Update app.py

app.py

@@ -7,17 +7,9 @@ import numpy as np
 import cv2
 from PIL import Image
 from diffusers.utils import load_image
-from diffusers.pipelines.flux.pipeline_flux_controlnet import FluxControlNetPipeline
-from diffusers.models.controlnet_flux import FluxControlNetModel
 from diffusers.utils import export_to_gif
 import random
 
-def process_controlnet_img(image):
-    controlnet_img = np.array(image)
-    controlnet_img = cv2.Canny(controlnet_img, 100, 200)
-    controlnet_img = HWC3(controlnet_img)
-    controlnet_img = Image.fromarray(controlnet_img)
-
 # load pipelines
 base_model = "black-forest-labs/FLUX.1-schnell"
 
@@ -32,11 +24,6 @@ pipe.transformer.to(memory_format=torch.channels_last)
 clip_slider = CLIPSliderFlux(pipe, device=torch.device("cuda"))
 
 
-# controlnet_model = 'InstantX/FLUX.1-dev-Controlnet-Canny-alpha'
-# controlnet = FluxControlNetModel.from_pretrained(controlnet_model, torch_dtype=torch.bfloat16)
-# pipe_controlnet = FluxControlNetPipeline.from_pretrained(base_model, controlnet=controlnet, torch_dtype=torch.bfloat16)
-# t5_slider_controlnet = T5SliderFlux(sd_pipe=pipe_controlnet,device=torch.device("cuda"))
-
 MAX_SEED = 2**32-1
 
 def convert_to_centered_scale(num):
@@ -56,8 +43,6 @@ def convert_to_centered_scale(num):
 def generate(concept_1, concept_2, scale, prompt, randomize_seed=True, seed=42, recalc_directions=True, iterations=200, steps=4, interm_steps=9, guidance_scale=3.5,
              x_concept_1="", x_concept_2="", 
              avg_diff_x=None, 
-             img2img_type = None, img = None, 
-             controlnet_scale= None, ip_adapter_scale=None,
              total_images=[],
              progress=gr.Progress(track_tqdm=True)
              ):
@@ -65,12 +50,10 @@ def generate(concept_1, concept_2, scale, prompt, randomize_seed=True, seed=42,
     # 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 randomize_seed:
             seed = random.randint(0, MAX_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)
         avg_diff = clip_slider.find_latent_direction(slider_x[0], slider_x[1], num_iterations=iterations)
         x_concept_1, x_concept_2 = slider_x[0], slider_x[1]
 
@@ -82,7 +65,7 @@ def generate(concept_1, concept_2, scale, prompt, randomize_seed=True, seed=42,
         image = clip_slider.generate(prompt, 
                                      width=768,
                                      height=768,
-                                     #guidance_scale=guidance_scale, 
+                                     guidance_scale=guidance_scale, 
                                      scale=cur_scale,  seed=seed, num_inference_steps=steps, avg_diff=avg_diff) 
         images.append(image)
     canvas = Image.new('RGB', (256*interm_steps, 256))
@@ -100,46 +83,6 @@ def generate(concept_1, concept_2, scale, prompt, randomize_seed=True, seed=42,
     
     return x_concept_1,x_concept_2, avg_diff_x, export_to_gif(images, "clip.gif", fps=5), canvas, images, images[scale_middle], post_generation_slider_update, seed
 
-@spaces.GPU
-def update_scales(x,prompt,seed, steps, interm_steps, guidance_scale,
-                  avg_diff_x, 
-                  img2img_type = None, img = None,
-                  controlnet_scale= None, ip_adapter_scale=None, total_images=[], progress=gr.Progress(track_tqdm=True)):
-    print("Hola", x)
-    avg_diff = avg_diff_x.cuda()
-
-    # for spectrum generation
-    images = []
-
-    high_scale = x
-    low_scale = -1 * x
-    
-    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:
-        for i in range(interm_steps):
-            cur_scale = low_scale + (high_scale - low_scale) * i / (steps - 1)
-            image = clip_slider.generate(prompt, 
-                                         width=768,
-                                         height=768,
-                                         #guidance_scale=guidance_scale, 
-                                         scale=cur_scale,  seed=seed, num_inference_steps=steps, avg_diff=avg_diff) 
-            images.append(image)
-        canvas = Image.new('RGB', (256*interm_steps, 256))
-        for i, im in enumerate(images):
-            canvas.paste(im.resize((256,256)), (256 * i, 0))
-    
-    scale_total = convert_to_centered_scale(interm_steps)
-    scale_min = scale_total[0]
-    scale_max = scale_total[-1]
-    scale_middle = scale_total.index(0)
-    post_generation_slider_update = gr.update(minimum=scale_min, maximum=scale_max, visible=True)
-    
-    return export_to_gif(images, "clip.gif", fps=5), canvas, images, images[scale_middle], post_generation_slider_update
-
 def update_pre_generated_images(slider_value, total_images):
     number_images = len(total_images)
     if(number_images > 0):
@@ -151,39 +94,11 @@ def update_pre_generated_images(slider_value, total_images):
 def reset_recalc_directions():
     return True
 
