86 lines
2.8 KiB
Python
86 lines
2.8 KiB
Python
import comfy.samplers
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import comfy.sd
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import comfy.utils
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import comfy_extras.clip_vision
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import model_management
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import importlib
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import folder_paths
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import torch
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import os
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import sys
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import json
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import hashlib
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import copy
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import traceback
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from PIL import Image
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from PIL.PngImagePlugin import PngInfo
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import numpy as np
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from .Colors import cprint, Colors as Colors
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import os
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py_name=os.path.basename(__file__)
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cprint(py_name, Colors.BLUE)
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#----------------------------
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# wildcards support check
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wildcardsOn=False
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try:
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from .wildcards import *
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wildcardsOn=True
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#wildcards.card_path=os.path.dirname(__file__)+"\\..\\wildcards\\**\\*.txt"
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cprint(f"{py_name} : import wildcards succ", Colors.GREEN )
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except:
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cprint(f"{py_name} : import wildcards fail", Colors.RED)
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wildcardsOn=False
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#----------------------------
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class SimpleSampler:
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@classmethod
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def INPUT_TYPES(s):
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return {"required":
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{
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"model": ("MODEL",),
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#"positive": ("CONDITIONING", ),
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"positive": ("STRING", {"multiline": True}), "clip": ("CLIP", ),
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#"negative": ("CONDITIONING", ),
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"negative": ("STRING", {"multiline": True}), "clip": ("CLIP", ),
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"width": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}),
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"height": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}),
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"batch_size": ("INT", {"default": 1, "min": 1, "max": 64}),
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
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"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
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"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
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"sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
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"scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
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"latent_image": ("LATENT", ),
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"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
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}}
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RETURN_TYPES = ("LATENT",)
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FUNCTION = "sample"
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CATEGORY = "sampling"
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def encode(self, clip, text):
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if wildcardsOn:
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text=wildcards.run(text)
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return ([[clip.encode(text), {}]], )
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def generate(self, width, height, batch_size=1):
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latent = torch.zeros([batch_size, 4, height // 8, width // 8])
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return ({"samples":latent}, )
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def sample(self,
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model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
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width, height, denoise=1.0, batch_size=1
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):
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return common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, self.encode(clip, positive), elf.encode(clip, negative), self.generate( width, height, batch_size=1), denoise=denoise)
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