fix denoising strength

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

TODO.md

@@ -4,6 +4,9 @@ Main ToDo list can be found at [GitHub projects](https://github.com/users/vladma
 
 ## Future Candidates
 
+- sd35 ip-adapter
+- flux.1 ip-adapter
+- flow-match scheudlers: <https://github.com/huggingface/diffusers/issues/9607>
 - async lowvram: <https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14855>
 - fp8: <https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14031>
 - ipadapter-negative: <https://github.com/huggingface/diffusers/discussions/7167>

modules/control/run.py

@@ -66,8 +66,8 @@ def control_run(state: str = '',
                 resize_mode_before: int = 0, resize_name_before: str = 'None', resize_context_before: str = 'None', width_before: int = 512, height_before: int = 512, scale_by_before: float = 1.0, selected_scale_tab_before: int = 0,
                 resize_mode_after: int = 0, resize_name_after: str = 'None', resize_context_after: str = 'None', width_after: int = 0, height_after: int = 0, scale_by_after: float = 1.0, selected_scale_tab_after: int = 0,
                 resize_mode_mask: int = 0, resize_name_mask: str = 'None', resize_context_mask: str = 'None', width_mask: int = 0, height_mask: int = 0, scale_by_mask: float = 1.0, selected_scale_tab_mask: int = 0,
-                denoising_strength: float = 0, batch_count: int = 1, batch_size: int = 1,
-                enable_hr: bool = False, hr_sampler_index: int = None, hr_denoising_strength: float = 0.3, hr_resize_mode: int = 0, hr_resize_context: str = 'None', hr_upscaler: str = None, hr_force: bool = False, hr_second_pass_steps: int = 20,
+                denoising_strength: float = 0.0, batch_count: int = 1, batch_size: int = 1,
+                enable_hr: bool = False, hr_sampler_index: int = None, hr_denoising_strength: float = 0.0, hr_resize_mode: int = 0, hr_resize_context: str = 'None', hr_upscaler: str = None, hr_force: bool = False, hr_second_pass_steps: int = 20,
                 hr_scale: float = 1.0, hr_resize_x: int = 0, hr_resize_y: int = 0, refiner_steps: int = 5, refiner_start: float = 0.0, refiner_prompt: str = '', refiner_negative: str = '',
                 video_skip_frames: int = 0, video_type: str = 'None', video_duration: float = 2.0, video_loop: bool = False, video_pad: int = 0, video_interpolate: int = 0,
                 *input_script_args,

modules/processing_class.py

@@ -72,7 +72,7 @@ def __init__(self,
                  resize_mode: int = 0,
                  resize_name: str = 'None',
                  resize_context: str = 'None',
-                 denoising_strength: float = 0.3,
+                 denoising_strength: float = 0.0,
                  image_cfg_scale: float = None,
                  initial_noise_multiplier: float = None, # pylint: disable=unused-argument # a1111 compatibility
                  scale_by: float = 1,
@@ -100,7 +100,7 @@ def __init__(self,
                  hr_second_pass_steps: int = 0,
                  hr_resize_x: int = 0,
                  hr_resize_y: int = 0,
-                 hr_denoising_strength: float = 0.50,
+                 hr_denoising_strength: float = 0.0,
                  refiner_steps: int = 5,
                  refiner_start: float = 0,
                  refiner_prompt: str = '',

modules/processing_diffusers.py

@@ -177,16 +177,16 @@ def process_hires(p: processing.StableDiffusionProcessing, output):
             sd_hijack_hypertile.hypertile_set(p, hr=True)
 
         latent_upscale = shared.latent_upscale_modes.get(p.hr_upscaler, None)
-        if (latent_upscale is not None or p.hr_force) and getattr(p, 'hr_denoising_strength', p.denoising_strength) > 0:
+        strength = p.hr_denoising_strength if p.hr_denoising_strength > 0 else p.denoising_strength
+        if (latent_upscale is not None or p.hr_force) and strength > 0:
             p.ops.append('hires')
             sd_models_compile.openvino_recompile_model(p, hires=True, refiner=False)
             if shared.sd_model.__class__.__name__ == "OnnxRawPipeline":
                 shared.sd_model = preprocess_onnx_pipeline(p)
             p.hr_force = True
 
         # hires
-        p.denoising_strength = getattr(p, 'hr_denoising_strength', p.denoising_strength)
-        if p.hr_force and p.denoising_strength == 0:
+        if p.hr_force and strength == 0:
             shared.log.warning('HiRes skip: denoising=0')
             p.hr_force = False
         if p.hr_force:
@@ -204,9 +204,9 @@ def process_hires(p: processing.StableDiffusionProcessing, output):
                 sd_models.move_model(shared.sd_model.unet, devices.device)
             if hasattr(shared.sd_model, 'transformer'):
                 sd_models.move_model(shared.sd_model.transformer, devices.device)
-            orig_denoise = p.denoising_strength
-            p.denoising_strength = getattr(p, 'hr_denoising_strength', p.denoising_strength)
             update_sampler(p, shared.sd_model, second_pass=True)
+            orig_denoise = p.denoising_strength
+            p.denoising_strength = strength
             hires_args = set_pipeline_args(
                 p=p,
                 model=shared.sd_model,
@@ -221,7 +221,7 @@ def process_hires(p: processing.StableDiffusionProcessing, output):
                 output_type='latent',
                 clip_skip=p.clip_skip,
                 image=output.images,
-                strength=p.denoising_strength,
+                strength=strength,
                 desc='Hires',
             )
             shared.state.job = 'HiRes'