20240205
This commit is contained in:
Fanghua-Yu
parent
12b8fa4c77
commit
377af5b01d
2 changed files with 61 additions and 41 deletions
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@ -1,23 +1,37 @@
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import gradio as gr
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from gradio_imageslider import ImageSlider
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import argparse
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from SUPIR.util import HWC3, upscale_image, fix_resize, convert_dtype
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import numpy as np
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import torch
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if torch.cuda.device_count() >= 2:
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use_llava = True
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else:
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use_llava = False
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from SUPIR.util import create_SUPIR_model, load_QF_ckpt
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from PIL import Image
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from llava.llava_agent import LLavaAgent
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from CKPT_PTH import LLAVA_MODEL_PATH
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import einops
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SUPIR_device = 'cuda:0'
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LLaVA_device = 'cuda:1'
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parser = argparse.ArgumentParser()
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parser.add_argument("--ip", type=str, default='0.0.0.0')
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parser.add_argument("--port", type=int, default='6688')
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parser.add_argument("--no_llava", action='store_true', default=False)
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parser.add_argument("--use_image_slider", action='store_true', default=False)
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args = parser.parse_args()
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server_ip = args.ip
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server_port = args.port
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use_llava = not args.no_llava
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if torch.cuda.device_count() >= 2:
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SUPIR_device = 'cuda:0'
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LLaVA_device = 'cuda:1'
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elif torch.cuda.device_count() == 1:
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SUPIR_device = 'cuda:0'
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LLaVA_device = 'cuda:0'
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else:
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raise ValueError('Currently support CUDA only.')
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# load SUPIR
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model = create_SUPIR_model('options/SUPIR_v0.yaml').to(SUPIR_device)
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model = create_SUPIR_model('options/SUPIR_v0.yaml', SUPIR_sign='Q').to(SUPIR_device)
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model.current_model = 'v0-Q'
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ckpt_Q, ckpt_F = load_QF_ckpt('options/SUPIR_v0.yaml')
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# load LLaVA
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if use_llava:
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@ -25,15 +39,13 @@ if use_llava:
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else:
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llava_agent = None
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parser = argparse.ArgumentParser()
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parser.add_argument("--ip", type=str, default='0.0.0.0')
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parser.add_argument("--port", type=int, default='6688')
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args = parser.parse_args()
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server_ip = args.ip
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server_port = args.port
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def stage1_process(input_image, gamma_correction):
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# force to v0-Q
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if model.current_model != 'v0-Q':
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print('load v0-Q')
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model.load_state_dict(ckpt_Q, strict=False)
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model.current_model = 'v0-Q'
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LQ = HWC3(input_image)
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LQ = fix_resize(LQ, 512)
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# stage1
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@ -59,7 +71,16 @@ def llave_process(input_image, temperature, top_p, qs=None):
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def stage2_process(input_image, prompt, a_prompt, n_prompt, num_samples, upscale, edm_steps, s_stage1, s_stage2,
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s_cfg, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction,
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linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2):
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linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select):
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if model_select != model.current_model:
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if model_select == 'v0-Q':
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print('load v0-Q')
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model.load_state_dict(ckpt_Q, strict=False)
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model.current_model = 'v0-Q'
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elif model_select == 'v0-F':
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print('load v0-F')
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model.load_state_dict(ckpt_F, strict=False)
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model.current_model = 'v0-F'
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input_image = HWC3(input_image)
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input_image = upscale_image(input_image, upscale, unit_resolution=32)
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@ -85,17 +106,8 @@ def stage2_process(input_image, prompt, a_prompt, n_prompt, num_samples, upscale
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results = [x_samples[i] for i in range(num_samples)]
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return [input_image] + results
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def load_and_reset(model_select, model_info):
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_model_select = model_info.replace('<p>', '').replace('</p>', '').replace('Current Model: ', '').strip()
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if model_select != _model_select:
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if model_select == 'v0-Q':
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print('load v0-Q')
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model.load_state_dict(ckpt_Q, strict=False)
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elif model_select == 'v0-F':
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print('load v0-F')
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model.load_state_dict(ckpt_F, strict=False)
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model_info = model_info.replace(_model_select, model_select)
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def load_and_reset(param_setting):
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edm_steps = 50
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s_stage2 = 1.0
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s_stage1 = -1.0
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s_churn = 5
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@ -109,17 +121,17 @@ def load_and_reset(model_select, model_info):
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color_fix_type = 'Wavelet'
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spt_linear_CFG = 1.0
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spt_linear_s_stage2 = 0.0
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if model_select == 'v0-Q':
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if param_setting == "Quality":
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s_cfg = 7.5
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linear_CFG = False
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linear_s_stage2 = True
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elif model_select == 'v0-F':
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elif param_setting == "Fidelity":
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s_cfg = 4.0
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linear_CFG = True
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linear_s_stage2 = False
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else:
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raise NotImplementedError
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return model_info, s_cfg, s_stage2, s_stage1, s_churn, s_noise, a_prompt, n_prompt, color_fix_type, linear_CFG, \
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return edm_steps, s_cfg, s_stage2, s_stage1, s_churn, s_noise, a_prompt, n_prompt, color_fix_type, linear_CFG, \
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linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2
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block = gr.Blocks(title='SUPIR').queue()
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@ -131,7 +143,7 @@ with block:
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with gr.Row(equal_height=True):
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with gr.Column():
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gr.Markdown("<center>Input</center>")
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input_image = gr.Image(sources='upload', type="numpy", elem_id="image-input")
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input_image = gr.Image(type="numpy", elem_id="image-input")
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with gr.Column():
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gr.Markdown("<center>Stage1 Output</center>")
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denoise_image = gr.Image(type="numpy", elem_id="image-s1")
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@ -143,7 +155,8 @@ with block:
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top_p = gr.Slider(label="Top P", minimum=0., maximum=1.0, value=0.7, step=0.1)
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qs = gr.Textbox(label="Question", value="Describe this image and its style in a very detailed manner.")
