nodes/ArcherFMY-Diffusion360_ComfyUI
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mengyang.fmy
2024-05-27 10:29:46 +08:00
parent 1bd67ebb9e
commit d1beb97a3a
4 changed files with 27 additions and 13 deletions
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Diffusion360_nodes_diffusers.py
+5 -4
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@@ -36,7 +36,7 @@ class InputImage:
FUNCTION = "load_image"
def load_image(self, image):
image_path = os.path.join('custom_nodes/Diffusion360_ComfyUI/data/', image)
image_path = os.path.join('custom_nodes', 'Diffusion360_ComfyUI', 'data', image)
img = node_helpers.pillow(Image.open, image_path)
output_images = []
@@ -123,7 +123,7 @@ class Diffusion360LoaderText2Pano:
CATEGORY = "Diffusion360/diffusers"
def load_models(self, model_path):
pipe = Text2360PanoramaImagePipeline(os.path.join('models/diffusers', model_path), torch_dtype=torch.float16)
pipe = Text2360PanoramaImagePipeline(os.path.join('models', 'diffusers', model_path), torch_dtype=torch.float16)
return (pipe, )
@@ -184,6 +184,7 @@ class Diffusion360LoaderImage2Pano:
CATEGORY = "Diffusion360/diffusers"
def load_models(self, model_path):
pipe = Image2360PanoramaImagePipeline(os.path.join('models/diffusers', model_path), torch_dtype=torch.float16)
mask = load_image('custom_nodes/Diffusion360_ComfyUI/data/i2p-mask.jpg')
pipe = Image2360PanoramaImagePipeline(os.path.join('models', 'diffusers', model_path), torch_dtype=torch.float16)
mask_path = os.path.join('custom_nodes', 'Diffusion360_ComfyUI', 'data', 'i2p-mask.jpg')
mask = load_image(mask_path)
return (pipe, mask)
img2panoimg/image_to_360panorama_image_pipeline.py
+12 -5
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@@ -1,6 +1,7 @@
# Copyright © Alibaba, Inc. and its affiliates.
import random
from typing import Any, Dict
import os
import numpy as np
import torch
@@ -47,10 +48,12 @@ class Image2360PanoramaImagePipeline(DiffusionPipeline):
enable_xformers_memory_efficient_attention = kwargs.get(
'enable_xformers_memory_efficient_attention', True)
model_id = model + '/sr-base/'
# model_id = model + '/sr-base/'
model_id = os.path.join(model, 'sr-base')
# init i2p model
controlnet = ControlNetModel.from_pretrained(model + '/sd-i2p', torch_dtype=torch.float16)
# controlnet = ControlNetModel.from_pretrained(model + '/sd-i2p', torch_dtype=torch.float16)
controlnet = ControlNetModel.from_pretrained(os.path.join(model, 'sd-i2p'), torch_dtype=torch.float16)
self.pipe = StableDiffusionImage2PanoPipeline.from_pretrained(
model_id, controlnet=controlnet, torch_dtype=torch_dtype).to(device)
@@ -66,8 +69,11 @@ class Image2360PanoramaImagePipeline(DiffusionPipeline):
self.pipe.enable_model_cpu_offload()
# init controlnet-sr model
base_model_path = model + '/sr-base'
controlnet_path = model + '/sr-control'
# base_model_path = model + '/sr-base'
# controlnet_path = model + '/sr-control'
base_model_path = os.path.join(model, 'sr-base')
controlnet_path = os.path.join(model, 'sr-control')
controlnet = ControlNetModel.from_pretrained(
controlnet_path, torch_dtype=torch_dtype)
self.pipe_sr = StableDiffusionControlNetImg2ImgPanoPipeline.from_pretrained(
@@ -94,7 +100,8 @@ class Image2360PanoramaImagePipeline(DiffusionPipeline):
scale=2)
netscale = 2
model_path = model + '/RealESRGAN_x2plus.pth'
# model_path = model + '/RealESRGAN_x2plus.pth'
model_path = os.path.join(model, 'RealESRGAN_x2plus.pth')
dni_weight = None
self.upsampler = RealESRGANer(
node.tar.gz
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txt2panoimg/text_to_360panorama_image_pipeline.py
+10 -4
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@@ -1,6 +1,7 @@
# Copyright © Alibaba, Inc. and its affiliates.
import random
from typing import Any, Dict
import os
import numpy as np
import torch
@@ -46,7 +47,8 @@ class Text2360PanoramaImagePipeline(DiffusionPipeline):
enable_xformers_memory_efficient_attention = kwargs.get(
'enable_xformers_memory_efficient_attention', True)
model_id = model + '/sd-base/'
# model_id = model + '/sd-base/'
model_id = os.path.join(model, 'sd-base')
# init base model
self.pipe = StableDiffusionBlendExtendPipeline.from_pretrained(
@@ -63,8 +65,11 @@ class Text2360PanoramaImagePipeline(DiffusionPipeline):
self.pipe.enable_model_cpu_offload()
# init controlnet-sr model
base_model_path = model + '/sr-base'
controlnet_path = model + '/sr-control'
# base_model_path = model + '/sr-base'
# controlnet_path = model + '/sr-control'
base_model_path = os.path.join(model, 'sr-base')
controlnet_path = os.path.join(model, 'sr-control')
controlnet = ControlNetModel.from_pretrained(
controlnet_path, torch_dtype=torch_dtype)
self.pipe_sr = StableDiffusionControlNetImg2ImgPanoPipeline.from_pretrained(
@@ -91,7 +96,8 @@ class Text2360PanoramaImagePipeline(DiffusionPipeline):
scale=2)
netscale = 2
model_path = model + '/RealESRGAN_x2plus.pth'
# model_path = model + '/RealESRGAN_x2plus.pth'
model_path = os.path.join(model, 'RealESRGAN_x2plus.pth')
dni_weight = None
self.upsampler = RealESRGANer(