Update app.py

app.py

@@ -42,28 +42,6 @@ run("apt install mpich libopenmpi-dev")
 run("python -m pip install --upgrade pip")
 run("pip install -r req.txt")
 
-def deps():
-    import spaces
-    import torch
-    import gradio as gr
-    import numpy as np
-    from lxml.html import fromstring
-    #from transformers import pipeline
-    from torch import multiprocessing as mp, nn
-    #from torch.multiprocessing import Pool
-    #from pathos.multiprocessing import ProcessPool as Pool
-    #from pathos.threading import ThreadPool as Pool
-    #from diffusers.pipelines.flux import FluxPipeline
-    from diffusers.utils import export_to_gif, load_image
-    from diffusers.models.modeling_utils import ModelMixin
-    from huggingface_hub import hf_hub_download
-    from safetensors.torch import load_file, save_file
-    from diffusers import DiffusionPipeline, AnimateDiffPipeline, MotionAdapter, EulerDiscreteScheduler, DDIMScheduler, StableDiffusionXLPipeline, UNet2DConditionModel, AutoencoderKL, UNet3DConditionModel
-    #import jax
-    #import jax.numpy as jnp
-    from pyina.launchers import TorqueMpiPool as Pool
-deps()
-
 last_motion=None
 dtype = torch.float16
 result=[]
@@ -125,6 +103,33 @@ function custom(){
 }
 """
 
+@njit(cache=True,nopython=True,parallel=True)
+def deps():
+  try:
+    import spaces
+    import torch
+    import gradio as gr
+    import numpy as np
+    from lxml.html import fromstring
+    #from transformers import pipeline
+    from torch import multiprocessing as mp, nn
+    #from torch.multiprocessing import Pool
+    #from pathos.multiprocessing import ProcessPool as Pool
+    #from pathos.threading import ThreadPool as Pool
+    #from diffusers.pipelines.flux import FluxPipeline
+    from diffusers.utils import export_to_gif, load_image
+    from diffusers.models.modeling_utils import ModelMixin
+    from huggingface_hub import hf_hub_download
+    from safetensors.torch import load_file, save_file
+    from diffusers import DiffusionPipeline, AnimateDiffPipeline, MotionAdapter, EulerDiscreteScheduler, DDIMScheduler, StableDiffusionXLPipeline, UNet2DConditionModel, AutoencoderKL, UNet3DConditionModel
+    #import jax
+    #import jax.numpy as jnp
+    from pyina.launchers import TorqueMpiPool as Pool
+    from numba import jit,njit
+  except Exception as e:
+    pass
+
+@njit(cache=True,nopython=True,parallel=True)
 def translate(text,lang):
     if text == None or lang == None:
         return ""       
@@ -166,11 +171,13 @@ def translate(text,lang):
     print(ret)
     return ret
 
+@njit(cache=True,nopython=True,parallel=True)
 def generate_random_string(length):
     characters = string.ascii_letters + string.digits
     return ''.join(random.choice(characters) for _ in range(length))
 
-@spaces.GPU(duration=65)
+#@spaces.GPU(duration=65)
+@njit(cache=True,nopython=True,parallel=True)
 def Piper(image,positive,negative,motion):
     global last_motion
     global ip_loaded
@@ -207,6 +214,7 @@ def Piper(image,positive,negative,motion):
         num_frames=(fps*time)
     )
 
+@njit(cache=True,nopython=True,parallel=True)
 def infer(pm):
         print("infer: started")
     
@@ -228,82 +236,94 @@ def infer(pm):
         export_to_gif(out.frames[0],name,fps=fps)
         return name
 
-def main(i,m,p1,p2,*result):
-    
-        p1_en = translate(p1,"english")
-        p2_en = translate(p2,"english")
-        pm = {"p":p1_en,"n":p2_en,"m":m,"i":i}
-        ln = len(result)
-        rng = list(range(ln))
-        arr = [pm for _ in rng]
+@njit(cache=True,nopython=True,parallel=True)
+def handle(i,m,p1,p2,*result):    
+    p1_en = translate(p1,"english")
+    p2_en = translate(p2,"english")
+    pm = {"p":p1_en,"n":p2_en,"m":m,"i":i}
+    ln = len(result)
+    rng = list(range(ln))
+    arr = [pm for _ in rng]
+    #with Pool(f'{ ln }:ppn=2', queue='productionQ', timelimit='5:00:00', workdir='.') as pool:
+        #return pool.map(infer,arr)
+    ret = []
+    ret.append(infer,pm) for _ in range(ln)
+    return ret
 
