Update app.py

app.py

@@ -1,81 +1,25 @@
 import gradio as gr
-import torch
-import os
-import spaces
+from gradio_client import Client, file
 import uuid
 
-from diffusers import AnimateDiffPipeline, MotionAdapter, EulerDiscreteScheduler
-from diffusers.utils import export_to_video
-from huggingface_hub import hf_hub_download
-from safetensors.torch import load_file
-from PIL import Image
-
 MORE = """ ## TRY Other Models
         ### JARVIS: Your VOICE Assistant -> https://huggingface.co/spaces/KingNish/JARVIS
         ### Instant Image: 4k images in 5 Second -> https://huggingface.co/spaces/KingNish/Instant-Image
         """
 
-# Constants
-bases = {
-    "Cartoon": "frankjoshua/toonyou_beta6",
-    "Realistic": "emilianJR/epiCRealism",
-    "3d": "Lykon/DreamShaper",
-    "Anime": "Yntec/mistoonAnime2"
-}
-step_loaded = None
-base_loaded = "Realistic"
-motion_loaded = None
-
-# Ensure model and scheduler are initialized in GPU-enabled function
-if not torch.cuda.is_available():
-    raise NotImplementedError("No GPU detected!")
-
-device = "cuda"
-dtype = torch.float16
-pipe = AnimateDiffPipeline.from_pretrained(bases[base_loaded], torch_dtype=dtype).to(device)
-pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing", beta_schedule="linear")
-
-# Safety checkers
-from transformers import CLIPFeatureExtractor
+# Gradio Client
+client = Client("KingNish/Instant-Video")
 
-feature_extractor = CLIPFeatureExtractor.from_pretrained("openai/clip-vit-base-patch32")
-
-# Function 
-@spaces.GPU(duration=60,queue=False)
+# Function
 def generate_image(prompt, base="Realistic", motion="", step=8, progress=gr.Progress()):
-    global step_loaded
-    global base_loaded
-    global motion_loaded
-    print(prompt, base, step)
-
-    if step_loaded != step:
-        repo = "ByteDance/AnimateDiff-Lightning"
-        ckpt = f"animatediff_lightning_{step}step_diffusers.safetensors"
-        pipe.unet.load_state_dict(load_file(hf_hub_download(repo, ckpt), device=device), strict=False)
-        step_loaded = step
-
-    if base_loaded != base:
-        pipe.unet.load_state_dict(torch.load(hf_hub_download(bases[base], "unet/diffusion_pytorch_model.bin"), map_location=device), strict=False)
-        base_loaded = base
-
-    if motion_loaded != motion:
-        pipe.unload_lora_weights()
-        if motion != "":
-            pipe.load_lora_weights(motion, adapter_name="motion")
-            pipe.set_adapters(["motion"], [0.7])
-        motion_loaded = motion
-
-    progress((0, step))
-    def progress_callback(i, t, z):
-        progress((i+1, step))
-
-    output = pipe(prompt=prompt, guidance_scale=1.2, num_inference_steps=step, callback=progress_callback, callback_steps=1)
-
-    name = str(uuid.uuid4()).replace("-", "")
-    path = f"/tmp/{name}.mp4"
-    export_to_video(output.frames[0], path, fps=10)
-    return path
-
+    result = client.predict(
+        prompt=prompt,
+        base=base,
+        motion=motion,
+        step=step,
+        api_name="/generate_image_1"
+    )
+    return result
 
 # Gradio Interface
 with gr.Blocks(css="style.css") as demo:
@@ -83,8 +27,8 @@ with gr.Blocks(css="style.css") as demo:
         "<h1><center>Instant⚡Video</center></h1>" +
         "<p><center><span style='color: red;'>You may change the steps from 4 to 8, if you didn't get satisfied results.</center></p>" +
         "<p><center><strong>First Video Generating takes time then Videos generate faster.</p>" +
-        "<p><center>To get best results Make Sure to Write prompts in style as Given in Examples/p>" +
-        "<p><a href='https://huggingface.co/spaces/KingNish/Instant-Video/discussions/1' >Must Share you Best Results with Community - Click HERE<a></p>"
+        "<p><center>To get best results Make Sure to Write prompts in style as Given in Examples</center></p>" +
+        "<p><a href='https://huggingface.co/spaces/KingNish/Instant-Video/discussions/1'>Must Share your Best Results with Community - Click HERE</a></p>"
     )
     with gr.Group():
         with gr.Row():
@@ -100,7 +44,7 @@ with gr.Blocks(css="style.css") as demo:
                     "3d",
                     "Anime",
                 ],
-                value=base_loaded,
+                value="Realistic",
                 interactive=True
             )
             select_motion = gr.Dropdown(
@@ -170,4 +114,4 @@ with gr.Blocks(css="style.css") as demo:
         cache_examples=True,
 )
 
-demo.queue().launch()
+demo.queue().launch()