20240205

gradio_demo.py

@@ -1,23 +1,37 @@
 import gradio as gr
+from gradio_imageslider import ImageSlider
 import argparse
 from SUPIR.util import HWC3, upscale_image, fix_resize, convert_dtype
 import numpy as np
 import torch
-if torch.cuda.device_count() >= 2:
-    use_llava = True
-else:
-    use_llava = False
 from SUPIR.util import create_SUPIR_model, load_QF_ckpt
 from PIL import Image
 from llava.llava_agent import LLavaAgent
 from CKPT_PTH import LLAVA_MODEL_PATH
 import einops
 
-SUPIR_device = 'cuda:0'
+parser = argparse.ArgumentParser()
-LLaVA_device = 'cuda:1'
+parser.add_argument("--ip", type=str, default='0.0.0.0')
+parser.add_argument("--port", type=int, default='6688')
+parser.add_argument("--no_llava", action='store_true', default=False)
+parser.add_argument("--use_image_slider", action='store_true', default=False)
+args = parser.parse_args()
+server_ip = args.ip
+server_port = args.port
+use_llava = not args.no_llava
+
+if torch.cuda.device_count() >= 2:
+    SUPIR_device = 'cuda:0'
+    LLaVA_device = 'cuda:1'
+elif torch.cuda.device_count() == 1:
+    SUPIR_device = 'cuda:0'
+    LLaVA_device = 'cuda:0'
+else:
+    raise ValueError('Currently support CUDA only.')
 
 # load SUPIR
-model = create_SUPIR_model('options/SUPIR_v0.yaml').to(SUPIR_device)
+model = create_SUPIR_model('options/SUPIR_v0.yaml', SUPIR_sign='Q').to(SUPIR_device)
+model.current_model = 'v0-Q'
 ckpt_Q, ckpt_F = load_QF_ckpt('options/SUPIR_v0.yaml')
 # load LLaVA
 if use_llava:
@@ -25,15 +39,13 @@ if use_llava:
 else:
     llava_agent = None
 
-parser = argparse.ArgumentParser()
-parser.add_argument("--ip", type=str, default='0.0.0.0')
-parser.add_argument("--port", type=int, default='6688')
-args = parser.parse_args()
-
-server_ip = args.ip
-server_port = args.port
 
 def stage1_process(input_image, gamma_correction):
+    # force to v0-Q
+    if model.current_model != 'v0-Q':
+        print('load v0-Q')
+        model.load_state_dict(ckpt_Q, strict=False)
+        model.current_model = 'v0-Q'
     LQ = HWC3(input_image)
     LQ = fix_resize(LQ, 512)
     # stage1
@@ -59,7 +71,16 @@ def llave_process(input_image, temperature, top_p, qs=None):
 
 def stage2_process(input_image, prompt, a_prompt, n_prompt, num_samples, upscale, edm_steps, s_stage1, s_stage2,
                    s_cfg, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction,
-                   linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2):
+                   linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select):
+    if model_select != model.current_model:
+        if model_select == 'v0-Q':
+            print('load v0-Q')
+            model.load_state_dict(ckpt_Q, strict=False)
+            model.current_model = 'v0-Q'
+        elif model_select == 'v0-F':
+            print('load v0-F')
+            model.load_state_dict(ckpt_F, strict=False)
+            model.current_model = 'v0-F'
     input_image = HWC3(input_image)
     input_image = upscale_image(input_image, upscale, unit_resolution=32)
 
@@ -85,17 +106,8 @@ def stage2_process(input_image, prompt, a_prompt, n_prompt, num_samples, upscale
     results = [x_samples[i] for i in range(num_samples)]
     return [input_image] + results
 
-def load_and_reset(model_select, model_info):
+def load_and_reset(param_setting):
-    _model_select = model_info.replace('<p>', '').replace('</p>', '').replace('Current Model: ', '').strip()
+    edm_steps = 50
-    if model_select != _model_select:
-        if model_select == 'v0-Q':
-            print('load v0-Q')
-            model.load_state_dict(ckpt_Q, strict=False)
-        elif model_select == 'v0-F':
-            print('load v0-F')
-            model.load_state_dict(ckpt_F, strict=False)
-        model_info = model_info.replace(_model_select, model_select)
-
     s_stage2 = 1.0
     s_stage1 = -1.0
     s_churn = 5
@@ -109,17 +121,17 @@ def load_and_reset(model_select, model_info):
     color_fix_type = 'Wavelet'
     spt_linear_CFG = 1.0
     spt_linear_s_stage2 = 0.0
-    if model_select == 'v0-Q':
+    if param_setting == "Quality":
         s_cfg = 7.5
         linear_CFG = False
         linear_s_stage2 = True
-    elif model_select == 'v0-F':
+    elif param_setting == "Fidelity":
         s_cfg = 4.0
         linear_CFG = True
         linear_s_stage2 = False
     else:
         raise NotImplementedError
-    return model_info, s_cfg, s_stage2, s_stage1, s_churn, s_noise, a_prompt, n_prompt, color_fix_type, linear_CFG, \
+    return edm_steps, s_cfg, s_stage2, s_stage1, s_churn, s_noise, a_prompt, n_prompt, color_fix_type, linear_CFG, \
         linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2
 
