mirror of
https://github.com/AcademiaSD/sd-forge-fluxtools-v2.git
synced 2026-01-27 03:39:57 +00:00
579 lines
36 KiB
Python
579 lines
36 KiB
Python
#v023
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from backend import memory_management
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from backend.diffusion_engine.flux import Flux
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import gradio
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from gradio_rangeslider import RangeSlider
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import torch, numpy as np
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from modules import scripts, shared
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from modules.ui_components import InputAccordion
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from modules.script_callbacks import on_cfg_denoiser, remove_current_script_callbacks
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from modules.sd_samplers_common import images_tensor_to_samples, approximation_indexes
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from modules_forge.forge_canvas.canvas import ForgeCanvas
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from PIL import Image, ImageDraw
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from controlnet_aux import CannyDetector
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from controlnet_aux.processor import Processor
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import gc
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from modules_forge import main_entry
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class flux1tools(scripts.Script):
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sorting_priority = 0
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glc_backup_flux = None
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clearConds = False
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text_encoder_device_backup = None
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sigmasBackup = None # Se conserva por si se requiere en ajustes futuros
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flux_use_T5 = True
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flux_use_CL = True
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def __init__(self):
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if flux1tools.glc_backup_flux is None:
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flux1tools.glc_backup_flux = Flux.get_learned_conditioning
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if flux1tools.text_encoder_device_backup is None:
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flux1tools.text_encoder_device_backup = memory_management.text_encoder_device
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# Función auxiliar para separar el prompt en 2 partes usando "SPLIT"
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def splitPrompt(prompt, countTextEncoders):
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promptTE1 = []
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promptTE2 = []
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for p in prompt:
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parts = p.split('SPLIT')
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if len(parts) >= 2:
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promptTE1.append(parts[0].strip())
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promptTE2.append(parts[1].strip())
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else:
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promptTE1.append(p)
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promptTE2.append(p)
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return promptTE1, promptTE2
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# Funciones para seleccionar el dispositivo de los text encoders
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def patched_text_encoder_gpu2():
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if torch.cuda.device_count() > 1:
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return torch.device("cuda:1")
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else:
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return torch.cuda.current_device()
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def patched_text_encoder_gpu():
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return torch.cuda.current_device()
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def patched_text_encoder_cpu():
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return memory_management.cpu
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@torch.inference_mode()
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def patched_glc_flux(self, prompt: list[str]):
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memory_management.load_model_gpu(self.forge_objects.clip.patcher)
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nprompt = len(prompt)
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CLIPprompt, T5prompt = flux1tools.splitPrompt(prompt, 2)
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if flux1tools.flux_use_CL:
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cond_l, pooled_l = self.text_processing_engine_l(CLIPprompt)
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else:
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pooled_l = torch.zeros([nprompt, 768])
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if flux1tools.flux_use_T5:
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cond_t5 = self.text_processing_engine_t5(prompt)
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else:
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cond_t5 = torch.zeros([nprompt, 256, 4096])
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cond = dict(crossattn=cond_t5, vector=pooled_l)
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if getattr(self, "use_distilled_cfg_scale", False):
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distilled_cfg_scale = getattr(prompt, 'distilled_cfg_scale', 3.5) or 3.5
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cond['guidance'] = torch.FloatTensor([distilled_cfg_scale] * len(prompt))
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print(f'Distilled CFG Scale: {distilled_cfg_scale}')
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else:
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print('Distilled CFG Scale will be ignored for Schnell')
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return cond
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def title(self):
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return "Flux1Tools-V2"
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def show(self, is_img2img):
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return scripts.AlwaysVisible
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# --- Funciones de preprocesamiento ---
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def run_canny_preprocessor(self, image, low_threshold, high_threshold, detect_resolution, image_resolution):
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try:
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if image is None:
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return None
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if not hasattr(image, "size"):
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image = Image.fromarray(np.array(image))
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if image.mode != "RGB":
