From 8c6d957a5308f4af957e32798b488ade1c2626a3 Mon Sep 17 00:00:00 2001 From: Sean Sube Date: Sun, 29 Jan 2023 15:23:19 -0600 Subject: [PATCH] continue converting upscale to ONNX --- .../pipeline_onnx_stable_diffusion_upscale.py | 14 +------------- 1 file changed, 1 insertion(+), 13 deletions(-) diff --git a/api/onnx_web/diffusion/pipeline_onnx_stable_diffusion_upscale.py b/api/onnx_web/diffusion/pipeline_onnx_stable_diffusion_upscale.py index e5d9592c..93c983af 100644 --- a/api/onnx_web/diffusion/pipeline_onnx_stable_diffusion_upscale.py +++ b/api/onnx_web/diffusion/pipeline_onnx_stable_diffusion_upscale.py @@ -6,6 +6,7 @@ from diffusers import ( from logging import getLogger from typing import ( Any, + List, ) import numpy as np @@ -56,12 +57,6 @@ class OnnxStableDiffusionUpscalePipeline(StableDiffusionUpscalePipeline): f" {self.tokenizer.model_max_length} tokens: {removed_text}" ) - if hasattr(text_inputs, 'attention_mask') and text_inputs.attention_mask is not None: - attention_mask = text_inputs.attention_mask.to(device) - else: - attention_mask = None - - # TODO: TypeError: __call__() takes 1 positional argument but 2 were given # no positional arguments to text_encoder text_embeddings = self.text_encoder( input_ids=text_input_ids.int().to(device), @@ -70,7 +65,6 @@ class OnnxStableDiffusionUpscalePipeline(StableDiffusionUpscalePipeline): text_embeddings = text_embeddings[0] # duplicate text embeddings for each generation per prompt, using mps friendly method - bs_embed, seq_len, _ = text_embeddings.shape text_embeddings = text_embeddings.repeat(1, num_images_per_prompt) #, 1) # text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1) @@ -104,11 +98,6 @@ class OnnxStableDiffusionUpscalePipeline(StableDiffusionUpscalePipeline): return_tensors="pt", ) - if hasattr(uncond_input, 'attention_mask') and uncond_input.attention_mask is not None: - attention_mask = uncond_input.attention_mask.to(device) - else: - attention_mask = None - uncond_embeddings = self.text_encoder( input_ids=uncond_input.input_ids.int().to(device), # attention_mask=attention_mask, @@ -116,7 +105,6 @@ class OnnxStableDiffusionUpscalePipeline(StableDiffusionUpscalePipeline): uncond_embeddings = uncond_embeddings[0] # duplicate unconditional embeddings for each generation per prompt, using mps friendly method - seq_len = uncond_embeddings.shape[1] uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt) #, 1) # uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)