Revert "[SDXL] Add SDXL pipeline to SHARK (#1731)" (#1882)

This reverts commit 9f0a421.

apps/stable_diffusion/scripts/txt2img.py

@@ -1,9 +1,9 @@
 import torch
+import transformers
 import time
 from apps.stable_diffusion.src import (
     args,
     Text2ImagePipeline,
-    Text2ImageSDXLPipeline,
     get_schedulers,
     set_init_device_flags,
     utils,
@@ -16,62 +16,31 @@ def main():
     if args.clear_all:
         clear_all()
 
-    # TODO: prompt_embeds and text_embeds form base_model.json requires fixing
     dtype = torch.float32 if args.precision == "fp32" else torch.half
     cpu_scheduling = not args.scheduler.startswith("Shark")
     set_init_device_flags()
     schedulers = get_schedulers(args.hf_model_id)
     scheduler_obj = schedulers[args.scheduler]
     seed = args.seed
-    if args.height == 1024:
-        assert (
-            args.width == 1024
-        ), "currently we support only 1024x1024 image size via SDXL"
-        assert args.precision == "fp16", "currently we support fp16 for SDXL"
-        # For SDXL we set max_length as 77.
-        args.max_length = 77
-        txt2img_obj = Text2ImageSDXLPipeline.from_pretrained(
-            scheduler=scheduler_obj,
-            import_mlir=args.import_mlir,
-            model_id=args.hf_model_id,
-            ckpt_loc=args.ckpt_loc,
-            precision=args.precision,
-            max_length=args.max_length,
-            batch_size=args.batch_size,
-            height=args.height,
-            width=args.width,
-            use_base_vae=args.use_base_vae,
-            use_tuned=args.use_tuned,
-            custom_vae=args.custom_vae,
-            low_cpu_mem_usage=args.low_cpu_mem_usage,
-            debug=args.import_debug if args.import_mlir else False,
-            use_lora=args.use_lora,
-            use_quantize=args.use_quantize,
-            ondemand=args.ondemand,
-        )
-    else:
-        assert (
-            args.height <= 768 and args.width <= 768
-        ), "height/width not in supported range"
-        txt2img_obj = Text2ImagePipeline.from_pretrained(
-            scheduler=scheduler_obj,
-            import_mlir=args.import_mlir,
-            model_id=args.hf_model_id,
-            ckpt_loc=args.ckpt_loc,
-            precision=args.precision,
-            max_length=args.max_length,
-            batch_size=args.batch_size,
-            height=args.height,
-            width=args.width,
-            use_base_vae=args.use_base_vae,
-            use_tuned=args.use_tuned,
-            custom_vae=args.custom_vae,
-            low_cpu_mem_usage=args.low_cpu_mem_usage,
-            debug=args.import_debug if args.import_mlir else False,
-            use_lora=args.use_lora,
-            use_quantize=args.use_quantize,
-            ondemand=args.ondemand,
-        )
+    txt2img_obj = Text2ImagePipeline.from_pretrained(
+        scheduler=scheduler_obj,
+        import_mlir=args.import_mlir,
+        model_id=args.hf_model_id,
+        ckpt_loc=args.ckpt_loc,
+        precision=args.precision,
+        max_length=args.max_length,
+        batch_size=args.batch_size,
+        height=args.height,
+        width=args.width,
+        use_base_vae=args.use_base_vae,
+        use_tuned=args.use_tuned,
+        custom_vae=args.custom_vae,
+        low_cpu_mem_usage=args.low_cpu_mem_usage,
+        debug=args.import_debug if args.import_mlir else False,
+        use_lora=args.use_lora,
+        use_quantize=args.use_quantize,
+        ondemand=args.ondemand,
+    )
 
     seeds = utils.batch_seeds(seed, args.batch_count, args.repeatable_seeds)
     for current_batch in range(args.batch_count):

apps/stable_diffusion/src/__init__.py

@@ -9,7 +9,6 @@
 )
 from apps.stable_diffusion.src.pipelines import (
     Text2ImagePipeline,
-    Text2ImageSDXLPipeline,
     Image2ImagePipeline,
     InpaintPipeline,
     OutpaintPipeline,