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README.md

@@ -1,21 +1,13 @@
 ---
-title: SUPIR Image Upscaler
+title: STAR
+emoji: 🌟
+colorFrom: purple
+colorTo: red
 sdk: gradio
-emoji: 📷
-sdk_version: 4.38.1
+sdk_version: 5.0.1
 app_file: app.py
-license: mit
-colorFrom: blue
-colorTo: pink
-tags:
-  - Upscaling
-  - Restoring
-  - Image-to-Image
-  - Image-2-Image
-  - Img-to-Img
-  - Img-2-Img
-  - language models
-  - LLMs
-short_description: Restore blurred or small images with prompt
-suggested_hardware: zero-a10g
----
+pinned: false
+short_description: Video Super-Resolution with Text-to-Video Model
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,909 +1,73 @@
+import spaces
 import os
 import gradio as gr
-import argparse
-import numpy as np
-import torch
-import einops
-import copy
-import math
-import time
-import random
-import spaces
-import re
-import uuid
-
-from gradio_imageslider import ImageSlider
-from PIL import Image
-from SUPIR.util import HWC3, upscale_image, fix_resize, convert_dtype, create_SUPIR_model, load_QF_ckpt
+from video_super_resolution.scripts.inference_sr import STAR_sr
 from huggingface_hub import hf_hub_download
-from pillow_heif import register_heif_opener
-
-register_heif_opener()
-
-max_64_bit_int = np.iinfo(np.int32).max
-
-hf_hub_download(repo_id="laion/CLIP-ViT-bigG-14-laion2B-39B-b160k", filename="open_clip_pytorch_model.bin", local_dir="laion_CLIP-ViT-bigG-14-laion2B-39B-b160k")
-hf_hub_download(repo_id="camenduru/SUPIR", filename="sd_xl_base_1.0_0.9vae.safetensors", local_dir="yushan777_SUPIR")
-hf_hub_download(repo_id="camenduru/SUPIR", filename="SUPIR-v0F.ckpt", local_dir="yushan777_SUPIR")
-hf_hub_download(repo_id="camenduru/SUPIR", filename="SUPIR-v0Q.ckpt", local_dir="yushan777_SUPIR")
-hf_hub_download(repo_id="RunDiffusion/Juggernaut-XL-Lightning", filename="Juggernaut_RunDiffusionPhoto2_Lightning_4Steps.safetensors", local_dir="RunDiffusion_Juggernaut-XL-Lightning")
-
-parser = argparse.ArgumentParser()
-parser.add_argument("--opt", type=str, default='options/SUPIR_v0.yaml')
-parser.add_argument("--ip", type=str, default='127.0.0.1')
-parser.add_argument("--port", type=int, default='6688')
-parser.add_argument("--no_llava", action='store_true', default=True)#False
-parser.add_argument("--use_image_slider", action='store_true', default=False)#False
-parser.add_argument("--log_history", action='store_true', default=False)
-parser.add_argument("--loading_half_params", action='store_true', default=False)#False
-parser.add_argument("--use_tile_vae", action='store_true', default=True)#False
-parser.add_argument("--encoder_tile_size", type=int, default=512)
-parser.add_argument("--decoder_tile_size", type=int, default=64)
-parser.add_argument("--load_8bit_llava", action='store_true', default=False)
-args = parser.parse_args()
-
-if torch.cuda.device_count() > 0:
-    SUPIR_device = 'cuda:0'
-
-    # Load SUPIR
-    model, default_setting = create_SUPIR_model(args.opt, SUPIR_sign='Q', load_default_setting=True)
-    if args.loading_half_params:
-        model = model.half()
-    if args.use_tile_vae:
-        model.init_tile_vae(encoder_tile_size=args.encoder_tile_size, decoder_tile_size=args.decoder_tile_size)
-    model = model.to(SUPIR_device)
-    model.first_stage_model.denoise_encoder_s1 = copy.deepcopy(model.first_stage_model.denoise_encoder)
-    model.current_model = 'v0-Q'
-    ckpt_Q, ckpt_F = load_QF_ckpt(args.opt)
-
-def check_upload(input_image):
-    if input_image is None:
-        raise gr.Error("Please provide an image to restore.")
-    return gr.update(visible = True)
-
-def update_seed(is_randomize_seed, seed):
-    if is_randomize_seed:
-        return random.randint(0, max_64_bit_int)
-    return seed
-
-def reset():
-    return [
-        None,
-        0,
-        None,
-        None,
-        "Cinematic, High Contrast, highly detailed, taken using a Canon EOS R camera, hyper detailed photo - realistic maximum detail, 32k, Color Grading, ultra HD, extreme meticulous detailing, skin pore detailing, hyper sharpness, perfect without deformations.",
-        "painting, oil painting, illustration, drawing, art, sketch, anime, cartoon, CG Style, 3D render, unreal engine, blurring, aliasing, pixel, unsharp, weird textures, ugly, dirty, messy, worst quality, low quality, frames, watermark, signature, jpeg artifacts, deformed, lowres, over-smooth",
-        1,
-        1024,
-        1,
-        2,
-        50,
-        -1.0,
-        1.,
-        default_setting.s_cfg_Quality if torch.cuda.device_count() > 0 else 1.0,
-        True,
-        random.randint(0, max_64_bit_int),
-        5,
-        1.003,
-        "Wavelet",
-        "fp32",
-        "fp32",
-        1.0,
-        True,
-        False,
-        default_setting.spt_linear_CFG_Quality if torch.cuda.device_count() > 0 else 1.0,
-        0.,
-        "v0-Q",
-        "input",
-        6
-    ]
-
-def check_and_update(input_image):
-    if input_image is None:
-        raise gr.Error("Please provide an image to restore.")
