diff --git a/__init__.py b/__init__.py index 0439ac0..4440b9f 100644 --- a/__init__.py +++ b/__init__.py @@ -45,19 +45,15 @@ def extract_nodes_from_source(filename): if isinstance(target, ast.Name) and target.id == "__nodes__": value = ast.get_source_segment(source_code, node.value) node_value = ast.parse(value).body[0].value - if isinstance(node_value, ast.List) or isinstance( - node_value, ast.Tuple - ): - for element in node_value.elts: - if isinstance(element, ast.Name): - print(element.id) - nodes.append(element.id) - + if isinstance(node_value, (ast.List, ast.Tuple)): + nodes.extend( + element.id + for element in node_value.elts + if isinstance(element, ast.Name) + ) break except SyntaxError: log.error("Failed to parse") - pass # File couldn't be parsed - return nodes @@ -187,6 +183,14 @@ def load_nodes(): from .endpoint import endlog if hasattr(PromptServer, "instance"): + restore_deps = ["basicsr"] + swap_deps = ["insightface", "onnxruntime"] + + node_dependency_mapping = { + "FaceSwap": swap_deps, + "LoadFaceSwapModel": swap_deps, + "LoadFaceAnalysisModel": restore_deps, + } @PromptServer.instance.routes.get("/mtb/status") async def get_full_library(request): @@ -202,7 +206,13 @@ async def get_full_library(request): NODE_CLASS_MAPPINGS_DEBUG, title="Registered" ) html_response += endpoint.render_table( - {k: "-" for k in failed}, title="Failed to load" + { + k: {"dependencies": node_dependency_mapping.get(k)} + if node_dependency_mapping.get(k) + else "-" + for k in failed + }, + title="Failed to load", ) return web.Response( @@ -226,11 +236,10 @@ async def set_debug(request): log.setLevel(logging.DEBUG) log.debug("Debug mode set from API (/mtb/debug POST route)") - else: - if "MTB_DEBUG" in os.environ: - # del os.environ["MTB_DEBUG"] - os.environ.pop("MTB_DEBUG") - log.setLevel(logging.INFO) + elif "MTB_DEBUG" in os.environ: + # del os.environ["MTB_DEBUG"] + os.environ.pop("MTB_DEBUG") + log.setLevel(logging.INFO) return web.json_response( {"message": f"Debug mode {'set' if enabled else 'unset'}"} @@ -244,7 +253,7 @@ async def get_home(request): # Check if the request prefers HTML content if "text/html" in request.headers.get("Accept", ""): # # Return an HTML page - html_response = f""" + html_response = """
debug status @@ -263,9 +272,7 @@ async def get_debug(request): from . import endpoint reload(endpoint) - enabled = False - if "MTB_DEBUG" in os.environ: - enabled = True + enabled = "MTB_DEBUG" in os.environ # Check if the request prefers HTML content if "text/html" in request.headers.get("Accept", ""): # # Return an HTML page @@ -285,7 +292,7 @@ async def no_route(request): from . import endpoint if "text/html" in request.headers.get("Accept", ""): - html_response = f""" + html_response = """

Actions has no get for now...

""" return web.Response( diff --git a/endpoint.py b/endpoint.py index d2030e0..9d137f7 100644 --- a/endpoint.py +++ b/endpoint.py @@ -1,11 +1,35 @@ -from .utils import here +from .utils import here, run_command, comfy_mode from aiohttp import web from .log import mklog -import os +import sys endlog = mklog("mtb endpoint") -#- ACTIONS +# - ACTIONS +import requirements + + + +def ACTIONS_installDependency(dependency_names=None): + if dependency_names is None: + return {"error": "No dependency name provided"} + endlog.debug(f"Received Install Dependency request for {dependency_names}") + reqs = [] + if comfy_mode == "embeded": + reqs = list(requirements.parse((here / "reqs_portable.txt").read_text())) + else: + reqs = list(requirements.parse((here / "reqs.txt").read_text())) + print([x.specs for x in reqs]) + print( + "\n".join([f"{x.line} {''.join(x.specs[0] if x.specs else '')}" for x in reqs]) + ) + for dependency_name in dependency_names: + for req in reqs: + if req.name == dependency_name: + endlog.debug(f"Dependency {dependency_name} installed") + break + return {"success": True} + def ACTIONS_getStyles(style_name=None): from .nodes.conditions import StylesLoader @@ -19,10 +43,7 @@ def ACTIONS_getStyles(style_name=None): if not key.startswith("__") and key not in match_list } if style_name: - if style_name in filtered_styles: - return filtered_styles[style_name] - else: - return {"error": "Style not found"} + return filtered_styles.get(style_name, {"error": "Style not found"}) return filtered_styles return {"error": "No styles found"} @@ -35,7 +56,7 @@ async def do_action(request) -> web.Response: endlog.debug(f"Received action request: {name} {args}") - method_name = "ACTIONS_" + name + method_name = f"ACTIONS_{name}" method = globals().get(method_name) if callable(method): @@ -53,16 +74,36 @@ async def do_action(request) -> web.Response: # - HTML UTILS + + +def dependencies_button(name, dependencies): + deps = ",".join([f"'{x}'" for x in dependencies]) + return f""" + + """ + + def render_table(table_dict, sort=True, title=None): - table_rows = "" table_dict = sorted( table_dict.items(), key=lambda item: item[0] ) # Sort the dictionary by keys - for name, description in table_dict: - table_rows += f"{name}{description}" + table_rows = "" + for name, item in table_dict: + if isinstance(item, dict): + if "dependencies" in item: + table_rows += f"{name}" + table_rows += f"{dependencies_button(name,item['dependencies'])}" + + table_rows += "" + else: + table_rows += f"{name}{render_table(item)}" + # elif isinstance(item, str): + # table_rows += f"{name}{item}" + else: + table_rows += f"{name}{item}" - html_response = f""" + return f"""
{"" if title is None else f"

{title}

"} @@ -78,7 +119,6 @@ def render_table(table_dict, sort=True, title=None):
""" - return html_response def render_base_template(title, content): @@ -98,6 +138,29 @@ def render_base_template(title, content): {css_content} +
