134 lines
5.9 KiB
Python
134 lines
5.9 KiB
Python
import node_helpers
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import math
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from typing_extensions import override
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from comfy_api.latest import ComfyExtension, io
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import comfy.model_management
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import torch
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import nodes
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class EmAySee_TextEncodeQwenImageEdit_noRS(io.ComfyNode):
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="EmAySee_TextEncodeQwenImageEdit_noRS",
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category="advanced/conditioning",
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inputs=[
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io.Clip.Input("clip"),
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io.String.Input("prompt", multiline=True, dynamic_prompts=True),
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io.Vae.Input("vae", optional=True),
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io.Image.Input("image", optional=True),
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],
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outputs=[
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io.Conditioning.Output(),
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],
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)
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@classmethod
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def execute(cls, clip, prompt, vae=None, image=None) -> io.NodeOutput:
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ref_latent = None
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if image is None:
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images = []
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else:
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image_rgb = image[:, :, :, :3]
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images = [image_rgb]
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if vae is not None:
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ref_latent = vae.encode(image_rgb)
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tokens = clip.tokenize(prompt, images=images)
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conditioning = clip.encode_from_tokens_scheduled(tokens)
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if ref_latent is not None:
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conditioning = node_helpers.conditioning_set_values(conditioning, {"reference_latents": [ref_latent]}, append=True)
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return io.NodeOutput(conditioning)
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class EmAySee_TextEncodeQwenImageEditPlus_noRS(io.ComfyNode):
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="EmAySee_TextEncodeQwenImageEditPlus_noRS",
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category="advanced/conditioning",
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inputs=[
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io.Clip.Input("clip"),
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io.String.Input("prompt", multiline=True, dynamic_prompts=True),
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io.Vae.Input("vae", optional=True),
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io.Image.Input("image1", optional=True),
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io.Image.Input("image2", optional=True),
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io.Image.Input("image3", optional=True),
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io.Image.Input("image4", optional=True),
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io.Image.Input("image5", optional=True),
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io.Image.Input("image6", optional=True),
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io.Image.Input("image7", optional=True),
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],
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outputs=[
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io.Conditioning.Output(),
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],
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)
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@classmethod
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def execute(cls, clip, prompt, vae=None, image1=None, image2=None, image3=None, image4=None, image5=None, image6=None, image7=None) -> io.NodeOutput:
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ref_latents = []
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images_input = [image1, image2, image3, image4, image5, image6, image7]
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images_vl = []
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llama_template = "<|im_start|>system\nDescribe the key features of the input image (color, shape, size, texture, objects, background), then explain how the user's text instruction should alter or modify the image. Generate a new image that meets the user's requirements while maintaining consistency with the original input where appropriate.<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n"
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image_prompt = ""
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for i, image in enumerate(images_input):
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if image is not None:
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image_rgb = image[:, :, :, :3]
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images_vl.append(image_rgb)
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if vae is not None:
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ref_latents.append(vae.encode(image_rgb))
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image_prompt += "Picture {}: <|vision_start|><|image_pad|><|vision_end|>".format(i + 1)
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tokens = clip.tokenize(image_prompt + prompt, images=images_vl, llama_template=llama_template)
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conditioning = clip.encode_from_tokens_scheduled(tokens)
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if len(ref_latents) > 0:
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conditioning = node_helpers.conditioning_set_values(conditioning, {"reference_latents": ref_latents}, append=True)
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return io.NodeOutput(conditioning)
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class EmAySee_EmptyQwenImageLayeredLatentImage_noRS(io.ComfyNode):
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="EmAySee_EmptyQwenImageLayeredLatentImage_noRS",
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display_name="EmAySee Empty Qwen Image Layered Latent noRS",
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category="latent/qwen",
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inputs=[
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io.Int.Input("width", default=640, min=16, max=nodes.MAX_RESOLUTION, step=16),
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io.Int.Input("height", default=640, min=16, max=nodes.MAX_RESOLUTION, step=16),
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io.Int.Input("layers", default=3, min=0, max=nodes.MAX_RESOLUTION, step=1, advanced=True),
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io.Int.Input("batch_size", default=1, min=1, max=4096),
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],
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outputs=[
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io.Latent.Output(),
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],
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)
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@classmethod
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def execute(cls, width, height, layers, batch_size=1) -> io.NodeOutput:
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latent = torch.zeros([batch_size, 16, layers + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
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return io.NodeOutput({"samples": latent})
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class EmAySee_QwenExtension_noRS(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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EmAySee_TextEncodeQwenImageEdit_noRS,
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EmAySee_TextEncodeQwenImageEditPlus_noRS,
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EmAySee_EmptyQwenImageLayeredLatentImage_noRS,
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]
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async def comfy_entrypoint() -> EmAySee_QwenExtension_noRS:
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return EmAySee_QwenExtension_noRS()
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NODE_CLASS_MAPPINGS = {
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"EmAySee_TextEncodeQwenImageEdit_noRS": EmAySee_TextEncodeQwenImageEdit_noRS,
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"EmAySee_TextEncodeQwenImageEditPlus_noRS": EmAySee_TextEncodeQwenImageEditPlus_noRS,
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"EmAySee_EmptyQwenImageLayeredLatentImage_noRS": EmAySee_EmptyQwenImageLayeredLatentImage_noRS,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"EmAySee_TextEncodeQwenImageEdit_noRS": "EmAySee Text Encode Qwen Image Edit noRS",
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"EmAySee_TextEncodeQwenImageEditPlus_noRS": "EmAySee Text Encode Qwen Image Edit Plus noRS",
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"EmAySee_EmptyQwenImageLayeredLatentImage_noRS": "EmAySee Empty Qwen Image Layered Latent noRS",
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} |