-    return gr.update(visible = True)
-
-@spaces.GPU(duration=420)
-def stage1_process(
-    input_image,
-    gamma_correction,
-    diff_dtype,
-    ae_dtype
-):
-    print('stage1_process ==>>')
-    if torch.cuda.device_count() == 0:
-        gr.Warning('Set this space to GPU config to make it work.')
-        return None, None
-    torch.cuda.set_device(SUPIR_device)
-    LQ = HWC3(np.array(Image.open(input_image)))
-    LQ = fix_resize(LQ, 512)
-    # stage1
-    LQ = np.array(LQ) / 255 * 2 - 1
-    LQ = torch.tensor(LQ, dtype=torch.float32).permute(2, 0, 1).unsqueeze(0).to(SUPIR_device)[:, :3, :, :]
-
-    model.ae_dtype = convert_dtype(ae_dtype)
-    model.model.dtype = convert_dtype(diff_dtype)
-
-    LQ = model.batchify_denoise(LQ, is_stage1=True)
-    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
-    LQ = np.power(LQ, gamma_correction)
-    LQ *= 255.0
-    LQ = LQ.round().clip(0, 255).astype(np.uint8)
-    print('<<== stage1_process')
-    return LQ, gr.update(visible = True)
-
-def stage2_process(*args, **kwargs):
-    try:
-        return restore_in_Xmin(*args, **kwargs)
-    except Exception as e:
-        # NO_GPU_MESSAGE_INQUEUE
-        print("gradio.exceptions.Error 'No GPU is currently available for you after 60s'")
-        print('str(type(e)): ' + str(type(e))) # <class 'gradio.exceptions.Error'>
-        print('str(e): ' + str(e)) # You have exceeded your GPU quota...
-        try:
-            print('e.message: ' + e.message) # No GPU is currently available for you after 60s
-        except Exception as e2:
-            print('Failure')
-        if str(e).startswith("No GPU is currently available for you after 60s"):
-            print('Exception identified!!!')
-        #if str(type(e)) == "<class 'gradio.exceptions.Error'>":
-            #print('Exception of name ' + type(e).__name__)
-        raise e
-
-def restore_in_Xmin(
-    noisy_image,
-    rotation,
-    denoise_image,
-    prompt,
-    a_prompt,
-    n_prompt,
-    num_samples,
-    min_size,
-    downscale,
-    upscale,
-    edm_steps,
-    s_stage1,
-    s_stage2,
-    s_cfg,
-    randomize_seed,
-    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,
-    model_select,
-    output_format,
-    allocation
-):
-    print("noisy_image:\n" + str(noisy_image))
-    print("denoise_image:\n" + str(denoise_image))
-    print("rotation: " + str(rotation))
-    print("prompt: " + str(prompt))
-    print("a_prompt: " + str(a_prompt))
-    print("n_prompt: " + str(n_prompt))
-    print("num_samples: " + str(num_samples))
-    print("min_size: " + str(min_size))
-    print("downscale: " + str(downscale))
-    print("upscale: " + str(upscale))
-    print("edm_steps: " + str(edm_steps))
-    print("s_stage1: " + str(s_stage1))
-    print("s_stage2: " + str(s_stage2))
-    print("s_cfg: " + str(s_cfg))
-    print("randomize_seed: " + str(randomize_seed))
-    print("seed: " + str(seed))
-    print("s_churn: " + str(s_churn))
-    print("s_noise: " + str(s_noise))
-    print("color_fix_type: " + str(color_fix_type))
-    print("diff_dtype: " + str(diff_dtype))
-    print("ae_dtype: " + str(ae_dtype))
-    print("gamma_correction: " + str(gamma_correction))
-    print("linear_CFG: " + str(linear_CFG))
-    print("linear_s_stage2: " + str(linear_s_stage2))
-    print("spt_linear_CFG: " + str(spt_linear_CFG))
-    print("spt_linear_s_stage2: " + str(spt_linear_s_stage2))
-    print("model_select: " + str(model_select))
-    print("GPU time allocation: " + str(allocation) + " min")
-    print("output_format: " + str(output_format))
-
-    input_format = re.sub(r"^.*\.([^\.]+)$", r"\1", noisy_image)
-
-    if input_format not in ['png', 'webp', 'jpg', 'jpeg', 'gif', 'bmp', 'heic']:
-        gr.Warning('Invalid image format. Please first convert into *.png, *.webp, *.jpg, *.jpeg, *.gif, *.bmp or *.heic.')
-        return None, None, None, None
-
-    if output_format == "input":
-        if noisy_image is None:
-            output_format = "png"
-        else:
-            output_format = input_format
-    print("final output_format: " + str(output_format))
 
-    if prompt is None:
-        prompt = ""
-
-    if a_prompt is None:
-        a_prompt = ""
-
-    if n_prompt is None:
-        n_prompt = ""
-
-    if prompt != "" and a_prompt != "":
-        a_prompt = prompt + ", " + a_prompt
-    else:
-        a_prompt = prompt + a_prompt
-    print("Final prompt: " + str(a_prompt))
-
-    denoise_image = np.array(Image.open(noisy_image if denoise_image is None else denoise_image))
-
-    if rotation == 90:
-        denoise_image = np.array(list(zip(*denoise_image[::-1])))
-    elif rotation == 180:
-        denoise_image = np.array(list(zip(*denoise_image[::-1])))
-        denoise_image = np.array(list(zip(*denoise_image[::-1])))
-    elif rotation == -90:
-        denoise_image = np.array(list(zip(*denoise_image))[::-1])
-
-    if 1 < downscale:
-        input_height, input_width, input_channel = denoise_image.shape
-        denoise_image = np.array(Image.fromarray(denoise_image).resize((input_width // downscale, input_height // downscale), Image.LANCZOS))
-
-    denoise_image = HWC3(denoise_image)
-
-    if torch.cuda.device_count() == 0:
-        gr.Warning('Set this space to GPU config to make it work.')
