fix ipadapter batch runs

CHANGELOG.md

@@ -5,9 +5,9 @@
 - resize type: fixed, fill, etc.
 - reference styles
 
-## Update for 2024-03-14
+## Update for 2024-03-15
 
-### Highlights 2024-03-14
+### Highlights 2024-03-15
 
 New models:
 - [Stable Cascade](https://github.com/Stability-AI/StableCascade) *Full* and *Lite*
@@ -16,16 +16,16 @@ New models:
 - [Stable Video Diffusion XT 1.1](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt-1-1)
 - [VGen](https://huggingface.co/ali-vilab/i2vgen-xl)  
 New pipelines and features:
-- Trajectory Consistency Distillation [TCD](https://mhh0318.github.io/tcd) for generate in even less steps
-- Image2image using [LEdit++](https://leditsplusplus-project.static.hf.space/index.html), context aware method with image analysis and positive/negative prompt handling
+- Trajectory Consistency Distillation [TCD](https://mhh0318.github.io/tcd) for processing in even less steps
+- Img2img using [LEdit++](https://leditsplusplus-project.static.hf.space/index.html), context aware method with image analysis and positive/negative prompt handling
 - Visual Query & Answer using [moondream2](https://github.com/vikhyat/moondream) as an addition to standard interrogate methods
 - Face-HiRes: simple detailer for face refinements
 - UI aspect-ratio controls and other UI improvements
 - User controllable invisibile and visible watermarking
 - Native composable LoRA
-**Styles**: Not just for prompts! Can apply generate parameters as templates and can be used to apply wildcards to prompts
-**Reference models**: *Networks -> Models -> Reference*: All reference models now come with recommended settings that can be auto-applied if desired
-Additional Improvements such as: Smooth tiling, Refine/HiRes workflow improvements, Control workflow improvements, Additional API endpoints
+**Styles**: Not just for prompts! Styles can apply *generate parameters* as templates and can be used to *apply wildcards* to prompts  
+**Reference models**: *Networks -> Models -> Reference*: All reference models now come with recommended settings that can be auto-applied if desired  
+Additional Improvements such as: Smooth tiling, Refine/HiRes workflow improvements, Control workflow improvements, Additional API endpoints  
 
 Further details:  
 - For basic instructions, see [README](https://github.com/vladmandic/automatic/blob/master/README.md)  
@@ -33,7 +33,7 @@ Further details:
 - For documentation, see [WiKi](https://github.com/vladmandic/automatic/wiki)
 - [Discord](https://discord.com/invite/sd-next-federal-batch-inspectors-1101998836328697867) server  
 
-### Full Changelog 2024-03-14
+### Full Changelog 2024-03-15
 
 - [Stable Cascade](https://github.com/Stability-AI/StableCascade) *Full* and *Lite*
   - large multi-stage high-quality model from warp-ai/wuerstchen team and released by stabilityai  
@@ -167,6 +167,7 @@ Further details:
   - fix *requires_aesthetics_score* errors
   - fix t2i-canny
   - fix *differenital diffusion* for manual mask, thanks @23pennies
+  - fix ipadapter apply/unapply on batch runs
   - use default model variant if specified variant doesnt exist
   - use diffusers lora load override for *lcm/tcd/turbo loras*
   - exception handler around vram memory stats gather

cli/clone.py

@@ -1,3 +1,4 @@
+#!/usr/bin/env python
 import os
 import logging
 import git

modules/ipadapter.py

@@ -96,6 +96,8 @@ def apply(pipe, p: processing.StableDiffusionProcessing, adapter_names=[], adapt
     adapters = [adapter for adapter in adapters if adapter is not None and adapter.lower() != 'none']
     if len(adapters) == 0:
         unapply(pipe)
+        if hasattr(p, 'ip_adapter_images'):
+            del p.ip_adapter_images
         return False
     if hasattr(p, 'ip_adapter_scales'):
         adapter_scales = p.ip_adapter_scales
@@ -125,6 +127,8 @@ def apply(pipe, p: processing.StableDiffusionProcessing, adapter_names=[], adapt
         adapters = [] # unload adapter if previously loaded as it will cause runtime errors
     if len(adapters) == 0:
         unapply(pipe)
+        if hasattr(p, 'ip_adapter_images'):
+            del p.ip_adapter_images
         return False
     if not hasattr(pipe, 'load_ip_adapter'):
         shared.log.error(f'IP adapter: pipeline not supported: {pipe.__class__.__name__}')

