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https://github.com/Sygil-Dev/sygil-webui.git
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Formatted tabs as spaces
This commit is contained in:
protoplm
parent
4f7adcaf42
commit
ca6fb326f3
@ -174,6 +174,7 @@ def load_models(continue_prev_run = False, use_GFPGAN=False, use_RealESRGAN=Fals
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del st.session_state.model
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del st.session_state.modelCS
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del st.session_state.modelFS
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del st.session_state.loaded_model
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except KeyError:
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pass
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@ -883,21 +884,26 @@ def slerp(device, t, v0:torch.Tensor, v1:torch.Tensor, DOT_THRESHOLD=0.9995):
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return v2
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#
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def optimize_update_preview_frequency(current_chunk_speed, previous_chunk_speed, update_preview_frequency):
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def optimize_update_preview_frequency(current_chunk_speed, previous_chunk_speed_list, update_preview_frequency, update_preview_frequency_list):
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"""Find the optimal update_preview_frequency value maximizing
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performance while minimizing the time between updates."""
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if current_chunk_speed >= previous_chunk_speed:
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from statistics import mean
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previous_chunk_avg_speed = mean(previous_chunk_speed_list)
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previous_chunk_speed_list.append(current_chunk_speed)
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current_chunk_avg_speed = mean(previous_chunk_speed_list)
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if current_chunk_avg_speed >= previous_chunk_avg_speed:
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#print(f"{current_chunk_speed} >= {previous_chunk_speed}")
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update_preview_frequency +=1
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previous_chunk_speed = current_chunk_speed
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update_preview_frequency_list.append(update_preview_frequency + 1)
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else:
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#print(f"{current_chunk_speed} <= {previous_chunk_speed}")
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update_preview_frequency -=1
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previous_chunk_speed = current_chunk_speed
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return current_chunk_speed, previous_chunk_speed, update_preview_frequency
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update_preview_frequency_list.append(update_preview_frequency - 1)
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update_preview_frequency = round(mean(update_preview_frequency_list))
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return current_chunk_speed, previous_chunk_speed_list, update_preview_frequency, update_preview_frequency_list
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def get_font(fontsize):
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@ -22,346 +22,346 @@ from streamlit.elements import image as STImage
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try:
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# this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
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from transformers import logging
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# this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
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from transformers import logging
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logging.set_verbosity_error()
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logging.set_verbosity_error()
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except:
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pass
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pass
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class plugin_info():
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plugname = "txt2img"
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description = "Text to Image"
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isTab = True
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displayPriority = 1
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plugname = "txt2img"
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description = "Text to Image"
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isTab = True
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displayPriority = 1
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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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GFPGAN_available = True
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GFPGAN_available = True
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else:
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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'].general.RealESRGAN_model}.pth")):
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RealESRGAN_available = True
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RealESRGAN_available = True
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else:
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RealESRGAN_available = False
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RealESRGAN_available = False
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#
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def txt2img(prompt: str, ddim_steps: int, sampler_name: str, realesrgan_model_name: str,
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n_iter: int, batch_size: int, cfg_scale: float, seed: Union[int, str, None],
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height: int, width: int, separate_prompts:bool = False, normalize_prompt_weights:bool = True,
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save_individual_images: bool = True, save_grid: bool = True, group_by_prompt: bool = True,
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save_as_jpg: bool = True, use_GFPGAN: bool = True, use_RealESRGAN: bool = True,
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RealESRGAN_model: str = "RealESRGAN_x4plus_anime_6B", fp = None, variant_amount: float = None,
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variant_seed: int = None, ddim_eta:float = 0.0, write_info_files:bool = True):
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n_iter: int, batch_size: int, cfg_scale: float, seed: Union[int, str, None],
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height: int, width: int, separate_prompts:bool = False, normalize_prompt_weights:bool = True,
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save_individual_images: bool = True, save_grid: bool = True, group_by_prompt: bool = True,
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save_as_jpg: bool = True, use_GFPGAN: bool = True, use_RealESRGAN: bool = True,