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with gr.Accordion("Stage2 options", open=False):
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num_samples = gr.Slider(label="Num Samples", minimum=1, maximum=4, value=1, step=1)
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num_samples = gr.Slider(label="Num Samples", minimum=1, maximum=4 if not args.use_image_slider else 1
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, value=1, step=1)
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upscale = gr.Slider(label="Upscale", minimum=1, maximum=8, value=1, step=1)
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edm_steps = gr.Slider(label="Steps", minimum=20, maximum=200, value=50, step=1)
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s_cfg = gr.Slider(label="Text Guidance Scale", minimum=1.0, maximum=15.0, value=7.5, step=0.1)
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@ -181,10 +194,16 @@ with block:
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with gr.Column():
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color_fix_type = gr.Radio(["None", "AdaIn", "Wavelet"], label="Color-Fix Type", value="Wavelet",
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interactive=True)
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with gr.Column():
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model_select = gr.Radio(["v0-Q", "v0-F"], label="Model Selection", value="v0-Q",
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interactive=True)
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with gr.Column():
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gr.Markdown("<center>Stage2 Output</center>")
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result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery1", rows=2, columns=1)
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if not args.use_image_slider:
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result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery1")
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else:
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result_gallery = ImageSlider(label='Output', show_label=False, elem_id="gallery1")
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with gr.Row():
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with gr.Column():
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denoise_button = gr.Button(value="Stage1 Run")
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@ -194,20 +213,20 @@ with block:
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diffusion_button = gr.Button(value="Stage2 Run")
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with gr.Row():
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with gr.Column():
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model_select = gr.Dropdown(["v0-Q", "v0-F"], interactive=True, label="Model List",
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value="v0-Q")
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param_setting = gr.Dropdown(["Quality", "Fidelity"], interactive=True, label="Param Setting",
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value="Quality")
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with gr.Column():
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restart_button = gr.Button(value="Load & Reset")
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model_info = gr.Markdown(f"Current Model: {model_select.value}")
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restart_button = gr.Button(value="Reset Param")
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llave_button.click(fn=llave_process, inputs=[denoise_image, temperature, top_p, qs], outputs=[prompt])
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denoise_button.click(fn=stage1_process, inputs=[input_image, gamma_correction],
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outputs=[denoise_image])
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stage2_ips = [input_image, prompt, a_prompt, n_prompt, num_samples, upscale, edm_steps, s_stage1, s_stage2,
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s_cfg, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction,
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linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2]
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linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select]
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diffusion_button.click(fn=stage2_process, inputs=stage2_ips, outputs=[result_gallery])
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restart_button.click(fn=load_and_reset, inputs=[model_select, model_info],
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outputs=[model_info, s_cfg, s_stage2, s_stage1, s_churn, s_noise, a_prompt, n_prompt,
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restart_button.click(fn=load_and_reset, inputs=[param_setting],
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outputs=[edm_steps, s_cfg, s_stage2, s_stage1, s_churn, s_noise, a_prompt, n_prompt,
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color_fix_type, linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2])
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block.launch(server_name=server_ip, server_port=server_port)
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@ -1,5 +1,6 @@
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fastapi==0.95.1
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gradio==3.4.0
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gradio==4.16.0
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gradio_imageslider==0.0.17
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Markdown==3.4.1
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numpy==1.24.2
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requests==2.28.2
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@ -35,4 +36,4 @@ tqdm==4.65.0
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triton==2.1.0
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urllib3==1.26.15
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webdataset==0.2.48
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xformers>=0.0.20
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xformers>=0.0.20
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