Create app.py

app.py

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+import spaces
+import torch
+import os
+from diffusers import AutoencoderKLLTXVideo, LTXImageToVideoPipeline, LTXVideoTransformer3DModel
+from diffusers.utils import export_to_video, load_image #, PIL_INTERPOLATION
+
+import gradio as gr
+import numpy as np
+import random
+from PIL import Image
+import imageio.v3
+
+torch.backends.cuda.matmul.allow_tf32 = False
+torch.backends.cuda.matmul.allow_bf16_reduced_precision_reduction = False
+torch.backends.cuda.matmul.allow_fp16_reduced_precision_reduction = False
+torch.backends.cudnn.allow_tf32 = False
+torch.backends.cudnn.deterministic = False
+torch.backends.cudnn.benchmark = False
+torch.backends.cuda.preferred_blas_library="cublas"
+#torch.backends.cuda.preferred_linalg_library="cusolver"
+torch.set_float32_matmul_precision("highest")
+os.putenv("HF_HUB_ENABLE_HF_TRANSFER","1")
+HF_TOKEN = os.getenv("HF_TOKEN")
+
+MAX_SEED = np.iinfo(np.int64).max
+
+single_file_url = "https://huggingface.co/Lightricks/LTX-Video/ltx-video-2b-v0.9.1.safetensors"
+#vae_url = 'https://huggingface.co/spacepxl/ltx-video-0.9-vae-finetune/ltx-video-v0.9-vae_finetune_decoder_111k_smooth.safetensors'
+
+transformer = LTXVideoTransformer3DModel.from_single_file(single_file_url,token=HF_TOKEN)
+
+#vae = AutoencoderKLLTXVideo.from_single_file(vae_url,token=HF_TOKEN)
+
+pipe = LTXImageToVideoPipeline.from_pretrained("Lightricks/LTX-Video",token=HF_TOKEN, transformer=transformer).to(torch.device("cuda"),torch.bfloat16)
+
+@spaces.GPU(duration=80)
+def generate_video(
+    image_url,
+    prompt,
+    negative_prompt,
+    width,
+    height,
+    num_frames,
+    guidance_scale,
+    num_inference_steps,
+    fps,
+    progress=gr.Progress(track_tqdm=True)
+):
+    seed=random.randint(0, MAX_SEED)
+    generator = torch.Generator(device="cuda").manual_seed(seed)
+    image = Image.open(image_url).convert("RGB")
+    image.resize((height,width), Image.LANCZOS)
+    video = pipe(
+        image=image,
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        width=width,
+        height=height,
+        num_frames=num_frames,
+        frame_rate=fps,
+        guidance_scale=guidance_scale,
+        generator=generator,
+        num_inference_steps=num_inference_steps,
+        output_type='pt',
+        max_sequence_length=512,
+    ).frames
+    video = video[0]
+    video = video.permute(0, 2, 3, 1).cpu().detach().to(torch.float32).numpy()
+    export_to_video(video, "output.mp4", fps=fps) 
+    return "output.mp4"
+
+iface = gr.Interface(
+    fn=generate_video,
+    inputs=[
+        gr.Image(type="filepath", label="Image"),
+        gr.Textbox(lines=2, label="Prompt"),
+        gr.Textbox(lines=2, label="Negative Prompt"),
+        gr.Slider(minimum=256, maximum=1024, step=8, value=704, label="Width"),
+        gr.Slider(minimum=256, maximum=1024, step=8, value=704, label="Height"),
+        gr.Slider(minimum=16, maximum=256, step=16, value=111, label="Number of Frames"),
+        gr.Slider(minimum=0.0, maximum=30.0, step=0.01, value=3.8, label="Guidance Scale"),
+        gr.Slider(minimum=1, maximum=100, step=1, value=40, label="Number of Inference Steps"),
+        gr.Slider(minimum=1, maximum=60, step=1, value=25, label="FPS"),
+    ],
+    outputs=gr.Video(label="Generated Video"),
+    title="LTX-Video Test D",
+    description="Generate video from image with LTX-Video.",
+)
+
+iface.launch()