mirror of https://github.com/vladmandic/automatic
fix hires and corrections with batch processing
Signed-off-by: Vladimir Mandic <mandic00@live.com>pull/3671/head
Vladimir Mandic
parent
86ac38d94f
commit
05d5ac03be
|
|
@ -1,6 +1,8 @@
|
|||
# Change Log for SD.Next
|
||||
|
||||
## Update for 2024-12-30
|
||||
## Update for 2024-12-31
|
||||
|
||||
NYE refresh release with quite a few optimizatios and bug fixes...
|
||||
|
||||
- **LoRA**:
|
||||
- **Sana** support
|
||||
|
|
@ -40,7 +42,9 @@
|
|||
- do not show disabled networks
|
||||
- enable debug logging by default
|
||||
- image width/height calculation when doing img2img
|
||||
|
||||
- corrections with batch processing
|
||||
- hires with refiner prompt and batch processing
|
||||
|
||||
## Update for 2024-12-24
|
||||
|
||||
### Highlights for 2024-12-24
|
||||
|
|
|
|||
|
|
@ -116,16 +116,18 @@ class StableDiffusionProcessing:
|
|||
# overrides
|
||||
override_settings: Dict[str, Any] = {},
|
||||
override_settings_restore_afterwards: bool = True,
|
||||
task_args: Dict[str, Any] = {},
|
||||
ops: List[str] = [],
|
||||
# metadata
|
||||
extra_generation_params: Dict[Any, Any] = {},
|
||||
):
|
||||
|
||||
# extra args set by processing loop
|
||||
self.task_args = {}
|
||||
self.task_args = task_args
|
||||
|
||||
# state items
|
||||
self.state: str = ''
|
||||
self.ops = []
|
||||
self.ops = ops
|
||||
self.skip = []
|
||||
self.color_corrections = []
|
||||
self.is_control = False
|
||||
|
|
@ -266,7 +268,6 @@ class StableDiffusionProcessing:
|
|||
self.s_max = shared.opts.s_max
|
||||
self.s_tmin = shared.opts.s_tmin
|
||||
self.s_tmax = float('inf') # not representable as a standard ui option
|
||||
self.task_args = {}
|
||||
|
||||
# ip adapter
|
||||
self.ip_adapter_names = []
|
||||
|
|
@ -299,6 +300,9 @@ class StableDiffusionProcessing:
|
|||
self.negative_embeds = []
|
||||
self.negative_pooleds = []
|
||||
|
||||
def __str__(self):
|
||||
return f'{self.__class__.__name__}: {self.__dict__}'
|
||||
|
||||
@property
|
||||
def sd_model(self):
|
||||
return shared.sd_model
|
||||
|
|
@ -339,9 +343,6 @@ class StableDiffusionProcessing:
|
|||
def close(self):
|
||||
self.sampler = None # pylint: disable=attribute-defined-outside-init
|
||||
|
||||
def __str__(self):
|
||||
return f'{self.__class__.__name__}: {self.__dict__}'
|
||||
|
||||
|
||||
class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
|
||||
def __init__(self, **kwargs):
|
||||
|
|
|
|||
|
|
@ -16,7 +16,7 @@ skip_correction = False
|
|||
|
||||
def sharpen_tensor(tensor, ratio=0):
|
||||
if ratio == 0:
|
||||
debug("Sharpen: Early exit")
|
||||
# debug("Sharpen: Early exit")
|
||||
return tensor
|
||||
kernel = torch.ones((3, 3), dtype=tensor.dtype, device=tensor.device)
|
||||
kernel[1, 1] = 5.0
|
||||
|
|
@ -42,18 +42,18 @@ def soft_clamp_tensor(tensor, threshold=0.8, boundary=4):
|
|||
min_replace = ((tensor + threshold) / (min_vals + threshold)) * (-boundary + threshold) - threshold
|
||||
under_mask = tensor < -threshold
|
||||
tensor = torch.where(over_mask, max_replace, torch.where(under_mask, min_replace, tensor))
