handler.py

handler.py

@@ -142,7 +142,7 @@ class EndpointHandler():
         Return:
             A :obj:`list` | `dict`: will be serialized and returned
         """
-        args = {
+        txt2img_args = {
             "do_not_save_samples": True,
             "do_not_save_grid": True,
             "outpath_samples": "./output",
@@ -155,15 +155,65 @@ class EndpointHandler():
             "height": 768,
             "seed": -1,
         }
-        if data["inputs"]:
-            for field in args:
-                if field in data["inputs"].keys():
-                    args[field] = data["inputs"][field]
-             # if "prompt" in data["inputs"].keys():
-             #    prompt = data["inputs"]["prompt"]
-             #    print("get prompt from request: ", prompt)
-             #    args["prompt"] = prompt
-        p = StableDiffusionProcessingTxt2Img(sd_model=self.shared.sd_model, **args)
+        img2img_args = {
+            "init_images": ["data:image/png;base64,"],
+            "resize_mode": 0,
+            "denoising_strength": 0.75,
+            "image_cfg_scale": 0,
+            "mask_blur": 4,
+            "inpainting_fill": 0,
+            "inpaint_full_res": 1,
+            "inpaint_full_res_padding": 0,
+            "inpainting_mask_invert": 0,
+            "initial_noise_multiplier": 0,
+            "prompt": "lora:koreanDollLikeness_v15:0.66, best quality, ultra high res, (photorealistic:1.4), 1girl, beige sweater, black choker, smile, laughing, bare shoulders, solo focus, ((full body), (brown hair:1), looking at viewer",
+            "styles": [],
+            "seed": -1,
+            "subseed": -1,
+            "subseed_strength": 0,
+            "seed_resize_from_h": -1,
+            "seed_resize_from_w": -1,
+            "sampler_name": "Euler a",
+            "batch_size": 1,
+            "n_iter": 1,
+            "steps": 50,
+            "cfg_scale": 7,
+            "width": 512,
+            "height": 512,
+            "restore_faces": 0,
+            "tiling": 0,
+            "negative_prompt": "paintings, sketches, (worst quality:2), (low quality:2), (normal quality:2), lowres, normal quality, ((monochrome)), ((grayscale)), skin spots, acnes, skin blemishes, age spot, glans, (ugly:1.331), (duplicate:1.331), (morbid:1.21), (mutilated:1.21), (tranny:1.331), mutated hands, (poorly drawn hands:1.331), blurry, 3hands,4fingers,3arms, bad anatomy, missing fingers, extra digit, fewer digits, cropped, jpeg artifacts,poorly drawn face,mutation,deformed",
+            "eta": 0,
+            "s_churn": 0,
+            "s_tmax": 0,
+            "s_tmin": 0,
+            "s_noise": 1,
+            "override_settings": {},
+            "override_settings_restore_afterwards": 1,
+            "script_args": [],
+            "sampler_index": "Euler",
+            "include_init_images": 0
+        }
+
+        p = None
+        if data["type"] == "txt2img":
+            if data["inputs"]:
+                for field in txt2img_args:
+                    if field in data["inputs"].keys():
+                        txt2img_args[field] = data["inputs"][field]
+                 # if "prompt" in data["inputs"].keys():
+                 #    prompt = data["inputs"]["prompt"]
+                 #    print("get prompt from request: ", prompt)
+                 #    args["prompt"] = prompt
+                p = StableDiffusionProcessingTxt2Img(sd_model=self.shared.sd_model, **txt2img_args)
+        if data["type"] == "txt2img":
+            if data["inputs"]:
+                for field in img2img_args:
+                    if field in data["inputs"].keys():
+                        img2img_args[field] = data["inputs"][field]
+                p = StableDiffusionProcessingImg2Img(sd_model=self.shared.sd_model, **img2img_args)
+        if p is None:
+            raise Exception("No processing object created")
         processed = process_images(p)
         single_image_b64 = encode_pil_to_base64(processed.images[0]).decode('utf-8')
         return {