diffusers.py

from functools import partial

import diffusers
import torch
import transformers

from ..registry import ModelAttribute, model_zoo

BATCH_SIZE = 2
SEQ_LENGTH = 5
HEIGHT = 224
WIDTH = 224
IN_CHANNELS = 3
LATENTS_SHAPE = (BATCH_SIZE, IN_CHANNELS, HEIGHT // 7, WIDTH // 7)
TIME_STEP = 3

data_vae_fn = lambda: dict(sample=torch.randn(2, 3, 32, 32))
data_unet_fn = lambda: dict(sample=torch.randn(2, 3, 32, 32), timestep=3)

identity_output = lambda x: x


def data_clip_model():
    input_ids = torch.zeros((BATCH_SIZE, SEQ_LENGTH), dtype=torch.int64)
    attention_mask = torch.zeros((BATCH_SIZE, SEQ_LENGTH), dtype=torch.int64)
    position_ids = torch.zeros((BATCH_SIZE, SEQ_LENGTH), dtype=torch.int64)
    pixel_values = torch.zeros((BATCH_SIZE, IN_CHANNELS, HEIGHT, WIDTH), dtype=torch.float32)
    return dict(input_ids=input_ids,
                pixel_values=pixel_values,
                attention_mask=attention_mask,
                position_ids=position_ids)


def data_clip_text():
    input_ids = torch.zeros((BATCH_SIZE, SEQ_LENGTH), dtype=torch.int64)
    attention_mask = torch.zeros((BATCH_SIZE, SEQ_LENGTH), dtype=torch.int64)
    return dict(input_ids=input_ids, attention_mask=attention_mask)


def data_clip_vision():
    pixel_values = torch.zeros((BATCH_SIZE, IN_CHANNELS, HEIGHT, WIDTH), dtype=torch.float32)
    return dict(pixel_values=pixel_values)


model_zoo.register(name='diffusers_auto_encoder_kl',
                   model_fn=diffusers.AutoencoderKL,
                   data_gen_fn=data_vae_fn,
                   output_transform_fn=identity_output)

model_zoo.register(name='diffusers_vq_model',
                   model_fn=diffusers.VQModel,
                   data_gen_fn=data_vae_fn,
                   output_transform_fn=identity_output)

model_zoo.register(name='diffusers_clip_model',
                   model_fn=partial(transformers.CLIPModel, config=transformers.CLIPConfig()),
                   data_gen_fn=data_clip_model,
                   output_transform_fn=identity_output)

model_zoo.register(name='diffusers_clip_text_model',
                   model_fn=partial(transformers.CLIPTextModel, config=transformers.CLIPTextConfig()),
                   data_gen_fn=data_clip_text,
                   output_transform_fn=identity_output)

model_zoo.register(name='diffusers_clip_vision_model',
                   model_fn=partial(transformers.CLIPVisionModel, config=transformers.CLIPVisionConfig()),
                   data_gen_fn=data_clip_vision,
                   output_transform_fn=identity_output)

model_zoo.register(name='diffusers_unet2d_model',
                   model_fn=diffusers.UNet2DModel,
                   data_gen_fn=data_unet_fn,
                   output_transform_fn=identity_output)