remove prints
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@@ -1116,9 +1116,7 @@ class WanVideoSampler:
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i, steps, latent_video_length, context_frames, context_stride, context_overlap,
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))
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for c in context_queue:
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print(c)
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partial_latent_model_input = [latent_model_input[0][:, c, :, :]]
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print("partial_latent_model_input", partial_latent_model_input[0].shape)
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# Model inference - returns [frames, channels, height, width]
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noise_pred_cond = transformer(
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partial_latent_model_input, t=timestep, **arg_c)[0].to(offload_device)
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@@ -1130,7 +1128,6 @@ class WanVideoSampler:
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noise_pred_cond - noise_pred_uncond)
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else:
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noise_pred_context = noise_pred_cond
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print("noise_pred_context", noise_pred.shape)
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noise_pred[:, c, :, :] += noise_pred_context
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counter[:, c, :, :] += 1
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#model inference end
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