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image = image.convert("RGB")
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processor = CannyDetector()
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processed = processor(
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image,
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low_threshold=int(low_threshold),
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high_threshold=int(high_threshold),
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detect_resolution=int(detect_resolution),
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image_resolution=int(image_resolution)
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)
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return processed
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except Exception as e:
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print("Error in run_canny_preprocessor:", e)
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return None
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def run_depth_preprocessor(self, image, processor_id):
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try:
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if image is None:
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return None
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if not hasattr(image, "size"):
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image = Image.fromarray(np.array(image))
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if image.mode != "RGB":
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image = image.convert("RGB")
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proc = Processor(processor_id)
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processed = proc(image)
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processed = processed.resize((512, 512), Image.BICUBIC)
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return processed
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except Exception as e:
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print("Error in run_depth_preprocessor:", e)
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return None
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# --- Nueva función para expandir lienzo (outpaint) ---
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def expand_canvas(self, image, mask, expand_up, expand_down, expand_left, expand_right):
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try:
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if image is None:
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return None, None
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# Convertir a PIL Image si es necesario
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if not hasattr(image, "size"):
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image = Image.fromarray(np.array(image))
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if not hasattr(mask, "size"):
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mask = Image.fromarray(np.array(mask))
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# Obtener dimensiones originales
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orig_width, orig_height = image.size
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# Calcular nuevas dimensiones
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new_width = orig_width + expand_left + expand_right
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new_height = orig_height + expand_up + expand_down
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# Crear nueva imagen y máscara expandidas
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new_image = Image.new("RGBA", (new_width, new_height), (0, 0, 0, 0))
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new_mask = Image.new("RGBA", (new_width, new_height), (0, 0, 0, 0))
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# Colocar imagen original en la nueva imagen
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new_image.paste(image, (expand_left, expand_up))
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# Crear máscara para areas expandidas (áreas a generar)
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draw = ImageDraw.Draw(new_mask)
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# Rellenar áreas expandidas en la máscara (blanco = áreas a generar)
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if expand_up > 0:
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draw.rectangle([(0, 0), (new_width, expand_up)], fill=(100, 255, 100, 255))
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if expand_down > 0:
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draw.rectangle([(0, new_height - expand_down), (new_width, new_height)], fill=(100, 255, 100, 255))
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if expand_left > 0:
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draw.rectangle([(0, expand_up), (expand_left, new_height - expand_down)], fill=(100, 255, 100, 255))
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if expand_right > 0:
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draw.rectangle([(new_width - expand_right, expand_up), (new_width, new_height - expand_down)], fill=(100, 255, 100, 255))
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return new_image, new_mask
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except Exception as e:
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print("Error in expand_canvas:", e)
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return image, mask
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# --- Interfaz de usuario ---
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def ui(self, *args, **kwargs):
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def update_ref_info(image):
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if image is None:
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return "Reference image aspect ratio: *no image*"
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else:
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return f"Reference image aspect ratio: {round(image.size[0]/image.size[1],3)} ({image.size[0]} × {image.size[1]})"
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def update_depth_info(image):
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if image is None:
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return "Depth image aspect ratio: *no image*"
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else:
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return f"Depth image aspect ratio: {round(image.size[0]/image.size[1],3)} ({image.size[0]} × {image.size[1]})"
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# Función que actualiza info y previsualización en Canny
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def update_canny_all(image, low, high, detect, imgres):
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info = update_ref_info(image)
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proc = self.run_canny_preprocessor(image, low, high, detect, imgres)
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return info, proc
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with InputAccordion(False, label=self.title()) as enabled:
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# Pestaña Canny
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with gradio.Tab("Canny", id="F2E_FT_Canny"):
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gradio.Markdown("Use an appropriately Flux Canny checkpoint.")
