Merge pull request #14390 from wangqyqq/sdxl-inpaint

Supporting for SDXL-Inpaint Model

1 files changed, 21 insertions, 0 deletions

diff --git a/modules/processing.py b/modules/processing.py
index 2f11d5f8..7789f9a4 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -113,6 +113,21 @@ def txt2img_image_conditioning(sd_model, x, width, height):
         return x.new_zeros(x.shape[0], 2*sd_model.noise_augmentor.time_embed.dim, dtype=x.dtype, device=x.device)

 

     else:

+        sd = sd_model.model.state_dict()

+        diffusion_model_input = sd.get('diffusion_model.input_blocks.0.0.weight', None)

+        if diffusion_model_input is not None:

+            if diffusion_model_input.shape[1] == 9:

+                # The "masked-image" in this case will just be all 0.5 since the entire image is masked.

+                image_conditioning = torch.ones(x.shape[0], 3, height, width, device=x.device) * 0.5

+                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)

+                image_conditioning = image_conditioning.to(x.dtype)

+

+                return image_conditioning

+

         # Dummy zero conditioning if we're not using inpainting or unclip models.

         # Still takes up a bit of memory, but no encoder call.

         # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.

@@ -371,6 +386,12 @@ class StableDiffusionProcessing:
         if self.sampler.conditioning_key == "crossattn-adm":

             return self.unclip_image_conditioning(source_image)

 

+        sd = self.sampler.model_wrap.inner_model.model.state_dict()

+        diffusion_model_input = sd.get('diffusion_model.input_blocks.0.0.weight', None)

+        if diffusion_model_input is not None:

+            if diffusion_model_input.shape[1] == 9:

+                return self.inpainting_image_conditioning(source_image, latent_image, image_mask=image_mask)

+

         # Dummy zero conditioning if we're not using inpainting or depth model.

         return latent_image.new_zeros(latent_image.shape[0], 5, 1, 1)