# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/). # Copyright 2022 Sygil-Dev team. # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Affero General Public License for more details. # You should have received a copy of the GNU Affero General Public License # along with this program. If not, see . # base webui import and utils. """ Implementation of Text to Video based on the https://github.com/nateraw/stable-diffusion-videos repo and the original gist script from https://gist.github.com/karpathy/00103b0037c5aaea32fe1da1af553355 """ from sd_utils import * # streamlit imports from streamlit import StopException from streamlit.elements import image as STImage #streamlit components section from streamlit_server_state import server_state, server_state_lock #other imports import os, sys from PIL import Image import torch import numpy as np import time, inspect, timeit import torch from torch import autocast from io import BytesIO import imageio from slugify import slugify from diffusers import StableDiffusionPipeline, DiffusionPipeline from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, \ PNDMScheduler # streamlit components from custom_components import key_phrase_suggestions # Temp imports # end of imports #--------------------------------------------------------------------------------------------------------------- key_phrase_suggestions.init() try: # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start. from transformers import logging logging.set_verbosity_error() except: pass class plugin_info(): plugname = "txt2vid" description = "Text to Image" isTab = True displayPriority = 1 # # ----------------------------------------------------------------------------- @torch.no_grad() def diffuse( pipe, cond_embeddings, # text conditioning, should be (1, 77, 768) cond_latents, # image conditioning, should be (1, 4, 64, 64) num_inference_steps, cfg_scale, eta, ): torch_device = cond_latents.get_device() # classifier guidance: add the unconditional embedding max_length = cond_embeddings.shape[1] # 77 uncond_input = pipe.tokenizer([""], padding="max_length", max_length=max_length, return_tensors="pt") uncond_embeddings = pipe.text_encoder(uncond_input.input_ids.to(torch_device))[0] text_embeddings = torch.cat([uncond_embeddings, cond_embeddings]) # if we use LMSDiscreteScheduler, let's make sure latents are mulitplied by sigmas if isinstance(pipe.scheduler, LMSDiscreteScheduler): cond_latents = cond_latents * pipe.scheduler.sigmas[0] # init the scheduler accepts_offset = "offset" in set(inspect.signature(pipe.scheduler.set_timesteps).parameters.keys()) extra_set_kwargs = {} if accepts_offset: extra_set_kwargs["offset"] = 1 pipe.scheduler.set_timesteps(num_inference_steps + st.session_state.sampling_steps, **extra_set_kwargs) # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(pipe.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta step_counter = 0 inference_counter = 0 if "current_chunk_speed" not in st.session_state: st.session_state["current_chunk_speed"] = 0 if "previous_chunk_speed_list" not in st.session_state: st.session_state["previous_chunk_speed_list"] = [0] st.session_state["previous_chunk_speed_list"].append(st.session_state["current_chunk_speed"]) if "update_preview_frequency_list" not in st.session_state: st.session_state["update_preview_frequency_list"] = [0] st.session_state["update_preview_frequency_list"].append(st.session_state["update_preview_frequency"]) try: # diffuse! for i, t in enumerate(pipe.scheduler.timesteps): start = timeit.default_timer() #status_text.text(f"Running step: {step_counter}{total_number_steps} {percent} | {duration:.2f}{speed}") # expand the latents for classifier free guidance latent_model_input = torch.cat([cond_latents] * 2) if isinstance(pipe.scheduler, LMSDiscreteScheduler): sigma = pipe.scheduler.sigmas[i] latent_model_input = latent_model_input / ((sigma**2 + 1) ** 0.5) # predict the noise residual noise_pred = pipe.unet(latent_model_input, t, encoder_hidden_states=text_embeddings)["sample"] # cfg noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + cfg_scale * (noise_pred_text - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 if isinstance(pipe.scheduler, LMSDiscreteScheduler): cond_latents = pipe.scheduler.step(noise_pred, i, cond_latents, **extra_step_kwargs)["prev_sample"] else: cond_latents = pipe.scheduler.step(noise_pred, t, cond_latents, **extra_step_kwargs)["prev_sample"] #update the preview image if it is enabled and the frequency matches the step_counter if st.session_state["update_preview"]: step_counter += 1 if step_counter == st.session_state["update_preview_frequency"]: if st.session_state.dynamic_preview_frequency: st.session_state["current_chunk_speed"], st.session_state["previous_chunk_speed_list"], st.session_state["update_preview_frequency"], st.session_state["avg_update_preview_frequency"] = optimize_update_preview_frequency(st.session_state["current_chunk_speed"], st.session_state["previous_chunk_speed_list"], st.session_state["update_preview_frequency"], st.session_state["update_preview_frequency_list"]) #scale and decode the image latents with vae cond_latents_2 = 1 / 0.18215 * cond_latents image = pipe.vae.decode(cond_latents_2) # generate output numpy image as uint8 image = torch.clamp((image["sample"] + 1.0) / 2.0, min=0.0, max=1.0) image2 = transforms.ToPILImage()(image.squeeze_(0)) st.session_state["preview_image"].image(image2) step_counter = 0 duration = timeit.default_timer() - start st.session_state["current_chunk_speed"] = duration if duration >= 1: speed = "s/it" else: speed = "it/s" duration = 1 / duration # total_frames = (st.session_state.sampling_steps + st.session_state.num_inference_steps) * st.session_state.max_duration_in_seconds total_steps = st.session_state.sampling_steps + st.session_state.num_inference_steps if i > st.session_state.sampling_steps: inference_counter += 1 inference_percent = int(100 * float(inference_counter + 1 if inference_counter < num_inference_steps else num_inference_steps)/float(num_inference_steps)) inference_progress = f"{inference_counter + 1 if inference_counter < num_inference_steps else num_inference_steps}/{num_inference_steps} {inference_percent}% " else: inference_progress = "" total_percent = int(100 * float(i+1 if i+1 < (num_inference_steps + st.session_state.sampling_steps) else (num_inference_steps + st.session_state.sampling_steps))/float((num_inference_steps + st.session_state.sampling_steps))) percent = int(100 * float(i+1 if i+1 < num_inference_steps else st.session_state.sampling_steps)/float(st.session_state.sampling_steps)) frames_percent = int(100 * float(st.session_state.current_frame if st.session_state.current_frame < total_frames else total_frames)/float(total_frames)) if "progress_bar_text" in st.session_state: st.session_state["progress_bar_text"].text( f"Running step: {i+1 if i+1 < st.session_state.sampling_steps else st.session_state.sampling_steps}/{st.session_state.sampling_steps} " f"{percent if percent < 100 else 100}% {inference_progress}{duration:.2f}{speed} | " f"Frame: {st.session_state.current_frame + 1 if st.session_state.current_frame < total_frames else total_frames}/{total_frames} " f"{frames_percent if frames_percent < 100 else 100}% {st.session_state.frame_duration:.2f}{st.session_state.frame_speed}" ) if "progress_bar" in st.session_state: st.session_state["progress_bar"].progress(total_percent if total_percent < 100 else 100) except KeyError: raise StopException #scale and decode the image latents with vae cond_latents_2 = 1 / 0.18215 * cond_latents image = pipe.vae.decode(cond_latents_2) # generate output numpy image as uint8 image = torch.clamp((image["sample"] + 1.0) / 2.0, min=0.0, max=1.0) image2 = transforms.ToPILImage()(image.squeeze_(0)) return image2 # def load_diffusers_model(weights_path,torch_device): with server_state_lock["model"]: if "model" in server_state: del server_state["model"] if "textual_inversion" in st.session_state: del st.session_state['textual_inversion'] try: with server_state_lock["pipe"]: if "pipe" not in server_state: if "weights_path" in st.session_state and st.session_state["weights_path"] != weights_path: del st.session_state["weights_path"] st.session_state["weights_path"] = weights_path server_state['float16'] = st.session_state['defaults'].general.use_float16 server_state['no_half'] = st.session_state['defaults'].general.no_half server_state['optimized'] = st.session_state['defaults'].general.optimized #if folder "models/diffusers/stable-diffusion-v1-4" exists, load the model from there if weights_path == "CompVis/stable-diffusion-v1-4": model_path = os.path.join("models", "diffusers", "stable-diffusion-v1-4") if weights_path == "runwayml/stable-diffusion-v1-5": model_path = os.path.join("models", "diffusers", "stable-diffusion-v1-5") if not os.path.exists(model_path + "/model_index.json"): server_state["pipe"] = DiffusionPipeline.from_pretrained( weights_path, use_local_file=True, use_auth_token=st.session_state["defaults"].general.huggingface_token, torch_dtype=torch.float16 if st.session_state['defaults'].general.use_float16 else None, revision="fp16" if not st.session_state['defaults'].general.no_half else None, safety_checker=None, # Very important for videos...lots of false positives while interpolating custom_pipeline="interpolate_stable_diffusion", ) DiffusionPipeline.save_pretrained(server_state["pipe"], model_path) else: server_state["pipe"] = DiffusionPipeline.from_pretrained( model_path, use_local_file=True, torch_dtype=torch.float16 if st.session_state['defaults'].general.use_float16 else None, revision="fp16" if not st.session_state['defaults'].general.no_half else None, safety_checker=None, # Very important for videos...lots of false positives while interpolating custom_pipeline="interpolate_stable_diffusion", ) server_state["pipe"].unet.to(torch_device) server_state["pipe"].vae.to(torch_device) server_state["pipe"].text_encoder.to(torch_device) if st.session_state.defaults.general.enable_attention_slicing: server_state["pipe"].enable_attention_slicing() if st.session_state.defaults.general.enable_minimal_memory_usage: server_state["pipe"].enable_minimal_memory_usage() logger.info("Tx2Vid Model Loaded") else: # if the float16 or no_half options have changed since the last time the model was loaded then we need to reload the model. if ("float16" in