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with gradio.Row():
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with gradio.Column():
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canny_image = gradio.Image(label="Reference Image", type="pil", height=500, sources=["upload", "clipboard"])
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canny_ref_info = gradio.Markdown("Reference image aspect ratio: *no image*")
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with gradio.Column():
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canny_preproc_preview = gradio.Image(label="Preprocessor Preview", type="pil", height=500)
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# Primera fila: Low Threshold, High Threshold y Run preprocessor en la misma línea
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with gradio.Row():
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canny_low_threshold = gradio.Slider(label="Low Threshold", minimum=0, maximum=255, step=1, value=100)
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canny_high_threshold = gradio.Slider(label="High Threshold", minimum=0, maximum=255, step=1, value=200)
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run_canny_button = gradio.Button("Run preprocessor", visible=False)
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# Segunda fila: Detect Resolution e Image Resolution
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with gradio.Row():
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canny_detect_resolution = gradio.Slider(label="Detect Resolution", minimum=128, maximum=1024, step=1, value=512)
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canny_image_resolution = gradio.Slider(label="Image Resolution", minimum=128, maximum=1024, step=1, value=512)
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# Tercera fila: Strength y Start/End
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with gradio.Row():
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canny_strength = gradio.Slider(label="Strength", minimum=0.0, maximum=2.0, step=0.01, value=1.0)
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canny_time = RangeSlider(label="Start / End", minimum=0.0, maximum=1.0, step=0.01, value=(0.0, 0.8))
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# Actualización automática solo al soltar los controles (on release)
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canny_low_threshold.release(fn=update_canny_all, inputs=[canny_image, canny_low_threshold, canny_high_threshold, canny_detect_resolution, canny_image_resolution], outputs=[canny_ref_info, canny_preproc_preview])
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canny_high_threshold.release(fn=update_canny_all, inputs=[canny_image, canny_low_threshold, canny_high_threshold, canny_detect_resolution, canny_image_resolution], outputs=[canny_ref_info, canny_preproc_preview])
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canny_detect_resolution.release(fn=update_canny_all, inputs=[canny_image, canny_low_threshold, canny_high_threshold, canny_detect_resolution, canny_image_resolution], outputs=[canny_ref_info, canny_preproc_preview])
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canny_image_resolution.release(fn=update_canny_all, inputs=[canny_image, canny_low_threshold, canny_high_threshold, canny_detect_resolution, canny_image_resolution], outputs=[canny_ref_info, canny_preproc_preview])
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canny_image.change(fn=update_canny_all, inputs=[canny_image, canny_low_threshold, canny_high_threshold, canny_detect_resolution, canny_image_resolution], outputs=[canny_ref_info, canny_preproc_preview])
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# También se deja el botón por si se desea una acción manual
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run_canny_button.click(fn=self.run_canny_preprocessor, inputs=[canny_image, canny_low_threshold, canny_high_threshold, canny_detect_resolution, canny_image_resolution], outputs=canny_preproc_preview)
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# Pestaña Depth
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with gradio.Tab("Depth", id="F2E_FT_Depth"):
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gradio.Markdown("Use an appropriately Flux Depth checkpoint..")
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with gradio.Row():
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with gradio.Column():
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depth_image = gradio.Image(label="Reference Image", type="pil", height=500, sources=["upload", "clipboard"])
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depth_ref_info = gradio.Markdown("Reference image aspect ratio: *no image*")
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with gradio.Column():
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depth_preproc_preview = gradio.Image(label="Preprocessor Preview", type="pil", height=500)
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# Se utiliza un Dropdown para seleccionar el procesador y se oculta el botón manual
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with gradio.Row():
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depth_processor_selector = gradio.Dropdown(label="Processor", choices=["depth_zoe", "depth_midas", "depth_leres", "depth_leres++"], value="depth_zoe")
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run_depth_button = gradio.Button("Run preprocessor", visible=False)
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with gradio.Row():
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depth_strength = gradio.Slider(label="Strength", minimum=0.0, maximum=2.0, step=0.01, value=1.0)
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depth_time = RangeSlider(label="Start / End", minimum=0.0, maximum=1.0, step=0.01, value=(0.0, 0.8))
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# Función que actualiza la información y previsualización en Depth
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def update_depth_all(image, processor):
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info = update_ref_info(image)
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proc = self.run_depth_preprocessor(image, processor)
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return info, proc
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# Ejecutar automáticamente al cambiar la imagen o el procesador
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depth_image.change(fn=update_depth_all, inputs=[depth_image, depth_processor_selector], outputs=[depth_ref_info, depth_preproc_preview])
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depth_processor_selector.change(fn=update_depth_all, inputs=[depth_image, depth_processor_selector], outputs=[depth_ref_info, depth_preproc_preview])
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# Pestaña Fill (modificada para incluir outpainting)
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with gradio.Tab("Fill", id="F2E_FT_f"):
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gradio.Markdown("Use an appropriately Flux Fill checkpoint.")
