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import os |
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import threading |
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import traceback |
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from aiohttp import web |
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import impact |
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import folder_paths |
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import torchvision |
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import impact.core as core |
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import impact.impact_pack as impact_pack |
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from impact.utils import to_tensor |
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from segment_anything import SamPredictor, sam_model_registry |
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import numpy as np |
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import nodes |
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from PIL import Image |
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import io |
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import impact.wildcards as wildcards |
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import comfy |
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from io import BytesIO |
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import random |
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from server import PromptServer |
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@PromptServer.instance.routes.post("/upload/temp") |
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async def upload_image(request): |
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upload_dir = folder_paths.get_temp_directory() |
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if not os.path.exists(upload_dir): |
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os.makedirs(upload_dir) |
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post = await request.post() |
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image = post.get("image") |
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if image and image.file: |
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filename = image.filename |
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if not filename: |
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return web.Response(status=400) |
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split = os.path.splitext(filename) |
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i = 1 |
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while os.path.exists(os.path.join(upload_dir, filename)): |
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filename = f"{split[0]} ({i}){split[1]}" |
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i += 1 |
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filepath = os.path.join(upload_dir, filename) |
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with open(filepath, "wb") as f: |
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f.write(image.file.read()) |
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return web.json_response({"name": filename}) |
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else: |
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return web.Response(status=400) |
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sam_predictor = None |
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default_sam_model_name = os.path.join(impact_pack.model_path, "sams", "sam_vit_b_01ec64.pth") |
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sam_lock = threading.Condition() |
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last_prepare_data = None |
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def async_prepare_sam(image_dir, model_name, filename): |
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with sam_lock: |
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global sam_predictor |
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if 'vit_h' in model_name: |
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model_kind = 'vit_h' |
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elif 'vit_l' in model_name: |
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model_kind = 'vit_l' |
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else: |
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model_kind = 'vit_b' |
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sam_model = sam_model_registry[model_kind](checkpoint=model_name) |
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sam_predictor = SamPredictor(sam_model) |
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image_path = os.path.join(image_dir, filename) |
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image = nodes.LoadImage().load_image(image_path)[0] |
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image = np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8) |
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if impact.config.get_config()['sam_editor_cpu']: |
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device = 'cpu' |
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else: |
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device = comfy.model_management.get_torch_device() |
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sam_predictor.model.to(device=device) |
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sam_predictor.set_image(image, "RGB") |
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sam_predictor.model.cpu() |
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@PromptServer.instance.routes.post("/sam/prepare") |
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async def sam_prepare(request): |
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global sam_predictor |
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global last_prepare_data |
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data = await request.json() |
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with sam_lock: |
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if last_prepare_data is not None and last_prepare_data == data: |
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return web.Response(status=200) |
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last_prepare_data = data |
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model_name = 'sam_vit_b_01ec64.pth' |
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if data['sam_model_name'] == 'auto': |
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model_name = impact.config.get_config()['sam_editor_model'] |
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model_name = os.path.join(impact_pack.model_path, "sams", model_name) |
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print(f"[INFO] ComfyUI-Impact-Pack: Loading SAM model '{impact_pack.model_path}'") |
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filename, image_dir = folder_paths.annotated_filepath(data["filename"]) |
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if image_dir is None: |
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typ = data['type'] if data['type'] != '' else 'output' |
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image_dir = folder_paths.get_directory_by_type(typ) |
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if data['subfolder'] is not None and data['subfolder'] != '': |
