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import random
import numpy as np
import gradio as gr
from PIL import Image, ImageChops
from typing import Dict, Any, List
from core.settings import INPUT_DIR
from utils.app_utils import (
sanitize_filename,
get_lora_path,
get_embedding_path,
ensure_controlnet_model_downloaded,
ensure_ipadapter_models_downloaded,
_ensure_model_downloaded,
ensure_sd3_ipadapter_models_downloaded,
get_vae_path,
)
def process_pipeline_inputs(ui_inputs: Dict[str, Any], progress: gr.Progress, workflow_model_type: str) -> Dict[str, Any]:
task_type = ui_inputs['task_type']
temp_files_to_clean = []
lora_data = ui_inputs.get('lora_data', [])
active_loras_for_gpu, active_loras_for_meta = [], []
if lora_data:
sources, ids, scales, files = lora_data[0::4], lora_data[1::4], lora_data[2::4], lora_data[3::4]
for i, (source, lora_id, scale, _) in enumerate(zip(sources, ids, scales, files)):
if scale > 0 and lora_id and lora_id.strip():
lora_filename = None
if source == "File":
lora_filename = sanitize_filename(lora_id)
elif source == "Civitai":
local_path, status = get_lora_path(source, lora_id, os.environ.get("CIVITAI_API_KEY", ""), progress)
if local_path: lora_filename = os.path.basename(local_path)
else: raise gr.Error(f"Failed to prepare LoRA {lora_id}: {status}")
if lora_filename:
active_loras_for_gpu.append({"lora_name": lora_filename, "strength_model": scale, "strength_clip": scale})
active_loras_for_meta.append(f"{source} {lora_id}:{scale}")
ui_inputs['denoise'] = 1.0
if task_type == 'img2img': ui_inputs['denoise'] = ui_inputs.get('img2img_denoise', 0.7)
elif task_type == 'hires_fix': ui_inputs['denoise'] = ui_inputs.get('hires_denoise', 0.55)
elif task_type == 'inpaint': ui_inputs['denoise'] = ui_inputs.get('inpaint_denoise', 1.0)
if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR)
if task_type == 'img2img':
input_image_pil = ui_inputs.get('img2img_image')
if not input_image_pil:
raise gr.Error("Please upload an image for Image-to-Image.")
temp_file_path = os.path.join(INPUT_DIR, f"temp_input_{random.randint(1000, 9999)}.png")
input_image_pil.save(temp_file_path, "PNG")
ui_inputs['input_image'] = os.path.basename(temp_file_path)
temp_files_to_clean.append(temp_file_path)
ui_inputs['width'] = input_image_pil.width
ui_inputs['height'] = input_image_pil.height
elif task_type == 'inpaint':
inpaint_dict = ui_inputs.get('inpaint_image_dict')
if not inpaint_dict or not inpaint_dict.get('background') or not inpaint_dict.get('layers'):
raise gr.Error("Inpainting requires an input image and a drawn mask.")
background_img = inpaint_dict['background'].convert("RGBA")
composite_mask_pil = Image.new('L', background_img.size, 0)
for layer in inpaint_dict['layers']:
if layer:
layer_alpha = layer.split()[-1]
composite_mask_pil = ImageChops.lighter(composite_mask_pil, layer_alpha)
inverted_mask_alpha = Image.fromarray(255 - np.array(composite_mask_pil), mode='L')
r, g, b, _ = background_img.split()
composite_image_with_mask = Image.merge('RGBA', [r, g, b, inverted_mask_alpha])
temp_file_path = os.path.join(INPUT_DIR, f"temp_inpaint_composite_{random.randint(1000, 9999)}.png")
composite_image_with_mask.save(temp_file_path, "PNG")
ui_inputs['input_image'] = os.path.basename(temp_file_path)
temp_files_to_clean.append(temp_file_path)
ui_inputs.pop('inpaint_mask', None)
elif task_type == 'outpaint':
input_image_pil = ui_inputs.get('outpaint_image')
if not input_image_pil:
raise gr.Error("Please upload an image for Outpainting.")
