ControlNet API (#162)

* ControlNet API

* update cache key

* nits

* disable controlnet tests

.github/workflows/run_tests.yaml

@@ -52,6 +52,18 @@ jobs:
               curl -Lo "$filename" "$url"
             fi
           done
+      # - name: Download ControlNet models
+      #   run: |
+      #     declare -a urls=(
+      #       "https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11p_sd15_canny.pth"
+      #     )
+
+      #     for url in "${urls[@]}"; do
+      #       filename="models/ControlNet/${url##*/}"  # Extracts the last part of the URL
+      #       if [ ! -f "$filename" ]; then
+      #         curl -Lo "$filename" "$url"
+      #       fi
+      #     done
       - name: Start test server
         run: >
           python -m coverage run
@@ -71,6 +83,16 @@ jobs:
         run: |
           wait-for-it --service 127.0.0.1:7860 -t 20
           python -m pytest -vv --junitxml=test/results.xml --cov . --cov-report=xml --verify-base-url test
+      # TODO(huchenlei): Enable ControlNet tests. Currently it is too slow to run these tests on CPU with
+      # real SD model. We need to find a way to load empty SD model.
+      # - name: Run ControlNet tests
+      #   run: >
+      #     python -m pytest
+      #     --junitxml=test/results.xml
+      #     --cov ./extensions-builtin/sd_forge_controlnet
+      #     --cov-report=xml
+      #     --verify-base-url
+      #     ./extensions-builtin/sd_forge_controlnet/tests
       - name: Kill test server
         if: always()
         run: curl -vv -XPOST http://127.0.0.1:7860/sdapi/v1/server-stop && sleep 10

.gitignore

@@ -40,4 +40,5 @@ notification.mp3
 /.coverage*
 /test/test_outputs
 /test/results.xml
-coverage.xml
+coverage.xml
+**/tests/**/expectations

extensions-builtin/sd_forge_controlnet/lib_controlnet/api.py

@@ -0,0 +1,108 @@
+from typing import List
+
+import numpy as np
+from fastapi import FastAPI, Body
+from fastapi.exceptions import HTTPException
+from PIL import Image
+import gradio as gr
+
+from modules.api import api
+from .global_state import (
+    get_all_preprocessor_names,
+    get_all_controlnet_names,
+    get_preprocessor,
+)
+from .logging import logger
+
+
+def encode_to_base64(image):
+    if isinstance(image, str):
+        return image
+    elif isinstance(image, Image.Image):
+        return api.encode_pil_to_base64(image)
+    elif isinstance(image, np.ndarray):
+        return encode_np_to_base64(image)
+    else:
+        return ""
+
+
+def encode_np_to_base64(image):
+    pil = Image.fromarray(image)
+    return api.encode_pil_to_base64(pil)
+
+
+def controlnet_api(_: gr.Blocks, app: FastAPI):
+    @app.get("/controlnet/model_list")
+    async def model_list():
+        up_to_date_model_list = get_all_controlnet_names()
+        logger.debug(up_to_date_model_list)
+        return {"model_list": up_to_date_model_list}
+
+    @app.get("/controlnet/module_list")
+    async def module_list():
+        module_list = get_all_preprocessor_names()
+        logger.debug(module_list)
+
+        return {
+            "module_list": module_list,
+            # TODO: Add back module detail.
+            # "module_detail": external_code.get_modules_detail(alias_names),
+        }
+
+    @app.post("/controlnet/detect")
+    async def detect(
+        controlnet_module: str = Body("none", title="Controlnet Module"),
+        controlnet_input_images: List[str] = Body([], title="Controlnet Input Images"),
+        controlnet_processor_res: int = Body(
+            512, title="Controlnet Processor Resolution"
+        ),
+        controlnet_threshold_a: float = Body(64, title="Controlnet Threshold a"),
+        controlnet_threshold_b: float = Body(64, title="Controlnet Threshold b"),
+    ):
+        processor_module = get_preprocessor(controlnet_module)
+        if processor_module is None:
+            raise HTTPException(status_code=422, detail="Module not available")
+
+        if len(controlnet_input_images) == 0:
+            raise HTTPException(status_code=422, detail="No image selected")
+
+        logger.debug(
+            f"Detecting {str(len(controlnet_input_images))} images with the {controlnet_module} module."
+        )
+
+        results = []
+        poses = []
+
+        for input_image in controlnet_input_images:
+            img = np.array(api.decode_base64_to_image(input_image)).astype('uint8')
+
+            class JsonAcceptor:
+                def __init__(self) -> None:
+                    self.value = None
+
+                def accept(self, json_dict: dict) -> None:
+                    self.value = json_dict
+
+            json_acceptor = JsonAcceptor()
+
+            results.append(
+                processor_module(
+                    img,
+                    res=controlnet_processor_res,
+                    thr_a=controlnet_threshold_a,
+                    thr_b=controlnet_threshold_b,
+                    json_pose_callback=json_acceptor.accept,
+                )[0]
+            )
+
+            if "openpose" in controlnet_module:
+                assert json_acceptor.value is not None
+                poses.append(json_acceptor.value)
+
+        results64 = list(map(encode_to_base64, results))
+        res = {"images": results64, "info": "Success"}
+        if poses:
+            res["poses"] = poses
+
+        return res
+

