fix k-diffusion

Signed-off-by: Vladimir Mandic <[email protected]>

modules/sd_samplers_kdiffusion.py

@@ -4,17 +4,18 @@
 from collections import deque
 import torch
 from modules import prompt_parser
-from modules import devices
+from modules import devices, errors
 from modules import sd_samplers_common
 import modules.shared as shared
 from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback
 from modules.script_callbacks import CFGDenoisedParams, cfg_denoised_callback
 from modules.script_callbacks import AfterCFGCallbackParams, cfg_after_cfg_callback
 from modules.script_callbacks import ExtraNoiseParams, extra_noise_callback
-
+from installer import install
 
 # deal with k-diffusion imports
 k_sampling = None
+install('clean-fid')
 try:
     import k_diffusion # pylint: disable=wrong-import-order
     k_sampling = k_diffusion.sampling
@@ -25,8 +26,8 @@
         import importlib
         k_diffusion = importlib.import_module('modules.k-diffusion.k_diffusion')
         k_sampling = k_diffusion.sampling
-except Exception:
-    pass
+except Exception as e:
+    errors.display(e, 'k-diffusion')
 if k_sampling is None:
     shared.log.info(f'Path search: {sys.path}')
     shared.log.error("Module not found: k-diffusion")

modules/ui_sections.py

@@ -270,7 +270,7 @@ def set_sampler_preset(preset):
             sampler_options = gr.CheckboxGroup(label='Sampler options', elem_id=f"{tabname}_sampler_options", choices=options, value=values, type='value')
         with gr.Row(elem_classes=['flex-break']):
             shared.opts.data['schedulers_sigma'] = shared.opts.data.get('schedulers_sigma', 'default')
-            sampler_algo = gr.Radio(label='Sigma algorithm', elem_id=f"{tabname}_sigma_algo", choices=['default', 'karras', 'exponential', 'polyexponential'], value=shared.opts.data.schedulers_sigma, type='value')
+            sampler_algo = gr.Dropdown(label='Sigma algorithm', elem_id=f"{tabname}_sigma_algo", choices=['default', 'karras', 'exponential', 'polyexponential'], value=shared.opts.schedulers_sigma, type='value')
         sampler_options.change(fn=set_sampler_original_options, inputs=[sampler_options, sampler_algo], outputs=[])
         sampler_algo.change(fn=set_sampler_original_options, inputs=[sampler_options, sampler_algo], outputs=[])