 block = gr.Blocks(title='SUPIR').queue()
@@ -131,7 +143,7 @@ with block:
             with gr.Row(equal_height=True):
                 with gr.Column():
                     gr.Markdown("<center>Input</center>")
-                    input_image = gr.Image(sources='upload', type="numpy", elem_id="image-input")
+                    input_image = gr.Image(type="numpy", elem_id="image-input")
                 with gr.Column():
                     gr.Markdown("<center>Stage1 Output</center>")
                     denoise_image = gr.Image(type="numpy", elem_id="image-s1")
@@ -143,7 +155,8 @@ with block:
                 top_p = gr.Slider(label="Top P", minimum=0., maximum=1.0, value=0.7, step=0.1)
                 qs = gr.Textbox(label="Question", value="Describe this image and its style in a very detailed manner.")
             with gr.Accordion("Stage2 options", open=False):
-                num_samples = gr.Slider(label="Num Samples", minimum=1, maximum=4, value=1, step=1)
+                num_samples = gr.Slider(label="Num Samples", minimum=1, maximum=4 if not args.use_image_slider else 1
+                                        , value=1, step=1)
                 upscale = gr.Slider(label="Upscale", minimum=1, maximum=8, value=1, step=1)
                 edm_steps = gr.Slider(label="Steps", minimum=20, maximum=200, value=50, step=1)
                 s_cfg = gr.Slider(label="Text Guidance Scale", minimum=1.0, maximum=15.0, value=7.5, step=0.1)
@@ -181,10 +194,16 @@ with block:
                     with gr.Column():
                         color_fix_type = gr.Radio(["None", "AdaIn", "Wavelet"], label="Color-Fix Type", value="Wavelet",
                                                   interactive=True)
+                    with gr.Column():
+                        model_select = gr.Radio(["v0-Q", "v0-F"], label="Model Selection", value="v0-Q",
+                                                interactive=True)
 
         with gr.Column():
             gr.Markdown("<center>Stage2 Output</center>")
-            result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery1", rows=2, columns=1)
+            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")
@@ -194,20 +213,20 @@ with block:
                     diffusion_button = gr.Button(value="Stage2 Run")
             with gr.Row():
                 with gr.Column():
-                    model_select = gr.Dropdown(["v0-Q", "v0-F"], interactive=True, label="Model List",
+                    param_setting = gr.Dropdown(["Quality", "Fidelity"], interactive=True, label="Param Setting",
-                                               value="v0-Q")
+                                               value="Quality")
                 with gr.Column():
-                    restart_button = gr.Button(value="Load & Reset")
+                    restart_button = gr.Button(value="Reset Param")
-                    model_info = gr.Markdown(f"Current Model: {model_select.value}")
+
 
     llave_button.click(fn=llave_process, inputs=[denoise_image, temperature, top_p, qs], outputs=[prompt])
     denoise_button.click(fn=stage1_process, inputs=[input_image, gamma_correction],
                          outputs=[denoise_image])
     stage2_ips = [input_image, prompt, a_prompt, n_prompt, num_samples, upscale, edm_steps, s_stage1, s_stage2,
                   s_cfg, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction,
-                  linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2]
+                  linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select]
     diffusion_button.click(fn=stage2_process, inputs=stage2_ips, outputs=[result_gallery])
-    restart_button.click(fn=load_and_reset, inputs=[model_select, model_info],
+    restart_button.click(fn=load_and_reset, inputs=[param_setting],
-                         outputs=[model_info, s_cfg, s_stage2, s_stage1, s_churn, s_noise, a_prompt, n_prompt,
+                         outputs=[edm_steps, s_cfg, s_stage2, s_stage1, s_churn, s_noise, a_prompt, n_prompt,
                                   color_fix_type, linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2])
 block.launch(server_name=server_ip, server_port=server_port)

requirements.txt

@@ -1,5 +1,6 @@
 fastapi==0.95.1
-gradio==3.4.0
+gradio==4.16.0
+gradio_imageslider==0.0.17
 Markdown==3.4.1
 numpy==1.24.2
 requests==2.28.2