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https://github.com/vladmandic/automatic
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all direct input images
Signed-off-by: Vladimir Mandic <mandic00@live.com>
This commit is contained in:
Vladimir Mandic
+14
-6
@@ -1,17 +1,25 @@
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# Change Log for SD.Next
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## Update for 2026-04-29
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## TODO
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- Tag multi-image pipes with `use_images_direct`
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## Update for 2026-04-30
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- **Features**
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- **Multi-image** workflows!
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for models that support multiple images as inputs, you can now add multiple stages in Kanvas
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prompts like "*place character from first image, add background from second image, render in style from third image*" are now possible
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- **UI**
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- add button to manually reorient input/output panels
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- all ui panels can be minimized/maximized by clicking on their header
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state is preserved across sessions and can be used to hide rarely used panels and declutter the workspace
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- **Kanvas** re-order stages by clicking on active stage
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- **Control**
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- remove buttons: *input/control/process*
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- move params *control input type* to control menu section
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- remove "processed preview" from ui
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preprocessor output can still be generated by clicking preview button in in control unit and it will render into normal output area
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- **UI**
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- all ui panels can be minimized/maximized by clicking on their header
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state is preserved across sessions and can be used to hide rarely used panels and declutter the workspace
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- **Kanvas**
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- re-order stages by clicking on active stage
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## Update for 2026-04-28
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Submodule extensions-builtin/sdnext-kanvas updated: 5c71a01449...68a9564f7c
Submodule extensions-builtin/sdnext-modernui updated: e282300b3d...2c1dc64a08
+94
-57
@@ -274,6 +274,68 @@ def init_units(units: list[unit.Unit]):
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u.process.override = u.override
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def control_process(p: StableDiffusionProcessingControl,
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input_script_args: list | None = None,
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override_script_name: str | None = None,
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override_script_args: list | None = None,
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input_image: Image.Image = None, # only used for tiling, otherwise processor.preprocess_image set p params
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):
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debug_log(f'Control exec pipeline: task={sd_models.get_diffusers_task(pipe)} class={pipe.__class__}')
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if sd_models.get_diffusers_task(pipe) != sd_models.DiffusersTaskType.TEXT_2_IMAGE: # force vae back to gpu if not in txt2img mode
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sd_models.move_model(pipe.vae, devices.device)
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# what are we doing?
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if 'control' in p.ops:
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p.outpath_samples = resolve_output_path(shared.opts.outdir_samples, shared.opts.outdir_control_samples)
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elif 'img2img' in p.ops:
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p.outpath_samples = resolve_output_path(shared.opts.outdir_samples, shared.opts.outdir_img2img_samples)
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elif 'txt2img' in p.ops:
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p.outpath_samples = resolve_output_path(shared.opts.outdir_samples, shared.opts.outdir_txt2img_samples)
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else: # fallback to txt2img
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p.outpath_samples = resolve_output_path(shared.opts.outdir_samples, shared.opts.outdir_txt2img_samples)
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# init scripts
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p.scripts = scripts_manager.scripts_control
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p.script_args = input_script_args or []
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if len(p.script_args) == 0:
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if not p.scripts:
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p.scripts.initialize_scripts(False)
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p.script_args = script.init_default_script_args(p.scripts)
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# init override scripts
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if override_script_name and override_script_args and len(override_script_name) > 0:
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selectable_scripts, selectable_script_idx = script.get_selectable_script(override_script_name, p.scripts)
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if selectable_scripts:
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for idx in range(len(override_script_args)):
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p.script_args[selectable_scripts.args_from + idx] = override_script_args[idx]
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p.script_args[0] = selectable_script_idx + 1
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# actual processing
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script_run = False
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processed: processing.Processed = None
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if p.is_tile:
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processed: processing.Processed = tile.run_tiling(p, input_image)
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if processed is None and p.scripts is not None:
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processed = p.scripts.run(p, *p.script_args)
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if processed is None:
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processed: processing.Processed = processing.process_images(p) # run actual pipeline
