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#!/usr/bin/env python
# coding: utf-8
# Uncomment to run on cpu
# import os
# os.environ["JAX_PLATFORM_NAME"] = "cpu"
import random
import gradio as gr
import jax
import numpy as np
from flax.jax_utils import replicate
from flax.training.common_utils import shard
from PIL import Image, ImageDraw, ImageFont
# ## CLIP Scoring
from transformers import BartTokenizer, CLIPProcessor, FlaxCLIPModel
from vqgan_jax.modeling_flax_vqgan import VQModel
from dalle_mini.model import CustomFlaxBartForConditionalGeneration
DALLE_REPO = "flax-community/dalle-mini"
DALLE_COMMIT_ID = "4d34126d0df8bc4a692ae933e3b902a1fa8b6114"
VQGAN_REPO = "flax-community/vqgan_f16_16384"
VQGAN_COMMIT_ID = "90cc46addd2dd8f5be21586a9a23e1b95aa506a9"
tokenizer = BartTokenizer.from_pretrained(DALLE_REPO, revision=DALLE_COMMIT_ID)
model = CustomFlaxBartForConditionalGeneration.from_pretrained(
DALLE_REPO, revision=DALLE_COMMIT_ID
)
vqgan = VQModel.from_pretrained(VQGAN_REPO, revision=VQGAN_COMMIT_ID)
def captioned_strip(images, caption=None, rows=1):
increased_h = 0 if caption is None else 48
w, h = images[0].size[0], images[0].size[1]
img = Image.new("RGB", (len(images) * w // rows, h * rows + increased_h))
for i, img_ in enumerate(images):
img.paste(img_, (i // rows * w, increased_h + (i % rows) * h))
if caption is not None:
draw = ImageDraw.Draw(img)
font = ImageFont.truetype(
"/usr/share/fonts/truetype/liberation2/LiberationMono-Bold.ttf", 40
)
draw.text((20, 3), caption, (255, 255, 255), font=font)
return img
def custom_to_pil(x):
x = np.clip(x, 0.0, 1.0)
x = (255 * x).astype(np.uint8)
x = Image.fromarray(x)
if not x.mode == "RGB":
x = x.convert("RGB")
return x
def generate(input, rng, params):
return model.generate(
**input,
max_length=257,
num_beams=1,
do_sample=True,
prng_key=rng,
eos_token_id=50000,
pad_token_id=50000,
params=params,
)
def get_images(indices, params):
return vqgan.decode_code(indices, params=params)
p_generate = jax.pmap(generate, "batch")
p_get_images = jax.pmap(get_images, "batch")
bart_params = replicate(model.params)
vqgan_params = replicate(vqgan.params)
clip = FlaxCLIPModel.from_pretrained("openai/clip-vit-base-patch32")
print("Initialize FlaxCLIPModel")
processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
print("Initialize CLIPProcessor")
def hallucinate(prompt, num_images=64):
prompt = [prompt] * jax.device_count()
inputs = tokenizer(
prompt,
return_tensors="jax",
padding="max_length",
truncation=True,
max_length=128,
).data
inputs = shard(inputs)
all_images = []
for i in range(num_images // jax.device_count()):
key = random.randint(0, 1e7)
rng = jax.random.PRNGKey(key)
rngs = jax.random.split(rng, jax.local_device_count())
indices = p_generate(inputs, rngs, bart_params).sequences
indices = indices[:, :, 1:]
images = p_get_images(indices, vqgan_params)
images = np.squeeze(np.asarray(images), 1)
for image in images:
all_images.append(custom_to_pil(image))
return all_images
def clip_top_k(prompt, images, k=8):
inputs = processor(text=prompt, images=images, return_tensors="np", padding=True)
outputs = clip(**inputs)
logits = outputs.logits_per_text
scores = np.array(logits[0]).argsort()[-k:][::-1]
return [images[score] for score in scores]
def compose_predictions(images, caption=None):
increased_h = 0 if caption is None else 48
w, h = images[0].size[0], images[0].size[1]
img = Image.new("RGB", (len(images) * w, h + increased_h))
for i, img_ in enumerate(images):
img.paste(img_, (i * w, increased_h))
if caption is not None:
draw = ImageDraw.Draw(img)
font = ImageFont.truetype(
"/usr/share/fonts/truetype/liberation2/LiberationMono-Bold.ttf", 40
)
draw.text((20, 3), caption, (255, 255, 255), font=font)
return img
def top_k_predictions(prompt, num_candidates=32, k=8):
images = hallucinate(prompt, num_images=num_candidates)
images = clip_top_k(prompt, images, k=k)
return images
def run_inference(prompt, num_images=32, num_preds=8):
images = top_k_predictions(prompt, num_candidates=num_images, k=num_preds)
predictions = captioned_strip(images)
output_title = f"""
<b>{prompt}</b>
"""
return (output_title, predictions)
outputs = [
gr.outputs.HTML(label=""), # To be used as title
gr.outputs.Image(label=""),
]
description = """
DALL路E-mini is an AI model that generates images from any prompt you give! Generate images from text:
"""
gr.Interface(
run_inference,
inputs=[gr.inputs.Textbox(label="What do you want to see?")],
outputs=outputs,
title="DALL路E mini",
description=description,
article="<p style='text-align: center'> Created by Boris Dayma et al. 2021 | <a href='https://github.com/borisdayma/dalle-mini'>GitHub</a> | <a href='https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini--Vmlldzo4NjIxODA'>Report</a></p>",
layout="vertical",
theme="huggingface",
examples=[
["an armchair in the shape of an avocado"],
["snowy mountains by the sea"],
],
allow_flagging=False,
live=False,
# server_port=8999
).launch(share=True)
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