From 74d5ba2d9e1bbab935a7a0fefa980c03e5e02d6d Mon Sep 17 00:00:00 2001 From: Yuwei Guo Date: Wed, 19 Jul 2023 23:27:43 +0800 Subject: [PATCH 1/5] update --- app.py | 378 ++++++++++++++++++++--------------------------- requirements.txt | 12 ++ 2 files changed, 170 insertions(+), 220 deletions(-) create mode 100644 requirements.txt diff --git a/app.py b/app.py index d4df5db9..e9b6519e 100644 --- a/app.py +++ b/app.py @@ -1,17 +1,15 @@ import os -import json import torch import random import gradio as gr from glob import glob from omegaconf import OmegaConf -from datetime import datetime from safetensors import safe_open from diffusers import AutoencoderKL -from diffusers import DDIMScheduler, EulerDiscreteScheduler, PNDMScheduler +from diffusers import EulerDiscreteScheduler, DDIMScheduler from diffusers.utils.import_utils import is_xformers_available from transformers import CLIPTextModel, CLIPTokenizer @@ -19,15 +17,10 @@ from animatediff.pipelines.pipeline_animation import AnimationPipeline from animatediff.utils.util import save_videos_grid from animatediff.utils.convert_from_ckpt import convert_ldm_unet_checkpoint, convert_ldm_clip_checkpoint, convert_ldm_vae_checkpoint -from animatediff.utils.convert_lora_safetensor_to_diffusers import convert_lora -sample_idx = 0 -scheduler_dict = { - "Euler": EulerDiscreteScheduler, - "PNDM": PNDMScheduler, - "DDIM": DDIMScheduler, -} +pretrained_model_path = "models/StableDiffusion/stable-diffusion-v1-5" +inference_config_path = "configs/inference/inference.yaml" css = """ .toolbutton { @@ -38,6 +31,49 @@ } """ +examples = [ + # 1-ToonYou + [ + "toonyou_beta3.safetensors", + "mm_sd_v14.ckpt", + "masterpiece, best quality, 1girl, solo, cherry blossoms, hanami, pink flower, white flower, spring season, wisteria, petals, flower, plum blossoms, outdoors, falling petals, white hair, black eyes", + "worst quality, low quality, nsfw, logo", + 512, 512, "13204175718326964000" + ], + # 2-Lyriel + [ + "lyriel_v16.safetensors", + "mm_sd_v15.ckpt", + "A forbidden castle high up in the mountains, pixel art, intricate details2, hdr, intricate details, hyperdetailed5, natural skin texture, hyperrealism, soft light, sharp, game art, key visual, surreal", + "3d, cartoon, anime, sketches, worst quality, low quality, normal quality, lowres, normal quality, monochrome, grayscale, skin spots, acnes, skin blemishes, bad anatomy, girl, loli, young, large breasts, red eyes, muscular", + 512, 512, "6681501646976930000" + ], + # 3-RCNZ + [ + "rcnzCartoon3d_v10.safetensors", + "mm_sd_v14.ckpt", + "Jane Eyre with headphones, natural skin texture,4mm,k textures, soft cinematic light, adobe lightroom, photolab, hdr, intricate, elegant, highly detailed, sharp focus, cinematic look, soothing tones, insane details, intricate details, hyperdetailed, low contrast, soft cinematic light, dim colors, exposure blend, hdr, faded", + "deformed, distorted, disfigured, poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, mutated hands and fingers, disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation", + 512, 512, "2416282124261060" + ], + # 4-MajicMix + [ + "majicmixRealistic_v5Preview.safetensors", + "mm_sd_v14.ckpt", + "1girl, offshoulder, light smile, shiny skin best quality, masterpiece, photorealistic", + "bad hand, worst quality, low quality, normal quality, lowres, bad anatomy, bad hands, watermark, moles", + 512, 512, "7132772652786303" + ], + # 5-RealisticVision + [ + "realisticVisionV20_v20.safetensors", + "mm_sd_v15.ckpt", + "photo of coastline, rocks, storm weather, wind, waves, lightning, 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3", + "blur, haze, deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers, deformed, distorted, disfigured, poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, ugly, disgusting, amputation", + 