Added nodes TT_SdxlClipTextEncoderNode, TT_SdxlClipTextEncoderContextNode, TT_Flux1ClipTextEncoderNode, TT_Flux1ClipTextEncoderContextNode, minor bugfixes and refactoring, added docs, changed version
This commit is contained in:
TenserTensor
+1
-1
@@ -9,7 +9,7 @@ __all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS', 'WEB_DIRECTORY']
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print()
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SEP = f"{CYAN}{'=' * 60}{RESET}"
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print(SEP)
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print(f"{YELLOW}TenserTensor ComfyUI Nodes Pack (v1.6.12): {BRIGHT_GREEN}{NODES_COUNT} Nodes Loaded{RESET}")
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print(f"{YELLOW}TenserTensor ComfyUI Nodes Pack (v1.6.13): {BRIGHT_GREEN}{NODES_COUNT} Nodes Loaded{RESET}")
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print(SEP)
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print(f"{BRIGHT_CYAN}[V] Repository: https://github.com/tenser-tensor/ComfyUI-TenserTensor{RESET}")
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print(f"{BRIGHT_CYAN}[V] ComfyUI Registry: https://registry.comfy.org/publishers/tenser-tensor{RESET}")
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@@ -20,7 +20,7 @@ class TT_ClipTextEncodeFlux():
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RETURN_TYPES = ("CONDITIONING", "CONDITIONING",)
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RETURN_NAMES = ("POSITIVE", "NEGATIVE",)
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FUNCTION = "execute"
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CATEGORY = "TenserTensor/Text Encoders/FLUX"
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CATEGORY = "TenserTensor/Deprecated/Text Encoders/FLUX"
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def execute(self, **kwargs):
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return encode_prompts_flux(**kwargs)
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@@ -15,7 +15,7 @@ class TT_ClipTextEncodeFluxContext():
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RETURN_TYPES = ("TT_CONTEXT", "CONDITIONING", "CONDITIONING",)
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RETURN_NAMES = ("CONTEXT", "POSITIVE", "NEGATIVE",)
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FUNCTION = "execute"
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CATEGORY = "TenserTensor/Text Encoders/FLUX"
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CATEGORY = "TenserTensor/Deprecated/Text Encoders/FLUX"
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def execute(self, context):
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clip = context["clip"]
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@@ -26,7 +26,7 @@ class TT_ClipTextEncodeSdxl():
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RETURN_TYPES = ("CONDITIONING", "CONDITIONING",)
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RETURN_NAMES = ("POSITIVE", "NEGATIVE",)
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FUNCTION = "execute"
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CATEGORY = "TenserTensor/Text Encoders/SDXL"
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CATEGORY = "TenserTensor/Deprecated/Text Encoders/SDXL"
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def execute(self, **kwargs):
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return encode_prompts_sdxl(**kwargs)
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@@ -15,7 +15,7 @@ class TT_ClipTextEncodeSdxlContext():
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RETURN_TYPES = ("TT_CONTEXT", "CONDITIONING", "CONDITIONING",)
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RETURN_NAMES = ("CONTEXT", "POSITIVE", "NEGATIVE",)
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FUNCTION = "execute"
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CATEGORY = "TenserTensor/Text Encoders/SDXL"
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CATEGORY = "TenserTensor/Deprecated/Text Encoders/SDXL"
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def execute(self, context):
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clip = context["clip"]
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@@ -16,7 +16,11 @@ def get_control_net_files():
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def load_control_net(filename):
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cnet_path = folder_paths.get_full_path_or_raise("controlnet", filename)
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print(f"CNET path: {cnet_path}")
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cnet = controlnet.load_controlnet(cnet_path)
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if cnet is None:
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raise RuntimeError("ERROR: ControlNet file is invalid or corrupted.")