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image_dir += f"/{data['subfolder']}" |
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if image_dir is None: |
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return web.Response(status=400) |
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thread = threading.Thread(target=async_prepare_sam, args=(image_dir, model_name, filename,)) |
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thread.start() |
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print(f"[INFO] ComfyUI-Impact-Pack: SAM model loaded. ") |
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return web.Response(status=200) |
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@PromptServer.instance.routes.post("/sam/release") |
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async def release_sam(request): |
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global sam_predictor |
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with sam_lock: |
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del sam_predictor |
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sam_predictor = None |
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print(f"[INFO] ComfyUI-Impact-Pack: unloading SAM model") |
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@PromptServer.instance.routes.post("/sam/detect") |
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async def sam_detect(request): |
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global sam_predictor |
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with sam_lock: |
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if sam_predictor is not None: |
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if impact.config.get_config()['sam_editor_cpu']: |
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device = 'cpu' |
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else: |
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device = comfy.model_management.get_torch_device() |
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sam_predictor.model.to(device=device) |
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try: |
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data = await request.json() |
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positive_points = data['positive_points'] |
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negative_points = data['negative_points'] |
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threshold = data['threshold'] |
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points = [] |
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plabs = [] |
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for p in positive_points: |
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points.append(p) |
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plabs.append(1) |
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for p in negative_points: |
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points.append(p) |
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plabs.append(0) |
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detected_masks = core.sam_predict(sam_predictor, points, plabs, None, threshold) |
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mask = core.combine_masks2(detected_masks) |
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if mask is None: |
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return web.Response(status=400) |
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image = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3) |
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i = 255. * image.cpu().numpy() |
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img = Image.fromarray(np.clip(i[0], 0, 255).astype(np.uint8)) |
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img_buffer = io.BytesIO() |
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img.save(img_buffer, format='png') |
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headers = {'Content-Type': 'image/png'} |
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finally: |
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sam_predictor.model.to(device="cpu") |
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return web.Response(body=img_buffer.getvalue(), headers=headers) |
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else: |
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return web.Response(status=400) |
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@PromptServer.instance.routes.get("/impact/wildcards/refresh") |
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async def wildcards_refresh(request): |
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impact.wildcards.wildcard_load() |
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return web.Response(status=200) |
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@PromptServer.instance.routes.get("/impact/wildcards/list") |
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async def wildcards_list(request): |
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data = {'data': impact.wildcards.get_wildcard_list()} |
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return web.json_response(data) |
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@PromptServer.instance.routes.post("/impact/wildcards") |
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async def populate_wildcards(request): |
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data = await request.json() |
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populated = wildcards.process(data['text'], data.get('seed', None)) |
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return web.json_response({"text": populated}) |
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segs_picker_map = {} |
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@PromptServer.instance.routes.get("/impact/segs/picker/count") |
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async def segs_picker_count(request): |
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node_id = request.rel_url.query.get('id', '') |
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if node_id in segs_picker_map: |
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res = len(segs_picker_map[node_id]) |
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return web.Response(status=200, text=str(res)) |
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return web.Response(status=400) |
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@PromptServer.instance.routes.get("/impact/segs/picker/view") |
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async def segs_picker(request): |
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node_id = request.rel_url.query.get('id', '') |
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idx = int(request.rel_url.query.get('idx', '')) |
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if node_id in segs_picker_map and idx < len(segs_picker_map[node_id]): |
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img = to_tensor(segs_picker_map[node_id][idx]).permute(0, 3, 1, 2).squeeze(0) |
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pil = torchvision.transforms.ToPILImage('RGB')(img) |
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image_bytes = BytesIO() |
