Merge branch 'master' into master

modules/api/api.py

@@ -0,0 +1,68 @@
+from modules.api.processing import StableDiffusionProcessingAPI
+from modules.processing import StableDiffusionProcessingTxt2Img, process_images
+from modules.sd_samplers import all_samplers
+from modules.extras import run_pnginfo
+import modules.shared as shared
+import uvicorn
+from fastapi import Body, APIRouter, HTTPException
+from fastapi.responses import JSONResponse
+from pydantic import BaseModel, Field, Json
+import json
+import io
+import base64
+
+sampler_to_index = lambda name: next(filter(lambda row: name.lower() == row[1].name.lower(), enumerate(all_samplers)), None)
+
+class TextToImageResponse(BaseModel):
+    images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
+    parameters: Json
+    info: Json
+
+
+class Api:
+    def __init__(self, app, queue_lock):
+        self.router = APIRouter()
+        self.app = app
+        self.queue_lock = queue_lock
+        self.app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"])
+
+    def text2imgapi(self, txt2imgreq: StableDiffusionProcessingAPI ):
+        sampler_index = sampler_to_index(txt2imgreq.sampler_index)
+        
+        if sampler_index is None:
+            raise HTTPException(status_code=404, detail="Sampler not found") 
+        
+        populate = txt2imgreq.copy(update={ # Override __init__ params
+            "sd_model": shared.sd_model, 
+            "sampler_index": sampler_index[0],
+            "do_not_save_samples": True,
+            "do_not_save_grid": True
+            }
+        )
+        p = StableDiffusionProcessingTxt2Img(**vars(populate))
+        # Override object param
+        with self.queue_lock:
+            processed = process_images(p)
+        
+        b64images = []
+        for i in processed.images:
+            buffer = io.BytesIO()
+            i.save(buffer, format="png")
+            b64images.append(base64.b64encode(buffer.getvalue()))
+
+        return TextToImageResponse(images=b64images, parameters=json.dumps(vars(txt2imgreq)), info=json.dumps(processed.info))
+        
+        
+
+    def img2imgapi(self):
+        raise NotImplementedError
+
+    def extrasapi(self):
+        raise NotImplementedError
+
+    def pnginfoapi(self):
+        raise NotImplementedError
+
+    def launch(self, server_name, port):
+        self.app.include_router(self.router)
+        uvicorn.run(self.app, host=server_name, port=port)

modules/api/processing.py

@@ -0,0 +1,99 @@
+from inflection import underscore
+from typing import Any, Dict, Optional
+from pydantic import BaseModel, Field, create_model
+from modules.processing import StableDiffusionProcessingTxt2Img
+import inspect
+
+
+API_NOT_ALLOWED = [
+    "self",
+    "kwargs",
+    "sd_model",
+    "outpath_samples",
+    "outpath_grids",
+    "sampler_index",
+    "do_not_save_samples",
+    "do_not_save_grid",
+    "extra_generation_params",
+    "overlay_images",
+    "do_not_reload_embeddings",
+    "seed_enable_extras",
+    "prompt_for_display",
+    "sampler_noise_scheduler_override",
+    "ddim_discretize"
+]
+
+class ModelDef(BaseModel):
+    """Assistance Class for Pydantic Dynamic Model Generation"""
+
+    field: str
+    field_alias: str
+    field_type: Any
+    field_value: Any
+
+
+class PydanticModelGenerator:
+    """
+    Takes in created classes and stubs them out in a way FastAPI/Pydantic is happy about:
+    source_data is a snapshot of the default values produced by the class
+    params are the names of the actual keys required by __init__
+    """
+
+    def __init__(
+        self,
+        model_name: str = None,
+        class_instance = None,
+        additional_fields = None,
+    ):
+        def field_type_generator(k, v):
+            # field_type = str if not overrides.get(k) else overrides[k]["type"]
+            # print(k, v.annotation, v.default)
+            field_type = v.annotation
+            
+            return Optional[field_type]
+        
+        def merge_class_params(class_):
+            all_classes = list(filter(lambda x: x is not object, inspect.getmro(class_)))
+            parameters = {}
+            for classes in all_classes:
+                parameters = {**parameters, **inspect.signature(classes.__init__).parameters}
+            return parameters
+            
+                
+        self._model_name = model_name
+        self._class_data = merge_class_params(class_instance)
+        self._model_def = [
+            ModelDef(
+                field=underscore(k),
+                field_alias=k,
+                field_type=field_type_generator(k, v),
+                field_value=v.default
+            )
+            for (k,v) in self._class_data.items() if k not in API_NOT_ALLOWED
+        ]
+        
+        for fields in additional_fields:
+            self._model_def.append(ModelDef(
+                field=underscore(fields["key"]), 
+                field_alias=fields["key"], 
+                field_type=fields["type"],
+                field_value=fields["default"]))
+
+    def generate_model(self):
+        """
+        Creates a pydantic BaseModel
+        from the json and overrides provided at initialization
+        """
+        fields = {
+            d.field: (d.field_type, Field(default=d.field_value, alias=d.field_alias)) for d in self._model_def
+        }
+        DynamicModel = create_model(self._model_name, **fields)
+        DynamicModel.__config__.allow_population_by_field_name = True
+        DynamicModel.__config__.allow_mutation = True
+        return DynamicModel
+    
+StableDiffusionProcessingAPI = PydanticModelGenerator(
+    "StableDiffusionProcessingTxt2Img", 
+    StableDiffusionProcessingTxt2Img,
+    [{"key": "sampler_index", "type": str, "default": "Euler"}]
+).generate_model()