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@@ -3,12 +3,192 @@
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from typing import override
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from comfy_api.latest import io, ComfyExtension
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from nodes import MAX_RESOLUTION
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from .nodes_context import Context
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from .nodes_image import SingleCondCFGGuider
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CATEGORY = "TenserTensor/Text Encoder"
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def encode_prompts_sdxl(**kwargs):
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clip = kwargs.get("clip")
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if clip is None:
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raise ValueError("ERROR: CLIP is required for text encoder")
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positive_tokens = clip.tokenize(kwargs.get("clip_g_positive"))
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positive_tokens["l"] = clip.tokenize(kwargs.get("clip_l_positive"))["l"]
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negative_tokens = clip.tokenize(kwargs.get("clip_g_negative"))
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negative_tokens["l"] = clip.tokenize(kwargs.get("clip_l_negative"))["l"]
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cond_dict = {
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"aesthetic_score": kwargs.get("ascore_positive"),
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"width": kwargs.get("width"),
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"height": kwargs.get("height"),
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"target_width": kwargs.get("target_width"),
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"target_height": kwargs.get("target_height"),
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}
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positive = clip.encode_from_tokens_scheduled(positive_tokens, add_dict=cond_dict)
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cond_dict["aesthetic_score"] = kwargs.get("ascore_negative")
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negative = clip.encode_from_tokens_scheduled(negative_tokens, add_dict=cond_dict)
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return positive, negative
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class TT_SdxlClipTextEncoderNode(io.ComfyNode):
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@classmethod
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def define_schema(cls) -> io.Schema:
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return io.Schema(
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node_id="TT_SdxlClipTextEncoderNode",
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display_name="TT SDXL CLIP Text Encoder",
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category=CATEGORY,
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description="",
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inputs=[
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io.Clip.Input("clip"),
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io.String.Input("clip_l_positive", multiline=True, placeholder="CLIP_L Positive", dynamic_prompts=True),
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io.String.Input("clip_g_positive", multiline=True, placeholder="CLIP_G Positive", dynamic_prompts=True),
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io.String.Input("clip_l_negative", multiline=True, placeholder="CLIP_L Negative", dynamic_prompts=True),
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io.String.Input("clip_g_negative", multiline=True, placeholder="CLIP_G Negative", dynamic_prompts=True),
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io.Float.Input("ascore_positive", default=9.0, min=0.0, max=100.0, step=0.1),
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io.Float.Input("ascore_negative", default=6.0, min=0.0, max=100.0, step=0.1),
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io.Int.Input("width", default=512, min=64, max=MAX_RESOLUTION, step=8),
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io.Int.Input("height", default=512, min=64, max=MAX_RESOLUTION, step=8),
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io.Int.Input("target_width", default=512, min=64, max=MAX_RESOLUTION, step=8),
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io.Int.Input("target_height", default=512, min=64, max=MAX_RESOLUTION, step=8),
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],
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outputs=[
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io.Conditioning.Output("POSITIVE"),
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io.Conditioning.Output("NEGATIVE"),
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]
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)
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@classmethod
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def execute(cls, **kwargs) -> io.NodeOutput:
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positive, negative = encode_prompts_sdxl(**kwargs)
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return io.NodeOutput(positive, negative)
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class TT_SdxlClipTextEncoderContextNode(io.ComfyNode):
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@classmethod
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def define_schema(cls) -> io.Schema:
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return io.Schema(
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node_id="TT_SdxlClipTextEncoderContextNode",
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display_name="TT SDXL CLIP Text Encoder (Context)",
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category=CATEGORY,
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description="",
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inputs=[
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Context.Input("context")
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],
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outputs=[
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Context.Output("CONTEXT"),
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io.Conditioning.Output("POSITIVE"),
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io.Conditioning.Output("NEGATIVE"),
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]
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)
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@classmethod
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def execute(cls, **kwargs) -> io.NodeOutput:
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context = kwargs.get("context")
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args = {}
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for key in (
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"clip", "clip_l_positive", "clip_g_positive", "clip_l_negative", "clip_g_negative",
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"ascore_positive", "ascore_negative", "width", "height", "target_width", "target_height",
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):
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args[key] = context.get_attr(key)
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positive, negative = encode_prompts_sdxl(**args)
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context.set_attr("positive", positive)
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context.set_attr("negative", negative)
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return io.NodeOutput(context, positive, negative)
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def encode_prompts_flux(**kwargs):
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clip = kwargs.get("clip")
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if clip is None:
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raise ValueError("ERROR: CLIP is required for text encoder")
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positive_tokens = clip.tokenize(kwargs.get("clip_l_positive"))