-        return [noisy_image, denoise_image], gr.update(label="Downloadable results in *." + output_format + " format", format = output_format, value = [denoise_image]), None, gr.update(visible=True)
-
-    if model_select != model.current_model:
-        print('load ' + model_select)
-        if model_select == 'v0-Q':
-            model.load_state_dict(ckpt_Q, strict=False)
-        elif model_select == 'v0-F':
-            model.load_state_dict(ckpt_F, strict=False)
-        model.current_model = model_select
+hf_hub_download(repo_id="SherryX/STAR", filename="I2VGen-XL-based/heavy_deg.pt", local_dir="pretrained_weight")
+
+# Example video and prompt pairs
+examples = [
+    ["star_examples/023_klingai_reedit.mp4", "The video shows a panda strumming a guitar on a rock by a tranquil lake at sunset. With its black-and-white fur, the panda sits against a backdrop of mountains and a vibrant sky painted in orange and pink hues. The serene scene highlights relaxation and whimsy, with the panda, guitar, and lake harmoniously positioned. The natural landscape's depth and perspective enhance the focus on the panda's peaceful interaction with the guitar.", 2, 24, 250],
+    ["star_examples/017_klingai_reedit.mp4", "The video depicts a majestic lion with eagle-like wings standing on a grassy hill against rolling green hills and a clear sky. The lion’s golden mane contrasts with the warm hues of the scene, and its intense gaze draws focus. The detailed, fully spread wings add a fantastical element. A 'PremiumBeat' watermark appears in the lower right, hinting at the image's source. The style blends realism with fantasy, showcasing the lion's mythical nature.", 4, 24, 250],
+    ["star_examples/016_video.mp4", "The video is a black-and-white silent film featuring two men in wheelchairs on a pier. The foreground man, in a suit and hat, holds a sign reading 'HELP CRIPPLE.' The background shows a building and a boat, with early 20th-century clothing and image quality suggesting a narrative of disability and assistance.", 4, 24, 300],
+]
+
+# Define a GPU-decorated function for enhancement
+@spaces.GPU(duration=180)
+def enhance_with_gpu(input_video, input_text, upscale, max_chunk_len, chunk_size):
+    """在每次调用时创建新的 STAR_sr 实例，确保参数正确传递"""
+    star = STAR_sr(
+        result_dir="./results/",
+        upscale=upscale,
+        max_chunk_len=max_chunk_len,
+        chunk_size=chunk_size
+    )
+    return star.enhance_a_video(input_video, input_text)
 
-    model.ae_dtype = convert_dtype(ae_dtype)
-    model.model.dtype = convert_dtype(diff_dtype)
+def star_demo(result_dir="./tmp/"):
+    css = """#input_video {max-width: 1024px !important} #output_vid {max-width: 2048px; max-height:1280px}"""
 
-    # Allocation
-    if allocation == 1:
-        return restore_in_1min(
-            noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, 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, model_select, output_format, allocation
-        )
-    if allocation == 2:
-        return restore_in_2min(
-            noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, 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, model_select, output_format, allocation
+    with gr.Blocks(analytics_enabled=False, css=css) as star_iface:
+        gr.Markdown(
+            "<div align='center'> <h1> STAR: Spatial-Temporal Augmentation with Text-to-Video Models for Real-World Video Super-Resolution </span> </h1> \
+                     <a style='font-size:18px;color: #000000' href='https://arxiv.org/abs/2501.02976'> [ArXiv] </a>\
+                     <a style='font-size:18px;color: #000000' href='https://nju-pcalab.github.io/projects/STAR'> [Project Page] </a> \
+                     <a style='font-size:18px;color: #000000' href='https://github.com/NJU-PCALab/STAR'> [Github] </a> </div>"
         )
-    if allocation == 3:
-        return restore_in_3min(
-            noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, 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, model_select, output_format, allocation
-        )
-    if allocation == 4:
-        return restore_in_4min(
-            noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, 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, model_select, output_format, allocation
-        )
-    if allocation == 5:
-        return restore_in_5min(
-            noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, 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, model_select, output_format, allocation
-        )
-    if allocation == 7:
-        return restore_in_7min(
-            noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, 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, model_select, output_format, allocation
-        )
-    if allocation == 8:
-        return restore_in_8min(
-            noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, 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, model_select, output_format, allocation
-        )
-    if allocation == 9:
-        return restore_in_9min(
-            noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, 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, model_select, output_format, allocation
-        )
-    if allocation == 10:
-        return restore_in_10min(
-            noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, 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, model_select, output_format, allocation
