webui support controlnet extension (#948)

only supported UNet inference with control, but controlnet model not
supported now

---------

Co-authored-by: Li Junliang <117806079+lijunliangTG@users.noreply.github.com>

onediff_sd_webui_extensions/compile/compile_utils.py

@@ -61,7 +61,9 @@ def get_calibrate_info(filename: str) -> Union[None, Dict]:
 
 
 def get_compiled_graph(sd_model, quantization) -> OneDiffCompiledGraph:
-    compiled_unet = compile_unet(
-        sd_model.model.diffusion_model, quantization=quantization
-    )
+    diffusion_model = sd_model.model.diffusion_model
+    # for controlnet
+    if "forward" in diffusion_model.__dict__:
+        diffusion_model.__dict__.pop("forward")
+    compiled_unet = compile_unet(diffusion_model, quantization=quantization)
     return OneDiffCompiledGraph(sd_model, compiled_unet, quantization)

onediff_sd_webui_extensions/compile/sd_webui_onediff_utils.py

@@ -13,17 +13,22 @@ def forward(self, x):
 
 
 # https://github.com/Stability-AI/generative-models/blob/059d8e9cd9c55aea1ef2ece39abf605efb8b7cc9/sgm/modules/diffusionmodules/util.py#L207
-def timestep_embedding(timesteps, dim, max_period=10000):
-    half = dim // 2
-    freqs = flow.exp(
-        -math.log(max_period)
-        * flow.arange(start=0, end=half, dtype=flow.float32)
-        / half
-    ).to(device=timesteps.device)
-    args = timesteps[:, None].float() * freqs[None]
-    embedding = flow.cat([flow.cos(args), flow.sin(args)], dim=-1)
-    if dim % 2:
-        embedding = flow.cat([embedding, flow.zeros_like(embedding[:, :1])], dim=-1)
+def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):
+    if not repeat_only:
+        half = dim // 2
+        freqs = flow.exp(
+            -math.log(max_period)
+            * flow.arange(start=0, end=half, dtype=flow.float32)
+            / half
+        ).to(device=timesteps.device)
+        args = timesteps[:, None].float() * freqs[None]
+        embedding = flow.cat([flow.cos(args), flow.sin(args)], dim=-1)
+        if dim % 2:
+            embedding = flow.cat([embedding, flow.zeros_like(embedding[:, :1])], dim=-1)
+    else:
+        raise NotImplementedError(
+            "repeat_only=True is not implemented in timestep_embedding"
+        )
     return embedding