mirror of
https://github.com/sd-webui/stable-diffusion-webui.git
synced 2024-12-15 15:22:55 +03:00
Added a progress bar and updated the Image-to-image tab. (#652)
* Added a progress bar as well as some extra info to know how the generation is going without having to check the console every time. * - Updated the Image-to-image tab, it is now working at a basic level. - Disabled RealESRGAN by default for the Image-to-Image tab as it is not working right now.
This commit is contained in:
ZeroCool
GitHub
parent
75a7ef77f0
commit
78ad3c3445
@ -87,7 +87,9 @@ os.environ["CUDA_VISIBLE_DEVICES"] = str(defaults.general.gpu)
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def load_models(continue_prev_run = False, use_GFPGAN=False, use_RealESRGAN=False, RealESRGAN_model="RealESRGAN_x4plus"):
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"""Load the different models. We also reuse the models that are already in memory to speed things up instead of loading them again. """
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print ("Loading models.")
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# Generate random run ID
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# Used to link runs linked w/ continue_prev_run which is not yet implemented
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# Use URL and filesystem safe version just in case.
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@ -180,8 +182,13 @@ def load_sd_from_config(ckpt, verbose=False):
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def generation_callback(img, i=0):
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if i == 0:
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if img['i']: i = img['i']
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try:
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if i == 0:
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if img['i']: i = img['i']
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except TypeError:
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pass
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if i % int(defaults.general.update_preview_frequency) == 0 and defaults.general.update_preview:
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#print (img)
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@ -200,8 +207,21 @@ def generation_callback(img, i=0):
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pil_image = transforms.ToPILImage()(x_samples_ddim.squeeze_(0))
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# update image on the UI so we can see the progress
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st.session_state["preview_image"].image(pil_image, width=512)
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# Show a progress bar so we can keep track of the progress even when the image progress is not been shown,
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# Dont worry, it doesnt affect the performance.
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if st.session_state["generation_mode"] == "txt2img":
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percent = int(100 * float(i+1)/float(st.session_state.sampling_steps))
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st.session_state["progress_bar_text"].text(f"Running step: {i+1}/{st.session_state.sampling_steps} {percent}%")
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else:
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percent = int(100 * float(i+1 )/float(round(st.session_state.sampling_steps * st.session_state["denoising_strength"])))
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st.session_state["progress_bar_text"].text(f"""Running step: {i+1}/{round(st.session_state.sampling_steps * st.session_state["denoising_strength"])} {percent}%""")
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st.session_state["progress_bar"] = st.session_state["progress_bar"].progress(percent)
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class MemUsageMonitor(threading.Thread):
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stop_flag = False
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@ -401,7 +421,7 @@ def load_RealESRGAN(model_name: str):
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}
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model_path = os.path.join(defaults.general.RealESRGAN_dir, 'experiments/pretrained_models', model_name + '.pth')
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if not os.path.isfile(model_path):
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if not os.path.exists(os.path.join(defaults.general.RealESRGAN_dir, "experiments","pretrained_models", f"{model_name}.pth")):
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raise Exception(model_name+".pth not found at path "+model_path)
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sys.path.append(os.path.abspath(defaults.general.RealESRGAN_dir))
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@ -505,27 +525,6 @@ def ModelLoader(models,load=False,unload=False,imgproc_realesrgan_model_name='Re
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torch_gc()
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def run_GFPGAN(image, strength):
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image = image.convert("RGB")
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cropped_faces, restored_faces, restored_img = GFPGAN.enhance(np.array(image, dtype=np.uint8), has_aligned=False, only_center_face=False, paste_back=True)
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res = Image.fromarray(restored_img)
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if strength < 1.0:
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res = Image.blend(image, res, strength)
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return res
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def run_RealESRGAN(image, model_name: str):
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if RealESRGAN.model.name != model_name:
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try_loading_RealESRGAN(model_name)
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image = image.convert("RGB")
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output, img_mode = RealESRGAN.enhance(np.array(image, dtype=np.uint8))
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res = Image.fromarray(output)
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return res
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def get_font(fontsize):
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fonts = ["arial.ttf", "DejaVuSans.ttf"]
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@ -957,18 +956,21 @@ def process_images(
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if use_RealESRGAN and st.session_state["RealESRGAN"] is not None and not use_GFPGAN:
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skip_save = True # #287 >_>
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torch_gc()
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if st.session_state["RealESRGAN"].model.name != realesrgan_model_name:
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try_loading_RealESRGAN(realesrgan_model_name)
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#try_loading_RealESRGAN(realesrgan_model_name)
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load_models(use_GFPGAN=use_GFPGAN, use_RealESRGAN=use_RealESRGAN, RealESRGAN_model=realesrgan_model_name)
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output, img_mode = st.session_state["RealESRGAN"].enhance(x_sample[:,:,::-1])
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esrgan_filename = original_filename + '-esrgan4x'
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esrgan_sample = output[:,:,::-1]
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esrgan_image = Image.fromarray(esrgan_sample)
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save_sample(image, sample_path_i, original_filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
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normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback, skip_save,
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normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback, skip_save,
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skip_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode)
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save_sample(esrgan_image, sample_path_i, esrgan_filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
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normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback, skip_save,
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normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback, skip_save,
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skip_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode)
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output_images.append(esrgan_image) #287
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@ -980,8 +982,11 @@ def process_images(
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torch_gc()
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cropped_faces, restored_faces, restored_img = st.session_state["GFPGAN"].enhance(x_sample[:,:,::-1], has_aligned=False, only_center_face=False, paste_back=True)
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gfpgan_sample = restored_img[:,:,::-1]
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if st.session_state["RealESRGAN"].model.name != realesrgan_model_name:
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try_loading_RealESRGAN(realesrgan_model_name)
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#try_loading_RealESRGAN(realesrgan_model_name)
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load_models(use_GFPGAN=use_GFPGAN, use_RealESRGAN=use_RealESRGAN, RealESRGAN_model=realesrgan_model_name)
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output, img_mode = st.session_state["RealESRGAN"].enhance(gfpgan_sample[:,:,::-1])
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gfpgan_esrgan_filename = original_filename + '-gfpgan-esrgan4x'
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gfpgan_esrgan_sample = output[:,:,::-1]
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@ -998,7 +1003,7 @@ def process_images(
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output_images.append(gfpgan_esrgan_image) #287
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if simple_templating:
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grid_captions.append( captions[i] + "\ngfpgan_esrgan" )
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if not skip_save or (not use_GFPGAN or not use_RealESRGAN):
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save_sample(image, sample_path_i, filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
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normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback, skip_save,
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@ -1043,7 +1048,7 @@ def process_images(
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time_diff = time.time()-start_time
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info = f"""
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{prompt}
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{prompt}
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Steps: {steps}, Sampler: {sampler_name}, CFG scale: {cfg_scale}, Seed: {seed}{', Denoising strength: '+str(denoising_strength) if init_img is not None else ''}{', GFPGAN' if use_GFPGAN and st.session_state["GFPGAN"] is not None else ''}{', '+realesrgan_model_name if use_RealESRGAN and st.session_state["RealESRGAN"] is not None else ''}{', Prompt Matrix Mode.' if prompt_matrix else ''}""".strip()
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stats = f'''
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Took { round(time_diff, 2) }s total ({ round(time_diff/(len(all_prompts)),2) }s per image)
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@ -1102,7 +1107,8 @@ def img2img(prompt: str = '', init_info: any = None, ddim_steps: int = 50, sampl
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write_info_files:bool = True, RealESRGAN_model: str = "RealESRGAN_x4plus_anime_6B",
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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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save_as_jpg: bool = True, use_GFPGAN: bool = True, use_RealESRGAN: bool = True, loopback: bool = True
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):
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outpath = defaults.general.outdir_img2img or defaults.general.outdir or "outputs/img2img-samples"
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err = False
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@ -1196,7 +1202,10 @@ def img2img(prompt: str = '', init_info: any = None, ddim_steps: int = 50, sampl
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sigma_sched = sigmas[ddim_steps - t_enc_steps - 1:]
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model_wrap_cfg = CFGMaskedDenoiser(sampler.model_wrap)
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samples_ddim = K.sampling.__dict__[f'sample_{sampler.get_sampler_name()}'](model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': cfg_scale, 'mask': z_mask, 'x0': x0, 'xi': xi}, disable=False)
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samples_ddim = K.sampling.__dict__[f'sample_{sampler.get_sampler_name()}'](model_wrap_cfg, xi, sigma_sched,
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extra_args={'cond': conditioning, 'uncond': unconditional_conditioning,
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'cond_scale': cfg_scale, 'mask': z_mask, 'x0': x0, 'xi': xi}, disable=False,
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callback=generation_callback)
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else:
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x0, z_mask = init_data