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positive_tokens["t5xxl"] = clip.tokenize(kwargs.get("t5xxl_positive"))["t5xxl"]
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negative_tokens = clip.tokenize(kwargs.get("clip_l_negative"))
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negative_tokens["t5xxl"] = clip.tokenize(kwargs.get("t5xxl_negative"))["t5xxl"]
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guidance = kwargs.get("guidance")
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positive = clip.encode_from_tokens_scheduled(positive_tokens, add_dict={"guidance": guidance, })
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negative = clip.encode_from_tokens_scheduled(negative_tokens, add_dict={"guidance": guidance, })
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return positive, negative
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class TT_Flux1ClipTextEncoderNode(io.ComfyNode):
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@classmethod
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def define_schema(cls) -> io.Schema:
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return io.Schema(
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node_id="TT_Flux1ClipTextEncoderNode",
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display_name="TT FLUX1 CLIP Text Encoder",
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category=CATEGORY,
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description="",
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inputs=[
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io.Clip.Input("clip"),
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io.String.Input("clip_l_positive", multiline=True, placeholder="CLIP_L Positive", dynamic_prompts=True),
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io.String.Input("t5xxl_positive", multiline=True, placeholder="T5XXL Positive", dynamic_prompts=True),
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io.String.Input("clip_l_negative", multiline=True, placeholder="CLIP_L Negative", dynamic_prompts=True),
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io.String.Input("t5xxl_negative", multiline=True, placeholder="T5XXL Negative", dynamic_prompts=True),
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io.Float.Input("guidance", default=9.0, min=0.0, max=100.0, step=0.1),
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],
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outputs=[
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io.Conditioning.Output("POSITIVE"),
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io.Conditioning.Output("NEGATIVE"),
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]
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)
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@classmethod
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def execute(cls, **kwargs) -> io.NodeOutput:
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positive, negative = encode_prompts_flux(**kwargs)
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return io.NodeOutput(positive, negative)
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class TT_Flux1ClipTextEncoderContextNode(io.ComfyNode):
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@classmethod
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def define_schema(cls) -> io.Schema:
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return io.Schema(
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node_id="TT_Flux1ClipTextEncoderContextNode",
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display_name="TT FLUX1 CLIP Text Encoder (Context)",
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category=CATEGORY,
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description="",
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inputs=[
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Context.Input("context")
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],
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outputs=[
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Context.Output("CONTEXT"),
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io.Conditioning.Output("POSITIVE"),
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io.Conditioning.Output("NEGATIVE"),
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]
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)
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@classmethod
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def execute(cls, **kwargs) -> io.NodeOutput:
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context = kwargs.get("context")
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args = {}
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for key in (
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"clip", "clip_l_positive", "t5xxl_positive", "clip_l_negative", "t5xxl_negative",
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"guidance",
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):
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args[key] = context.get_attr(key)
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positive, negative = encode_prompts_flux(**args)
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context.set_attr("positive", positive)
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context.set_attr("negative", negative)
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return io.NodeOutput(context, positive, negative)
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def encode_prompts_flux2(**kwargs):
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clip = kwargs.get("clip")
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model = kwargs.get("model")
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@@ -103,6 +283,10 @@ class TextEncodeNodesExtension(ComfyExtension):
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@override
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async def get_node_list(self) -> list[type[io.ComfyNode]]:
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return [
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TT_SdxlClipTextEncoderNode,
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TT_SdxlClipTextEncoderContextNode,
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TT_Flux1ClipTextEncoderNode,
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TT_Flux1ClipTextEncoderContextNode,
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TT_Flux2TextEncoderNode,
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TT_Flux2TextEncoderContextNode,
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]
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@@ -117,6 +301,10 @@ async def comfy_entrypoint() -> TextEncodeNodesExtension:
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# ==============================================================================
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__all__ = [
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"TT_SdxlClipTextEncoderNode",
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"TT_SdxlClipTextEncoderContextNode",
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"TT_Flux1ClipTextEncoderNode",
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"TT_Flux1ClipTextEncoderContextNode",
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"TT_Flux2TextEncoderNode",
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"TT_Flux2TextEncoderContextNode",
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]
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+1
-1
@@ -2,7 +2,7 @@
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[project]
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name = "ComfyUI-TenserTensor"
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description = "A collection of custom nodes for ComfyUI that streamline common workflows. Includes all-in-one model loaders (checkpoint/unet, LoRA, CLIP, VAE), GGUF quantized model support (FLUX, FLUX2, SDXL), workflow settings nodes, latent factory (aspect ratio, megapixels, FLUX/SDXL), context nodes, CLIP text encoders, KSamplers (context, advanced, two-stage - draft/refine), VAE encoders/decoders (context, tiled), and post-production tools (brightness, contrast, LUT color grading, film grain, upscaler)."
