Remove dead test code

Deleting tests that don't seem to test any SD modules.
Also deleted code for old variant of SD that used 224x224 input shapes.

models/demos/wormhole/stable_diffusion/tests/test_resnet_block_2d_new_conv.py

@@ -25,90 +25,6 @@ def ttnn_to_torch(input):
     return input
 
 
-@skip_for_grayskull()
-@pytest.mark.parametrize(
-    "batch_size, in_channels, input_height, input_width, index1,index2,block_name,out_channels",
-    [
-        (2, 320, 32, 32, 0, 0, "down", None),
-        (2, 320, 16, 16, 0, 0, "down", None),
-        (2, 640, 16, 16, 1, 1, "down", None),
-        (2, 640, 8, 8, 1, 1, "down", None),
-        (2, 1280, 8, 8, 2, 1, "down", None),
-        (2, 1280, 4, 4, 2, 1, "down", None),
-        (2, 2560, 4, 4, 0, 0, "up", 1280),
-        (2, 2560, 8, 8, 0, 0, "up", 1280),
-        (2, 1920, 8, 8, 2, 0, "up", 640),
-        (2, 1920, 16, 16, 2, 0, "up", 640),
-        (2, 1280, 16, 16, 3, 0, "down", None),
-        (2, 960, 16, 16, 3, 0, "up", 320),
-        (2, 960, 32, 32, 3, 0, "up", 320),
-        (2, 640, 32, 32, 3, 1, "up", 320),
-    ],
-)
-def test_resnet_block_2d_256x256(
-    device, batch_size, in_channels, input_height, input_width, index1, index2, block_name, out_channels
-):
-    pytest.skip()
-    # setup pytorch model
-    model_name = "CompVis/stable-diffusion-v1-4"
-    pipe = StableDiffusionPipeline.from_pretrained(model_name, torch_dtype=torch.float32)
-
-    model = pipe.unet
-    model.eval()
-
-    parameters = preprocess_model_parameters(
-        model_name=model_name, initialize_model=lambda: model, custom_preprocessor=custom_preprocessor, device=device
-    )
-
-    if block_name == "up":
-        parameters = parameters.up_blocks[index1].resnets[index2]
-        resnet = pipe.unet.up_blocks[index1].resnets[index2]
-    elif block_name == "down":
-        parameters = parameters.down_blocks[index1].resnets[index2]
-        resnet = pipe.unet.down_blocks[index1].resnets[index2]
-    else:
-        parameters = parameters.mid_block.resnets[index2]
-        resnet = pipe.unet.mid_block.resnets[index2]
-
-    ############ start of residual block #############
-    temb_channels = 1280
-    groups = 32
-    time_embedding_norm = "default"
-    output_scale_factor = 1
-    use_in_shortcut = None
-    ########## end of residual block #############
-    hidden_states_shape = [batch_size, in_channels, input_height, input_width]
-    temb_shape = [1, 1, 2, 1280]
-
-    input = torch.randn(hidden_states_shape)
-    temb = torch.randn(temb_shape)
-
-    torch_output = resnet(input, temb.squeeze(0).squeeze(0))
-
-    input = ttnn.from_torch(input, ttnn.bfloat16)
-    input = ttnn.to_layout(input, ttnn.TILE_LAYOUT)
-    input = ttnn.to_device(input, device, memory_config=ttnn.L1_MEMORY_CONFIG)
-
-    temb = ttnn.from_torch(temb, ttnn.bfloat16)
-    temb = ttnn.to_layout(temb, ttnn.TILE_LAYOUT)
-    temb = ttnn.to_device(temb, device, memory_config=ttnn.L1_MEMORY_CONFIG)
-    ttnn_output = resnetBlock2D(
-        input,
-        temb=temb,
-        temb_channels=temb_channels,
-        time_embedding_norm=time_embedding_norm,
-        in_channels=in_channels,
-        out_channels=out_channels,
-        use_in_shortcut=use_in_shortcut,
-        groups=groups,
-        output_scale_factor=output_scale_factor,
-        parameters=parameters,
-        device=device,
-    )
-    ttnn_output = ttnn_to_torch(ttnn_output)
-    assert_with_pcc(torch_output, ttnn_output, pcc=0.99)
-
-
 @skip_for_grayskull()
 @pytest.mark.parametrize("device_params", [{"l1_small_size": 32768}], indirect=True)
 @pytest.mark.parametrize(