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@ -1225,41 +1234,41 @@ def img2img(prompt: str = '', init_info: any = None, ddim_steps: int = 50, sampl
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for i in range(n_iter):
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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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skip_save=skip_save,
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skip_grid=skip_grid,
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batch_size=1,
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n_iter=1,
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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=prompt_matrix,
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use_GFPGAN=use_GFPGAN,
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use_RealESRGAN=False, # Forcefully disable upscaling when using loopback
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realesrgan_model_name=realesrgan_model_name,
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fp=fp,
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do_not_save_grid=True,
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normalize_prompt_weights=normalize_prompt_weights,
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init_img=init_img,
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init_mask=init_mask,
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keep_mask=keep_mask,
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mask_blur_strength=mask_blur_strength,
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denoising_strength=denoising_strength,
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resize_mode=resize_mode,
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uses_loopback=loopback,
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uses_random_seed_loopback=random_seed_loopback,
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sort_samples=sort_samples,
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write_info_files=write_info_files,
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write_sample_info_to_log_file=write_sample_info_to_log_file,
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jpg_sample=save_as_jpg,
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job_info=job_info
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)
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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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skip_save=skip_save,
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skip_grid=skip_grid,
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batch_size=1,
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n_iter=1,
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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=prompt_matrix,
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use_GFPGAN=use_GFPGAN,
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use_RealESRGAN=False, # Forcefully disable upscaling when using loopback
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realesrgan_model_name=realesrgan_model_name,
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fp=fp,
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do_not_save_grid=True,
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normalize_prompt_weights=normalize_prompt_weights,
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init_img=init_img,
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init_mask=init_mask,
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keep_mask=keep_mask,
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mask_blur_strength=mask_blur_strength,
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denoising_strength=denoising_strength,
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resize_mode=resize_mode,
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uses_loopback=loopback,
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uses_random_seed_loopback=random_seed_loopback,
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sort_samples=sort_samples,
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write_info_files=write_info_files,
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write_sample_info_to_log_file=write_sample_info_to_log_file,
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jpg_sample=save_as_jpg,
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job_info=job_info
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)
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if initial_seed is None:
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initial_seed = seed
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@ -1448,7 +1457,7 @@ def layout():
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else:
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GFPGAN_available = False
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if os.path.exists(os.path.join(defaults.general.RealESRGAN_dir, "experiments","pretrained_models", "RealESRGAN_x4plus.pth")):
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if os.path.exists(os.path.join(defaults.general.RealESRGAN_dir, "experiments","pretrained_models", f"{defaults.general.RealESRGAN_model}.pth")):
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RealESRGAN_available = True
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else:
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RealESRGAN_available = False
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@ -1468,6 +1477,8 @@ def layout():
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txt2img_tab, img2img_tab, postprocessing_tab = st.tabs(["Stable Diffusion Text-to-Image Unified", "Stable Diffusion Image-to-Image Unified", "Post-Processing"])
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with txt2img_tab:
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st.session_state["generation_mode"] = "txt2img"
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with st.form("txt2img-inputs"):
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input_col1, generate_col1 = st.columns([10,1])
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@ -1484,8 +1495,8 @@ def layout():
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col1, col2, col3 = st.columns([1,2,1], gap="large")
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with col1:
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height = st.slider("Height:", min_value=64, max_value=2048, value=defaults.txt2img.height, step=64)
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width = st.slider("Width:", min_value=64, max_value=2048, value=defaults.txt2img.width, step=64)
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height = st.slider("Height:", min_value=64, max_value=2048, value=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=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=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=500, value=defaults.txt2img.batch_count, step=1, help="How many iterations or batches of images to generate in total.")