server_state and server_state['float16'] != st.session_state['defaults'].general.use_float16) \ or ("no_half" in server_state and server_state['no_half'] != st.session_state['defaults'].general.no_half) \ or ("optimized" in server_state and server_state['optimized'] != st.session_state['defaults'].general.optimized): del server_state['float16'] del server_state['no_half'] with server_state_lock["pipe"]: del server_state["pipe"] torch_gc() del server_state['optimized'] server_state['float16'] = st.session_state['defaults'].general.use_float16 server_state['no_half'] = st.session_state['defaults'].general.no_half server_state['optimized'] = st.session_state['defaults'].general.optimized load_diffusers_model(weights_path, torch_device) else: logger.info("Tx2Vid Model already Loaded") except (EnvironmentError, OSError) as e: if "huggingface_token" not in st.session_state or st.session_state["defaults"].general.huggingface_token == "None": if "progress_bar_text" in st.session_state: st.session_state["progress_bar_text"].error( "You need a huggingface token in order to use the Text to Video tab. Use the Settings page from the sidebar on the left to add your token." ) raise OSError("You need a huggingface token in order to use the Text to Video tab. Use the Settings page from the sidebar on the left to add your token.") else: if "progress_bar_text" in st.session_state: st.session_state["progress_bar_text"].error(e) # def save_video_to_disk(frames, seeds, sanitized_prompt, fps=6,save_video=True, outdir='outputs'): if save_video: # write video to memory #output = io.BytesIO() #writer = imageio.get_writer(os.path.join(os.getcwd(), st.session_state['defaults'].general.outdir, "txt2vid"), im, extension=".mp4", fps=30) #try: video_path = os.path.join(os.getcwd(), outdir, "txt2vid",f"{seeds}_{sanitized_prompt}{datetime.now().strftime('%Y%m-%d%H-%M%S-') + str(uuid4())[:8]}.mp4") writer = imageio.get_writer(video_path, fps=fps) for frame in frames: writer.append_data(frame) writer.close() #except: # print("Can't save video, skipping.") return video_path # def txt2vid( # -------------------------------------- # args you probably want to change prompts = ["blueberry spaghetti", "strawberry spaghetti"], # prompt to dream about gpu:int = st.session_state['defaults'].general.gpu, # id of the gpu to run on #name:str = 'test', # name of this project, for the output directory #rootdir:str = st.session_state['defaults'].general.outdir, num_steps:int = 200, # number of steps between each pair of sampled points max_duration_in_seconds:int = 30, # number of frames to write and then exit the script num_inference_steps:int = 50, # more (e.g. 100, 200 etc) can create slightly better images cfg_scale:float = 5.0, # can depend on the prompt. usually somewhere between 3-10 is good save_video = True, save_video_on_stop = False, outdir='outputs', do_loop = False, use_lerp_for_text = False, seeds = None, quality:int = 100, # for jpeg compression of the output images eta:float = 0.0, width:int = 256, height:int = 256, weights_path = "runwayml/stable-diffusion-v1-5", scheduler="klms", # choices: default, ddim, klms disable_tqdm = False, #----------------------------------------------- beta_start = 0.0001, beta_end = 0.00012, beta_schedule = "scaled_linear", starting_image=None ): """ prompt = ["blueberry spaghetti", "strawberry spaghetti"], # prompt to dream about gpu:int = st.session_state['defaults'].general.gpu, # id of the gpu to run on #name:str = 'test', # name of this project, for the output directory #rootdir:str = st.session_state['defaults'].general.outdir, num_steps:int = 200, # number of steps between each pair of sampled points max_duration_in_seconds:int = 10000, # number of frames to write and then exit the script num_inference_steps:int = 50, # more (e.g. 100, 200 etc) can create slightly better images cfg_scale:float = 5.0, # can depend on the prompt. usually somewhere between 3-10 is good do_loop = False, use_lerp_for_text = False, seed = None, quality:int = 100, # for jpeg compression of the output images eta:float = 0.0, width:int = 256, height:int = 256, weights_path = "runwayml/stable-diffusion-v1-5", scheduler="klms", # choices: default, ddim, klms disable_tqdm = False, beta_start = 0.0001, beta_end = 0.00012, beta_schedule = "scaled_linear" """ mem_mon = MemUsageMonitor('MemMon') mem_mon.start() seeds = seed_to_int(seeds) # We add an extra frame because most # of the time the first frame is just the noise. #max_duration_in_seconds +=1 assert torch.cuda.is_available() assert height % 8 == 0 and width % 8 == 0 torch.manual_seed(seeds) torch_device = f"cuda:{gpu}" # init the output dir sanitized_prompt = slugify(prompts) full_path = os.path.join(os.getcwd(), st.session_state['defaults'].general.outdir, "txt2vid", "samples", sanitized_prompt) if len(full_path) > 220: sanitized_prompt = sanitized_prompt[:220-len(full_path)] full_path = os.path.join(os.getcwd(), st.session_state['defaults'].general.outdir, "txt2vid", "samples", sanitized_prompt) os.makedirs(full_path, exist_ok=True) # Write prompt info to file in output dir so we can keep track of what we did if st.session_state.write_info_files: with