face module apply style

CHANGELOG.md

@@ -12,11 +12,7 @@
 ## TODO for Dev merge
 
 - update docs  
-- face apply style  
-- embeddings disappear  
 - control reference mode  
-- control init image same as control, separate init image  
-- control t2i-adapter with ip-adapter  
 
 ## Update for 2023-02-02
 

modules/face/__init__.py

@@ -107,6 +107,7 @@ class Script(scripts.Script):
                 input_images[i] = Image.open(image['name'])
         source_image = input_images[0]
 
+        processing.process_init(p)
         if mode == 'FaceID': # faceid runs as ipadapter in its own pipeline
             from modules.face.faceid import face_id
             from modules.face.insightface import get_app

modules/face/faceid.py

@@ -32,7 +32,6 @@ def face_id(p: processing.StableDiffusionProcessing, app, source_image: Image.Im
         shared.log.error(f'FaceID download failed: model={model} file={ip_ckpt}')
         return None
 
-    processing.process_init(p)
     if override:
         shared.sd_model.scheduler = diffusers.DDIMScheduler(
             num_train_timesteps=1000,

modules/face/instantid.py

@@ -34,8 +34,9 @@ def instant_id(p: processing.StableDiffusionProcessing, app, source_image, stren
     shared.log.debug(f'InstantID face: score={face.det_score:.2f} gender={"female" if face.gender==0 else "male"} age={face.age} bbox={face.bbox}')
     shared.log.debug(f'InstantID loading: model={REPO_ID}')
     face_adapter = hf.hf_hub_download(repo_id=REPO_ID, filename="ip-adapter.bin")
-    if controlnet_model is None:
+    if controlnet_model is None or not cache:
         controlnet_model = ControlNetModel.from_pretrained(REPO_ID, subfolder="ControlNetModel", torch_dtype=devices.dtype, cache_dir=shared.opts.diffusers_dir)
+        controlnet_model.to(devices.device, devices.dtype)
 
     processing.process_init(p)
 
@@ -57,12 +58,13 @@ def instant_id(p: processing.StableDiffusionProcessing, app, source_image, stren
     shared.sd_model.load_ip_adapter_instantid(face_adapter, scale=strength)
     shared.sd_model.set_ip_adapter_scale(strength)
     if not ((shared.opts.diffusers_model_cpu_offload or shared.cmd_opts.medvram) or (shared.opts.diffusers_seq_cpu_offload or shared.cmd_opts.lowvram)):
+        print('HERE1')
         shared.sd_model.to(shared.device, devices.dtype) # move pipeline if needed, but don't touch if its under automatic managment
 
     # pipeline specific args
     orig_prompt_attention = shared.opts.prompt_attention
     shared.opts.data['prompt_attention'] = 'Fixed attention' # otherwise need to deal with class_tokens_mask
-    p.task_args['prompt'] = p.prompt # override all logic
+    p.task_args['prompt'] = p.all_prompts[0] # override all logic
     p.task_args['image_embeds'] = face_emb
     p.task_args['image'] = face_kps
     p.task_args['controlnet_conditioning_scale'] = float(conditioning)

modules/face/photomaker.py

@@ -18,14 +18,14 @@ def photo_maker(p: processing.StableDiffusionProcessing, input_images, trigger,
 
     # validate prompt
     trigger_ids = shared.sd_model.tokenizer.encode(trigger) + shared.sd_model.tokenizer_2.encode(trigger)
-    prompt_ids1 = shared.sd_model.tokenizer.encode(p.prompt)
-    prompt_ids2 = shared.sd_model.tokenizer_2.encode(p.prompt)
+    prompt_ids1 = shared.sd_model.tokenizer.encode(p.all_prompts[0])
+    prompt_ids2 = shared.sd_model.tokenizer_2.encode(p.all_prompts[0])
     for t in trigger_ids:
         if prompt_ids1.count(t) != 1:
-            shared.log.error(f'PhotoMaker: trigger word not matched in prompt: {trigger} ids={trigger_ids} prompt={p.prompt} ids={prompt_ids1}')
+            shared.log.error(f'PhotoMaker: trigger word not matched in prompt: {trigger} ids={trigger_ids} prompt={p.all_prompts[0]} ids={prompt_ids1}')
             return None
         if prompt_ids2.count(t) != 1:
-            shared.log.error(f'PhotoMaker: trigger word not matched in prompt: {trigger} ids={trigger_ids} prompt={p.prompt} ids={prompt_ids1}')
+            shared.log.error(f'PhotoMaker: trigger word not matched in prompt: {trigger} ids={trigger_ids} prompt={p.all_prompts[0]} ids={prompt_ids1}')
             return None
 
     # create new pipeline
@@ -49,7 +49,7 @@ def photo_maker(p: processing.StableDiffusionProcessing, input_images, trigger,
     shared.opts.data['prompt_attention'] = 'Fixed attention' # otherwise need to deal with class_tokens_mask
     p.task_args['input_id_images'] = input_images
     p.task_args['start_merge_step'] = int(start * p.steps)
-    p.task_args['prompt'] = p.prompt # override all logic
+    p.task_args['prompt'] = p.all_prompts[0] # override all logic
 
     photomaker_path = hf.hf_hub_download(repo_id="TencentARC/PhotoMaker", filename="photomaker-v1.bin", repo_type="model", cache_dir=shared.opts.diffusers_dir)
     shared.log.debug(f'PhotoMaker: model={photomaker_path} images={len(input_images)} trigger={trigger} args={p.task_args}')