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2.1.821

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* New UI for LoRAs.
* Improved preset system: normalized preset keys and file names.
* Improved session system: now multiple users can use one Fooocus at the same time without seeing others' results.
* Improved some computation related to model precision.
* Improved config loading system with user-friendly prints.
This commit is contained in:
lllyasviel
2023-11-17 11:25:39 -08:00
committed by GitHub
parent 3b97e49dd8
commit 675805960a
24 changed files with 587 additions and 215 deletions
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modules/async_worker.py
+70 -62
View File
@@ -1,13 +1,18 @@
import threading
buffer = []
outputs = []
global_results = []
class AsyncTask:
def __init__(self, args):
self.args = args
self.yields = []
self.results = []
async_tasks = []
def worker():
global buffer, outputs, global_results
global async_tasks
import traceback
import math
@@ -46,42 +51,40 @@ def worker():
except Exception as e:
print(e)
def progressbar(number, text):
def progressbar(async_task, number, text):
print(f'[Fooocus] {text}')
outputs.append(['preview', (number, text, None)])
def yield_result(imgs, do_not_show_finished_images=False):
global global_results
async_task.yields.append(['preview', (number, text, None)])
def yield_result(async_task, imgs, do_not_show_finished_images=False):
if not isinstance(imgs, list):
imgs = [imgs]
global_results = global_results + imgs
async_task.results = async_task.results + imgs
if do_not_show_finished_images:
return
outputs.append(['results', global_results])
async_task.yields.append(['results', async_task.results])
return
def build_image_wall():
def build_image_wall(async_task):
if not advanced_parameters.generate_image_grid:
return
global global_results
results = async_task.results
if len(global_results) < 2:
if len(results) < 2:
return
for img in global_results:
for img in results:
if not isinstance(img, np.ndarray):
return
if img.ndim != 3:
return
H, W, C = global_results[0].shape
H, W, C = results[0].shape
for img in global_results:
for img in results:
Hn, Wn, Cn = img.shape
if H != Hn:
return
@@ -90,28 +93,29 @@ def worker():
if C != Cn:
return
cols = float(len(global_results)) ** 0.5
cols = float(len(results)) ** 0.5
cols = int(math.ceil(cols))
rows = float(len(global_results)) / float(cols)
rows = float(len(results)) / float(cols)
rows = int(math.ceil(rows))
wall = np.zeros(shape=(H * rows, W * cols, C), dtype=np.uint8)
for y in range(rows):
for x in range(cols):
if y * cols + x < len(global_results):
img = global_results[y * cols + x]
if y * cols + x < len(results):
img = results[y * cols + x]
wall[y * H:y * H + H, x * W:x * W + W, :] = img
# must use deep copy otherwise gradio is super laggy. Do not use list.append() .
global_results = global_results + [wall]
async_task.results = async_task.results + [wall]
return
@torch.no_grad()
@torch.inference_mode()
def handler(args):
def handler(async_task):
execution_start_time = time.perf_counter()
args = async_task.args
args.reverse()
prompt = args.pop()
@@ -172,7 +176,7 @@ def worker():
if performance_selection == 'Extreme Speed':
print('Enter LCM mode.')
progressbar(1, 'Downloading LCM components ...')
progressbar(async_task, 1, 'Downloading LCM components ...')
base_model_additional_loras += [(modules.config.downloading_sdxl_lcm_lora(), 1.0)]
if refiner_model_name != 'None':
@@ -199,7 +203,8 @@ def worker():
modules.patch.positive_adm_scale = advanced_parameters.adm_scaler_positive
modules.patch.negative_adm_scale = advanced_parameters.adm_scaler_negative
modules.patch.adm_scaler_end = advanced_parameters.adm_scaler_end
print(f'[Parameters] ADM Scale = {modules.patch.positive_adm_scale} : {modules.patch.negative_adm_scale} : {modules.patch.adm_scaler_end}')
print(
f'[Parameters] ADM Scale = {modules.patch.positive_adm_scale} : {modules.patch.negative_adm_scale} : {modules.patch.adm_scaler_end}')
cfg_scale = float(guidance_scale)
print(f'[Parameters] CFG = {cfg_scale}')
@@ -232,7 +237,8 @@ def worker():
tasks = []
if input_image_checkbox:
if (current_tab == 'uov' or (current_tab == 'ip' and advanced_parameters.mixing_image_prompt_and_vary_upscale)) \
if (current_tab == 'uov' or (
current_tab == 'ip' and advanced_parameters.mixing_image_prompt_and_vary_upscale)) \
and uov_method != flags.disabled and uov_input_image is not None:
uov_input_image = HWC3(uov_input_image)
if 'vary' in uov_method:
@@ -253,17 +259,19 @@ def worker():
if performance_selection == 'Extreme Speed':
steps = 8
progressbar(1, 'Downloading upscale models ...')
