update samplers

modules/sd_models.py

@@ -10,13 +10,12 @@ from rich import print, progress # pylint: disable=redefined-builtin
 import torch
 import safetensors.torch
 from omegaconf import OmegaConf
+import tomesd
 import ldm.modules.midas as midas
 from ldm.util import instantiate_from_config
-
 from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config
 from modules.sd_hijack_inpainting import do_inpainting_hijack
 from modules.timer import Timer
-import tomesd
 
 
 model_dir = "Stable-diffusion"
@@ -231,7 +230,7 @@ def read_metadata_from_safetensors(filename):
         return res
 
 
-def read_state_dict(checkpoint_file):
+def read_state_dict(checkpoint_file, map_location=None): # pylint: disable=unused-argument
     try:
         with progress.open(checkpoint_file, 'rb', description=f'Loading weights: [cyan]{checkpoint_file}', auto_refresh=True) as f:
             _, extension = os.path.splitext(checkpoint_file)

modules/shared.py

@@ -327,7 +327,7 @@ options_templates.update(options_section(('cuda', "CUDA Settings"), {
     "no_half_vae": OptionInfo(False, "Use full precision for VAE (--no-half-vae)"),
     "upcast_sampling": OptionInfo(False, "Enable upcast sampling. Usually produces similar results to --no-half with better performance while using less memory"),
     "disable_nan_check": OptionInfo(True, "Do not check if produced images/latent spaces have NaN values"),
-    "rollback_vae": OptionInfo(False, "Attempt to roll back VAE when produced NaN values, requires NaN check"),
+    "rollback_vae": OptionInfo(False, "Attempt to roll back VAE when produced NaN values, requires NaN check (experimental)"),
     "opt_channelslast": OptionInfo(False, "Use channels last as torch memory format "),
     "cudnn_benchmark": OptionInfo(False, "Enable cuDNN benchmark feature"),
     "cuda_allow_tf32": OptionInfo(True, "Allow TF32 math ops"),
@@ -429,14 +429,14 @@ options_templates.update(options_section(('ui', "Live previews"), {
 }))
 
 options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
-    "show_samplers": OptionInfo(["Euler", "LMS", "Heun", "DPM2", "DPM2 a", "DPM++ 2M", "DPM fast", "DPM adaptive", "DPM++ 2S a Karras", "DPM++ 2S a", "DPM++ SDE Karras", "DPM2 a Karras", "LMS Karras"], "Show samplers in user interface", gr.CheckboxGroup, lambda: {"choices": [x.name for x in list_samplers()]}),
-    "eta_ddim": OptionInfo(0.0, "eta (noise multiplier) for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-    "eta_ancestral": OptionInfo(1.0, "eta (noise multiplier) for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-    "ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform', 'quad']}),
+    "show_samplers": OptionInfo(["Euler a", "UniPC", "DDIM", "DPM++ SDE", "DPM++ SDE", "DPM2 Karras", "DPM++ 2M Karras"], "Show samplers in user interface", gr.CheckboxGroup, lambda: {"choices": [x.name for x in list_samplers()]}),
+    "eta_ancestral": OptionInfo(1.0, "Noise multiplier for ancestral samplers (eta)", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+    "eta_ddim": OptionInfo(0.0, "Noise multiplier for DDIM (eta)", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+    "ddim_discretize": OptionInfo('uniform', "DDIM discretize img2img", gr.Radio, {"choices": ['uniform', 'quad']}),
     's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
     's_tmin':  OptionInfo(0.0, "sigma tmin",  gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
     's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-    'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}),
+    'eta_noise_seed_delta': OptionInfo(0, "Noise seed delta (eta)", gr.Number, {"precision": 0}),
     'always_discard_next_to_last_sigma': OptionInfo(False, "Always discard next-to-last sigma"),
     'uni_pc_variant': OptionInfo("bh1", "UniPC variant", gr.Radio, {"choices": ["bh1", "bh2", "vary_coeff"]}),
     'uni_pc_skip_type': OptionInfo("time_uniform", "UniPC skip type", gr.Radio, {"choices": ["time_uniform", "time_quadratic", "logSNR"]}),

modules/ui.py

@@ -1514,10 +1514,6 @@ def create_ui():
                 with gr.TabItem(label, id=ifid, elem_id='tab_' + ifid):
                     interface.render()
 
-        footer = shared.html("footer.html")
-        footer = footer.format(versions=versions_html())
-        gr.HTML(footer, elem_id="footer")
-
         text_settings = gr.Textbox(elem_id="settings_json", value=lambda: opts.dumpjson(), visible=False)
         settings_submit.click(
             fn=wrap_gradio_call(run_settings, extra_outputs=[gr.update()]),

setup.py

@@ -217,7 +217,7 @@ def check_torch():
         log.debug(f'Cannot install xformers package: {e}')
     try:
         tensorflow_package = os.environ.get('TENSORFLOW_PACKAGE', 'tensorflow==2.12.0')
-        install(tensorflow_package, ignore=True)
+        install(tensorflow_package, 'tensorflow', ignore=True)
     except Exception as e:
         log.debug(f'Cannot install tensorflow package: {e}')
 

webui.py

@@ -66,11 +66,11 @@ else:
 
 def check_rollback_vae():
     if shared.cmd_opts.rollback_vae:
-        if version.parse(torch.__version__) < version.parse('2.1'):
-            print("If your PyTorch version is lower than PyTorch 2.1, Rollback VAE will not work.")
+        if not torch.__version__.startswith('2.1'):
+            print("Rollback VAE functionality requires Torch 2.1 or higher")
             shared.cmd_opts.rollback_vae = False
-        elif 0 < torch.cuda.get_device_capability()[0] < 8:
-            print('Rollback VAE will not work because your device does not support it.')
+        if 0 < torch.cuda.get_device_capability()[0] < 8:
+            print('Rollback VAE functionality device capabilities not met')
             shared.cmd_opts.rollback_vae = False