add faceid module

2026-09-20 01:31:13 +02:00 · 2023-12-26 13:19:38 -05:00
parent 54deae7746
commit 54acb47f41
3 changed files with 117 additions and 1 deletions
@@ -0,0 +1,109 @@
+import os
+import cv2
+import torch
+import numpy as np
+import gradio as gr
+import diffusers
+import huggingface_hub as hf
+from modules import scripts, processing, shared, devices
+
+
+app = None
+try:
+    import onnxruntime
+    from insightface.app import FaceAnalysis
+    from ip_adapter.ip_adapter_faceid import IPAdapterFaceID
+    ok = True
+except Exception as e:
+    shared.log.error(f'FaceID: {e}')
+    ok = False
+
+
+class Script(scripts.Script):
+    def title(self):
+        return 'FaceID'
+
+    def show(self, is_img2img):
+        return ok if shared.backend == shared.Backend.DIFFUSERS else False
+
+    # return signature is array of gradio components
+    def ui(self, _is_img2img):
+        with gr.Row():
+            scale = gr.Slider(label='Scale', minimum=0.0, maximum=1.0, step=0.01, value=1.0)
+        with gr.Row():
+            image = gr.Image(image_mode='RGB', label='Image', source='upload', type='pil', width=512)
+        return [scale, image]
+
+    def run(self, p: processing.StableDiffusionProcessing, scale, image): # pylint: disable=arguments-differ, unused-argument
+        if not ok:
+            shared.log.error('FaceID: missing dependencies')
+            return None
+        if image is None:
+            shared.log.error('FaceID: no init_images')
+            return None
+        if shared.sd_model_type != 'sd':
+            shared.log.error('FaceID: base model not supported')
+            return None
+
+        global app # pylint: disable=global-statement
+        if app is None:
+            shared.log.debug(f"ONNX: device={onnxruntime.get_device()} providers={onnxruntime.get_available_providers()}")
+            app = FaceAnalysis(name="buffalo_l", providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
+            onnxruntime.set_default_logger_severity(3)
+            app.prepare(ctx_id=0, det_thresh=0.5, det_size=(640, 640))
+
+        image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
+        faces = app.get(image)
+        if len(faces) == 0:
+            shared.log.error('FaceID: no faces found')
+            return None
+        for face in faces:
+            shared.log.debug(f'FaceID face: score={face.det_score:.2f} gender={"female" if face.gender==0 else "male"} age={face.age} bbox={face.bbox}')
+        embeds = torch.from_numpy(faces[0].normed_embedding).unsqueeze(0)
+
+        ip_ckpt = "h94/IP-Adapter-FaceID/ip-adapter-faceid_sd15.bin"
+        shared.log.debug(f'FaceID model load: {ip_ckpt}')
+        folder, filename = os.path.split(ip_ckpt)
+        model_path = hf.hf_hub_download(repo_id=folder, filename=filename, cache_dir=shared.opts.diffusers_dir)
+        if model_path is None:
+            shared.log.error(f'FaceID: model download failed: {ip_ckpt}')
+            return None
+
+        processing.process_init(p)
+        shared.sd_model.scheduler = diffusers.DDIMScheduler(
+            num_train_timesteps=1000,
+            beta_start=0.00085,
+            beta_end=0.012,
+            beta_schedule="scaled_linear",
+            clip_sample=False,
+            set_alpha_to_one=False,
+            steps_offset=1,
+        )
+        ip_model = IPAdapterFaceID(shared.sd_model, model_path, devices.device)
+        ip_model_dict = {
+            'prompt': p.all_prompts[0],
+            'negative_prompt': p.all_negative_prompts[0],
+            'num_samples': p.batch_size,
+            'width': p.width,
+            'height': p.height,
+            'num_inference_steps': p.steps,
+            'scale': scale,
+            'guidance_scale': p.cfg_scale,
+            'seed': int(p.all_seeds[0]),
+            'faceid_embeds': None,
+        }
+        shared.log.debug(f'FaceID args: {ip_model_dict}')
+        ip_model_dict['faceid_embeds'] = embeds
+        images = ip_model.generate(**ip_model_dict)
+
+        processed = processing.Processed(
+            p,
+            images_list=images,
+            seed=p.seed,
+            subseed=p.subseed,
+            index_of_first_image=0,
+        )
+        processed.infotexts = processed.infotext(p, 0)
+        ip_model = None
+        devices.torch_gc()
+        return processed