progressbar(async_task, 1, 'Downloading upscale models ...')
modules.config.downloading_upscale_model()
if (current_tab == 'inpaint' or (current_tab == 'ip' and advanced_parameters.mixing_image_prompt_and_inpaint))\
if (current_tab == 'inpaint' or (
current_tab == 'ip' and advanced_parameters.mixing_image_prompt_and_inpaint)) \
and isinstance(inpaint_input_image, dict):
inpaint_image = inpaint_input_image['image']
inpaint_mask = inpaint_input_image['mask'][:, :, 0]
inpaint_image = HWC3(inpaint_image)
if isinstance(inpaint_image, np.ndarray) and isinstance(inpaint_mask, np.ndarray) \
and (np.any(inpaint_mask > 127) or len(outpaint_selections) > 0):
progressbar(1, 'Downloading inpainter ...')
inpaint_head_model_path, inpaint_patch_model_path = modules.config.downloading_inpaint_models(advanced_parameters.inpaint_engine)
progressbar(async_task, 1, 'Downloading inpainter ...')
inpaint_head_model_path, inpaint_patch_model_path = modules.config.downloading_inpaint_models(
advanced_parameters.inpaint_engine)
base_model_additional_loras += [(inpaint_patch_model_path, 1.0)]
print(f'[Inpaint] Current inpaint model is {inpaint_patch_model_path}')
goals.append('inpaint')
@@ -271,7 +279,7 @@ def worker():
advanced_parameters.mixing_image_prompt_and_inpaint or \
advanced_parameters.mixing_image_prompt_and_vary_upscale:
goals.append('cn')
progressbar(1, 'Downloading control models ...')
progressbar(async_task, 1, 'Downloading control models ...')
if len(cn_tasks[flags.cn_canny]) > 0:
controlnet_canny_path = modules.config.downloading_controlnet_canny()
if len(cn_tasks[flags.cn_cpds]) > 0:
@@ -279,8 +287,9 @@ def worker():
if len(cn_tasks[flags.cn_ip]) > 0:
clip_vision_path, ip_negative_path, ip_adapter_path = modules.config.downloading_ip_adapters('ip')
if len(cn_tasks[flags.cn_ip_face]) > 0:
clip_vision_path, ip_negative_path, ip_adapter_face_path = modules.config.downloading_ip_adapters('face')
progressbar(1, 'Loading control models ...')
clip_vision_path, ip_negative_path, ip_adapter_face_path = modules.config.downloading_ip_adapters(
'face')
progressbar(async_task, 1, 'Loading control models ...')
# Load or unload CNs
pipeline.refresh_controlnets([controlnet_canny_path, controlnet_cpds_path])
@@ -304,7 +313,7 @@ def worker():
print(f'[Parameters] Sampler = {sampler_name} - {scheduler_name}')
print(f'[Parameters] Steps = {steps} - {switch}')
progressbar(1, 'Initializing ...')
progressbar(async_task, 1, 'Initializing ...')
if not skip_prompt_processing:
@@ -321,11 +330,11 @@ def worker():
extra_positive_prompts = prompts[1:] if len(prompts) > 1 else []
extra_negative_prompts = negative_prompts[1:] if len(negative_prompts) > 1 else []
progressbar(3, 'Loading models ...')
progressbar(async_task, 3, 'Loading models ...')