512, 512, "1490157606650685400" + ] +] + class AnimateController: def __init__(self): @@ -46,156 +82,120 @@ def __init__(self): self.stable_diffusion_dir = os.path.join(self.basedir, "models", "StableDiffusion") self.motion_module_dir = os.path.join(self.basedir, "models", "Motion_Module") self.personalized_model_dir = os.path.join(self.basedir, "models", "DreamBooth_LoRA") - self.savedir = os.path.join(self.basedir, "samples", datetime.now().strftime("Gradio-%Y-%m-%dT%H-%M-%S")) - self.savedir_sample = os.path.join(self.savedir, "sample") + self.savedir = os.path.join(self.basedir, "samples") os.makedirs(self.savedir, exist_ok=True) - self.stable_diffusion_list = [] - self.motion_module_list = [] - self.personalized_model_list = [] + self.base_model_list = [] + self.motion_module_list = [] + + self.selected_base_model = None + self.selected_motion_module = None - self.refresh_stable_diffusion() self.refresh_motion_module() self.refresh_personalized_model() # config models - self.tokenizer = None - self.text_encoder = None - self.vae = None - self.unet = None - self.pipeline = None - self.lora_model_state_dict = {} - - self.inference_config = OmegaConf.load("configs/inference/inference.yaml") - - def refresh_stable_diffusion(self): - self.stable_diffusion_list = glob(os.path.join(self.stable_diffusion_dir, "*/")) + self.inference_config = OmegaConf.load(inference_config_path) + self.tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_path, subfolder="tokenizer") + self.text_encoder = CLIPTextModel.from_pretrained(pretrained_model_path, subfolder="text_encoder").cuda() + self.vae = AutoencoderKL.from_pretrained(pretrained_model_path, subfolder="vae").cuda() + self.unet = UNet3DConditionModel.from_pretrained_2d(pretrained_model_path, subfolder="unet", unet_additional_kwargs=OmegaConf.to_container(self.inference_config.unet_additional_kwargs)).cuda() + + self.update_base_model(self.base_model_list[0]) + self.update_motion_module(self.motion_module_list[0]) + + def refresh_motion_module(self): motion_module_list = glob(os.path.join(self.motion_module_dir, "*.ckpt")) self.motion_module_list = [os.path.basename(p) for p in motion_module_list] def refresh_personalized_model(self): - personalized_model_list = glob(os.path.join(self.personalized_model_dir, "*.safetensors")) - self.personalized_model_list = [os.path.basename(p) for p in personalized_model_list] - - def update_stable_diffusion(self, stable_diffusion_dropdown): - self.tokenizer = CLIPTokenizer.from_pretrained(stable_diffusion_dropdown, subfolder="tokenizer") - self.text_encoder = CLIPTextModel.from_pretrained(stable_diffusion_dropdown, subfolder="text_encoder").cuda() - self.vae = AutoencoderKL.from_pretrained(stable_diffusion_dropdown, subfolder="vae").cuda() - self.unet = UNet3DConditionModel.from_pretrained_2d(stable_diffusion_dropdown, subfolder="unet", unet_additional_kwargs=OmegaConf.to_container(self.inference_config.unet_additional_kwargs)).cuda() - return gr.Dropdown.update() + base_model_list = glob(os.path.join(self.personalized_model_dir, "*.safetensors")) + self.base_model_list = [os.path.basename(p) for p in base_model_list] - def update_motion_module(self, motion_module_dropdown): - if self.unet is None: - gr.Info(f"Please select a pretrained model path.") - return gr.Dropdown.update(value=None) - else: - motion_module_dropdown = os.path.join(self.motion_module_dir, motion_module_dropdown) - motion_module_state_dict = torch.load(motion_module_dropdown, map_location="cpu") - missing, unexpected = self.unet.load_state_dict(motion_module_state_dict, strict=False) - assert len(unexpected) == 0 - return gr.Dropdown.update() def update_base_model(self, base_model_dropdown): - if