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with gradio.Row():
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fill_image = ForgeCanvas(
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height=500,
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contrast_scribbles=shared.opts.img2img_inpaint_mask_high_contrast,
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scribble_color=shared.opts.img2img_inpaint_mask_brush_color,
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scribble_color_fixed=True,
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scribble_alpha=75,
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scribble_alpha_fixed=True,
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scribble_softness_fixed=True
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)
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# Añadimos nueva sección para outpainting
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with gradio.Accordion("Outpaint Options", open=False):
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# Sliders para controlar la expansión en cada dirección
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with gradio.Row():
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fill_expand_up = gradio.Slider(label="Expand Up ↑", minimum=0, maximum=512, step=64, value=0)
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fill_expand_down = gradio.Slider(label="Expand Down ↓", minimum=0, maximum=512, step=64, value=0)
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with gradio.Row():
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fill_expand_left = gradio.Slider(label="Expand Left ←", minimum=0, maximum=512, step=64, value=0)
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fill_expand_right = gradio.Slider(label="Expand Right →", minimum=0, maximum=512, step=64, value=0)
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# Botón para aplicar expansión
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with gradio.Row():
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apply_outpaint_button = gradio.Button("Apply Outpaint")
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# Información sobre dimensiones actuales y nuevas
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fill_dimensions_info = gradio.Markdown("Current dimensions: N/A")
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# Función para actualizar la información de dimensiones
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def update_dimensions_info(image, up, down, left, right):
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if image is None:
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return "Current dimensions: N/A"
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width, height = image.size
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new_width = width + left + right
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new_height = height + up + down
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return f"Current dimensions: {width}×{height}"
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#return f"Current dimensions: {width}×{height} → New dimensions: {new_width}×{new_height}"
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# Actualizar info al cambiar valores
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fill_image.background.change(
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fn=update_dimensions_info,
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inputs=[fill_image.background, fill_expand_up, fill_expand_down, fill_expand_left, fill_expand_right],
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outputs=fill_dimensions_info
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)
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for slider in [fill_expand_up, fill_expand_down, fill_expand_left, fill_expand_right]:
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slider.release(
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fn=update_dimensions_info,
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inputs=[fill_image.background, fill_expand_up, fill_expand_down, fill_expand_left, fill_expand_right],
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outputs=fill_dimensions_info
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)
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# Función para aplicar outpaint
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def apply_outpaint(background, foreground, up, down, left, right):
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if background is None:
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return background, foreground
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new_bg, new_fg = self.expand_canvas(background, foreground, up, down, left, right)
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return new_bg, new_fg
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# Conectar botón a función
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apply_outpaint_button.click(
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fn=apply_outpaint,
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inputs=[fill_image.background, fill_image.foreground,
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fill_expand_up, fill_expand_down, fill_expand_left, fill_expand_right],
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outputs=[fill_image.background, fill_image.foreground]
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)
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with gradio.Row():
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fill_strength = gradio.Slider(label="Strength", minimum=0.0, maximum=2.0, step=0.01, value=1.0, visible=False)
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fill_time = RangeSlider(label=" Start / End", minimum=0.0, maximum=1.0, step=0.01, value=(0.0, 1.0), visible=False)
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# Pestaña Redux (se mantiene igual)
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with gradio.Tab("Redux", id="F2E_FT_r1"):
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gradio.Markdown("Use an appropriately Flux checkpoint.")
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with gradio.Row():
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with gradio.Column():
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redux_image1 = gradio.Image(show_label=False, type="pil", height=500, sources=["upload", "clipboard"])
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with gradio.Column():
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redux_str1 = gradio.Slider(label="Strength", minimum=0.0, maximum=2.0, step=0.01, value=1.0)
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redux_time1 = RangeSlider(label="Start / End", minimum=0.0, maximum=1.0, step=0.01, value=(0.0, 0.8))
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swap12 = gradio.Button("swap redux 1 and 2")
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swap13 = gradio.Button("swap redux 1 and 3")
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swap14 = gradio.Button("swap redux 1 and 4")
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with gradio.Tab("Redux-2", id="F2E_FT_r2"):
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gradio.Markdown("Use an appropriately Flux checkpoint.")