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RealESRGAN_model: str = "RealESRGAN_x4plus_anime_6B", fp = None, variant_amount: float = None,
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variant_seed: int = None, ddim_eta:float = 0.0, write_info_files:bool = True):
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outpath = st.session_state['defaults'].general.outdir_txt2img or st.session_state['defaults'].general.outdir or "outputs/txt2img-samples"
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outpath = st.session_state['defaults'].general.outdir_txt2img or st.session_state['defaults'].general.outdir or "outputs/txt2img-samples"
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seed = seed_to_int(seed)
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seed = seed_to_int(seed)
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#prompt_matrix = 0 in toggles
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#normalize_prompt_weights = 1 in toggles
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#skip_save = 2 not in toggles
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#save_grid = 3 not in toggles
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#sort_samples = 4 in toggles
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#write_info_files = 5 in toggles
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#jpg_sample = 6 in toggles
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#use_GFPGAN = 7 in toggles
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#use_RealESRGAN = 8 in toggles
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#prompt_matrix = 0 in toggles
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#normalize_prompt_weights = 1 in toggles
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#skip_save = 2 not in toggles
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#save_grid = 3 not in toggles
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#sort_samples = 4 in toggles
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#write_info_files = 5 in toggles
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#jpg_sample = 6 in toggles
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#use_GFPGAN = 7 in toggles
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#use_RealESRGAN = 8 in toggles
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if sampler_name == 'PLMS':
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sampler = PLMSSampler(st.session_state["model"])
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elif sampler_name == 'DDIM':
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sampler = DDIMSampler(st.session_state["model"])
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elif sampler_name == 'k_dpm_2_a':
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sampler = KDiffusionSampler(st.session_state["model"],'dpm_2_ancestral')
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elif sampler_name == 'k_dpm_2':
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sampler = KDiffusionSampler(st.session_state["model"],'dpm_2')
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elif sampler_name == 'k_euler_a':
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sampler = KDiffusionSampler(st.session_state["model"],'euler_ancestral')
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elif sampler_name == 'k_euler':
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sampler = KDiffusionSampler(st.session_state["model"],'euler')
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elif sampler_name == 'k_heun':
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sampler = KDiffusionSampler(st.session_state["model"],'heun')
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elif sampler_name == 'k_lms':
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sampler = KDiffusionSampler(st.session_state["model"],'lms')
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else:
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raise Exception("Unknown sampler: " + sampler_name)
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if sampler_name == 'PLMS':
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sampler = PLMSSampler(st.session_state["model"])
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elif sampler_name == 'DDIM':
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sampler = DDIMSampler(st.session_state["model"])
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elif sampler_name == 'k_dpm_2_a':
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sampler = KDiffusionSampler(st.session_state["model"],'dpm_2_ancestral')
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elif sampler_name == 'k_dpm_2':
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sampler = KDiffusionSampler(st.session_state["model"],'dpm_2')
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elif sampler_name == 'k_euler_a':
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sampler = KDiffusionSampler(st.session_state["model"],'euler_ancestral')
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elif sampler_name == 'k_euler':
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sampler = KDiffusionSampler(st.session_state["model"],'euler')
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elif sampler_name == 'k_heun':
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sampler = KDiffusionSampler(st.session_state["model"],'heun')
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elif sampler_name == 'k_lms':
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sampler = KDiffusionSampler(st.session_state["model"],'lms')
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else:
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raise Exception("Unknown sampler: " + sampler_name)
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def init():
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pass
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def init():
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pass
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def sample(init_data, x, conditioning, unconditional_conditioning, sampler_name):
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samples_ddim, _ = sampler.sample(S=ddim_steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=cfg_scale,
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unconditional_conditioning=unconditional_conditioning, eta=ddim_eta, x_T=x, img_callback=generation_callback,
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log_every_t=int(st.session_state['defaults'].general.update_preview_frequency))
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def sample(init_data, x, conditioning, unconditional_conditioning, sampler_name):
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samples_ddim, _ = sampler.sample(S=ddim_steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=cfg_scale,
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unconditional_conditioning=unconditional_conditioning, eta=ddim_eta, x_T=x, img_callback=generation_callback,
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log_every_t=int(st.session_state['defaults'].general.update_preview_frequency))
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return samples_ddim
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return samples_ddim
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#try:
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output_images, seed, info, stats = process_images(