self.unet is None: - gr.Info(f"Please select a pretrained model path.") - return gr.Dropdown.update(value=None) - else: - base_model_dropdown = os.path.join(self.personalized_model_dir, base_model_dropdown) - base_model_state_dict = {} - with safe_open(base_model_dropdown, framework="pt", device="cpu") as f: - for key in f.keys(): - base_model_state_dict[key] = f.get_tensor(key) - - converted_vae_checkpoint = convert_ldm_vae_checkpoint(base_model_state_dict, self.vae.config) - self.vae.load_state_dict(converted_vae_checkpoint) - - converted_unet_checkpoint = convert_ldm_unet_checkpoint(base_model_state_dict, self.unet.config) - self.unet.load_state_dict(converted_unet_checkpoint, strict=False) + self.selected_base_model = base_model_dropdown + + base_model_dropdown = os.path.join(self.personalized_model_dir, base_model_dropdown) + base_model_state_dict = {} + with safe_open(base_model_dropdown, framework="pt", device="cpu") as f: + for key in f.keys(): base_model_state_dict[key] = f.get_tensor(key) + + converted_vae_checkpoint = convert_ldm_vae_checkpoint(base_model_state_dict, self.vae.config) + self.vae.load_state_dict(converted_vae_checkpoint) - self.text_encoder = convert_ldm_clip_checkpoint(base_model_state_dict) - return gr.Dropdown.update() + converted_unet_checkpoint = convert_ldm_unet_checkpoint(base_model_state_dict, self.unet.config) + self.unet.load_state_dict(converted_unet_checkpoint, strict=False) - def update_lora_model(self, lora_model_dropdown): - lora_model_dropdown = os.path.join(self.personalized_model_dir, lora_model_dropdown) - self.lora_model_state_dict = {} - if lora_model_dropdown == "none": pass - else: - with safe_open(lora_model_dropdown, framework="pt", device="cpu") as f: - for key in f.keys(): - self.lora_model_state_dict[key] = f.get_tensor(key) + self.text_encoder = convert_ldm_clip_checkpoint(base_model_state_dict) return gr.Dropdown.update() + def update_motion_module(self, motion_module_dropdown): + self.selected_motion_module = motion_module_dropdown + + motion_module_dropdown = os.path.join(self.motion_module_dir, motion_module_dropdown) + motion_module_state_dict = torch.load(motion_module_dropdown, map_location="cpu") + _, unexpected = self.unet.load_state_dict(motion_module_state_dict, strict=False) + assert len(unexpected) == 0 + return gr.Dropdown.update() + + def animate( self, - stable_diffusion_dropdown, - motion_module_dropdown, base_model_dropdown, - lora_alpha_slider, - prompt_textbox, - negative_prompt_textbox, - sampler_dropdown, - sample_step_slider, - width_slider, - length_slider, - height_slider, - cfg_scale_slider, - seed_textbox - ): - if self.unet is None: - raise gr.Error(f"Please select a pretrained model path.") - if motion_module_dropdown == "": - raise gr.Error(f"Please select a motion module.") - if base_model_dropdown == "": - raise gr.Error(f"Please select a base DreamBooth model.") - + motion_module_dropdown, + prompt_textbox, + negative_prompt_textbox, + width_slider, + height_slider, + seed_textbox, + ): + if self.selected_base_model != base_model_dropdown: self.update_base_model(base_model_dropdown) + if self.selected_motion_module != motion_module_dropdown: self.update_motion_module(motion_module_dropdown) + if is_xformers_available(): self.unet.enable_xformers_memory_efficient_attention() pipeline = AnimationPipeline( vae=self.vae, text_encoder=self.text_encoder, tokenizer=self.tokenizer, unet=self.unet, - scheduler=scheduler_dict[sampler_dropdown](**OmegaConf.to_container(self.inference_config.noise_scheduler_kwargs)) + scheduler=DDIMScheduler(**OmegaConf.to_container(self.inference_config.noise_scheduler_kwargs)) ).to("cuda") - if self.lora_model_state_dict != {}: - pipeline = convert_lora(pipeline, self.lora_model_state_dict, alpha=lora_alpha_slider) - - pipeline.to("cuda") - - if seed_textbox != -1 and seed_textbox != "": torch.manual_seed(int(seed_textbox)) - else: torch.seed() - seed = torch.initial_seed() + if int(seed_textbox) > 0: seed = int(seed_textbox) + else: seed = random.randint(1, 1e16) + torch.manual_seed(int(seed)) + + assert seed == torch.initial_seed() + print(f"### seed: {seed}") + + generator = torch.Generator(device="cuda") + generator.manual_seed(seed) sample = pipeline( prompt_textbox, negative_prompt = negative_prompt_textbox, - num_inference_steps = sample_step_slider, - guidance_scale = cfg_scale_slider, + num_inference_steps = 25, + guidance_scale = 8., width = width_slider, height = height_slider, - video_length = length_slider, + video_length = 16, + generator = generator, ).videos - save_sample_path = os.path.join(self.savedir_sample, f"{sample_idx}.mp4") + save_sample_path = os.path.join(self.savedir, f"sample.mp4") save_videos_grid(sample, save_sample_path) - sample_config = { + json_config = { "prompt": prompt_textbox, "n_prompt": negative_prompt_textbox, - "sampler": sampler_dropdown, - "num_inference_steps": sample_step_slider, - "guidance_scale": cfg_scale_slider, "width": width_slider, "height": height_slider, - "video_length": length_slider, - "seed": seed + "seed": seed, + "base_model": base_model_dropdown, + "motion_module": motion_module_dropdown, } - json_str = json.dumps(sample_config, indent=4) - with open(os.path.join(self.savedir, "logs.json"), "a") as f: - f.write(json_str) - f.write("\n\n") - - return gr.Video.update(value=save_sample_path) + return gr.Video.update(value=save_sample_path), gr.Json.update(value=json_config) controller = AnimateController() @@ -205,124 +205,62 @@ def ui(): with gr.Blocks(css=css) as demo: gr.Markdown( """ - # [AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning](https://arxiv.org/abs/2307.04725) + # AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning Yuwei Guo, Ceyuan Yang*, Anyi Rao, Yaohui Wang, Yu Qiao, Dahua Lin, Bo Dai (*Corresponding Author)
[Arxiv Report](https://arxiv.org/abs/2307.04725) | [Project Page](https://animatediff.github.io/) | [Github](https://github.com/guoyww/animatediff/) """ ) - with gr.Column(variant="panel"): - gr.Markdown( - """ - ### 1. Model checkpoints (select pretrained model path first). - """ - ) - with gr.Row(): - stable_diffusion_dropdown = gr.Dropdown( - label="Pretrained Model Path", - choices=controller.stable_diffusion_list, - interactive=True, - ) - stable_diffusion_dropdown.change(fn=controller.update_stable_diffusion, inputs=[stable_diffusion_dropdown], outputs=[stable_diffusion_dropdown]) - - stable_diffusion_refresh_button = gr.Button(value="\U0001F503", elem_classes="toolbutton") - def update_stable_diffusion(): - controller.refresh_stable_diffusion() - return gr.Dropdown.update(choices=controller.stable_diffusion_list) - stable_diffusion_refresh_button.click(fn=update_stable_diffusion, inputs=[], outputs=[stable_diffusion_dropdown]) + gr.Markdown( + """ + ### Quick Start + 1. Select desired `Base DreamBooth Model`. + 2. Select `Motion Module` from `mm_sd_v14.ckpt` and `mm_sd_v15.ckpt`. We recommend trying both of them for the best results. + 3. Provide `Prompt` and `Negative Prompt` for each model. You are encouraged to refer to each model's webpage on CivitAI to learn how to write prompts for them. Below are the DreamBooth models in this demo. Click to visit their homepage. + - [`toonyou_beta3.safetensors`](https://civitai.com/models/30240?modelVersionId=78775) + - [`lyriel_v16.safetensors`](https://civitai.com/models/22922/lyriel) + - [`rcnzCartoon3d_v10.safetensors`](https://civitai.com/models/66347?modelVersionId=71009) + - [`majicmixRealistic_v5Preview.safetensors`](https://civitai.com/models/43331?modelVersionId=79068) + - [`realisticVisionV20_v20.safetensors`](https://civitai.com/models/4201?modelVersionId=29460) + 