open(os.path.join(full_path , f'{slugify(str(seeds))}_config.json' if len(prompts) > 1 else "prompts_config.json"), "w") as outfile: outfile.write(json.dumps( dict( prompts = prompts, gpu = gpu, num_steps = num_steps, max_duration_in_seconds = max_duration_in_seconds, num_inference_steps = num_inference_steps, cfg_scale = cfg_scale, do_loop = do_loop, use_lerp_for_text = use_lerp_for_text, seeds = seeds, quality = quality, eta = eta, width = width, height = height, weights_path = weights_path, scheduler=scheduler, disable_tqdm = disable_tqdm, beta_start = beta_start, beta_end = beta_end, beta_schedule = beta_schedule ), indent=2, sort_keys=False, )) #print(scheduler) default_scheduler = PNDMScheduler( beta_start=beta_start, beta_end=beta_end, beta_schedule=beta_schedule ) # ------------------------------------------------------------------------------ #Schedulers ddim_scheduler = DDIMScheduler( beta_start=beta_start, beta_end=beta_end, beta_schedule=beta_schedule, clip_sample=False, set_alpha_to_one=False, ) klms_scheduler = LMSDiscreteScheduler( beta_start=beta_start, beta_end=beta_end, beta_schedule=beta_schedule ) SCHEDULERS = dict(default=default_scheduler, ddim=ddim_scheduler, klms=klms_scheduler) with st.session_state["progress_bar_text"].container(): with hc.HyLoader('Loading Models...', hc.Loaders.standard_loaders,index=[0]): load_diffusers_model(weights_path, torch_device) if "pipe" not in server_state: logger.error('wtf') server_state["pipe"].scheduler = SCHEDULERS[scheduler] server_state["pipe"].use_multiprocessing_for_evaluation = False server_state["pipe"].use_multiprocessed_decoding = False if do_loop: prompts = str([prompts, prompts]) seeds = [seeds, seeds] #first_seed, *seeds = seeds #prompts.append(prompts) #seeds.append(first_seed) with torch.autocast('cuda'): # get the conditional text embeddings based on the prompt text_input = server_state["pipe"].tokenizer(prompts, padding="max_length", max_length=server_state["pipe"].tokenizer.model_max_length, truncation=True, return_tensors="pt") cond_embeddings = server_state["pipe"].text_encoder(text_input.input_ids.to(torch_device) )[0] # if st.session_state.defaults.general.use_sd_concepts_library: prompt_tokens = re.findall('<([a-zA-Z0-9-]+)>', prompts) if prompt_tokens: # compviz #tokenizer = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else st.session_state.modelCS).cond_stage_model.tokenizer #text_encoder = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else st.session_state.modelCS).cond_stage_model.transformer # diffusers tokenizer = st.session_state.pipe.tokenizer text_encoder = st.session_state.pipe.text_encoder ext = ('pt', 'bin') #print (prompt_tokens) if len(prompt_tokens) > 1: for token_name in prompt_tokens: embedding_path = os.path.join(st.session_state['defaults'].general.sd_concepts_library_folder, token_name) if os.path.exists(embedding_path): for files in os.listdir(embedding_path): if files.endswith(ext): load_learned_embed_in_clip(f"{os.path.join(embedding_path, files)}", text_encoder, tokenizer, f"<{token_name}>") else: embedding_path = os.path.join(st.session_state['defaults'].general.sd_concepts_library_folder, prompt_tokens[0]) if os.path.exists(embedding_path): for files in os.listdir(embedding_path): if files.endswith(ext): load_learned_embed_in_clip(f"{os.path.join(embedding_path, files)}", text_encoder, tokenizer, f"<{prompt_tokens[0]}>") # sample a source init1 = torch.randn((1, server_state["pipe"].unet.in_channels, height // 8, width // 8), device=torch_device) # iterate the loop frames = [] frame_index = 0 second_count = 1 st.session_state["total_frames_avg_duration"] = [] st.session_state["total_frames_avg_speed"] = [] try: while second_count < max_duration_in_seconds: st.session_state["frame_duration"] = 0 st.session_state["frame_speed"] = 0 st.session_state["current_frame"] = frame_index #print(f"Second: {second_count+1}/{max_duration_in_seconds}") # sample the destination init2 = torch.randn((1, server_state["pipe"].unet.in_channels, height // 8, width // 8), device=torch_device) for i, t in enumerate(np.linspace(0, 1, num_steps)): start = timeit.default_timer() logger.info(f"COUNT: {frame_index+1}/{num_steps}") if use_lerp_for_text: init = torch.lerp(init1, init2, float(t)) else: init = slerp(gpu, float(t), init1, init2) #init = slerp(gpu, float(t), init1, init2) with autocast("cuda"): image = diffuse(server_state["pipe"], cond_embeddings, init, num_inference_steps, cfg_scale, eta) if st.session_state["save_individual_images"] and not st.session_state["use_GFPGAN"] and not st.session_state["use_RealESRGAN"]: #im = Image.fromarray(image) outpath = os.path.join(full_path, 'frame%06d.png' % frame_index) image.save(outpath, quality=quality) # send the image to the UI to update it #st.session_state["preview_image"].image(im) #append the frames to the frames list so we can use them later. frames.append(np.asarray(image)) # #try: #if st.session_state["use_GFPGAN"] and server_state["GFPGAN"] is not None and not st.session_state["use_RealESRGAN"]: if st.session_state["use_GFPGAN"] and server_state["GFPGAN"] is not None: #print("Running GFPGAN on image ...") if "progress_bar_text" in st.session_state: st.session_state["progress_bar_text"].text("Running GFPGAN on image ...") #skip_save = True # #287 >_> torch_gc() cropped_faces, restored_faces, restored_img = server_state["GFPGAN"].enhance(np.array(image)[:,:,::-1], has_aligned=False, only_center_face=False, paste_back=True) gfpgan_sample = restored_img[:,:,::-1] gfpgan_image = Image.fromarray(gfpgan_sample) outpath = os.path.join(full_path, 'frame%06d.png' % frame_index) gfpgan_image.save(outpath, quality=quality) #append the frames to the frames list so we can use them later. frames.append(np.asarray(gfpgan_image)) try: st.session_state["preview_image"].image(gfpgan_image) except KeyError: logger.error ("Cant get session_state, skipping image preview.") #except (AttributeError, KeyError): #print("Cant perform GFPGAN, skipping.") #increase frame_index counter. frame_index += 1 st.session_state["current_frame"] = frame_index duration = timeit.default_timer() - start if duration >= 1: speed = "s/it" else: speed = "it/s" duration = 1 / duration st.session_state["frame_duration"] = duration st.session_state["frame_speed"] = speed init1 = init2 # save the video after the generation is done. video_path = save_video_to_disk(frames, seeds, sanitized_prompt, save_video=save_video, outdir=outdir) except StopException: if save_video_on_stop: logger.info("Streamlit Stop Exception Received. Saving video") video_path = save_video_to_disk(frames, seeds, sanitized_prompt, save_video=save_video, outdir=outdir) else: video_path = None if video_path and "preview_video" in st.session_state: # show video preview on the UI st.session_state["preview_video"].video(open(video_path, 'rb').read()) mem_max_used, mem_total = mem_mon.read_and_stop() time_diff = time.time()- start info = f""" {prompts} Sampling Steps: {num_steps}, Sampler: {scheduler}, CFG scale: {cfg_scale}, Seed: {seeds}, Max Duration In Seconds: {max_duration_in_seconds}""".strip() stats = f''' Took { round(time_diff, 2) }s total ({ round(time_diff/(max_duration_in_seconds),2) }s per image) Peak memory usage: { -(mem_max_used // -1_048_576) } MiB / { -(mem_total // -1_048_576) } MiB / { round(mem_max_used/mem_total*100, 3) }%''' return video_path, seeds, info, stats # def layout(): with st.form("txt2vid-inputs"): st.session_state["generation_mode"] = "txt2vid" input_col1, generate_col1 = st.columns([10,1]) with input_col1: #prompt = st.text_area("Input Text","") placeholder = "A corgi wearing a top hat as an oil painting." prompt = st.text_area("Input Text","", placeholder=placeholder, height=54) key_phrase_suggestions.suggestion_area(placeholder) # Every form must have a submit button, the extra blank spaces is a temp way to align it with the input field. Needs to be done in CSS or some other way. generate_col1.write("") generate_col1.write("") generate_button = generate_col1.form_submit_button("Generate") # creating the page layout using columns col1, col2, col3 = st.columns([1,2,1], gap="large") with col1: width = st.slider("Width:", min_value=st.session_state['defaults'].txt2vid.width.min_value, max_value=st.session_state['defaults'].txt2vid.width.max_value, value=st.session_state['defaults'].txt2vid.width.value, step=st.session_state['defaults'].txt2vid.width.step) height = st.slider("Height:", min_value=st.session_state['defaults'].txt2vid.height.min_value, max_value=st.session_state['defaults'].txt2vid.height.max_value, value=st.session_state['defaults'].txt2vid.height.value, step=st.session_state['defaults'].txt2vid.height.step) cfg_scale = st.number_input("CFG (Classifier Free Guidance Scale):", min_value=st.session_state['defaults'].txt2vid.cfg_scale.min_value, value=st.session_state['defaults'].txt2vid.cfg_scale.value, step=st.session_state['defaults'].txt2vid.cfg_scale.step, help="How strongly the image should follow the prompt.") #uploaded_images = st.file_uploader("Upload Image", accept_multiple_files=False, type=["png", "jpg", "jpeg", "webp"], #help="Upload an image which will be used for the image to image generation.") seed = st.text_input("Seed:", value=st.session_state['defaults'].txt2vid.seed, help=" The seed to use, if left blank a random seed will be generated.") #batch_count = st.slider("Batch count.", min_value=1, max_value=100, value=st.session_state['defaults'].txt2vid.batch_count, # step=1, help="How many iterations or batches of images to generate in total.") #batch_size = st.slider("Batch size", min_value=1, max_value=250, value=st.session_state['defaults'].txt2vid.batch_size, step=1, #help="How many images are at once in a batch.\ #It increases the VRAM usage a lot but if you have enough VRAM it can reduce the time it takes to finish generation as more images are generated at once.