extensions-builtin/sd_forge_controlnet/lib_controlnet/controlnet_ui/controlnet_ui_group.py

@@ -850,7 +850,6 @@ def is_openpose(module: str):
                 slider_1=pthr_a,
                 slider_2=pthr_b,
                 input_mask=mask,
-                low_vram=shared.opts.data.get("controlnet_clip_detector_on_cpu", False),
                 json_pose_callback=json_acceptor.accept
                 if is_openpose(module)
                 else None,

extensions-builtin/sd_forge_controlnet/lib_controlnet/external_code.py

@@ -148,13 +148,15 @@ def pixel_perfect_resolution(
 @dataclass
 class UiControlNetUnit:
     input_mode: InputMode = InputMode.SIMPLE
-    use_preview_as_input: bool = False,
-    batch_image_dir: str = '',
-    batch_mask_dir: str = '',
-    batch_input_gallery: list = [],
-    batch_mask_gallery: list = [],
-    generated_image: Optional[np.ndarray] = None,
-    mask_image: Optional[np.ndarray] = None,
+    use_preview_as_input: bool = False
+    batch_image_dir: str = ''
+    batch_mask_dir: str = ''
+    batch_input_gallery: Optional[List[str]] = None
+    batch_mask_gallery: Optional[List[str]] = None
+    generated_image: Optional[np.ndarray] = None
+    mask_image: Optional[np.ndarray] = None
+    # If hires fix is enabled in A1111, how should this ControlNet unit be applied.
+    # The value is ignored if the generation is not using hires fix.
     hr_option: Union[HiResFixOption, int, str] = HiResFixOption.BOTH
     enabled: bool = True
     module: str = "None"
@@ -169,6 +171,13 @@ class UiControlNetUnit:
     guidance_end: float = 1.0
     pixel_perfect: bool = False
     control_mode: Union[ControlMode, int, str] = ControlMode.BALANCED
+    # ====== Start of API only fields ======
+    # Whether save the detected map of this unit. Setting this option to False
+    # prevents saving the detected map or sending detected map along with
+    # generated images via API. Currently the option is only accessible in API
+    # calls.
+    save_detected_map: bool = True
+    # ====== End of API only fields ======
 
     @staticmethod
     def infotext_fields():
@@ -192,6 +201,23 @@ def infotext_fields():
             "hr_option",
         )
 
+    @staticmethod
+    def from_dict(d: Dict) -> "UiControlNetUnit":
+        """Create UiControlNetUnit from dict. This is primarily used to convert
+        API json dict to UiControlNetUnit."""
+        unit = UiControlNetUnit(
+            **{k: v for k, v in d.items() if k in vars(UiControlNetUnit)}
+        )
+        if isinstance(unit.image, str):
+            img = np.array(api.decode_base64_to_image(unit.image)).astype('uint8')
+            unit.image = {
+                "image": img,
+                "mask": np.zeros_like(img),
+            }
+        if isinstance(unit.mask_image, str):
+            unit.mask_image = np.array(api.decode_base64_to_image(unit.mask_image)).astype('uint8')
+        return unit
+
 
