ipadapter optional face autocrop input image

CHANGELOG.md

@@ -76,6 +76,7 @@ To use and of the new models, simply select model from *Networks -> Reference* a
   for example: set size->before->method:nearest, mode:fixed or mode:fill  
 - control tab includes superset of txt and img scripts
 - automatically offload disabled controlnet units  
+- ipadapter option to auto-crop input images to faces to improve efficiency of face-transfter ipadapters  
 - update **IPEX** to 2.1.40+xpu on Linux, thanks @Disty0!  
 - general **ROCm** fixes, thanks @lshqqytiger!  
 - support for HIP SDK 6.1 on ZLUDA backend, thanks @lshqqytiger!

cli/api-control.py

@@ -132,7 +132,7 @@ def generate(args): # pylint: disable=redefined-outer-name
 
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description = 'api-img2img')
+    parser = argparse.ArgumentParser(description = 'api-control')
     parser.add_argument('--init', required=False, default=None, help='init image')
     parser.add_argument('--input', required=False, default=None, help='input image')
     parser.add_argument('--mask', required=False, help='mask image')
@@ -148,5 +148,5 @@ if __name__ == "__main__":
     parser.add_argument('--control', required=False, help='control units')
     parser.add_argument('--ipadapter', required=False, help='ipadapter units')
     args = parser.parse_args()
-    log.info(f'img2img: {args}')
+    log.info(f'api-control: {args}')
     generate(args)

cli/api-faceid.py

@@ -95,7 +95,7 @@ if __name__ == "__main__":
     parser.add_argument('--output', required=False, default=None, help='output image file')
     parser.add_argument('--model', required=False, help='model name')
     args = parser.parse_args()
-    log.info(f'img2img: {args}')
+    log.info(f'api-faceid: {args}')
     generate(args)
 
 """

cli/api-faces.py

@@ -0,0 +1,59 @@
+#!/usr/bin/env python
+import os
+import io
+import base64
+import logging
+import argparse
+import requests
+import urllib3
+from PIL import Image
+
+sd_url = os.environ.get('SDAPI_URL', "http://127.0.0.1:7860")
+sd_username = os.environ.get('SDAPI_USR', None)
+sd_password = os.environ.get('SDAPI_PWD', None)
+
+logging.basicConfig(level = logging.INFO, format = '%(asctime)s %(levelname)s: %(message)s')
+log = logging.getLogger(__name__)
+urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
+
+
+def auth():
+    if sd_username is not None and sd_password is not None:
+        return requests.auth.HTTPBasicAuth(sd_username, sd_password)
+    return None
+
+
+def post(endpoint: str, dct: dict = None):
+    req = requests.post(f'{sd_url}{endpoint}', json = dct, timeout=300, verify=False, auth=auth())
+    if req.status_code != 200:
+        return { 'error': req.status_code, 'reason': req.reason, 'url': req.url }
+    else:
+        return req.json()
+
+
+def encode(f):
+    image = Image.open(f)
+    if image.mode == 'RGBA':
+        image = image.convert('RGB')
+    with io.BytesIO() as stream:
+        image.save(stream, 'JPEG')
+        image.close()
+        values = stream.getvalue()
+        encoded = base64.b64encode(values).decode()
+        return encoded
+
+
+def detect(args): # pylint: disable=redefined-outer-name
+    data = post('/sdapi/v1/faces', { 'image': encode(args.image) })
+    for face in zip(data['images'], data['scores']):
+        log.info(f'Face: score={face[1]}')
+        image = Image.open(io.BytesIO(base64.b64decode(face[0])))
+        image.save(f'/tmp/face_{face[1]}.jpg')
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description = 'api-faces')
+    parser.add_argument('--image', required=True, help='input image')
+    args = parser.parse_args()
+    log.info(f'api-faces: {args}')
+    detect(args)

