Clean up the v-diffusion install pipeline (#327)

tank/pytorch/v_diffusion_pytorch/README.md

@@ -27,7 +27,25 @@ Run the script setup_v_diffusion_pytorch.sh
 ./v-diffusion-pytorch/cfg_sample.py "New York City, oil on canvas":5 -n 5 -bs 5
 ```
 
+The runtime device can be specified with `--runtime_device=<device string>`
+
 ### Run the v-diffusion model via torch-mlir
 ```shell
 ./cfg_sample.py "New York City, oil on canvas":5 -n 1 -bs 1 --steps 2
 ```
+
+### Run the model stored in the tank
+```shell
+./cfg_sample_from_mlir.py "New York City, oil on canvas":5 -n 1 -bs 1 --steps 2
+```
+Note that the current model in the tank requires batch size 1 statically.
+
+### Run the model with preprocessing elements taken out
+To run the model without preprocessing copy `cc12m_1.py` to replace the version in `v-diffusion-pytorch`
+```shell
+cp cc12m_1.py v-diffusion-pytorch/diffusion/models
+```
+Then run
+```shell
+./cfg_sample_preprocess.py "New York City, oil on canvas":5 -n 1 -bs 1 --steps 2
+```

tank/pytorch/v_diffusion_pytorch/cfg_sample.py

@@ -67,6 +67,12 @@ p.add_argument(
 )
 p.add_argument("--checkpoint", type=str, help="the checkpoint to use")
 p.add_argument("--device", type=str, help="the device to use")
+p.add_argument(
+    "--runtime_device",
+    type=str,
+    help="the device to use with SHARK",
+    default="cpu",
+)
 p.add_argument(
     "--eta",
     type=float,
@@ -235,7 +241,7 @@ mlir_model = module
 func_name = "forward"
 
 shark_module = SharkInference(
-    mlir_model, func_name, device="cuda", mlir_dialect="linalg"
+    mlir_model, func_name, device=args.runtime_device, mlir_dialect="linalg"
 )
 shark_module.compile()
 

tank/pytorch/v_diffusion_pytorch/cfg_sample_from_mlir.py

@@ -69,6 +69,12 @@ p.add_argument(
 )
 p.add_argument("--checkpoint", type=str, help="the checkpoint to use")
 p.add_argument("--device", type=str, help="the device to use")
+p.add_argument(
+    "--runtime_device",
+    type=str,
+    help="the device to use with SHARK",
+    default="cpu",
+)
 p.add_argument(
     "--eta",
     type=float,
@@ -188,7 +194,7 @@ t_in = t[0] * ts
 mlir_model, func_name, inputs, golden_out = download_torch_model("v_diffusion")
 
 shark_module = SharkInference(
-    mlir_model, func_name, device="cpu", mlir_dialect="linalg"
+    mlir_model, func_name, device=args.runtime_device, mlir_dialect="linalg"
 )
 shark_module.compile()
 

tank/pytorch/v_diffusion_pytorch/cfg_sample_preprocess.py

@@ -69,6 +69,12 @@ p.add_argument(
 )
 p.add_argument("--checkpoint", type=str, help="the checkpoint to use")
 p.add_argument("--device", type=str, help="the device to use")
+p.add_argument(
+    "--runtime_device",
+    type=str,
+    help="the device to use with SHARK",
+    default="intel-gpu",
+)
 p.add_argument(
     "--eta",
     type=float,
@@ -260,7 +266,7 @@ mlir_model = module
 func_name = "forward"
 
 shark_module = SharkInference(
-    mlir_model, func_name, device="intel-gpu", mlir_dialect="linalg"
+    mlir_model, func_name, device=args.runtime_device, mlir_dialect="linalg"
 )
 shark_module.compile()
 

tank/pytorch/v_diffusion_pytorch/setup_v_diffusion_pytorch.sh

@@ -24,4 +24,3 @@ mkdir checkpoints
 wget https://the-eye.eu/public/AI/models/v-diffusion/cc12m_1_cfg.pth -P checkpoints/
 
 cp -r checkpoints/ v-diffusion-pytorch/
-cp cc12m_1.py v-diffusion-pytorch/diffusion/models/.