-        )
-    else:
-        return restore_in_6min(
-            noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, 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, model_select, output_format, allocation
-        )
-
-@spaces.GPU(duration=59)
-def restore_in_1min(*args, **kwargs):
-    return restore_on_gpu(*args, **kwargs)
-
-@spaces.GPU(duration=119)
-def restore_in_2min(*args, **kwargs):
-    return restore_on_gpu(*args, **kwargs)
-
-@spaces.GPU(duration=179)
-def restore_in_3min(*args, **kwargs):
-    return restore_on_gpu(*args, **kwargs)
-
-@spaces.GPU(duration=239)
-def restore_in_4min(*args, **kwargs):
-    return restore_on_gpu(*args, **kwargs)
-
-@spaces.GPU(duration=299)
-def restore_in_5min(*args, **kwargs):
-    return restore_on_gpu(*args, **kwargs)
-
-@spaces.GPU(duration=359)
-def restore_in_6min(*args, **kwargs):
-    return restore_on_gpu(*args, **kwargs)
-
-@spaces.GPU(duration=419)
-def restore_in_7min(*args, **kwargs):
-    return restore_on_gpu(*args, **kwargs)
-
-@spaces.GPU(duration=479)
-def restore_in_8min(*args, **kwargs):
-    return restore_on_gpu(*args, **kwargs)
-
-@spaces.GPU(duration=539)
-def restore_in_9min(*args, **kwargs):
-    return restore_on_gpu(*args, **kwargs)
-
-@spaces.GPU(duration=599)
-def restore_in_10min(*args, **kwargs):
-    return restore_on_gpu(*args, **kwargs)
-
-def restore_on_gpu(
-    noisy_image,
-    input_image,
-    prompt,
-    a_prompt,
-    n_prompt,
-    num_samples,
-    min_size,
-    downscale,
-    upscale,
-    edm_steps,
-    s_stage1,
-    s_stage2,
-    s_cfg,
-    randomize_seed,
-    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,
-    model_select,
-    output_format,
-    allocation
-):
-    start = time.time()
-    print('restore ==>>')
-
-    torch.cuda.set_device(SUPIR_device)
-
-    with torch.no_grad():
-        input_image = upscale_image(input_image, upscale, unit_resolution=32, min_size=min_size)
-        LQ = np.array(input_image) / 255.0
-        LQ = np.power(LQ, gamma_correction)
-        LQ *= 255.0
-        LQ = LQ.round().clip(0, 255).astype(np.uint8)
-        LQ = LQ / 255 * 2 - 1
-        LQ = torch.tensor(LQ, dtype=torch.float32).permute(2, 0, 1).unsqueeze(0).to(SUPIR_device)[:, :3, :, :]
-        captions = ['']
-
-        samples = model.batchify_sample(LQ, captions, num_steps=edm_steps, restoration_scale=s_stage1, s_churn=s_churn,
-                                    s_noise=s_noise, cfg_scale=s_cfg, control_scale=s_stage2, seed=seed,
-                                    num_samples=num_samples, p_p=a_prompt, n_p=n_prompt, color_fix_type=color_fix_type,
-                                    use_linear_CFG=linear_CFG, use_linear_control_scale=linear_s_stage2,
-                                    cfg_scale_start=spt_linear_CFG, control_scale_start=spt_linear_s_stage2)
-
-        x_samples = (einops.rearrange(samples, 'b c h w -> b h w c') * 127.5 + 127.5).cpu().numpy().round().clip(
-            0, 255).astype(np.uint8)
-        results = [x_samples[i] for i in range(num_samples)]
-    torch.cuda.empty_cache()
-
-    # All the results have the same size
-    input_height, input_width, input_channel = np.array(input_image).shape
-    result_height, result_width, result_channel = np.array(results[0]).shape
-
-    print('<<== restore')
-    end = time.time()
-    secondes = int(end - start)
-    minutes = math.floor(secondes / 60)
-    secondes = secondes - (minutes * 60)
-    hours = math.floor(minutes / 60)
-    minutes = minutes - (hours * 60)
-    information = ("Start the process again if you want a different result. " if randomize_seed else "") + \
-    "If you don't get the image you wanted, add more details in the « Image description ». " + \
-    "Wait " + str(allocation) + " min before a new run to avoid quota penalty or use another computer. " + \
-    "The image" + (" has" if len(results) == 1 else "s have") + " been generated in " + \
-    ((str(hours) + " h, ") if hours != 0 else "") + \
-    ((str(minutes) + " min, ") if hours != 0 or minutes != 0 else "") + \
-    str(secondes) + " sec. " + \
-    "The new image resolution is " + str(result_width) + \
-    " pixels large and " + str(result_height) + \
-    " pixels high, so a resolution of " + f'{result_width * result_height:,}' + " pixels."
-    print(information)
-    try:
-        print("Initial resolution: " + f'{input_width * input_height:,}')
-        print("Final resolution: " + f'{result_width * result_height:,}')
-        print("edm_steps: " + str(edm_steps))
-        print("num_samples: " + str(num_samples))
-        print("downscale: " + str(downscale))
-        print("Estimated minutes: " + f'{(((result_width * result_height**(1/1.75)) * input_width * input_height * (edm_steps**(1/2)) * (num_samples**(1/2.5)))**(1/2.5)) / 25000:,}')
-    except Exception as e:
-        print('Exception of Estimation')
-
-    # Only one image can be shown in the slider
-    return [noisy_image] + [results[0]], gr.update(label="Downloadable results in *." + output_format + " format", format = output_format, value = results), gr.update(value = information, visible = True), gr.update(visible=True)
-
-def load_and_reset(param_setting):
-    print('load_and_reset ==>>')
-    if torch.cuda.device_count() == 0:
-        gr.Warning('Set this space to GPU config to make it work.')