cli/api-img2img.py

@@ -94,5 +94,5 @@ if __name__ == "__main__":
     parser.add_argument('--output', required=False, default=None, help='output image file')
     parser.add_argument('--model', required=False, help='model name')
     args = parser.parse_args()
-    log.info(f'img2img: {args}')
+    log.info(f'api-img2img: {args}')
     generate(args)

cli/api-info.py

@@ -53,5 +53,5 @@ if __name__ == "__main__":
     parser = argparse.ArgumentParser(description = 'api-info')
     parser.add_argument('--input', required=True, help='input image')
     args = parser.parse_args()
-    log.info(f'info: {args}')
+    log.info(f'api-info: {args}')
     info(args)

cli/api-json.py

@@ -38,7 +38,7 @@ def post(endpoint: str, payload: dict = None):
 
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description = 'api-txt2img')
+    parser = argparse.ArgumentParser(description = 'api-json')
     parser.add_argument('endpoint', nargs=1, help='endpoint')
     parser.add_argument('json', nargs=1, help='json data or file')
     args = parser.parse_args()

cli/api-mask.py

@@ -79,5 +79,5 @@ if __name__ == "__main__":
     parser.add_argument('--type', required=False, help='output mask type')
     parser.add_argument('--output', required=False, help='output image')
     args = parser.parse_args()
-    log.info(f'info: {args}')
+    log.info(f'api-mask: {args}')
     info(args)

cli/api-preprocess.py

@@ -72,5 +72,5 @@ if __name__ == "__main__":
     parser.add_argument('--model', required=True, help='preprocessing model')
     parser.add_argument('--output', required=False, help='output image')
     args = parser.parse_args()
-    log.info(f'info: {args}')
+    log.info(f'api-preprocess: {args}')
     info(args)

cli/api-txt2img.py

@@ -80,5 +80,5 @@ if __name__ == "__main__":
     parser.add_argument('--output', required=False, default=None, help='output image file')
     parser.add_argument('--model', required=False, help='model name')
     args = parser.parse_args()
-    log.info(f'txt2img: {args}')
+    log.info(f'api-txt2img: {args}')
     generate(args)

cli/api-upscale.py

@@ -86,5 +86,5 @@ if __name__ == "__main__":
     parser.add_argument('--upscaler', required=False, default='Nearest', help='upscaler name')
     parser.add_argument('--scale', required=False, default=2, help='upscaler scale')
     args = parser.parse_args()
-    log.info(f'upscale: {args}')
+    log.info(f'api-upscale: {args}')
     upscale(args)

cli/api-vqa.py

@@ -60,5 +60,5 @@ if __name__ == "__main__":
     parser.add_argument('--model', required=False, help='vqa model')
     parser.add_argument('--question', required=False, help='question')
     args = parser.parse_args()
-    log.info(f'info: {args}')
+    log.info(f'api-vqa: {args}')
     info(args)

html/reference.json

@@ -1,4 +1,29 @@
 {
+  "Juggernaut SD-XL XI": {
+    "path": "juggernautXL_juggXIByRundiffusion.safetensors@https://civitai.com/api/download/models/782002",
+    "preview": "juggernautXL_v9Rundiffusionphoto2.jpg",
+    "desc": "Showcase finetuned model based on Stable diffusion XL",
+    "extras": "width: 1024, height: 1024, sampler: DEIS, steps: 20, cfg_scale: 6.0"
+  },
+  "Juggernaut SD-XL X Hyper": {
+    "path": "Juggernaut_X_RunDiffusion_Hyper.safetensors@https://civitai.com/api/download/models/471120",
+    "preview": "juggernautXL_v9Rundiffusionphoto2.jpg",
+    "desc": "Showcase finetuned model based on Stable diffusion XL",
+    "extras": "width: 1024, height: 1024, sampler: DEIS, steps: 20, cfg_scale: 6.0"
+  },
+  "Juggernaut SD-XL IX Lightning": {
+    "path": "juggernautXL_v9Rdphoto2Lightning.safetensors@https://civitai.com/api/download/models/357609",
+    "preview": "juggernautXL_v9Rdphoto2Lightning.jpg",
+    "desc": "Showcase finetuned model based on Stable diffusion XL",
+    "extras": "width: 1024, height: 1024, sampler: DPM SDE, steps: 6, cfg_scale: 2.0"
+  },
+  "Juggernaut SD Reborn": {
+    "original": true,
+    "path": "juggernaut_reborn.safetensors@https://civitai.com/api/download/models/274039",
+    "preview": "juggernaut_reborn.jpg",
+    "desc": "Showcase finetuned model based on Stable diffusion 1.5",
+    "extras": "width: 512, height: 512, sampler: DEIS, steps: 20, cfg_scale: 6.0"
+  },
 
