Update app.py

app.py

@@ -25,7 +25,6 @@ pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell",
 
 pipe.transformer.to(memory_format=torch.channels_last)
 pipe.transformer = torch.compile(pipe.transformer, mode="max-autotune", fullgraph=True)
-#pipe.enable_model_cpu_offload()
 clip_slider = CLIPSliderFlux(pipe, device=torch.device("cuda"))
 
 
@@ -47,7 +46,6 @@ def generate(slider_x, prompt, seed, recalc_directions, iterations, steps, guida
     # check if avg diff for directions need to be re-calculated
     print("slider_x", slider_x)
     print("x_concept_1", x_concept_1, "x_concept_2", x_concept_2)
-    #torch.manual_seed(seed)
     
     if not sorted(slider_x) == sorted([x_concept_1, x_concept_2]) or recalc_directions:
         #avg_diff = clip_slider.find_latent_direction(slider_x[0], slider_x[1], num_iterations=iterations).to(torch.float16)
@@ -65,8 +63,6 @@ def generate(slider_x, prompt, seed, recalc_directions, iterations, steps, guida
                                      scale=0, scale_2nd=0, 
                                      seed=seed, num_inference_steps=steps, avg_diff=avg_diff)
     
-    
-    #comma_concepts_x = ', '.join(slider_x)
     comma_concepts_x = f"{slider_x[1]}, {slider_x[0]}"
 
     avg_diff_x = avg_diff.cpu()
@@ -79,36 +75,16 @@ def update_scales(x,prompt,seed, steps, guidance_scale,
                   img2img_type = None, img = None,
                   controlnet_scale= None, ip_adapter_scale=None,):
     avg_diff = avg_diff_x.cuda()
-    torch.manual_seed(seed)
     if img2img_type=="controlnet canny" and img is not None:
         control_img = process_controlnet_img(img)
         image = t5_slider_controlnet.generate(prompt, guidance_scale=guidance_scale, image=control_img, controlnet_conditioning_scale =controlnet_scale, scale=x, seed=seed, num_inference_steps=steps, avg_diff=avg_diff) 
     elif img2img_type=="ip adapter" and img is not None:
         image = clip_slider.generate(prompt, guidance_scale=guidance_scale, ip_adapter_image=img, scale=x,seed=seed, num_inference_steps=steps, avg_diff=avg_diff) 
     else:     
-        image = clip_slider.generate(prompt, guidance_scale=guidance_scale, scale=x,  seed=seed, num_inference_steps=steps, avg_diff=avg_diff) 
-    return image
-
-
-
-@spaces.GPU
-def update_x(x,y,prompt,seed, steps, 
-             avg_diff_x, avg_diff_y,
-             img2img_type = None,
-             img = None):
-    avg_diff = avg_diff_x.cuda()
-    avg_diff_2nd = avg_diff_y.cuda()
-    image = clip_slider.generate(prompt, scale=x, scale_2nd=y, seed=seed, num_inference_steps=steps, avg_diff=avg_diff,avg_diff_2nd=avg_diff_2nd) 
-    return image
-
-@spaces.GPU
-def update_y(x,y,prompt,seed, steps, 
-             avg_diff_x, avg_diff_y,
-             img2img_type = None,
-             img = None):
-    avg_diff = avg_diff_x.cuda()
-    avg_diff_2nd = avg_diff_y.cuda()
-    image = clip_slider.generate(prompt, scale=x, scale_2nd=y, seed=seed, num_inference_steps=steps, avg_diff=avg_diff,avg_diff_2nd=avg_diff_2nd) 
+        image = clip_slider.generate(prompt, 
+                                     #guidance_scale=guidance_scale, 
+                                     scale=x,  
+                                     seed=seed, num_inference_steps=steps, avg_diff=avg_diff) 
     return image
 
 def reset_recalc_directions():