', '').split('[')[0].split('x')
if conditioning_aspect and conditioning_aspect != "width x height [custom]":
conditioning_factor = conditioning_aspect.split('x')[0]
conditioning_width = int(conditioning_factor) * int(empty_latent_width)
conditioning_height = int(conditioning_factor) * int(empty_latent_height)
if target_aspect and target_aspect != "width x height [custom]":
target_factor = target_aspect.split('x')[0]
target_width = int(target_factor) * int(empty_latent_width)
target_height = int(target_factor) * int(empty_latent_height)
positive_embedding, refiner_positive_embedding = loader.embedding_encodeXL(positive_g, clip, clip_skip, seed=seed, title='pipeLoaderSDXL Positive', my_unique_id=my_unique_id, prepend_text=prepend_positive_g, text2=positive_l, prepend_text2=prepend_positive_l, width=conditioning_width, height=conditioning_height, crop_width=crop_width, crop_height=crop_height, target_width=target_width, target_height=target_height, refiner_clip=refiner_clip, ascore=positive_ascore)
negative_embedding, refiner_negative_embedding = loader.embedding_encodeXL(negative_g, clip, clip_skip, seed=seed, title='pipeLoaderSDXL Negative', my_unique_id=my_unique_id, prepend_text=prepend_negative_g, text2=negative_l, prepend_text2=prepend_negative_l, width=conditioning_width, height=conditioning_height, crop_width=crop_width, crop_height=crop_height, target_width=target_width, target_height=target_height, refiner_clip=refiner_clip, ascore=negative_ascore)
if optional_controlnet_stack is not None:
for cnt in optional_controlnet_stack:
positive_embedding, negative_embedding = loader.load_controlNet(self, positive_embedding, negative_embedding, cnt[0], cnt[1], cnt[2], cnt[3], cnt[4])
refiner_positive_embedding, refiner_negative_embedding = loader.load_controlNet(self, refiner_positive_embedding, refiner_negative_embedding, cnt[0], cnt[1], cnt[2], cnt[3], cnt[4])
image = None
sdxl_pipe = {"model": model,
"positive": positive_embedding,
"negative": negative_embedding,
"vae": vae,
"clip": clip,
"refiner_model": refiner_model,
"refiner_positive": refiner_positive_embedding,
"refiner_negative": refiner_negative_embedding,
"refiner_clip": refiner_clip,
"samples": samples,
"images": image,
"seed": seed,
"loader_settings": {"ckpt_name": ckpt_name,
"config_name": config_name,
"vae_name": vae_name,
"clip_skip": None,
"loras": loras,
"refiner_ckpt_name": refiner_ckpt_name,
"refiner_config_name": refiner_config_name,
"refiner_clip_skip": None,
"model_override": model_override,
"clip_override": clip_override,
"optional_lora_stack": optional_lora_stack,
"optional_controlnet_stack": optional_controlnet_stack,
"refiner_model_override": refiner_model_override,
"refiner_clip_override": refiner_clip_override,
"prepend_positive_g": prepend_positive_g,
"prepend_positive_l": prepend_positive_l,
"prepend_negative_g": prepend_negative_g,
"prepend_negative_l": prepend_negative_l,
"conditioning_width": conditioning_width,
"conditioning_height": conditioning_height,
"positive_g": positive_g,
"positive_l": positive_l,
"negative_g": negative_g,
"negative_l": negative_l,
"positive_ascore": positive_ascore,
"negative_ascore": negative_ascore,
"empty_latent_width": empty_latent_width,
"empty_latent_height": empty_latent_height,
"seed": seed,
"empty_samples": samples,}
}
final_positive = (prepend_positive_g + ' ' if prepend_positive_g else '') + (positive_g + ' ' if positive_g else '') + (prepend_positive_l + ' ' if prepend_positive_l else '') + (positive_l + ' ' if positive_l else '')
final_negative = (prepend_negative_g + ' ' if prepend_negative_g else '') + (negative_g + ' ' if negative_g else '') + (prepend_negative_l + ' ' if prepend_negative_l else '') + (negative_l + ' ' if negative_l else '')
return (sdxl_pipe, model, positive_embedding, negative_embedding, vae, clip, refiner_model, refiner_positive_embedding, refiner_negative_embedding, refiner_clip, samples, seed, empty_latent_width, empty_latent_height, final_positive, final_negative)