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@ -1498,26 +1509,33 @@ def layout():
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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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#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)
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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 and use session_state to hold it globally.
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preview_image = st.empty()
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st.session_state["preview_image"] = preview_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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st.session_state["loading"] = st.empty()
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st.session_state["progress_bar_text"] = st.empty()
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st.session_state["progress_bar"] = st.empty()
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message = st.empty()
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with gallery_tab:
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st.write('Here should be the image gallery, if I could make a grid in streamlit.')
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with col3:
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sampling_steps = st.slider("Sampling Steps", value=defaults.txt2img.sampling_steps, min_value=1, max_value=250)
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st.session_state.sampling_steps = st.slider("Sampling Steps", value=defaults.txt2img.sampling_steps, min_value=1, max_value=250)
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sampler_name = st.selectbox("Sampling method",
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["k_lms", "k_euler", "k_euler_a", "k_dpm_2", "k_dpm_2_a", "k_heun", "PLMS", "DDIM"],
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index=0, help="Sampling method to use. Default: k_lms")
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#basic_tab, advanced_tab = st.tabs(["Basic", "Advanced"])
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#with basic_tab:
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@ -1552,14 +1570,17 @@ 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 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, use_GFPGAN, use_RealESRGAN, RealESRGAN_model)
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try:
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output_images, seed, info, stats = txt2img(prompt, sampling_steps, sampler_name, RealESRGAN_model, batch_count, batch_size,
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output_images, seed, info, stats = txt2img(prompt, st.session_state.sampling_steps, sampler_name, RealESRGAN_model, batch_count, batch_size,
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cfg_scale, seed, height, width, separate_prompts, normalize_prompt_weights, save_individual_images,
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save_grid, group_by_prompt, save_as_jpg, use_GFPGAN, use_RealESRGAN, RealESRGAN_model, fp=defaults.general.fp,
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variant_amount=variant_amount, variant_seed=variant_seed, write_info_files=write_info_files)
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message.success('Done!', icon="✅")
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except (StopException, KeyError):
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print(f"Received Streamlit StopException")
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@ -1568,6 +1589,8 @@ def layout():
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#preview_image.image(output_images, width=750)
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with img2img_tab:
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st.session_state["generation_mode"] = "img2img"
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with st.form("img2img-inputs"):
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img2img_input_col, img2img_generate_col = st.columns([10,1])
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@ -1593,8 +1616,8 @@ def layout():
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help="Upload an image which will be used for the image to image generation."
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)
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height = st.slider("Height:", min_value=64, max_value=2048, value=defaults.img2img.height, step=64)
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width = st.slider("Width:", min_value=64, max_value=2048, value=defaults.img2img.width, step=64)
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height = st.slider("Height:", min_value=64, max_value=2048, value=defaults.img2img.height, step=64)
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seed = st.text_input("Seed:", value=defaults.img2img.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=500, value=defaults.img2img.batch_count, step=1, help="How many iterations or batches of images to generate in total.")
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@ -1631,29 +1654,45 @@ def layout():
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batch_size = st.slider("Batch size", min_value=1, max_value=500, value=defaults.img2img.batch_size, 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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Default: 1")
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st.session_state["denoising_strength"] = st.slider("Denoising Strength:", value=defaults.img2img.denoising_strength, min_value=0.0, max_value=1.0, step=0.01)
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with col2_img2img_layout:
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preview_tab, gallery_tab = st.tabs(["Preview", "Gallery"])
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editor_tab = st.tabs(["Editor"])
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editor_image = st.empty()
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st.session_state["editor_image"] = editor_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)
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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 and use session_state to hold it globally.