-        return None, None, None, None, None, None, None, None, None, None, None, None, None, None
-    edm_steps = default_setting.edm_steps
-    s_stage2 = 1.0
-    s_stage1 = -1.0
-    s_churn = 5
-    s_noise = 1.003
-    a_prompt = 'Cinematic, High Contrast, highly detailed, taken using a Canon EOS R camera, hyper detailed photo - ' \
-               'realistic maximum detail, 32k, Color Grading, ultra HD, extreme meticulous detailing, skin pore ' \
-               'detailing, hyper sharpness, perfect without deformations.'
-    n_prompt = 'painting, oil painting, illustration, drawing, art, sketch, anime, cartoon, CG Style, ' \
-               '3D render, unreal engine, blurring, dirty, messy, worst quality, low quality, frames, watermark, ' \
-               'signature, jpeg artifacts, deformed, lowres, over-smooth'
-    color_fix_type = 'Wavelet'
-    spt_linear_s_stage2 = 0.0
-    linear_s_stage2 = False
-    linear_CFG = True
-    if param_setting == "Quality":
-        s_cfg = default_setting.s_cfg_Quality
-        spt_linear_CFG = default_setting.spt_linear_CFG_Quality
-        model_select = "v0-Q"
-    elif param_setting == "Fidelity":
-        s_cfg = default_setting.s_cfg_Fidelity
-        spt_linear_CFG = default_setting.spt_linear_CFG_Fidelity
-        model_select = "v0-F"
-    else:
-        raise NotImplementedError
-    gr.Info('The parameters are reset.')
-    print('<<== load_and_reset')
-    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, model_select
-
-def log_information(result_gallery):
-    print('log_information')
-    if result_gallery is not None:
-        for i, result in enumerate(result_gallery):
-            print(result[0])
-
-def on_select_result(result_slider, result_gallery, evt: gr.SelectData):
-    print('on_select_result')
-    if result_gallery is not None:
-        for i, result in enumerate(result_gallery):
-            print(result[0])
-    return [result_slider[0], result_gallery[evt.index][0]]
-
-title_html = """
-    <h1><center>SUPIR</center></h1>
-    <big><center>Upscale your images freely, without account, without watermark and download it</center></big>
-    <center><big><big>��<big><big><big><big><big><big>🤸</big></big></big></big></big></big></big></big></center>
-    
-    <p>This is an online demo of SUPIR, a practicing model scaling for photo-realistic image restoration.
-    The content added by SUPIR is <b><u>imagination, not real-world information</u></b>.
-    SUPIR is for beauty and illustration only.
-    Most of the processes last few minutes.
-    If you want to upscale AI-generated images, be noticed that <i>PixArt Sigma</i> space can directly generate 5984x5984 images.
-    Due to Gradio issues, the generated image is slightly less satured than the original.
-    Please leave a <a href="https://huggingface.co/spaces/Fabrice-TIERCELIN/SUPIR/discussions/new">message in discussion</a> if you encounter issues.
-    You can also use <a href="https://huggingface.co/spaces/gokaygokay/AuraSR">AuraSR</a> to upscale x4.
-    
-    <p><center><a href="https://arxiv.org/abs/2401.13627">Paper</a> &emsp; <a href="http://supir.xpixel.group/">Project Page</a> &emsp; <a href="https://huggingface.co/blog/MonsterMMORPG/supir-sota-image-upscale-better-than-magnific-ai">Local Install Guide</a></center></p>
-    <p><center><a style="display:inline-block" href='https://github.com/Fanghua-Yu/SUPIR'><img alt="GitHub Repo stars" src="https://img.shields.io/github/stars/Fanghua-Yu/SUPIR?style=social"></a></center></p>
-    """
-
-
-claim_md = """
-## **Piracy**
-The images are not stored but the logs are saved during a month.
-## **How to get SUPIR**
-You can get SUPIR on HuggingFace by [duplicating this space](https://huggingface.co/spaces/Fabrice-TIERCELIN/SUPIR?duplicate=true) and set GPU.
-You can also install SUPIR on your computer following [this tutorial](https://huggingface.co/blog/MonsterMMORPG/supir-sota-image-upscale-better-than-magnific-ai).
-You can install _Pinokio_ on your computer and then install _SUPIR_ into it. It should be quite easy if you have an Nvidia GPU.
-## **Terms of use**
-By using this service, users are required to agree to the following terms: The service is a research preview intended for non-commercial use only. It only provides limited safety measures and may generate offensive content. It must not be used for any illegal, harmful, violent, racist, or sexual purposes. The service may collect user dialogue data for future research. Please submit a feedback to us if you get any inappropriate answer! We will collect those to keep improving our models. For an optimal experience, please use desktop computers for this demo, as mobile devices may compromise its quality.
-## **License**
-The service is a research preview intended for non-commercial use only, subject to the model [License](https://github.com/Fanghua-Yu/SUPIR) of SUPIR.
-"""
-
-# Gradio interface
-with gr.Blocks() as interface:
-    if torch.cuda.device_count() == 0:
-        with gr.Row():
-            gr.HTML("""
-    <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 is not enough for SUPIR. Please provide <a href="https://huggingface.co/spaces/Fabrice-TIERCELIN/SUPIR/discussions/new">feedback</a> if you have issues.