modules/processing.py

@@ -9,6 +9,7 @@ from PIL import Image, ImageFilter, ImageOps
 import random
 import cv2
 from skimage import exposure
+from typing import Any, Dict, List, Optional
 
 import modules.sd_hijack
 from modules import devices, prompt_parser, masking, sd_samplers, lowvram
@@ -51,9 +52,15 @@ def get_correct_sampler(p):
         return sd_samplers.samplers
     elif isinstance(p, modules.processing.StableDiffusionProcessingImg2Img):
         return sd_samplers.samplers_for_img2img
+    elif isinstance(p, modules.api.processing.StableDiffusionProcessingAPI):
+        return sd_samplers.samplers
 
-class StableDiffusionProcessing:
-    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", styles=None, seed=-1, subseed=-1, subseed_strength=0, seed_resize_from_h=-1, seed_resize_from_w=-1, seed_enable_extras=True, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, restore_faces=False, tiling=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None, eta=None, do_not_reload_embeddings=False):
+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_index: int=0, 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 = "uniform", s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0):
         self.sd_model = sd_model
         self.outpath_samples: str = outpath_samples
         self.outpath_grids: str = outpath_grids
@@ -86,10 +93,10 @@ class StableDiffusionProcessing:
         self.denoising_strength: float = 0
         self.sampler_noise_scheduler_override = None
         self.ddim_discretize = opts.ddim_discretize
-        self.s_churn = opts.s_churn
-        self.s_tmin = opts.s_tmin
-        self.s_tmax = float('inf')  # not representable as a standard ui option
-        self.s_noise = opts.s_noise
+        self.s_churn = s_churn or opts.s_churn
+        self.s_tmin = s_tmin or opts.s_tmin
+        self.s_tmax = s_tmax or float('inf')  # not representable as a standard ui option
+        self.s_noise = s_noise or opts.s_noise
 
         if not seed_enable_extras:
             self.subseed = -1
@@ -97,6 +104,7 @@ class StableDiffusionProcessing:
             self.seed_resize_from_h = 0
             self.seed_resize_from_w = 0
 
+
     def init(self, all_prompts, all_seeds, all_subseeds):
         pass
 
@@ -491,7 +499,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
 class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
     sampler = None
 
-    def __init__(self, enable_hr=False, denoising_strength=0.75, firstphase_width=0, firstphase_height=0, **kwargs):
+    def __init__(self, enable_hr: bool=False, denoising_strength: float=0.75, firstphase_width: int=0, firstphase_height: int=0, **kwargs):
         super().__init__(**kwargs)
         self.enable_hr = enable_hr
         self.denoising_strength = denoising_strength
@@ -717,4 +725,4 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         del x
         devices.torch_gc()
 
-        return samples
+        return samples

modules/sd_models.py

@@ -122,11 +122,33 @@ def select_checkpoint():
     return checkpoint_info
 
 
+chckpoint_dict_replacements = {
+    'cond_stage_model.transformer.embeddings.': 'cond_stage_model.transformer.text_model.embeddings.',
+    'cond_stage_model.transformer.encoder.': 'cond_stage_model.transformer.text_model.encoder.',
+    'cond_stage_model.transformer.final_layer_norm.': 'cond_stage_model.transformer.text_model.final_layer_norm.',
+}
+
+
+def transform_checkpoint_dict_key(k):
+    for text, replacement in chckpoint_dict_replacements.items():
+        if k.startswith(text):
+            k = replacement + k[len(text):]
+
+    return k
+
+
 def get_state_dict_from_checkpoint(pl_sd):
     if "state_dict" in pl_sd:
-        return pl_sd["state_dict"]
+        pl_sd = pl_sd["state_dict"]
 
