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Add optional noise_seed to make augmentation deterministic
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@ -34,7 +34,10 @@ class SVD_img2vid_Conditioning:
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"motion_bucket_id": ("INT", {"default": 127, "min": 1, "max": 1023}),
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"fps": ("INT", {"default": 6, "min": 1, "max": 1024}),
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"augmentation_level": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.01})
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}}
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},
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"optional": { "noise_seed": ("INT", {"default": 0, "min": 0, "max": 2**32-1}),
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}
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}
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RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
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RETURN_NAMES = ("positive", "negative", "latent")
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@ -42,12 +45,15 @@ class SVD_img2vid_Conditioning:
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CATEGORY = "conditioning/video_models"
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def encode(self, clip_vision, init_image, vae, width, height, video_frames, motion_bucket_id, fps, augmentation_level):
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def encode(self, clip_vision, init_image, vae, width, height, video_frames, motion_bucket_id,
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fps, augmentation_level, noise_seed=None):
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output = clip_vision.encode_image(init_image)
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pooled = output.image_embeds.unsqueeze(0)
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pixels = comfy.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1)
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encode_pixels = pixels[:,:,:,:3]
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if augmentation_level > 0:
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if noise_seed is not None:
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torch.manual_seed(noise_seed)
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encode_pixels += torch.randn_like(pixels) * augmentation_level
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t = vae.encode(encode_pixels)
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positive = [[pooled, {"motion_bucket_id": motion_bucket_id, "fps": fps, "augmentation_level": augmentation_level, "concat_latent_image": t}]]
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