Back to Comfy @@ -117,5 +180,6 @@ def render_base_template(title, content): + """ diff --git a/examples/01-faceswap.json b/examples/01-faceswap.json index d3690c0..2e52c14 100644 --- a/examples/01-faceswap.json +++ b/examples/01-faceswap.json @@ -265,7 +265,7 @@ "Node name for S&R": "CLIPTextEncode" }, "widgets_values": [ - "Closeup portrait of an old bearded Caucasian man smiling, (NYC 1995), trench coat, golden ring, brown eyes" + "Medium cinematic shot of an old Caucasian man smiling, (NYC 1995), trench coat, golden ring, brown eyes, (with a blue light saber)" ] }, { @@ -424,65 +424,6 @@ "embedding:EasyNegative, embedding:EasyNegativeV2, watermark, text, deformed, disfigured, blurry" ] }, - { - "id": 3, - "type": "KSampler", - "pos": [ - -483, - -21 - ], - "size": [ - 315, - 474 - ], - "flags": {}, - "order": 14, - "mode": 0, - "inputs": [ - { - "name": "model", - "type": "MODEL", - "link": 120 - }, - { - "name": "positive", - "type": "CONDITIONING", - "link": 4 - }, - { - "name": "negative", - "type": "CONDITIONING", - "link": 6 - }, - { - "name": "latent_image", - "type": "LATENT", - "link": 2 - } - ], - "outputs": [ - { - "name": "LATENT", - "type": "LATENT", - "links": [ - 138 - ], - "slot_index": 0 - } - ], - "properties": { - "Node name for S&R": "KSampler" - }, - "widgets_values": [ - 542821171533322, - "fixed", - 45, - 8, - "dpmpp_sde", - "simple", - 1 - ] - }, { "id": 66, "type": "VAEDecodeTiled", @@ -584,7 +525,7 @@ 52 ], "size": [ - 260.3902351585391, + 260.3902282714844, 58 ], "flags": {}, @@ -615,8 +556,8 @@ 40 ], "size": [ - 265.97600515853924, - 87.31192548828142 + 265.97601318359375, + 87.31192779541016 ], "flags": {}, "order": 11, @@ -795,8 +736,66 @@ "Node name for S&R": "Face Swap (mtb)" }, "widgets_values": [ - "0", - false + "0" + ] + }, + { + "id": 3, + "type": "KSampler", + "pos": [ + -483, + -21 + ], + "size": [ + 315, + 474 + ], + "flags": {}, + "order": 14, + "mode": 0, + "inputs": [ + { + "name": "model", + "type": "MODEL", + "link": 120 + }, + { + "name": "positive", + "type": "CONDITIONING", + "link": 4 + }, + { + "name": "negative", + "type": "CONDITIONING", + "link": 6 + }, + { + "name": "latent_image", + "type": "LATENT", + "link": 2 + } + ], + "outputs": [ + { + "name": "LATENT", + "type": "LATENT", + "links": [ + 138 + ], + "slot_index": 0 + } + ], + "properties": { + "Node name for S&R": "KSampler" + }, + "widgets_values": [ + 542071534529, + "fixed", + 32, + 9, + "dpmpp_2m", + "normal", + 1 ] } ], diff --git a/examples/02-film_interpolation.json b/examples/02-film_interpolation.json index eb8ef7e..d031a25 100644 --- a/examples/02-film_interpolation.json +++ b/examples/02-film_interpolation.json @@ -1,6 +1,6 @@ { "last_node_id": 86, - "last_link_id": 171, + "last_link_id": 172, "nodes": [ { "id": 59, @@ -111,40 +111,6 @@ "horizontal": false } }, - { - "id": 5, - "type": "EmptyLatentImage", - "pos": [ - -1410, - 660 - ], - "size": [ - 315, - 106 - ], - "flags": {}, - "order": 0, - "mode": 0, - "outputs": [ - { - "name": "LATENT", - "type": "LATENT", - "links": [ - 2, - 153 - ], - "slot_index": 0 - } - ], - "properties": { - "Node name for S&R": "EmptyLatentImage" - }, - "widgets_values": [ - 768, - 512, - 1 - ] - }, { "id": 3, "type": "KSampler", @@ -211,7 +177,7 @@ "Node name for S&R": "KSampler" }, "widgets_values": [ - 45, + 1682, "fixed", 45, 8, @@ -232,7 +198,7 @@ 58 ], "flags": {}, - "order": 1, + "order": 0, "mode": 0, "outputs": [ { @@ -263,7 +229,7 @@ 246 ], "flags": {}, - "order": 23, + "order": 22, "mode": 0, "inputs": [ { @@ -289,7 +255,7 @@ 75.28300476074219 ], "flags": {}, - "order": 18, + "order": 16, "mode": 0, "inputs": [ { @@ -301,7 +267,7 @@ { "name": "text", "type": "STRING", - "link": 171, + "link": 172, "widget": { "name": "text", "config": [ @@ -437,7 +403,7 @@ "flags": { "collapsed": false }, - "order": 20, + "order": 19, "mode": 0, "inputs": [ { @@ -480,7 +446,7 @@ 474 ], "flags": {}, - "order": 19, + "order": 18, "mode": 0, "inputs": [ { @@ -536,7 +502,7 @@ "Node name for S&R": "KSampler" }, "widgets_values": [ - 45, + 1682, "fixed", 45, 8, @@ -557,7 +523,7 @@ 98 ], "flags": {}, - "order": 2, + "order": 1, "mode": 0, "outputs": [ { @@ -648,7 +614,7 @@ 46 ], "flags": {}, - "order": 21, + "order": 20, "mode": 0, "inputs": [ { @@ -757,10 +723,10 @@ ], "size": [ 294, - 104.4554216999652 + 104.4554214477539 ], "flags": {}, - "order": 3, + "order": 2, "mode": 0, "outputs": [ { @@ -847,7 +813,7 @@ 78 ], "flags": {}, - "order": 22, + "order": 21, "mode": 0, "inputs": [ { @@ -882,34 +848,36 @@ ] }, { - "id": 85, - "type": "Save Gif (mtb)", + "id": 83, + "type": "Text box", "pos": [ - 1546, - 401 + -2456, + 236 ], "size": [ - 210, - 336 + 400, + 200 ], "flags": {}, - "order": 24, + "order": 3, "mode": 0, - "inputs": [ + "outputs": [ { - "name": "image", - "type": "IMAGE", - "link": 169 + "name": "STRING", + "type": "STRING", + "links": [ + 166, + 167 + ], + "shape": 3, + "slot_index": 0 } ], "properties": { - "Node name for S&R": "Save Gif (mtb)" + "Node name for S&R": "Text box" }, "widgets_values": [ - 12, - 0.7, - true, - "/view?filename=eda71478da.gif&subfolder=&type=output" + "Close up photo of the face of a Caucasian young man (looking down, and frowning), rim lighting, Tokyo 1987, Bernard, over a blue sky, blue eyes shaved close" ] }, { @@ -951,121 +919,115 @@ "title": "seed", "properties": {}, "widgets_values": [ - 45, + 1682, "fixed" ] }, { - "id": 83, - "type": "Text box", + "id": 69, + "type": "String Replace (mtb)", "pos": [ - -2456, - 236 + -1529, + -3 ], "size": [ - 400, - 200 + 315, + 82 ], "flags": {}, - "order": 5, + "order": 14, "mode": 0, + "inputs": [ + { + "name": "string", + "type": "STRING", + "link": 165 + } + ], "outputs": [ { "name": "STRING", "type": "STRING", "links": [ - 166, - 167 + 172 ], "shape": 3, "slot_index": 0 } ], "properties": { - "Node name for S&R": "Text box" + "Node name for S&R": "String Replace (mtb)" }, "widgets_values": [ - "Close up photo of the face of a man (looking down, and frowning), rim lighting, Tokyo 1987, Bernard, over a blue sky, blue eyes and a long beard" + "a Caucasian young", + "an African