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| # Prediction interface for Cog ⚙️ | |
| # https://github.com/replicate/cog/blob/main/docs/python.md | |
| from cog import BasePredictor, Input, Path | |
| from typing import List | |
| from omni_zero import OmniZeroCouple | |
| class Predictor(BasePredictor): | |
| def setup(self): | |
| """Load the model into memory to make running multiple predictions efficient""" | |
| self.omni_zero = OmniZeroCouple( | |
| base_model="frankjoshua/albedobaseXL_v13", | |
| ) | |
| def predict( | |
| self, | |
| seed: int = Input(description="Random seed for the model", default=42), | |
| prompt: str = Input(description="Prompt for the model", default="Cinematic still photo of a couple. emotional, harmonious, vignette, 4k epic detailed, shot on kodak, 35mm photo, sharp focus, high budget, cinemascope, moody, epic, gorgeous, film grain, grainy"), | |
| negative_prompt: str = Input(description="Negative prompt for the model", default="anime, cartoon, graphic, (blur, blurry, bokeh), text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured"), | |
| guidance_scale: float = Input(description="Guidance scale for the model", default=3.0, ge=0.0, le=14.0), | |
| number_of_images: int = Input(description="Number of images to generate", default=1, ge=1, le=4), | |
| number_of_steps: int = Input(description="Number of steps for the model", default=10, ge=1, le=50), | |
| base_image: Path = Input(description="Base image for the model", default=None), | |
| base_image_strength: float = Input(description="Base image strength for the model", default=0.3, ge=0.0, le=1.0), | |
| style_image: Path = Input(description="Style image for the model", default=None), | |
| style_image_strength: float = Input(description="Style image strength for the model", default=1.0, ge=0.0, le=1.0), | |
| identity_image_1: Path = Input(description="First identity image for the model", default=None), | |
| identity_image_strength_1: float = Input(description="First identity image strength for the model", default=1.0, ge=0.0, le=1.0), | |
| identity_image_2: Path = Input(description="Second identity image for the model", default=None), | |
| identity_image_strength_2: float = Input(description="Second identity image strength for the model", default=1.0, ge=0.0, le=1.0), | |
| depth_image: Path = Input(description="Depth image for the model", default=None), | |
| depth_image_strength: float = Input(description="Depth image strength for the model", default=0.2, ge=0.0, le=1.0), | |
| mask_guidance_start: float = Input(description="Mask guidance start value", default=0.0, ge=0.0, le=1.0), | |
| mask_guidance_end: float = Input(description="Mask guidance end value", default=1.0, ge=0.0, le=1.0), | |
| ) -> List[Path]: | |
| """Run a single prediction on the model""" | |
| # base_image = Image.open(base_image) if base_image else None | |
| # style_image = Image.open(style_image) if style_image else None | |
| # identity_image_1 = Image.open(identity_image_1) if identity_image_1 else None | |
| # identity_image_2 = Image.open(identity_image_2) if identity_image_2 else None | |
| # depth_image = Image.open(depth_image) if depth_image else None | |
| images = self.omni_zero.generate( | |
| seed=seed, | |
| prompt=prompt, | |
| negative_prompt=negative_prompt, | |
| guidance_scale=guidance_scale, | |
| number_of_images=number_of_images, | |
| number_of_steps=number_of_steps, | |
| base_image=base_image, | |
| base_image_strength=base_image_strength, | |
| style_image=style_image, | |
| style_image_strength=style_image_strength, | |
| identity_image_1=identity_image_1, | |
| identity_image_strength_1=identity_image_strength_1, | |
| identity_image_2=identity_image_2, | |
| identity_image_strength_2=identity_image_strength_2, | |
| depth_image=depth_image, | |
| depth_image_strength=depth_image_strength, | |
| mask_guidance_start=mask_guidance_start, | |
| mask_guidance_end=mask_guidance_end, | |
| ) | |
| outputs = [] | |
| for i, image in enumerate(images): | |
| output_path = f"oz_output_{i}.jpg" | |
| image.save(output_path) | |
| outputs.append(Path(output_path)) | |
| return outputs |