Create app.py

app.py

@@ -0,0 +1,149 @@
+import gradio as gr
+import torch
+import os
+import spaces
+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
+
+# 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
+
+feature_extractor = CLIPFeatureExtractor.from_pretrained("openai/clip-vit-base-patch32")
+
+# Function 
+@spaces.GPU(duration=30,queue=False)
+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
+
+
+# Gradio Interface
+with gr.Blocks(css="style.css") as demo:
+    gr.HTML(
+        "<h1><center>Textual Imagination : A Text To Video Synthesis</center></h1>"
+        )
+    with gr.Group():
+        with gr.Row():
+            prompt = gr.Textbox(
+                label='Prompt'
+            )
+        with gr.Row():
+            select_base = gr.Dropdown(
+                label='Base model',
+                choices=[
+                    "Cartoon", 
+                    "Realistic",
+                    "3d",
+                    "Anime",
+                ],
+                value=base_loaded,
+                interactive=True
+            )
+            select_motion = gr.Dropdown(
+                label='Motion',
+                choices=[
+                    ("Default", ""),
+                    ("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="guoyww/animatediff-motion-lora-zoom-in",
+                interactive=True
+            )
+            select_step = gr.Dropdown(
+                label='Inference steps',
+                choices=[
+                    ('1-Step', 1), 
+                    ('2-Step', 2),
+                    ('4-Step', 4),
+                    ('8-Step', 8),
+                ],
+                value=4,
+                interactive=True
+            )
+            submit = gr.Button(
+                scale=1,
+                variant='primary'
+            )
+    video = gr.Video(
+        label='AnimateDiff-Lightning',
+        autoplay=True,
+        height=512,
+        width=512,
+        elem_id="video_output"
+    )
+
+    gr.on(triggers=[
+            submit.click,
+            prompt.submit
+    ],
+        fn = generate_image,
+        inputs = [prompt, select_base, select_motion, select_step],
+        outputs = [video],
+        api_name = "instant_video",
+        queue = False
+    )
+
+demo.queue().launch()
+
+Translate