2023 08 07 add inference mode

README.md

@@ -11,98 +11,100 @@ The `ComfyUI-to-Python-Extension` is a powerful tool that translates ComfyUI wor
 
 ```
 import random
+import torch
 import sys
 
 sys.path.append("../")
 from nodes import (
+ VAEDecode,
  KSamplerAdvanced,
  EmptyLatentImage,
- VAEDecodeTiled,
  SaveImage,
- CLIPTextEncode,
  CheckpointLoaderSimple,
+ CLIPTextEncode,
 )
 
 
 def main():
- checkpointloadersimple = CheckpointLoaderSimple()
- checkpointloadersimple_4 = checkpointloadersimple.load_checkpoint(
- ckpt_name="sd_xl_base_1.0.safetensors"
- )
-
- emptylatentimage = EmptyLatentImage()
- emptylatentimage_5 = emptylatentimage.generate(
- width=1024, height=1024, batch_size=1
- )
-
- cliptextencode = CLIPTextEncode()
- cliptextencode_6 = cliptextencode.encode(
- text="evening sunset scenery blue sky nature, glass bottle with a galaxy in it",
- clip=checkpointloadersimple_4[1],
- )
-
- cliptextencode_7 = cliptextencode.encode(
- text="text, watermark", clip=checkpointloadersimple_4[1]
- )
-
- checkpointloadersimple_12 = checkpointloadersimple.load_checkpoint(
- ckpt_name="sd_xl_refiner_1.0.safetensors"
- )
-
- cliptextencode_15 = cliptextencode.encode(
- text="evening sunset scenery blue sky nature, glass bottle with a galaxy in it",
- clip=checkpointloadersimple_12[1],
- )
-
- cliptextencode_16 = cliptextencode.encode(
- text="text, watermark", clip=checkpointloadersimple_12[1]
- )
-
- ksampleradvanced = KSamplerAdvanced()
- vaedecodetiled = VAEDecodeTiled()
- saveimage = SaveImage()
-
- for q in range(10):
- ksampleradvanced_10 = ksampleradvanced.sample(
- add_noise="enable",
- noise_seed=random.randint(1, 2**64),
- steps=25,
- cfg=8,
- sampler_name="euler",
- scheduler="normal",
- start_at_step=0,
- end_at_step=20,
- return_with_leftover_noise="enable",
- model=checkpointloadersimple_4[0],
- positive=cliptextencode_6[0],
- negative=cliptextencode_7[0],
- latent_image=emptylatentimage_5[0],
+ with torch.inference_mode():
+ checkpointloadersimple = CheckpointLoaderSimple()
+ checkpointloadersimple_4 = checkpointloadersimple.load_checkpoint(
+ ckpt_name="sd_xl_base_1.0.safetensors"
  )
 
- ksampleradvanced_11 = ksampleradvanced.sample(
- add_noise="disable",
- noise_seed=random.randint(1, 2**64),
- steps=25,
- cfg=8,
- sampler_name="euler",
- scheduler="normal",
- start_at_step=20,
- end_at_step=10000,
- return_with_leftover_noise="disable",
- model=checkpointloadersimple_12[0],
- positive=cliptextencode_15[0],
- negative=cliptextencode_16[0],
- latent_image=ksampleradvanced_10[0],
+ emptylatentimage = EmptyLatentImage()
+ emptylatentimage_5 = emptylatentimage.generate(
+ width=1024, height=1024, batch_size=1
  )
 
- vaedecodetiled_17 = vaedecodetiled.decode(
- samples=ksampleradvanced_11[0], vae=checkpointloadersimple_12[2]
+ cliptextencode = CLIPTextEncode()
+ cliptextencode_6 = cliptextencode.encode(
+ text="evening sunset scenery blue sky nature, glass bottle with a galaxy in it",
+ clip=checkpointloadersimple_4[1],
  )
 
- saveimage_19 = saveimage.save_images(
- filename_prefix="ComfyUI", images=vaedecodetiled_17[0].detach()
+ cliptextencode_7 = cliptextencode.encode(
+ text="text, watermark", clip=checkpointloadersimple_4[1]
  )
 
+ checkpointloadersimple_12 = checkpointloadersimple.load_checkpoint(
+ ckpt_name="sd_xl_refiner_1.0.safetensors"
+ )
+
+ cliptextencode_15 = cliptextencode.encode(
+ text="evening sunset scenery blue sky nature, glass bottle with a galaxy in it",
+ clip=checkpointloadersimple_12[1],
+ )
+
+ cliptextencode_16 = cliptextencode.encode(
+ text="text, watermark", clip=checkpointloadersimple_12[1]
+ )
+
+ ksampleradvanced = KSamplerAdvanced()
+ vaedecode = VAEDecode()
+ saveimage = SaveImage()
+
+ for q in range(10):
+ ksampleradvanced_10 = ksampleradvanced.sample(
+ add_noise="enable",
+ noise_seed=random.randint(1, 2**64),
+ steps=25,
+ cfg=8,
+ sampler_name="euler",
+ scheduler="normal",
+ start_at_step=0,
+ end_at_step=20,
+ return_with_leftover_noise="enable",
+ model=checkpointloadersimple_4[0],
+ positive=cliptextencode_6[0],
+ negative=cliptextencode_7[0],
+ latent_image=emptylatentimage_5[0],
+ )
+
+ ksampleradvanced_11 = ksampleradvanced.sample(
+ add_noise="disable",
+ noise_seed=random.randint(1, 2**64),
+ steps=25,
+ cfg=8,
+ sampler_name="euler",
+ scheduler="normal",
+ start_at_step=20,
+ end_at_step=10000,
+ return_with_leftover_noise="disable",
+ model=checkpointloadersimple_12[0],
+ positive=cliptextencode_15[0],
+ negative=cliptextencode_16[0],
+ latent_image=ksampleradvanced_10[0],
+ )
+
+ vaedecode_17 = vaedecode.decode(
+ samples=ksampleradvanced_11[0], vae=checkpointloadersimple_12[2]
+ )
+
+ saveimage_19 = saveimage.save_images(
+ filename_prefix="ComfyUI", images=vaedecode_17[0].detach()
+ )
+
 
 if __name__ == "__main__":
  main()

comfyui_to_python.py

@@ -197,10 +197,6 @@ def get_class_info(class_type: str) -> (str, str, str):
  import_statement (str): Import statement string
  class_code (str): Class initialization code
  """
- # If the class is 'VAEDecode', adjust the class name
- if class_type == 'VAEDecode':
- class_type = 'VAEDecodeTiled'
-
  import_statement = class_type
  class_code = f'{class_type.lower()} = {class_type}()'
 
@@ -219,13 +215,12 @@ def assemble_python_code(import_statements: set, loader_code: List[str], code: L
  Returns:
  final_code (str): Generated final code as a string
  """
- static_imports = ['import random']
+ static_imports = ['import random', 'import torch']
  imports_code = [f"from nodes import {', '.join([class_name for class_name in import_statements])}" ]
- main_function_code = f"def main():\n\t" + '\n\t'.join(loader_code) + f'\n\n\tfor q in range({queue_size}):\n\t' + '\n\t'.join(code)
+ main_function_code = f"def main():\n\t" + 'with torch.inference_mode():\n\t\t' + '\n\t\t'.join(loader_code) + f'\n\n\t\tfor q in range({queue_size}):\n\t\t' + '\n\t\t'.join(code)
  final_code = '\n'.join(static_imports + ['import sys\nsys.path.append("../")'] + imports_code + ['', main_function_code, '', 'if __name__ == "__main__":', '\tmain()'])
  final_code = black.format_str(final_code, mode=black.Mode())
 
-
  return final_code