-css_old = '''
-#group {
-    position: relative;
-    width: 600px; /* Increased width */
-    height: 600px; /* Increased height */
-    margin-bottom: 20px;
-    background-color: white;
-}
-#x {
-    position: absolute;
-    bottom: 20px; /* Moved further down */
-    left: 30px; /* Adjusted left margin */
-    width: 540px; /* Increased width to match the new container size */
-}
-#y {
-    position: absolute;
-    bottom: 200px; /* Increased bottom margin to ensure proper spacing from #x */
-    left: 20px; /* Adjusted left margin */
-    width: 540px; /* Increased width to match the new container size */
-    transform: rotate(-90deg);
-    transform-origin: left bottom;
-}
-#image_out {
-    position: absolute;
-    width: 80%; /* Adjust width as needed */
-    right: 10px;
-    top: 10px; /* Increased top margin to clear space occupied by #x */
-}
-'''
+
 intro = """
 <div style="display: flex;align-items: center;justify-content: center">
-    <img src="https://huggingface.co/spaces/LatentNavigation/latentnavigation-flux/resolve/main/Group 4-16.png" width="100" style="display: inline-block">
-    <h1 style="margin-left: 12px;text-align: center;margin-bottom: 7px;display: inline-block">Latent Navigation</h1>
+    <img src="https://huggingface.co/spaces/LatentNavigation/latentnavigation-flux/resolve/main/Group 4-16.png" width="120" style="display: inline-block">
+    <h1 style="margin-left: 12px;text-align: center;margin-bottom: 7px;display: inline-block;font-size:1.1em">Latent Navigation</h1>
 </div>
 <div style="display: flex;align-items: center;justify-content: center">
     <h3 style="display: inline-block;margin-left: 10px;margin-top: 6px;font-weight: 500">Exploring CLIP text space with FLUX.1 schnell 🪐</h3>
@@ -205,6 +120,8 @@ image_seq = gr.Image(label="Strip", elem_id="strip")
 output_image = gr.Image(label="Gif", elem_id="gif")
 post_generation_image = gr.Image(label="Generated Images")
 post_generation_slider = gr.Slider(minimum=-2, maximum=2, value=0, step=1, interactive=False)
+seed = gr.Slider(minimum=0, maximum=MAX_SEED, step=1, label="Seed", interactive=True, randomize=True)
+
 with gr.Blocks(css=css) as demo:
 
     gr.HTML(intro)
@@ -212,22 +129,16 @@ with gr.Blocks(css=css) as demo:
     x_concept_1 = gr.State("")
     x_concept_2 = gr.State("")
     total_images = gr.State([])
-    # y_concept_1 = gr.State("")
-    # y_concept_2 = gr.State("")
 
     avg_diff_x = gr.State()
-    #avg_diff_y = gr.State()
 
     recalc_directions = gr.State(False)
     
-    #with gr.Tab("text2image"):
     with gr.Row():
         with gr.Column():
             with gr.Row():
                 concept_1 = gr.Textbox(label="1st direction to steer", placeholder="winter")
                 concept_2 = gr.Textbox(label="2nd direction to steer", placeholder="summer")
-            #slider_x = gr.Dropdown(label="Slider concept range", allow_custom_value=True, multiselect=True, max_choices=2)
-            #slider_y = gr.Dropdown(label="Slider Y concept range", allow_custom_value=True, multiselect=True, max_choices=2)
             prompt = gr.Textbox(label="Prompt", info="Describe what you to be steered by the directions", placeholder="A dog in the park")
             x = gr.Slider(minimum=0, value=1.5, step=0.1, maximum=4.0, label="Strength", info="maximum strength on each direction (unstable beyond 2.5)")
             submit = gr.Button("Generate directions")
@@ -242,15 +153,12 @@ with gr.Blocks(css=css) as demo:
             with gr.Group(elem_id="group"):
                 post_generation_image.render()
                 post_generation_slider.render()
-              #y = gr.Slider(minimum=-10, value=0, maximum=10, elem_id="y", interactive=False)
             with gr.Row():
                 with gr.Column(scale=4, min_width=50):
                     image_seq.render()
                     
                 with gr.Column(scale=2, min_width=50):
                     output_image.render()
-            # with gr.Row():
-            #     generate_butt = gr.Button("generate")
     
