Fix different first gen with Approx NN previews

The loading of the model for approx nn live previews can change the internal state of PyTorch, resulting in a different image. This can be avoided by preloading the approx nn model in advance.

1 files changed, 5 insertions, 1 deletions

diff --git a/modules/processing.py b/modules/processing.py
index bc541e2f..3bd590ba 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -13,7 +13,7 @@ from skimage import exposure
 from typing import Any, Dict, List, Optional

 

 import modules.sd_hijack

-from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, script_callbacks, extra_networks

+from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, script_callbacks, extra_networks, sd_vae_approx

 from modules.sd_hijack import model_hijack

 from modules.shared import opts, cmd_opts, state

 import modules.shared as shared

@@ -568,6 +568,10 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
         with devices.autocast():

             p.init(p.all_prompts, p.all_seeds, p.all_subseeds)

 

+            if shared.opts.live_previews_enable and sd_samplers.approximation_indexes.get(shared.opts.show_progress_type, 0) == 1:

+                # preload approx nn model before sampling for a more deterministic result

+                sd_vae_approx.model()

+

             if not p.disable_extra_networks:

                 extra_networks.activate(p, extra_network_data)