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Fix for potentially dangerous code #27

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Fix for potentially dangerous code #27

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ClownsharkBatwing Jul 26, 2024
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ClownsharkBatwing Jul 27, 2024
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ClownsharkBatwing Jul 27, 2024
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4 changes: 2 additions & 2 deletions nodes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -129,11 +129,11 @@ def INPUT_TYPES(s):
"model": ("ULTRAPIXELMODEL",),
"height": (
"INT",
{"default": 2048, "min": 512, "max": 5120, "step": 256},
{"default": 2048, "min": 512, "max": 5120, "step": 8},
),
"width": (
"INT",
{"default": 2048, "min": 512, "max": 5120, "step": 256},
{"default": 2048, "min": 512, "max": 5120, "step": 8},
),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFFFFFFFFFFFF}),
"dtype": (["bf16", "fp32"],),
Expand Down
18 changes: 11 additions & 7 deletions ultrapixel.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,6 +16,7 @@
)
from .train import WurstCore_t2i as WurstCoreC

from safetensors.torch import load_file as load_safetensors

class UltraPixel:
def __init__(
Expand Down Expand Up @@ -88,6 +89,9 @@ def set_config(
self.prompt = prompt
self.controlnet_image = controlnet_image




def process(self):
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
torch.manual_seed(self.seed)
Expand Down Expand Up @@ -140,11 +144,11 @@ def process(self):

captions = [self.prompt]
height, width = self.height, self.width

sdd = torch.load(self.pretrained, map_location="cpu")
collect_sd = {}
for k, v in sdd.items():
collect_sd[k[7:]] = v
sdd = load_safetensors(self.pretrained) # this is the equivalent code for loading the real safetensors versions of ultrapixel_t2i and lora_cat.
collect_sd = {k: v for k, v in sdd.items()}
collect_sd = {k[7:] if k.startswith('module.') else k: v for k, v in collect_sd.items()}
models.train_norm.load_state_dict(collect_sd)

if self.controlnet_image == None:
models.train_norm.load_state_dict(collect_sd)
Expand All @@ -154,8 +158,8 @@ def process(self):
load_or_fail(self.controlnet), strict=True
)

models.generator.eval()
models.train_norm.eval()
models.generator.eval() # stage C
models.train_norm.eval() # stage UP

batch_size = 1
edge_image = None
Expand Down