created app.py

app.py

@@ -0,0 +1,91 @@
+import os
+import torch
+import numpy as np
+from diffusers import DiffusionPipeline, AutoencoderKL, UNet2DConditionModel, DDPMScheduler
+from transformers import CLIPVisionModelWithProjection, CLIPFeatureExtractor
+from diffusers.utils import load_image
+from pipeline_zero1to3_stable import Zero1to3StableDiffusionPipeline, CCProjection
+import math
+import imageio
+import gradio as gr
+from PIL import Image
+import cv2
+# Define the background removal function
+
+def preprocess_image(input_im):
+    '''
+    :param input_im (PIL Image).
+    :return input_im (H, W, 3) array in [0, 1].
+    '''
+
+    input_im  = input_im.convert('RGB')
+    print("shape1 = ",input_im.size)
+    input_im = input_im.resize([256, 256], Image.Resampling.LANCZOS)
+    input_im = np.asarray(input_im, dtype=np.float32) / 255.0
+    # input_im[input_im[:, :, -1] <= 0.9] = [1., 1., 1.]
+    return input_im
+    
+
+    return input_im
+
+# Load model and set paths
+model_id = "mirza152/zero123-face"
+cc_projection = CCProjection.from_pretrained(model_id, subfolder="cc_projection", use_safetensors=True)
+unet = UNet2DConditionModel.from_pretrained(model_id, subfolder="unet", use_safetensors=True)
+feature_extractor = CLIPFeatureExtractor.from_pretrained(model_id, subfolder="feature_extractor", use_safetensors=True)
+image_encoder = CLIPVisionModelWithProjection.from_pretrained(model_id, subfolder="image_encoder")
+scheduler = DDPMScheduler.from_pretrained(model_id, subfolder="scheduler", use_safetensors=True)
+vae = AutoencoderKL.from_pretrained(model_id, subfolder="vae", use_safetensors=True)
+
+# Instantiate pipeline
+pipe = Zero1to3StableDiffusionPipeline(
+    unet=unet,
+    cc_projection=cc_projection,
+    vae=vae,
+    scheduler=scheduler,
+    feature_extractor=feature_extractor,
+    image_encoder=image_encoder,
+    safety_checker=None,
+)
+pipe.enable_vae_tiling()
+pipe.enable_attention_slicing()
+
+# Define the function to process and generate GIFs
+def process_image(input_image):
+
+    input_image = preprocess_image(input_image)
+    H, W = input_image.shape[:2]
+    input_image = Image.fromarray((input_image * 255.0).astype(np.uint8))
+    total_frames = 8
+    input_images = [input_image]*total_frames
+    pitch_range, yaw_range = 0.20, 0.20
+    avg_polar, avg_azimuth = 1.52, 1.57
+    all_poses = []
+
+    # Generate poses for GIF frames
+    for frame_idx in range(total_frames):
+        theta_target = 3.14 / 2 + yaw_range * np.sin(2 * 3.14 * frame_idx / total_frames)
+        polar = avg_polar - theta_target
+        azimuth_cond = 3.14 / 2 - 0.05 + pitch_range * np.cos(2 * 3.14 * frame_idx / total_frames)
+        azimuth = avg_azimuth - azimuth_cond
+        query_pose = torch.tensor([(1.5708 - theta_target) - (1.5708 - avg_polar), math.sin(azimuth), math.cos(azimuth), 1.5708 - avg_azimuth])
+        all_poses.append(query_pose)
+    
+    query_poses = torch.stack(all_poses)
+    images = pipe(input_imgs=input_images, prompt_imgs=input_images, poses=query_poses, height=H, width=W, guidance_scale=4, num_images_per_prompt=1, num_inference_steps=1).images
+
+    # Save images to GIF
+    gif_path = "output.gif"
+    images[0].save(gif_path, save_all=True, append_images=images[1:], duration=100, loop=0)
+    return gif_path
+
+# Create Gradio Interface
+iface = gr.Interface(
+    fn=process_image,
+    inputs=gr.Image(type="pil", label="Input Image"),
+    outputs=gr.Image(type="filepath", label="Output GIF"),
+    title="Image to GIF Pipeline",
+    description="Upload an image to generate a GIF.",
+    allow_flagging="never",
+)
+iface.launch()