\ #Default: 1") st.session_state["max_duration_in_seconds"] = st.number_input("Max Duration In Seconds:", value=st.session_state['defaults'].txt2vid.max_duration_in_seconds, help="Specify the max duration in seconds you want your video to be.") with st.expander("Preview Settings"): #st.session_state["update_preview"] = st.checkbox("Update Image Preview", value=st.session_state['defaults'].txt2vid.update_preview, #help="If enabled the image preview will be updated during the generation instead of at the end. \ #You can use the Update Preview \Frequency option bellow to customize how frequent it's updated. \ #By default this is enabled and the frequency is set to 1 step.") st.session_state["update_preview"] = st.session_state["defaults"].general.update_preview st.session_state["update_preview_frequency"] = st.number_input("Update Image Preview Frequency", min_value=0, value=st.session_state['defaults'].txt2vid.update_preview_frequency, help="Frequency in steps at which the the preview image is updated. By default the frequency \ is set to 1 step.") st.session_state["dynamic_preview_frequency"] = st.checkbox("Dynamic Preview Frequency", value=st.session_state['defaults'].txt2vid.dynamic_preview_frequency, help="This option tries to find the best value at which we can update \ the preview image during generation while minimizing the impact it has in performance. Default: True") # with col2: preview_tab, gallery_tab = st.tabs(["Preview", "Gallery"]) with preview_tab: #st.write("Image") #Image for testing #image = Image.open(requests.get("https://icon-library.com/images/image-placeholder-icon/image-placeholder-icon-13.jpg", stream=True).raw).convert('RGB') #new_image = image.resize((175, 240)) #preview_image = st.image(image) # create an empty container for the image, progress bar, etc so we can update it later and use session_state to hold them globally. st.session_state["preview_image"] = st.empty() st.session_state["loading"] = st.empty() st.session_state["progress_bar_text"] = st.empty() st.session_state["progress_bar"] = st.empty() #generate_video = st.empty() st.session_state["preview_video"] = st.empty() preview_video = st.session_state["preview_video"] message = st.empty() with gallery_tab: st.write('Here should be the image gallery, if I could make a grid in streamlit.') with col3: # If we have custom models available on the "models/custom" #folder then we show a menu to select which model we want to use, otherwise we use the main model for SD custom_models_available() if server_state["CustomModel_available"]: custom_model = st.selectbox("Custom Model:", st.session_state["defaults"].txt2vid.custom_models_list, index=st.session_state["defaults"].txt2vid.custom_models_list.index(st.session_state["defaults"].txt2vid.default_model), help="Select the model you want to use. This option is only available if you have custom models \ on your 'models/custom' folder. The model name that will be shown here is the same as the name\ the file for the model has on said folder, it is recommended to give the .ckpt file a name that \ will make it easier for you to distinguish it from other models. Default: Stable Diffusion v1.5") else: custom_model = "runwayml/stable-diffusion-v1-5" #st.session_state["weights_path"] = custom_model #else: #custom_model = "runwayml/stable-diffusion-v1-5" #st.session_state["weights_path"] = f"CompVis/{slugify(custom_model.lower())}" st.session_state.sampling_steps = st.number_input("Sampling Steps", value=st.session_state['defaults'].txt2vid.sampling_steps.value, min_value=st.session_state['defaults'].txt2vid.sampling_steps.min_value, step=st.session_state['defaults'].txt2vid.sampling_steps.step, help="Number of steps between each pair of sampled points") st.session_state.num_inference_steps = st.number_input("Inference Steps:", value=st.session_state['defaults'].txt2vid.num_inference_steps.value, min_value=st.session_state['defaults'].txt2vid.num_inference_steps.min_value, step=st.session_state['defaults'].txt2vid.num_inference_steps.step, help="Higher values (e.g. 100, 200 etc) can create better images.") #sampler_name_list = ["k_lms", "k_euler", "k_euler_a", "k_dpm_2", "k_dpm_2_a", "k_heun", "PLMS", "DDIM"] #sampler_name = st.selectbox("Sampling method", sampler_name_list, #index=sampler_name_list.index(st.session_state['defaults'].txt2vid.default_sampler), help="Sampling method to use. Default: k_euler") scheduler_name_list = ["klms", "ddim"] scheduler_name = st.selectbox("Scheduler:", scheduler_name_list, index=scheduler_name_list.index(st.session_state['defaults'].txt2vid.scheduler_name), help="Scheduler to use. Default: klms") beta_scheduler_type_list = ["scaled_linear", "linear"] beta_scheduler_type = st.selectbox("Beta Schedule Type:", beta_scheduler_type_list, index=beta_scheduler_type_list.index(st.session_state['defaults'].txt2vid.beta_scheduler_type), help="Schedule Type to use. Default: linear") #basic_tab, advanced_tab = st.tabs(["Basic", "Advanced"]) #with basic_tab: #summit_on_enter = st.radio("Submit on enter?", ("Yes", "No"), horizontal=True, #help="Press the Enter key to summit, when 'No' is selected you can use the Enter key to write multiple lines.") with st.expander("Advanced"): with st.expander("Output Settings"): st.session_state["separate_prompts"] = st.checkbox("Create Prompt Matrix.", value=st.session_state['defaults'].txt2vid.separate_prompts, help="Separate multiple prompts using the `|` character, and get all combinations of them.") st.session_state["normalize_prompt_weights"] = st.checkbox("Normalize Prompt Weights.", value=st.session_state['defaults'].txt2vid.normalize_prompt_weights, help="Ensure the sum of all weights add up to 