temp_file_path = os.path.join(INPUT_DIR, f"temp_input_{random.randint(1000, 9999)}.png")
input_image_pil.save(temp_file_path, "PNG")
ui_inputs['input_image'] = os.path.basename(temp_file_path)
temp_files_to_clean.append(temp_file_path)
ui_inputs['megapixels'] = 0.25
ui_inputs['grow_mask_by'] = ui_inputs.get('feathering', 10)
elif task_type == 'hires_fix':
input_image_pil = ui_inputs.get('hires_image')
if not input_image_pil:
raise gr.Error("Please upload an image for Hires Fix.")
temp_file_path = os.path.join(INPUT_DIR, f"temp_input_{random.randint(1000, 9999)}.png")
input_image_pil.save(temp_file_path, "PNG")
ui_inputs['input_image'] = os.path.basename(temp_file_path)
temp_files_to_clean.append(temp_file_path)
embedding_data = ui_inputs.get('embedding_data', [])
embedding_filenames = []
if embedding_data:
emb_sources, emb_ids, emb_files = embedding_data[0::3], embedding_data[1::3], embedding_data[2::3]
for i, (source, emb_id, _) in enumerate(zip(emb_sources, emb_ids, emb_files)):
if emb_id and emb_id.strip():
emb_filename = None
if source == "File":
emb_filename = sanitize_filename(emb_id)
elif source == "Civitai":
local_path, status = get_embedding_path(source, emb_id, os.environ.get("CIVITAI_API_KEY", ""), progress)
if local_path: emb_filename = os.path.basename(local_path)
else: raise gr.Error(f"Failed to prepare Embedding {emb_id}: {status}")
if emb_filename:
embedding_filenames.append(emb_filename)
if embedding_filenames:
embedding_prompt_text = " ".join([f"embedding:{f}" for f in embedding_filenames])
if ui_inputs['positive_prompt']:
ui_inputs['positive_prompt'] = f"{ui_inputs['positive_prompt']}, {embedding_prompt_text}"
else:
ui_inputs['positive_prompt'] = embedding_prompt_text
controlnet_data = ui_inputs.get('controlnet_data', [])
active_controlnets = []
if controlnet_data:
(cn_images, _, _, cn_strengths, cn_filepaths) = [controlnet_data[i::5] for i in range(5)]
for i in range(len(cn_images)):
if cn_images[i] and cn_strengths[i] > 0 and cn_filepaths[i] and cn_filepaths[i] != "None":
ensure_controlnet_model_downloaded(cn_filepaths[i], progress)
if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR)
cn_temp_path = os.path.join(INPUT_DIR, f"temp_cn_{i}_{random.randint(1000, 9999)}.png")
cn_images[i].save(cn_temp_path, "PNG")
temp_files_to_clean.append(cn_temp_path)
active_controlnets.append({
"image": os.path.basename(cn_temp_path), "strength": cn_strengths[i],
"start_percent": 0.0, "end_percent": 1.0, "control_net_name": cn_filepaths[i]
})
anima_controlnet_lllite_data = ui_inputs.get('anima_controlnet_lllite_data', [])
active_anima_controlnets = []
if anima_controlnet_lllite_data:
(cn_images, _, _, cn_strengths, cn_filepaths, cn_starts, cn_ends) = [anima_controlnet_lllite_data[i::7] for i in range(7)]
for i in range(len(cn_images)):
if cn_images[i] and cn_strengths[i] > 0 and cn_filepaths[i] and cn_filepaths[i] != "None":
_ensure_model_downloaded(cn_filepaths[i], progress)
if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR)
cn_temp_path = os.path.join(INPUT_DIR, f"temp_anima_cn_{i}_{random.randint(1000, 9999)}.png")
cn_images[i].save(cn_temp_path, "PNG")
