From 72403181794604b4818acbfb2fdb3c8365a9d6ea Mon Sep 17 00:00:00 2001 From: Anton Lozhkov Date: Fri, 18 Nov 2022 16:30:07 +0100 Subject: [PATCH] Fix the order of casts for onnx inpainting (#1338) --- .../stable_diffusion/pipeline_onnx_stable_diffusion_inpaint.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint.py b/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint.py index c353217d75..b933c52bf6 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint.py @@ -409,8 +409,8 @@ class OnnxStableDiffusionInpaintPipeline(DiffusionPipeline): latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents # concat latents, mask, masked_image_latnets in the channel dimension latent_model_input = self.scheduler.scale_model_input(torch.from_numpy(latent_model_input), t) - latent_model_input = np.concatenate([latent_model_input, mask, masked_image_latents], axis=1) latent_model_input = latent_model_input.cpu().numpy() + latent_model_input = np.concatenate([latent_model_input, mask, masked_image_latents], axis=1) # predict the noise residual timestep = np.array([t], dtype=timestep_dtype)