nodes/TashaSkyUp-EternalKernelPytorchNodes
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changes to exec node and the ability to return TinyTextToImage as a function

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Tasha Upchurch
2023-05-29 22:38:37 -04:00
parent eb6f956051
commit dacbed79c2
1 changed files with 92 additions and 21 deletions
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image.py
+92 -21
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@@ -1,9 +1,10 @@
try:
import comfy.samplers
#import custom_nodes.Derfuu_ComfyUI_ModdedNodes.components.fields as field
# import custom_nodes.Derfuu_ComfyUI_ModdedNodes.components.fields as field
from nodes import CLIPTextEncode, VAEEncode, VAEDecode, KSampler, CheckpointLoaderSimple, EmptyLatentImage, \
SaveImage
from custom_nodes.ComfyUI_ADV_CLIP_emb.nodes import AdvancedCLIPTextEncode as CLIPTextEncodeAdvanced
from nodes import common_ksampler
except ImportError as e:
print("ETK> comfy.samplers not found, skipping comfyui")
SaveImage = None
@@ -107,19 +108,21 @@ class TinyTxtToImg:
"default": "tinytxt2img"}
),
"overrides": ("STRING", {"multiline": True,
"default": ""})
"default": ""}),
"FUNC": ("FUNC",)
}
}
CATEGORY = "ETK"
RETURN_TYPES = ("IMAGE",)
RETURN_TYPES = ("IMAGE","FUNC",)
FUNCTION = "tinytxt2img"
def tinytxt2img(self, prompt, neg_prompt, name="tinytxt2img", overrides: str = "",
clip_encoder="comfy",
clip_encoder="comfy -ignore below",
token_normalization="length+mean",
weight_interpretation="comfy++"):
weight_interpretation="comfy++",
FUNC: callable = None):
""" use the imports from nodes to generate an image from text """
import random
@@ -150,6 +153,8 @@ class TinyTxtToImg:
self.height = 512
self.batch_size = 1
self.denoise = 1.0
self.one_seed_per_batch = False
if overrides:
if overrides != "":
overrides = overrides.replace("'", "\"")
@@ -161,6 +166,9 @@ class TinyTxtToImg:
self.__setattr__(k, v)
else:
print(f"invalid override key: {k}")
## TODO: probably need to prepare the execution better to make sure that applying the incomming func can change everything
if FUNC is not None:
self.__dict__.update(FUNC(self.__dict__))
if clip_encoder == "comfy -ignore below":
self.pos_cond = CLIPTextEncode.encode(None, self.clp, self.positive)[0]
self.neg_cond = CLIPTextEncode.encode(None, self.clp, self.negative)[0]
@@ -182,12 +190,23 @@ class TinyTxtToImg:
self.mdl,
self.seed, self.steps, self.cfg, self.sampler_name, self.scheduler,
self.pos_cond, self.neg_cond,
self.latent_image, denoise=self.denoise)[0]
self.latent_image,
denoise=self.denoise,
one_seed_per_batch=self.one_seed_per_batch)[0]
image = VAEDecode.decode(None, self.vae, samples)[0]
image = image.detach().cpu()
return (image,)
return (image,lambda : self.tinytxt2img(prompt,
neg_prompt,
name,
overrides,
clip_encoder,
token_normalization,
weight_interpretation,
FUNC
)
,)
if SaveImage != None:
@@ -230,10 +249,17 @@ class ExecWidget:
def INPUT_TYPES(cls):
return {"optional":
{
"text_to_eval": ("STRING", {"multiline": True}),
"text_to_eval": ("STRING",
{"multiline": True,
"default":"int_out=int_out\n"
"float_out=float_out\n"
"string_out=string_out\n"
"image_out=image_out\n"
}),
"image1_in": ("IMAGE", {"multiline": False}),
"float1_in": ("FLOAT", {"multiline": False}),
"string1_in": ("STRING", {"multiline": False}),
"int1_in": ("INT", {"multiline": False}),
},
"required": {
@@ -244,16 +270,20 @@ class ExecWidget:
}
CATEGORY = "text"
RETURN_TYPES = ("STRING", "IMAGE", "FLOAT")
RETURN_TYPES = ("STRING", "IMAGE", "FLOAT", "INT")
FUNCTION = "exec_handler"
def exec_handler(self, text_to_eval, image1_in: torch.Tensor = None, float1_in: float = 0.0, string1_in: str = "",
name: str = "exec_func"):
int1_in: int = 0,name: str = "exec_func"):
"""
>>> ExecWidget().exec_handler("2 + 3")
'5'
"""
result = None
out_image = None
out_float = None
out_string = None
if image1_in is not None:
image_in = image1_in.clone()
else:
@@ -262,21 +292,34 @@ class ExecWidget:
new_locals = {"float_out": float1_in,
"image_out": image_in,
"string_out": string1_in,
"int_out": int1_in,
}
exec(text_to_eval, globals(), new_locals)
try:
if isinstance(text_to_eval, list):
text_to_eval = text_to_eval[0]
# merge new_locals into globals
# globals().update(new_locals)
# locals().update(new_locals)
# print(globals())
out_string = new_locals.get("string_out", None)
out_image = new_locals.get("image_out", None)
out_float = new_locals.get("float_out", None)
return (out_string, out_image, out_float,)
if "<code>" in text_to_eval and "</code>" in text_to_eval:
start = text_to_eval.find("<code>")
end = text_to_eval.find("</code>")
text_to_eval = text_to_eval[start + 6:end]
exec(text_to_eval, globals(), new_locals)
out_string = new_locals.get("string_out", None)
out_image = new_locals.get("image_out", None)
out_float = new_locals.get("float_out", None)
out_int = new_locals.get("int_out", None)
except Exception as e:
print(e)
out_string = str(e)
return (out_string, out_image, out_float, out_int)
@classmethod
def IS_CHANGED(s, text_to_eval, image1_in: torch.Tensor = None, float1_in: float = 0.0, string1_in: str = "",
name: str = "exec_func"):
def IS_CHANGED(s, text_to_eval, image1_in: torch.Tensor = None, float1_in: float = 0.0, string1_in: str = "",
int1_in: int = 0, name: str = "exec_func"):
m = hashlib.sha256()
m.update(name.encode("utf-8"))
return m.digest().hex()
@@ -1196,6 +1239,34 @@ def get_image_size(image):
return (width * height)
class KSampler:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"model": ("MODEL",),
"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,),
"positive": ("CONDITIONING",),
"negative": ("CONDITIONING",),
"latent_image": ("LATENT",),
"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"one_seed_per_batch": ([True, False], {"default": False}),
}}
RETURN_TYPES = ("LATENT",)
FUNCTION = "sample"
CATEGORY = "sampling"
def sample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0,
one_seed_per_batch=False):
return common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
denoise=denoise, one_seed_per_batch=one_seed_per_batch)
if __name__ == "__main__":
import doctest