nodes/niknah-MiniT2I-ComfyUI
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Remove need for trust_remote_code

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niknah
2026-06-27 22:39:02 +10:00
parent b412e056d3
commit 1190fbb080
1 changed files with 32 additions and 30 deletions
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MiniT2I.py
+32 -30
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@@ -5,6 +5,7 @@ from comfy_api.latest import io
from PIL import Image
import torch
from diffusers import DiffusionPipeline
from .pipeline import MiniT2IPipeline
# import os
model_downloaded = False
@@ -89,33 +90,33 @@ class MiniT2ISampler(io.ComfyNode):
# return []
@classmethod
def pil2tensor(cls, image: Union[Image.Image, List[Image.Image]]) -> torch.Tensor:
"""
Convert PIL image(s) to tensor, matching ComfyUI's implementation.
Args:
image: Single PIL Image or list of PIL Images
Returns:
torch.Tensor: Image tensor with values normalized to [0, 1]
"""
if isinstance(image, list):
if len(image) == 0:
return torch.empty(0)
return torch.cat([cls.pil2tensor(img) for img in image], dim=0)
# Convert PIL image to RGB if needed
if image.mode == 'RGBA':
image = image.convert('RGB')
elif image.mode != 'RGB':
image = image.convert('RGB')
# Convert to numpy array and normalize to [0, 1]
img_array = np.array(image).astype(np.float32) / 255.0
# Return tensor with shape [1, H, W, 3]
return torch.from_numpy(img_array)[None,]
# @classmethod
# def pil2tensor(cls, image: Union[Image.Image, List[Image.Image]]) -> torch.Tensor:
# """
# Convert PIL image(s) to tensor, matching ComfyUI's implementation.
#
# Args:
# image: Single PIL Image or list of PIL Images
#
# Returns:
# torch.Tensor: Image tensor with values normalized to [0, 1]
# """
# if isinstance(image, list):
# if len(image) == 0:
# return torch.empty(0)
# return torch.cat([cls.pil2tensor(img) for img in image], dim=0)
#
# # Convert PIL image to RGB if needed
# if image.mode == 'RGBA':
# image = image.convert('RGB')
# elif image.mode != 'RGB':
# image = image.convert('RGB')
#
# # Convert to numpy array and normalize to [0, 1]
# img_array = np.array(image).astype(np.float32) / 255.0
#
# # Return tensor with shape [1, H, W, 3]
# return torch.from_numpy(img_array)[None,]
@classmethod
def execute(cls, prompt, steps, guidance, model_type, seed) -> io.NodeOutput:
@@ -128,11 +129,12 @@ class MiniT2ISampler(io.ComfyNode):
HUB_MODEL_ID = "MiniT2I/MiniT2I"
pipe = DiffusionPipeline.from_pretrained(
pipe = MiniT2IPipeline.from_pretrained(
# pipe = DiffusionPipeline.from_pretrained(
HUB_MODEL_ID,
custom_pipeline=str(script_dir / "pipeline.py"),
# custom_pipeline=str(script_dir / "pipeline.py"),
local_files_only=model_downloaded,
trust_remote_code=True,
# trust_remote_code=True,
)
if pipe:
model_downloaded = True