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with gradio.Row():
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with gradio.Column():
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redux_image2 = gradio.Image(show_label=False, type="pil", height=500, sources=["upload", "clipboard"])
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with gradio.Column():
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redux_str2 = gradio.Slider(label="Strength", minimum=0.0, maximum=2.0, step=0.01, value=1.0)
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redux_time2 = RangeSlider(label="Start / End", minimum=0.0, maximum=1.0, step=0.01, value=(0.0, 0.8))
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swap21 = gradio.Button("swap redux 2 and 1")
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swap23 = gradio.Button("swap redux 2 and 3")
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swap24 = gradio.Button("swap redux 2 and 4")
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with gradio.Tab("Redux-3", id="F2E_FT_r3"):
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gradio.Markdown("Use an appropriately Flux checkpoint.")
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with gradio.Row():
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with gradio.Column():
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redux_image3 = gradio.Image(show_label=False, type="pil", height=500, sources=["upload", "clipboard"])
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with gradio.Column():
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redux_str3 = gradio.Slider(label="Strength", minimum=0.0, maximum=2.0, step=0.01, value=1.0)
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redux_time3 = RangeSlider(label="Start / End", minimum=0.0, maximum=1.0, step=0.01, value=(0.0, 0.8))
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swap31 = gradio.Button("swap redux 3 and 1")
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swap32 = gradio.Button("swap redux 3 and 2")
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swap34 = gradio.Button("swap redux 3 and 4")
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with gradio.Tab("Redux-4", id="F2E_FT_r4"):
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gradio.Markdown("Use an appropriately Flux checkpoint.")
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with gradio.Row():
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with gradio.Column():
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redux_image4 = gradio.Image(show_label=False, type="pil", height=500, sources=["upload", "clipboard"])
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with gradio.Column():
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redux_str4 = gradio.Slider(label="Strength", minimum=0.0, maximum=2.0, step=0.01, value=1.0)
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redux_time4 = RangeSlider(label="Start / End", minimum=0.0, maximum=1.0, step=0.01, value=(0.0, 0.8))
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swap41 = gradio.Button("swap redux 4 and 1")
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swap42 = gradio.Button("swap redux 4 and 2")
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swap43 = gradio.Button("swap redux 4 and 3")
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def redux_swap(image1, image2, str1, str2, time1, time2):
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return image2, image1, str2, str1, time2, time1
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swap12.click(redux_swap, inputs=[redux_image1, redux_image2, redux_str1, redux_str2, redux_time1, redux_time2],
|
||
outputs=[redux_image1, redux_image2, redux_str1, redux_str2, redux_time1, redux_time2])
|
||
swap13.click(redux_swap, inputs=[redux_image1, redux_image3, redux_str1, redux_str3, redux_time1, redux_time3],
|
||