modules/modelloader.py

@@ -399,7 +399,10 @@ def load_file_from_url(url: str, *, model_dir: str, progress: bool = True, file_
     if not os.path.exists(cached_file):
         shared.log.info(f'Downloading: url="{url}" file={cached_file}')
         download_url_to_file(url, cached_file)
-    return cached_file
+    if os.path.exists(cached_file):
+        return cached_file
+    else:
+        return None
 
 
 def load_models(model_path: str, model_url: str = None, command_path: str = None, ext_filter=None, download_name=None, ext_blacklist=None) -> list:

modules/processing.py

@@ -251,10 +251,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
     if p.scripts is not None and isinstance(p.scripts, scripts.ScriptRunner):
         p.scripts.process(p)
 
-    if shared.backend == shared.Backend.DIFFUSERS:
-        from modules import ipadapter
-        ipadapter.apply(shared.sd_model, p)
-
     def infotext(_inxex=0): # dummy function overriden if there are iterations
         return ''
 
@@ -277,6 +273,10 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             if shared.state.interrupted:
                 shared.log.debug(f'Process interrupted: {n+1}/{p.n_iter}')
                 break
+
+            if shared.backend == shared.Backend.DIFFUSERS:
+                from modules import ipadapter
+                ipadapter.apply(shared.sd_model, p)
             p.prompts = p.all_prompts[n * p.batch_size:(n+1) * p.batch_size]
             p.negative_prompts = p.all_negative_prompts[n * p.batch_size:(n+1) * p.batch_size]
             p.seeds = p.all_seeds[n * p.batch_size:(n+1) * p.batch_size]

scripts/face-details.py

@@ -23,6 +23,8 @@ class FaceRestorerYolo(FaceRestoration):
         self.model_dir = os.path.join(paths.models_path, 'yolo')
         self.model_name = 'yolov8n-face.pt'
         self.model_url = 'https://github.com/akanametov/yolov8-face/releases/download/v0.0.0/yolov8n-face.pt'
+        # self.model_name = 'yolov9-c-face.pt'
+        # self.model_url = 'https://github.com/akanametov/yolov9-face/releases/download/1.0/yolov9-c-face.pt'
 
     def dependencies(self):
         import installer
@@ -81,12 +83,11 @@ class FaceRestorerYolo(FaceRestoration):
         from modules import modelloader
         self.dependencies()
         if self.model is None:
-            model_files = modelloader.load_models(model_path=self.model_dir, model_url=self.model_url, download_name=self.model_name)
-            for f in model_files:
-                if self.model_name in f:
-                    shared.log.info(f'Loading: type=FaceHires model={f}')
-                    from ultralytics import YOLO # pylint: disable=import-outside-toplevel
-                    self.model = YOLO(f)
+            model_file = modelloader.load_file_from_url(url=self.model_url, model_dir=self.model_dir, file_name=self.model_name)
+            if model_file is not None:
+                shared.log.info(f'Loading: type=FaceHires model={model_file}')
+                from ultralytics import YOLO # pylint: disable=import-outside-toplevel
+                self.model = YOLO(model_file)
 
     def restore(self, np_image, p: processing.StableDiffusionProcessing = None):
         from modules import devices, processing_class
@@ -96,7 +97,7 @@ class FaceRestorerYolo(FaceRestoration):
             return np_image
         self.load()
         if self.model is None:
-            shared.log.error(f"Model load: type=FaceHires model={self.model_name} dir={self.model_dir} url={self.model_url}")
+            shared.log.error(f"Model load: type=FaceHires model='{self.model_name}' dir={self.model_dir} url={self.model_url}")
             return np_image
         image = Image.fromarray(np_image)
         faces = self.predict(image, mask=True, device=devices.device, offload=shared.opts.face_restoration_unload)

scripts/ipadapter.py

@@ -82,4 +82,4 @@ class Script(scripts.Script):
             p.ip_adapter_starts = args[MAX_ADAPTERS*3:MAX_ADAPTERS*4][:units]
         if p.ip_adapter_ends == [1.0]:
             p.ip_adapter_ends = args[MAX_ADAPTERS*4:MAX_ADAPTERS*5][:units]
-        # ipadapter.apply(shared.sd_model, p, adapter_name, scale, image) # called directly from processing.process_images_inner
+        # ipadapter.apply(shared.sd_model, p, p.ip_adapter_names, p.ip_adapter_scales, p.ip_adapter_starts, p.ip_adapter_ends, p.ip_adapter_images) # called directly from processing.process_images_inner