old" ] }, { - "id": 69, - "type": "String Replace (mtb)", + "id": 5, + "type": "EmptyLatentImage", "pos": [ - -1442, - -1 + -1410, + 660 ], "size": [ 315, - 82 + 106 ], "flags": {}, - "order": 14, + "order": 5, "mode": 0, - "inputs": [ - { - "name": "string", - "type": "STRING", - "link": 165 - } - ], "outputs": [ { - "name": "STRING", - "type": "STRING", + "name": "LATENT", + "type": "LATENT", "links": [ - 170 + 2, + 153 ], - "shape": 3, "slot_index": 0 } ], "properties": { - "Node name for S&R": "String Replace (mtb)" + "Node name for S&R": "EmptyLatentImage" }, "widgets_values": [ - "looking down", - "looking up" + 768, + 320, + 1 ] }, { - "id": 86, - "type": "String Replace (mtb)", + "id": 85, + "type": "Save Gif (mtb)", "pos": [ - -1082, - 1 + 1519, + 364 ], "size": [ - 315, - 82 + 862.6054045703117, + 496.63413712402314 ], "flags": {}, - "order": 16, + "order": 23, "mode": 0, "inputs": [ { - "name": "string", - "type": "STRING", - "link": 170 - } - ], - "outputs": [ - { - "name": "STRING", - "type": "STRING", - "links": [ - 171 - ], - "shape": 3, - "slot_index": 0 + "name": "image", + "type": "IMAGE", + "link": 169 } ], "properties": { - "Node name for S&R": "String Replace (mtb)" + "Node name for S&R": "Save Gif (mtb)" }, "widgets_values": [ - "frowning", - "smiling" + 12, + 0.7, + true, + "/view?filename=eda71478da.gif&subfolder=&type=output", + "nearest", + "/view?filename=421883da47.gif&subfolder=&type=output" ] } ], @@ -1327,17 +1289,9 @@ "IMAGE" ], [ - 170, + 172, 69, 0, - 86, - 0, - "STRING" - ], - [ - 171, - 86, - 0, 71, 1, "STRING" diff --git a/examples/03-animation_builder-condition-lerp.json b/examples/03-animation_builder-condition-lerp.json index 7262e17..f6556bb 100644 --- a/examples/03-animation_builder-condition-lerp.json +++ b/examples/03-animation_builder-condition-lerp.json @@ -1,1248 +1 @@ -{ - "last_node_id": 82, - "last_link_id": 171, - "nodes": [ - { - "id": 59, - "type": "Reroute", - "pos": [ - -150.35178124999982, - 644.4360633544919 - ], - "size": [ - 75, - 26 - ], - "flags": {}, - "order": 13, - "mode": 0, - "inputs": [ - { - "name": "", - "type": "*", - "link": 124 - } - ], - "outputs": [ - { - "name": "", - "type": "VAE", - "links": [ - 139 - ], - "slot_index": 0 - } - ], - "properties": { - "showOutputText": false, - "horizontal": false - } - }, - { - "id": 56, - "type": "Reroute", - "pos": [ - -1580.8297949218763, - 644.7740239257807 - ], - "size": [ - 75, - 26 - ], - "flags": {}, - "order": 7, - "mode": 0, - "inputs": [ - { - "name": "", - "type": "*", - "link": 117 - } - ], - "outputs": [ - { - "name": "", - "type": "VAE", - "links": [ - 124 - ] - } - ], - "properties": { - "showOutputText": false, - "horizontal": false - } - }, - { - "id": 57, - "type": "Reroute", - "pos": [ - -673.8297949218747, - -185.22597607421872 - ], - "size": [ - 75, - 26 - ], - "flags": {}, - "order": 10, - "mode": 0, - "inputs": [ - { - "name": "", - "type": "*", - "link": 135 - } - ], - "outputs": [ - { - "name": "", - "type": "MODEL", - "links": [ - 120 - ], - "slot_index": 0 - } - ], - "properties": { - "showOutputText": false, - "horizontal": false - } - }, - { - "id": 65, - "type": "Reroute", - "pos": [ - -1512.8297949218763, - -181.22597607421872 - ], - "size": [ - 75, - 26 - ], - "flags": {}, - "order": 5, - "mode": 0, - "inputs": [ - { - "name": "", - "type": "*", - "link": 134 - } - ], - "outputs": [ - { - "name": "", - "type": "MODEL", - "links": [ - 135 - ] - } - ], - "properties": { - "showOutputText": false, - "horizontal": false - } - }, - { - "id": 16, - "type": "CheckpointLoaderSimple", - "pos": [ - -2000.8297949218756, - 192.77402392578128 - ], - "size": [ - 315, - 98 - ], - "flags": {}, - "order": 0, - "mode": 0, - "outputs": [ - { - "name": "MODEL", - "type": "MODEL", - "links": [ - 134 - ], - "slot_index": 0 - }, - { - "name": "CLIP", - "type": "CLIP", - "links": [ - 116 - ], - "slot_index": 1 - }, - { - "name": "VAE", - "type": "VAE", - "links": [ - 117 - ], - "slot_index": 2 - } - ], - "properties": { - "Node name for S&R": "CheckpointLoaderSimple" - }, - "widgets_values": [ - "revAnimated_v122.safetensors" - ] - }, - { - "id": 19, - "type": "CLIPSetLastLayer", - "pos": [ - -1597, - 209 - ], - "size": [ - 315, - 58 - ], - "flags": {}, - "order": 6, - "mode": 0, - "inputs": [ - { - "name": "clip", - "type": "CLIP", - "link": 116 - } - ], - "outputs": [ - { - "name": "CLIP", - "type": "CLIP", - "links": [ - 28, - 29, - 149 - ], - "shape": 3, - "slot_index": 0 - } - ], - "properties": { - "Node name for S&R": "CLIPSetLastLayer" - }, - "widgets_values": [ - -2 - ] - }, - { - "id": 7, - "type": "CLIPTextEncode", - "pos": [ - -839, - 335 - ], - "size": [ - 210, - 54 - ], - "flags": {}, - "order": 11, - "mode": 0, - "inputs": [ - { - "name": "clip", - "type": "CLIP", - "link": 29 - }, - { - "name": "text", - "type": "STRING", - "link": 163, - "widget": { - "name": "text", - "config": [ - "STRING", - { - "multiline": true - } - ] - }, - "slot_index": 1 - } - ], - "outputs": [ - { - "name": "CONDITIONING", - "type": "CONDITIONING", - "links": [ - 6 - ], - "slot_index": 0 - } - ], - "properties": { - "Node name for S&R": "CLIPTextEncode" - }, - "widgets_values": [ - "worst quality, hands, embedding:EasyNegative," - ] - }, - { - "id": 5, - "type": "EmptyLatentImage", - "pos": [ - -969, - 484 - ], - "size": [ - 315, - 106 - ], - "flags": {}, - "order": 1, - "mode": 0, - "outputs": [ - { - "name": "LATENT", - "type": "LATENT", - "links": [ - 2 - ], - "slot_index": 0 - } - ], - "properties": { - "Node name for S&R": "EmptyLatentImage" - }, - "widgets_values": [ - 768, - 512, - 1 - ] - }, - { - "id": 80, - "type": "Text box", - "pos": [ - -1397, - 356 - ], - "size": [ - 294, - 93.11674499511719 - ], - "flags": {}, - "order": 2, - "mode": 0, - "outputs": [ - { - "name": "STRING", - "type": "STRING", - "links": [ - 163 - ], - "shape": 3, - "slot_index": 0 - } - ], - "title": "❌Mel Negatives (general) (Negative)", - "properties": { - "Node name for S&R": "Text box" - }, - "widgets_values": [ - "embedding:EasyNegative, embedding:EasyNegativeV2, watermark, text, deformed" - ] - }, - { - "id": 72, - "type": "ConditioningAverage ", - "pos": [ - -960, - 2 - ], - "size": [ - 380.4000244140625, - 78 - ], - "flags": {}, - "order": 16, - "mode": 0, - "inputs": [ - { - "name": "conditioning_to", - "type": "CONDITIONING", - "link": 151 - }, - { - "name": "conditioning_from", - "type": "CONDITIONING", - "link": 152, - "slot_index": 1 - }, - { - "name": "conditioning_to_strength", - "type": "FLOAT", - "link": 154, - "widget": { - "name": "conditioning_to_strength", - "config": [ - "FLOAT", - { - "default": 1, - "min": 0, - "max": 1, - "step": 0.01 - } - ] - }, - "slot_index": 2 - } - ], - "outputs": [ - { - "name": "CONDITIONING", - "type": "CONDITIONING", - "links": [ - 153 - ], - "shape": 3, - "slot_index": 0 - } - ], - "properties": { - "Node name for S&R": "ConditioningAverage " - }, - "widgets_values": [ - 1 - ] - }, - { - "id": 6, - "type": "CLIPTextEncode", - "pos": [ - -1261, - -46 - ], - "size": [ - 210, - 54 - ], - "flags": {}, - "order": 14, - "mode": 0, - "inputs": [ - { - "name": "clip", - "type": "CLIP", - "link": 28 - }, - { - "name": "text", - "type": "STRING", - "link": 145, - "widget": { - "name": "text", - "config": [ - "STRING", - { - "multiline": true - } - ] - }, - "slot_index": 1 - } - ], - "outputs": [ - { - "name": "CONDITIONING", - "type": "CONDITIONING", - "links": [ - 151 - ], - "slot_index": 0 - } - ], - "properties": { - "Node name for S&R": "CLIPTextEncode" - }, - "widgets_values": [ - "A majestic Lion, fur in the wind, (smirk smile)" - ] - }, - { - "id": 70, - "type": "CLIPTextEncode", - "pos": [ - -1245, - 87 - ], - "size": [ - 210, - 54 - ], - "flags": {}, - "order": 12, - "mode": 0, - "inputs": [ - { - "name": "clip", - "type": "CLIP", - "link": 149, - "slot_index": 0 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@@ def print_formatted(text, *formats, color=None, background=None, **kwargs): encoded_header = header.encode("utf-8", errors="replace").decode("utf-8") encoded_text = formatted_text.encode("utf-8", errors="replace").decode("utf-8") - if sys.platform == "win32": - output_text = ( - " " * len(encoded_header) - if kwargs.get("no_header") - else apply_color(apply_format(encoded_header, "bold"), color="yellow") - ) - output_text += encoded_text + "\n" - sys.stdout.buffer.write(output_text.encode("utf-8")) - else: - print( - " " * len(encoded_header) - if kwargs.get("no_header") - else apply_color(apply_format(encoded_header, "bold"), color="yellow"), - encoded_text, - file=file, - ) + print( + " " * len(encoded_header) + if kwargs.get("no_header") + else apply_color(apply_format(encoded_header, "bold"), color="yellow"), + encoded_text, + file=file, + ) # endregion @@ -142,12 +133,15 @@ def print_formatted(text, *formats, color=None, background=None, **kwargs): # region utils def enqueue_output(out, queue): - for line in iter(out.readline, b""): - queue.put(line) + for char in iter(lambda: out.read(1), b""): + queue.put(char) out.close() -def run_command(cmd): +def run_command(cmd, ignored_lines_start=None): + if ignored_lines_start is None: + ignored_lines_start = [] + if isinstance(cmd, str): shell_cmd = cmd elif isinstance(cmd, list): @@ -193,18 +187,37 @@ def signal_handler(signum, frame): # Register the signal handler for keyboard interrupts (SIGINT) signal.signal(signal.SIGINT, signal_handler) + stdout_buffer = "" + stderr_buffer = "" + # Process output from both streams until the process completes or interrupted while not interrupted and ( process.poll() is None or not stdout_queue.empty() or not stderr_queue.empty() ): with suppress(Empty): - stdout_line = stdout_queue.get_nowait() - if stdout_line.strip() != "": - print(stdout_line.strip()) + stdout_char = stdout_queue.get_nowait() + stdout_buffer += stdout_char + if stdout_char == "\n": + if not any( + stdout_buffer.startswith(ign) for ign in ignored_lines_start + ): + print(stdout_buffer.strip()) + stdout_buffer = "" with suppress(Empty): - stderr_line = stderr_queue.get_nowait() - if stderr_line.strip() != "": - print(stderr_line.strip()) + stderr_char = stderr_queue.get_nowait() + stderr_buffer += stderr_char + if stderr_char == "\n": + print(stderr_buffer.strip()) + stderr_buffer = "" + + # Print any remaining content in buffers + if stdout_buffer and not any( + stdout_buffer.startswith(ign) for ign in ignored_lines_start + ): + print(stdout_buffer.strip()) + if stderr_buffer: + print(stderr_buffer.strip()) + return_code = process.returncode if return_code == 0 and not interrupted: @@ -231,8 +244,6 @@ def signal_handler(signum, frame): print_formatted("Installing tqdm...", "italic", color="yellow") run_command([sys.executable, "-m", "pip", "install", "--upgrade", "tqdm"]) from tqdm import tqdm -import importlib - pip_map = { "onnxruntime-gpu": "onnxruntime", @@ -462,6 +473,7 @@ def install_dependencies(dry=False): # default=get_local_version(), # help="Version to check against the GitHub API", # ) + print_formatted("mtb install", "bold", color="yellow") args = parser.parse_args() @@ -472,7 +484,7 @@ def install_dependencies(dry=False): clone_dir = Path(args.path) if not clone_dir.exists(): print_formatted( - f"The path provided does not exist on disk... It must be pointing to ComfyUI's custom_nodes directory" + "The path provided does not exist on disk... It must be pointing to ComfyUI's custom_nodes directory" ) sys.exit() @@ -493,48 +505,49 @@ def install_dependencies(dry=False): # Install dependencies from requirements.txt # if args.requirements or mode == "venv": - if (not args.wheels and mode not in ["colab", "embeded"]) and not full: - print_formatted( - "Skipping wheel installation. Use --wheels to install wheel dependencies. (only needed for Comfy embed)", - "italic", - color="yellow", - ) - - install_dependencies(dry=args.dry) - sys.exit() - - if