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pil.save(image_bytes, format="PNG") |
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image_bytes.seek(0) |
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return web.Response(status=200, body=image_bytes, content_type='image/png', headers={"Content-Disposition": f"filename={node_id}{idx}.png"}) |
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return web.Response(status=400) |
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@PromptServer.instance.routes.get("/view/validate") |
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async def view_validate(request): |
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if "filename" in request.rel_url.query: |
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filename = request.rel_url.query["filename"] |
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subfolder = request.rel_url.query["subfolder"] |
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filename, base_dir = folder_paths.annotated_filepath(filename) |
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if filename == '' or filename[0] == '/' or '..' in filename: |
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return web.Response(status=400) |
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if base_dir is None: |
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base_dir = folder_paths.get_input_directory() |
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file = os.path.join(base_dir, subfolder, filename) |
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if os.path.isfile(file): |
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return web.Response(status=200) |
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return web.Response(status=400) |
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@PromptServer.instance.routes.get("/impact/validate/pb_id_image") |
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async def view_validate(request): |
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if "id" in request.rel_url.query: |
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pb_id = request.rel_url.query["id"] |
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if pb_id not in core.preview_bridge_image_id_map: |
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return web.Response(status=400) |
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file = core.preview_bridge_image_id_map[pb_id] |
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if os.path.isfile(file): |
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return web.Response(status=200) |
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return web.Response(status=400) |
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@PromptServer.instance.routes.get("/impact/set/pb_id_image") |
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async def set_previewbridge_image(request): |
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try: |
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if "filename" in request.rel_url.query: |
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node_id = request.rel_url.query["node_id"] |
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filename = request.rel_url.query["filename"] |
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path_type = request.rel_url.query["type"] |
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subfolder = request.rel_url.query["subfolder"] |
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filename, output_dir = folder_paths.annotated_filepath(filename) |
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if filename == '' or filename[0] == '/' or '..' in filename: |
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return web.Response(status=400) |
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if output_dir is None: |
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if path_type == 'input': |
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output_dir = folder_paths.get_input_directory() |
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elif path_type == 'output': |
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output_dir = folder_paths.get_output_directory() |
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else: |
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output_dir = folder_paths.get_temp_directory() |
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file = os.path.join(output_dir, subfolder, filename) |
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item = { |
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'filename': filename, |
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'type': path_type, |
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'subfolder': subfolder, |
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} |
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pb_id = core.set_previewbridge_image(node_id, file, item) |
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return web.Response(status=200, text=pb_id) |
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except Exception: |
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traceback.print_exc() |
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return web.Response(status=400) |
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@PromptServer.instance.routes.get("/impact/get/pb_id_image") |
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async def get_previewbridge_image(request): |
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if "id" in request.rel_url.query: |
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pb_id = request.rel_url.query["id"] |
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if pb_id in core.preview_bridge_image_id_map: |
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_, path_item = core.preview_bridge_image_id_map[pb_id] |
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return web.json_response(path_item) |
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return web.Response(status=400) |
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@PromptServer.instance.routes.get("/impact/view/pb_id_image") |
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async def view_previewbridge_image(request): |
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if "id" in request.rel_url.query: |
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pb_id = request.rel_url.query["id"] |
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if pb_id in core.preview_bridge_image_id_map: |
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file = core.preview_bridge_image_id_map[pb_id] |
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with Image.open(file) as img: |
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filename = os.path.basename(file) |
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return web.FileResponse(file, headers={"Content-Disposition": f"filename=\"{filename}\""}) |
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return web.Response(status=400) |
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def onprompt_for_switch(json_data): |
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inversed_switch_info = {} |
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onprompt_switch_info = {} |
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onprompt_cond_branch_info = {} |
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for k, v in json_data['prompt'].items(): |