|
||||
debug(f'HDR soft clamp: threshold={threshold} boundary={boundary} shape={tensor.shape}')
|
||||
# debug(f'HDR soft clamp: threshold={threshold} boundary={boundary} shape={tensor.shape}')
|
||||
return tensor
|
||||
|
||||
|
||||
def center_tensor(tensor, channel_shift=0.0, full_shift=0.0, offset=0.0):
|
||||
if channel_shift == 0 and full_shift == 0 and offset == 0:
|
||||
return tensor
|
||||
debug(f'HDR center: Before Adjustment: Full mean={tensor.mean().item()} Channel means={tensor.mean(dim=(-1, -2)).float().cpu().numpy()}')
|
||||
# debug(f'HDR center: Before Adjustment: Full mean={tensor.mean().item()} Channel means={tensor.mean(dim=(-1, -2)).float().cpu().numpy()}')
|
||||
tensor -= tensor.mean(dim=(-1, -2), keepdim=True) * channel_shift
|
||||
tensor -= tensor.mean() * full_shift - offset
|
||||
debug(f'HDR center: channel-shift={channel_shift} full-shift={full_shift}')
|
||||
debug(f'HDR center: After Adjustment: Full mean={tensor.mean().item()} Channel means={tensor.mean(dim=(-1, -2)).float().cpu().numpy()}')
|
||||
# debug(f'HDR center: channel-shift={channel_shift} full-shift={full_shift}')
|
||||
# debug(f'HDR center: After Adjustment: Full mean={tensor.mean().item()} Channel means={tensor.mean(dim=(-1, -2)).float().cpu().numpy()}')
|
||||
return tensor
|
||||
|
||||
|
||||
|
|
@ -65,7 +65,7 @@ def maximize_tensor(tensor, boundary=1.0):
|
|||
max_val = tensor.max()
|
||||
normalization_factor = boundary / max(abs(min_val), abs(max_val))
|
||||
tensor *= normalization_factor
|
||||
debug(f'HDR maximize: boundary={boundary} min={min_val} max={max_val} factor={normalization_factor}')
|
||||
# debug(f'HDR maximize: boundary={boundary} min={min_val} max={max_val} factor={normalization_factor}')
|
||||
return tensor
|
||||
|
||||
|
||||
|
|
@ -78,7 +78,7 @@ def get_color(colorstr):
|
|||
|
||||
def color_adjust(tensor, colorstr, ratio):
|
||||
color = get_color(colorstr)
|
||||
debug(f'HDR tint: str={colorstr} color={color} ratio={ratio}')
|
||||
# debug(f'HDR tint: str={colorstr} color={color} ratio={ratio}')
|
||||
for i in range(3):
|
||||
tensor[i] = center_tensor(tensor[i], full_shift=1, offset=color[i]*(ratio/2))
|
||||
return tensor
|
||||
|
|
@ -86,35 +86,26 @@ def color_adjust(tensor, colorstr, ratio):
|
|||
|
||||
def correction(p, timestep, latent):
|
||||
if timestep > 950 and p.hdr_clamp:
|
||||
p.extra_generation_params["HDR clamp"] = f'{p.hdr_threshold}/{p.hdr_boundary}'
|
||||
latent = soft_clamp_tensor(latent, threshold=p.hdr_threshold, boundary=p.hdr_boundary)
|
||||
if 600 < timestep < 900 and (p.hdr_color != 0 or p.hdr_tint_ratio != 0):
|
||||
if p.hdr_brightness != 0:
|
||||
latent[0:1] = center_tensor(latent[0:1], full_shift=float(p.hdr_mode), offset=2*p.hdr_brightness) # Brightness
|
||||
p.extra_generation_params["HDR brightness"] = f'{p.hdr_brightness}'
|
||||
p.hdr_brightness = 0
|
||||
if p.hdr_color != 0:
|
||||
latent[1:] = center_tensor(latent[1:], channel_shift=p.hdr_color, full_shift=float(p.hdr_mode)) # Color
|
||||
p.extra_generation_params["HDR color"] = f'{p.hdr_color}'
|
||||