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outpath=outpath,
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func_init=init,
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func_sample=sample,
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prompt=prompt,
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seed=seed,
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sampler_name=sampler_name,
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save_grid=save_grid,
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batch_size=batch_size,
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n_iter=n_iter,
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steps=ddim_steps,
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cfg_scale=cfg_scale,
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width=width,
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height=height,
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prompt_matrix=separate_prompts,
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use_GFPGAN=st.session_state["use_GFPGAN"],
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use_RealESRGAN=st.session_state["use_RealESRGAN"],
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realesrgan_model_name=realesrgan_model_name,
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fp=fp,
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ddim_eta=ddim_eta,
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normalize_prompt_weights=normalize_prompt_weights,
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save_individual_images=save_individual_images,
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sort_samples=group_by_prompt,
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write_info_files=write_info_files,
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jpg_sample=save_as_jpg,
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variant_amount=variant_amount,
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variant_seed=variant_seed,
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)
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#try:
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output_images, seed, info, stats = process_images(
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outpath=outpath,
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func_init=init,
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func_sample=sample,
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prompt=prompt,
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seed=seed,
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sampler_name=sampler_name,
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save_grid=save_grid,
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batch_size=batch_size,
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n_iter=n_iter,
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steps=ddim_steps,
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cfg_scale=cfg_scale,
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width=width,
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height=height,
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prompt_matrix=separate_prompts,
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use_GFPGAN=st.session_state["use_GFPGAN"],
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use_RealESRGAN=st.session_state["use_RealESRGAN"],
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realesrgan_model_name=realesrgan_model_name,
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fp=fp,
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ddim_eta=ddim_eta,
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normalize_prompt_weights=normalize_prompt_weights,
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save_individual_images=save_individual_images,
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sort_samples=group_by_prompt,
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write_info_files=write_info_files,
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jpg_sample=save_as_jpg,
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variant_amount=variant_amount,
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variant_seed=variant_seed,
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)
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del sampler
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del sampler
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return output_images, seed, info, stats
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return output_images, seed, info, stats
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#except RuntimeError as e:
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#err = e
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#err_msg = f'CRASHED:<br><textarea rows="5" style="color:white;background: black;width: -webkit-fill-available;font-family: monospace;font-size: small;font-weight: bold;">{str(e)}</textarea><br><br>Please wait while the program restarts.'
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#stats = err_msg
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#return [], seed, 'err', stats
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#except RuntimeError as e:
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#err = e
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#err_msg = f'CRASHED:<br><textarea rows="5" style="color:white;background: black;width: -webkit-fill-available;font-family: monospace;font-size: small;font-weight: bold;">{str(e)}</textarea><br><br>Please wait while the program restarts.'
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#stats = err_msg
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#return [], seed, 'err', stats
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def layout():
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with st.form("txt2img-inputs"):
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st.session_state["generation_mode"] = "txt2img"
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with st.form("txt2img-inputs"):
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st.session_state["generation_mode"] = "txt2img"
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input_col1, generate_col1 = st.columns([10,1])
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input_col1, generate_col1 = st.columns([10,1])
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with input_col1:
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#prompt = st.text_area("Input Text","")
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prompt = st.text_input("Input Text","", placeholder="A corgi wearing a top hat as an oil painting.")
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with input_col1:
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#prompt = st.text_area("Input Text","")
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prompt = st.text_input("Input Text","", placeholder="A corgi wearing a top hat as an oil painting.")
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# 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.
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generate_col1.write("")
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generate_col1.write("")
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generate_button = generate_col1.form_submit_button("Generate")
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# 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.
