futureproof

Signed-off-by: Vladimir Mandic <mandic00@live.com>

CHANGELOG.md

@@ -13,7 +13,9 @@
   - [GLM-Image](https://huggingface.co/zai-org/GLM-Image)  
     GLM-image is a new image generation model that adopts a hybrid autoregressive with diffusion decoder architecture  
     available in both *original* and *sdnq-dynamic prequantized* variants, thanks @CalamitousFelicitousness  
-    *note*: model requires usage of `--new` flag to install pre-release versions of required package  
+    *note*: model requires pre-release versions of `transformers` package:  
+    > pip install --upgrade git+https://github.com/huggingface/transformers.git  
+    > ./webui.sh --experimental  
   - [Nunchaku Z-Image Turbo](https://huggingface.co/nunchaku-tech/nunchaku-z-image-turbo)  
 - **Feaures**
   - **SDNQ**: add *dynamic* quantization method  

modules/loader.py

@@ -99,14 +99,23 @@ except Exception:
     _bnb = False
 timer.startup.record("bnb")
 
+import huggingface_hub # pylint: disable=W0611,C0411
+logging.getLogger("huggingface_hub.file_download").setLevel(logging.ERROR)
+if huggingface_hub.__version__.startswith('0.'):
+    huggingface_hub.is_offline_mode = lambda: False
+timer.startup.record("hfhub")
+
+import accelerate # pylint: disable=W0611,C0411
+timer.startup.record("accelerate")
+
+import pydantic # pylint: disable=W0611,C0411
+timer.startup.record("pydantic")
+
 import transformers # pylint: disable=W0611,C0411
 from transformers import logging as transformers_logging # pylint: disable=W0611,C0411
 transformers_logging.set_verbosity_error()
 timer.startup.record("transformers")
 
-import accelerate # pylint: disable=W0611,C0411
-timer.startup.record("accelerate")
-
 try:
     import onnxruntime # pylint: disable=W0611,C0411
     onnxruntime.set_default_logger_severity(4)
@@ -121,9 +130,6 @@ import gradio # pylint: disable=W0611,C0411
 timer.startup.record("gradio")
 errors.install([gradio])
 
-import pydantic # pylint: disable=W0611,C0411
-timer.startup.record("pydantic")
-
 # patch different progress bars
 import tqdm as tqdm_lib # pylint: disable=C0411
 from tqdm.rich import tqdm # pylint: disable=W0611,C0411
@@ -145,10 +151,6 @@ except Exception as e:
     errors.log.error('Please restart re-run the installer')
     sys.exit(1)
 
-import huggingface_hub # pylint: disable=W0611,C0411
-logging.getLogger("huggingface_hub.file_download").setLevel(logging.ERROR)
-timer.startup.record("hfhub")
-
 try:
     import pillow_jxl # pylint: disable=W0611,C0411
 except Exception:
@@ -185,6 +187,7 @@ def get_packages():
         "gradio": gradio.__version__,
         "transformers": transformers.__version__,
         "accelerate": accelerate.__version__,
+        "hub": huggingface_hub.__version__,
     }
 
 try:

modules/modelloader.py

@@ -45,7 +45,7 @@ def hf_login(token=None):
         except Exception:
             pass
         with contextlib.redirect_stdout(stdout):
-            hf.login(token=token, add_to_git_credential=False, write_permission=False)
+            hf.login(token=token, add_to_git_credential=False)
         os.environ['HF_TOKEN'] = token
         text = stdout.getvalue() or ''
         obfuscated_token = 'hf_...' + token[-4:]

modules/ui_extra_networks.py

@@ -326,6 +326,7 @@ class ExtraNetworksPage:
             else:
                 style = 'network-folder'
             subdirs_html += f'<button class="lg secondary gradio-button custom-button {style}" onclick="extraNetworksSearchButton(event)">{html.escape(subdir)}</button><br>'
+
         self.html = ''
         self.create_items(tabname)
         versions = sorted({item.get("version", "") for item in self.items if item.get("version")})

modules/ui_extra_networks_checkpoints.py

@@ -19,6 +19,8 @@ version_map = {
     "StableDiffusionXL": "SD XL",
     "WanToVideo": "Wan",
     "WanVACE": "Wan",
+    "Z": "Z-Image",
+    "Glm": "GLM-Image",
 }
 
 class ExtraNetworksPageCheckpoints(ui_extra_networks.ExtraNetworksPage):
@@ -91,7 +93,7 @@ class ExtraNetworksPageCheckpoints(ui_extra_networks.ExtraNetworksPage):
             ready = reference_downloaded(url)
             version = "ready" if ready else "download"
             if tag == 'cloud':
-                version = 'cloud'
+                version = 'Cloud'
             if not ready and shared.opts.offline_mode:
                 count['hidden'] += 1
                 continue

scripts/differential_diffusion.py

@@ -15,7 +15,7 @@ import PIL.Image
 import numpy as np
 import torch
 import torchvision
-from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
 from diffusers.image_processor import VaeImageProcessor
 from diffusers.loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin
 from diffusers.models import AutoencoderKL, UNet2DConditionModel
@@ -1059,7 +1059,7 @@ class StableDiffusionDiffImg2ImgPipeline(DiffusionPipeline):
         unet: UNet2DConditionModel,
         scheduler: KarrasDiffusionSchedulers,
         safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPFeatureExtractor,
+        feature_extractor: CLIPImageProcessor,
         requires_safety_checker: bool = False,
     ):
         super().__init__()
@@ -1353,17 +1353,6 @@ class StableDiffusionDiffImg2ImgPipeline(DiffusionPipeline):
 
         return prompt_embeds
 
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
-    def run_safety_checker(self, image, device, dtype):
-        if self.safety_checker is not None:
-            safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
-            image, has_nsfw_concept = self.safety_checker(
-                images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
-            )
-        else:
-            has_nsfw_concept = None
-        return image, has_nsfw_concept
-
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
     def decode_latents(self, latents):
         latents = 1 / self.vae.config.scaling_factor * latents