   "DreamShaper SD v8": {
     "original": true,
@@ -19,31 +44,14 @@
     "desc": "Showcase finetuned model based on Stable diffusion XL",
     "extras": "width: 1024, height: 1024, sampler: DPM SDE, steps: 8, cfg_scale: 2.0"
   }, 
-  "Juggernaut SD Reborn": {
-    "original": true,
-    "path": "juggernaut_reborn.safetensors@https://civitai.com/api/download/models/274039",
-    "preview": "juggernaut_reborn.jpg",
-    "desc": "Showcase finetuned model based on Stable diffusion 1.5",
-    "extras": "width: 512, height: 512, sampler: DEIS, steps: 20, cfg_scale: 6.0"
-  },
-  "Juggernaut SD-XL v9": {
-    "path": "juggernautXL_v9Rundiffusionphoto2.safetensors@https://civitai.com/api/download/models/348913",
-    "preview": "juggernautXL_v9Rundiffusionphoto2.jpg",
-    "desc": "Showcase finetuned model based on Stable diffusion XL",
-    "extras": "width: 1024, height: 1024, sampler: DEIS, steps: 20, cfg_scale: 6.0"
-  },
-  "Juggernaut SD-XL v9 Lightning": {
-    "path": "juggernautXL_v9Rdphoto2Lightning.safetensors@https://civitai.com/api/download/models/357609",
-    "preview": "juggernautXL_v9Rdphoto2Lightning.jpg",
-    "desc": "Showcase finetuned model based on Stable diffusion XL",
-    "extras": "width: 1024, height: 1024, sampler: DPM SDE, steps: 6, cfg_scale: 2.0"
-  },
+
   "Tempest SD-XL v0.1": {
     "path": "TempestV0.1-Artistic.safetensors@https://huggingface.co/dataautogpt3/TempestV0.1/resolve/main/TempestV0.1-Artistic.safetensors?download=true",
     "preview": "TempestV0.1-Artistic.jpg",
     "desc": "The TempestV0.1 Initiative is a powerhouse in image generation, leveraging an unparalleled dataset of over 6 million images. The collection's vast scale, with resolutions from 1400x2100 to 4800x7200, encompasses 200GB of high-quality content.",
     "extras": "width: 2048, height: 1024, sampler: DEIS, steps: 40, cfg_scale: 6.0"
   },
+
   "SDXS DreamShaper 512": {
     "path": "IDKiro/sdxs-512-dreamshaper",
     "preview": "IDKiro--sdxs-512-dreamshaper.jpg",
@@ -152,7 +160,7 @@
   "AuraFlow 0.3": {
     "path": "fal/AuraFlow-v0.3",
     "desc": "AuraFlow v0.3 is the fully open-sourced flow-based text-to-image generation model. The model was trained with more compute compared to the previous version, AuraFlow-v0.2. Compared to AuraFlow-v0.2, the model is fine-tuned on more aesthetic datasets and now supports various aspect ratio, (now width and height up to 1536 pixels).",
-    "preview": "fal--AuraFlow.jpg",
+    "preview": "fal--AuraFlow-v0.3.jpg",
     "skip": true,
     "extras": "width: 1024, height: 1024"
   }, 