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version = "1.6.12"
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version = "1.6.13"
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license = { file = "LICENSE" }
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dependencies = ["gguf", "kornia"]
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@@ -0,0 +1,29 @@
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## TT FLUX1 CLIP Text Encoder (Context) *TT_Flux1ClipTextEncoderContextNode*
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*This description was generated with AI assistance. If you spot any errors, please create an issue on GitHub.*
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Context-based variant of the FLUX1 CLIP text encoder. Reads all required parameters from a Context object and writes the resulting conditioning back
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into it.
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### Inputs
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| Parameter | Type | Required | Description |
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|-----------|------------|----------|-----------------------------------------------------------|
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| `context` | TT_CONTEXT | Yes | Context object carrying CLIP, prompts, and guidance scale |
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### Outputs
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| Parameter | Type | Description |
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|------------|--------------|-----------------------------------------------------------------------|
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| `CONTEXT` | TT_CONTEXT | Updated Context with positive and negative conditioning stored inside |
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| `POSITIVE` | CONDITIONING | Positive conditioning |
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| `NEGATIVE` | CONDITIONING | Negative conditioning |
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### Usage
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Drop-in replacement for [TT FLUX1 CLIP Text Encoder](TT_Flux1ClipTextEncoderNode.md) in Context-based workflows. The Context must have `clip`,
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`clip_l_positive`, `t5xxl_positive`, `clip_l_negative`, `t5xxl_negative`, and `guidance` set before reaching this node.
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---
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[View on GitHub](https://github.com/tenser-tensor/ComfyUI-TenserTensor)
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@@ -0,0 +1,32 @@
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## TT FLUX1 CLIP Text Encoder *TT_Flux1ClipTextEncoderNode*
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*This description was generated with AI assistance. If you spot any errors, please create an issue on GitHub.*
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Encodes positive and negative text prompts for FLUX1 models using dual text encoders (CLIP-L and T5-XXL) with guidance scale conditioning.
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### Inputs
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| Parameter | Type | Required | Description |
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|-------------------|--------|----------|-------------------------------------------|
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| `clip` | CLIP | Yes | Dual text encoder model (CLIP-L + T5-XXL) |
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| `clip_l_positive` | STRING | Yes | CLIP-L positive prompt |
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| `t5xxl_positive` | STRING | Yes | T5-XXL positive prompt |
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| `clip_l_negative` | STRING | Yes | CLIP-L negative prompt |
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| `t5xxl_negative` | STRING | Yes | T5-XXL negative prompt |
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| `guidance` | FLOAT | Yes | Guidance scale, default 9.0 |
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### Outputs
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| Parameter | Type | Description |
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|------------|--------------|-----------------------|
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| `POSITIVE` | CONDITIONING | Positive conditioning |
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| `NEGATIVE` | CONDITIONING | Negative conditioning |
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### Usage
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Connect `POSITIVE` and `NEGATIVE` outputs to a sampler. CLIP-L handles short, structured descriptions; T5-XXL understands longer, more complex
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prompts — you can use the same text in both or tailor each separately.
