Merge pull request #6469 from noodleanon/scripts-from-api

Run scripts from API

modules/api/api.py

@@ -11,7 +11,7 @@ from fastapi.security import HTTPBasic, HTTPBasicCredentials
 from secrets import compare_digest
 
 import modules.shared as shared
-from modules import sd_samplers, deepbooru, sd_hijack, images
+from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui
 from modules.api.models import *
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
 from modules.extras import run_extras
@@ -28,8 +28,13 @@ def upscaler_to_index(name: str):
     try:
         return [x.name.lower() for x in shared.sd_upscalers].index(name.lower())
     except:
-        raise HTTPException(status_code=400, detail=f"Invalid upscaler, needs to be on of these: {' , '.join([x.name for x in sd_upscalers])}")
+        raise HTTPException(status_code=400, detail=f"Invalid upscaler, needs to be one of these: {' , '.join([x.name for x in sd_upscalers])}")
 
+def script_name_to_index(name, scripts):
+    try:
+        return [script.title().lower() for script in scripts].index(name.lower())
+    except:
+        raise HTTPException(status_code=422, detail=f"Script '{name}' not found")
 
 def validate_sampler_name(name):
     config = sd_samplers.all_samplers_map.get(name, None)
@@ -144,6 +149,14 @@ class Api:
         raise HTTPException(status_code=401, detail="Incorrect username or password", headers={"WWW-Authenticate": "Basic"})
 
     def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
+        if txt2imgreq.script_name is not None:
+            if scripts.scripts_txt2img.scripts == []:
+                scripts.scripts_txt2img.initialize_scripts(False)
+                ui.create_ui()
+
+            script_idx = script_name_to_index(txt2imgreq.script_name, scripts.scripts_txt2img.selectable_scripts)
+            script = scripts.scripts_txt2img.selectable_scripts[script_idx]
+
         populate = txt2imgreq.copy(update={ # Override __init__ params
             "sampler_name": validate_sampler_name(txt2imgreq.sampler_name or txt2imgreq.sampler_index),
             "do_not_save_samples": True,
@@ -153,11 +166,20 @@ class Api:
         if populate.sampler_name:
             populate.sampler_index = None  # prevent a warning later on
 
+        args = vars(populate)
+        args.pop('script_name', None)
+
         with self.queue_lock:
-            p = StableDiffusionProcessingTxt2Img(sd_model=shared.sd_model, **vars(populate))
+            p = StableDiffusionProcessingTxt2Img(sd_model=shared.sd_model, **args)
 
             shared.state.begin()
-            processed = process_images(p)
+            if 'script' in locals():
+                p.outpath_grids = opts.outdir_txt2img_grids
+                p.outpath_samples = opts.outdir_txt2img_samples
+                p.script_args = [script_idx + 1] + [None] * (script.args_from - 1) + p.script_args
+                processed = scripts.scripts_txt2img.run(p, *p.script_args)
+            else:
+                processed = process_images(p)
             shared.state.end()
 
 
@@ -170,6 +192,14 @@ class Api:
         if init_images is None:
             raise HTTPException(status_code=404, detail="Init image not found")
 
+        if img2imgreq.script_name is not None:
+            if scripts.scripts_img2img.scripts == []:
+                scripts.scripts_img2img.initialize_scripts(True)
+                ui.create_ui()
+
+            script_idx = script_name_to_index(img2imgreq.script_name, scripts.scripts_img2img.selectable_scripts)
+            script = scripts.scripts_img2img.selectable_scripts[script_idx]
+
         mask = img2imgreq.mask
         if mask:
             mask = decode_base64_to_image(mask)
@@ -186,13 +216,20 @@ class Api:
 
         args = vars(populate)
         args.pop('include_init_images', None)  # this is meant to be done by "exclude": True in model, but it's for a reason that I cannot determine.
+        args.pop('script_name', None)
 
         with self.queue_lock:
             p = StableDiffusionProcessingImg2Img(sd_model=shared.sd_model, **args)
             p.init_images = [decode_base64_to_image(x) for x in init_images]
 
             shared.state.begin()
-            processed = process_images(p)
+            if 'script' in locals():
+                p.outpath_grids = opts.outdir_img2img_grids
+                p.outpath_samples = opts.outdir_img2img_samples
+                p.script_args = [script_idx + 1] + [None] * (script.args_from - 1) + p.script_args
+                processed = scripts.scripts_img2img.run(p, *p.script_args)
+            else:
+                processed = process_images(p)
             shared.state.end()
 
         b64images = list(map(encode_pil_to_base64, processed.images))

modules/api/models.py

@@ -100,13 +100,13 @@ class PydanticModelGenerator:
 StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator(
     "StableDiffusionProcessingTxt2Img",
     StableDiffusionProcessingTxt2Img,
-    [{"key": "sampler_index", "type": str, "default": "Euler"}]
+    [{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "script_name", "type": str, "default": None}, {"key": "script_args", "type": list, "default": []}]
 ).generate_model()
 
 StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator(
     "StableDiffusionProcessingImg2Img",
     StableDiffusionProcessingImg2Img,
-    [{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "init_images", "type": list, "default": None}, {"key": "denoising_strength", "type": float, "default": 0.75}, {"key": "mask", "type": str, "default": None}, {"key": "include_init_images", "type": bool, "default": False, "exclude" : True}]
+    [{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "init_images", "type": list, "default": None}, {"key": "denoising_strength", "type": float, "default": 0.75}, {"key": "mask", "type": str, "default": None}, {"key": "include_init_images", "type": bool, "default": False, "exclude" : True}, {"key": "script_name", "type": str, "default": None}, {"key": "script_args", "type": list, "default": []}]
 ).generate_model()
 
 class TextToImageResponse(BaseModel):

modules/processing.py

@@ -98,7 +98,7 @@ class StableDiffusionProcessing():
     """
     The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing
     """
-    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None):
+    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None, script_args: list = None):
         if sampler_index is not None:
             print("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name", file=sys.stderr)
 
@@ -149,7 +149,7 @@ class StableDiffusionProcessing():
             self.seed_resize_from_w = 0
 
         self.scripts = None
-        self.script_args = None
+        self.script_args = script_args
         self.all_prompts = None
         self.all_negative_prompts = None
         self.all_seeds = None

scripts/sd_upscale.py

@@ -25,6 +25,8 @@ class Script(scripts.Script):
         return [info, overlap, upscaler_index, scale_factor]
 
     def run(self, p, _, overlap, upscaler_index, scale_factor):
+        if isinstance(upscaler_index, str):
+            upscaler_index = [x.name.lower() for x in shared.sd_upscalers].index(upscaler_index.lower())
         processing.fix_seed(p)
         upscaler = shared.sd_upscalers[upscaler_index]