modules/api/api.py

@@ -55,6 +55,7 @@ class Api:
         self.add_api_route("/sdapi/v1/extra-batch-images", self.extras_batch_images_api, methods=["POST"], response_model=models.ResProcessBatch)
         self.add_api_route("/sdapi/v1/preprocess", self.process.post_preprocess, methods=["POST"])
         self.add_api_route("/sdapi/v1/mask", self.process.post_mask, methods=["POST"])
+        self.add_api_route("/sdapi/v1/faces", self.process.post_face, methods=["POST"])
 
         # api dealing with optional scripts
         self.add_api_route("/sdapi/v1/scripts", script.get_scripts_list, methods=["GET"], response_model=models.ResScripts)

modules/api/control.py

@@ -95,7 +95,7 @@ class APIControl():
 
     def prepare_ip_adapter(self, request):
         if hasattr(request, "ip_adapter") and request.ip_adapter:
-            args = { 'ip_adapter_names': [], 'ip_adapter_scales': [], 'ip_adapter_starts': [], 'ip_adapter_ends': [], 'ip_adapter_images': [], 'ip_adapter_masks': [] }
+            args = { 'ip_adapter_names': [], 'ip_adapter_scales': [], 'ip_adapter_crops': [], 'ip_adapter_starts': [], 'ip_adapter_ends': [], 'ip_adapter_images': [], 'ip_adapter_masks': [] }
             for ipadapter in request.ip_adapter:
                 if not ipadapter.images or len(ipadapter.images) == 0:
                     continue

modules/api/generate.py

@@ -64,6 +64,7 @@ class APIGenerate():
         if hasattr(request, "ip_adapter") and request.ip_adapter:
             p.ip_adapter_names = []
             p.ip_adapter_scales = []
+            p.ip_adapter_crops = []
             p.ip_adapter_starts = []
             p.ip_adapter_ends = []
             p.ip_adapter_images = []
@@ -72,6 +73,7 @@ class APIGenerate():
                     continue
                 p.ip_adapter_names.append(ipadapter.adapter)
                 p.ip_adapter_scales.append(ipadapter.scale)
+                p.ip_adapter_crops.append(ipadapter.crop)
                 p.ip_adapter_starts.append(ipadapter.start)
                 p.ip_adapter_ends.append(ipadapter.end)
                 p.ip_adapter_images.append([helpers.decode_base64_to_image(x) for x in ipadapter.images])

modules/api/process.py

@@ -26,6 +26,13 @@ class ReqMask(BaseModel):
     model: Optional[str] = Field(title="Model", description="The model to use for preprocessing")
     params: Optional[dict] = Field(default={}, title="Settings", description="Preprocessor settings")
 
+class ReqFace(BaseModel):
+    image: str = Field(title="Image", description="The base64 encoded image")
+
+class ResFace(BaseModel):
+    images: List[str] = Field(title="Image", description="The base64 encoded images of detected faces")
+    scores: List[float] = Field(title="Scores", description="The scores of the detected faces")
+
 class ResMask(BaseModel):
     mask: str = Field(default='', title="Image", description="The processed image in base64 format")
 
@@ -98,3 +105,18 @@ class APIProcess():
             return JSONResponse(status_code=400, content={"error": "Mask is none"})
         image = encode_pil_to_base64(processed)
         return ResMask(mask=image)
+
+    def post_face(self, req: ReqFace):
+        from scripts.face_details import yolo # pylint: disable=no-name-in-module
+        image = decode_base64_to_image(req.image)
+        shared.state.begin('API-FACE', api=True)
+        images = []
+        scores = []
+        with self.queue_lock:
+            yolo.load()
+            faces = yolo.predict(image)
+            for face in faces:
+                images.append(encode_pil_to_base64(face.face))
+                scores.append(face.score)
+        shared.state.end(api=False)
+        return ResFace(images=images, scores=scores)