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preview_image = st.empty()
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st.session_state["preview_image"] = preview_image
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if uploaded_images:
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image = Image.open(uploaded_images)
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#img_array = np.array(image) # if you want to pass it to OpenCV
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new_img = image.resize((width, height))
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st.image(new_img)
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|
||||
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||||
with col3_img2img_layout:
|
||||
result_tab = st.tabs(["Result"])
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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.
|
||||
preview_image = st.empty()
|
||||
st.session_state["preview_image"] = preview_image
|
||||
|
||||
#st.session_state["loading"] = st.empty()
|
||||
|
||||
with gallery_tab:
|
||||
st.write('Here should be the image gallery, if I could make a grid in streamlit.')
|
||||
st.session_state["progress_bar_text"] = st.empty()
|
||||
st.session_state["progress_bar"] = st.empty()
|
||||
|
||||
|
||||
message = st.empty()
|
||||
|
||||
# if uploaded_images:
|
||||
# image = Image.open(uploaded_images)
|
||||
# img_array = np.array(image) # if you want to pass it to OpenCV
|
||||
# st.image(image, use_column_width=True)
|
||||
#if uploaded_images:
|
||||
#image = Image.open(uploaded_images)
|
||||
##img_array = np.array(image) # if you want to pass it to OpenCV
|
||||
#new_img = image.resize((width, height))
|
||||
#st.image(new_img, use_column_width=True)
|
||||
|
||||
|
||||
if generate_button:
|
||||
#print("Loading models")
|
||||
@ -1661,9 +1700,24 @@ def layout():
|
||||
load_models(False, use_GFPGAN, use_RealESRGAN, RealESRGAN_model)
|
||||
if uploaded_images:
|
||||
image = Image.open(uploaded_images)
|
||||
img_array = np.array(image) # if you want to pass it to OpenCV
|
||||
new_img = image.resize((width, height))
|
||||
#img_array = np.array(image) # if you want to pass it to OpenCV
|
||||
|
||||
try:
|
||||
output_images, seed, info, stats = img2img(prompt=prompt, init_info=image, ddim_steps=sampling_steps, sampler_name=sampler_name, n_iter=batch_count)
|
||||
#output_images, seed, info, stats = img2img(prompt=prompt, init_info=new_img, ddim_steps=sampling_steps, sampler_name=sampler_name, n_iter=batch_count)
|
||||
output_images, seed, info, stats = img2img(prompt=prompt, init_info=new_img, ddim_steps=sampling_steps, sampler_name=sampler_name, n_iter=batch_count,
|
||||
cfg_scale=cfg_scale, denoising_strength=st.session_state["denoising_strength"], variant_seed=variant_seed,
|
||||
seed=seed, width=width, height=height, fp=defaults.general.fp, variant_amount=variant_amount,
|
||||
ddim_eta=0.0, write_info_files=True, RealESRGAN_model="RealESRGAN_x4plus_anime_6B",
|
||||
separate_prompts=separate_prompts, normalize_prompt_weights=normalize_prompt_weights,
|
||||
save_individual_images=save_individual_images, save_grid=save_grid,
|
||||
group_by_prompt=group_by_prompt, save_as_jpg=save_as_jpg, use_GFPGAN=use_GFPGAN,
|
||||
use_RealESRGAN=use_RealESRGAN
|
||||
)
|
||||
|
||||
#show a message when the generation is complete.
|
||||
message.success('Done!', icon="✅")
|
||||
|
||||
except (StopException, KeyError):
|
||||
print(f"Received Streamlit StopException")
|
||||
|
||||
|
||||
@ -6,8 +6,8 @@ general:
|
||||
ckpt: "models/ldm/stable-diffusion-v1/model.ckpt"
|
||||
fp:
|
||||
name: 'embeddings/alex/embeddings_gs-11000.pt'
|
||||
GFPGAN_dir: "./src/GFPGAN"
|
||||
RealESRGAN_dir: "./src/RealESRGAN"
|
||||
GFPGAN_dir: "./src/gfpgan"
|
||||
RealESRGAN_dir: "./src/realesrgan"
|
||||
RealESRGAN_model: "RealESRGAN_x4plus"
|
||||
outdir_txt2img: outputs/txt2img-samples
|
||||
outdir_img2img: outputs/img2img-samples
|
||||
@ -91,7 +91,7 @@ img2img:
|
||||
group_by_prompt: True
|
||||
save_as_jpg: False
|
||||
use_GFPGAN: True
|
||||
use_RealESRGAN: True
|
||||
use_RealESRGAN: False
|
||||
RealESRGAN_model: "RealESRGAN_x4plus"
|
||||
variant_amount: 0.0
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user