     with gr.Accordion(label="advanced options", open=False):
         iterations = gr.Slider(label = "num iterations for clip directions", minimum=0, value=200, maximum=500, step=1)
@@ -264,67 +172,14 @@ with gr.Blocks(css=css) as demo:
                 value=3.5,
             )
         randomize_seed = gr.Checkbox(True, label="Randomize seed")
-        seed = gr.Slider(minimum=0, maximum=MAX_SEED, step=1, label="Seed", interactive=True, randomize=True)
-        
-       
-    # with gr.Tab(label="image2image"):
-    #     with gr.Row():
-    #         with gr.Column():
-    #             image = gr.ImageEditor(type="pil", image_mode="L", crop_size=(512, 512))
-    #             slider_x_a = gr.Dropdown(label="Slider X concept range", allow_custom_value=True, multiselect=True, max_choices=2)
-    #             slider_y_a = gr.Dropdown(label="Slider X concept range", allow_custom_value=True, multiselect=True, max_choices=2)
-    #             img2img_type = gr.Radio(["controlnet canny", "ip adapter"], label="", info="", visible=False, value="controlnet canny")
-    #             prompt_a = gr.Textbox(label="Prompt")
-    #             submit_a = gr.Button("Submit")
-    #         with gr.Column():
-    #             with gr.Group(elem_id="group"):
-    #               x_a = gr.Slider(minimum=-10, value=0, maximum=10, elem_id="x", interactive=False)
-    #               y_a = gr.Slider(minimum=-10, value=0, maximum=10, elem_id="y", interactive=False)
-    #               output_image_a = gr.Image(elem_id="image_out")
-    #             with gr.Row():
-    #                 generate_butt_a = gr.Button("generate")
-        
-    #     with gr.Accordion(label="advanced options", open=False):
-    #         iterations_a = gr.Slider(label = "num iterations", minimum=0, value=200, maximum=300)
-    #         steps_a = gr.Slider(label = "num inference steps", minimum=1, value=8, maximum=30)
-    #         guidance_scale_a = gr.Slider(
-    #                 label="Guidance scale",
-    #                 minimum=0.1,
-    #                 maximum=10.0,
-    #                 step=0.1,
-    #                 value=5,
-    #             )
-    #         controlnet_conditioning_scale = gr.Slider(
-    #                 label="controlnet conditioning scale",
-    #                 minimum=0.5,
-    #                 maximum=5.0,
-    #                 step=0.1,
-    #                 value=0.7,
-    #             )
-    #         ip_adapter_scale = gr.Slider(
-    #                 label="ip adapter scale",
-    #                 minimum=0.5,
-    #                 maximum=5.0,
-    #                 step=0.1,
-    #                 value=0.8,
-    #                 visible=False
-    #             )
-    #         seed_a  = gr.Slider(minimum=0, maximum=np.iinfo(np.int32).max, label="Seed", interactive=True, randomize=True)
-        
-    # submit.click(fn=generate,
-    #                  inputs=[slider_x, slider_y, prompt, seed, iterations, steps, guidance_scale, x_concept_1, x_concept_2, y_concept_1, y_concept_2, avg_diff_x, avg_diff_y],
-    #                  outputs=[x, y, x_concept_1, x_concept_2, y_concept_1, y_concept_2, avg_diff_x, avg_diff_y, output_image])
+        seed.render()
+     
     submit.click(fn=generate,
                      inputs=[concept_1, concept_2, x, prompt, randomize_seed, seed, recalc_directions, iterations, steps, interm_steps, guidance_scale, x_concept_1, x_concept_2, avg_diff_x, total_images],
                      outputs=[x_concept_1, x_concept_2, avg_diff_x, output_image, image_seq, total_images, post_generation_image, post_generation_slider, seed])
 
     iterations.change(fn=reset_recalc_directions, outputs=[recalc_directions])
     seed.change(fn=reset_recalc_directions, outputs=[recalc_directions])
-    #x.release(fn=update_scales, inputs=[x, prompt, seed, steps, interm_steps, guidance_scale, avg_diff_x, total_images], outputs=[output_image, image_seq, total_images, post_generation_image, post_generation_slider], trigger_mode='always_last')
-    # generate_butt_a.click(fn=update_scales, inputs=[x_a,y_a, prompt_a, seed_a, steps_a, guidance_scale_a, avg_diff_x, avg_diff_y, img2img_type, image, controlnet_conditioning_scale, ip_adapter_scale], outputs=[output_image_a])
-    # submit_a.click(fn=generate,
-    #                  inputs=[slider_x_a, slider_y_a, prompt_a, seed_a, iterations_a, steps_a, guidance_scale_a, x_concept_1, x_concept_2, y_concept_1, y_concept_2, avg_diff_x, avg_diff_y, img2img_type, image, controlnet_conditioning_scale, ip_adapter_scale],
-    #                  outputs=[x_a, y_a, x_concept_1, x_concept_2, y_concept_1, y_concept_2, avg_diff_x, avg_diff_y, output_image_a])
     post_generation_slider.change(fn=update_pre_generated_images, inputs=[post_generation_slider, total_images], outputs=[post_generation_image], queue=False, show_progress="hidden", concurrency_limit=None)
         
 if __name__ == "__main__":