massive img downscaling for fast sending...
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30f4aaaa24
commit
b0555f1954
20
gradio_ui.py
20
gradio_ui.py
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@ -67,14 +67,15 @@ class BlendingFrontend():
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self.nmb_imgs_show = 5
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self.fps = 30
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self.duration = 10
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self.max_size_imgs = 200 # gradio otherwise mega slow
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if not self.use_debug:
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self.lb.sdh.num_inference_steps = self.num_inference_steps
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self.height = self.lb.sdh.height
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self.width = self.lb.sdh.width
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else:
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self.height = 420
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self.width = 420
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self.height = 768
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self.width = 768
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def change_depth_strength(self, value):
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self.depth_strength = value
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@ -150,6 +151,10 @@ class BlendingFrontend():
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print(f"randomize_seed2: new seed = {self.seed2}")
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return seed
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def downscale_imgs(self, list_imgs):
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return [l.resize((self.max_size_imgs, self.max_size_imgs)) for l in list_imgs]
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def run(self, x):
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print("STARTING DIFFUSION!")
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self.state_prev = self.state_current.copy()
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@ -159,7 +164,10 @@ class BlendingFrontend():
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if self.use_debug:
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list_imgs = [(255*np.random.rand(self.height,self.width,3)).astype(np.uint8) for l in range(5)]
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list_imgs = [Image.fromarray(l) for l in list_imgs]
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list_imgs = self.downscale_imgs(list_imgs)
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self.imgs_show_current = copy.deepcopy(list_imgs)
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print("DONE! SENDING BACK RESULTS")
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return list_imgs
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self.lb.set_width(self.width)
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@ -190,8 +198,10 @@ class BlendingFrontend():
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list_imgs = []
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for j in idx_list:
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list_imgs.append(imgs_transition[j])
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self.imgs_show_current = copy.deepcopy(list_imgs)
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list_imgs = self.downscale_imgs(list_imgs)
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self.imgs_show_current = copy.deepcopy(list_imgs)
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return list_imgs
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@ -261,9 +271,7 @@ class BlendingFrontend():
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if __name__ == "__main__":
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fp_ckpt = "../stable_diffusion_models/ckpt/v2-1_512-ema-pruned.ckpt"
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fp_config = 'configs/v2-inference.yaml'
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sdh = StableDiffusionHolder(fp_ckpt, fp_config)
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sdh = StableDiffusionHolder(fp_ckpt)
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self = BlendingFrontend(sdh)
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