mode in ["colab", "embeded"]: - print_formatted( - f"Downloading and installing release wheels since we are in a Comfy {apply_color(mode,'cyan')} environment", - "italic", - color="yellow", - ) - if full: - print_formatted( - f"Downloading and installing release wheels since no arguments where provided", - "italic", - color="yellow", - ) - - # - Check the env before proceeding. + # if (not args.wheels and mode not in ["colab", "embeded"]) and not full: + # print_formatted( + # "Skipping wheel installation. Use --wheels to install wheel dependencies. (only needed for Comfy embed)", + # "italic", + # color="yellow", + # ) + + # install_dependencies(dry=args.dry) + # sys.exit() + + # if mode in ["colab", "embeded"]: + # print_formatted( + # f"Downloading and installing release wheels since we are in a Comfy {apply_color(mode,'cyan')} environment", + # "italic", + # color="yellow", + # ) + # if full: + # print_formatted( + # f"Downloading and installing release wheels since no arguments where provided", + # "italic", + # color="yellow", + # ) + + print_formatted("Checking environment...", "italic", color="yellow") missing_deps = [] - parsed_requirements = get_requirements(here / "reqs.txt") - if parsed_requirements: + if parsed_requirements := get_requirements(here / "reqs.txt"): for requirement in parsed_requirements: installed, pip_name, pip_spec, import_name = try_import(requirement) if not installed: missing_deps.append(pip_name.split("-")[0]) - if len(missing_deps) == 0: + if not missing_deps: print_formatted( - f"All requirements are already installed. Enjoy 🚀", "italic", color="green" + "All requirements are already installed. Enjoy 🚀", + "italic", + color="green", ) sys.exit() - # - Get the tag version from the GitHub API - tag_data, tag_name = get_github_assets(tag=None) + # # - Get the tag version from the GitHub API + # tag_data, tag_name = get_github_assets(tag=None) - # - keep + # # - keep # version = args.version # # Compare the local and tag versions # if version and tag_name: @@ -552,58 +565,58 @@ def install_dependencies(dry=False): # ) # sys.exit() - matching_assets = [ - asset - for asset in tag_data["assets"] - if asset["name"].endswith(".whl") - and ( - "any" in asset["name"] or short_platform[current_platform] in asset["name"] - ) - ] - if not matching_assets: - print_formatted( - f"Unsupported operating system: {current_platform}", color="yellow" - ) - wheel_order_asset = next( - (asset for asset in tag_data["assets"] if asset["name"] == "wheel_order.txt"), - None, - ) - if wheel_order_asset is not None: - print_formatted( - "⚙️ Sorting the release wheels using wheels order", "italic", color="yellow" - ) - response = requests.get(wheel_order_asset["browser_download_url"]) - if response.status_code == 200: - wheel_order = [line.strip() for line in response.text.splitlines()] - - def get_order_index(val): - try: - return wheel_order.index(val) - except ValueError: - return len(wheel_order) - - matching_assets = sorted( - matching_assets, - key=lambda x: get_order_index(x["name"].split("-")[0]), - ) - else: - print("Failed to fetch wheel_order.txt. Status code:", response.status_code) - - missing_deps_urls = [] - for whl_file in matching_assets: - # check if installed - missing_deps_urls.append(whl_file["browser_download_url"]) + # matching_assets = [ + # asset + # for asset in tag_data["assets"] + # if asset["name"].endswith(".whl") + # and ( + # "any" in asset["name"] or short_platform[current_platform] in asset["name"] + # ) + # ] + # if not matching_assets: + # print_formatted( + # f"Unsupported operating system: {current_platform}", color="yellow" + # ) + # wheel_order_asset = next( + # (asset for asset in tag_data["assets"] if asset["name"] == "wheel_order.txt"), + # None, + # ) + # if wheel_order_asset is not None: + # print_formatted( + # "⚙️ Sorting the release wheels using wheels order", "italic", color="yellow" + # ) + # response = requests.get(wheel_order_asset["browser_download_url"]) + # if response.status_code == 200: + # wheel_order = [line.strip() for line in response.text.splitlines()] + + # def get_order_index(val): + # try: + # return wheel_order.index(val) + # except ValueError: + # return len(wheel_order) + + # matching_assets = sorted( + # matching_assets, + # key=lambda x: get_order_index(x["name"].split("-")[0]), + # ) + # else: + # print("Failed to fetch wheel_order.txt. Status code:", response.status_code) + + # missing_deps_urls = [] + # for whl_file in matching_assets: + # # check if installed + # missing_deps_urls.append(whl_file["browser_download_url"]) install_cmd = [sys.executable, "-m", "pip", "install"] - wheel_cmd = install_cmd + missing_deps_urls - # - Install all deps if not args.dry: - # - first install the ordered wheel files + if platform.system() == "Windows": + wheel_cmd = install_cmd + ["-r", (here / "reqs_windows.txt")] + else: + wheel_cmd = install_cmd + ["-r", (here / "reqs.txt")] + run_command(wheel_cmd) - # - then install the rest of the deps - run_command(install_cmd + ["-r", (here / "reqs.txt")]) print_formatted( "✅ Successfully installed all dependencies.", "italic", color="green" ) diff --git a/log.py b/log.py index c538bff..977c113 100644 --- a/log.py +++ b/log.py @@ -3,7 +3,6 @@ import os base_log_level = logging.DEBUG if os.environ.get("MTB_DEBUG") else logging.INFO -print(f"Log level: {base_log_level}") # Custom object that discards the output diff --git a/node_list.json b/node_list.json index 7ddc03e..7770a2b 100644 --- a/node_list.json +++ b/node_list.json @@ -1,5 +1,6 @@ { "Animation Builder (mtb)": "Convenient way to manage basic animation maths at the core of many of my workflows", + "Any To String (mtb)": "Tries to take any input and convert it to a string", "Bbox (mtb)": "The bounding box (BBOX) custom type used by other nodes", "Bbox From Mask (mtb)": "From a mask extract the bounding box", "Blur (mtb)": "Blur an image using a Gaussian filter.", @@ -9,7 +10,7 @@ "Crop (mtb)": "Crops an image and an optional mask to a given bounding box\n\n The bounding box can be given as a tuple of (x, y, width, height) or as a BBOX type\n The BBOX input takes precedence over the tuple input\n ", "Debug (mtb)": "Experimental node to debug any Comfy values, support for more types and widgets is planned", "Deep Bump (mtb)": "Normal & height maps generation from single pictures", - "Export To Prores (mtb)": "Export to ProRes 4444 (Experimental)", + "Export With Ffmpeg (mtb)": "Export with FFmpeg (Experimental)", "Face Swap (mtb)": "Face swap using deepinsight/insightface models", "Film Interpolation (mtb)": "Google Research FILM frame interpolation for large motion", "Fit Number (mtb)": "Fit the input float using a source and target range", diff --git a/nodes/animation.py b/nodes/animation.py index da22df1..9822685 100644 --- a/nodes/animation.py +++ b/nodes/animation.py @@ -17,7 +17,7 @@ def INPUT_TYPES(cls): }, } - RETURN_TYPES = ("INT", "FLOAT", "INT", "BOOL") + RETURN_TYPES = ("INT", "FLOAT", "INT", "BOOLEAN") RETURN_NAMES = ("frame", "0-1 (scaled)", "count", "loop_ended") CATEGORY = "mtb/animation" FUNCTION = "build_animation" diff --git a/nodes/conditions.py b/nodes/conditions.py index 71eeef0..33d50e9 100644 --- a/nodes/conditions.py +++ b/nodes/conditions.py @@ -1,5 +1,4 @@ -from ..utils import pil2tensor -from ..utils import here, comfy_dir +from ..utils import here from ..log import log import folder_paths from pathlib import Path @@ -97,110 +96,4 @@ def load_style(self, style_name): return (self.options[style_name][0], self.options[style_name][1]) -class TextToImage: - """Utils to convert text to image using a font - - - The tool looks for any .ttf file in the Comfy folder hierarchy. - """ - - fonts = {} - - def __init__(self): - # - This is executed when the graph is executed, we could conditionaly reload fonts there - pass - - @classmethod - def CACHE_FONTS(cls): - font_extensions = ["*.ttf", "*.otf", "*.woff", "*.woff2", "*.eot"] - fonts = [] - - for extension in font_extensions: - fonts.extend(comfy_dir.glob(f"**/{extension}")) - - if not fonts: - log.warn( - "> No fonts found in the comfy folder, place at least one font file somewhere in ComfyUI's hierarchy" - ) - else: - log.debug(f"> Found {len(fonts)} fonts") - - for font in fonts: - log.debug(f"Adding font {font}") - cls.fonts[font.stem] = font.as_posix() - - @classmethod - def INPUT_TYPES(cls): - if not cls.fonts: - cls.CACHE_FONTS() - else: - log.debug(f"Using cached fonts (count: {len(cls.fonts)})") - return { - "required": { - "text": ( - "STRING", - {"default": "Hello world!"}, - ), - "font": ((sorted(cls.fonts.keys())),), - "wrap": ( - "INT", - {"default": 120, "min": 0, "max": 8096, "step": 1}, - ), - "font_size": ( - "INT", - {"default": 12, "min": 1, "max": 2500, "step": 1}, - ), - "width": ( - "INT", - {"default": 512, "min": 1, "max": 8096, "step": 1}, - ), - "height": ( - "INT", - {"default": 512, "min": 1, "max": 8096, "step": 1}, - ), - # "position": (["INT"], {"default": 0, "min": 0, "max": 100, "step": 1}), - "color": ( - "COLOR", - {"default": "black"}, - ), - "background": ( - "COLOR", - {"default": "white"}, - ), - } - } - - RETURN_TYPES = ("IMAGE",) - RETURN_NAMES = ("image",) - FUNCTION = "text_to_image" - CATEGORY = "mtb/generate" - - def text_to_image( - self, text, font, wrap, font_size, width, height, color, background - ): - from PIL import Image, ImageDraw, ImageFont - import textwrap - - font = self.fonts[font] - font = ImageFont.truetype(font, font_size) - if wrap == 0: - wrap = width / font_size - lines = textwrap.wrap(text, width=wrap) - log.debug(f"Lines: {lines}") - line_height = font.getsize("hg")[1] - img_height = height # line_height * len(lines) - img_width = width # max(font.getsize(line)[0] for line in lines) - - img = Image.new("RGBA", (img_width, img_height), background) - draw = ImageDraw.Draw(img) - y_text = 0 - for line in lines: - width, height = font.getsize(line) - draw.text((0, y_text), line, color, font=font) - y_text += height - - # img.save(os.path.join(folder_paths.base_path, f'{str(uuid.uuid4())}.png')) - return (pil2tensor(img),) - - -__nodes__ = [SmartStep, TextToImage, StylesLoader] +__nodes__ = [SmartStep, StylesLoader] diff --git a/nodes/debug.py b/nodes/debug.py index 2ca0ff9..1aaf5f0 100644 --- a/nodes/debug.py +++ b/nodes/debug.py @@ -38,9 +38,9 @@ def do_debug(self, **kwargs): b64_imgs = [] for im in image: buffered = io.BytesIO() - im.save(buffered, format="JPEG") + im.save(buffered, format="PNG") b64_imgs.append( - "data:image/jpeg;base64," + "data:image/png;base64," + base64.b64encode(buffered.getvalue()).decode("utf-8") ) diff --git a/nodes/deep_bump.py b/nodes/deep_bump.py index 1444d35..47a4d26 100644 --- a/nodes/deep_bump.py +++ b/nodes/deep_bump.py @@ -261,7 +261,7 @@ def INPUT_TYPES(cls): "LARGEST", ], ), - "normals_to_height_seamless": ("BOOL", {"default": False}), + "normals_to_height_seamless": ("BOOLEAN", {"default": False}), }, } diff --git a/nodes/faceenhance.py b/nodes/faceenhance.py index 0c97db4..2e81861 100644 --- a/nodes/faceenhance.py +++ b/nodes/faceenhance.py @@ -165,12 +165,12 @@ def INPUT_TYPES(cls): "image": ("IMAGE",), "model": ("FACEENHANCE_MODEL",), # Input are aligned faces - "aligned": ("BOOL", {"default": False}), + "aligned": ("BOOLEAN", {"default": False}), # Only restore the center face - "only_center_face": ("BOOL", {"default": False}), + "only_center_face": ("BOOLEAN", {"default": False}), # Adjustable weights "weight": ("FLOAT", {"default": 0.5}), - "save_tmp_steps": ("BOOL", {"default": True}), + "save_tmp_steps": ("BOOLEAN", {"default": True}), } } diff --git a/nodes/faceswap.py b/nodes/faceswap.py index b21396b..0047ba6 100644 --- a/nodes/faceswap.py +++ b/nodes/faceswap.py @@ -2,14 +2,13 @@ import onnxruntime from pathlib import Path from PIL import Image -from