pipeline.refresh_everything(refiner_model_name=refiner_model_name, base_model_name=base_model_name,
loras=loras, base_model_additional_loras=base_model_additional_loras)
progressbar(3, 'Processing prompts ...')
progressbar(async_task, 3, 'Processing prompts ...')
tasks = []
for i in range(image_number):
task_seed = (seed + i) % (constants.MAX_SEED + 1) # randint is inclusive, % is not
@@ -372,26 +381,25 @@ def worker():
if use_expansion:
for i, t in enumerate(tasks):
progressbar(5, f'Preparing Fooocus text #{i + 1} ...')
progressbar(async_task, 5, f'Preparing Fooocus text #{i + 1} ...')
expansion = pipeline.final_expansion(t['task_prompt'], t['task_seed'])
print(f'[Prompt Expansion] {expansion}')
t['expansion'] = expansion
t['positive'] = copy.deepcopy(t['positive']) + [expansion] # Deep copy.
for i, t in enumerate(tasks):
progressbar(7, f'Encoding positive #{i + 1} ...')
progressbar(async_task, 7, f'Encoding positive #{i + 1} ...')
t['c'] = pipeline.clip_encode(texts=t['positive'], pool_top_k=t['positive_top_k'])
for i, t in enumerate(tasks):
if abs(float(cfg_scale) - 1.0) < 1e-4:
# progressbar(10, f'Skipped negative #{i + 1} ...')
t['uc'] = pipeline.clone_cond(t['c'])
else:
progressbar(10, f'Encoding negative #{i + 1} ...')
progressbar(async_task, 10, f'Encoding negative #{i + 1} ...')
t['uc'] = pipeline.clip_encode(texts=t['negative'], pool_top_k=t['negative_top_k'])
if len(goals) > 0:
progressbar(13, 'Image processing ...')
progressbar(async_task, 13, 'Image processing ...')
if 'vary' in goals:
if 'subtle' in uov_method:
@@ -412,7 +420,7 @@ def worker():
uov_input_image = set_image_shape_ceil(uov_input_image, shape_ceil)
initial_pixels = core.numpy_to_pytorch(uov_input_image)
progressbar(13, 'VAE encoding ...')
progressbar(async_task, 13, 'VAE encoding ...')
initial_latent = core.encode_vae(vae=pipeline.final_vae, pixels=initial_pixels)
B, C, H, W = initial_latent['samples'].shape
width = W * 8
@@ -421,7 +429,7 @@ def worker():
if 'upscale' in goals:
H, W, C = uov_input_image.shape
progressbar(13, f'Upscaling image from {str((H, W))} ...')
progressbar(async_task, 13, f'Upscaling image from {str((H, W))} ...')
uov_input_image = core.numpy_to_pytorch(uov_input_image)
uov_input_image = perform_upscale(uov_input_image)
@@ -459,7 +467,7 @@ def worker():
if direct_return:
d = [('Upscale (Fast)', '2x')]
log(uov_input_image, d, single_line_number=1)
yield_result(uov_input_image, do_not_show_finished_images=True)
yield_result(async_task, uov_input_image, do_not_show_finished_images=True)
return
tiled = True
@@ -469,7 +477,7 @@ def worker():
denoising_strength = advanced_parameters.overwrite_upscale_strength
initial_pixels = core.numpy_to_pytorch(uov_input_image)
progressbar(13, 'VAE encoding ...')
progressbar(async_task, 13, 'VAE encoding ...')
initial_latent = core.encode_vae(
vae=pipeline.final_vae if pipeline.final_refiner_vae is None else pipeline.final_refiner_vae,
@@ -511,10 +519,11 @@ def worker():
pipeline.final_unet.model.diffusion_model.in_inpaint = True
if advanced_parameters.debugging_cn_preprocessor:
yield_result(inpaint_worker.current_task.visualize_mask_processing(), do_not_show_finished_images=True)
yield_result(async_task, inpaint_worker.current_task.visualize_mask_processing(),
do_not_show_finished_images=True)
return
progressbar(13, 'VAE Inpaint encoding ...')
progressbar(async_task, 13, 'VAE Inpaint encoding ...')
