143 lines
5 KiB
Python
143 lines
5 KiB
Python
from contextlib import nullcontext
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from functools import partial
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import fire
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import gradio as gr
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import numpy as np
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import torch
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from einops import rearrange
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from ldm.models.diffusion.ddim import DDIMSampler
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from ldm.models.diffusion.plms import PLMSSampler
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from omegaconf import OmegaConf
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from PIL import Image
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from torch import autocast
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from torchvision import transforms
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from scripts.image_variations import load_model_from_config
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@torch.no_grad()
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def sample_model(input_im, model, sampler, precision, h, w, ddim_steps, n_samples, scale, ddim_eta):
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precision_scope = autocast if precision=="autocast" else nullcontext
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with precision_scope("cuda"):
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with model.ema_scope():
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c = model.get_learned_conditioning(input_im).tile(n_samples,1,1)
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if scale != 1.0:
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uc = torch.zeros_like(c)
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else:
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uc = None
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shape = [4, h // 8, w // 8]
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samples_ddim, _ = sampler.sample(S=ddim_steps,
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conditioning=c,
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batch_size=n_samples,
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shape=shape,
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verbose=False,
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unconditional_guidance_scale=scale,
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unconditional_conditioning=uc,
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eta=ddim_eta,
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x_T=None)
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x_samples_ddim = model.decode_first_stage(samples_ddim)
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return torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0).cpu()
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def main(
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model,
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device,
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input_im,
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scale=3.0,
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n_samples=4,
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plms=True,
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ddim_steps=50,
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ddim_eta=1.0,
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precision="fp32",
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h=512,
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w=512,
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):
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input_im = transforms.ToTensor()(input_im).unsqueeze(0).to(device)
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input_im = input_im*2-1
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if plms:
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sampler = PLMSSampler(model)
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ddim_eta = 0.0
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else:
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sampler = DDIMSampler(model)
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x_samples_ddim = sample_model(input_im, model, sampler, precision, h, w, ddim_steps, n_samples, scale, ddim_eta)
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output_ims = []
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for x_sample in x_samples_ddim:
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x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
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output_ims.append(Image.fromarray(x_sample.astype(np.uint8)))
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return output_ims
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description = \
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"""Generate variations on an input image using a fine-tuned version of Stable Diffision.
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Trained by [Justin Pinkney](https://www.justinpinkney.com) ([@Buntworthy](https://twitter.com/Buntworthy)) at [Lambda](https://lambdalabs.com/)
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__Get the [code](https://github.com/justinpinkney/stable-diffusion) and [model](https://huggingface.co/lambdalabs/stable-diffusion-image-conditioned).__
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"""
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article = \
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"""
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## How does this work?
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The normal Stable Diffusion model is trained to be conditioned on text input. This version has had the original text encoder (from CLIP) removed, and replaced with
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the CLIP _image_ encoder instead. So instead of generating images based a text input, images are generated to match CLIP's embedding of the image.
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This creates images which have the same rough style and content, but different details, in particular the composition is generally quite different.
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This is a totally different approach to the img2img script of the original Stable Diffusion and gives very different results.
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The model was fine tuned on the [LAION aethetics v2 6+ dataset](https://laion.ai/blog/laion-aesthetics/) to accept the new conditioning.
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Training was done on 4xA6000 GPUs on [Lambda GPU Cloud](https://lambdalabs.com/service/gpu-cloud).
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More details on the method and training will come in a future blog post.
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"""
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def run_demo(
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device_idx=0,
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ckpt="models/ldm/stable-diffusion-v1/sd-clip-vit-l14-img-embed_ema_only.ckpt",
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config="configs/stable-diffusion/sd-image-condition-finetune.yaml",
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):
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device = f"cuda:{device_idx}"
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config = OmegaConf.load(config)
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model = load_model_from_config(config, ckpt, device=device)
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inputs = [
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gr.Image(),
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gr.Slider(0, 25, value=3, step=1, label="cfg scale"),
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gr.Slider(1, 4, value=1, step=1, label="Number images"),
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gr.Checkbox(True, label="plms"),
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gr.Slider(5, 50, value=25, step=5, label="steps"),
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]
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output = gr.Gallery(label="Generated variations")
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output.style(grid=2)
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fn_with_model = partial(main, model, device)
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fn_with_model.__name__ = "fn_with_model"
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examples = [
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["assets/im-examples/vermeer.jpg", 3, 1, True, 25],
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["assets/im-examples/matisse.jpg", 3, 1, True, 25],
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]
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demo = gr.Interface(
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fn=fn_with_model,
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title="Stable Diffusion Image Variations",
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description=description,
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article=article,
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inputs=inputs,
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outputs=output,
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examples=examples,
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allow_flagging="never",
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)
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demo.launch(enable_queue=True, share=True)
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if __name__ == "__main__":
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fire.Fire(run_demo)
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