4. Click `Generate`, wait for ~1 min, and enjoy. + """ + ) + with gr.Row(): + with gr.Column(): + base_model_dropdown = gr.Dropdown( label="Base DreamBooth Model", choices=controller.base_model_list, value=controller.base_model_list[0], interactive=True ) + motion_module_dropdown = gr.Dropdown( label="Motion Module", choices=controller.motion_module_list, value=controller.motion_module_list[0], interactive=True ) - with gr.Row(): - motion_module_dropdown = gr.Dropdown( - label="Select motion module", - choices=controller.motion_module_list, - interactive=True, - ) + base_model_dropdown.change(fn=controller.update_base_model, inputs=[base_model_dropdown], outputs=[base_model_dropdown]) motion_module_dropdown.change(fn=controller.update_motion_module, inputs=[motion_module_dropdown], outputs=[motion_module_dropdown]) - - motion_module_refresh_button = gr.Button(value="\U0001F503", elem_classes="toolbutton") - def update_motion_module(): - controller.refresh_motion_module() - return gr.Dropdown.update(choices=controller.motion_module_list) - motion_module_refresh_button.click(fn=update_motion_module, inputs=[], outputs=[motion_module_dropdown]) - - base_model_dropdown = gr.Dropdown( - label="Select base Dreambooth model (required)", - choices=controller.personalized_model_list, - interactive=True, - ) - base_model_dropdown.change(fn=controller.update_base_model, inputs=[base_model_dropdown], outputs=[base_model_dropdown]) - - lora_model_dropdown = gr.Dropdown( - label="Select LoRA model (optional)", - choices=["none"] + controller.personalized_model_list, - value="none", - interactive=True, - ) - lora_model_dropdown.change(fn=controller.update_lora_model, inputs=[lora_model_dropdown], outputs=[lora_model_dropdown]) - - lora_alpha_slider = gr.Slider(label="LoRA alpha", value=0.8, minimum=0, maximum=2, interactive=True) - - personalized_refresh_button = gr.Button(value="\U0001F503", elem_classes="toolbutton") - def update_personalized_model(): - controller.refresh_personalized_model() - return [ - gr.Dropdown.update(choices=controller.personalized_model_list), - gr.Dropdown.update(choices=["none"] + controller.personalized_model_list) - ] - personalized_refresh_button.click(fn=update_personalized_model, inputs=[], outputs=[base_model_dropdown, lora_model_dropdown]) - with gr.Column(variant="panel"): - gr.Markdown( - """ - ### 2. Configs for AnimateDiff. - """ - ) - - prompt_textbox = gr.Textbox(label="Prompt", lines=2) - negative_prompt_textbox = gr.Textbox(label="Negative prompt", lines=2) - - with gr.Row().style(equal_height=False): - with gr.Column(): + prompt_textbox = gr.Textbox( label="Prompt", lines=3 ) + negative_prompt_textbox = gr.Textbox( label="Negative Prompt", lines=3, value="worst quality, low quality, nsfw, logo") + + with gr.Accordion("Advance", open=False): with gr.Row(): - sampler_dropdown = gr.Dropdown(label="Sampling method", choices=list(scheduler_dict.keys()), value=list(scheduler_dict.keys())[0]) - sample_step_slider = gr.Slider(label="Sampling steps", value=25, minimum=10, maximum=100, step=1) - - width_slider = gr.Slider(label="Width", value=512, minimum=256, maximum=1024, step=64) - height_slider = gr.Slider(label="Height", value=512, minimum=256, maximum=1024, step=64) - length_slider = gr.Slider(label="Animation length", value=16, minimum=8, maximum=24, step=1) - cfg_scale_slider = gr.Slider(label="CFG Scale", value=7.5, minimum=0, maximum=20) - + width_slider = gr.Slider( label="Width", value=512, minimum=256, maximum=1024, step=64 ) + height_slider = gr.Slider( label="Height", value=512, minimum=256, maximum=1024, step=64 ) with