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https://github.com/lllyasviel/stable-diffusion-webui-forge.git
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Update forge_svd.py
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@@ -7,6 +7,7 @@ from modules.paths import models_path
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from modules.ui_common import ToolButton, refresh_symbol
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from modules import shared
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from modules_forge.forge_util import numpy_to_pytorch, pytorch_to_numpy
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from ldm_patched.modules.sd import load_checkpoint_guess_config
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from ldm_patched.contrib.external_video_model import VideoLinearCFGGuidance, SVD_img2vid_Conditioning
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from ldm_patched.contrib.external import KSampler, VAEDecode
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@@ -41,8 +42,11 @@ def predict(filename, width, height, video_frames, motion_bucket_id, fps, augmen
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sampling_denoise, guidance_min_cfg, input_image):
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filename = os.path.join(svd_root, filename)
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model_raw, _, vae, clip_vision = load_checkpoint_guess_config(filename, output_vae=True, output_clip=False, output_clipvision=True)
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model = opVideoLinearCFGGuidance.patch(model_raw, guidance_min_cfg)
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a = 0
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model = opVideoLinearCFGGuidance.patch(model_raw, guidance_min_cfg)[0]
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init_image = numpy_to_pytorch(input_image)
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positive, negative, latent_image = opSVD_img2vid_Conditioning.encode(clip_vision, init_image, vae, width, height, video_frames, motion_bucket_id, fps, augmentation_level)
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output_latent = opKSampler.sample(model, sampling_seed, sampling_steps, sampling_cfg, sampling_sampler_name, sampling_scheduler, positive, negative, latent_image, sampling_denoise)
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output_pixels = opVAEDecode.decode(vae, output_latent)[0]
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return
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