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---
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[View on GitHub](https://github.com/tenser-tensor/ComfyUI-TenserTensor)
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@@ -0,0 +1,30 @@
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## TT SDXL CLIP Text Encoder (Context) *TT_SdxlClipTextEncoderContextNode*
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*This description was generated with AI assistance. If you spot any errors, please create an issue on GitHub.*
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Context-based variant of the SDXL CLIP text encoder. Reads all required parameters from a Context object and writes the resulting conditioning back
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into it.
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### Inputs
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| Parameter | Type | Required | Description |
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|-----------|------------|----------|------------------------------------------------------------------------------------|
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| `context` | TT_CONTEXT | Yes | Context object carrying CLIP, prompts, aesthetic scores, and resolution parameters |
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### Outputs
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| Parameter | Type | Description |
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|------------|--------------|-----------------------------------------------------------------------|
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| `CONTEXT` | TT_CONTEXT | Updated Context with positive and negative conditioning stored inside |
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| `POSITIVE` | CONDITIONING | Positive conditioning |
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| `NEGATIVE` | CONDITIONING | Negative conditioning |
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### Usage
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Drop-in replacement for [TT SDXL CLIP Text Encoder](TT_SdxlClipTextEncoderNode.md) in Context-based workflows. The Context must have `clip`,
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`clip_l_positive`, `clip_g_positive`, `clip_l_negative`, `clip_g_negative`, `ascore_positive`, `ascore_negative`, `width`, `height`, `target_width`,
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and `target_height` set before reaching this node.
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---
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[View on GitHub](https://github.com/tenser-tensor/ComfyUI-TenserTensor)
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@@ -0,0 +1,39 @@
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## TT SDXL CLIP Text Encoder *TT_SdxlClipTextEncoderNode*
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*This description was generated with AI assistance. If you spot any errors, please create an issue on GitHub.*
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Encodes positive and negative text prompts for SDXL models using dual CLIP encoders (CLIP-L and CLIP-G) with aesthetic score and resolution
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conditioning.
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### Inputs
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| Parameter | Type | Required | Description |
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|-------------------|--------|----------|--------------------------------------------------------|
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| `clip` | CLIP | Yes | Dual CLIP model (CLIP-L + CLIP-G) |
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| `clip_l_positive` | STRING | Yes | CLIP-L positive prompt |
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| `clip_g_positive` | STRING | Yes | CLIP-G positive prompt |
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| `clip_l_negative` | STRING | Yes | CLIP-L negative prompt |
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| `clip_g_negative` | STRING | Yes | CLIP-G negative prompt |
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| `ascore_positive` | FLOAT | Yes | Aesthetic score for positive conditioning, default 9.0 |
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| `ascore_negative` | FLOAT | Yes | Aesthetic score for negative conditioning, default 6.0 |
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| `width` | INT | Yes | Source image width |
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| `height` | INT | Yes | Source image height |
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| `target_width` | INT | Yes | Target output width |
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| `target_height` | INT | Yes | Target output height |
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### Outputs
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| Parameter | Type | Description |
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|------------|--------------|-----------------------|
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| `POSITIVE` | CONDITIONING | Positive conditioning |
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| `NEGATIVE` | CONDITIONING | Negative conditioning |
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### Usage
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||||
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Connect `POSITIVE` and `NEGATIVE` outputs to a sampler. Set `width`/`height` to match your source image dimensions and `target_width`/`target_height`
|
||||
to your desired output resolution. Higher `ascore_positive` (8–10) steers generation toward higher aesthetic quality; lower `ascore_negative` (4–6)
|
||||
pushes away from low-quality outputs.
|
||||
|
||||
---
|
||||
|
||||
[View on GitHub](https://github.com/tenser-tensor/ComfyUI-TenserTensor)
|
||||
Reference in New Issue
Block a user