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generate_col1.write("")
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generate_col1.write("")
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generate_button = generate_col1.form_submit_button("Generate")
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# creating the page layout using columns
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col1, col2, col3 = st.columns([1,2,1], gap="large")
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# creating the page layout using columns
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col1, col2, col3 = st.columns([1,2,1], gap="large")
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with col1:
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width = st.slider("Width:", min_value=64, max_value=1024, value=st.session_state['defaults'].txt2img.width, step=64)
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height = st.slider("Height:", min_value=64, max_value=1024, value=st.session_state['defaults'].txt2img.height, step=64)
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cfg_scale = st.slider("CFG (Classifier Free Guidance Scale):", min_value=1.0, max_value=30.0, value=st.session_state['defaults'].txt2img.cfg_scale, step=0.5, help="How strongly the image should follow the prompt.")
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seed = st.text_input("Seed:", value=st.session_state['defaults'].txt2img.seed, help=" The seed to use, if left blank a random seed will be generated.")
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batch_count = st.slider("Batch count.", min_value=1, max_value=100, value=st.session_state['defaults'].txt2img.batch_count, step=1, help="How many iterations or batches of images to generate in total.")
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with col1:
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width = st.slider("Width:", min_value=64, max_value=1024, value=st.session_state['defaults'].txt2img.width, step=64)
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height = st.slider("Height:", min_value=64, max_value=1024, value=st.session_state['defaults'].txt2img.height, step=64)
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cfg_scale = st.slider("CFG (Classifier Free Guidance Scale):", min_value=1.0, max_value=30.0, value=st.session_state['defaults'].txt2img.cfg_scale, step=0.5, help="How strongly the image should follow the prompt.")
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seed = st.text_input("Seed:", value=st.session_state['defaults'].txt2img.seed, help=" The seed to use, if left blank a random seed will be generated.")
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batch_count = st.slider("Batch count.", min_value=1, max_value=100, value=st.session_state['defaults'].txt2img.batch_count, step=1, help="How many iterations or batches of images to generate in total.")
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bs_slider_max_value = 5
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if st.session_state.defaults.general.optimized:
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bs_slider_max_value = 100
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bs_slider_max_value = 5
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if st.session_state.defaults.general.optimized:
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bs_slider_max_value = 100
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batch_size = st.slider(
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"Batch size",
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min_value=1,
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max_value=bs_slider_max_value,
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value=st.session_state.defaults.txt2img.batch_size,
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step=1,
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help="How many images are at once in a batch.\
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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.\
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Default: 1")
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batch_size = st.slider(
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"Batch size",
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min_value=1,
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max_value=bs_slider_max_value,
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value=st.session_state.defaults.txt2img.batch_size,
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step=1,
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help="How many images are at once in a batch.\
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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.\
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Default: 1")
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with st.expander("Preview Settings"):
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st.session_state["update_preview"] = st.checkbox("Update Image Preview", value=st.session_state['defaults'].txt2img.update_preview,
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help="If enabled the image preview will be updated during the generation instead of at the end. \
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You can use the Update Preview \Frequency option bellow to customize how frequent it's updated. \
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By default this is enabled and the frequency is set to 1 step.")
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with st.expander("Preview Settings"):
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st.session_state["update_preview"] = st.checkbox("Update Image Preview", value=st.session_state['defaults'].txt2img.update_preview,
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help="If enabled the image preview will be updated during the generation instead of at the end. \
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You can use the Update Preview \Frequency option bellow to customize how frequent it's updated. \
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By default this is enabled and the frequency is set to 1 step.")
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st.session_state["update_preview_frequency"] = st.text_input("Update Image Preview Frequency", value=st.session_state['defaults'].txt2img.update_preview_frequency,
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help="Frequency in steps at which the the preview image is updated. By default the frequency \
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is set to 1 step.")
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st.session_state["update_preview_frequency"] = st.text_input("Update Image Preview Frequency", value=st.session_state['defaults'].txt2img.update_preview_frequency,
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help="Frequency in steps at which the the preview image is updated. By default the frequency \
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is set to 1 step.")