gr.Row(): - seed_textbox = gr.Textbox(label="Seed", value=-1) + seed_textbox = gr.Textbox( label="Seed", value=-1) seed_button = gr.Button(value="\U0001F3B2", elem_classes="toolbutton") - seed_button.click(fn=lambda: gr.Textbox.update(value=random.randint(1, 1e8)), inputs=[], outputs=[seed_textbox]) - - generate_button = gr.Button(value="Generate", variant='primary') - - result_video = gr.Video(label="Generated Animation", interactive=False) + seed_button.click(fn=lambda: gr.Textbox.update(value=random.randint(1, 1e16)), inputs=[], outputs=[seed_textbox]) + + generate_button = gr.Button( value="Generate", variant='primary' ) + + with gr.Column(): + result_video = gr.Video( label="Generated Animation", interactive=False ) + json_config = gr.Json( label="Config", value=None ) - generate_button.click( - fn=controller.animate, - inputs=[ - stable_diffusion_dropdown, - motion_module_dropdown, - base_model_dropdown, - lora_alpha_slider, - prompt_textbox, - negative_prompt_textbox, - sampler_dropdown, - sample_step_slider, - width_slider, - length_slider, - height_slider, - cfg_scale_slider, - seed_textbox, - ], - outputs=[result_video] - ) + inputs = [base_model_dropdown, motion_module_dropdown, prompt_textbox, negative_prompt_textbox, width_slider, height_slider, seed_textbox] + outputs = [result_video, json_config] + generate_button.click( fn=controller.animate, inputs=inputs, outputs=outputs ) + + gr.Examples( fn=controller.animate, examples=examples, inputs=inputs, outputs=outputs, cache_examples=True ) + return demo if __name__ == "__main__": demo = ui() - demo.launch(share=True) + demo.queue(max_size=20) + demo.launch() diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 00000000..d2dda00e --- /dev/null +++ b/requirements.txt @@ -0,0 +1,12 @@ +torch==1.13.1 +torchvision==0.14.1 +torchaudio==0.13.1 +diffusers==0.11.1 +transformers==4.25.1 +xformers==0.0.16 +imageio==2.27.0 +gdown +einops +omegaconf +safetensors +gradio From 5ce09707952083643c7b826e22163c38073b7d16 Mon Sep 17 00:00:00 2001 From: Yuwei Guo Date: Thu, 20 Jul 2023 10:53:43 +0800 Subject: [PATCH 2/5] update --- requirements.txt | 3 +++ 1 file changed, 3 insertions(+) diff --git a/requirements.txt b/requirements.txt index d2dda00e..eae99794 100644 --- a/requirements.txt +++ b/requirements.txt @@ -10,3 +10,6 @@ einops omegaconf safetensors gradio +imageio[ffmpeg] +imageio[pyav] +accelerate \ No newline at end of file From e73420a4d7a324d2cfb0ce0a1790d5aa5b3d383b Mon Sep 17 00:00:00 2001 From: Yuwei Guo Date: Thu, 20 Jul 2023 11:23:05 +0800 Subject: [PATCH 3/5] update --- app.py | 22 +++++++++++++++++++++- 1 file changed, 21 insertions(+), 1 deletion(-) diff --git a/app.py b/app.py index e9b6519e..daeab2fa 100644 --- a/app.py +++ b/app.py @@ -1,4 +1,3 @@ - import os import torch import random @@ -74,6 +73,27 @@ ] ] +# clean unrelated ckpts +ckpts = [ + "realisticVisionV20_v20.safetensors", + "majicmixRealistic_v5Preview.safetensors", + "rcnzCartoon3d_v10.safetensors", + "lyriel_v16.safetensors", + "toonyou_beta3.safetensors" +] + +for path in glob(os.path.join("models", "DreamBooth_LoRA", "*.safetensors")): + for ckpt in ckpts: + if path.endswith(ckpt): break + else: + print(f"### Cleaning {path} ...") + os.system(f"rm -rf {path}") + +# clean Grdio cache +print(f"### Cleaning cached examples ...") +os.system(f"rm -rf gradio_cached_examples/") + + class AnimateController: def __init__(self): From 11f8e3313eeeced75a8c3397f806ee74a418dfeb Mon Sep 17 00:00:00 2001 From: Yuwei Guo Date: Thu, 20 Jul 2023 12:53:09 +0800 Subject: [PATCH 4/5] update --- app.py | 35 +++++++++++++++++++++-------------- 1 file changed, 21 insertions(+), 14 deletions(-) diff --git a/app.py b/app.py index daeab2fa..e2ff3687 100644 --- a/app.py +++ b/app.py @@ -74,20 +74,27 @@ ] # clean unrelated ckpts -ckpts = [ - "realisticVisionV20_v20.safetensors", - "majicmixRealistic_v5Preview.safetensors", - "rcnzCartoon3d_v10.safetensors", - "lyriel_v16.safetensors", - "toonyou_beta3.safetensors" -] - -for path in glob(os.path.join("models", "DreamBooth_LoRA", "*.safetensors")): - for ckpt in ckpts: - if path.endswith(ckpt): break - else: - print(f"### Cleaning {path} ...") - os.system(f"rm -rf {path}") +# ckpts = [ +# "realisticVisionV20_v20.safetensors", +# "majicmixRealistic_v5Preview.safetensors", +# "rcnzCartoon3d_v10.safetensors", +# "lyriel_v16.safetensors", +# "toonyou_beta3.safetensors" +# ] + +# for path in glob(os.path.join("models", "DreamBooth_LoRA", "*.safetensors")): +# for ckpt in ckpts: +# if path.endswith(ckpt): break +# else: +# print(f"### Cleaning {path} ...") +# os.system(f"rm -rf {path}") +os.system(f"rm -rf {os.path.join('models', 'DreamBooth_LoRA', '*.safetensors')}") + +os.system(f"bash download_bashscripts/1-ToonYou.sh") +os.system(f"bash download_bashscripts/2-Lyriel.sh") +os.system(f"bash download_bashscripts/3-RcnzCartoon.sh") +os.system(f"bash download_bashscripts/4-MajicMix.sh") +os.system(f"bash download_bashscripts/5-RealisticVision.sh") # clean Grdio cache print(f"### Cleaning cached examples ...") From c34de4d6b0879c75fce909f1a2ef1730a2a90cf7 Mon Sep 17 00:00:00 2001 From: Yuwei Guo Date: Thu, 20 Jul 2023 14:37:31 +0800 Subject: [PATCH 5/5] update --- app.py | 45 +++++++++++++++++++++++---------------------- 1 file changed, 23 insertions(+), 22 deletions(-) diff --git a/app.py b/app.py index e2ff3687..1dd62db7 100644 --- a/app.py +++ b/app.py @@ -65,7 +65,7 @@ ], # 5-RealisticVision [ - "realisticVisionV20_v20.safetensors", + "realisticVisionV40_v20Novae.safetensors", "mm_sd_v15.ckpt", "photo of coastline, rocks, storm weather, wind, waves, lightning, 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3", "blur, haze, deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers, deformed, distorted, disfigured, poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, ugly, disgusting, amputation", @@ -74,27 +74,28 @@ ] # clean unrelated ckpts -# ckpts = [ -# "realisticVisionV20_v20.safetensors", -# "majicmixRealistic_v5Preview.safetensors", -# "rcnzCartoon3d_v10.safetensors", -# "lyriel_v16.safetensors", -# "toonyou_beta3.safetensors" -# ] - -# for path in glob(os.path.join("models", "DreamBooth_LoRA", "*.safetensors")): -# for ckpt in ckpts: -# if path.endswith(ckpt): break -# else: -# print(f"### Cleaning {path} ...") -# os.system(f"rm -rf {path}") -os.system(f"rm -rf {os.path.join('models', 'DreamBooth_LoRA', '*.safetensors')}") - -os.system(f"bash download_bashscripts/1-ToonYou.sh") -os.system(f"bash download_bashscripts/2-Lyriel.sh") -os.system(f"bash download_bashscripts/3-RcnzCartoon.sh") -os.system(f"bash download_bashscripts/4-MajicMix.sh") -os.system(f"bash download_bashscripts/5-RealisticVision.sh") +ckpts = [ + "realisticVisionV40_v20Novae.safetensors", + "majicmixRealistic_v5Preview.safetensors", + "rcnzCartoon3d_v10.safetensors", + "lyriel_v16.safetensors", + "toonyou_beta3.safetensors" +] + +for path in glob(os.path.join("models", "DreamBooth_LoRA", "*.safetensors")): + for ckpt in ckpts: + if path.endswith(ckpt): break + else: + print(f"### Cleaning {path} ...") + os.system(f"rm -rf {path}") + +# os.system(f"rm -rf {os.path.join('models', 'DreamBooth_LoRA', '*.safetensors')}") + +# os.system(f"bash download_bashscripts/1-ToonYou.sh") +# os.system(f"bash download_bashscripts/2-Lyriel.sh") +# os.system(f"bash download_bashscripts/3-RcnzCartoon.sh") +# os.system(f"bash download_bashscripts/4-MajicMix.sh") +# os.system(f"bash download_bashscripts/5-RealisticVision.sh") # clean Grdio cache print(f"### Cleaning cached examples ...")