outputs=[redux_image1, redux_image3, redux_str1, redux_str3, redux_time1, redux_time3])
|
||
swap14.click(redux_swap, inputs=[redux_image1, redux_image4, redux_str1, redux_str4, redux_time1, redux_time4],
|
||
outputs=[redux_image1, redux_image4, redux_str1, redux_str4, redux_time1, redux_time4])
|
||
swap21.click(redux_swap, inputs=[redux_image2, redux_image1, redux_str2, redux_str1, redux_time2, redux_time1],
|
||
outputs=[redux_image2, redux_image1, redux_str2, redux_str1, redux_time2, redux_time1])
|
||
swap23.click(redux_swap, inputs=[redux_image2, redux_image3, redux_str2, redux_str3, redux_time2, redux_time3],
|
||
outputs=[redux_image2, redux_image3, redux_str2, redux_str3, redux_time2, redux_time3])
|
||
swap24.click(redux_swap, inputs=[redux_image2, redux_image4, redux_str2, redux_str4, redux_time2, redux_time4],
|
||
outputs=[redux_image2, redux_image4, redux_str2, redux_str4, redux_time2, redux_time4])
|
||
swap31.click(redux_swap, inputs=[redux_image3, redux_image1, redux_str3, redux_str1, redux_time3, redux_time1],
|
||
outputs=[redux_image3, redux_image1, redux_str3, redux_str1, redux_time3, redux_time1])
|
||
swap32.click(redux_swap, inputs=[redux_image3, redux_image2, redux_str3, redux_str2, redux_time3, redux_time2],
|
||
outputs=[redux_image3, redux_image2, redux_str3, redux_str2, redux_time3, redux_time2])
|
||
swap34.click(redux_swap, inputs=[redux_image3, redux_image4, redux_str3, redux_str4, redux_time3, redux_time4],
|
||
outputs=[redux_image3, redux_image4, redux_str3, redux_str4, redux_time3, redux_time4])
|
||
swap41.click(redux_swap, inputs=[redux_image4, redux_image1, redux_str4, redux_str1, redux_time4, redux_time1],
|
||
outputs=[redux_image4, redux_image1, redux_str4, redux_str1, redux_time4, redux_time1])
|
||
swap42.click(redux_swap, inputs=[redux_image4, redux_image2, redux_str4, redux_str2, redux_time4, redux_time2],
|
||
outputs=[redux_image4, redux_image2, redux_str4, redux_str2, redux_time4, redux_time2])
|
||
swap43.click(redux_swap, inputs=[redux_image4, redux_image3, redux_str4, redux_str3, redux_time4, redux_time3],
|
||
outputs=[redux_image4, redux_image3, redux_str4, redux_str3, redux_time4, redux_time3])
|
||
with gradio.Accordion('Text encoders control', open=False, visible=False):
|
||
te_device = gradio.Radio(label="Device for text encoders", choices=["default", "cpu", "gpu", "gpu-2"], value="default")
|
||
with gradio.Row():
|
||
flux_use_T5 = gradio.Checkbox(value=flux1tools.flux_use_T5, label="Flux: Use T5")
|
||
flux_use_CL = gradio.Checkbox(value=flux1tools.flux_use_CL, label="Flux: Use CLIP (pooled)")
|
||
self.infotext_fields = [
|
||
(enabled, lambda d: d.get("fmp_enabled", False)),
|
||
(te_device, "fmp_te_device"),
|
||
(flux_use_T5, "fmp_fluxT5"),
|
||
(flux_use_CL, "fmp_fluxCL"),
|
||
]
|
||
def clearCondCache():
|
||
flux1tools.clearConds = True
|
||
enabled.change(fn=clearCondCache, inputs=None, outputs=None)
|
||
flux_use_T5.change(fn=clearCondCache, inputs=None, outputs=None)
|
||
flux_use_CL.change(fn=clearCondCache, inputs=None, outputs=None)
|
||
|
||
# Añadir nuevos componentes de outpainting a la lista de retorno
|
||
return enabled, te_device, flux_use_T5, flux_use_CL, \
|
||
canny_image, canny_ref_info, canny_preproc_preview, canny_low_threshold, canny_high_threshold, canny_detect_resolution, canny_image_resolution, canny_strength, canny_time, \
|
||
depth_image, depth_ref_info, depth_preproc_preview, depth_processor_selector, depth_strength, depth_time, \
|
||
redux_image1, redux_image2, redux_image3, redux_image4, \
|
||
redux_str1, redux_str2, redux_str3, redux_str4, \
|
||
redux_time1, redux_time2, redux_time3, redux_time4, \
|
||
fill_image.background, fill_image.foreground, fill_strength, fill_time, \
|
||
fill_expand_up, fill_expand_down, fill_expand_left, fill_expand_right, fill_dimensions_info
|
||
|
||
def after_extra_networks_activate(self, p, *script_args, **kwargs):
|
||
enabled = script_args[0]