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with col2:
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preview_tab, gallery_tab = st.tabs(["Preview", "Gallery"])
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with col2:
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preview_tab, gallery_tab = st.tabs(["Preview", "Gallery"])
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with preview_tab:
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#st.write("Image")
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#Image for testing
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#image = Image.open(requests.get("https://icon-library.com/images/image-placeholder-icon/image-placeholder-icon-13.jpg", stream=True).raw).convert('RGB')
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#new_image = image.resize((175, 240))
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#preview_image = st.image(image)
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with preview_tab:
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#st.write("Image")
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#Image for testing
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#image = Image.open(requests.get("https://icon-library.com/images/image-placeholder-icon/image-placeholder-icon-13.jpg", stream=True).raw).convert('RGB')
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#new_image = image.resize((175, 240))
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#preview_image = st.image(image)
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# create an empty container for the image, progress bar, etc so we can update it later and use session_state to hold them globally.
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st.session_state["preview_image"] = st.empty()
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# 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["loading"] = st.empty()
|
||||
|
||||
st.session_state["progress_bar_text"] = st.empty()
|
||||
st.session_state["progress_bar"] = st.empty()
|
||||
st.session_state["progress_bar_text"] = st.empty()
|
||||
st.session_state["progress_bar"] = st.empty()
|
||||
|
||||
message = st.empty()
|
||||
message = st.empty()
|
||||
|
||||
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"]:
|
||||
st.session_state["custom_model"] = st.selectbox("Custom Model:", st.session_state["custom_models"],
|
||||
index=st.session_state["custom_models"].index(st.session_state['defaults'].general.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")
|
||||
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"]:
|
||||
st.session_state["custom_model"] = st.selectbox("Custom Model:", st.session_state["custom_models"],
|
||||
index=st.session_state["custom_models"].index(st.session_state['defaults'].general.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")
|
||||
|
||||
st.session_state.sampling_steps = st.slider("Sampling Steps", value=st.session_state['defaults'].txt2img.sampling_steps, min_value=1, max_value=250)
|
||||
st.session_state.sampling_steps = st.slider("Sampling Steps", value=st.session_state['defaults'].txt2img.sampling_steps, min_value=1, max_value=250)
|
||||
|
||||
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'].txt2img.default_sampler), help="Sampling method to use. Default: k_euler")
|
||||
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'].txt2img.default_sampler), help="Sampling method to use. Default: k_euler")
|
||||
|
||||
|
||||
|
||||
#basic_tab, advanced_tab = st.tabs(["Basic", "Advanced"])
|
||||
#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 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"):
|
||||
separate_prompts = st.checkbox("Create Prompt Matrix.", value=False, help="Separate multiple prompts using the `|` character, and get all combinations of them.")
|
||||
normalize_prompt_weights = st.checkbox("Normalize Prompt Weights.", value=True, help="Ensure the sum of all weights add up to 1.0")
|
||||
save_individual_images = st.checkbox("Save individual images.", value=True, help="Save each image generated before any filter or enhancement is applied.")
|
||||
save_grid = st.checkbox("Save grid",value=True, help="Save a grid with all the images generated into a single image.")
|
||||
group_by_prompt = st.checkbox("Group results by prompt", value=True,
|
||||
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.")
|
||||
write_info_files = st.checkbox("Write Info file", value=True, help="Save a file next to the image with informartion about the generation.")
|
||||
save_as_jpg = st.checkbox("Save samples as jpg", value=False, help="Saves the images as jpg instead of png.")
|
||||
with st.expander("Advanced"):
|
||||
separate_prompts = st.checkbox("Create Prompt Matrix.", value=False, help="Separate multiple prompts using the `|` character, and get all combinations of them.")
|
||||
normalize_prompt_weights = st.checkbox("Normalize Prompt Weights.", value=True, help="Ensure the sum of all weights add up to 1.0")
|
||||
save_individual_images = st.checkbox("Save individual images.", value=True, help="Save each image generated before any filter or enhancement is applied.")
|
||||
save_grid = st.checkbox("Save grid",value=True, help="Save a grid with all the images generated into a single image.")
|
||||
group_by_prompt = st.checkbox("Group results by prompt", value=True,
|
||||
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.")
|
||||
write_info_files = st.checkbox("Write Info file", value=True, help="Save a file next to the image with informartion about the generation.")
|
||||
save_as_jpg = st.checkbox("Save samples as jpg", value=False, help="Saves the images as jpg instead of png.")