p.hdr_color = 0
|
||||
if p.hdr_tint_ratio != 0:
|
||||
latent = color_adjust(latent, p.hdr_color_picker, p.hdr_tint_ratio)
|
||||
p.hdr_tint_ratio = 0
|
||||
p.extra_generation_params["HDR clamp"] = f'{p.hdr_threshold}/{p.hdr_boundary}'
|
||||
if 600 < timestep < 900 and p.hdr_color != 0:
|
||||
latent[1:] = center_tensor(latent[1:], channel_shift=p.hdr_color, full_shift=float(p.hdr_mode)) # Color
|
||||
p.extra_generation_params["HDR color"] = f'{p.hdr_color}'
|
||||
if 600 < timestep < 900 and p.hdr_tint_ratio != 0:
|
||||
latent = color_adjust(latent, p.hdr_color_picker, p.hdr_tint_ratio)
|
||||
p.extra_generation_params["HDR tint"] = f'{p.hdr_tint_ratio}'
|
||||
if timestep < 200 and (p.hdr_brightness != 0): # do it late so it doesn't change the composition
|
||||
if p.hdr_brightness != 0:
|
||||
latent[0:1] = center_tensor(latent[0:1], full_shift=float(p.hdr_mode), offset=2*p.hdr_brightness) # Brightness
|
||||
p.extra_generation_params["HDR brightness"] = f'{p.hdr_brightness}'
|
||||
p.hdr_brightness = 0
|
||||
latent[0:1] = center_tensor(latent[0:1], full_shift=float(p.hdr_mode), offset=p.hdr_brightness) # Brightness
|
||||
p.extra_generation_params["HDR brightness"] = f'{p.hdr_brightness}'
|
||||
if timestep < 350 and p.hdr_sharpen != 0:
|
||||
p.extra_generation_params["HDR sharpen"] = f'{p.hdr_sharpen}'
|
||||
per_step_ratio = 2 ** (timestep / 250) * p.hdr_sharpen / 16
|
||||
if abs(per_step_ratio) > 0.01:
|
||||
debug(f"HDR Sharpen: timestep={timestep} ratio={p.hdr_sharpen} val={per_step_ratio}")
|
||||
latent = sharpen_tensor(latent, ratio=per_step_ratio)
|
||||
p.extra_generation_params["HDR sharpen"] = f'{p.hdr_sharpen}'
|
||||
if 1 < timestep < 100 and p.hdr_maximize:
|
||||
p.extra_generation_params["HDR max"] = f'{p.hdr_max_center}/{p.hdr_max_boundry}'
|
||||
latent = center_tensor(latent, channel_shift=p.hdr_max_center, full_shift=1.0)
|
||||
latent = maximize_tensor(latent, boundary=p.hdr_max_boundry)
|
||||
p.extra_generation_params["HDR max"] = f'{p.hdr_max_center}/{p.hdr_max_boundry}'
|
||||
return latent
|
||||
|
||||
|
||||
|
|
@ -129,9 +120,6 @@ def correction_callback(p, timestep, kwargs, initial: bool = False):
|
|||
elif skip_correction:
|
||||
return kwargs
|
||||
latents = kwargs["latents"]
|
||||
if debug_enabled:
|
||||
debug('')
|
||||
debug(f' Timestep: {timestep}')
|
||||
# debug(f'HDR correction: latents={latents.shape}')
|
||||
if len(latents.shape) == 4: # standard batched latent
|
||||
for i in range(latents.shape[0]):
|
||||
|
|
|
|||
|
|
@ -205,10 +205,10 @@ def process_hires(p: processing.StableDiffusionProcessing, output):
|
|||
hires_args = set_pipeline_args(
|
||||
p=p,
|
||||
model=shared.sd_model,
|
||||
prompts=[p.refiner_prompt] if len(p.refiner_prompt) > 0 else p.prompts,
|
||||
negative_prompts=[p.refiner_negative] if len(p.refiner_negative) > 0 else p.negative_prompts,
|
||||
prompts_2=[p.refiner_prompt] if len(p.refiner_prompt) > 0 else p.prompts,
|
||||
negative_prompts_2=[p.refiner_negative] if len(p.refiner_negative) > 0 else p.negative_prompts,
|
||||
