Support SVD dynamic shape[feat] (#564)

This PR is done:

issue: siliconflow/sd-team#240

- [x] SVD acceleration supports dynamic resolution switching. 
- [x] Add example.

Run:
```
export ONEFLOW_RUN_GRAPH_BY_VM="1"
python3 examples/image_to_video.py --variant fp16 --output-video vm.mp4 --decode-chunk-size 4 --run_multiple_resolutions true
```
Output:
```
Loading pipeline components...: 100%|█████████████████████████████████████████████████████| 5/5 [00:00<00:00,  7.50it/s]
warmup:
100%|███████████████████████████████████████████████████████████████████████████████████| 25/25 [02:29<00:00,  5.99s/it]
infer:
100%|███████████████████████████████████████████████████████████████████████████████████| 25/25 [00:28<00:00,  1.15s/it]
test dynamic resolution switch:
100%|███████████████████████████████████████████████████████████████████████████████████| 25/25 [00:28<00:00,  1.15s/it]
```
<img width="1024" alt="image"
src="https://github.com/siliconflow/onediff/assets/54010254/7204f043-1611-43d5-83ba-ced69482f3be">

benchmarks/image_to_video.py

@@ -26,10 +26,13 @@
 import argparse
 import time
 import json
+import random
 from PIL import Image, ImageDraw
+
+import oneflow as flow
 import torch
+
 from diffusers.utils import load_image, export_to_video
-import oneflow as flow
 from onediff.infer_compiler import oneflow_compile
 from onediff.infer_compiler.utils import set_boolean_env_var
 
@@ -73,6 +76,11 @@ def parse_args():
         type=int,
         default=ATTENTION_FP16_SCORE_ACCUM_MAX_M,
     )
+    parser.add_argument(
+        "--run_multiple_resolutions",
+        type=(lambda x: str(x).lower() in ["true", "1", "yes"]),
+        default=False,
+    )
     return parser.parse_args()
 
 
@@ -189,6 +197,11 @@ def callback_on_step_end(self, pipe, i, t, callback_kwargs={}):
         return callback_kwargs
 
 
+def adjust_resolution():
+    resolutions = [(1024, 512),(768, 576)]
+    return random.choice(resolutions)
+
+
 def main():
     args = parse_args()
     if args.deepcache:
@@ -229,6 +242,11 @@ def main():
     input_image = load_image(args.input_image)
     input_image.resize((width, height), Image.LANCZOS)
 
+    if args.run_multiple_resolutions:
+        new_width, new_height = adjust_resolution()
+        otherResImg = load_image(args.input_image)
+        otherResImg.resize((new_width, new_height), Image.LANCZOS)
+
     if args.control_image is None:
         if args.controlnet is None:
             control_image = None
@@ -301,6 +319,13 @@ def get_kwarg_inputs():
     print(f"CUDA Mem after: {cuda_mem_after_used / 1024:.3f}GiB")
     print(f"Host Mem after: {host_mem_after_used / 1024:.3f}GiB")
 
+    if args.run_multiple_resolutions:
+        kwarg_inputs['image'] = otherResImg
+        kwarg_inputs['height'] = new_height
+        kwarg_inputs['width'] = new_width
+        print("Test run with multiple resolutions...")
+        output_frames = pipe(**kwarg_inputs).frames
+
     if args.output_video is not None:
         export_to_video(output_frames[0], args.output_video, fps=args.fps)
     else:

src/infer_compiler_registry/register_diffusers/__init__.py

@@ -12,6 +12,9 @@
 from diffusers.models.transformer_2d import Transformer2DModel
 if diffusers_version >= version.parse("0.24.00"):
     from diffusers.models.resnet import SpatioTemporalResBlock
+    from diffusers.models.transformer_temporal import TransformerSpatioTemporalModel
+    from diffusers.models.attention import TemporalBasicTransformerBlock 
+    from diffusers.models.unet_spatio_temporal_condition import UNetSpatioTemporalConditionModel
 
     if diffusers_version >= version.parse("0.25.00"):
         from diffusers.models.autoencoders.autoencoder_kl_temporal_decoder import TemporalDecoder
@@ -27,6 +30,9 @@
     SpatioTemporalResBlock as SpatioTemporalResBlockOflow,
 )
 from .spatio_temporal_oflow import TemporalDecoder as TemporalDecoderOflow
+from .spatio_temporal_oflow import TransformerSpatioTemporalModel as TransformerSpatioTemporalModelOflow
+from .spatio_temporal_oflow import TemporalBasicTransformerBlock as TemporalBasicTransformerBlockOflow
+from .spatio_temporal_oflow import UNetSpatioTemporalConditionModel as UNetSpatioTemporalConditionModelOflow
 
 # For CI
 if diffusers_version >= version.parse("0.24.00"):
@@ -37,6 +43,9 @@
         LoRAAttnProcessor2_0: LoRAAttnProcessorOflow,
         SpatioTemporalResBlock: SpatioTemporalResBlockOflow,
         TemporalDecoder: TemporalDecoderOflow,
+        TransformerSpatioTemporalModel: TransformerSpatioTemporalModelOflow, 
+        TemporalBasicTransformerBlock: TemporalBasicTransformerBlockOflow,
+        UNetSpatioTemporalConditionModel: UNetSpatioTemporalConditionModelOflow,
     }
 else:
     torch2oflow_class_map = {