|
||
if enabled:
|
||
te_device = script_args[1]
|
||
match te_device:
|
||
case "gpu-2":
|
||
memory_management.text_encoder_device = flux1tools.patched_text_encoder_gpu2
|
||
case "gpu":
|
||
memory_management.text_encoder_device = flux1tools.patched_text_encoder_gpu
|
||
case "cpu":
|
||
memory_management.text_encoder_device = flux1tools.patched_text_encoder_cpu
|
||
case _:
|
||
pass
|
||
|
||
def process(self, params, *script_args, **kwargs):
|
||
# Desempaquetamos los argumentos incluyendo los nuevos componentes de outpainting
|
||
(enabled, te_device, flux_use_T5, flux_use_CL,
|
||
canny_image, canny_ref_info, canny_preproc_preview, canny_low_threshold, canny_high_threshold, canny_detect_resolution, canny_image_resolution, canny_strength, canny_time,
|
||
depth_image, depth_ref_info, depth_preproc_preview, depth_processor_selector, depth_strength, depth_time,
|
||
redux_image1, redux_image2, redux_image3, redux_image4,
|
||
redux_str1, redux_str2, redux_str3, redux_str4,
|
||
redux_time1, redux_time2, redux_time3, redux_time4,
|
||
fill_image, fill_mask, fill_strength, fill_time,
|
||
fill_expand_up, fill_expand_down, fill_expand_left, fill_expand_right, fill_dimensions_info) = script_args
|
||
|
||
def process_before_every_sampling(self, params, *script_args, **kwargs):
|
||
(enabled, te_device, flux_use_T5, flux_use_CL,
|
||
canny_image, canny_ref_info, canny_preproc_preview, canny_low_threshold, canny_high_threshold, canny_detect_resolution, canny_image_resolution, canny_strength, canny_time,
|
||
depth_image, depth_ref_info, depth_preproc_preview, depth_processor_selector, depth_strength, depth_time,
|
||
redux_image1, redux_image2, redux_image3, redux_image4,
|
||
redux_str1, redux_str2, redux_str3, redux_str4,
|
||
redux_time1, redux_time2, redux_time3, redux_time4,
|
||
fill_image, fill_mask, fill_strength, fill_time,
|
||
fill_expand_up, fill_expand_down, fill_expand_left, fill_expand_right, fill_dimensions_info) = script_args
|
||
|
||
if enabled:
|
||
if not params.sd_model.is_webui_legacy_model():
|
||
x = kwargs['x']
|
||
n, c, h, w = x.size()
|
||
# Prioridad: Fill > Canny > Depth
|
||
if fill_image is not None and fill_mask is not None:
|
||
mask_A = fill_mask.getchannel('A').convert('L')
|
||
mask_A_I = mask_A.point(lambda v: 0 if v > 128 else 255)
|
||
mask_A = mask_A.point(lambda v: 255 if v > 128 else 0)
|
||
mask = Image.merge('RGBA', (mask_A_I, mask_A_I, mask_A_I, mask_A))
|
||
image = Image.alpha_composite(fill_image, mask).convert('RGB')
|
||
image = image.resize((w * 8, h * 8))
|
||
image_np = np.array(image) / 255.0
|
||
image_np = np.transpose(image_np, (2, 0, 1))
|
||
image_tensor = torch.tensor(image_np).unsqueeze(0)
|
||
latent = images_tensor_to_samples(image_tensor, approximation_indexes.get(shared.opts.sd_vae_encode_method), params.sd_model)
|
||
|
||
mask_resized = mask_A.resize((w * 8, h * 8))
|
||
mask_np = np.array(mask_resized) / 255.0
|
||
mask_tensor = torch.tensor(mask_np).unsqueeze(0).unsqueeze(0)
|
||
mask_tensor = mask_tensor[:, 0, :, :]
|
||
mask_tensor = mask_tensor.view(1, h, 8, w, 8)
|
||
mask_tensor = mask_tensor.permute(0, 2, 4, 1, 3)
|
||
mask_tensor = mask_tensor.reshape(1, 64, h, w)
|
||
|
||
flux1tools.latent = torch.cat([latent, mask_tensor.to(latent.device)], dim=1)
|
||
flux1tools.unmasked_latent = None
|
||
del image_tensor, mask_tensor
|
||
flux1tools.start = 0.0
|
||
flux1tools.end = 1.0
|
||
flux1tools.strength = 1.0
|
||
elif canny_preproc_preview is not None and canny_strength > 0:
|
||
image = canny_preproc_preview.resize((w * 8, h * 8))
|
||
image = np.array(image) / 255.0
|
||
image = np.transpose(image, (2, 0, 1))
|
||
image = torch.tensor(image).unsqueeze(0)
|
||
latent = images_tensor_to_samples(image, approximation_indexes.get(shared.opts.sd_vae_encode_method), params.sd_model)
|
||
flux1tools.latent = latent
|
||
flux1tools.unmasked_latent = None
|
||
del image
|
||
