dev tool (#526)

modules/async_worker.py

@@ -47,7 +47,7 @@ def worker():
         execution_start_time = time.perf_counter()
 
         prompt, negative_prompt, style_selections, performance_selction, \
-            aspect_ratios_selction, image_number, image_seed, sharpness, adm_scaler_positive, adm_scaler_negative, guidance_scale, adaptive_cfg, sampler_name, \
+            aspect_ratios_selction, image_number, image_seed, sharpness, adm_scaler_positive, adm_scaler_negative, guidance_scale, adaptive_cfg, sampler_name, scheduler_name, \
             base_model_name, refiner_model_name, \
             l1, w1, l2, w2, l3, w3, l4, w4, l5, w5, \
             input_image_checkbox, current_tab, \
@@ -241,7 +241,7 @@ def worker():
 
                     sampler_name = 'dpmpp_fooocus_2m_sde_inpaint_seamless'
 
-        print(f'[Parameters] Sampler = {sampler_name}')
+        print(f'[Parameters] Sampler = {sampler_name} - {scheduler_name}')
 
         progressbar(1, 'Initializing ...')
 
@@ -360,6 +360,7 @@ def worker():
                     image_seed=task['task_seed'],
                     callback=callback,
                     sampler_name=sampler_name,
+                    scheduler_name=scheduler_name,
                     latent=initial_latent,
                     denoise=denoising_strength,
                     tiled=tiled,
@@ -383,6 +384,7 @@ def worker():
                         ('Base Model', base_model_name),
                         ('Refiner Model', refiner_model_name),
                         ('Sampler', sampler_name),
+                        ('Scheduler', scheduler_name),
                         ('Seed', task['task_seed'])
                     ]
                     for n, w in loras_user_raw_input:

modules/default_pipeline.py

@@ -198,7 +198,7 @@ expansion = FooocusExpansion()
 
 @torch.no_grad()
 @torch.inference_mode()
-def process_diffusion(positive_cond, negative_cond, steps, switch, width, height, image_seed, callback, sampler_name, latent=None, denoise=1.0, tiled=False, cfg_scale=7.0):
+def process_diffusion(positive_cond, negative_cond, steps, switch, width, height, image_seed, callback, sampler_name, scheduler_name, latent=None, denoise=1.0, tiled=False, cfg_scale=7.0):
     if latent is None:
         empty_latent = core.generate_empty_latent(width=width, height=height, batch_size=1)
     else:
@@ -219,7 +219,8 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
             denoise=denoise,
             callback_function=callback,
             cfg=cfg_scale,
-            sampler_name=sampler_name
+            sampler_name=sampler_name,
+            scheduler=scheduler_name
         )
     else:
         sampled_latent = core.ksampler(
@@ -232,7 +233,8 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
             denoise=denoise,
             callback_function=callback,
             cfg=cfg_scale,
-            sampler_name=sampler_name
+            sampler_name=sampler_name,
+            scheduler=scheduler_name
         )
 
     decoded_latent = core.decode_vae(vae=xl_base_patched.vae, latent_image=sampled_latent, tiled=tiled)

modules/flags.py

@@ -18,3 +18,6 @@ sampler_list = ["euler", "euler_ancestral", "heun", "dpm_2", "dpm_2_ancestral",
                 # "dpmpp_fooocus_2m_sde_inpaint_seamless"
                 ]
 default_sampler = 'dpmpp_2m_sde_gpu'
+
+scheduler_list = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform"]
+default_scheduler = "karras"