-    </big></big></big></p>
-    """)
-    gr.HTML(title_html)
-
-    input_image = gr.Image(label="Input (*.png, *.webp, *.jpeg, *.jpg, *.gif, *.bmp, *.heic)", show_label=True, type="filepath", height=600, elem_id="image-input")
-    rotation = gr.Radio([["No rotation", 0], ["⤵ Rotate +90°", 90], ["↩ Return 180°", 180], ["⤴ Rotate -90°", -90]], label="Orientation correction", info="Will apply the following rotation before restoring the image; the AI needs a good orientation to understand the content", value=0, interactive=True, visible=False)
-    with gr.Group():
-        prompt = gr.Textbox(label="Image description", info="Help the AI understand what the image represents; describe as much as possible, especially the details we can't see on the original image; you can write in any language", value="", placeholder="A 33 years old man, walking, in the street, Santiago, morning, Summer, photorealistic", lines=3)
-        prompt_hint = gr.HTML("You can use a <a href='"'https://huggingface.co/spaces/badayvedat/LLaVA'"'>LlaVa space</a> to auto-generate the description of your image.")
-        upscale = gr.Radio([["x1", 1], ["x2", 2], ["x3", 3], ["x4", 4], ["x5", 5], ["x6", 6], ["x7", 7], ["x8", 8], ["x9", 9], ["x10", 10], ["x20", 20], ["x100", 100]], label="Upscale factor", info="Resolution x1 to x100", value=2, interactive=True)
-        output_format = gr.Radio([["As input", "input"], ["*.png", "png"], ["*.webp", "webp"], ["*.jpeg", "jpeg"], ["*.gif", "gif"], ["*.bmp", "bmp"]], label="Image format for result", info="File extention", value="input", interactive=True)
-        allocation = gr.Radio([["1 min", 1], ["2 min", 2], ["3 min", 3], ["4 min", 4], ["5 min", 5]], label="GPU allocation time", info="lower=May abort run, higher=Quota penalty for next runs", value=3, interactive=True)
-
-    with gr.Accordion("Pre-denoising (optional)", open=False):
-        gamma_correction = gr.Slider(label="Gamma Correction", info = "lower=lighter, higher=darker", minimum=0.1, maximum=2.0, value=1.0, step=0.1)
-        denoise_button = gr.Button(value="Pre-denoise")
-        denoise_image = gr.Image(label="Denoised image", show_label=True, type="filepath", sources=[], interactive = False, height=600, elem_id="image-s1")
-        denoise_information = gr.HTML(value="If present, the denoised image will be used for the restoration instead of the input image.", visible=False)
-
-    with gr.Accordion("Advanced options", open=False):
-        a_prompt = gr.Textbox(label="Additional image description",
-                              info="Completes the main image description",
-                              value='Cinematic, High Contrast, highly detailed, taken using a Canon EOS R '
-                                    'camera, hyper detailed photo - realistic maximum detail, 32k, Color '
-                                    'Grading, ultra HD, extreme meticulous detailing, skin pore detailing, clothing fabric detailing, '
-                                    'hyper sharpness, perfect without deformations.',
-                              lines=3)
-        n_prompt = gr.Textbox(label="Negative image description",
-                              info="Disambiguate by listing what the image does NOT represent",
-                              value='painting, oil painting, illustration, drawing, art, sketch, anime, '
-                                    'cartoon, CG Style, 3D render, unreal engine, blurring, aliasing, pixel, unsharp, weird textures, ugly, dirty, messy, '
-                                    'worst quality, low quality, frames, watermark, signature, jpeg artifacts, '
-                                    'deformed, lowres, over-smooth',
-                              lines=3)
-        edm_steps = gr.Slider(label="Steps", info="lower=faster, higher=more details; too many steps create a checker effect", minimum=1, maximum=200, value=default_setting.edm_steps if torch.cuda.device_count() > 0 else 1, step=1)
-        num_samples = gr.Slider(label="Num Samples", info="Number of generated results", minimum=1, maximum=4 if not args.use_image_slider else 1
-                                , value=1, step=1)
-        min_size = gr.Slider(label="Minimum size", info="Minimum height, minimum width of the result", minimum=32, maximum=4096, value=1024, step=32)
-        downscale = gr.Radio([["/1", 1], ["/2", 2], ["/3", 3], ["/4", 4], ["/5", 5], ["/6", 6], ["/7", 7], ["/8", 8], ["/9", 9], ["/10", 10]], label="Pre-downscale factor", info="Reducing blurred image reduce the process time", value=1, interactive=True)
-        with gr.Row():
-            with gr.Column():
-                model_select = gr.Radio([["💃 Quality (v0-Q)", "v0-Q"], ["🎯 Fidelity (v0-F)", "v0-F"]], label="Model Selection", info="Pretrained model", value="v0-Q",
-                                        interactive=True)
-            with gr.Column():
-                color_fix_type = gr.Radio([["None", "None"], ["AdaIn (improve as a photo)", "AdaIn"], ["Wavelet (for JPEG artifacts)", "Wavelet"]], label="Color-Fix Type", info="AdaIn=Improve following a style, Wavelet=For JPEG artifacts", value="AdaIn",
-                                          interactive=True)
-        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="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)
-        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)
-        with gr.Row():
-            with gr.Column():
-                linear_CFG = gr.Checkbox(label="Linear CFG", value=True)
-                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 Restoring Guidance", value=False)
-                spt_linear_s_stage2 = gr.Slider(label="Guidance Start", minimum=0.,
-                                                maximum=1., value=0., step=0.05)
-            with gr.Column():
-                diff_dtype = gr.Radio([["fp32 (precision)", "fp32"], ["fp16 (medium)", "fp16"], ["bf16 (speed)", "bf16"]], label="Diffusion Data Type", value="fp32",
-                                      interactive=True)
+        with gr.Tab(label="STAR"):