 # Backward Compatible
 ControlNetUnit = UiControlNetUnit

extensions-builtin/sd_forge_controlnet/scripts/controlnet.py

@@ -11,16 +11,18 @@
 import gradio as gr
 
 from lib_controlnet import global_state, external_code
+from lib_controlnet.external_code import ControlNetUnit
 from lib_controlnet.utils import align_dim_latent, image_dict_from_any, set_numpy_seed, crop_and_resize_image, \
     prepare_mask, judge_image_type
-from lib_controlnet.controlnet_ui.controlnet_ui_group import ControlNetUiGroup, UiControlNetUnit
+from lib_controlnet.controlnet_ui.controlnet_ui_group import ControlNetUiGroup
 from lib_controlnet.controlnet_ui.photopea import Photopea
 from lib_controlnet.logging import logger
 from modules.processing import StableDiffusionProcessingImg2Img, StableDiffusionProcessingTxt2Img, \
     StableDiffusionProcessing
 from lib_controlnet.infotext import Infotext
 from modules_forge.forge_util import HWC3, numpy_to_pytorch
 from lib_controlnet.enums import HiResFixOption
+from lib_controlnet.api import controlnet_api
 
 import numpy as np
 import functools
@@ -67,7 +69,7 @@ def ui(self, is_img2img):
         max_models = shared.opts.data.get("control_net_unit_count", 3)
         gen_type = "img2img" if is_img2img else "txt2img"
         elem_id_tabname = gen_type + "_controlnet"
-        default_unit = UiControlNetUnit(enabled=False, module="None", model="None")
+        default_unit = ControlNetUnit(enabled=False, module="None", model="None")
         with gr.Group(elem_id=elem_id_tabname):
             with gr.Accordion(f"ControlNet Integrated", open=False, elem_id="controlnet",
                               elem_classes=["controlnet"]):
@@ -95,13 +97,19 @@ def ui(self, is_img2img):
         return tuple(controls)
 
     def get_enabled_units(self, units):
+        # Parse dict from API calls.
+        units = [
+            ControlNetUnit.from_dict(unit) if isinstance(unit, dict) else unit
+            for unit in units
+        ]
+        assert all(isinstance(unit, ControlNetUnit) for unit in units)
         enabled_units = [x for x in units if x.enabled]
         return enabled_units
 
     @staticmethod
     def try_crop_image_with_a1111_mask(
             p: StableDiffusionProcessing,
-            unit: external_code.ControlNetUnit,
+            unit: ControlNetUnit,
             input_image: np.ndarray,
             resize_mode: external_code.ResizeMode,
             preprocessor
@@ -252,7 +260,7 @@ def get_target_dimensions(p: StableDiffusionProcessing) -> Tuple[int, int, int,
     @torch.no_grad()
     def process_unit_after_click_generate(self,
                                           p: StableDiffusionProcessing,
-                                          unit: external_code.ControlNetUnit,
+                                          unit: ControlNetUnit,
                                           params: ControlNetCachedParameters,
                                           *args, **kwargs):
 
@@ -279,8 +287,6 @@ def optional_tqdm(iterable, use_tqdm):
             return tqdm(iterable) if use_tqdm else iterable
 
         for input_image, input_mask in optional_tqdm(input_list, len(input_list) > 1):
-            # p.extra_result_images.append(input_image)
-
             if unit.pixel_perfect:
                 unit.processor_res = external_code.pixel_perfect_resolution(
                     input_image,
@@ -319,31 +325,36 @@ def optional_tqdm(iterable, use_tqdm):
             hr_option = HiResFixOption.BOTH
 
         alignment_indices = [i % len(preprocessor_outputs) for i in range(p.batch_size)]
+        def attach_extra_result_image(img: np.ndarray, is_high_res: bool = False):
+            if (
+                (is_high_res and hr_option.high_res_enabled) or
+                (not is_high_res and hr_option.low_res_enabled)
+            ) and unit.save_detected_map:
+                p.extra_result_images.append(img)
+
         if preprocessor_output_is_image:
             params.control_cond = []
             params.control_cond_for_hr_fix = []
 
             for preprocessor_output in preprocessor_outputs:
                 control_cond = crop_and_resize_image(preprocessor_output, resize_mode, h, w)
-                if hr_option.low_res_enabled:
-                    p.extra_result_images.append(external_code.visualize_inpaint_mask(control_cond))
+                attach_extra_result_image(external_code.visualize_inpaint_mask(control_cond))
                 params.control_cond.append(numpy_to_pytorch(control_cond).movedim(-1, 1))
 
             params.control_cond = torch.cat(params.control_cond, dim=0)[alignment_indices].contiguous()
 