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if 'class_type' not in v: |
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continue |
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cls = v['class_type'] |
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if cls == 'ImpactInversedSwitch': |
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select_input = v['inputs']['select'] |
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if isinstance(select_input, list) and len(select_input) == 2: |
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input_node = json_data['prompt'][select_input[0]] |
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if input_node['class_type'] == 'ImpactInt' and 'inputs' in input_node and 'value' in input_node['inputs']: |
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inversed_switch_info[k] = input_node['inputs']['value'] |
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else: |
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inversed_switch_info[k] = select_input |
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elif cls in ['ImpactSwitch', 'LatentSwitch', 'SEGSSwitch', 'ImpactMakeImageList']: |
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if 'sel_mode' in v['inputs'] and v['inputs']['sel_mode'] and 'select' in v['inputs']: |
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select_input = v['inputs']['select'] |
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if isinstance(select_input, list) and len(select_input) == 2: |
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input_node = json_data['prompt'][select_input[0]] |
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if input_node['class_type'] == 'ImpactInt' and 'inputs' in input_node and 'value' in input_node['inputs']: |
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onprompt_switch_info[k] = input_node['inputs']['value'] |
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if input_node['class_type'] == 'ImpactSwitch' and 'inputs' in input_node and 'select' in input_node['inputs']: |
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if isinstance(input_node['inputs']['select'], int): |
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onprompt_switch_info[k] = input_node['inputs']['select'] |
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else: |
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print(f"\n##### ##### #####\n[WARN] {cls}: For the 'select' operation, only 'select_index' of the 'ImpactSwitch', which is not an input, or 'ImpactInt' and 'Primitive' are allowed as inputs.\n##### ##### #####\n") |
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else: |
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onprompt_switch_info[k] = select_input |
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elif cls == 'ImpactConditionalBranchSelMode': |
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if 'sel_mode' in v['inputs'] and v['inputs']['sel_mode'] and 'cond' in v['inputs']: |
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cond_input = v['inputs']['cond'] |
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if isinstance(cond_input, list) and len(cond_input) == 2: |
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input_node = json_data['prompt'][cond_input[0]] |
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if (input_node['class_type'] == 'ImpactValueReceiver' and 'inputs' in input_node |
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and 'value' in input_node['inputs'] and 'typ' in input_node['inputs']): |
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if 'BOOLEAN' == input_node['inputs']['typ']: |
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try: |
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onprompt_cond_branch_info[k] = input_node['inputs']['value'].lower() == "true" |
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except: |
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pass |
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else: |
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onprompt_cond_branch_info[k] = cond_input |
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for k, v in json_data['prompt'].items(): |
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disable_targets = set() |
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for kk, vv in v['inputs'].items(): |
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if isinstance(vv, list) and len(vv) == 2: |
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if vv[0] in inversed_switch_info: |
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if vv[1] + 1 != inversed_switch_info[vv[0]]: |
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disable_targets.add(kk) |
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if k in onprompt_switch_info: |
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selected_slot_name = f"input{onprompt_switch_info[k]}" |
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for kk, vv in v['inputs'].items(): |
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if kk != selected_slot_name and kk.startswith('input'): |
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disable_targets.add(kk) |
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if k in onprompt_cond_branch_info: |
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selected_slot_name = "tt_value" if onprompt_cond_branch_info[k] else "ff_value" |
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for kk, vv in v['inputs'].items(): |
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if kk in ['tt_value', 'ff_value'] and kk != selected_slot_name: |
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disable_targets.add(kk) |
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for kk in disable_targets: |
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del v['inputs'][kk] |
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def onprompt_for_pickers(json_data): |
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detected_pickers = set() |
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for k, v in json_data['prompt'].items(): |
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if 'class_type' not in v: |
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continue |
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cls = v['class_type'] |
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if cls == 'ImpactSEGSPicker': |
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detected_pickers.add(k) |
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keys_to_remove = [key for key in segs_picker_map if key not in detected_pickers] |
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for key in keys_to_remove: |
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del segs_picker_map[key] |
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def gc_preview_bridge_cache(json_data): |
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prompt_keys = json_data['prompt'].keys() |
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for key in list(core.preview_bridge_cache.keys()): |
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if key not in prompt_keys: |
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print(f"key deleted: {key}") |
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del core.preview_bridge_cache[key] |
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def workflow_imagereceiver_update(json_data): |