-        with Pool(f'{ ln }:ppn=2', queue='productionQ', timelimit='5:00:00', workdir='.') as pool:
-            return pool.map(infer,arr)
+@njit(cache=True,nopython=True,parallel=True)
+def ui():
+    with gr.Blocks(theme=gr.themes.Soft(),css=css,js=js) as demo:
+        with gr.Column(elem_id="col-container"):
+            gr.Markdown(f"""
+                # MULTI-LANGUAGE IMAGE GENERATOR
+            """)
+            with gr.Row():
+                img = gr.Image(label="STATIC PHOTO",show_label=True,container=True,type="pil")
+            with gr.Row():
+                prompt = gr.Textbox(
+                    elem_id="prompt",
+                    placeholder="INCLUDE",
+                    container=False,
+                    max_lines=1
+                )
+            with gr.Row():
+                prompt2 = gr.Textbox(
+                    elem_id="prompt2",
+                    placeholder="EXCLUDE",
+                    container=False,
+                    max_lines=1
+                )
+            with gr.Row():
+                    motion = gr.Dropdown(
+                        label='CAMERA',
+                        show_label=True,
+                        container=True,
+                        choices=[
+                            ("(No Effect)", ""),
+                            ("Zoom in", "guoyww/animatediff-motion-lora-zoom-in"),
+                            ("Zoom out", "guoyww/animatediff-motion-lora-zoom-out"),
+                            ("Tilt up", "guoyww/animatediff-motion-lora-tilt-up"),
+                            ("Tilt down", "guoyww/animatediff-motion-lora-tilt-down"),
+                            ("Pan left", "guoyww/animatediff-motion-lora-pan-left"),
+                            ("Pan right", "guoyww/animatediff-motion-lora-pan-right"),
+                            ("Roll left", "guoyww/animatediff-motion-lora-rolling-anticlockwise"),
+                            ("Roll right", "guoyww/animatediff-motion-lora-rolling-clockwise"),
+                        ],
+                        value="",
+                        interactive=True
+                    )
+            with gr.Row():
+                    run_button = gr.Button("START",elem_classes="btn",scale=0)
+            with gr.Row():
+                    result.append(gr.Image(interactive=False,elem_classes="image-container", label="Result", show_label=False, type='filepath', show_share_button=False))
+                    result.append(gr.Image(interactive=False,elem_classes="image-container", label="Result", show_label=False, type='filepath', show_share_button=False))
+                
+        gr.on(
+            triggers=[run_button.click, prompt.submit, prompt2.submit],
+            fn=handle,inputs=[img,motion,prompt,prompt2,*result],outputs=result
+        )
+        demo.queue().launch()
+
+@njit(cache=True,nopython=True,parallel=True)
+def pre():
+    global pipe
 
-pipe = AnimateDiffPipeline.from_pretrained(base, vae=vae, motion_adapter=adapter, torch_dtype=dtype).to(device)
-pipe.scheduler = DDIMScheduler(
+    pipe = AnimateDiffPipeline.from_pretrained(base, vae=vae, motion_adapter=adapter, torch_dtype=dtype).to(device)
+    pipe.scheduler = DDIMScheduler(
         clip_sample=False,
         beta_start=0.00085,
         beta_end=0.012,
         beta_schedule="linear",
         timestep_spacing="trailing",
         steps_offset=1
-)
-pipe.unet.load_state_dict(load_file(hf_hub_download(repo, ckpt), device=device), strict=False)
-pipe.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter_sd15.bin")
-pipe.enable_vae_slicing()
-pipe.enable_free_init(method="butterworth", use_fast_sampling=fast)
+    )
+    pipe.unet.load_state_dict(load_file(hf_hub_download(repo, ckpt), device=device), strict=False)
+    pipe.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter_sd15.bin")
+    pipe.enable_vae_slicing()
+    pipe.enable_free_init(method="butterworth", use_fast_sampling=fast)
 
-mp.set_start_method("spawn", force=True)
+    mp.set_start_method("spawn", force=True)
 
-with gr.Blocks(theme=gr.themes.Soft(),css=css,js=js) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(f"""
-            # MULTI-LANGUAGE IMAGE GENERATOR
-        """)
-        with gr.Row():
-            img = gr.Image(label="STATIC PHOTO",show_label=True,container=True,type="pil")
-        with gr.Row():
-            prompt = gr.Textbox(
-                elem_id="prompt",
-                placeholder="INCLUDE",
-                container=False,
-                max_lines=1
-            )
-        with gr.Row():
-            prompt2 = gr.Textbox(
-                elem_id="prompt2",
-                placeholder="EXCLUDE",
-                container=False,
-                max_lines=1
-            )
-        with gr.Row():
-                motion = gr.Dropdown(
-                    label='CAMERA',
-                    show_label=True,
-                    container=True,
-                    choices=[
-                        ("(No Effect)", ""),
-                        ("Zoom in", "guoyww/animatediff-motion-lora-zoom-in"),
-                        ("Zoom out", "guoyww/animatediff-motion-lora-zoom-out"),
-                        ("Tilt up", "guoyww/animatediff-motion-lora-tilt-up"),
-                        ("Tilt down", "guoyww/animatediff-motion-lora-tilt-down"),
-                        ("Pan left", "guoyww/animatediff-motion-lora-pan-left"),
-                        ("Pan right", "guoyww/animatediff-motion-lora-pan-right"),
-                        ("Roll left", "guoyww/animatediff-motion-lora-rolling-anticlockwise"),
-                        ("Roll right", "guoyww/animatediff-motion-lora-rolling-clockwise"),
-                    ],
-                    value="",
-                    interactive=True
-                )
-        with gr.Row():
-                run_button = gr.Button("START",elem_classes="btn",scale=0)
-        with gr.Row():
-                result.append(gr.Image(interactive=False,elem_classes="image-container", label="Result", show_label=False, type='filepath', show_share_button=False))
-                result.append(gr.Image(interactive=False,elem_classes="image-container", label="Result", show_label=False, type='filepath', show_share_button=False))
-                
-    gr.on(
-            triggers=[run_button.click, prompt.submit, prompt2.submit],
-            fn=main,inputs=[img,motion,prompt,prompt2,*result],outputs=result
-    )
-    demo.queue().launch()
+deps()
+pre()
+ui()