             with gr.Column():
-                ae_dtype = gr.Radio([["fp32 (precision)", "fp32"], ["bf16 (speed)", "bf16"]], label="Auto-Encoder Data Type", value="fp32",
-                                    interactive=True)
-        randomize_seed = gr.Checkbox(label = "\U0001F3B2 Randomize seed", value = True, info = "If checked, result is always different")
-        seed = gr.Slider(label="Seed", minimum=0, maximum=max_64_bit_int, step=1, randomize=True)
-        with gr.Group():
-            param_setting = gr.Radio(["Quality", "Fidelity"], interactive=True, label="Presetting", value = "Quality")
-            restart_button = gr.Button(value="Apply presetting")
-
-    with gr.Column():
-        diffusion_button = gr.Button(value="🚀 Upscale/Restore", variant = "primary", elem_id = "process_button")
-        reset_btn = gr.Button(value="🧹 Reinit page", variant="stop", elem_id="reset_button", visible = False)
-
-        warning = gr.HTML(value = "<center><big>Your computer must <u>not</u> enter into standby mode.</big><br/>On Chrome, you can force to keep a tab alive in <code>chrome://discards/</code></center>", visible = False)
-        restore_information = gr.HTML(value = "Restart the process to get another result.", visible = False)
-        result_slider = ImageSlider(label = 'Comparator', show_label = False, interactive = False, elem_id = "slider1", show_download_button = False)
-        result_gallery = gr.Gallery(label = 'Downloadable results', show_label = True, interactive = False, elem_id = "gallery1")
-
-    gr.Examples(
-        examples = [
-                [
-                    "./Examples/Example1.png",
-                    0,
-                    None,
-                    "Group of people, walking, happy, in the street, photorealistic, 8k, extremely detailled",
-                    "Cinematic, High Contrast, highly detailed, taken using a Canon EOS R camera, hyper detailed photo - realistic maximum detail, 32k, Color Grading, ultra HD, extreme meticulous detailing, skin pore detailing, hyper sharpness, perfect without deformations.",
-                    "painting, oil painting, illustration, drawing, art, sketch, anime, cartoon, CG Style, 3D render, unreal engine, blurring, aliasing, pixel, unsharp, weird textures, ugly, dirty, messy, worst quality, low quality, frames, watermark, signature, jpeg artifacts, deformed, lowres, over-smooth",
-                    2,
-                    1024,
-                    1,
-                    8,
-                    100,
-                    -1,
-                    1,
-                    7.5,
-                    False,
-                    42,
-                    5,
-                    1.003,
-                    "AdaIn",
-                    "fp16",
-                    "bf16",
-                    1.0,
-                    True,
-                    4,
-                    False,
-                    0.,
-                    "v0-Q",
-                    "input",
-                    3
-                ],
-                [
-                    "./Examples/Example2.jpeg",
-                    0,
-                    None,
-                    "La cabeza de un gato atigrado, en una casa, fotorrealista, 8k, extremadamente detallada",
-                    "Cinematic, High Contrast, highly detailed, taken using a Canon EOS R camera, hyper detailed photo - realistic maximum detail, 32k, Color Grading, ultra HD, extreme meticulous detailing, skin pore detailing, hyper sharpness, perfect without deformations.",
-                    "painting, oil painting, illustration, drawing, art, sketch, anime, cartoon, CG Style, 3D render, unreal engine, blurring, aliasing, pixel, unsharp, weird textures, ugly, dirty, messy, worst quality, low quality, frames, watermark, signature, jpeg artifacts, deformed, lowres, over-smooth",
-                    1,
-                    1024,
-                    1,
-                    1,
-                    200,
-                    -1,
-                    1,
-                    7.5,
-                    False,
-                    42,
-                    5,
-                    1.003,
-                    "Wavelet",
-                    "fp16",
-                    "bf16",
-                    1.0,
-                    True,
-                    4,
-                    False,
-                    0.,
-                    "v0-Q",
-                    "input",
-                    3
-                ],
-                [
-                    "./Examples/Example3.webp",
-                    0,
-                    None,
-                    "A red apple",
-                    "Cinematic, High Contrast, highly detailed, taken using a Canon EOS R camera, hyper detailed photo - realistic maximum detail, 32k, Color Grading, ultra HD, extreme meticulous detailing, skin pore detailing, hyper sharpness, perfect without deformations.",
-                    "painting, oil painting, illustration, drawing, art, sketch, anime, cartoon, CG Style, 3D render, unreal engine, blurring, aliasing, pixel, unsharp, weird textures, ugly, dirty, messy, worst quality, low quality, frames, watermark, signature, jpeg artifacts, deformed, lowres, over-smooth",
-                    1,
-                    1024,
-                    1,
-                    1,
-                    200,
-                    -1,
-                    1,
-                    7.5,
-                    False,
-                    42,
-                    5,
-                    1.003,
-                    "Wavelet",
-                    "fp16",
-                    "bf16",
-                    1.0,
-                    True,
-                    4,
-                    False,
-                    0.,
-                    "v0-Q",
-                    "input",
-                    3
-                ],
-                [
-                    "./Examples/Example3.webp",
-                    0,
-                    None,
-                    "A red marble",