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else:
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script_run = True
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# postprocessing
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if p.scripts is not None:
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processed = p.scripts.after(p, processed, *p.script_args)
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output = None
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info = None
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if processed is not None and processed.images is not None:
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output = processed.images
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info = [processed.infotext(p, i) for i in range(len(output))]
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# output = pipe(**vars(p)).images # debug: direct pipe exec call
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return output, info, script_run
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def control_run(state: str = '', # pylint: disable=keyword-arg-before-vararg
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units: list[unit.Unit] | None = None, inputs: list[Image.Image] | None = None, inits: list[Image.Image] | None = None, mask: Image.Image = None, unit_type: str | None = None, is_generator: bool = True,
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input_type: int = 0,
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@@ -633,17 +695,20 @@ def control_run(state: str = '', # pylint: disable=keyword-arg-before-vararg
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processed_image = None
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if frame is not None:
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inputs = [Image.fromarray(frame)] # cv2 to pil
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for i, input_image in enumerate(inputs):
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if input_image is not None:
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p.ops.append('img2img')
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for i, input_image in enumerate(inputs): # loop per-input, but with early-break
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if pipe is None: # pipe may have been reset externally
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if video is None:
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break # non-video: pipeline was consumed, no need to re-process remaining inputs
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pipe = set_pipe(p, has_models, unit_type, selected_models, active_model, active_strength, active_units, control_conditioning, control_guidance_start, control_guidance_end, inits)
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debug_log(f'Control pipeline reinit: class={pipe.__class__.__name__}')
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pipe.restore_pipeline = restore_pipeline
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shared.sd_model.restore_pipeline = restore_pipeline
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debug_log(f'Control Control image: {i + 1} of {len(inputs)}')
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if input_image is not None:
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p.ops.append('img2img')
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if shared.state.skipped:
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shared.state.skipped = False
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continue
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@@ -652,6 +717,7 @@ def control_run(state: str = '', # pylint: disable=keyword-arg-before-vararg
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if is_generator:
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yield terminate('Interrupted')
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return terminate('Interrupted')
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# get input
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if isinstance(input_image, str) and os.path.exists(input_image):
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try:
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@@ -664,7 +730,7 @@ def control_run(state: str = '', # pylint: disable=keyword-arg-before-vararg
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debug_log('Control Init image: same as control')
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init_image = input_image
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elif inits is None or len(inits) == 0:
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debug_log('Control Init image: none')
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debug_log('Control init image: none')
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init_image = None
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elif len(inits) > i and isinstance(inits[i], str):
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debug_log(f'Control: init image: {inits[i]}')
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@@ -683,7 +749,22 @@ def control_run(state: str = '', # pylint: disable=keyword-arg-before-vararg
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continue
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index += 1
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processed_image, blended_image = preprocess_image(p, pipe, input_image, init_image, mask, input_type, unit_type, active_process, active_model, selected_models, has_models, active_units)
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if getattr(pipe, 'use_images_direct', False):
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p.init_images = inputs
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else:
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processed_image, blended_image = preprocess_image(p,
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pipe,
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input_image,
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init_image,
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mask,
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input_type,
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unit_type,
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active_process,
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active_model,
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selected_models,
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has_models,
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active_units,
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)
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if is_generator:
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yield (None, blended_image, '') # result is control_output, proces_output
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@@ -701,62 +782,18 @@ def control_run(state: str = '', # pylint: disable=keyword-arg-before-vararg
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if unit_type == 'lite':
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instance.apply(selected_models, processed_image, control_conditioning)
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# what are we doing?