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prompt = json_data['prompt'] |
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for v in prompt.values(): |
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if 'class_type' in v and v['class_type'] == 'ImageReceiver': |
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if v['inputs']['save_to_workflow']: |
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v['inputs']['image'] = "#DATA" |
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def regional_sampler_seed_update(json_data): |
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prompt = json_data['prompt'] |
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for k, v in prompt.items(): |
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if 'class_type' in v and v['class_type'] == 'RegionalSampler': |
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seed_2nd_mode = v['inputs']['seed_2nd_mode'] |
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new_seed = None |
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if seed_2nd_mode == 'increment': |
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new_seed = v['inputs']['seed_2nd']+1 |
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if new_seed > 1125899906842624: |
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new_seed = 0 |
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elif seed_2nd_mode == 'decrement': |
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new_seed = v['inputs']['seed_2nd']-1 |
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if new_seed < 0: |
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new_seed = 1125899906842624 |
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elif seed_2nd_mode == 'randomize': |
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new_seed = random.randint(0, 1125899906842624) |
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if new_seed is not None: |
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PromptServer.instance.send_sync("impact-node-feedback", {"node_id": k, "widget_name": "seed_2nd", "type": "INT", "value": new_seed}) |
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def onprompt_populate_wildcards(json_data): |
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prompt = json_data['prompt'] |
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updated_widget_values = {} |
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for k, v in prompt.items(): |
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if 'class_type' in v and (v['class_type'] == 'ImpactWildcardEncode' or v['class_type'] == 'ImpactWildcardProcessor'): |
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inputs = v['inputs'] |
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if inputs['mode'] and isinstance(inputs['populated_text'], str): |
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if isinstance(inputs['seed'], list): |
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try: |
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input_node = prompt[inputs['seed'][0]] |
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if input_node['class_type'] == 'ImpactInt': |
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input_seed = int(input_node['inputs']['value']) |
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if not isinstance(input_seed, int): |
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continue |
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if input_node['class_type'] == 'Seed (rgthree)': |
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input_seed = int(input_node['inputs']['seed']) |
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if not isinstance(input_seed, int): |
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continue |
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else: |
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print(f"[Impact Pack] Only `ImpactInt`, `Seed (rgthree)` and `Primitive` Node are allowed as the seed for '{v['class_type']}'. It will be ignored. ") |
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continue |
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except: |
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continue |
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else: |
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input_seed = int(inputs['seed']) |
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inputs['populated_text'] = wildcards.process(inputs['wildcard_text'], input_seed) |
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inputs['mode'] = False |
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PromptServer.instance.send_sync("impact-node-feedback", {"node_id": k, "widget_name": "populated_text", "type": "STRING", "value": inputs['populated_text']}) |
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updated_widget_values[k] = inputs['populated_text'] |
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|
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if 'extra_data' in json_data and 'extra_pnginfo' in json_data['extra_data']: |
|
for node in json_data['extra_data']['extra_pnginfo']['workflow']['nodes']: |
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key = str(node['id']) |
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if key in updated_widget_values: |
|
node['widgets_values'][1] = updated_widget_values[key] |
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node['widgets_values'][2] = False |
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|
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def onprompt_for_remote(json_data): |
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prompt = json_data['prompt'] |
|
|
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for v in prompt.values(): |
|
if 'class_type' in v: |
|
cls = v['class_type'] |
|
if cls == 'ImpactRemoteBoolean' or cls == 'ImpactRemoteInt': |
|
inputs = v['inputs'] |
|
node_id = str(inputs['node_id']) |
|
|
|
if node_id not in prompt: |
|
continue |
|
|
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target_inputs = prompt[node_id]['inputs'] |
|
|
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widget_name = inputs['widget_name'] |
|
if widget_name in target_inputs: |
|
widget_type = None |
|
if cls == 'ImpactRemoteBoolean' and isinstance(target_inputs[widget_name], bool): |
|
widget_type = 'BOOLEAN' |
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|
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elif cls == 'ImpactRemoteInt' and (isinstance(target_inputs[widget_name], int) or isinstance(target_inputs[widget_name], float)): |
|
widget_type = 'INT' |
|
|
|
if widget_type is None: |
|
break |
|
|
|
target_inputs[widget_name] = inputs['value'] |
|
PromptServer.instance.send_sync("impact-node-feedback", {"node_id": node_id, "widget_name": widget_name, "type": widget_type, "value": inputs['value']}) |
|
|
|
|
|
def onprompt(json_data): |
|
try: |
|
onprompt_for_remote(json_data) |
|
onprompt_for_switch(json_data) |
|
onprompt_for_pickers(json_data) |
|
onprompt_populate_wildcards(json_data) |
|
gc_preview_bridge_cache(json_data) |
|
workflow_imagereceiver_update(json_data) |
|
regional_sampler_seed_update(json_data) |
|
core.current_prompt = json_data |
|
except Exception as e: |
|
print(f"[WARN] ComfyUI-Impact-Pack: Error on prompt - several features will not work.\n{e}") |
|
|
|
return json_data |
|
|
|
|
|
PromptServer.instance.add_on_prompt_handler(onprompt) |
|
|
