update

cli/modules/preview.py

@@ -0,0 +1,58 @@
+#!/bin/env python
+import io
+import json
+import base64
+import logging
+from PIL import Image
+
+from util import Map, log
+from sdapi import postsync
+
+template = 'photo of "{name}", {suffix}, high detailed, skin texture, facing camera, 135mm, shot on dslr, 4k, modelshoot style'
+opt = {
+    'prompt': None,
+    'negative_prompt': '',
+    'init_images': [],
+    'sampler_name': 'DPM2 Karras',
+    'batch_size': 1,
+    'n_iter': 1,
+    'steps': 30,
+    'cfg_scale': 6,
+    'width': 512,
+    'height': 512,
+    'restore_faces': False
+}
+
+def encode(f):
+    img = Image.open(f)
+    with io.BytesIO() as stream:
+        img.save(stream, 'JPEG')
+        values = stream.getvalue()
+        encoded = base64.b64encode(values).decode()
+        return encoded
+
+def img2img(name: str, suffix: str):
+    opt['prompt'] = template.format(name = name, suffix = suffix)
+    log.info({ 'preview prompt': opt['prompt'] })
+    log.info({ 'preview options': opt })
+    opt['init_images'].append(encode('sillouethe.jpg'))
+    data = postsync('/sdapi/v1/img2img', opt)
+    if 'error' in data:
+        log.error({ 'preview': data['error'], 'reason': data['reason'] })
+        return
+    info = Map(json.loads(data['info']))
+    log.debug({ 'preview info': info })
+    if not 'images' in data:
+        log.error({ 'preview': 'no images' })
+        return
+    obj = data.copy()
+    del obj['images']
+    log.info({ 'preview response': obj })
+    for b64 in data['images']:
+        image = Image.open(io.BytesIO(base64.b64decode(b64.split(",",1)[0])))
+        image.save('test.jpg')
+
+if __name__ == "__main__":
+    # log.setLevel(logging.DEBUG)
+    log.info({ 'preview': 'start' })
+    img2img('hanna', 'person, woman, girl, model')

cli/train.py

@@ -415,7 +415,7 @@ async def main():
     parser.add_argument("--init", type = str, default = "person", required = False, help = "initialization class, default: %(default)s")
     parser.add_argument("--dst", type = str, default = "/tmp", required = False, help = "destination image folder for processed images, default: %(default)s")
     parser.add_argument("--steps", type = int, default = -1, required = False, help = "training steps, default: %(default)s")
-    parser.add_argument("--maxsteps", type = int, default = 2500, required = False, help = "max training steps used when dynamic gradient is active, default: %(default)s")
+    parser.add_argument("--maxsteps", type = int, default = 5000, required = False, help = "max training steps used when dynamic gradient is active, default: %(default)s")
     parser.add_argument("--vectors", type = int, default = -1, required = False, help = "number of vectors per token, default: dynamic based on number of input images")
     parser.add_argument("--batch", type = int, default = 1, required = False, help = "batch size, default: %(default)s")
     parser.add_argument("--rate", type = str, default = "", required = False, help = "learn rate, default: dynamic")

config.json

@@ -57,7 +57,7 @@
     "training_image_repeats_per_epoch": 1,
     "training_write_csv_every": 1.0,
     "training_xattention_optimizations": false,
-    "training_enable_tensorboard": true,
+    "training_enable_tensorboard": false,
     "training_tensorboard_save_images": false,
     "training_tensorboard_flush_every": 120,
     "sd_model_checkpoint": "sd-v15-runwayml.ckpt [cc6cb27103]",