             if has_high_res_fix:
                 for preprocessor_output in preprocessor_outputs:
                     control_cond_for_hr_fix = crop_and_resize_image(preprocessor_output, resize_mode, hr_y, hr_x)
-                    if hr_option.high_res_enabled:
-                        p.extra_result_images.append(external_code.visualize_inpaint_mask(control_cond_for_hr_fix))
+                    attach_extra_result_image(external_code.visualize_inpaint_mask(control_cond_for_hr_fix), is_high_res=True)
                     params.control_cond_for_hr_fix.append(numpy_to_pytorch(control_cond_for_hr_fix).movedim(-1, 1))
                 params.control_cond_for_hr_fix = torch.cat(params.control_cond_for_hr_fix, dim=0)[alignment_indices].contiguous()
             else:
                 params.control_cond_for_hr_fix = params.control_cond
         else:
             params.control_cond = preprocessor_output
             params.control_cond_for_hr_fix = preprocessor_output
-            p.extra_result_images.append(input_image)
+            attach_extra_result_image(input_image)
 
         if len(control_masks) > 0:
             params.control_mask = []
@@ -352,15 +363,13 @@ def optional_tqdm(iterable, use_tqdm):
             for input_mask in control_masks:
                 fill_border = preprocessor.fill_mask_with_one_when_resize_and_fill
                 control_mask = crop_and_resize_image(input_mask, resize_mode, h, w, fill_border)
-                if hr_option.low_res_enabled:
-                    p.extra_result_images.append(control_mask)
+                attach_extra_result_image(control_mask)
                 control_mask = numpy_to_pytorch(control_mask).movedim(-1, 1)[:, :1]
                 params.control_mask.append(control_mask)
 
                 if has_high_res_fix:
                     control_mask_for_hr_fix = crop_and_resize_image(input_mask, resize_mode, hr_y, hr_x, fill_border)
-                    if hr_option.high_res_enabled:
-                        p.extra_result_images.append(control_mask_for_hr_fix)
+                    attach_extra_result_image(control_mask_for_hr_fix, is_high_res=True)
                     control_mask_for_hr_fix = numpy_to_pytorch(control_mask_for_hr_fix).movedim(-1, 1)[:, :1]
                     params.control_mask_for_hr_fix.append(control_mask_for_hr_fix)
 
@@ -390,7 +399,7 @@ def optional_tqdm(iterable, use_tqdm):
     @torch.no_grad()
     def process_unit_before_every_sampling(self,
                                            p: StableDiffusionProcessing,
-                                           unit: external_code.ControlNetUnit,
+                                           unit: ControlNetUnit,
                                            params: ControlNetCachedParameters,
                                            *args, **kwargs):
 
@@ -473,14 +482,14 @@ def process_unit_before_every_sampling(self,
         return
 
     @staticmethod
-    def bound_check_params(unit: external_code.ControlNetUnit) -> None:
+    def bound_check_params(unit: ControlNetUnit) -> None:
         """
         Checks and corrects negative parameters in ControlNetUnit 'unit'.
         Parameters 'processor_res', 'threshold_a', 'threshold_b' are reset to
         their default values if negative.
 
         Args:
-            unit (external_code.ControlNetUnit): The ControlNetUnit instance to check.
+            unit (ControlNetUnit): The ControlNetUnit instance to check.
         """
         preprocessor = global_state.get_preprocessor(unit.module)
 
@@ -498,7 +507,7 @@ def bound_check_params(unit: external_code.ControlNetUnit) -> None:
     @torch.no_grad()
     def process_unit_after_every_sampling(self,
                                           p: StableDiffusionProcessing,
-                                          unit: external_code.ControlNetUnit,
+                                          unit: ControlNetUnit,
                                           params: ControlNetCachedParameters,
                                           *args, **kwargs):
 
@@ -577,3 +586,4 @@ def on_ui_settings():
 script_callbacks.on_infotext_pasted(Infotext.on_infotext_pasted)
 script_callbacks.on_after_component(ControlNetUiGroup.on_after_component)
 script_callbacks.on_before_reload(ControlNetUiGroup.reset)
+script_callbacks.on_app_started(controlnet_api)

extensions-builtin/sd_forge_controlnet/tests/conftest.py

@@ -0,0 +1,8 @@
+import os
+
+
+def pytest_configure(config):
+    # We don't want to fail on Py.test command line arguments being
+    # parsed by webui:
+    os.environ.setdefault("IGNORE_CMD_ARGS_ERRORS", "1")
+    os.environ.setdefault("FORGE_CQ_TEST", "1")