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Fooocus Prompt Expansion (#329)
* add vae approx download * files * files * files * i * i * i * i * i * i * i * i * i * i
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lllyasviel
+73
-21
@@ -15,7 +15,7 @@ def worker():
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import modules.path
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import modules.patch
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from modules.sdxl_styles import apply_style, aspect_ratios
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from modules.sdxl_styles import apply_style_negative, apply_style_positive, aspect_ratios
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from modules.private_logger import log
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try:
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@@ -29,19 +29,69 @@ def worker():
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def handler(task):
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prompt, negative_prompt, style_selction, performance_selction, \
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aspect_ratios_selction, image_number, image_seed, sharpness, base_model_name, refiner_model_name, \
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aspect_ratios_selction, image_number, image_seed, sharpness, raw_mode, \
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base_model_name, refiner_model_name, \
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l1, w1, l2, w2, l3, w3, l4, w4, l5, w5 = task
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loras = [(l1, w1), (l2, w2), (l3, w3), (l4, w4), (l5, w5)]
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modules.patch.sharpness = sharpness
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outputs.append(['preview', (1, 'Initializing ...', None)])
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seed = image_seed
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max_seed = int(1024 * 1024 * 1024)
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if not isinstance(seed, int):
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seed = random.randint(1, max_seed)
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if seed < 0:
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seed = - seed
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seed = seed % max_seed
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outputs.append(['preview', (3, 'Load models ...', None)])
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pipeline.refresh_base_model(base_model_name)
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pipeline.refresh_refiner_model(refiner_model_name)
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pipeline.refresh_loras(loras)
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pipeline.clean_prompt_cond_caches()
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p_txt, n_txt = apply_style(style_selction, prompt, negative_prompt)
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outputs.append(['preview', (5, 'Encoding negative text ...', None)])
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n_txt = apply_style_negative(style_selction, negative_prompt)
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n_cond = pipeline.process_prompt(n_txt)
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tasks = []
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if raw_mode:
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outputs.append(['preview', (9, 'Encoding positive text ...', None)])
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p_txt = apply_style_positive(style_selction, prompt)
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p_cond = pipeline.process_prompt(p_txt)
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for i in range(image_number):
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tasks.append(dict(
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prompt=prompt,
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negative_prompt=negative_prompt,
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seed=seed + i,
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n_cond=n_cond,
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p_cond=p_cond,
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real_positive_prompt=p_txt,
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real_negative_prompt=n_txt
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))
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else:
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for i in range(image_number):
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outputs.append(['preview', (9, f'Preparing positive text #{i + 1} ...', None)])
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current_seed = seed + i
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p_txt = pipeline.expand_txt(prompt, current_seed)
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print(f'Expanded positive prompt: {p_txt}')
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p_txt = apply_style_positive(style_selction, p_txt)
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tasks.append(dict(
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prompt=prompt,
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negative_prompt=negative_prompt,
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seed=current_seed,
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n_cond=n_cond,
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real_positive_prompt=p_txt,
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real_negative_prompt=n_txt
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))
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for i, t in enumerate(tasks):
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outputs.append(['preview', (12, f'Encoding positive text #{i + 1} ...', None)])
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t['p_cond'] = pipeline.process_prompt(t['real_positive_prompt'])
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if performance_selction == 'Speed':
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steps = 30
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@@ -53,45 +103,47 @@ def worker():
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width, height = aspect_ratios[aspect_ratios_selction]
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results = []
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seed = image_seed
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max_seed = int(1024*1024*1024)
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if not isinstance(seed, int):
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seed = random.randint(1, max_seed)
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if seed < 0:
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seed = - seed
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seed = seed % max_seed
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all_steps = steps * image_number
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def callback(step, x0, x, total_steps, y):
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done_steps = i * steps + step
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done_steps = current_task_id * steps + step
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outputs.append(['preview', (
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int(100.0 * float(done_steps) / float(all_steps)),
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int(15.0 + 85.0 * float(done_steps) / float(all_steps)),
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f'Step {step}/{total_steps} in the {i}-th Sampling',
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y)])
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for i in range(image_number):
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imgs = pipeline.process(p_txt, n_txt, steps, switch, width, height, seed, callback=callback)
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outputs.append(['preview', (13, 'Starting tasks ...', None)])
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for current_task_id, task in enumerate(tasks):
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imgs = pipeline.process_diffusion(
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positive_cond=task['p_cond'],
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negative_cond=task['n_cond'],
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steps=steps,
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switch=switch,
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width=width,
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height=height,
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image_seed=task['seed'],
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callback=callback)
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for x in imgs:
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d = [
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('Prompt', prompt),
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('Negative Prompt', negative_prompt),
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('Prompt', task['prompt']),
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('Negative Prompt', task['negative_prompt']),
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('Real Positive Prompt', task['real_positive_prompt']),
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('Real Negative Prompt', task['real_negative_prompt']),
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('Raw Mode', str(raw_mode)),
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('Style', style_selction),
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('Performance', performance_selction),
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('Resolution', str((width, height))),
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('Sharpness', sharpness),
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('Base Model', base_model_name),
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('Refiner Model', refiner_model_name),
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('Seed', seed)
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('Seed', task['seed'])
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]
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for n, w in loras:
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if n != 'None':
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d.append((f'LoRA [{n}] weight', w))
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log(x, d)
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seed += 1
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results += imgs
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outputs.append(['results', results])
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