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if 'control' in p.ops:
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p.outpath_samples = resolve_output_path(shared.opts.outdir_samples, shared.opts.outdir_control_samples)
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elif 'img2img' in p.ops:
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p.outpath_samples = resolve_output_path(shared.opts.outdir_samples, shared.opts.outdir_img2img_samples)
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elif 'txt2img' in p.ops:
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p.outpath_samples = resolve_output_path(shared.opts.outdir_samples, shared.opts.outdir_txt2img_samples)
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else: # fallback to txt2img
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p.outpath_samples = resolve_output_path(shared.opts.outdir_samples, shared.opts.outdir_txt2img_samples)
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# pipeline
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output = None
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script_run = False
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if pipe is not None: # run new pipeline
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debug_log(f'Control exec pipeline: task={sd_models.get_diffusers_task(pipe)} class={pipe.__class__}')
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if sd_models.get_diffusers_task(pipe) != sd_models.DiffusersTaskType.TEXT_2_IMAGE: # force vae back to gpu if not in txt2img mode
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sd_models.move_model(pipe.vae, devices.device)
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# init scripts
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p.scripts = scripts_manager.scripts_control
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p.script_args = input_script_args or []
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if len(p.script_args) == 0:
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if not p.scripts:
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p.scripts.initialize_scripts(False)
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p.script_args = script.init_default_script_args(p.scripts)
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# init override scripts
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if override_script_name and override_script_args and len(override_script_name) > 0:
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selectable_scripts, selectable_script_idx = script.get_selectable_script(override_script_name, p.scripts)
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if selectable_scripts:
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for idx in range(len(override_script_args)):
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p.script_args[selectable_scripts.args_from + idx] = override_script_args[idx]
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p.script_args[0] = selectable_script_idx + 1
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# actual processing
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processed: processing.Processed = None
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if p.is_tile:
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processed: processing.Processed = tile.run_tiling(p, input_image)
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if processed is None and p.scripts is not None:
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processed = p.scripts.run(p, *p.script_args)
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if processed is None:
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processed: processing.Processed = processing.process_images(p) # run actual pipeline
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else:
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script_run = True
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# postprocessing
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if p.scripts is not None:
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processed = p.scripts.after(p, processed, *p.script_args)
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output = None
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if processed is not None and processed.images is not None:
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output = processed.images
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info_txt = [processed.infotext(p, i) for i in range(len(output))]
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# output = pipe(**vars(p)).images # alternative direct pipe exec call
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else: # blend all processed images and return
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if pipe is None: # blend all processed images and return
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output = processed_image
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else: # run new pipeline
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output, info_txt, script_run = control_process(p,
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input_script_args,
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override_script_name,
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override_script_args,
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input_image,
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)
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# outputs
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output = output or []
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@@ -156,10 +156,7 @@ def task_specific_kwargs(p, model):
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if ('WanVACEPipeline' in model_cls) and (p.init_images is not None) and (len(p.init_images) > 0):
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task_args['reference_images'] = p.init_images
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if ('GoogleNanoBananaPipeline' in model_cls) and (p.init_images is not None) and (len(p.init_images) > 0):
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if hasattr(p, 'orig_init_images') and (p.orig_init_images is not None) and len(p.orig_init_images) > 0:
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task_args['images'] = p.orig_init_images
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else:
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task_args['images'] = p.init_images
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task_args['images'] = p.init_images
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if ('GlmImagePipeline' in model_cls) and (p.init_images is not None) and (len(p.init_images) > 0):
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task_args['image'] = p.init_images
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if 'BlipDiffusionPipeline' in model_cls:
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@@ -1290,7 +1290,7 @@ def set_diffuser_pipe(pipe, new_pipe_type):
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add_noise_pred_to_diffusers_callback(new_pipe.pipe)
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fn = f'{sys._getframe(2).f_code.co_name}:{sys._getframe(1).f_code.co_name}' # pylint: disable=protected-access
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log.debug(f"Pipeline class change: original={cls} target={new_pipe.__class__.__name__} device={pipe.device} fn={fn}") # pylint: disable=protected-access
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log.debug(f"Pipeline class change: source={cls} target={new_pipe.__class__.__name__} device={pipe.device} fn={fn}") # pylint: disable=protected-access
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if shared.opts.diffusers_offload_mode == 'none':
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move_model(new_pipe, pipe.device)
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@@ -40,6 +40,7 @@ def get_size_buckets(width: int, height: int) -> tuple[str, str]:
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class GoogleNanoBananaPipeline():
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def __init__(self, model_name: str):
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self.use_images_direct = True
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self.model = model_name
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self.client = None
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self.config = None
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