-    return pl_sd
+    sd = {}
+    for k, v in pl_sd.items():
+        new_key = transform_checkpoint_dict_key(k)
+
+        if new_key is not None:
+            sd[new_key] = v
+
+    return sd
 
 
 def load_model_weights(model, checkpoint_info):
@@ -141,7 +163,7 @@ def load_model_weights(model, checkpoint_info):
             print(f"Global Step: {pl_sd['global_step']}")
 
         sd = get_state_dict_from_checkpoint(pl_sd)
-        model.load_state_dict(sd, strict=False)
+        missing, extra = model.load_state_dict(sd, strict=False)
 
         if shared.cmd_opts.opt_channelslast:
             model.to(memory_format=torch.channels_last)

modules/shared.py

@@ -76,6 +76,8 @@ parser.add_argument("--disable-console-progressbars", action='store_true', help=
 parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)
 parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencoders model', default=None)
 parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
+parser.add_argument("--api", action='store_true', help="use api=True to launch the api with the webui")
+parser.add_argument("--nowebui", action='store_true', help="use api=True to launch the api instead of the webui")
 
 cmd_opts = parser.parse_args()
 restricted_opts = [

requirements.txt

@@ -23,3 +23,4 @@ resize-right
 torchdiffeq
 kornia
 lark
+inflection

requirements_versions.txt

@@ -22,3 +22,4 @@ resize-right==0.0.2
 torchdiffeq==0.2.3
 kornia==0.6.7
 lark==1.1.2
+inflection==0.5.1

webui.py

@@ -4,7 +4,7 @@ import time
 import importlib
 import signal
 import threading
-
+from fastapi import FastAPI
 from fastapi.middleware.gzip import GZipMiddleware
 
 from modules.paths import script_path
@@ -31,7 +31,6 @@ from modules.paths import script_path
 from modules.shared import cmd_opts
 import modules.hypernetworks.hypernetwork
 
-
 queue_lock = threading.Lock()
 
 
@@ -88,11 +87,7 @@ def initialize():
     shared.opts.onchange("sd_hypernetwork_strength", modules.hypernetworks.hypernetwork.apply_strength)
     shared.opts.onchange("sd_hypernetwork_layer_structure", modules.hypernetworks.hypernetwork.apply_layer_structure)
     shared.opts.onchange("sd_hypernetwork_add_layer_norm", modules.hypernetworks.hypernetwork.apply_layer_norm)
-
-
-def webui():
-    initialize()
-
+    
     # make the program just exit at ctrl+c without waiting for anything
     def sigint_handler(sig, frame):
         print(f'Interrupted with signal {sig} in {frame}')
@@ -100,8 +95,35 @@ def webui():
 
     signal.signal(signal.SIGINT, sigint_handler)
 
-    while 1:
 
+def create_api(app):
+    from modules.api.api import Api
+    api = Api(app, queue_lock)
+    return api
+
+def wait_on_server(demo=None):
+    while 1:
+        time.sleep(0.5)
+        if demo and getattr(demo, 'do_restart', False):
+            time.sleep(0.5)
+            demo.close()
+            time.sleep(0.5)
+            break
+
+def api_only():
+    initialize()
+
+    app = FastAPI()
+    app.add_middleware(GZipMiddleware, minimum_size=1000)
+    api = create_api(app)
+
+    api.launch(server_name="0.0.0.0" if cmd_opts.listen else "127.0.0.1", port=cmd_opts.port if cmd_opts.port else 7861)
+
+
+def webui(launch_api=False):
+    initialize()
+
+    while 1:
         demo = modules.ui.create_ui(wrap_gradio_gpu_call=wrap_gradio_gpu_call)
 
         app, local_url, share_url = demo.launch(
@@ -113,17 +135,14 @@ def webui():
             inbrowser=cmd_opts.autolaunch,
             prevent_thread_lock=True
         )
-        
+
         app.add_middleware(GZipMiddleware, minimum_size=1000)
 
-        while 1:
-            time.sleep(0.5)
-            if getattr(demo, 'do_restart', False):
-                time.sleep(0.5)
-                demo.close()
-                time.sleep(0.5)
-                break
+        if (launch_api):
+            create_api(app)
 
+        wait_on_server(demo)
+        
         sd_samplers.set_samplers()
 
         print('Reloading Custom Scripts')
@@ -135,5 +154,10 @@ def webui():
         print('Restarting Gradio')
 
 
+
+task = []
 if __name__ == "__main__":
-    webui()
+    if cmd_opts.nowebui:
+        api_only()
+    else:
+        webui(cmd_opts.api)