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https://github.com/Sygil-Dev/sygil-webui.git
synced 2024-12-15 06:21:34 +03:00
updated weights code to match latest
This commit is contained in:
xra
parent
f2f866e67f
commit
f502897a06
57
webui.py
57
webui.py
@ -258,7 +258,13 @@ def image_grid(imgs, batch_size, round_down=False, force_n_rows=None):
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return grid
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def seed_to_int(s):
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if s == 'random':
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return random.randint(0,2**32)
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n = abs(int(s) if s.isdigit() else hash(s))
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while n > 2**32:
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n = n >> 32
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return n
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def draw_prompt_matrix(im, width, height, all_prompts):
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def wrap(text, d, font, line_length):
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@ -390,10 +396,6 @@ def process_images(outpath, func_init, func_sample, prompt, seed, sampler_name,
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mem_mon = MemUsageMonitor('MemMon')
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mem_mon.start()
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if seed == -1:
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seed = random.randrange(4294967294)
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seed = int(seed)
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os.makedirs(outpath, exist_ok=True)
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sample_path = os.path.join(outpath, "samples")
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@ -450,7 +452,7 @@ def process_images(outpath, func_init, func_sample, prompt, seed, sampler_name,
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uc = model.get_learned_conditioning(len(prompts) * [""])
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if isinstance(prompts, tuple):
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prompts = list(prompts)
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# split the prompt if it has : for weighting
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# TODO for speed it might help to have this occur when all_prompts filled??
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subprompts,weights = split_weighted_subprompts(prompts[0])
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@ -469,7 +471,7 @@ def process_images(outpath, func_init, func_sample, prompt, seed, sampler_name,
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c = torch.add(c,model.get_learned_conditioning(subprompts[i]), alpha=weight)
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else: # just behave like usual
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c = model.get_learned_conditioning(prompts)
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shape = [opt_C, height // opt_f, width // opt_f]
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# we manually generate all input noises because each one should have a specific seed
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@ -479,23 +481,22 @@ def process_images(outpath, func_init, func_sample, prompt, seed, sampler_name,
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x_samples_ddim = model.decode_first_stage(samples_ddim)
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x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
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# removed or not skip_grid, both skip = benchmark ;)
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if prompt_matrix or not skip_save:
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for i, x_sample in enumerate(x_samples_ddim):
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x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
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x_sample = x_sample.astype(np.uint8)
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for i, x_sample in enumerate(x_samples_ddim):
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x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
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x_sample = x_sample.astype(np.uint8)
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if use_GFPGAN and GFPGAN is not None:
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cropped_faces, restored_faces, restored_img = GFPGAN.enhance(x_sample, has_aligned=False, only_center_face=False, paste_back=True)
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x_sample = restored_img
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if use_GFPGAN and GFPGAN is not None:
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cropped_faces, restored_faces, restored_img = GFPGAN.enhance(x_sample, has_aligned=False, only_center_face=False, paste_back=True)
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x_sample = restored_img
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image = Image.fromarray(x_sample)
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filename = f"{base_count:05}-{seeds[i]}_{prompts[i].replace(' ', '_').translate({ord(x): '' for x in invalid_filename_chars})[:128]}.png"
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image = Image.fromarray(x_sample)
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filename = f"{base_count:05}-{seeds[i]}_{prompts[i].replace(' ', '_').translate({ord(x): '' for x in invalid_filename_chars})[:128]}.png"
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if not skip_save:
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image.save(os.path.join(sample_path, filename))
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output_images.append(image)
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base_count += 1
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output_images.append(image)
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base_count += 1
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if (prompt_matrix or not skip_grid) and not do_not_save_grid:
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grid = image_grid(output_images, batch_size, round_down=prompt_matrix)
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@ -512,7 +513,7 @@ def process_images(outpath, func_init, func_sample, prompt, seed, sampler_name,
