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Revert "Made sure GFPGAN and RealESRGAN are on server_state. (#1319)"
This reverts commit 1fd28eed1e.
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ZeroCool
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1fd28eed1e
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c8a8d6cea9
@ -686,15 +686,13 @@ def load_GFPGAN():
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sys.path.append(os.path.abspath(st.session_state['defaults'].general.GFPGAN_dir))
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from gfpgan import GFPGANer
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with server_state_lock['GFPGAN']:
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if st.session_state['defaults'].general.gfpgan_cpu or st.session_state['defaults'].general.extra_models_cpu:
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server_state['GFPGAN'] = GFPGANer(model_path=model_path, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None, device=torch.device('cpu'))
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elif st.session_state['defaults'].general.extra_models_gpu:
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server_state['GFPGAN'] = GFPGANer(model_path=model_path, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None, device=torch.device(f"cuda:{st.session_state['defaults'].general.gfpgan_gpu}"))
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else:
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server_state['GFPGAN'] = GFPGANer(model_path=model_path, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None, device=torch.device(f"cuda:{st.session_state['defaults'].general.gpu}"))
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return server_state['GFPGAN']
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if st.session_state['defaults'].general.gfpgan_cpu or st.session_state['defaults'].general.extra_models_cpu:
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instance = GFPGANer(model_path=model_path, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None, device=torch.device('cpu'))
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elif st.session_state['defaults'].general.extra_models_gpu:
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instance = GFPGANer(model_path=model_path, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None, device=torch.device(f"cuda:{st.session_state['defaults'].general.gfpgan_gpu}"))
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else:
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instance = GFPGANer(model_path=model_path, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None, device=torch.device(f"cuda:{st.session_state['defaults'].general.gpu}"))
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return instance
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@retry(tries=5)
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def load_RealESRGAN(model_name: str):
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@ -711,18 +709,17 @@ def load_RealESRGAN(model_name: str):
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sys.path.append(os.path.abspath(st.session_state['defaults'].general.RealESRGAN_dir))
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from realesrgan import RealESRGANer
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with server_state_lock['RealESRGAN']:
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if st.session_state['defaults'].general.esrgan_cpu or st.session_state['defaults'].general.extra_models_cpu:
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server_state['RealESRGAN'] = RealESRGANer(scale=2, model_path=model_path, model=RealESRGAN_models[model_name], pre_pad=0, half=False) # cpu does not support half
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server_state['RealESRGAN'].device = torch.device('cpu')
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server_state['RealESRGAN'].model.to('cpu')
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elif st.session_state['defaults'].general.extra_models_gpu:
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server_state['RealESRGAN'] = RealESRGANer(scale=2, model_path=model_path, model=RealESRGAN_models[model_name], pre_pad=0, half=not st.session_state['defaults'].general.no_half, device=torch.device(f"cuda:{st.session_state['defaults'].general.esrgan_gpu}"))
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else:
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server_state['RealESRGAN'] = RealESRGANer(scale=2, model_path=model_path, model=RealESRGAN_models[model_name], pre_pad=0, half=not st.session_state['defaults'].general.no_half, device=torch.device(f"cuda:{st.session_state['defaults'].general.gpu}"))
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server_state['RealESRGAN'].model.name = model_name
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if st.session_state['defaults'].general.esrgan_cpu or st.session_state['defaults'].general.extra_models_cpu:
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instance = RealESRGANer(scale=2, model_path=model_path, model=RealESRGAN_models[model_name], pre_pad=0, half=False) # cpu does not support half
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instance.device = torch.device('cpu')
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instance.model.to('cpu')
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elif st.session_state['defaults'].general.extra_models_gpu:
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instance = RealESRGANer(scale=2, model_path=model_path, model=RealESRGAN_models[model_name], pre_pad=0, half=not st.session_state['defaults'].general.no_half, device=torch.device(f"cuda:{st.session_state['defaults'].general.esrgan_gpu}"))
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else:
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instance = RealESRGANer(scale=2, model_path=model_path, model=RealESRGAN_models[model_name], pre_pad=0, half=not st.session_state['defaults'].general.no_half, device=torch.device(f"cuda:{st.session_state['defaults'].general.gpu}"))
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instance.model.name = model_name
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return server_state['RealESRGAN']
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return instance
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#
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@retry(tries=5)
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@ -731,7 +728,6 @@ def load_LDSR(checking=False):
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yaml_name = 'project'
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model_path = os.path.join(st.session_state['defaults'].general.LDSR_dir, 'experiments/pretrained_models', model_name + '.ckpt')
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yaml_path = os.path.join(st.session_state['defaults'].general.LDSR_dir, 'experiments/pretrained_models', yaml_name + '.yaml')
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if not os.path.isfile(model_path):
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raise Exception("LDSR model not found at path "+model_path)
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if not os.path.isfile(yaml_path):
