fix repeating subseeds for every batch #221
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@ -229,6 +229,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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prompts = all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
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seeds = all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
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subseeds = all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]
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uc = p.sd_model.get_learned_conditioning(len(prompts) * [p.negative_prompt])
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c = p.sd_model.get_learned_conditioning(prompts)
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@ -237,7 +238,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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comments += model_hijack.comments
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# we manually generate all input noises because each one should have a specific seed
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x = create_random_tensors([opt_C, p.height // opt_f, p.width // opt_f], seeds=seeds, subseeds=all_subseeds, subseed_strength=p.subseed_strength, seed_resize_from_h=p.seed_resize_from_h, seed_resize_from_w=p.seed_resize_from_w)
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x = create_random_tensors([opt_C, p.height // opt_f, p.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, seed_resize_from_h=p.seed_resize_from_h, seed_resize_from_w=p.seed_resize_from_w)
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if p.n_iter > 1:
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shared.state.job = f"Batch {n+1} out of {p.n_iter}"
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