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| # Text to Vedio | |
| # import torch | |
| # from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler | |
| # from diffusers.utils import export_to_video | |
| # import streamlit as st | |
| # import numpy as np | |
| # # Title and User Input | |
| # st.title("Text-to-Video with Streamlit") | |
| # prompt = st.text_input("Enter your text prompt:", "Spiderman is surfing") | |
| # # Button to trigger generation | |
| # if st.button("Generate Video"): | |
| # # Ensure you have 'accelerate' version 0.17.0 or higher | |
| # import accelerate | |
| # if accelerate.__version__ < "0.17.0": | |
| # st.warning("Please upgrade 'accelerate' to version 0.17.0 or higher for CPU offloading.") | |
| # else: | |
| # with st.spinner("Generating video..."): | |
| # # Define the pipeline for image generation | |
| # pipe = DiffusionPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b", | |
| # torch_dtype=torch.float16, variant="fp16", device="cpu") | |
| # pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) | |
| # pipe.enable_model_cpu_offload() | |
| # # Generate video frames | |
| # video_frames = pipe(prompt, num_inference_steps=25).frames | |
| # # Create dummy frames for testing (replace with actual manipulation later) | |
| # dummy_frames = [np.ones((256, 256, 3), dtype=np.uint8) for _ in range(20)] | |
| # # Export to video | |
| # video_path = export_to_video(dummy_frames) | |
| # # Display the video in the Streamlit app | |
| # st.video(video_path) | |
| # Text to 3D | |
| import streamlit as st | |
| import torch | |
| from diffusers import ShapEPipeline | |
| from diffusers.utils import export_to_gif | |
| from PIL import Image | |
| import numpy as np | |
| import PyTorch | |
| # Model loading (Ideally done once at the start for efficiency) | |
| ckpt_id = "openai/shap-e" | |
| # Caches the model for faster subsequent runs | |
| def process_image_for_pil(image): | |
| if isinstance(image, torch.Tensor): | |
| # Your existing PyTorch conversion | |
| elif isinstance(image, np.ndarray): | |
| # Your existing Numpy conversion | |
| else: | |
| raise TypeError("Unsupported image format. Please provide conversion logic.") | |
| def should_resize(): | |
| """Determines whether to resize images (replace with your own logic)""" | |
| # Example: Resize only if the image dimensions exceed a threshold | |
| if image.width > 512 or image.height > 512: | |
| return True | |
| else: | |
| return False | |
| def load_model(): | |
| return ShapEPipeline.from_pretrained(ckpt_id).to("cuda") | |
| pipe = load_model() | |
| # App Title | |
| st.title("Shark 3D Image Generator") | |
| # User Inputs | |
| prompt = st.text_input("Enter your prompt:", "a shark") | |
| guidance_scale = st.slider("Guidance Scale", 0.0, 20.0, 15.0, step=0.5) | |
| # Generate and Display Images | |
| if st.button("Generate"): | |
| with st.spinner("Generating images..."): | |
| images = pipe(prompt, guidance_scale=guidance_scale, num_inference_steps=64).images | |
| # Process images for PIL conversion (This will be customized) | |
| pil_images = [] | |
| for image in images: | |
| processed_image = process_image_for_pil(image) | |
| pil_images.append(processed_image) | |
| # Resize Images (Optional) | |
| if should_resize(): # Add a function to control if resizing is needed | |
| for i in range(len(pil_images)): | |
| pil_images[i] = pil_images[i].resize((256, 256)) | |
| gif_path = export_to_gif(pil_images, "shark_3d.gif") | |
| st.image(pil_images[0]) | |
| st.success("GIF saved as shark_3d.gif") | |