temp_files_to_clean.append(cn_temp_path)
active_anima_controlnets.append({
"image": os.path.basename(cn_temp_path), "strength": cn_strengths[i],
"start_percent": cn_starts[i], "end_percent": cn_ends[i], "control_net_name": cn_filepaths[i]
})
diffsynth_controlnet_data = ui_inputs.get('diffsynth_controlnet_data', [])
active_diffsynth_controlnets = []
if diffsynth_controlnet_data:
(cn_images, _, _, cn_strengths, cn_filepaths) = [diffsynth_controlnet_data[i::5] for i in range(5)]
for i in range(len(cn_images)):
if cn_images[i] and cn_strengths[i] > 0 and cn_filepaths[i] and cn_filepaths[i] != "None":
ensure_controlnet_model_downloaded(cn_filepaths[i], progress)
if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR)
cn_temp_path = os.path.join(INPUT_DIR, f"temp_diffsynth_cn_{i}_{random.randint(1000, 9999)}.png")
cn_images[i].save(cn_temp_path, "PNG")
temp_files_to_clean.append(cn_temp_path)
active_diffsynth_controlnets.append({
"image": os.path.basename(cn_temp_path), "strength": cn_strengths[i],
"control_net_name": cn_filepaths[i]
})
ipadapter_data = ui_inputs.get('ipadapter_data', [])
active_ipadapters = []
if ipadapter_data:
num_ipa_units = (len(ipadapter_data) - 5) // 3
final_preset, final_weight, final_lora_strength, final_embeds_scaling, final_combine_method = ipadapter_data[-5:]
ipa_images, ipa_weights, ipa_lora_strengths = [ipadapter_data[i*num_ipa_units:(i+1)*num_ipa_units] for i in range(3)]
all_presets_to_download = set()
for i in range(num_ipa_units):
if ipa_images[i] and ipa_weights[i] > 0 and final_preset:
all_presets_to_download.add(final_preset)
if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR)
ipa_temp_path = os.path.join(INPUT_DIR, f"temp_ipa_{i}_{random.randint(1000, 9999)}.png")
ipa_images[i].save(ipa_temp_path, "PNG")
temp_files_to_clean.append(ipa_temp_path)
active_ipadapters.append({
"image": os.path.basename(ipa_temp_path), "preset": final_preset,
"weight": ipa_weights[i], "lora_strength": ipa_lora_strengths[i]
})
if active_ipadapters and final_preset:
all_presets_to_download.add(final_preset)
for preset in all_presets_to_download:
ensure_ipadapter_models_downloaded(preset, progress)
model_type_key = 'sd15' if workflow_model_type == 'sd15' else 'sdxl'
if active_ipadapters:
active_ipadapters.append({
'is_final_settings': True, 'model_type': model_type_key, 'final_preset': final_preset,
'final_weight': final_weight, 'final_lora_strength': final_lora_strength,
'final_embeds_scaling': final_embeds_scaling, 'final_combine_method': final_combine_method
})
flux1_ipadapter_data = ui_inputs.get('flux1_ipadapter_data', [])
active_flux1_ipadapters = []
if flux1_ipadapter_data:
num_units = len(flux1_ipadapter_data) // 4
f_images = flux1_ipadapter_data[0*num_units : 1*num_units]
f_weights = flux1_ipadapter_data[1*num_units : 2*num_units]
f_starts = flux1_ipadapter_data[2*num_units : 3*num_units]
f_ends = flux1_ipadapter_data[3*num_units : 4*num_units]
for i in range(len(f_images)):
if f_images[i] and f_weights[i] > 0:
for filename in ["ip-adapter.bin"]:
_ensure_model_downloaded(filename, progress)
from huggingface_hub import snapshot_download
progress(0.5, desc="Caching HF SigLIP model...")
