Add logs

gradio_demo.py

@@ -72,19 +72,30 @@ def stage1_process(input_image, gamma_correction):
     if torch.cuda.device_count() == 0:
         gr.Warning('Set this space to GPU config to make it work.')
         return None
+    print ("stage1_process 1")
     torch.cuda.set_device(SUPIR_device)
+    print ("stage1_process 2")
     LQ = HWC3(input_image)
+    print ("stage1_process 3")
     LQ = fix_resize(LQ, 512)
+    print ("stage1_process 4")
     # stage1
     LQ = np.array(LQ) / 255 * 2 - 1
+    print ("stage1_process 5")
     LQ = torch.tensor(LQ, dtype=torch.float32).permute(2, 0, 1).unsqueeze(0).to(SUPIR_device)[:, :3, :, :]
+    print ("stage1_process 6")
     LQ = model.batchify_denoise(LQ, is_stage1=True)
+    print ("stage1_process 7")
     LQ = (LQ[0].permute(1, 2, 0) * 127.5 + 127.5).cpu().numpy().round().clip(0, 255).astype(np.uint8)
     # gamma correction
     LQ = LQ / 255.0
+    print ("stage1_process 8")
     LQ = np.power(LQ, gamma_correction)
+    print ("stage1_process 9")
     LQ *= 255.0
+    print ("stage1_process 10")
     LQ = LQ.round().clip(0, 255).astype(np.uint8)
+    print ("stage1_process 11")
     return LQ
 
 @spaces.GPU(duration=120)
@@ -165,6 +176,7 @@ def stage2_process(input_image, prompt, a_prompt, n_prompt, num_samples, upscale
             Image.fromarray(result).save(f'./history/{event_id[:5]}/{event_id[5:]}/HQ_{i}.png')
     return [input_image] + results, event_id, 3, ''
 
+@spaces.GPU(duration=120)
 def load_and_reset(param_setting):
     if torch.cuda.device_count() == 0:
         gr.Warning('Set this space to GPU config to make it work.')
@@ -215,7 +227,7 @@ if torch.cuda.device_count() == 0:
     
     <p style="background-color: red;"><big><big><big><b>⚠️To use SUPIR, <a href="https://huggingface.co/spaces/Fabrice-TIERCELIN/SUPIR?duplicate=true">Duplicate this space</a> and set a GPU with 30 GB VRAM.</b>
     
-    You can't use SUPIR directly here because this space runs on a CPU, which not enough for SUPIR. This is a template space. Please provide feedback if you have issues.
+    You can't use SUPIR directly here because this space runs on a CPU, which is not enough for SUPIR. This is a template space. Please provide feedback if you have issues.
     </big></big></big></p>
     """
 else:
@@ -224,8 +236,8 @@ else:
     
     ⚠️SUPIR is still a research project under tested and is not yet a stable commercial product.
     
-    <a href="https://arxiv.org/abs/2401.13627">Paper</a> &emsp; <a href="http://supir.xpixel.group/">Project Page</a> &emsp; <a href="https://github.com/Fanghua-Yu/SUPIR/blob/master/assets/DemoGuide.png">How to play</a>
-    <p style="background-color: orange;">For now, only the stage 2 is working (the most important one). The stage 1 and LLaVa are failing. LLaVa is disabled.</p>
+    <a href="https://arxiv.org/abs/2401.13627">Paper</a> &emsp; <a href="http://supir.xpixel.group/">Project Page</a> &emsp; <a href="https://github.com/Fanghua-Yu/SUPIR/blob/master/assets/DemoGuide.png">How to play</a> &emsp; <a href="https://huggingface.co/blog/MonsterMMORPG/supir-sota-image-upscale-better-than-magnific-ai">Local Install Guide</a>
+    <p style="background-color: blue;">For now, only the stage 2 is working (the most important one). The stage 1 and LLaVa are failing. LLaVa is disabled.</p>
     """
 
 
@@ -243,6 +255,7 @@ The service is a research preview intended for non-commercial use only, subject
 with gr.Blocks(title='SUPIR') as interface:
     with gr.Row():
         gr.HTML(title_md)
+
     with gr.Row():
         with gr.Column():
             with gr.Row(equal_height=True):
@@ -250,11 +263,11 @@ with gr.Blocks(title='SUPIR') as interface:
                     gr.Markdown("<center>Input</center>")
                     input_image = gr.Image(type="numpy", elem_id="image-input", height=400, width=400)
                 with gr.Column():
-                    gr.Markdown("<center>Stage1 Output</center>")
+                    gr.Markdown("<center>Pre-denoising Output</center>")
                     denoise_image = gr.Image(type="numpy", elem_id="image-s1", height=400, width=400)
-            prompt = gr.Textbox(label="Prompt", value="", placeholder="A person, walking, in a town, Summer, photorealistic")
+            prompt = gr.Textbox(label="Image description", value="", placeholder="A person, walking, in a town, Summer, photorealistic")
 
