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SimpleSampler.py

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