|
||||
|
||||
if st.session_state["GFPGAN_available"]:
|
||||
st.session_state["use_GFPGAN"] = st.checkbox("Use GFPGAN", value=st.session_state['defaults'].txt2img.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 st.session_state["GFPGAN_available"]:
|
||||
st.session_state["use_GFPGAN"] = st.checkbox("Use GFPGAN", value=st.session_state['defaults'].txt2img.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 st.session_state["RealESRGAN_available"]:
|
||||
st.session_state["use_RealESRGAN"] = st.checkbox("Use RealESRGAN", value=st.session_state['defaults'].txt2img.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"
|
||||
if st.session_state["RealESRGAN_available"]:
|
||||
st.session_state["use_RealESRGAN"] = st.checkbox("Use RealESRGAN", value=st.session_state['defaults'].txt2img.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"
|
||||
|
||||
variant_amount = st.slider("Variant Amount:", value=st.session_state['defaults'].txt2img.variant_amount, min_value=0.0, max_value=1.0, step=0.01)
|
||||
variant_seed = st.text_input("Variant Seed:", value=st.session_state['defaults'].txt2img.seed, help="The seed to use when generating a variant, if left blank a random seed will be generated.")
|
||||
galleryCont = st.empty()
|
||||
variant_amount = st.slider("Variant Amount:", value=st.session_state['defaults'].txt2img.variant_amount, min_value=0.0, max_value=1.0, step=0.01)
|
||||
variant_seed = st.text_input("Variant Seed:", value=st.session_state['defaults'].txt2img.seed, help="The seed to use when generating a variant, if left blank a random seed will be generated.")
|
||||
galleryCont = st.empty()
|
||||
|
||||
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"], st.session_state["use_RealESRGAN"], st.session_state["RealESRGAN_model"], st.session_state["CustomModel_available"],
|
||||
st.session_state["custom_model"])
|
||||
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"], st.session_state["use_RealESRGAN"], st.session_state["RealESRGAN_model"], st.session_state["CustomModel_available"],
|
||||
st.session_state["custom_model"])
|
||||
|
||||
try:
|
||||
output_images, seeds, info, stats = txt2img(prompt, st.session_state.sampling_steps, sampler_name, st.session_state["RealESRGAN_model"], batch_count, batch_size,
|
||||
cfg_scale, seed, height, width, separate_prompts, normalize_prompt_weights, save_individual_images,
|
||||
save_grid, group_by_prompt, save_as_jpg, st.session_state["use_GFPGAN"], st.session_state["use_RealESRGAN"], st.session_state["RealESRGAN_model"],
|
||||
fp=st.session_state.defaults.general.fp, variant_amount=variant_amount, variant_seed=variant_seed, write_info_files=write_info_files)
|
||||
try:
|
||||
output_images, seeds, info, stats = txt2img(prompt, st.session_state.sampling_steps, sampler_name, st.session_state["RealESRGAN_model"], batch_count, batch_size,
|
||||
cfg_scale, seed, height, width, separate_prompts, normalize_prompt_weights, save_individual_images,
|
||||
save_grid, group_by_prompt, save_as_jpg, st.session_state["use_GFPGAN"], st.session_state["use_RealESRGAN"], st.session_state["RealESRGAN_model"],
|
||||
fp=st.session_state.defaults.general.fp, variant_amount=variant_amount, variant_seed=variant_seed, write_info_files=write_info_files)
|
||||
|
||||
message.success('Render Complete: ' + info + '; Stats: ' + stats, 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']
|
||||
history_tab,col1,col2,col3,PlaceHolder,col1_cont,col2_cont,col3_cont = st.session_state['historyTab']
|
||||
|
||||
if 'latestImages' in st.session_state:
|
||||
for i in output_images:
|
||||
#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['latestImages'].pop()
|
||||
#add the new image to the start of the list
|
||||
st.session_state['latestImages'].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['latestImages'][0])
|
||||
st.image(st.session_state['latestImages'][3])
|
||||
st.image(st.session_state['latestImages'][6])
