add infotext

2 files changed, 11 insertions, 0 deletions

diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index a3448be9..0713dbf0 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -304,6 +304,12 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
     if "Schedule rho" not in res:

         res["Schedule rho"] = 0

 

+    if "VAE Encoder" not in res:

+        res["VAE Encoder"] = "Full"

+

+    if "VAE Decoder" not in res:

+        res["VAE Decoder"] = "Full"

+

     return res

 

 

@@ -329,6 +335,8 @@ infotext_to_setting_name_mapping = [
     ('RNG', 'randn_source'),

     ('NGMS', 's_min_uncond'),

     ('Pad conds', 'pad_cond_uncond'),

+    ('VAE Encoder', 'sd_vae_encode_method'),

+    ('VAE Decoder', 'sd_vae_decode_method'),

 ]

 

 

diff --git a/modules/processing.py b/modules/processing.py
index aa6d4d2a..a9ee7507 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -788,6 +788,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             with devices.without_autocast() if devices.unet_needs_upcast else devices.autocast():

                 samples_ddim = p.sample(conditioning=p.c, unconditional_conditioning=p.uc, seeds=p.seeds, subseeds=p.subseeds, subseed_strength=p.subseed_strength, prompts=p.prompts)

 

+            p.extra_generation_params['VAE Decoder'] = opts.sd_vae_decode_method

             x_samples_ddim = decode_latent_batch(p.sd_model, samples_ddim, target_device=devices.cpu, check_for_nans=True)

             x_samples_ddim = torch.stack(x_samples_ddim).float()

             x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)

@@ -1100,6 +1101,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             decoded_samples = torch.from_numpy(np.array(batch_images))

             decoded_samples = decoded_samples.to(shared.device)

 

+            self.extra_generation_params['VAE Encoder'] = opts.sd_vae_encode_method

             samples = images_tensor_to_samples(decoded_samples, approximation_indexes.get(opts.sd_vae_encode_method))

 

             image_conditioning = self.img2img_image_conditioning(decoded_samples, samples)

@@ -1338,6 +1340,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             raise RuntimeError(f"bad number of images passed: {len(imgs)}; expecting {self.batch_size} or less")

 

         image = torch.from_numpy(batch_images)

+        self.extra_generation_params['VAE Encoder'] = opts.sd_vae_encode_method

         self.init_latent = images_tensor_to_samples(image, approximation_indexes.get(opts.sd_vae_encode_method), self.sd_model)

         devices.torch_gc()