Disable TGS in frontend for testing.

invokeai/app/invocations/flux_denoise.py

@@ -143,7 +143,9 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
         )
 
         # Clip the timesteps schedule based on denoising_start and denoising_end.
-        timesteps = clip_timestep_schedule(timesteps, self.denoising_start, self.denoising_end)
+        # HACK(ryand): This should be applied in the frontend.
+        denoising_start = 1.0 - (1.0 - self.denoising_start) ** 0.2
+        timesteps = clip_timestep_schedule(timesteps, denoising_start, self.denoising_end)
 
         # Prepare input latent image.
         if init_latents is not None:
@@ -193,7 +195,8 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
             traj_guidance_extension = TrajectoryGuidanceExtension(
                 init_latents=init_latents,
                 inpaint_mask=inpaint_mask,
-                trajectory_guidance_strength=self.trajectory_guidance_strength,
+                # HACK(ryand): This should not be hardcoded.
+                trajectory_guidance_strength=0,
             )
 
         with (

invokeai/backend/flux/sampling_utils.py

@@ -92,7 +92,9 @@ def clip_timestep_schedule(timesteps: list[float], denoising_start: float, denoi
     t_start_idx = _find_last_index_ge_val(timesteps, t_start_val)
     t_end_idx = _find_last_index_ge_val(timesteps, t_end_val)
 
-    clipped_timesteps = timesteps[t_start_idx : t_end_idx + 1]
+    # TODO(ryand): Make fractional cutoffs optional and apply to both the start and end.
+    clipped_timesteps = timesteps[t_start_idx + 1 : t_end_idx + 1]
+    clipped_timesteps = [t_start_val] + clipped_timesteps
 
     return clipped_timesteps
 

invokeai/frontend/web/src/features/nodes/util/graph/generation/buildFLUXGraph.ts

@@ -46,7 +46,6 @@ export const buildFLUXGraph = async (
     t5EncoderModel,
     clipEmbedModel,
     img2imgStrength,
-    optimizedDenoisingEnabled,
   } = params;
 
   assert(model, 'No model found in state');
@@ -155,12 +154,12 @@ export const buildFLUXGraph = async (
       denoisingValue,
       false
     );
-    if (optimizedDenoisingEnabled) {
-      g.updateNode(noise, {
-        denoising_start: 0,
-        trajectory_guidance_strength: denoisingValue,
-      });
-    }
+    // if (optimizedDenoisingEnabled) {
+    //   g.updateNode(noise, {
+    //     denoising_start: 0,
+    //     trajectory_guidance_strength: denoisingValue,
+    //   });
+    // }
   } else if (generationMode === 'outpaint') {
     canvasOutput = await addOutpaint(
       state,