use a torch rng
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@ -200,8 +200,6 @@ def process_tiles(
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tiles_y = height // tile
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tiles_y = height // tile
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total = tiles_x * tiles_y
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total = tiles_x * tiles_y
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rng = random.RandomState(0)
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for y in range(tiles_y):
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for y in range(tiles_y):
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for x in range(tiles_x):
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for x in range(tiles_x):
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idx = (y * tiles_x) + x
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idx = (y * tiles_x) + x
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@ -11,6 +11,7 @@ from realesrgan import RealESRGANer
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from typing import Literal, Union
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from typing import Literal, Union
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import numpy as np
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import numpy as np
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import torch
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from .image import (
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from .image import (
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process_tiles
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process_tiles
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@ -146,8 +147,15 @@ def upscale_stable_diffusion(ctx: ServerContext, params: UpscaleParams, image: I
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# )
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# )
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# result = pipeline('', image=image)
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# result = pipeline('', image=image)
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generator = torch.manual_seed(0)
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seed = generator.initial_seed()
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pipeline = StableDiffusionUpscalePipeline.from_pretrained('stabilityai/stable-diffusion-x4-upscaling')
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pipeline = StableDiffusionUpscalePipeline.from_pretrained('stabilityai/stable-diffusion-x4-upscaling')
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upscale = lambda i: pipeline('an astronaut eating a hamburger', image=i, rng=np.random.RandomState(0)).images[0]
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upscale = lambda i: pipeline(
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'an astronaut eating a hamburger',
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image=i,
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generator=torch.manual_seed(initial_seed),
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).images[0]
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result = process_tiles(image, 128, 4, [upscale])
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result = process_tiles(image, 128, 4, [upscale])
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return result
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return result
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