prompts=len(output.images)* [p.refiner_prompt] if len(p.refiner_prompt) > 0 else p.prompts,
|
||||
negative_prompts=len(output.images) * [p.refiner_negative] if len(p.refiner_negative) > 0 else p.negative_prompts,
|
||||
prompts_2=len(output.images) * [p.refiner_prompt] if len(p.refiner_prompt) > 0 else p.prompts,
|
||||
negative_prompts_2=len(output.images) * [p.refiner_negative] if len(p.refiner_negative) > 0 else p.negative_prompts,
|
||||
num_inference_steps=calculate_hires_steps(p),
|
||||
eta=shared.opts.scheduler_eta,
|
||||
guidance_scale=p.image_cfg_scale if p.image_cfg_scale is not None else p.cfg_scale,
|
||||
|
|
|
|||
|
|
@ -35,16 +35,16 @@ class Script(scripts.Script):
|
|||
s1_restart = gr.Slider(minimum=0, maximum=1.0, value=0.75, label='Restart step')
|
||||
with gr.Row():
|
||||
s2_enable = gr.Checkbox(value=True, label='2nd Stage')
|
||||
s2_scale = gr.Slider(minimum=1, maximum=8.0, value=2.0, label='Scale')
|
||||
s2_restart = gr.Slider(minimum=0, maximum=1.0, value=0.75, label='Restart step')
|
||||
s2_scale = gr.Slider(minimum=1, maximum=8.0, value=2.0, label='2nd Scale')
|
||||
s2_restart = gr.Slider(minimum=0, maximum=1.0, value=0.75, label='2nd Restart step')
|
||||
with gr.Row():
|
||||
s3_enable = gr.Checkbox(value=False, label='3rd Stage')
|
||||
s3_scale = gr.Slider(minimum=1, maximum=8.0, value=3.0, label='Scale')
|
||||
s3_restart = gr.Slider(minimum=0, maximum=1.0, value=0.75, label='Restart step')
|
||||
s3_scale = gr.Slider(minimum=1, maximum=8.0, value=3.0, label='3rd Scale')
|
||||
s3_restart = gr.Slider(minimum=0, maximum=1.0, value=0.75, label='3rd Restart step')
|
||||
with gr.Row():
|
||||
s4_enable = gr.Checkbox(value=False, label='4th Stage')
|
||||
s4_scale = gr.Slider(minimum=1, maximum=8.0, value=4.0, label='Scale')
|
||||
s4_restart = gr.Slider(minimum=0, maximum=1.0, value=0.75, label='Restart step')
|
||||
s4_scale = gr.Slider(minimum=1, maximum=8.0, value=4.0, label='4th Scale')
|
||||
s4_restart = gr.Slider(minimum=0, maximum=1.0, value=0.75, label='4th Restart step')
|
||||
return [cosine_scale, override_sampler, cosine_scale_bg, dilate_tau, s1_enable, s1_scale, s1_restart, s2_enable, s2_scale, s2_restart, s3_enable, s3_scale, s3_restart, s4_enable, s4_scale, s4_restart]
|
||||
|
||||
def run(self, p: processing.StableDiffusionProcessing, cosine_scale, override_sampler, cosine_scale_bg, dilate_tau, s1_enable, s1_scale, s1_restart, s2_enable, s2_scale, s2_restart, s3_enable, s3_scale, s3_restart, s4_enable, s4_scale, s4_restart): # pylint: disable=arguments-differ
|
||||
|
|
|
|||
2
webui.py
2
webui.py
|
|
@ -303,7 +303,7 @@ def start_ui():
|
|||
shared.log.info(f'API ReDocs: {local_url[:-1]}/redocs') # pylint: disable=unsubscriptable-object
|
||||
if share_url is not None:
|
||||
shared.log.info(f'Share URL: {share_url}')
|
||||
shared.log.debug(f'Gradio functions: registered={len(shared.demo.fns)}')
|
||||
# shared.log.debug(f'Gradio functions: registered={len(shared.demo.fns)}')
|
||||
shared.demo.server.wants_restart = False
|
||||
setup_middleware(app, cmd_opts)
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue