mirror of
https://github.com/Sygil-Dev/sygil-webui.git
synced 2024-12-15 14:31:44 +03:00
GFPGAN can now be used on the txt2vid tab.
This commit is contained in:
ZeroCool940711
parent
300e6865d6
commit
a7d7955721
@ -151,12 +151,12 @@ def diffuse(
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# generate output numpy image as uint8
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image = torch.clamp((image["sample"] + 1.0) / 2.0, min=0.0, max=1.0)
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image = transforms.ToPILImage()(image.squeeze_(0))
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image2 = transforms.ToPILImage()(image.squeeze_(0))
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st.session_state["preview_image"].image(image)
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st.session_state["preview_image"].image(image2)
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step_counter = 0
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duration = timeit.default_timer() - start
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st.session_state["current_chunk_speed"] = duration
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@ -185,7 +185,7 @@ def diffuse(
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)
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st.session_state["progress_bar"].progress(percent if percent < 100 else 100)
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return image
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return image2
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#
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def txt2vid(
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@ -375,6 +375,13 @@ def txt2vid(
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print("Tx2Vid Model Loaded")
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st.session_state["pipe"].scheduler = SCHEDULERS[scheduler]
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if do_loop:
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prompts = str([prompts, prompts])
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seeds = [seeds, seeds]
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#first_seed, *seeds = seeds
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#prompts.append(prompts)
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#seeds.append(first_seed)
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# get the conditional text embeddings based on the prompt
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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")
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@ -414,13 +421,6 @@ def txt2vid(
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# sample a source
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init1 = torch.randn((1, st.session_state["pipe"].unet.in_channels, height // 8, width // 8), device=torch_device)
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if do_loop:
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prompts = [prompts, prompts]
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seeds = [seeds, seeds]
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#first_seed, *seeds = seeds
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#prompts.append(prompts)
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#seeds.append(first_seed)
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# iterate the loop
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frames = []
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@ -451,17 +451,42 @@ def txt2vid(
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with autocast("cuda"):
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image = diffuse(st.session_state["pipe"], cond_embeddings, init, num_inference_steps, cfg_scale, eta)
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#im = Image.fromarray(image)
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outpath = os.path.join(full_path, 'frame%06d.png' % frame_index)
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image.save(outpath, quality=quality)
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# send the image to the UI to update it
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#st.session_state["preview_image"].image(im)
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#append the frames to the frames list so we can use them later.
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frames.append(np.asarray(image))
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if st.session_state["save_individual_images"] and not st.session_state["use_GFPGAN"] and not st.session_state["use_RealESRGAN"]:
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#im = Image.fromarray(image)
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outpath = os.path.join(full_path, 'frame%06d.png' % frame_index)
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image.save(outpath, quality=quality)
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# send the image to the UI to update it
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#st.session_state["preview_image"].image(im)
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#append the frames to the frames list so we can use them later.
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frames.append(np.asarray(image))
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#
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#try:
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#if st.session_state["use_GFPGAN"] and st.session_state["GFPGAN"] is not None and not st.session_state["use_RealESRGAN"]:
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if st.session_state["use_GFPGAN"] and st.session_state["GFPGAN"] is not None:
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#print("Running GFPGAN on image ...")
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st.session_state["progress_bar_text"].text("Running GFPGAN on image ...")
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#skip_save = True # #287 >_>
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torch_gc()
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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)
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gfpgan_sample = restored_img[:,:,::-1]
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gfpgan_image = Image.fromarray(gfpgan_sample)
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outpath = os.path.join(full_path, 'frame%06d.png' % frame_index)
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gfpgan_image.save(outpath, quality=quality)
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#append the frames to the frames list so we can use them later.
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frames.append(np.asarray(gfpgan_image))
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st.session_state["preview_image"].image(gfpgan_image)
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#except AttributeError:
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#print("Cant perform GFPGAN, skipping.")
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#pass
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#increase frame_index counter.
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frame_index += 1
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@ -661,7 +686,7 @@ def layout():
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st.session_state.num_inference_steps = st.slider("Inference Steps:", value=st.session_state['defaults'].txt2vid.num_inference_steps.value,
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min_value=st.session_state['defaults'].txt2vid.num_inference_steps.min_value,
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step=st.session_state['defaults'].txt2vid.num_inference_steps.max_value,
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step=st.session_state['defaults'].txt2vid.num_inference_steps.step,
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max_value=st.session_state['defaults'].txt2vid.num_inference_steps.max_value,
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help="Higher values (e.g. 100, 200 etc) can create better images.")
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@ -731,65 +756,82 @@ def layout():
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if generate_button:
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#print("Loading models")
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# load the models when we hit the generate button for the first time, it wont be loaded after that so dont worry.
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#load_models(False, False, False, st.session_state["RealESRGAN_model"], CustomModel_available=st.session_state["CustomModel_available"], custom_model=custom_model)
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#load_models(False, st.session_state["use_GFPGAN"], True, st.session_state["RealESRGAN_model"])
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try:
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# run video generation
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video, seed, info, stats = txt2vid(prompts=prompt, gpu=st.session_state["defaults"].general.gpu,
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num_steps=st.session_state.sampling_steps, max_frames=int(st.session_state.max_frames),
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num_inference_steps=st.session_state.num_inference_steps,
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cfg_scale=cfg_scale,do_loop=st.session_state["do_loop"],
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seeds=seed, quality=100, eta=0.0, width=width,
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height=height, weights_path=custom_model, scheduler=scheduler_name,
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disable_tqdm=False, beta_start=st.session_state['defaults'].txt2vid.beta_start.value,
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beta_end=st.session_state['defaults'].txt2vid.beta_end.value,
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beta_schedule=beta_scheduler_type, starting_image=None)
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if st.session_state["use_GFPGAN"]:
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if "GFPGAN" in st.session_state:
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print("GFPGAN already loaded")
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else:
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# Load GFPGAN
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if os.path.exists(st.session_state["defaults"].general.GFPGAN_dir):
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try:
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st.session_state["GFPGAN"] = load_GFPGAN()
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print("Loaded GFPGAN")
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except Exception:
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import traceback
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print("Error loading GFPGAN:", file=sys.stderr)
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print(traceback.format_exc(), file=sys.stderr)
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else:
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if "GFPGAN" in st.session_state:
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del st.session_state["GFPGAN"]
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#message.success('Done!', icon="✅")
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message.success('Render Complete: ' + info + '; Stats: ' + stats, icon="✅")
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#try:
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# run video generation
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video, seed, info, stats = txt2vid(prompts=prompt, gpu=st.session_state["defaults"].general.gpu,
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num_steps=st.session_state.sampling_steps, max_frames=int(st.session_state.max_frames),
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num_inference_steps=st.session_state.num_inference_steps,
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cfg_scale=cfg_scale,do_loop=st.session_state["do_loop"],
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seeds=seed, quality=100, eta=0.0, width=width,
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height=height, weights_path=custom_model, scheduler=scheduler_name,
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disable_tqdm=False, beta_start=st.session_state['defaults'].txt2vid.beta_start.value,
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beta_end=st.session_state['defaults'].txt2vid.beta_end.value,
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beta_schedule=beta_scheduler_type, starting_image=None)
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#history_tab,col1,col2,col3,PlaceHolder,col1_cont,col2_cont,col3_cont = st.session_state['historyTab']
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#message.success('Done!', icon="✅")
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message.success('Render Complete: ' + info + '; Stats: ' + stats, icon="✅")
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#if 'latestVideos' in st.session_state:
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#for i in video:
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##push the new image to the list of latest images and remove the oldest one
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##remove the last index from the list\
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#st.session_state['latestVideos'].pop()
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##add the new image to the start of the list
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#st.session_state['latestVideos'].insert(0, i)
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#PlaceHolder.empty()
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#history_tab,col1,col2,col3,PlaceHolder,col1_cont,col2_cont,col3_cont = st.session_state['historyTab']
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#with PlaceHolder.container():
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#col1, col2, col3 = st.columns(3)
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#col1_cont = st.container()
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#col2_cont = st.container()
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#col3_cont = st.container()
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#if 'latestVideos' in st.session_state:
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#for i in video:
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##push the new image to the list of latest images and remove the oldest one
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##remove the last index from the list\
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#st.session_state['latestVideos'].pop()
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##add the new image to the start of the list
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#st.session_state['latestVideos'].insert(0, i)
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#PlaceHolder.empty()
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#with col1_cont:
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#with col1:
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#st.image(st.session_state['latestVideos'][0])
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#st.image(st.session_state['latestVideos'][3])
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#st.image(st.session_state['latestVideos'][6])
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#with col2_cont:
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#with col2:
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#st.image(st.session_state['latestVideos'][1])
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#st.image(st.session_state['latestVideos'][4])
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#st.image(st.session_state['latestVideos'][7])
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#with col3_cont:
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#with col3:
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#st.image(st.session_state['latestVideos'][2])
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#st.image(st.session_state['latestVideos'][5])
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#st.image(st.session_state['latestVideos'][8])
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#historyGallery = st.empty()
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#with PlaceHolder.container():
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#col1, col2, col3 = st.columns(3)
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#col1_cont = st.container()
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#col2_cont = st.container()
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#col3_cont = st.container()
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## check if output_images length is the same as seeds length
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#with gallery_tab:
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#st.markdown(createHTMLGallery(video,seed), unsafe_allow_html=True)
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#with col1_cont:
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#with col1:
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#st.image(st.session_state['latestVideos'][0])
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#st.image(st.session_state['latestVideos'][3])
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#st.image(st.session_state['latestVideos'][6])
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#with col2_cont:
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#with col2:
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#st.image(st.session_state['latestVideos'][1])
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#st.image(st.session_state['latestVideos'][4])
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#st.image(st.session_state['latestVideos'][7])
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#with col3_cont:
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#with col3:
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#st.image(st.session_state['latestVideos'][2])
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#st.image(st.session_state['latestVideos'][5])
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#st.image(st.session_state['latestVideos'][8])
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#historyGallery = st.empty()
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## check if output_images length is the same as seeds length
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#with gallery_tab:
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#st.markdown(createHTMLGallery(video,seed), unsafe_allow_html=True)
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#st.session_state['historyTab'] = [history_tab,col1,col2,col3,PlaceHolder,col1_cont,col2_cont,col3_cont]
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#st.session_state['historyTab'] = [history_tab,col1,col2,col3,PlaceHolder,col1_cont,col2_cont,col3_cont]
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except (StopException, KeyError):
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print(f"Received Streamlit StopException")
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#except (StopException, KeyError):
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#print(f"Received Streamlit StopException")
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