silence the warning from transformers

add feature demonstrations to readme

1 files changed, 22 insertions, 12 deletions

diff --git a/webui.py b/webui.py
index 5b990a5f..1a2fa56c 100644
--- a/webui.py
+++ b/webui.py
@@ -13,13 +13,20 @@ from contextlib import contextmanager, nullcontext
 import mimetypes

 import random

 import math

-import csv

 

 import k_diffusion as K

 from ldm.util import instantiate_from_config

 from ldm.models.diffusion.ddim import DDIMSampler

 from ldm.models.diffusion.plms import PLMSSampler

 

+try:

+    # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.

+

+    from transformers import logging

+    logging.set_verbosity_error()

+except:

+    pass

+

 # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the bowser will not show any UI

 mimetypes.init()

 mimetypes.add_type('application/javascript', '.js')

@@ -28,7 +35,7 @@ mimetypes.add_type('application/javascript', '.js')
 opt_C = 4

 opt_f = 8

 

-invalid_filename_chars = '<>:"/\|?*'

+invalid_filename_chars = '<>:"/\|?*\n'

 

 parser = argparse.ArgumentParser()

 parser.add_argument("--outdir", type=str, nargs="?", help="dir to write results to", default=None)

@@ -121,7 +128,6 @@ if os.path.exists(GFPGAN_dir):
         print("Error loading GFPGAN:", file=sys.stderr)

         print(traceback.format_exc(), file=sys.stderr)

 

-

 config = OmegaConf.load("configs/stable-diffusion/v1-inference.yaml")

 model = load_model_from_config(config, "models/ldm/stable-diffusion-v1/model.ckpt")

 

@@ -296,7 +302,9 @@ class Flagging(gr.FlaggingCallback):
     def setup(self, components, flagging_dir: str):

         pass

 

-    def flag(self, flag_data, flag_option=None, flag_index=None, username=None) -> int:

+    def flag(self, flag_data, flag_option=None, flag_index=None, username=None):

+        import csv

+

         os.makedirs("log/images", exist_ok=True)

 

         # those must match the "dream" function

@@ -341,7 +349,7 @@ dream_interface = gr.Interface(
         gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="DDIM ETA", value=0.0, visible=False),

         gr.Slider(minimum=1, maximum=16, step=1, label='Batch count (how many batches of images to generate)', value=1),

         gr.Slider(minimum=1, maximum=4, step=1, label='Batch size (how many images are in a batch; memory-hungry)', value=1),

-        gr.Slider(minimum=1.0, maximum=15.0, step=0.5, label='Classifier Free Guidance Scale (how strongly should the image follow the prompt)', value=7.0),

+        gr.Slider(minimum=1.0, maximum=15.0, step=0.5, label='Classifier Free Guidance Scale (how strongly the image should follow the prompt)', value=7.0),

         gr.Number(label='Seed', value=-1),

         gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512),

         gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512),

@@ -456,13 +464,13 @@ img2img_interface = gr.Interface(
         gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=50),

         gr.Checkbox(label='Fix faces using GFPGAN', value=False, visible=GFPGAN is not None),

         gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="DDIM ETA", value=0.0, visible=False),

-        gr.Slider(minimum=1, maximum=16, step=1, label='Sampling iterations', value=1),

-        gr.Slider(minimum=1, maximum=4, step=1, label='Samples per iteration', value=1),

-        gr.Slider(minimum=1.0, maximum=15.0, step=0.5, label='Classifier Free Guidance Scale', value=7.0),

+        gr.Slider(minimum=1, maximum=16, step=1, label='Batch count (how many batches of images to generate)', value=1),

+        gr.Slider(minimum=1, maximum=4, step=1, label='Batch size (how many images are in a batch; memory-hungry)', value=1),

+        gr.Slider(minimum=1.0, maximum=15.0, step=0.5, label='Classifier Free Guidance Scale (how strongly the image should follow the prompt)', value=7.0),

         gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising Strength', value=0.75),

         gr.Number(label='Seed', value=-1),

-        gr.Slider(minimum=64, maximum=2048, step=64, label="Resize Height", value=512),

-        gr.Slider(minimum=64, maximum=2048, step=64, label="Resize Width", value=512),

+        gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512),

+        gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512),

     ],

     outputs=[

         gr.Gallery(),

@@ -470,11 +478,12 @@ img2img_interface = gr.Interface(
     ],

     title="Stable Diffusion Image-to-Image",

     description="Generate images from images with Stable Diffusion",

+    allow_flagging="never",

 )

 

 interfaces = [

-    (dream_interface, "Dream"),

-    (img2img_interface, "Image Translation")

+    (dream_interface, "txt2img"),

+    (img2img_interface, "img2img")

 ]

 

 def run_GFPGAN(image, strength):

@@ -501,6 +510,7 @@ if GFPGAN is not None:
         ],

         title="GFPGAN",

         description="Fix faces on images",

+        allow_flagging="never",

     ), "GFPGAN"))

 

 demo = gr.TabbedInterface(interface_list=[x[0] for x in interfaces], tab_names=[x[1] for x in interfaces])