inpaint_pixel_fill = core.numpy_to_pytorch(inpaint_worker.current_task.interested_fill)
inpaint_pixel_image = core.numpy_to_pytorch(inpaint_worker.current_task.interested_image)
@@ -527,12 +536,12 @@ def worker():
latent_swap = None
if pipeline.final_refiner_vae is not None:
progressbar(13, 'VAE Inpaint SD15 encoding ...')
progressbar(async_task, 13, 'VAE Inpaint SD15 encoding ...')
latent_swap = core.encode_vae(
vae=pipeline.final_refiner_vae,
pixels=inpaint_pixel_fill)['samples']
progressbar(13, 'VAE encoding ...')
progressbar(async_task, 13, 'VAE encoding ...')
latent_fill = core.encode_vae(
vae=pipeline.final_vae,
pixels=inpaint_pixel_fill)['samples']
@@ -560,7 +569,7 @@ def worker():
cn_img = HWC3(cn_img)
task[0] = core.numpy_to_pytorch(cn_img)
if advanced_parameters.debugging_cn_preprocessor:
yield_result(cn_img, do_not_show_finished_images=True)
yield_result(async_task, cn_img, do_not_show_finished_images=True)
return
for task in cn_tasks[flags.cn_cpds]:
cn_img, cn_stop, cn_weight = task
@@ -572,7 +581,7 @@ def worker():
cn_img = HWC3(cn_img)
task[0] = core.numpy_to_pytorch(cn_img)
if advanced_parameters.debugging_cn_preprocessor:
yield_result(cn_img, do_not_show_finished_images=True)
yield_result(async_task, cn_img, do_not_show_finished_images=True)
return
for task in cn_tasks[flags.cn_ip]:
cn_img, cn_stop, cn_weight = task
@@ -583,7 +592,7 @@ def worker():
task[0] = ip_adapter.preprocess(cn_img, ip_adapter_path=ip_adapter_path)
if advanced_parameters.debugging_cn_preprocessor:
yield_result(cn_img, do_not_show_finished_images=True)
yield_result(async_task, cn_img, do_not_show_finished_images=True)
return
for task in cn_tasks[flags.cn_ip_face]:
cn_img, cn_stop, cn_weight = task
@@ -597,7 +606,7 @@ def worker():
task[0] = ip_adapter.preprocess(cn_img, ip_adapter_path=ip_adapter_face_path)
if advanced_parameters.debugging_cn_preprocessor:
yield_result(cn_img, do_not_show_finished_images=True)
yield_result(async_task, cn_img, do_not_show_finished_images=True)
return
all_ip_tasks = cn_tasks[flags.cn_ip] + cn_tasks[flags.cn_ip_face]
@@ -637,11 +646,11 @@ def worker():
zsnr=False)[0]
print('Using lcm scheduler.')
outputs.append(['preview', (13, 'Moving model to GPU ...', None)])
async_task.yields.append(['preview', (13, 'Moving model to GPU ...', None)])
def callback(step, x0, x, total_steps, y):
done_steps = current_task_id * steps + step
outputs.append(['preview', (
async_task.yields.append(['preview', (
int(15.0 + 85.0 * float(done_steps) / float(all_steps)),
f'Step {step}/{total_steps} in the {current_task_id + 1}-th Sampling',
y)])
@@ -711,7 +720,7 @@ def worker():
d.append((f'LoRA [{n}] weight', w))
log(x, d, single_line_number=3)
yield_result(imgs, do_not_show_finished_images=len(tasks) == 1)
yield_result(async_task, imgs, do_not_show_finished_images=len(tasks) == 1)
except fcbh.model_management.InterruptProcessingException as e:
if shared.last_stop == 'skip':
print('User skipped')
@@ -727,16 +736,15 @@ def worker():
while True:
time.sleep(0.01)
if len(buffer) > 0:
task = buffer.pop(0)
if len(async_tasks) > 0:
task = async_tasks.pop(0)
try:
handler(task)
except:
traceback.print_exc()
if len(buffer) == 0:
build_image_wall()
outputs.append(['finish', global_results])
global_results = []
finally:
build_image_wall(task)
task.yields.append(['finish', task.results])
pipeline.prepare_text_encoder(async_call=True)
pass