from optimum.habana.diffusers import GaudiDDIMScheduler, GaudiStableDiffusionLDM3DPipeline import gradio as gr import torch from PIL import Image import base64 from io import BytesIO from tempfile import NamedTemporaryFile from pathlib import Path Path("tmp").mkdir(exist_ok=True) device = "hpu" print(f"Device is {device}") model_name = "Intel/ldm3d-pano" scheduler = GaudiDDIMScheduler.from_pretrained(model_name, subfolder="scheduler") pipe = GaudiStableDiffusionLDM3DPipeline.from_pretrained( model_name, scheduler=scheduler, use_habana=True, use_hpu_graphs=True, gaudi_config="Habana/stable-diffusion") pipe.to(device) def get_iframe(rgb_path: str, depth_path: str, viewer_mode: str = "6DOF"): # buffered = BytesIO() # rgb.convert("RGB").save(buffered, format="JPEG") # rgb_base64 = base64.b64encode(buffered.getvalue()) # buffered = BytesIO() # depth.convert("RGB").save(buffered, format="JPEG") # depth_base64 = base64.b64encode(buffered.getvalue()) # rgb_base64 = "data:image/jpeg;base64," + rgb_base64.decode("utf-8") # depth_base64 = "data:image/jpeg;base64," + depth_base64.decode("utf-8") rgb_base64 = f"/file={rgb_path}" depth_base64 = f"/file={depth_path}" if viewer_mode == "6DOF": return f"""""" else: return f"""""" def predict( prompt: str, negative_prompt: str, guidance_scale: float = 5.0, seed: int = 0, randomize_seed: bool = True, ): generator = torch.Generator() if randomize_seed else torch.manual_seed(seed) output = pipe( prompt, width=1024, height=512, negative_prompt=negative_prompt, guidance_scale=guidance_scale, generator=generator, num_inference_steps=50, ) # type: ignore rgb_image, depth_image = output.rgb[0], output.depth[0] # type: ignore with NamedTemporaryFile(suffix=".png", delete=False, dir="tmp") as rgb_file: rgb_image.save(rgb_file.name) rgb_image = rgb_file.name with NamedTemporaryFile(suffix=".png", delete=False, dir="tmp") as depth_file: depth_image.save(depth_file.name) depth_image = depth_file.name iframe = get_iframe(rgb_image, depth_image) return rgb_image, depth_image, generator.seed(), iframe with gr.Blocks() as block: gr.Markdown( """ ## LDM3d Demo [Model card](https://huggingface.co/Intel/ldm3d-pano) [Diffusers docs](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/ldm3d_diffusion) For better results, specify "360 view of" or "panoramic view of" in the prompt """ ) with gr.Row(): with gr.Column(scale=1): prompt = gr.Textbox(label="Prompt") negative_prompt = gr.Textbox(label="Negative Prompt") guidance_scale = gr.Slider( label="Guidance Scale", minimum=0, maximum=10, step=0.1, value=5.0 ) randomize_seed = gr.Checkbox(label="Randomize Seed", value=True) seed = gr.Slider(label="Seed", minimum=0, maximum=2**64 - 1, step=1) generated_seed = gr.Number(label="Generated Seed") markdown = gr.Markdown(label="Output Box") with gr.Row(): new_btn = gr.Button("New Image") with gr.Column(scale=2): html = gr.HTML(height='50%') with gr.Row(): rgb = gr.Image(label="RGB Image", type="filepath") depth = gr.Image(label="Depth Image", type="filepath") gr.Examples( examples=[ ["360 view of a large bedroom", "", 7.0, 42, False]], inputs=[prompt, negative_prompt, guidance_scale, seed, randomize_seed], outputs=[rgb, depth, generated_seed, html], fn=predict, cache_examples=True) new_btn.click( fn=predict, inputs=[prompt, negative_prompt, guidance_scale, seed, randomize_seed], outputs=[rgb, depth, generated_seed, html], ) block.launch()