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@ -742,7 +738,6 @@ def load_LDSR(checking=False):
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sys.path.append(os.path.abspath(st.session_state['defaults'].general.LDSR_dir))
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from LDSR import LDSR
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LDSRObject = LDSR(model_path, yaml_path)
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return LDSRObject
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#
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@ -47,14 +47,14 @@ class plugin_info():
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if os.path.exists(os.path.join(st.session_state['defaults'].general.GFPGAN_dir, "experiments", "pretrained_models", "GFPGANv1.3.pth")):
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server_state["GFPGAN_available"] = True
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GFPGAN_available = True
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else:
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server_state["GFPGAN_available"] = False
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GFPGAN_available = False
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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")):
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server_state["RealESRGAN_available"] = True
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RealESRGAN_available = True
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else:
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server_state["RealESRGAN_available"] = False
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RealESRGAN_available = False
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#
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# -----------------------------------------------------------------------------
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@ -484,7 +484,7 @@ def txt2vid(
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with autocast("cuda"):
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image = diffuse(server_state["pipe"], cond_embeddings, init, num_inference_steps, cfg_scale, eta)
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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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if st.session_state["save_individual_images"] and not server_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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@ -498,8 +498,8 @@ def txt2vid(
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#
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#try:
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#if st.session_state["use_GFPGAN"] and server_state["GFPGAN"] is not None and not st.session_state["use_RealESRGAN"]:
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if st.session_state["use_GFPGAN"] and server_state["GFPGAN"] is not None:
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#if server_state["use_GFPGAN"] and server_state["GFPGAN"] is not None and not st.session_state["use_RealESRGAN"]:
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if server_state["use_GFPGAN"] and server_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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@ -714,12 +714,12 @@ def layout():
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help="Do loop")
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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.")
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if server_state["GFPGAN_available"]:
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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.")
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if GFPGAN_available:
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server_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.")
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else:
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st.session_state["use_GFPGAN"] = False
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server_state["use_GFPGAN"] = False
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if server_state["RealESRGAN_available"]:
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if RealESRGAN_available:
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st.session_state["use_RealESRGAN"] = st.checkbox("Use RealESRGAN", value=st.session_state['defaults'].txt2vid.use_RealESRGAN,
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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.")
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st.session_state["RealESRGAN_model"] = st.selectbox("RealESRGAN model", ["RealESRGAN_x4plus", "RealESRGAN_x4plus_anime_6B"], index=0)
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@ -743,9 +743,9 @@ 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, st.session_state["use_GFPGAN"], True, st.session_state["RealESRGAN_model"])
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#load_models(False, server_state["use_GFPGAN"], True, st.session_state["RealESRGAN_model"])
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if st.session_state["use_GFPGAN"]:
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if server_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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@ -762,63 +762,63 @@ def layout():
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if "GFPGAN" in st.session_state:
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del server_state["GFPGAN"]
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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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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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#message.success('Done!', icon="✅")
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message.success('Render Complete: ' + info + '; Stats: ' + stats, icon="✅")
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#history_tab,col1,col2,col3,PlaceHolder,col1_cont,col2_cont,col3_cont = st.session_state['historyTab']
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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 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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#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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#message.success('Done!', icon="✅")
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message.success('Render Complete: ' + info + '; Stats: ' + stats, icon="✅")
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#history_tab,col1,col2,col3,PlaceHolder,col1_cont,col2_cont,col3_cont = st.session_state['historyTab']
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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 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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#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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#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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