1.0") st.session_state["save_individual_images"] = st.checkbox("Save individual images.", value=st.session_state['defaults'].txt2vid.save_individual_images, help="Save each image generated before any filter or enhancement is applied.") st.session_state["save_video"] = st.checkbox("Save video",value=st.session_state['defaults'].txt2vid.save_video, help="Save a video with all the images generated as frames at the end of the generation.") save_video_on_stop = st.checkbox("Save video on Stop",value=st.session_state['defaults'].txt2vid.save_video_on_stop, help="Save a video with all the images generated as frames when we hit the stop button during a generation.") st.session_state["group_by_prompt"] = st.checkbox("Group results by prompt", value=st.session_state['defaults'].txt2vid.group_by_prompt, help="Saves all the images with the same prompt into the same folder. When using a prompt \ matrix each prompt combination will have its own folder.") st.session_state["write_info_files"] = st.checkbox("Write Info file", value=st.session_state['defaults'].txt2vid.write_info_files, help="Save a file next to the image with informartion about the generation.") st.session_state["do_loop"] = st.checkbox("Do Loop", value=st.session_state['defaults'].txt2vid.do_loop, help="Loop the prompt making two prompts from a single one.") st.session_state["use_lerp_for_text"] = st.checkbox("Use Lerp Instead of Slerp", value=st.session_state['defaults'].txt2vid.use_lerp_for_text, help="Uses torch.lerp() instead of slerp. When interpolating between related prompts. \ e.g. 'a lion in a grassy meadow' -> 'a bear in a grassy meadow' tends to keep the meadow \ the whole way through when lerped, but slerping will often find a path where the meadow \ disappears in the middle") st.session_state["save_as_jpg"] = st.checkbox("Save samples as jpg", value=st.session_state['defaults'].txt2vid.save_as_jpg, help="Saves the images as jpg instead of png.") # if "GFPGAN_available" not in st.session_state: GFPGAN_available() if "RealESRGAN_available" not in st.session_state: RealESRGAN_available() if "LDSR_available" not in st.session_state: LDSR_available() if st.session_state["GFPGAN_available"] or st.session_state["RealESRGAN_available"] or st.session_state["LDSR_available"]: with st.expander("Post-Processing"): face_restoration_tab, upscaling_tab = st.tabs(["Face Restoration", "Upscaling"]) with face_restoration_tab: # GFPGAN used for face restoration if st.session_state["GFPGAN_available"]: #with st.expander("Face Restoration"): #if st.session_state["GFPGAN_available"]: #with st.expander("GFPGAN"): st.session_state["use_GFPGAN"] = st.checkbox("Use GFPGAN", value=st.session_state['defaults'].txt2vid.use_GFPGAN, help="Uses the GFPGAN model to improve faces after the generation.\ This greatly improve the quality and consistency of faces but uses\ extra VRAM. Disable if you need the extra VRAM.") st.session_state["GFPGAN_model"] = st.selectbox("GFPGAN model", st.session_state["GFPGAN_models"], index=st.session_state["GFPGAN_models"].index(st.session_state['defaults'].general.GFPGAN_model)) #st.session_state["GFPGAN_strenght"] = st.slider("Effect Strenght", min_value=1, max_value=100, value=1, step=1, help='') else: st.session_state["use_GFPGAN"] = False with upscaling_tab: st.session_state['us_upscaling'] = st.checkbox("Use Upscaling", value=st.session_state['defaults'].txt2vid.use_upscaling) # RealESRGAN and LDSR used for upscaling. if st.session_state["RealESRGAN_available"] or st.session_state["LDSR_available"]: upscaling_method_list = [] if st.session_state["RealESRGAN_available"]: upscaling_method_list.append("RealESRGAN") if st.session_state["LDSR_available"]: upscaling_method_list.append("LDSR") st.session_state["upscaling_method"] = st.selectbox("Upscaling Method", upscaling_method_list, index=upscaling_method_list.index(st.session_state['defaults'].general.upscaling_method)) if st.session_state["RealESRGAN_available"]: with st.expander("RealESRGAN"): if st.session_state["upscaling_method"] == "RealESRGAN" and st.session_state['us_upscaling']: st.session_state["use_RealESRGAN"] = True else: st.session_state["use_RealESRGAN"] = False st.session_state["RealESRGAN_model"] = st.selectbox("RealESRGAN model", st.session_state["RealESRGAN_models"], index=st.session_state["RealESRGAN_models"].index(st.session_state['defaults'].general.RealESRGAN_model)) else: st.session_state["use_RealESRGAN"] = False st.session_state["RealESRGAN_model"] = "RealESRGAN_x4plus" # if st.session_state["LDSR_available"]: with st.expander("LDSR"): if st.session_state["upscaling_method"] == "LDSR" and st.session_state['us_upscaling']: st.session_state["use_LDSR"] = True else: st.session_state["use_LDSR"] = False st.session_state["LDSR_model"] = st.selectbox("LDSR model", st.session_state["LDSR_models"], index=st.session_state["LDSR_models"].index(st.session_state['defaults'].general.LDSR_model)) st.session_state["ldsr_sampling_steps"] = st.number_input("Sampling Steps", value=st.session_state['defaults'].txt2vid.LDSR_config.sampling_steps, help="") st.session_state["preDownScale"] = st.number_input("PreDownScale", value=st.session_state['defaults'].txt2vid.LDSR_config.preDownScale, help="") st.session_state["postDownScale"] = st.number_input("postDownScale", value=st.session_state['defaults'].txt2vid.LDSR_config.postDownScale, help="") downsample_method_list = ['Nearest', 'Lanczos'] st.session_state["downsample_method"] = st.selectbox("Downsample Method", downsample_method_list, index=downsample_method_list.index(st.session_state['defaults'].txt2vid.LDSR_config.downsample_method)) else: st.session_state["use_LDSR"] = False st.session_state["LDSR_model"] = "model" with st.expander("Variant"): st.session_state["variant_amount"] = st.number_input("Variant Amount:", value=st.session_state['defaults'].txt2vid.variant_amount.value, min_value=st.session_state['defaults'].txt2vid.variant_amount.min_value, max_value=st.session_state['defaults'].txt2vid.variant_amount.max_value, step=st.session_state['defaults'].txt2vid.variant_amount.step) st.session_state["variant_seed"] = st.text_input("Variant Seed:", value=st.session_state['defaults'].txt2vid.seed, help="The seed to use when generating a variant, if left blank a random seed will be generated.") #st.session_state["beta_start"] = st.slider("Beta Start:", value=st.session_state['defaults'].txt2vid.beta_start.value, #min_value=st.session_state['defaults'].txt2vid.beta_start.min_value, #max_value=st.session_state['defaults'].txt2vid.beta_start.max_value, #step=st.session_state['defaults'].txt2vid.beta_start.step, format=st.session_state['defaults'].txt2vid.beta_start.format) #st.session_state["beta_end"] = st.slider("Beta End:", value=st.session_state['defaults'].txt2vid.beta_end.value, #min_value=st.session_state['defaults'].txt2vid.beta_end.min_value, max_value=st.session_state['defaults'].txt2vid.beta_end.max_value, #step=st.session_state['defaults'].txt2vid.beta_end.step, format=st.session_state['defaults'].txt2vid.beta_end.format) if generate_button: #print("Loading models") # load the models when we hit the generate button for the first time, it wont be loaded after that so dont worry. #load_models(False, st.session_state["use_GFPGAN"], True, st.session_state["RealESRGAN_model"]) if st.session_state["use_GFPGAN"]: if "GFPGAN" in server_state: logger.info("GFPGAN already loaded") else: with col2: with hc.HyLoader('Loading Models...', hc.Loaders.standard_loaders,index=[0]): # Load GFPGAN if os.path.exists(st.session_state["defaults"].general.GFPGAN_dir): try: load_GFPGAN() logger.info("Loaded GFPGAN") except Exception: import traceback logger.error("Error loading GFPGAN:", file=sys.stderr) logger.error(traceback.format_exc(), file=sys.stderr) else: if "GFPGAN" in server_state: del server_state["GFPGAN"] #try: # run video generation video, seed, info, stats = txt2vid(prompts=prompt, gpu=st.session_state["defaults"].general.gpu, num_steps=st.session_state.sampling_steps, max_duration_in_seconds=st.session_state.max_duration_in_seconds, num_inference_steps=st.session_state.num_inference_steps, cfg_scale=cfg_scale, save_video_on_stop=save_video_on_stop, outdir=st.session_state["defaults"].general.outdir, do_loop=st.session_state["do_loop"], use_lerp_for_text=st.session_state["use_lerp_for_text"], seeds=seed, quality=100, eta=0.0, width=width, height=height, weights_path=custom_model, scheduler=scheduler_name, disable_tqdm=False, beta_start=st.session_state['defaults'].txt2vid.beta_start.value, beta_end=st.session_state['defaults'].txt2vid.beta_end.value, beta_schedule=beta_scheduler_type, starting_image=None) if video and save_video_on_stop: # show video preview on the UI after we hit the stop button # currently not working as session_state is cleared on StopException preview_video.video(open(video, 'rb').read()) #message.success('Done!', icon="✅") message.success('Render Complete: ' + info + '; Stats: ' + stats, icon="✅") #history_tab,col1,col2,col3,PlaceHolder,col1_cont,col2_cont,col3_cont = st.session_state['historyTab'] #if 'latestVideos' in st.session_state: #for i in video: ##push the new image to the list of latest images and remove the oldest one ##remove the last index from the list\ #st.session_state['latestVideos'].pop() ##add the new image to the start of the list #st.session_state['latestVideos'].insert(0, i) #PlaceHolder.empty() #with PlaceHolder.container(): #col1, col2, col3 = st.columns(3) #col1_cont = st.container() #col2_cont = st.container() #col3_cont = st.container() #with col1_cont: #with col1: #st.image(st.session_state['latestVideos'][0]) #st.image(st.session_state['latestVideos'][3]) #st.image(st.session_state['latestVideos'][6]) #with col2_cont: #with col2: #st.image(st.session_state['latestVideos'][1]) #st.image(st.session_state['latestVideos'][4]) #st.image(st.session_state['latestVideos'][7]) #with col3_cont: #with col3: #st.image(st.session_state['latestVideos'][2]) #st.image(st.session_state['latestVideos'][5]) #st.image(st.session_state['latestVideos'][8]) #historyGallery = st.empty() ## check if output_images length is the same as seeds length #with gallery_tab: #st.markdown(createHTMLGallery(video,seed), unsafe_allow_html=True) #st.session_state['historyTab'] = [history_tab,col1,col2,col3,PlaceHolder,col1_cont,col2_cont,col3_cont] #except (StopException, KeyError): #print(f"Received Streamlit StopException")