-            with gr.Accordion("Stage1 options", open=False):
+            with gr.Accordion("Pre-denoising options", open=False):
                 gamma_correction = gr.Slider(label="Gamma Correction", minimum=0.1, maximum=2.0, value=1.0, step=0.1)
 
             with gr.Accordion("LLaVA options", open=False):
@@ -262,15 +275,15 @@ with gr.Blocks(title='SUPIR') as interface:
                 top_p = gr.Slider(label="Top P", info = "Percent of tokens shortlisted", minimum=0., maximum=1.0, value=0.7, step=0.1)
                 qs = gr.Textbox(label="Question", info="Describe the image and its style in a very detailed manner", placeholder="The image is a realistic photography, not an art painting.")
 
-            with gr.Accordion("Stage2 options", open=False):
+            with gr.Accordion("Restoring options", open=False):
                 num_samples = gr.Slider(label="Num Samples", info="Number of generated results; I discourage to increase because the process is limited to 2 min", minimum=1, maximum=4 if not args.use_image_slider else 1
                                         , value=1, step=1)
                 upscale = gr.Slider(label="Upscale", info="The resolution increase factor", minimum=1, maximum=8, value=1, step=1)
                 edm_steps = gr.Slider(label="Steps", info="lower=faster, higher=more details", minimum=1, maximum=200, value=default_setting.edm_steps if torch.cuda.device_count() > 0 else 1, step=1)
                 s_cfg = gr.Slider(label="Text Guidance Scale", info="lower=follow the image, higher=follow the prompt", minimum=1.0, maximum=15.0,
                                   value=default_setting.s_cfg_Quality if torch.cuda.device_count() > 0 else 1.0, step=0.1)
-                s_stage2 = gr.Slider(label="Stage2 Guidance Strength", minimum=0., maximum=1., value=1., step=0.05)
-                s_stage1 = gr.Slider(label="Stage1 Guidance Strength", minimum=-1.0, maximum=6.0, value=-1.0, step=1.0)
+                s_stage2 = gr.Slider(label="Restoring Guidance Strength", minimum=0., maximum=1., value=1., step=0.05)
+                s_stage1 = gr.Slider(label="Pre-denoising Guidance Strength", minimum=-1.0, maximum=6.0, value=-1.0, step=1.0)
                 seed = gr.Slider(label="Seed", info="-1=Different each time, other=Reproducible", minimum=-1, maximum=2147483647, step=1, randomize=True)
                 s_churn = gr.Slider(label="S-Churn", minimum=0, maximum=40, value=5, step=1)
                 s_noise = gr.Slider(label="S-Noise", minimum=1.0, maximum=1.1, value=1.003, step=0.001)
@@ -292,7 +305,7 @@ with gr.Blocks(title='SUPIR') as interface:
                         spt_linear_CFG = gr.Slider(label="CFG Start", minimum=1.0,
                                                         maximum=9.0, value=default_setting.spt_linear_CFG_Quality if torch.cuda.device_count() > 0 else 1.0, step=0.5)
                     with gr.Column():
-                        linear_s_stage2 = gr.Checkbox(label="Linear Stage2 Guidance", value=False)
+                        linear_s_stage2 = gr.Checkbox(label="Linear Restoring Guidance", value=False)
                         spt_linear_s_stage2 = gr.Slider(label="Guidance Start", minimum=0.,
                                                         maximum=1., value=0., step=0.05)
                 with gr.Row():
@@ -310,18 +323,18 @@ with gr.Blocks(title='SUPIR') as interface:
                                                 interactive=True)
 
         with gr.Column():
-            gr.Markdown("<center>Stage2 Output</center>")
+            gr.Markdown("<center>Restoring Output</center>")
             if not args.use_image_slider:
                 result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery1")
             else:
                 result_gallery = ImageSlider(label='Output', show_label=False, elem_id="gallery1")
             with gr.Row():
                 with gr.Column():
-                    denoise_button = gr.Button(value="Stage1 Run (out of work)")
+                    denoise_button = gr.Button(value="Pre-denoise (KO)")
                 with gr.Column():
-                    llave_button = gr.Button(value="LlaVa Run")
+                    llave_button = gr.Button(value="Auto-generate description (LlaVa)")
                 with gr.Column():
-                    diffusion_button = gr.Button(value="Stage2 Run (actual upscaling)", variant = "primary")
+                    diffusion_button = gr.Button(value="🚀 Restore", variant = "primary")
             with gr.Row():
                 with gr.Column():
                     param_setting = gr.Radio(["Quality", "Fidelity"], interactive=True, label="Param Setting", value="Quality")