# base webui import and utils. from webui_streamlit import st from sd_utils import * # streamlit imports from streamlit import StopException from streamlit.runtime.in_memory_file_manager import in_memory_file_manager from streamlit.elements import image as STImage #other imports import os 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 # Temp imports from diffusers import StableDiffusionPipeline from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, \ PNDMScheduler # end of imports #--------------------------------------------------------------------------------------------------------------- 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 = "txt2img" description = "Text to Image" isTab = True displayPriority = 1 if os.path.exists(os.path.join(st.session_state['defaults'].general.GFPGAN_dir, "experiments", "pretrained_models", "GFPGANv1.3.pth")): GFPGAN_available = True else: GFPGAN_available = False if os.path.exists(os.path.join(st.session_state['defaults'].general.RealESRGAN_dir, "experiments","pretrained_models", f"{st.session_state['defaults'].txt2vid.RealESRGAN_model}.pth")): RealESRGAN_available = True else: RealESRGAN_available = False # # ----------------------------------------------------------------------------- @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['defaults'].txt2vid.update_preview_frequency) # 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"] #print (st.session_state["update_preview_frequency"]) #update the preview image if it is enabled and the frequency matches the step_counter if st.session_state['defaults'].txt2vid.update_preview: step_counter += 1 if st.session_state['defaults'].txt2vid.update_preview_frequency == step_counter or step_counter == st.session_state.sampling_steps: if st.session_state.dynamic_preview_frequency: st.session_state["current_chunk_speed"], st.session_state["previous_chunk_speed_list"], st.session_state['defaults'].txt2vid.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['defaults'].txt2vid.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 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 = "" percent = int(100 * float(i+1 if i+1 < st.session_state.sampling_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 < st.session_state.max_frames else st.session_state.max_frames)/float(st.session_state.max_frames)) 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 < st.session_state.max_frames else st.session_state.max_frames}/{st.session_state.max_frames} " f"{frames_percent if frames_percent < 100 else 100}% {st.session_state.frame_duration:.2f}{st.session_state.frame_speed}" ) st.session_state["progress_bar"].progress(percent if percent < 100 else 100) #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 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_frames: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, seeds = None, quality:int = 100, # for jpeg compression of the output images eta:float = 0.0, width:int = 256, height:int = 256, weights_path = "CompVis/stable-diffusion-v1-4", 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_frames: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 = "CompVis/stable-diffusion-v1-4", 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_frames +=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", "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", "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_frames = max_frames, 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) # ------------------------------------------------------------------------------ st.session_state["progress_bar_text"].text("Loading models...") try: if "model" in st.session_state: del st.session_state["model"] except: pass #print (st.session_state["weights_path"] != weights_path) try: if not "pipe" in st.session_state or st.session_state["weights_path"] != weights_path: if st.session_state["weights_path"] != weights_path: del st.session_state["weights_path"] st.session_state["weights_path"] = weights_path st.session_state["pipe"] = StableDiffusionPipeline.from_pretrained( weights_path, use_local_file=True, use_auth_token=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 ) st.session_state["pipe"].unet.to(torch_device) st.session_state["pipe"].vae.to(torch_device) st.session_state["pipe"].text_encoder.to(torch_device) if st.session_state.defaults.general.enable_attention_slicing: st.session_state["pipe"].enable_attention_slicing() if st.session_state.defaults.general.enable_minimal_memory_usage: st.session_state["pipe"].enable_minimal_memory_usage() print("Tx2Vid Model Loaded") else: print("Tx2Vid Model already Loaded") except: #del st.session_state["weights_path"] #del st.session_state["pipe"] st.session_state["weights_path"] = weights_path st.session_state["pipe"] = StableDiffusionPipeline.from_pretrained( weights_path, use_local_file=True, use_auth_token=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 ) st.session_state["pipe"].unet.to(torch_device) st.session_state["pipe"].vae.to(torch_device) st.session_state["pipe"].text_encoder.to(torch_device) if st.session_state.defaults.general.enable_attention_slicing: st.session_state["pipe"].enable_attention_slicing() if st.session_state.defaults.general.enable_minimal_memory_usage: st.session_state["pipe"].enable_minimal_memory_usage() print("Tx2Vid Model Loaded") st.session_state["pipe"].scheduler = SCHEDULERS[scheduler] if do_loop: prompts = str([prompts, prompts]) seeds = [seeds, seeds] #first_seed, *seeds = seeds #prompts.append(prompts) #seeds.append(first_seed) # get the conditional text embeddings based on the prompt text_input = st.session_state["pipe"].tokenizer(prompts, padding="max_length", max_length=st.session_state["pipe"].tokenizer.model_max_length, truncation=True, return_tensors="pt") cond_embeddings = st.session_state["pipe"].text_encoder(text_input.input_ids.to(torch_device))[0] # shape [1, 77, 768] # 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, st.session_state["pipe"].unet.in_channels, height // 8, width // 8), device=torch_device) # iterate the loop frames = [] frame_index = 0 st.session_state["total_frames_avg_duration"] = [] st.session_state["total_frames_avg_speed"] = [] try: while frame_index < max_frames: st.session_state["frame_duration"] = 0 st.session_state["frame_speed"] = 0 st.session_state["current_frame"] = frame_index # sample the destination init2 = torch.randn((1, st.session_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() print(f"COUNT: {frame_index+1}/{max_frames}") #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(st.session_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 st.session_state["GFPGAN"] is not None and not st.session_state["use_RealESRGAN"]: if st.session_state["use_GFPGAN"] and st.session_state["GFPGAN"] is not None: #print("Running GFPGAN on image ...") st.session_state["progress_bar_text"].text("Running GFPGAN on image ...") #skip_save = True # #287 >_> torch_gc() cropped_faces, restored_faces, restored_img = st.session_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)) st.session_state["preview_image"].image(gfpgan_image) #except AttributeError: #print("Cant perform GFPGAN, skipping.") #pass #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 except StopException: pass if st.session_state['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-samples"), im, extension=".mp4", fps=30) try: video_path = os.path.join(os.getcwd(), st.session_state['defaults'].general.outdir, "txt2vid-samples",f"{seeds}_{sanitized_prompt}.mp4") writer = imageio.get_writer(video_path, fps=6) for frame in frames: writer.append_data(frame) writer.close() except: print("Can't save video, skipping.") # 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 Frames: {max_frames}""".strip() stats = f''' Took { round(time_diff, 2) }s total ({ round(time_diff/(max_frames),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 #on import run init def createHTMLGallery(images,info): html3 = """
""" mkdwn_array = [] for i in images: try: seed = info[images.index(i)] except: seed = ' ' image_io = BytesIO() i.save(image_io, 'PNG') width, height = i.size #get random number for the id image_id = "%s" % (str(images.index(i))) (data, mimetype) = STImage._normalize_to_bytes(image_io.getvalue(), width, 'auto') this_file = in_memory_file_manager.add(data, mimetype, image_id) img_str = this_file.url #img_str = 'data:image/png;base64,' + b64encode(image_io.getvalue()).decode('ascii') #get image size #make sure the image is not bigger then 150px but keep the aspect ratio if width > 150: height = int(height * (150/width)) width = 150 if height > 150: width = int(width * (150/height)) height = 150 #mkdwn = f"""Image""" mkdwn = f'''
Image
{seed}
''' mkdwn_array.append(mkdwn) html3 += "".join(mkdwn_array) html3 += '
' return html3 # 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","") prompt = st.text_input("Input Text","", placeholder="A corgi wearing a top hat as an oil painting.") # 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.slider("CFG (Classifier Free Guidance Scale):", min_value=st.session_state['defaults'].txt2vid.cfg_scale.min_value, max_value=st.session_state['defaults'].txt2vid.cfg_scale.max_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_frames"] = int(st.text_input("Max Frames:", value=st.session_state['defaults'].txt2vid.max_frames, help="Specify the max number of frames you want to generate.")) 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_frequency"] = st.text_input("Update Image Preview Frequency", 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.") # 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() 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 if st.session_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 v1.4") else: custom_model = "CompVis/stable-diffusion-v1-4" #st.session_state["weights_path"] = custom_model #else: #custom_model = "CompVis/stable-diffusion-v1-4" #st.session_state["weights_path"] = f"CompVis/{slugify(custom_model.lower())}" st.session_state.sampling_steps = st.slider("Sampling Steps", value=st.session_state['defaults'].txt2vid.sampling_steps.value, min_value=st.session_state['defaults'].txt2vid.sampling_steps.min_value, max_value=st.session_state['defaults'].txt2vid.sampling_steps.max_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.slider("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, max_value=st.session_state['defaults'].txt2vid.num_inference_steps.max_value, 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"): 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.") 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["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") st.session_state["do_loop"] = st.checkbox("Do Loop", value=st.session_state['defaults'].txt2vid.do_loop, help="Do loop") 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: 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.") else: st.session_state["use_GFPGAN"] = False if RealESRGAN_available: st.session_state["use_RealESRGAN"] = st.checkbox("Use RealESRGAN", value=st.session_state['defaults'].txt2vid.use_RealESRGAN, help="Uses the RealESRGAN model to upscale the images after the generation. This greatly improve the quality and lets you have high resolution images but uses extra VRAM. Disable if you need the extra VRAM.") st.session_state["RealESRGAN_model"] = st.selectbox("RealESRGAN model", ["RealESRGAN_x4plus", "RealESRGAN_x4plus_anime_6B"], index=0) else: st.session_state["use_RealESRGAN"] = False st.session_state["RealESRGAN_model"] = "RealESRGAN_x4plus" st.session_state["variant_amount"] = st.slider("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 st.session_state: print("GFPGAN already loaded") else: # Load GFPGAN if os.path.exists(st.session_state["defaults"].general.GFPGAN_dir): try: st.session_state["GFPGAN"] = load_GFPGAN() print("Loaded GFPGAN") except Exception: import traceback print("Error loading GFPGAN:", file=sys.stderr) print(traceback.format_exc(), file=sys.stderr) else: if "GFPGAN" in st.session_state: del st.session_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_frames=int(st.session_state.max_frames), num_inference_steps=st.session_state.num_inference_steps, cfg_scale=cfg_scale,do_loop=st.session_state["do_loop"], 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) #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")