SDXL Prompt Encoder (Searge)¶
Documentation¶
- Class name:
SeargeSDXLPromptEncoder - Category:
Searge/_deprecated_/ClipEncoding - Output node:
False
The SeargeSDXLPromptEncoder node is designed to encode prompts for the SDXL model, facilitating the generation of text prompts that are optimized for image synthesis tasks. It supports the encoding of both base and refined prompts, enabling users to fine-tune the prompt details for more precise image generation outcomes.
Input types¶
Required¶
base_clip- unknown
- Comfy dtype:
CLIP - Python dtype:
unknown
refiner_clip- unknown
- Comfy dtype:
CLIP - Python dtype:
unknown
pos_g- unknown
- Comfy dtype:
STRING - Python dtype:
unknown
pos_l- unknown
- Comfy dtype:
STRING - Python dtype:
unknown
pos_r- unknown
- Comfy dtype:
STRING - Python dtype:
unknown
neg_g- unknown
- Comfy dtype:
STRING - Python dtype:
unknown
neg_l- unknown
- Comfy dtype:
STRING - Python dtype:
unknown
neg_r- unknown
- Comfy dtype:
STRING - Python dtype:
unknown
base_width- unknown
- Comfy dtype:
INT - Python dtype:
unknown
base_height- unknown
- Comfy dtype:
INT - Python dtype:
unknown
crop_w- unknown
- Comfy dtype:
INT - Python dtype:
unknown
crop_h- unknown
- Comfy dtype:
INT - Python dtype:
unknown
target_width- unknown
- Comfy dtype:
INT - Python dtype:
unknown
target_height- unknown
- Comfy dtype:
INT - Python dtype:
unknown
pos_ascore- unknown
- Comfy dtype:
FLOAT - Python dtype:
unknown
neg_ascore- unknown
- Comfy dtype:
FLOAT - Python dtype:
unknown
refiner_width- unknown
- Comfy dtype:
INT - Python dtype:
unknown
refiner_height- unknown
- Comfy dtype:
INT - Python dtype:
unknown
Output types¶
base_positive- Comfy dtype:
CONDITIONING - unknown
- Python dtype:
unknown
- Comfy dtype:
base_negative- Comfy dtype:
CONDITIONING - unknown
- Python dtype:
unknown
- Comfy dtype:
refiner_positive- Comfy dtype:
CONDITIONING - unknown
- Python dtype:
unknown
- Comfy dtype:
refiner_negative- Comfy dtype:
CONDITIONING - unknown
- Python dtype:
unknown
- Comfy dtype:
Usage tips¶
- Infra type:
CPU - Common nodes: unknown
Source code¶
class SeargeSDXLPromptEncoder:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"base_clip": ("CLIP",),
"refiner_clip": ("CLIP",),
"pos_g": ("STRING", {"multiline": True, "default": "POS_G"}),
"pos_l": ("STRING", {"multiline": True, "default": "POS_L"}),
"pos_r": ("STRING", {"multiline": True, "default": "POS_R"}),
"neg_g": ("STRING", {"multiline": True, "default": "NEG_G"}),
"neg_l": ("STRING", {"multiline": True, "default": "NEG_L"}),
"neg_r": ("STRING", {"multiline": True, "default": "NEG_R"}),
"base_width": ("INT", {"default": 4096, "min": 0, "max": nodes.MAX_RESOLUTION, "step": 8}),
"base_height": ("INT", {"default": 4096, "min": 0, "max": nodes.MAX_RESOLUTION, "step": 8}),
"crop_w": ("INT", {"default": 0, "min": 0, "max": nodes.MAX_RESOLUTION, "step": 8}),
"crop_h": ("INT", {"default": 0, "min": 0, "max": nodes.MAX_RESOLUTION, "step": 8}),
"target_width": ("INT", {"default": 4096, "min": 0, "max": nodes.MAX_RESOLUTION, "step": 8}),
"target_height": ("INT", {"default": 4096, "min": 0, "max": nodes.MAX_RESOLUTION, "step": 8}),
"pos_ascore": ("FLOAT", {"default": 6.0, "min": 0.0, "max": 1000.0, "step": 0.01}),
"neg_ascore": ("FLOAT", {"default": 2.5, "min": 0.0, "max": 1000.0, "step": 0.01}),
"refiner_width": ("INT", {"default": 2048, "min": 0, "max": nodes.MAX_RESOLUTION, "step": 8}),
"refiner_height": ("INT", {"default": 2048, "min": 0, "max": nodes.MAX_RESOLUTION, "step": 8}),
},
}
RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "CONDITIONING", "CONDITIONING",)
RETURN_NAMES = ("base_positive", "base_negative", "refiner_positive", "refiner_negative",)
FUNCTION = "encode"
CATEGORY = "Searge/_deprecated_/ClipEncoding"
def encode(self, base_clip, refiner_clip, pos_g, pos_l, pos_r, neg_g, neg_l, neg_r, base_width, base_height,
crop_w, crop_h, target_width, target_height, pos_ascore, neg_ascore, refiner_width, refiner_height):
empty = base_clip.tokenize("")
# positive base prompt
tokens1 = base_clip.tokenize(pos_g)
tokens1["l"] = base_clip.tokenize(pos_l)["l"]
if len(tokens1["l"]) != len(tokens1["g"]):
while len(tokens1["l"]) < len(tokens1["g"]):
tokens1["l"] += empty["l"]
while len(tokens1["l"]) > len(tokens1["g"]):
tokens1["g"] += empty["g"]
cond1, pooled1 = base_clip.encode_from_tokens(tokens1, return_pooled=True)
res1 = [[cond1, {"pooled_output": pooled1, "width": base_width, "height": base_height, "crop_w": crop_w,
"crop_h": crop_h, "target_width": target_width, "target_height": target_height}]]
# negative base prompt
tokens2 = base_clip.tokenize(neg_g)
tokens2["l"] = base_clip.tokenize(neg_l)["l"]
if len(tokens2["l"]) != len(tokens2["g"]):
while len(tokens2["l"]) < len(tokens2["g"]):
tokens2["l"] += empty["l"]
while len(tokens2["l"]) > len(tokens2["g"]):
tokens2["g"] += empty["g"]
cond2, pooled2 = base_clip.encode_from_tokens(tokens2, return_pooled=True)
res2 = [[cond2, {"pooled_output": pooled2, "width": base_width, "height": base_height, "crop_w": crop_w,
"crop_h": crop_h, "target_width": target_width, "target_height": target_height}]]
# positive refiner prompt
tokens3 = refiner_clip.tokenize(pos_r)
cond3, pooled3 = refiner_clip.encode_from_tokens(tokens3, return_pooled=True)
res3 = [[cond3, {"pooled_output": pooled3, "aesthetic_score": pos_ascore, "width": refiner_width,
"height": refiner_height}]]
# negative refiner prompt
tokens4 = refiner_clip.tokenize(neg_r)
cond4, pooled4 = refiner_clip.encode_from_tokens(tokens4, return_pooled=True)
res4 = [[cond4, {"pooled_output": pooled4, "aesthetic_score": neg_ascore, "width": refiner_width,
"height": refiner_height}]]
return (res1, res2, res3, res4,)