modules/control/processors.py

@@ -212,7 +212,6 @@ class Processor():
     def __call__(self, image_input: Image, mode: str = 'RGB', resize_mode: int = 0, resize_name: str = 'None', scale_tab: int = 1, scale_by: float = 1.0, local_config: dict = {}):
         if self.override is not None:
             debug(f'Control Processor: id="{self.processor_id}" override={self.override}')
-            print('HERE1', image_input)
             if image_input is not None and image_input.size != self.override.size:
                 debug(f'Control resize: op=override image={self.override} width={image_input.width} height={image_input.height} mode={resize_mode} name={resize_name}')
                 image_input = images.resize_image(resize_mode, self.override, image_input.width, image_input.height, resize_name)
@@ -224,7 +223,6 @@ class Processor():
                     debug(f'Control resize: op=before image={image_input} width={width_before} height={height_before} mode={resize_mode} name={resize_name}')
                     image_input = images.resize_image(resize_mode, image_input, width_before, height_before, resize_name)
         if self.processor_id is None or self.processor_id == 'None':
-            print('HERE3', self.override)
             return image_input
         image_process = image_input
         if image_input is None:

modules/ipadapter.py

@@ -72,6 +72,34 @@ def get_scales(adapter_scales, adapter_images):
     return output_scales
 
 
+def get_crops(adapter_crops, adapter_images):
+    output_crops = [adapter_crops] if not isinstance(adapter_crops, list) else adapter_crops
+    while len(output_crops) < len(adapter_images):
+        output_crops.append(output_crops[-1])
+    return output_crops
+
+
+def crop_images(images, crops):
+    try:
+        for i in range(len(images)):
+            if crops[i]:
+                from scripts.face_details import yolo # pylint: disable=no-name-in-module
+                yolo.load()
+                cropped = []
+                for image in images[i]:
+                    faces = yolo.predict(image)
+                    if len(faces) > 0:
+                        cropped.append(faces[0].face)
+                if len(cropped) == len(images[i]):
+                    print('HERE0')
+                    images[i] = cropped
+                else:
+                    shared.log.error(f'IP adapter: failed to crop image: source={len(images[i])} faces={len(cropped)}')
+    except Exception as e:
+        shared.log.error(f'IP adapter: failed to crop image: {e}')
+    return images
+
+
 def unapply(pipe): # pylint: disable=arguments-differ
     try:
         if hasattr(pipe, 'set_ip_adapter_scale'):
@@ -84,7 +112,7 @@ def unapply(pipe): # pylint: disable=arguments-differ
         pass
 
