Create app.py

app.py

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+import os
+import torch
+import gradio as gr
+from PIL import Image
+from huggingface_hub import snapshot_download
+from pyramid_dit import PyramidDiTForVideoGeneration
+from diffusers.utils import export_to_video
+
+# Constants
+MODEL_PATH = "pyramid-flow-model"
+MODEL_REPO = "rain1011/pyramid-flow-sd3"
+MODEL_VARIANT = "diffusion_transformer_768p"
+MODEL_DTYPE = "bf16"
+
+# Download and load the model
+def load_model():
+ if not os.path.exists(MODEL_PATH):
+ snapshot_download(MODEL_REPO, local_dir=MODEL_PATH, local_dir_use_symlinks=False, repo_type='model')
+ 
+ model = PyramidDiTForVideoGeneration(
+ MODEL_PATH,
+ MODEL_DTYPE,
+ model_variant=MODEL_VARIANT,
+ )
+ 
+ model.vae.to("cuda")
+ model.dit.to("cuda")
+ model.text_encoder.to("cuda")
+ model.vae.enable_tiling()
+ 
+ return model
+
+# Global model variable
+model = load_model()
+
+# Text-to-video generation function
+def generate_video(prompt, duration, guidance_scale, video_guidance_scale):
+ temp = int(duration * 2.4) # Convert seconds to temp value (assuming 24 FPS)
+ torch_dtype = torch.bfloat16 if MODEL_DTYPE == "bf16" else torch.float32
+ 
+ with torch.no_grad(), torch.cuda.amp.autocast(enabled=True, dtype=torch_dtype):
+ frames = model.generate(
+ prompt=prompt,
+ num_inference_steps=[20, 20, 20],
+ video_num_inference_steps=[10, 10, 10],
+ height=768,
+ width=1280,
+ temp=temp,
+ guidance_scale=guidance_scale,
+ video_guidance_scale=video_guidance_scale,
+ output_type="pil",
+ save_memory=True,
+ )
+ 
+ output_path = "output_video.mp4"
+ export_to_video(frames, output_path, fps=24)
+ return output_path
+
+# Image-to-video generation function
+def generate_video_from_image(image, prompt, duration, video_guidance_scale):
+ temp = int(duration * 2.4) # Convert seconds to temp value (assuming 24 FPS)
+ torch_dtype = torch.bfloat16 if MODEL_DTYPE == "bf16" else torch.float32
+ 
+ image = image.resize((1280, 768))
+ 
+ with torch.no_grad(), torch.cuda.amp.autocast(enabled=True, dtype=torch_dtype):
+ frames = model.generate_i2v(
+ prompt=prompt,
+ input_image=image,
+ num_inference_steps=[10, 10, 10],
+ temp=temp,
+ guidance_scale=7.0,
+ video_guidance_scale=video_guidance_scale,
+ output_type="pil",
+ save_memory=True,
+ )
+ 
+ output_path = "output_video_i2v.mp4"
+ export_to_video(frames, output_path, fps=24)
+ return output_path
+
+# Gradio interface
+with gr.Blocks() as demo:
+ gr.Markdown("# Pyramid Flow Video Generation Demo")
+ 
+ with gr.Tab("Text-to-Video"):
+ with gr.Row():
+ with gr.Column():
+ t2v_prompt = gr.Textbox(label="Prompt")
+ t2v_duration = gr.Slider(minimum=1, maximum=10, value=5, step=1, label="Duration (seconds)")
+ t2v_guidance_scale = gr.Slider(minimum=1, maximum=15, value=9, step=0.1, label="Guidance Scale")
+ t2v_video_guidance_scale = gr.Slider(minimum=1, maximum=15, value=5, step=0.1, label="Video Guidance Scale")
+ t2v_generate_btn = gr.Button("Generate Video")
+ with gr.Column():
+ t2v_output = gr.Video(label="Generated Video")
+ 
+ t2v_generate_btn.click(
+ generate_video,
+ inputs=[t2v_prompt, t2v_duration, t2v_guidance_scale, t2v_video_guidance_scale],
+ outputs=t2v_output
+ )
+ 
+ with gr.Tab("Image-to-Video"):
+ with gr.Row():
+ with gr.Column():
+ i2v_image = gr.Image(type="pil", label="Input Image")
+ i2v_prompt = gr.Textbox(label="Prompt")
+ i2v_duration = gr.Slider(minimum=1, maximum=10, value=5, step=1, label="Duration (seconds)")
+ i2v_video_guidance_scale = gr.Slider(minimum=1, maximum=15, value=4, step=0.1, label="Video Guidance Scale")
+ i2v_generate_btn = gr.Button("Generate Video")
+ with gr.Column():
+ i2v_output = gr.Video(label="Generated Video")
+ 
+ i2v_generate_btn.click(
+ generate_video_from_image,
+ inputs=[i2v_image, i2v_prompt, i2v_duration, i2v_video_guidance_scale],
+ outputs=i2v_output
+ )
+
+demo.launch()