typing import List, Set, Tuple, Union, Optional +from typing import List, Set, Union, Optional import cv2 import folder_paths import glob import insightface import numpy as np import os -import tempfile import torch from insightface.model_zoo.inswapper import INSwapper from ..utils import pil2tensor, tensor2pil, download_antelopev2 diff --git a/nodes/fun.py b/nodes/generate.py similarity index 56% rename from nodes/fun.py rename to nodes/generate.py index d12af8f..8fa55ee 100644 --- a/nodes/fun.py +++ b/nodes/generate.py @@ -1,5 +1,7 @@ import qrcode from ..utils import pil2tensor +from ..utils import comfy_dir +from typing import cast from PIL import Image from ..log import log @@ -121,7 +123,7 @@ def INPUT_TYPES(cls): "error_correct": (("L", "M", "Q", "H"), {"default": "L"}), "box_size": ("INT", {"default": 10, "max": 8096, "min": 0, "step": 1}), "border": ("INT", {"default": 4, "max": 8096, "min": 0, "step": 1}), - "invert": (("BOOL",), {"default": False}), + "invert": (("BOOLEAN",), {"default": False}), } } @@ -130,6 +132,9 @@ def INPUT_TYPES(cls): CATEGORY = "mtb/generate" def do_qr(self, url, width, height, error_correct, box_size, border, invert): + log.warning( + "This node will soon be deprecated, there are much better alternatives like https://github.com/coreyryanhanson/comfy-qr" + ) if error_correct == "L" or error_correct not in ["M", "Q", "H"]: error_correct = qrcode.constants.ERROR_CORRECT_L elif error_correct == "M": @@ -159,8 +164,123 @@ def do_qr(self, url, width, height, error_correct, box_size, border, invert): return (pil2tensor(code),) +def bbox_dim(bbox): + left, upper, right, lower = bbox + width = right - left + height = lower - upper + return width, height + + +class TextToImage: + """Utils to convert text to image using a font + + + The tool looks for any .ttf file in the Comfy folder hierarchy. + """ + + fonts = {} + + def __init__(self): + # - This is executed when the graph is executed, we could conditionaly reload fonts there + pass + + @classmethod + def CACHE_FONTS(cls): + font_extensions = ["*.ttf", "*.otf", "*.woff", "*.woff2", "*.eot"] + fonts = [] + + for extension in font_extensions: + fonts.extend(comfy_dir.glob(f"**/{extension}")) + + if not fonts: + log.warn( + "> No fonts found in the comfy folder, place at least one font file somewhere in ComfyUI's hierarchy" + ) + else: + log.debug(f"> Found {len(fonts)} fonts") + + for font in fonts: + log.debug(f"Adding font {font}") + cls.fonts[font.stem] = font.as_posix() + + @classmethod + def INPUT_TYPES(cls): + if not cls.fonts: + cls.CACHE_FONTS() + else: + log.debug(f"Using cached fonts (count: {len(cls.fonts)})") + return { + "required": { + "text": ( + "STRING", + {"default": "Hello world!"}, + ), + "font": ((sorted(cls.fonts.keys())),), + "wrap": ( + "INT", + {"default": 120, "min": 0, "max": 8096, "step": 1}, + ), + "font_size": ( + "INT", + {"default": 12, "min": 1, "max": 2500, "step": 1}, + ), + "width": ( + "INT", + {"default": 512, "min": 1, "max": 8096, "step": 1}, + ), + "height": ( + "INT", + {"default": 512, "min": 1, "max": 8096, "step": 1}, + ), + # "position": (["INT"], {"default": 0, "min": 0, "max": 100, "step": 1}), + "color": ( + "COLOR", + {"default": "black"}, + ), + "background": ( + "COLOR", + {"default": "white"}, + ), + } + } + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("image",) + FUNCTION = "text_to_image" + CATEGORY = "mtb/generate" + + def text_to_image( + self, text, font, wrap, font_size, width, height, color, background + ): + from PIL import Image, ImageDraw, ImageFont + import textwrap + + font = self.fonts[font] + font = cast(ImageFont.FreeTypeFont, ImageFont.truetype(font, font_size)) + if wrap == 0: + wrap = width / font_size + lines = textwrap.wrap(text, width=wrap) + log.debug(f"Lines: {lines}") + line_height = bbox_dim(font.getbbox("hg"))[1] + img_height = height # line_height * len(lines) + img_width = width # max(font.getsize(line)[0] for line in lines) + + img = Image.new("RGBA", (img_width, img_height), background) + draw = ImageDraw.Draw(img) + y_text = 0 + # - bbox is [left, upper, right, lower] + for line in lines: + width, height = bbox_dim(font.getbbox(line)) + draw.text((0, y_text), line, color, font=font) + y_text += height + + # img.save(os.path.join(folder_paths.base_path, f'{str(uuid.uuid4())}.png')) + return (pil2tensor(img),) + + __nodes__ = [ QrCode, - UnsplashImage + UnsplashImage, + TextToImage # MtbExamples, ] diff --git a/nodes/graph_utils.py b/nodes/graph_utils.py index e191fbc..5c1b856 100644 --- a/nodes/graph_utils.py +++ b/nodes/graph_utils.py @@ -1,4 +1,133 @@ from ..log import log +from PIL import Image +import urllib.request +import urllib.parse +import torch +import json +from comfy.cli_args import args +from ..utils import pil2tensor, apply_easing +import io +import numpy as np + + +def get_image(filename, subfolder, folder_type): + log.debug(f"Getting image {filename} from {subfolder} of {folder_type}") + data = {"filename": filename, "subfolder": subfolder, "type": folder_type} + url_values = urllib.parse.urlencode(data) + with urllib.request.urlopen( + f"http://{args.listen}:{args.port}/view?