 
-def apply(pipe, p: processing.StableDiffusionProcessing, adapter_names=[], adapter_scales=[1.0], adapter_starts=[0.0], adapter_ends=[1.0], adapter_images=[]):
+def apply(pipe, p: processing.StableDiffusionProcessing, adapter_names=[], adapter_scales=[1.0], adapter_crops=[False], adapter_starts=[0.0], adapter_ends=[1.0], adapter_images=[]):
     global clip_loaded # pylint: disable=global-statement
     # overrides
     if hasattr(p, 'ip_adapter_names'):
@@ -104,6 +132,8 @@ def apply(pipe, p: processing.StableDiffusionProcessing, adapter_names=[], adapt
         return False
     if hasattr(p, 'ip_adapter_scales'):
         adapter_scales = p.ip_adapter_scales
+    if hasattr(p, 'ip_adapter_crops'):
+        adapter_crops = p.ip_adapter_crops
     if hasattr(p, 'ip_adapter_starts'):
         adapter_starts = p.ip_adapter_starts
     if hasattr(p, 'ip_adapter_ends'):
@@ -131,6 +161,8 @@ def apply(pipe, p: processing.StableDiffusionProcessing, adapter_names=[], adapt
         return False
     adapter_scales = get_scales(adapter_scales, adapter_images)
     p.ip_adapter_scales = adapter_scales.copy()
+    adapter_crops = get_crops(adapter_crops, adapter_images)
+    p.ip_adapter_crops = adapter_crops.copy()
     adapter_starts = get_scales(adapter_starts, adapter_images)
     p.ip_adapter_starts = adapter_starts.copy()
     adapter_ends = get_scales(adapter_ends, adapter_images)
@@ -202,7 +234,7 @@ def apply(pipe, p: processing.StableDiffusionProcessing, adapter_names=[], adapt
                     adapter_scales[i] = 0.00
             pipe.set_ip_adapter_scale(adapter_scales)
             ip_str =  [f'{os.path.splitext(adapter)[0]}:{scale}:{start}:{end}' for adapter, scale, start, end in zip(adapter_names, adapter_scales, adapter_starts, adapter_ends)]
-        p.task_args['ip_adapter_image'] = adapter_images
+        p.task_args['ip_adapter_image'] = crop_images(adapter_images, adapter_crops)
         if len(adapter_masks) > 0:
             p.cross_attention_kwargs = { 'ip_adapter_masks': adapter_masks }
         p.extra_generation_params["IP Adapter"] = ';'.join(ip_str)

modules/processing.py

@@ -328,7 +328,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                 p.scripts.postprocess_batch_list(p, batch_params, batch_number=n)
                 x_samples_ddim = batch_params.images
 
-            def infotext(index): # pylint: disable=function-redefined # noqa: F811
+            def infotext(index): # pylint: disable=function-redefined
                 return create_infotext(p, p.prompts, p.seeds, p.subseeds, index=index, all_negative_prompts=p.negative_prompts)
 
             for i, x_sample in enumerate(x_samples_ddim):

modules/processing_class.py

@@ -122,6 +122,7 @@ class StableDiffusionProcessing:
         self.ip_adapter_images = []
         self.ip_adapter_starts = [0.0]
         self.ip_adapter_ends = [1.0]
+        self.ip_adapter_crops = []
         # hdr
         self.hdr_mode=hdr_mode
         self.hdr_brightness=hdr_brightness

modules/sd_models.py

@@ -211,7 +211,8 @@ def get_closet_checkpoint_match(search_string):
     if found and len(found) > 0:
         return found[0]
     for v in shared.reference_models.values():
-        if search_string in v['path'] or os.path.basename(search_string) in v['path']:
+        pth = v['path'].split('@')[-1]
+        if search_string in pth or os.path.basename(search_string) in pth:
             model_name = search_string.replace('huggingface/', '')
             checkpoint_info = CheckpointInfo(v['path']) # create a virutal model info
             checkpoint_info.type = 'huggingface'

scripts/face_details.py

@@ -7,10 +7,11 @@ from modules import devices, processing_class
 
 
 class YoLoResult:
-    def __init__(self, score: float, box: list[int], mask: Image.Image = None, size: float = 0, width = 0, height = 0, args = {}):
+    def __init__(self, score: float, box: list[int], mask: Image.Image = None, face: Image.Image = None, size: float = 0, width = 0, height = 0, args = {}):
         self.score = score
         self.box = box
         self.mask = mask
+        self.face = face
         self.size = size
         self.width = width
         self.height = height
@@ -82,7 +83,8 @@ class FaceRestorerYolo(FaceRestoration):
                         mask_image = Image.new('L', image.size, 0)
                         draw = ImageDraw.Draw(mask_image)
                         draw.rectangle(box, fill="white", outline=None, width=0)
-                    result.append(YoLoResult(score=round(score, 2), box=box, mask=mask_image, size=size, width=w, height=h, args=args))
+                        face_image = image.crop(box)
+                    result.append(YoLoResult(score=round(score, 2), box=box, mask=mask_image, face=face_image, size=size, width=w, height=h, args=args))
         return result
 
