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on
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Running
on
Zero
| import gradio as gr | |
| import torch | |
| from PIL import Image | |
| from diffusers import AutoPipelineForText2Image, DDIMScheduler | |
| from transformers import CLIPVisionModelWithProjection | |
| import numpy as np | |
| import spaces | |
| # Initialize the image encoder and pipeline outside the function | |
| image_encoder = CLIPVisionModelWithProjection.from_pretrained( | |
| "h94/IP-Adapter", | |
| subfolder="models/image_encoder", | |
| torch_dtype=torch.float16, | |
| ) | |
| pipeline = AutoPipelineForText2Image.from_pretrained( | |
| "stabilityai/stable-diffusion-xl-base-1.0", | |
| torch_dtype=torch.float16, | |
| image_encoder=image_encoder, | |
| ) | |
| pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config) | |
| pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="sdxl_models", weight_name=["ip-adapter-plus_sdxl_vit-h.safetensors", "ip-adapter-plus-face_sdxl_vit-h.safetensors"]) | |
| pipeline.enable_model_cpu_offload() | |
| def transform_image(face_image, soy_strength, face_strength): | |
| generator = torch.Generator(device="cpu").manual_seed(0) | |
| if isinstance(face_image, Image.Image): | |
| processed_face_image = face_image | |
| elif isinstance(face_image, np.ndarray): | |
| processed_face_image = Image.fromarray(face_image) | |
| else: | |
| raise ValueError("Unsupported image format") | |
| style_image_path = "examples/soyjak2.jpg" | |
| style_image = Image.open(style_image_path) | |
| # Set the IP adapter scale dynamically based on the sliders | |
| pipeline.set_ip_adapter_scale([soy_strength, face_strength]) | |
| image = pipeline( | |
| prompt="soyjak", | |
| ip_adapter_image=[style_image, processed_face_image], | |
| negative_prompt="monochrome, lowres, bad anatomy, worst quality, low quality", | |
| num_inference_steps=50, | |
| generator=generator, | |
| ).images[0] | |
| return image | |
| # Gradio interface setup with dynamic sliders | |
| demo = gr.Interface( | |
| fn=transform_image, | |
| inputs=[ | |
| gr.Image(label="Upload your face image"), | |
| gr.Slider(minimum=0, maximum=1, step=0.05, value=0.7, label="Soy Strength"), | |
| gr.Slider(minimum=0, maximum=1, step=0.05, value=1.0, label="Face Strength") # Renamed to Face Strength | |
| ], | |
| outputs=gr.Image(label="Your Soyjak"), | |
| title="InstaSoyjak - turn anyone into a Soyjak", | |
| description="All you need to do is upload an image and adjust the strengths. **Please use responsibly.**", | |
| ) | |
| demo.queue(max_size=20) | |
| demo.launch() | |