🌟 CR SDXL Base Prompt Encoder¶

Documentation¶

Class name: CR SDXL Base Prompt Encoder
Category: 🧩 Comfyroll Studio/✨ Essential/🌟 SDXL
Output node: False

The CR SDXL Base Prompt Encoder node is designed to encode base prompts for the SDXL model, providing foundational text inputs that can be further customized or mixed with other prompts. It plays a crucial role in establishing the initial context or theme for text-to-image generation tasks within the SDXL framework.

Input types¶

Required¶

base_clip
- Represents the foundational CLIP model used for encoding the base prompts, setting the initial visual-textual context for the SDXL model.
- Comfy dtype: CLIP
- Python dtype: torch.nn.Module
pos_g
- Positive guidance text for enhancing specific attributes or themes in the generated images.
- Comfy dtype: STRING
- Python dtype: str
pos_l
- Localized positive guidance text for fine-tuning specific areas or aspects within the generated images.
- Comfy dtype: STRING
- Python dtype: str
neg_g
- Negative guidance text for suppressing undesired attributes or themes in the generated images.
- Comfy dtype: STRING
- Python dtype: str
neg_l
- Localized negative guidance text for reducing specific undesired areas or aspects within the generated images.
- Comfy dtype: STRING
- Python dtype: str
preset
- A predefined set of parameters or settings that influence the encoding process and the resulting image generation.
- Comfy dtype: COMBO[STRING]
- Python dtype: str
base_width
- The width of the base image or context area for the encoding process.
- Comfy dtype: INT
- Python dtype: int
base_height
- The height of the base image or context area for the encoding process.
- Comfy dtype: INT
- Python dtype: int
crop_w
- Width of the cropping area applied to the base context, allowing for focus on specific regions during encoding.
- Comfy dtype: INT
- Python dtype: int
crop_h
- Height of the cropping area applied to the base context, enabling emphasis on particular regions during encoding.
- Comfy dtype: INT
- Python dtype: int
target_width
- The target width for the output image, guiding the scaling and aspect ratio of the generated content.
- Comfy dtype: INT
- Python dtype: int
target_height
- The target height for the output image, directing the scaling and aspect ratio of the generated content.
- Comfy dtype: INT
- Python dtype: int

Output types¶

base_positive
- Comfy dtype: CONDITIONING
- The encoded positive base prompt, ready for further processing or combination with other prompts for image generation.
- Python dtype: torch.Tensor
base_negative
- Comfy dtype: CONDITIONING
- The encoded negative base prompt, prepared for additional processing or merging with other prompts for image generation.
- Python dtype: torch.Tensor
show_help
- Comfy dtype: STRING
- A helper output providing guidance or suggestions for optimizing the use of encoded prompts in the SDXL model.
- Python dtype: str

Usage tips¶

Infra type: CPU
Common nodes:
- KSampler
- DetailerForEach

Source code¶

class CR_SDXLBasePromptEncoder:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {
                    "base_clip": ("CLIP", ),
                    "pos_g": ("STRING", {"multiline": True, "default": "POS_G"}),
                    "pos_l": ("STRING", {"multiline": True, "default": "POS_L"}),
                    "neg_g": ("STRING", {"multiline": True, "default": "NEG_G"}),
                    "neg_l": ("STRING", {"multiline": True, "default": "NEG_L"}),
                    "preset": (["preset A", "preset B", "preset C"],), 
                    "base_width": ("INT", {"default": 4096.0, "min": 0, "max": MAX_RESOLUTION, "step": 64}),
                    "base_height": ("INT", {"default": 4096.0, "min": 0, "max": MAX_RESOLUTION, "step": 64}),
                    "crop_w": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 64}),
                    "crop_h": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 64}),
                    "target_width": ("INT", {"default": 4096.0, "min": 0, "max": MAX_RESOLUTION, "step": 64}),
                    "target_height": ("INT", {"default": 4096.0, "min": 0, "max": MAX_RESOLUTION, "step": 64}),
                    },
                }

    RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "STRING", )
    RETURN_NAMES = ("base_positive", "base_negative", "show_help", )
    FUNCTION = "encode"
    CATEGORY = icons.get("Comfyroll/SDXL")

    def encode(self, base_clip, pos_g, pos_l, neg_g, neg_l, base_width, base_height, crop_w, crop_h, target_width, target_height, preset,):
        empty = base_clip.tokenize("")

        # positive 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 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 style
        tokens2 = base_clip.tokenize(pos_l)
        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)
        res3 = [[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}]]

        # negative style
        tokens2 = base_clip.tokenize(neg_l)
        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)
        res4 = [[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}]]

        if preset == "preset A":
            base_positive = res1
            base_negative = res2
        elif preset == "preset B":
            base_positive = res3
            base_negative = res4
        elif preset == "preset C":
            base_positive = res1 + res3
            base_negative = res2 + res4

        show_help = "https://github.com/Suzie1/ComfyUI_Comfyroll_CustomNodes/wiki/SDXL-Nodes#cr-sdxl-base-prompt-encoder"
        return (base_positive, base_negative, show_help, )