     def load(self):

scripts/ipadapter.py

@@ -51,6 +51,7 @@ class Script(scripts.Script):
             starts = []
             ends = []
             files = []
+            crops = []
             masks = []
             image_galleries = []
             mask_galleries = []
@@ -61,6 +62,7 @@ class Script(scripts.Script):
                     with gr.Row():
                         adapters.append(gr.Dropdown(label='Adapter', choices=list(ipadapter.ADAPTERS), value='None'))
                         scales.append(gr.Slider(label='Scale', minimum=0.0, maximum=1.0, step=0.01, value=0.5))
+                        crops.append(gr.Checkbox(label='Crop', default=False, interactive=True))
                     with gr.Row():
                         starts.append(gr.Slider(label='Start', minimum=0.0, maximum=1.0, step=0.1, value=0))
                         ends.append(gr.Slider(label='End', minimum=0.0, maximum=1.0, step=0.1, value=1))
@@ -80,7 +82,7 @@ class Script(scripts.Script):
             layers_label = gr.HTML('<a href="https://huggingface.co/docs/diffusers/main/en/using-diffusers/ip_adapter#style--layout-control" target="_blank">InstantStyle: advanced layer activation</a>', visible=False)
             layers = gr.Text(label='Layer scales', placeholder='{\n"down": {"block_2": [0.0, 1.0]},\n"up": {"block_0": [0.0, 1.0, 0.0]}\n}', rows=1, type='text', interactive=True, lines=5, visible=False, show_label=False)
             layers_active.change(fn=self.display_advanced, inputs=[layers_active], outputs=[layers_label, layers])
-        return [num_adapters] + adapters + scales + files + starts + ends + masks + [layers_active] + [layers]
+        return [num_adapters] + adapters + scales + files + crops + starts + ends + masks + [layers_active] + [layers]
 
     def process(self, p: processing.StableDiffusionProcessing, *args): # pylint: disable=arguments-differ
         if not shared.native:
@@ -95,14 +97,16 @@ class Script(scripts.Script):
             p.ip_adapter_scales = args[MAX_ADAPTERS:MAX_ADAPTERS*2][:units]
         if getattr(p, 'ip_adapter_images', []) == []:
             p.ip_adapter_images = args[MAX_ADAPTERS*2:MAX_ADAPTERS*3][:units]
+        if getattr(p, 'ip_adapter_crops', []) == []:
+            p.ip_adapter_crops = args[MAX_ADAPTERS*3:MAX_ADAPTERS*4][:units]
         if getattr(p, 'ip_adapter_starts', [0.0]) == [0.0]:
-            p.ip_adapter_starts = args[MAX_ADAPTERS*3:MAX_ADAPTERS*4][:units]
+            p.ip_adapter_starts = args[MAX_ADAPTERS*4:MAX_ADAPTERS*5][:units]
         if getattr(p, 'ip_adapter_ends', [1.0]) == [1.0]:
-            p.ip_adapter_ends = args[MAX_ADAPTERS*4:MAX_ADAPTERS*5][:units]
+            p.ip_adapter_ends = args[MAX_ADAPTERS*5:MAX_ADAPTERS*6][:units]
         if getattr(p, 'ip_adapter_masks', []) == []:
-            p.ip_adapter_masks = args[MAX_ADAPTERS*5:MAX_ADAPTERS*6][:units]
+            p.ip_adapter_masks = args[MAX_ADAPTERS*6:MAX_ADAPTERS*7][:units]
             p.ip_adapter_masks = [x for x in p.ip_adapter_masks if x]
-        layers_active, layers = args[MAX_ADAPTERS*6:MAX_ADAPTERS*7]
+        layers_active, layers = args[MAX_ADAPTERS*7:MAX_ADAPTERS*8]
         if layers_active and len(layers) > 0:
             try:
                 layers = json.loads(layers)