Infinity/predict.py at main · octo-patch/Infinity · GitHub

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# Prediction interface for Cog ⚙️

# https://cog.run/python

import os

import argparse

import subprocess

import time

from cog import BasePredictor, Input, Path

import torch

import cv2

import numpy as np

from tools.run_infinity import (

load_tokenizer,

load_infinity,

load_visual_tokenizer,

gen_one_img,

)

from infinity.utils.dynamic_resolution import dynamic_resolution_h_w, h_div_w_templates

MODEL_CACHE = "model_cache"

MODEL_URL = f"https://weights.replicate.delivery/default/FoundationVision/Infinity/model_cache.tar"

def download_weights(url, dest):

start = time.time()

print("downloading url: ", url)

print("downloading to: ", dest)

subprocess.check_call(["pget", "-x", url, dest], close_fds=False)

print("downloading took: ", time.time() - start)

def load_transformer(vae, args):

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

model_path = args.model_path

# Define model configuration based on type

model_configurations = {

"infinity_2b": dict(

depth=32,

embed_dim=2048,

num_heads=2048 // 128,

drop_path_rate=0.1,

mlp_ratio=4,

block_chunks=8,

"infinity_layer12": dict(

depth=12,

embed_dim=768,

num_heads=8,

drop_path_rate=0.1,

mlp_ratio=4,

block_chunks=4,

"infinity_layer16": dict(

depth=16,

embed_dim=1152,

num_heads=12,

drop_path_rate=0.1,

mlp_ratio=4,

block_chunks=4,

"infinity_layer24": dict(

depth=24,

embed_dim=1536,

num_heads=16,

drop_path_rate=0.1,

mlp_ratio=4,

block_chunks=4,

"infinity_layer32": dict(

depth=32,

embed_dim=2080,

num_heads=20,

drop_path_rate=0.1,

mlp_ratio=4,

block_chunks=4,

"infinity_layer40": dict(

depth=40,

embed_dim=2688,

num_heads=24,

drop_path_rate=0.1,

mlp_ratio=4,

block_chunks=4,

"infinity_layer48": dict(

depth=48,

embed_dim=3360,

num_heads=28,

drop_path_rate=0.1,

mlp_ratio=4,

block_chunks=4,

}

kwargs_model = model_configurations.get(args.model_type, {})

if not kwargs_model:

raise ValueError(f"Unknown model type: {args.model_type}")

infinity = load_infinity(

rope2d_each_sa_layer=args.rope2d_each_sa_layer,

rope2d_normalized_by_hw=args.rope2d_normalized_by_hw,

use_scale_schedule_embedding=args.use_scale_schedule_embedding,

pn=args.pn,

use_bit_label=args.use_bit_label,

add_lvl_embeding_only_first_block=args.add_lvl_embeding_only_first_block,

model_path=model_path, # Directly use model_path

scale_schedule=None,

vae=vae,

device=device,

model_kwargs=kwargs_model,

text_channels=args.text_channels,

apply_spatial_patchify=args.apply_spatial_patchify,

use_flex_attn=args.use_flex_attn,

bf16=args.bf16,

)

return infinity

class Predictor(BasePredictor):

def setup(self) -> None:

"""Load the model into memory to make running multiple predictions efficient"""

if not os.path.exists(MODEL_CACHE):

print("downloading")

download_weights(MODEL_URL, MODEL_CACHE)

model_path = f"{MODEL_CACHE}/FoundationVision/Infinity/infinity_2b_reg.pth"

vae_path = f"{MODEL_CACHE}/FoundationVision/Infinity/infinity_vae_d32reg.pth"

text_encoder_ckpt = f"{MODEL_CACHE}/google/flan-t5-xl"

self.args = argparse.Namespace(

pn="1M",

model_path=model_path,

cfg_insertion_layer=0,

vae_type=32,

vae_path=vae_path,

add_lvl_embeding_only_first_block=1,

use_bit_label=1,

model_type="infinity_2b",

rope2d_each_sa_layer=1,

rope2d_normalized_by_hw=2,

use_scale_schedule_embedding=0,

sampling_per_bits=1,

text_encoder_ckpt=text_encoder_ckpt,

text_channels=2048,

apply_spatial_patchify=0,

h_div_w_template=1.000,

use_flex_attn=0,

cache_dir="/tmp/cache",

checkpoint_type="torch",

bf16=1,

)

self.text_tokenizer, self.text_encoder = load_tokenizer(

t5_path=text_encoder_ckpt

)

# load vae

self.vae = load_visual_tokenizer(self.args)

# load infinity

self.infinity = load_transformer(self.vae, self.args)

def predict(

self,

prompt: str = Input(

description="Input prompt",

default="alien spaceship enterprise",

guidance_scale: float = Input(

description="Scale for classifier-free guidance", ge=1, le=10, default=3

tau: float = Input(description="tau in self attention", default=0.5),

seed: int = Input(

description="Random seed. Leave blank to randomize the seed", default=None

) -> Path:

"""Run a single prediction on the model"""

if seed is None:

seed = int.from_bytes(os.urandom(2), "big")

print(f"Using seed: {seed}")

h_div_w = 1 / 1 # aspect ratio, height:width

h_div_w_template_ = h_div_w_templates[

np.argmin(np.abs(h_div_w_templates - h_div_w))

]

scale_schedule = dynamic_resolution_h_w[h_div_w_template_][self.args.pn][

"scales"

]

scale_schedule = [(1, h, w) for (_, h, w) in scale_schedule]

generated_image = gen_one_img(

self.infinity,

self.vae,

self.text_tokenizer,

self.text_encoder,

prompt,

g_seed=seed,

gt_leak=0,

gt_ls_Bl=None,

cfg_list=guidance_scale,

tau_list=tau,

scale_schedule=scale_schedule,

cfg_insertion_layer=[self.args.cfg_insertion_layer],

vae_type=self.args.vae_type,

sampling_per_bits=self.args.sampling_per_bits,

enable_positive_prompt=0,

)

output_path = "/tmp/out.png"

cv2.imwrite(output_path, generated_image.cpu().numpy())

return Path(output_path)

Infinity/predict.py at main · octo-patch/Infinity · GitHub

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