server.py~

import base64
from io import BytesIO
from fastapi import FastAPI
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from PIL import Image
import torch as th

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from PIL import Image
import torch as th
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from glide_text2im.download import load_checkpoint
from glide_text2im.model_creation import (
    create_model_and_diffusion,
    model_and_diffusion_defaults,
    model_and_diffusion_defaults_upsampler
)
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print("Loading models...")
app = FastAPI()

# This notebook supports both CPU and GPU.
# On CPU, generating one sample may take on the order of 20 minutes.
# On a GPU, it should be under a minute.

has_cuda = th.cuda.is_available()
device = th.device('cpu' if not has_cuda else 'cuda')

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print("Loading models...")
app = FastAPI()
# This notebook supports both CPU and GPU.
# On CPU, generating one sample may take on the order of 20 minutes.
# On a GPU, it should be under a minute.
has_cuda = th.cuda.is_available()
device = th.device('cpu' if not has_cuda else 'cuda')
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# Create base model.
options = model_and_diffusion_defaults()
options['use_fp16'] = has_cuda
options['timestep_respacing'] = '100' # use 100 diffusion steps for fast sampling
model, diffusion = create_model_and_diffusion(**options)
model.eval()
if has_cuda:
    model.convert_to_fp16()
model.to(device)
model.load_state_dict(load_checkpoint('base', device))
print('total base parameters', sum(x.numel() for x in model.parameters()))
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# Create upsampler model.
options_up = model_and_diffusion_defaults_upsampler()
options_up['use_fp16'] = has_cuda
options_up['timestep_respacing'] = 'fast27' # use 27 diffusion steps for very fast sampling
model_up, diffusion_up = create_model_and_diffusion(**options_up)
model_up.eval()
if has_cuda:
    model_up.convert_to_fp16()
model_up.to(device)
model_up.load_state_dict(load_checkpoint('upsample', device))
print('total upsampler parameters', sum(x.numel() for x in model_up.parameters()))
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def get_images(batch: th.Tensor):
    """ Display a batch of images inline. """
    scaled = ((batch + 1)*127.5).round().clamp(0,255).to(th.uint8).cpu()
    reshaped = scaled.permute(2, 0, 3, 1).reshape([batch.shape[2], -1, 3])
    Image.fromarray(reshaped.numpy())
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# Create a classifier-free guidance sampling function
guidance_scale = 3.0

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# Create a classifier-free guidance sampling function
guidance_scale = 3.0
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def model_fn(x_t, ts, **kwargs):
    half = x_t[: len(x_t) // 2]
    combined = th.cat([half, half], dim=0)
    model_out = model(combined, ts, **kwargs)
    eps, rest = model_out[:, :3], model_out[:, 3:]
    cond_eps, uncond_eps = th.split(eps, len(eps) // 2, dim=0)
    half_eps = uncond_eps + guidance_scale * (cond_eps - uncond_eps)
    eps = th.cat([half_eps, half_eps], dim=0)
    return th.cat([eps, rest], dim=1)
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@app.get("/")
def read_root():
    return {"glide!"}

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@app.get("/")
def read_root():
    return {"glide!"}
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@app.get("/{generate}")
def sample(prompt):
    # Sampling parameters
    batch_size = 1
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    # Tune this parameter to control the sharpness of 256x256 images.
    # A value of 1.0 is sharper, but sometimes results in grainy artifacts.
    upsample_temp = 0.997

    ##############################
    # Sample from the base model #
    ##############################

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    # Tune this parameter to control the sharpness of 256x256 images.
    # A value of 1.0 is sharper, but sometimes results in grainy artifacts.
    upsample_temp = 0.997
    ##############################
    # Sample from the base model #
    ##############################
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    # Create the text tokens to feed to the model.
    tokens = model.tokenizer.encode(prompt)
    tokens, mask = model.tokenizer.padded_tokens_and_mask(
        tokens, options['text_ctx']
    )
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    # Create the classifier-free guidance tokens (empty)
    full_batch_size = batch_size * 2
    uncond_tokens, uncond_mask = model.tokenizer.padded_tokens_and_mask(
        [], options['text_ctx']
    )
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    # Pack the tokens together into model kwargs.
    model_kwargs = dict(
        tokens=th.tensor(
            [tokens] * batch_size + [uncond_tokens] * batch_size, device=device
        ),
        mask=th.tensor(
            [mask] * batch_size + [uncond_mask] * batch_size,
            dtype=th.bool,
            device=device,
        ),
    )
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    # Sample from the base model.
    model.del_cache()
    samples = diffusion.p_sample_loop(
        model_fn,
        (full_batch_size, 3, options["image_size"], options["image_size"]),
        device=device,
        clip_denoised=True,
        progress=True,
        model_kwargs=model_kwargs,
        cond_fn=None,
    )[:batch_size]
    model.del_cache()
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    ##############################
    # Upsample the 64x64 samples #
    ##############################

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    ##############################
    # Upsample the 64x64 samples #
    ##############################
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    tokens = model_up.tokenizer.encode(prompt)
    tokens, mask = model_up.tokenizer.padded_tokens_and_mask(
        tokens, options_up['text_ctx']
    )
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    # Create the model conditioning dict.
    model_kwargs = dict(
        # Low-res image to upsample.
        low_res=((samples+1)*127.5).round()/127.5 - 1,
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        # Text tokens
        tokens=th.tensor(
            [tokens] * batch_size, device=device
        ),
        mask=th.tensor(
            [mask] * batch_size,
            dtype=th.bool,
            device=device,
        ),
    )
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    # Sample from the base model.
    model_up.del_cache()
    up_shape = (batch_size, 3, options_up["image_size"], options_up["image_size"])
    up_samples = diffusion_up.ddim_sample_loop(
        model_up,
        up_shape,
        noise=th.randn(up_shape, device=device) * upsample_temp,
        device=device,
        clip_denoised=True,
        progress=True,
        model_kwargs=model_kwargs,
        cond_fn=None,
    )[:batch_size]
    model_up.del_cache()
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    # Show the output
    image = get_images(up_samples)
    image = to_base64(image)
    return {"image": image}
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def to_base64(pil_image):
    buffered = BytesIO()
    pil_image.save(buffered, format="JPEG")
    return base64.b64encode(buffered.getvalue())