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from pytorch_lightning import seed_everything |
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from scripts.demo.streamlit_helpers import * |
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from scripts.util.detection.nsfw_and_watermark_dectection import DeepFloydDataFiltering |
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import argparse |
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import tqdm |
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if __name__ == "__main__": |
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parser = argparse.ArgumentParser() |
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parser.add_argument('--model_version', type=str, default='2.1', |
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choices=['2.1', '2.1-768', 'xl']) |
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parser.add_argument("--num_samples", type=int, default=4) |
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parser.add_argument("--seed", type=int, default=42) |
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parser.add_argument("--prompt", type=str, default="a corgi is sitting on a couch") |
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parser.add_argument("--prompt_listpath", type=str, default="", help="path to a txt file with a list of prompts") |
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parser.add_argument("--negative_prompt", type=str, default="ugly, low quality") |
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parser.add_argument('--save_path', type=str, default='outputs/demo/txt2img/') |
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args = parser.parse_args() |
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seed_everything(args.seed) |
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save_path = args.save_path |
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version_map = { |
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'2.1': 'sd-2.1', |
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'2.1-768': 'sd-2.1-768', |
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'xl': 'SD-XL base', |
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} |
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SD_XL_BASE_RATIOS = { |
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"0.5": (704, 1408), |
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"0.52": (704, 1344), |
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"0.57": (768, 1344), |
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"0.6": (768, 1280), |
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"0.68": (832, 1216), |
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"0.72": (832, 1152), |
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"0.78": (896, 1152), |
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"0.82": (896, 1088), |
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"0.88": (960, 1088), |
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"0.94": (960, 1024), |
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"1.0": (1024, 1024), |
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"1.07": (1024, 960), |
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"1.13": (1088, 960), |
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"1.21": (1088, 896), |
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"1.29": (1152, 896), |
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"1.38": (1152, 832), |
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"1.46": (1216, 832), |
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"1.67": (1280, 768), |
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"1.75": (1344, 768), |
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"1.91": (1344, 704), |
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"2.0": (1408, 704), |
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"2.09": (1472, 704), |
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"2.4": (1536, 640), |
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"2.5": (1600, 640), |
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"2.89": (1664, 576), |
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"3.0": (1728, 576), |
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} |
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VERSION2SPECS = { |
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"SD-XL base": { |
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"H": 1024, |
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"W": 1024, |
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"C": 4, |
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"f": 8, |
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"is_legacy": False, |
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"config": "configs/inference/sd_xl_base.yaml", |
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"ckpt": "checkpoints/sd_xl_base_0.9.safetensors", |
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"is_guided": True, |
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}, |
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"sd-2.1": { |
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"H": 512, |
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"W": 512, |
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"C": 4, |
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"f": 8, |
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"is_legacy": True, |
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"config": "configs/inference/sd_2_1.yaml", |
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"ckpt": "checkpoints/v2-1_512-ema-pruned.safetensors", |
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"is_guided": True, |
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}, |
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"sd-2.1-768": { |
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"H": 768, |
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"W": 768, |
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"C": 4, |
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"f": 8, |
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"is_legacy": True, |
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"config": "configs/inference/sd_2_1_768.yaml", |
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"ckpt": "checkpoints/v2-1_768-ema-pruned.safetensors", |
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}, |
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"SDXL-Refiner": { |
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"H": 1024, |
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"W": 1024, |
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"C": 4, |
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"f": 8, |
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"is_legacy": True, |
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"config": "configs/inference/sd_xl_refiner.yaml", |
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"ckpt": "checkpoints/sd_xl_refiner_0.9.safetensors", |
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"is_guided": True, |
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}, |
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} |
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version = args.model_version |
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version = version_map[version] |
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version_dict = VERSION2SPECS[version] |
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state = init_st(version_dict) |
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if state["msg"]: |
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st.info(state["msg"]) |
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model = state["model"] |
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if version == "SD-XL base": |
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ratio = '1.0' |
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W, H = SD_XL_BASE_RATIOS[ratio] |
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else: |
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W, H = version_dict['W'], version_dict['H'] |
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C = version_dict["C"] |
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F = version_dict["f"] |
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if args.prompt_listpath: |
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with open(args.prompt_listpath, 'r') as f: |
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prompts = f.readlines() |
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prompts = [p.strip() for p in prompts] |
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else: |
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prompts = [args.prompt] |
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negative_prompt = args.negative_prompt |
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init_dict = { |
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"orig_width": W, |
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"orig_height": H, |
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"target_width": W, |
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"target_height": H, |
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} |
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for prompt in tqdm.tqdm(prompts): |
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print('Current Prompt: >>>>> {} <<<<<'.format(prompt)) |
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value_dict = init_embedder_options( |
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get_unique_embedder_keys_from_conditioner(state["model"].conditioner), |
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init_dict, |
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prompt=prompt, |
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negative_prompt=negative_prompt, |
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) |
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_, _, sampler = init_sampling( |
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use_identity_guider=not version_dict["is_guided"] |
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) |
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num_samples = args.num_samples |
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is_legacy=False |
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return_latents = False |
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filter=None |
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with torch.no_grad(): |
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samples = do_sample( |
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state["model"], |
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sampler, |
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value_dict, |
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num_samples, |
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H, |
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W, |
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C, |
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F, |
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force_uc_zero_embeddings=["txt"] if not is_legacy else [], |
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return_latents=return_latents, |
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filter=filter, |
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) |
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if samples is not None: |
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perform_save_locally(save_path, samples) |
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print("Saved samples to {}. Enjoy.".format(save_path)) |
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