|
||||
with col2_cont:
|
||||
with col2:
|
||||
st.image(st.session_state['latestImages'][1])
|
||||
st.image(st.session_state['latestImages'][4])
|
||||
st.image(st.session_state['latestImages'][7])
|
||||
with col3_cont:
|
||||
with col3:
|
||||
st.image(st.session_state['latestImages'][2])
|
||||
st.image(st.session_state['latestImages'][5])
|
||||
st.image(st.session_state['latestImages'][8])
|
||||
historyGallery = st.empty()
|
||||
if 'latestImages' in st.session_state:
|
||||
for i in output_images:
|
||||
#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['latestImages'].pop()
|
||||
#add the new image to the start of the list
|
||||
st.session_state['latestImages'].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['latestImages'][0])
|
||||
st.image(st.session_state['latestImages'][3])
|
||||
st.image(st.session_state['latestImages'][6])
|
||||
with col2_cont:
|
||||
with col2:
|
||||
st.image(st.session_state['latestImages'][1])
|
||||
st.image(st.session_state['latestImages'][4])
|
||||
st.image(st.session_state['latestImages'][7])
|
||||
with col3_cont:
|
||||
with col3:
|
||||
st.image(st.session_state['latestImages'][2])
|
||||
st.image(st.session_state['latestImages'][5])
|
||||
st.image(st.session_state['latestImages'][8])
|
||||
historyGallery = st.empty()
|
||||
|
||||
# check if output_images length is the same as seeds length
|
||||
with gallery_tab:
|
||||
st.markdown(createHTMLGallery(output_images,seeds), unsafe_allow_html=True)
|
||||
# check if output_images length is the same as seeds length
|
||||
with gallery_tab:
|
||||
st.markdown(createHTMLGallery(output_images,seeds), 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")
|
||||
st.session_state['historyTab'] = [history_tab,col1,col2,col3,PlaceHolder,col1_cont,col2_cont,col3_cont]
|
||||
except (StopException, KeyError):
|
||||
print(f"Received Streamlit StopException")
|
||||
|
||||
# this will render all the images at the end of the generation but its better if its moved to a second tab inside col2 and shown as a gallery.
|
||||
# use the current col2 first tab to show the preview_img and update it as its generated.
|
||||
#preview_image.image(output_images)
|
||||
# this will render all the images at the end of the generation but its better if its moved to a second tab inside col2 and shown as a gallery.
|
||||
# use the current col2 first tab to show the preview_img and update it as its generated.
|
||||
#preview_image.image(output_images)
|
||||
|
||||
#on import run init
|
||||
def createHTMLGallery(images,info):
|
||||
html3 = """
|
||||
<div class="gallery-history" style="
|
||||
display: flex;
|
||||
flex-wrap: wrap;
|
||||
align-items: flex-start;">
|
||||
"""
|
||||
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
|
||||
html3 = """
|
||||
<div class="gallery-history" style="
|
||||
display: flex;
|
||||
flex-wrap: wrap;
|
||||
align-items: flex-start;">
|
||||
"""
|
||||
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
|
||||
#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"""<img src="{img_str}" alt="Image" with="200" height="200" />"""
|
||||
mkdwn = f'''<div class="gallery" style="margin: 3px;" >
|
||||
#mkdwn = f"""<img src="{img_str}" alt="Image" with="200" height="200" />"""
|
||||
mkdwn = f'''<div class="gallery" style="margin: 3px;" >
|
||||
<a href="{img_str}">
|
||||
<img src="{img_str}" alt="Image" width="{width}" height="{height}">
|
||||
<img src="{img_str}" alt="Image" width="{width}" height="{height}">
|
||||
</a>
|
||||
<div class="desc" style="text-align: center; opacity: 40%;">{seed}</div>
|
||||
</div>
|
||||
'''
|
||||
mkdwn_array.append(mkdwn)
|
||||
html3 += "".join(mkdwn_array)
|
||||
html3 += '</div>'
|
||||
return html3
|
||||
mkdwn_array.append(mkdwn)
|
||||
html3 += "".join(mkdwn_array)
|
||||
html3 += '</div>'
|
||||
return html3
|
||||
Loading…
Reference in New Issue
Block a user