flux1tools.start = canny_time[0]
|
||
flux1tools.end = canny_time[1]
|
||
flux1tools.strength = canny_strength
|
||
elif depth_preproc_preview is not None and depth_strength > 0:
|
||
image = depth_preproc_preview.resize((w * 8, h * 8))
|
||
image = np.array(image) / 255.0
|
||
image = np.transpose(image, (2, 0, 1))
|
||
image = torch.tensor(image).unsqueeze(0)
|
||
latent = images_tensor_to_samples(image, approximation_indexes.get(shared.opts.sd_vae_encode_method), params.sd_model)
|
||
flux1tools.latent = latent
|
||
flux1tools.unmasked_latent = None
|
||
del image
|
||
flux1tools.start = depth_time[0]
|
||
flux1tools.end = depth_time[1]
|
||
flux1tools.strength = depth_strength
|
||
else:
|
||
flux1tools.latent = None
|
||
|
||
redux_images = [redux_image1, redux_image2, redux_image3, redux_image4]
|
||
redux_strengths = [redux_str1, redux_str2, redux_str3, redux_str4]
|
||
redux_times = [redux_time1, redux_time2, redux_time3, redux_time4]
|
||
if redux_images != [None, None, None, None] and redux_strengths != [0, 0, 0, 0]:
|
||
from transformers import SiglipImageProcessor, SiglipVisionModel
|
||
from diffusers.pipelines.flux.modeling_flux import ReduxImageEncoder
|
||
embeds = []
|
||
for i in range(len(redux_images)):
|
||
if redux_images[i] is None or redux_strengths[i] == 0:
|
||
continue
|
||
feature = SiglipImageProcessor.from_pretrained("Runware/FLUX.1-Redux-dev", subfolder="feature_extractor")
|
||
image = feature.preprocess(images=redux_images[i], do_resize=True, return_tensors="pt", do_convert_rgb=True)
|
||
del feature
|
||
encoder = SiglipVisionModel.from_pretrained("Runware/FLUX.1-Redux-dev", subfolder="image_encoder")
|
||
image_enc_hidden_states = encoder(**image).last_hidden_state
|
||
del encoder
|
||
embedder = ReduxImageEncoder.from_pretrained("Runware/FLUX.1-Redux-dev", subfolder="image_embedder")
|
||
embeds.append((redux_strengths[i] * embedder(image_enc_hidden_states).image_embeds, redux_times[i][0], redux_times[i][1]))
|
||
del embedder, image_enc_hidden_states
|
||
flux1tools.image_embeds = embeds
|
||
else:
|
||
flux1tools.image_embeds = None
|
||
|
||
def apply_control(self):
|
||
lastStep = self.total_sampling_steps - 1
|
||
thisStep = self.sampling_step
|
||
if flux1tools.image_embeds is not None:
|
||
embeds = flux1tools.image_embeds
|
||
cond = self.text_cond["crossattn"]
|
||
for e in embeds:
|
||
if thisStep >= e[1] * lastStep and thisStep <= e[2] * lastStep:
|
||
image_embeds = e[0].repeat_interleave(len(self.text_cond["crossattn"]), dim=0)
|
||
image_embeds *= (256 / 729)
|
||
cond = torch.cat([cond, image_embeds.to(cond.device)], dim=1)
|
||
del image_embeds
|
||
cond = torch.sum(cond, dim=0, keepdim=True)
|
||
self.text_cond["crossattn"] = cond
|
||
if flux1tools.latent is not None:
|
||
if thisStep >= flux1tools.start * lastStep and thisStep <= flux1tools.end * lastStep:
|
||
latent_strength = flux1tools.latent * flux1tools.strength
|
||
shared.sd_model.forge_objects.unet.extra_concat_condition = latent_strength
|
||
else:
|
||
if flux1tools.unmasked_latent is not None:
|
||
shared.sd_model.forge_objects.unet.extra_concat_condition = flux1tools.unmasked_latent
|
||
else:
|
||
shared.sd_model.forge_objects.unet.extra_concat_condition = flux1tools.latent * 0.0
|
||
on_cfg_denoiser(apply_control)
|
||
return
|
||
|
||
def postprocess(self, params, processed, *args):
|
||
enabled = args[0]
|
||
if enabled:
|
||
Flux.get_learned_conditioning = flux1tools.glc_backup_flux
|
||
memory_management.text_encoder_device = flux1tools.text_encoder_device_backup
|
||
if flux1tools.sigmasBackup is not None:
|
||
shared.sd_model.forge_objects.unet.model.predictor.sigmas = flux1tools.sigmasBackup
|
||
flux1tools.sigmasBackup = None
|
||
shared.sd_model.forge_objects.unet.extra_concat_condition = None
|
||
flux1tools.image_embeds = None
|
||
flux1tools.latent = None
|
||
flux1tools.unmasked_latent = None
|
||
remove_current_script_callbacks()
|
||
return
|