nodes/pydn-ComfyUI-to-Python-Extension
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Merge pull request #2 from pydn/2023-08-07_add_inference_mode

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2023 08 07 add inference mode
This commit is contained in:
Peyton DeNiro
2023-08-07 22:43:27 -05:00
committed by GitHub
parent 654e6a2650 a7a43f4f5d
commit de8b64de9f
2 changed files with 76 additions and 79 deletions
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README.md
+74 -72
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@@ -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
+2 -7
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@@ -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