fix samplers config

DIFFUSERS.md

@@ -68,12 +68,14 @@ whats implemented so far?
 even if extensions are not supported, runtime errors are never nice  
 will need to handle in the code before we get out of alpha
 
-- controlnet
-  `sd_model.model?.diffusion_model?`
-- multi-diffusion  
-  `sd_model.first_stage_model?.encoder?`
-- lycoris
+- lycoris  
   `lyco_patch_lora`
+- controlnet
+  > sd_model.model?.diffusion_model?
+- multi-diffusion  
+  > sd_model.first_stage_model?.encoder?
+- dynamic-thresholding
+  > AttributeError: 'DiffusionSampler' object has no attribute 'model_wrap_cfg'
 
 ## Issues
 
@@ -120,6 +122,8 @@ will need to handle in the code before we get out of alpha
   > In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag.  
 - do you have plans to implement [Restart](https://github.com/vladmandic/automatic/issues/1537) sampler in diffusers?
 - `torch.nonzero()` performance issue <https://github.com/huggingface/diffusers/issues/3950>
+- `enable_sequential_cpu_offload()` results in error
+  > NotImplementedError: Cannot copy out of meta tensor; no data!
 
 ## Update
 
@@ -132,3 +136,17 @@ will need to handle in the code before we get out of alpha
 - redid samplers
 - fixed "it looks like the config file at 'xxx.safetensors' is not a valid JSON file"  
 - ui settings -> samplers is now dynamic depending if backend is original or diffusers
+
+## Performance
+
+| pipeline | performance it/s | memory cpu/gpu |
+| --- | --- | --- |
+| original | | |
+| diffusers | 8.98 / 7.44 / 8.16 / 8.41 / 7.04 | 4.3 / 9.0 |
+| diffusers with safetensors | 8.91 / 7.35 / 8.11 / 8.4 / 7.09 | 5.9 / 9.0 |
+| diffusers medvram | 7.52 / 6.72 / 7.53 / 7.84 / 7.21 | 6.6 / 8.2 |
+| diffusers lowvram | | |
+
+Notes:
+
+- Performance is measured for batch sizes 1, 2, 4, 8 16

javascript/style.css

@@ -541,7 +541,7 @@ table.settings-value-table td{
 .extra-networks-tab { padding: 0 !important; }
 .extra-network-subdirs { background: var(--input-background-fill); overflow-x: hidden; overflow-y: auto; max-height: 50vh; min-width: 80px; max-width: 120px; }
 .extra-networks-page { display: flex }
-.extra-networks .custom-button { min-width: 80px; max-width: 120px; width: 100%; background: none; justify-content: left; text-align: left; padding: 2px 8px 2px 8px; box-shadow: none; line-break: anywhere; }
+.extra-networks .custom-button { min-width: 80px; max-width: 120px; width: 100%; background: none; justify-content: left; text-align: left; padding: 2px 8px 2px 8px; box-shadow: none; line-break: auto; }
 .extra-networks .custom-button:hover { background: var(--button-primary-background-fill) }
 .extra-network-cards { display: flex; flex-wrap: wrap; height: 50vh; max-height: 50vh; overflow-y: scroll; overflow-x: hidden; width: -webkit-fill-available; }
 .extra-network-cards .card { height: fit-content; margin: 0.5em; position: relative; scroll-snap-align: start; scroll-margin-top: 0; }

modules/sd_models.py

@@ -582,28 +582,28 @@ def load_diffuser(checkpoint_info=None, already_loaded_state_dict=None, timer=No
             prior = diffusers.DiffusionPipeline.from_pretrained(prior_id, **diffusers_load_config)
             sd_model = PriorPipeline(prior=prior, main=sd_model) # wrap sd_model
 
-        if hasattr(sd_model, "enable_sequential_cpu_offload"):
-            if shared.cmd_opts.lowvram or shared.opts.diffusers_seq_cpu_offload:
-                sd_model.enable_sequential_cpu_offload()
-                shared.log.debug('Diffusers: enable sequenctial CPU offload')
         if hasattr(sd_model, "enable_model_cpu_offload"):
             if shared.cmd_opts.medvram or shared.opts.diffusers_model_cpu_offload:
                 shared.log.debug('Diffusers: enable model CPU offload')
                 sd_model.enable_model_cpu_offload()
+        if hasattr(sd_model, "enable_sequential_cpu_offload"):
+            if shared.opts.diffusers_seq_cpu_offload:
+                sd_model.enable_sequential_cpu_offload()
+                shared.log.debug('Diffusers: enable sequential CPU offload')
         if hasattr(sd_model, "enable_vae_slicing"):
-            if shared.opts.diffusers_vae_slicing:
+            if shared.cmd_opts.lowvram or shared.opts.diffusers_vae_slicing:
                 shared.log.debug('Diffusers: enable VAE slicing')
                 sd_model.enable_vae_slicing()
             else:
                 sd_model.disable_vae_slicing()
         if hasattr(sd_model, "enable_vae_tiling"):
-            if shared.opts.diffusers_vae_tiling:
+            if shared.cmd_opts.lowvram or shared.opts.diffusers_vae_tiling:
                 shared.log.debug('Diffusers: enable VAE tiling')
                 sd_model.enable_vae_tiling()
             else:
                 sd_model.disable_vae_tiling()
         if hasattr(sd_model, "enable_attention_slicing"):
-            if shared.opts.diffusers_attention_slicing:
+            if shared.cmd_opts.lowvram or shared.opts.diffusers_attention_slicing:
                 shared.log.debug('Diffusers: enable attention slicing')
                 sd_model.enable_attention_slicing()
             else:

modules/shared.py

@@ -472,11 +472,11 @@ options_templates.update(options_section(('sampler-params', "Sampler Settings"),
     "show_samplers": OptionInfo(["Euler a", "UniPC", "DEIS", "DDIM", "DPM 1S", "DPM 2M", "DPM++ 2M SDE", "DPM++ 2M SDE Karras", "DPM2 Karras", "DPM++ 2M Karras"], "Show samplers in user interface", gr.CheckboxGroup, lambda: {"choices": [x.name for x in list_samplers() if x.name != "PLMS"]}),
     "fallback_sampler": OptionInfo("Euler a", "Secondary sampler", gr.Dropdown, lambda: {"choices": ["None"] + [x.name for x in list_samplers()]}),
     "force_latent_sampler": OptionInfo("None", "Force latent upscaler sampler", gr.Dropdown, lambda: {"choices": ["None"] + [x.name for x in list_samplers()]}),
+    "always_batch_cond_uncond": OptionInfo(False, "Disable conditional batching enabled on low memory systems"),
 }))
 
 if backend == Backend.ORIGINAL:
     options_templates.update(options_section(('sampler-params', "Sampler Settings"), {
-        "always_batch_cond_uncond": OptionInfo(False, "Disable conditional batching enabled on low memory systems"),
         "enable_quantization": OptionInfo(True, "Enable samplers quantization for sharper and cleaner results"),
         "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}),