mirror of
https://github.com/Stability-AI/stablediffusion.git
synced 2024-09-21 10:16:27 +00:00
198 lines
7.3 KiB
Python
198 lines
7.3 KiB
Python
import sys
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import torch
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import numpy as np
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import gradio as gr
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from PIL import Image
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from omegaconf import OmegaConf
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from einops import repeat, rearrange
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from pytorch_lightning import seed_everything
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from imwatermark import WatermarkEncoder
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from scripts.txt2img import put_watermark
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from ldm.models.diffusion.ddim import DDIMSampler
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from ldm.models.diffusion.ddpm import LatentUpscaleDiffusion, LatentUpscaleFinetuneDiffusion
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from ldm.util import exists, instantiate_from_config
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torch.set_grad_enabled(False)
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def initialize_model(config, ckpt):
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config = OmegaConf.load(config)
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model = instantiate_from_config(config.model)
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model.load_state_dict(torch.load(ckpt)["state_dict"], strict=False)
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device = torch.device(
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"cuda") if torch.cuda.is_available() else torch.device("cpu")
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model = model.to(device)
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sampler = DDIMSampler(model)
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return sampler
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def make_batch_sd(
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image,
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txt,
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device,
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num_samples=1,
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):
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image = np.array(image.convert("RGB"))
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image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
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batch = {
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"lr": rearrange(image, 'h w c -> 1 c h w'),
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"txt": num_samples * [txt],
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}
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batch["lr"] = repeat(batch["lr"].to(device=device),
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"1 ... -> n ...", n=num_samples)
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return batch
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def make_noise_augmentation(model, batch, noise_level=None):
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x_low = batch[model.low_scale_key]
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x_low = x_low.to(memory_format=torch.contiguous_format).float()
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x_aug, noise_level = model.low_scale_model(x_low, noise_level)
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return x_aug, noise_level
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def paint(sampler, image, prompt, seed, scale, h, w, steps, num_samples=1, callback=None, eta=0., noise_level=None):
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device = torch.device(
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"cuda") if torch.cuda.is_available() else torch.device("cpu")
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model = sampler.model
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seed_everything(seed)
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prng = np.random.RandomState(seed)
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start_code = prng.randn(num_samples, model.channels, h, w)
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start_code = torch.from_numpy(start_code).to(
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device=device, dtype=torch.float32)
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print("Creating invisible watermark encoder (see https://github.com/ShieldMnt/invisible-watermark)...")
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wm = "SDV2"
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wm_encoder = WatermarkEncoder()
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wm_encoder.set_watermark('bytes', wm.encode('utf-8'))
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with torch.no_grad(),\
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torch.autocast("cuda"):
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batch = make_batch_sd(
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image, txt=prompt, device=device, num_samples=num_samples)
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c = model.cond_stage_model.encode(batch["txt"])
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c_cat = list()
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if isinstance(model, LatentUpscaleFinetuneDiffusion):
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for ck in model.concat_keys:
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cc = batch[ck]
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if exists(model.reshuffle_patch_size):
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assert isinstance(model.reshuffle_patch_size, int)
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cc = rearrange(cc, 'b c (p1 h) (p2 w) -> b (p1 p2 c) h w',
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p1=model.reshuffle_patch_size, p2=model.reshuffle_patch_size)
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c_cat.append(cc)
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c_cat = torch.cat(c_cat, dim=1)
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# cond
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cond = {"c_concat": [c_cat], "c_crossattn": [c]}
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# uncond cond
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uc_cross = model.get_unconditional_conditioning(num_samples, "")
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uc_full = {"c_concat": [c_cat], "c_crossattn": [uc_cross]}
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elif isinstance(model, LatentUpscaleDiffusion):
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x_augment, noise_level = make_noise_augmentation(
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model, batch, noise_level)
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cond = {"c_concat": [x_augment],
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"c_crossattn": [c], "c_adm": noise_level}
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# uncond cond
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uc_cross = model.get_unconditional_conditioning(num_samples, "")
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uc_full = {"c_concat": [x_augment], "c_crossattn": [
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uc_cross], "c_adm": noise_level}
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else:
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raise NotImplementedError()
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shape = [model.channels, h, w]
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samples, intermediates = sampler.sample(
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steps,
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num_samples,
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shape,
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cond,
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verbose=False,
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eta=eta,
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unconditional_guidance_scale=scale,
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unconditional_conditioning=uc_full,
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x_T=start_code,
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callback=callback
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)
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with torch.no_grad():
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x_samples_ddim = model.decode_first_stage(samples)
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result = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
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result = result.cpu().numpy().transpose(0, 2, 3, 1) * 255
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return [put_watermark(Image.fromarray(img.astype(np.uint8)), wm_encoder) for img in result]
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def pad_image(input_image):
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pad_w, pad_h = np.max(((2, 2), np.ceil(
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np.array(input_image.size) / 64).astype(int)), axis=0) * 64 - input_image.size
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im_padded = Image.fromarray(
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np.pad(np.array(input_image), ((0, pad_h), (0, pad_w), (0, 0)), mode='edge'))
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return im_padded
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def predict(input_image, prompt, steps, num_samples, scale, seed, eta, noise_level):
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init_image = input_image.convert("RGB")
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image = pad_image(init_image) # resize to integer multiple of 32
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width, height = image.size
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noise_level = torch.Tensor(
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num_samples * [noise_level]).to(sampler.model.device).long()
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sampler.make_schedule(steps, ddim_eta=eta, verbose=True)
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result = paint(
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sampler=sampler,
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image=image,
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prompt=prompt,
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seed=seed,
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scale=scale,
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h=height, w=width, steps=steps,
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num_samples=num_samples,
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callback=None,
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noise_level=noise_level
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)
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return result
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sampler = initialize_model(sys.argv[1], sys.argv[2])
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block = gr.Blocks().queue()
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with block:
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with gr.Row():
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gr.Markdown("## Stable Diffusion Upscaling")
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with gr.Row():
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with gr.Column():
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input_image = gr.Image(source='upload', type="pil")
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gr.Markdown(
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"Tip: Add a description of the object that should be upscaled, e.g.: 'a professional photograph of a cat")
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prompt = gr.Textbox(label="Prompt")
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run_button = gr.Button(label="Run")
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with gr.Accordion("Advanced options", open=False):
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num_samples = gr.Slider(
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label="Number of Samples", minimum=1, maximum=4, value=1, step=1)
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steps = gr.Slider(label="DDIM Steps", minimum=2,
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maximum=200, value=75, step=1)
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scale = gr.Slider(
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label="Scale", minimum=0.1, maximum=30.0, value=10, step=0.1
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)
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seed = gr.Slider(
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label="Seed",
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minimum=0,
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maximum=2147483647,
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step=1,
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randomize=True,
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)
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eta = gr.Number(label="eta (DDIM)",
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value=0.0, min=0.0, max=1.0)
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noise_level = None
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if isinstance(sampler.model, LatentUpscaleDiffusion):
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# TODO: make this work for all models
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noise_level = gr.Number(
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label="Noise Augmentation", min=0, max=350, value=20, step=1)
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with gr.Column():
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gallery = gr.Gallery(label="Generated images", show_label=False).style(
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grid=[2], height="auto")
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run_button.click(fn=predict, inputs=[
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input_image, prompt, steps, num_samples, scale, seed, eta, noise_level], outputs=[gallery])
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block.launch()
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