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grid_file = f"grid-{grid_count:05}-{seed}_{prompts[i].replace(' ', '_').translate({ord(x): '' for x in invalid_filename_chars})[:128]}.jpg"
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grid.save(os.path.join(outpath, grid_file), 'jpeg', quality=80, optimize=True)
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grid.save(os.path.join(outpath, grid_file), 'jpeg', quality=100, optimize=True)
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grid_count += 1
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toc = time.time()
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@ -539,6 +540,7 @@ Peak memory usage: { -(mem_max_used // -1_048_576) } MiB / { -(mem_total // -1_0
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def txt2img(prompt: str, ddim_steps: int, sampler_name: str, use_GFPGAN: bool, prompt_matrix: bool, skip_grid: bool, skip_save: bool, ddim_eta: float, n_iter: int, batch_size: int, cfg_scale: float, seed: int, height: int, width: int, normalize_prompt_weights: bool):
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outpath = opt.outdir or "outputs/txt2img-samples"
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err = False
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seed = seed_to_int(seed)
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if sampler_name == 'PLMS':
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sampler = PLMSSampler(model)
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@ -645,7 +647,7 @@ txt2img_interface = gr.Interface(
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gr.Slider(minimum=1, maximum=16, step=1, label='Batch count (how many batches of images to generate)', value=1),
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gr.Slider(minimum=1, maximum=8, step=1, label='Batch size (how many images are in a batch; memory-hungry)', value=1),
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gr.Slider(minimum=1.0, maximum=15.0, step=0.5, label='Classifier Free Guidance Scale (how strongly the image should follow the prompt)', value=7.0),
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gr.Number(label='Seed', value=-1),
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gr.Textbox(label="Seed ('random' to randomize)", lines=1, value="random"),
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gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512),
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gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512),
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gr.Checkbox(label="Normalize Prompt Weights (ensure sum of weights add up to 1.0)", value=True),
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@ -666,6 +668,7 @@ txt2img_interface = gr.Interface(
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def img2img(prompt: str, init_img, ddim_steps: int, sampler_name: str, use_GFPGAN: bool, prompt_matrix, loopback: bool, skip_grid: bool, skip_save: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, height: int, width: int, resize_mode: int, normalize_prompt_weights: bool):
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outpath = opt.outdir or "outputs/img2img-samples"
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err = False
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seed = seed_to_int(seed)
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if sampler_name == 'DDIM':
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sampler = DDIMSampler(model)
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@ -748,10 +751,12 @@ def img2img(prompt: str, init_img, ddim_steps: int, sampler_name: str, use_GFPGA
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denoising_strength = max(denoising_strength * 0.95, 0.1)
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history.append(init_img)
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grid_count = len(os.listdir(outpath)) - 1
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grid = image_grid(history, batch_size, force_n_rows=1)
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grid_file = f"grid-{grid_count:05}-{seed}_{prompt.replace(' ', '_').translate({ord(x): '' for x in invalid_filename_chars})[:128]}.jpg"
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grid.save(os.path.join(outpath, grid_file), 'jpeg', quality=80, optimize=True)
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if not skip_grid:
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grid_count = len(os.listdir(outpath)) - 1
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grid = image_grid(history, batch_size, force_n_rows=1)
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grid_file = f"grid-{grid_count:05}-{seed}_{prompt.replace(' ', '_').translate({ord(x): '' for x in invalid_filename_chars})[:128]}.jpg"
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grid.save(os.path.join(outpath, grid_file), 'jpeg', quality=100, optimize=True)
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output_images = history
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seed = initial_seed
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@ -810,7 +815,7 @@ img2img_interface = gr.Interface(
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gr.Slider(minimum=1, maximum=8, step=1, label='Batch size (how many images are in a batch; memory-hungry)', value=1),
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gr.Slider(minimum=1.0, maximum=15.0, step=0.5, label='Classifier Free Guidance Scale (how strongly the image should follow the prompt)', value=7.0),
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gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising Strength', value=0.75),
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gr.Number(label='Seed', value=-1),
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gr.Textbox(label="Seed ('random' to randomize)", lines=1, value="random"),
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gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512),
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gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512),
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gr.Radio(label="Resize mode", choices=["Just resize", "Crop and resize", "Resize and fill"], type="index", value="Just resize"),
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