snapshot_download(
repo_id="google/siglip-so400m-patch14-384",
allow_patterns=["*.json", "*.safetensors", "*.txt"],
ignore_patterns=["*.msgpack", "*.h5", "*.bin"]
)
temp_path = os.path.join(INPUT_DIR, f"temp_fipa_{i}_{random.randint(1000, 9999)}.png")
f_images[i].save(temp_path, "PNG")
temp_files_to_clean.append(temp_path)
active_flux1_ipadapters.append({
"image": os.path.basename(temp_path),
"weight": f_weights[i], "start_percent": f_starts[i], "end_percent": f_ends[i]
})
sd3_ipadapter_data = ui_inputs.get('sd3_ipadapter_chain', [])
active_sd3_ipadapters = []
if sd3_ipadapter_data:
num_units = len(sd3_ipadapter_data) // 4
s_images = sd3_ipadapter_data[0*num_units : 1*num_units]
s_weights = sd3_ipadapter_data[1*num_units : 2*num_units]
s_starts = sd3_ipadapter_data[2*num_units : 3*num_units]
s_ends = sd3_ipadapter_data[3*num_units : 4*num_units]
sd3_ipa_downloaded = False
for i in range(len(s_images)):
if s_images[i] and s_weights[i] > 0:
if not sd3_ipa_downloaded:
ensure_sd3_ipadapter_models_downloaded(progress)
sd3_ipa_downloaded = True
temp_path = os.path.join(INPUT_DIR, f"temp_s3ipa_{i}_{random.randint(1000, 9999)}.png")
s_images[i].save(temp_path, "PNG")
temp_files_to_clean.append(temp_path)
active_sd3_ipadapters.append({
"image": os.path.basename(temp_path),
"weight": s_weights[i], "start_percent": s_starts[i], "end_percent": s_ends[i]
})
style_data = ui_inputs.get('style_data', [])
active_styles = []
if style_data:
num_units = len(style_data) // 2
st_images = style_data[0*num_units : 1*num_units]
st_strengths = style_data[1*num_units : 2*num_units]
for i in range(len(st_images)):
if st_images[i] and st_strengths[i] > 0:
_ensure_model_downloaded("sigclip_vision_patch14_384.safetensors", progress)
temp_path = os.path.join(INPUT_DIR, f"temp_style_{i}_{random.randint(1000, 9999)}.png")
st_images[i].save(temp_path, "PNG")
temp_files_to_clean.append(temp_path)
active_styles.append({
"image": os.path.basename(temp_path), "strength": st_strengths[i]
})
reference_latent_data = ui_inputs.get('reference_latent_data', [])
active_reference_latents = []
if reference_latent_data:
for img in reference_latent_data:
if img:
if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR)
temp_path = os.path.join(INPUT_DIR, f"temp_ref_{random.randint(1000, 9999)}.png")
img.save(temp_path, "PNG")
temp_files_to_clean.append(temp_path)
active_reference_latents.append(os.path.basename(temp_path))
hidream_o1_reference_data = ui_inputs.get('hidream_o1_reference_data', [])
active_hidream_o1_reference = []
if hidream_o1_reference_data:
for img in hidream_o1_reference_data:
if img:
if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR)
temp_path = os.path.join(INPUT_DIR, f"temp_ho1_ref_{random.randint(1000, 9999)}.png")
img.save(temp_path, "PNG")
temp_files_to_clean.append(temp_path)
active_hidream_o1_reference.append(os.path.basename(temp_path))
vae_source = ui_inputs.get('vae_source')
vae_id = ui_inputs.get('vae_id')
vae_name_override = None
if vae_source and vae_source != "None":
if vae_source == "File":
vae_name_override = sanitize_filename(vae_id)
elif vae_source == "Civitai" and vae_id and vae_id.strip():
local_path, status = get_vae_path(vae_source, vae_id, os.environ.get("CIVITAI_API_KEY", ""), progress)
if local_path: vae_name_override = os.path.basename(local_path)
else: raise gr.Error(f"Failed to prepare VAE {vae_id}: {status}")
if vae_name_override:
ui_inputs['vae_name'] = vae_name_override
conditioning_data = ui_inputs.get('conditioning_data', [])
active_conditioning = []
if conditioning_data:
num_units = len(conditioning_data) // 6
prompts, widths, heights, xs, ys, strengths = [conditioning_data[i*num_units : (i+1)*num_units] for i in range(6)]
for i in range(num_units):
if prompts[i] and prompts[i].strip():
active_conditioning.append({
"prompt": prompts[i], "width": int(widths[i]), "height": int(heights[i]),
"x": int(xs[i]), "y": int(ys[i]), "strength": float(strengths[i])
})
return {
"active_loras_for_gpu": active_loras_for_gpu,
"active_loras_for_meta": active_loras_for_meta,
"active_controlnets": active_controlnets,
"active_anima_controlnets": active_anima_controlnets,
"active_diffsynth_controlnets": active_diffsynth_controlnets,
"active_ipadapters": active_ipadapters,
"active_flux1_ipadapters": active_flux1_ipadapters,
"active_sd3_ipadapters": active_sd3_ipadapters,
"active_styles": active_styles,
"active_reference_latents": active_reference_latents,
"active_hidream_o1_reference": active_hidream_o1_reference,
"active_conditioning": active_conditioning,
"temp_files_to_clean": temp_files_to_clean
} |