-                    "Cinematic, High Contrast, highly detailed, taken using a Canon EOS R camera, hyper detailed photo - realistic maximum detail, 32k, Color Grading, ultra HD, extreme meticulous detailing, skin pore detailing, hyper sharpness, perfect without deformations.",
-                    "painting, oil painting, illustration, drawing, art, sketch, anime, cartoon, CG Style, 3D render, unreal engine, blurring, aliasing, pixel, unsharp, weird textures, ugly, dirty, messy, worst quality, low quality, frames, watermark, signature, jpeg artifacts, deformed, lowres, over-smooth",
-                    1,
-                    1024,
-                    1,
-                    1,
-                    200,
-                    -1,
-                    1,
-                    7.5,
-                    False,
-                    42,
-                    5,
-                    1.003,
-                    "Wavelet",
-                    "fp16",
-                    "bf16",
-                    1.0,
-                    True,
-                    4,
-                    False,
-                    0.,
-                    "v0-Q",
-                    "input",
-                    3
-                ],
-            ],
-        run_on_click = True,
-        fn = stage2_process,
-	    inputs = [
-            input_image,
-            rotation,
-            denoise_image,
-            prompt,
-            a_prompt,
-            n_prompt,
-            num_samples,
-            min_size,
-            downscale,
-            upscale,
-            edm_steps,
-            s_stage1,
-            s_stage2,
-            s_cfg,
-            randomize_seed,
-            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,
-            model_select,
-            output_format,
-            allocation
-        ],
-	    outputs = [
-            result_slider,
-            result_gallery,
-            restore_information,
-            reset_btn
-        ],
-        cache_examples = False,
-    )
-
-    with gr.Row():
-        gr.Markdown(claim_md)
-    
-    input_image.upload(fn = check_upload, inputs = [
-        input_image
-    ], outputs = [
-        rotation
-    ], queue = False, show_progress = False)
-
-    denoise_button.click(fn = check_and_update, inputs = [
-        input_image
-    ], outputs = [warning], queue = False, show_progress = False).success(fn = stage1_process, inputs = [
-        input_image,
-        gamma_correction,
-        diff_dtype,
-        ae_dtype
-    ], outputs=[
-        denoise_image,
-        denoise_information
-    ])
-
-    diffusion_button.click(fn = update_seed, inputs = [
-        randomize_seed,
-        seed
-    ], outputs = [
-        seed
-    ], queue = False, show_progress = False).then(fn = check_and_update, inputs = [
-        input_image
-    ], outputs = [warning], queue = False, show_progress = False).success(fn=stage2_process, inputs = [
-        input_image,
-        rotation,
-        denoise_image,
-        prompt,
-        a_prompt,
-        n_prompt,
-        num_samples,
-        min_size,
-        downscale,
-        upscale,
-        edm_steps,
-        s_stage1,
-        s_stage2,
-        s_cfg,
-        randomize_seed,
-        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,
-        model_select,
-        output_format,
-        allocation
-    ], outputs = [
-        result_slider,
-        result_gallery,
-        restore_information,
-        reset_btn
-    ]).success(fn = log_information, inputs = [
-        result_gallery
-    ], outputs = [], queue = False, show_progress = False)
-
-    result_gallery.change(on_select_result, [result_slider, result_gallery], result_slider)
-    result_gallery.select(on_select_result, [result_slider, result_gallery], result_slider)
-
-    restart_button.click(fn = load_and_reset, inputs = [
-        param_setting
-    ], 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,
-        model_select
-    ])
-
-    reset_btn.click(fn = reset, inputs = [], outputs = [
-            input_image,
-            rotation,
-            denoise_image,
-            prompt,
-            a_prompt,
-            n_prompt,
-            num_samples,
-            min_size,
-            downscale,
-            upscale,
-            edm_steps,
-            s_stage1,
-            s_stage2,
-            s_cfg,
-            randomize_seed,
-            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,
-            model_select,
-            output_format,
-            allocation
-        ], queue = False, show_progress = False)
-        
-    interface.queue(10).launch()
+                with gr.Row():
+                    with gr.Column():
+                        input_video = gr.Video(label="Input Video", elem_id="input_video")
+                        input_text = gr.Text(label="Prompts")
+                        
+                        upscale = gr.Slider(1, 4, value=4, step=1, label="Upscale Factor")
+                        max_chunk_len = gr.Slider(1, 32, value=24, step=1, label="Input Chunk Length")
+                        chunk_size = gr.Slider(1, 5, value=3, step=1, label="Decode Chunk Size")
+
+                        end_btn = gr.Button("Generate")
+
+                    output_video = gr.Video(label="Generated Video", elem_id="output_vid", autoplay=True, show_share_button=True)
+
+                gr.Examples(
+                    examples=examples,
+                    inputs=[input_video, input_text, upscale, max_chunk_len, chunk_size],
+                    outputs=[output_video],
+                    fn=enhance_with_gpu,  # Use the GPU-decorated function
+                    cache_examples=True,
+                )
+
+            end_btn.click(
+                inputs=[input_video, input_text, upscale, max_chunk_len, chunk_size],
+                outputs=[output_video],
+                fn=enhance_with_gpu,  # Use the GPU-decorated function
+            )
+
+    return star_iface
+
+if __name__ == "__main__":
+    result_dir = os.path.join("./", "results")
+    star_iface = star_demo(result_dir)
+    star_iface.queue(max_size=12)
+    star_iface.launch(max_threads=1)