replace get_first_stage_encoding

modules/processing.py

@@ -16,6 +16,7 @@ from typing import Any, Dict, List
 import modules.sd_hijack
 from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, extra_networks, sd_vae_approx, scripts, sd_samplers_common, sd_unet, errors
 from modules.sd_hijack import model_hijack
+from modules.sd_samplers_common import images_tensor_to_samples, decode_first_stage, approximation_indexes
 from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
 import modules.paths as paths
@@ -30,7 +31,6 @@ from ldm.models.diffusion.ddpm import LatentDepth2ImageDiffusion
 from einops import repeat, rearrange
 from blendmodes.blend import blendLayers, BlendType
 
-decode_first_stage = sd_samplers_common.decode_first_stage
 
 # some of those options should not be changed at all because they would break the model, so I removed them from options.
 opt_C = 4
@@ -84,7 +84,7 @@ def txt2img_image_conditioning(sd_model, x, width, height):
 
         # The "masked-image" in this case will just be all zeros since the entire image is masked.
         image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
-        image_conditioning = sd_model.get_first_stage_encoding(sd_model.encode_first_stage(image_conditioning))
+        image_conditioning = images_tensor_to_samples(image_conditioning, approximation_indexes.get(opts.sd_vae_encode_method))
 
         # Add the fake full 1s mask to the first dimension.
         image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
@@ -203,7 +203,7 @@ class StableDiffusionProcessing:
         midas_in = torch.from_numpy(transformed["midas_in"][None, ...]).to(device=shared.device)
         midas_in = repeat(midas_in, "1 ... -> n ...", n=self.batch_size)
 
-        conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(source_image))
+        conditioning_image = images_tensor_to_samples(source_image*0.5+0.5, approximation_indexes.get(opts.sd_vae_encode_method))
         conditioning = torch.nn.functional.interpolate(
             self.sd_model.depth_model(midas_in),
             size=conditioning_image.shape[2:],
@@ -216,7 +216,7 @@ class StableDiffusionProcessing:
         return conditioning
 
     def edit_image_conditioning(self, source_image):
-        conditioning_image = self.sd_model.encode_first_stage(source_image).mode()
+        conditioning_image = images_tensor_to_samples(source_image*0.5+0.5, approximation_indexes.get(opts.sd_vae_encode_method))
 
         return conditioning_image
 
@@ -1099,9 +1099,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
             decoded_samples = torch.from_numpy(np.array(batch_images))
             decoded_samples = decoded_samples.to(shared.device)
-            decoded_samples = 2. * decoded_samples - 1.
 
-            samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples))
+            samples = images_tensor_to_samples(decoded_samples, approximation_indexes.get(opts.sd_vae_encode_method))
 
             image_conditioning = self.img2img_image_conditioning(decoded_samples, samples)
 
@@ -1339,7 +1338,6 @@ 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)
-        from modules.sd_samplers_common import images_tensor_to_samples, approximation_indexes
         self.init_latent = images_tensor_to_samples(image, approximation_indexes.get(opts.sd_vae_encode_method), self.sd_model)
         devices.torch_gc()