{url_values}" + ) as response: + return io.BytesIO(response.read()) + + +class GetBatchFromHistory: + """Very experimental node to load images from the history of the server. + + Queue items without output are ignored in the count.""" + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "enable": ("BOOLEAN", {"default": True}), + "count": ("INT", {"default": 1, "min": 0}), + "offset": ("INT", {"default": 0, "min": -1e9, "max": 1e9}), + "internal_count": ("INT", {"default": 0}), + }, + "optional": { + "passthrough_image": ("IMAGE",), + }, + } + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("images",) + CATEGORY = "mtb/animation" + FUNCTION = "load_from_history" + + def load_from_history( + self, + enable=True, + count=0, + offset=0, + internal_count=0, # hacky way to invalidate the node + passthrough_image=None, + ): + if not enable or count == 0: + if passthrough_image is not None: + log.debug("Using passthrough image") + return (passthrough_image,) + log.debug("Load from history is disabled for this iteration") + return (torch.zeros(0),) + frames = [] + + with urllib.request.urlopen( + f"http://{args.listen}:{args.port}/history" + ) as response: + return self.load_batch_frames(response, offset, count, frames) + + def load_batch_frames(self, response, offset, count, frames): + history = json.loads(response.read()) + + output_images = [] + + for run in history.values(): + for node_output in run["outputs"].values(): + if "images" in node_output: + for image in node_output["images"]: + image_data = get_image( + image["filename"], image["subfolder"], image["type"] + ) + output_images.append(image_data) + + if not output_images: + return (torch.zeros(0),) + + # Directly get desired range of images + start_index = max(len(output_images) - offset - count, 0) + end_index = len(output_images) - offset + selected_images = output_images[start_index:end_index] + + frames = [Image.open(image) for image in selected_images] + + if not frames: + return (torch.zeros(0),) + elif len(frames) != count: + log.warning(f"Expected {count} images, got {len(frames)} instead") + + output = pil2tensor(frames) + + return (output,) + + +class AnyToString: + """Tries to take any input and convert it to a string""" + + @classmethod + def INPUT_TYPES(cls): + return { + "required": {"input": ("*")}, + } + + RETURN_TYPES = ("STRING",) + FUNCTION = "do_str" + CATEGORY = "mtb/converters" + + def do_str(self, input): + if isinstance(input, str): + return (input,) + elif isinstance(input, torch.Tensor): + return (f"Tensor of shape {input.shape} and dtype {input.dtype}",) + elif isinstance(input, Image.Image): + return (f"PIL Image of size {input.size} and mode {input.mode}",) + elif isinstance(input, np.ndarray): + return (f"Numpy array of shape {input.shape} and dtype {input.dtype}",) + + elif isinstance(input, dict): + return (f"Dictionary of {len(input)} items, with keys {input.keys()}",) + + else: + log.debug(f"Falling back to string conversion of {input}") + return (str(input),) class StringReplace: @@ -38,11 +167,38 @@ def INPUT_TYPES(cls): return { "required": { "value": ("FLOAT", {"default": 0, "forceInput": True}), - "clamp": ("BOOL", {"default": False}), + "clamp": ("BOOLEAN", {"default": False}), "source_min": ("FLOAT", {"default": 0.0}), "source_max": ("FLOAT", {"default": 1.0}), "target_min": ("FLOAT", {"default": 0.0}), "target_max": ("FLOAT", {"default": 1.0}), + "easing": ( + [ + "Linear", + "Sine In", + "Sine Out", + "Sine In/Out", + "Quart In", + "Quart Out", + "Quart In/Out", + "Cubic In", + "Cubic Out", + "Cubic In/Out", + "Circ In", + "Circ Out", + "Circ In/Out", + "Back In", + "Back Out", + "Back In/Out", + "Elastic In", + "Elastic Out", + "Elastic In/Out", + "Bounce In", + "Bounce Out", + "Bounce In/Out", + ], + {"default": "Linear"}, + ), } } @@ -58,10 +214,14 @@ def set_range( source_max: float, target_min: float, target_max: float, + easing: str, ): - res = target_min + (target_max - target_min) * (value - source_min) / ( - source_max - source_min - ) + normalized_value = (value - source_min) / (source_max - source_min) + + eased_value = apply_easing(normalized_value, easing) + + # - Convert the eased value to the target range + res = target_min + (target_max - target_min) * eased_value if clamp: if target_min > target_max: @@ -72,4 +232,4 @@ def set_range( return (res,) -__nodes__ = [StringReplace, FitNumber] +__nodes__ = [StringReplace, FitNumber, GetBatchFromHistory, AnyToString] diff --git a/nodes/image_interpolation.py b/nodes/image_interpolation.py index 60541cc..6bd39ae 100644 --- a/nodes/image_interpolation.py +++ b/nodes/image_interpolation.py @@ -6,107 +6,11 @@ from ..log import log import torch from frame_interpolation.eval import util, interpolator -from ..utils import tensor2np import numpy as np import comfy -from PIL import Image -import urllib.request -import urllib.parse -import json +import comfy.utils import tensorflow as tf import comfy.model_management as model_management -import io - -from comfy.cli_args import args -from ..utils import pil2tensor - - -def get_image(filename, subfolder, folder_type): - data = {"filename": filename, "subfolder": subfolder, "type": folder_type} - url_values = urllib.parse.urlencode(data) - with urllib.request.urlopen( - "http://{}:{}/view?{}".format(args.listen, args.port, url_values) - ) as response: - return io.BytesIO(response.read()) - - -class GetBatchFromHistory: - """Very experimental node to load images from the history of the server. - - Queue items without output are ignore in the count.""" - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "enable": ("BOOL", {"default": True}), - "count": ("INT", {"default": 1, "min": 0}), - "offset": ("INT", {"default": 0, "min": -1e9, "max": 1e9}), - "internal_count": ("INT", {"default": 0}), - }, - "optional": {"passthrough_image": ("IMAGE",)}, - } - - RETURN_TYPES = ("IMAGE",) - RETURN_NAMES = "images" - CATEGORY = "mtb/animation" - FUNCTION = "load_from_history" - - def load_from_history( - self, - enable=True, - count=0, - offset=0, - internal_count=0, # hacky way to invalidate the node - passthrough_image=None, - ): - if not enable or count == 0: - if passthrough_image is not None: - return (passthrough_image,) - log.debug("Load from history is disabled for this iteration") - return (torch.zeros(0),) - frames = [] - - with urllib.request.urlopen( - "http://{}:{}/history".format(args.listen, args.port) - ) as response: - history = json.loads(response.read()) - - output_images = [] - for k, run in history.items(): - for o in run["outputs"]: - for node_id in run["outputs"]: - node_output = run["outputs"][node_id] - if "images" in node_output: - images_output = [] - for image in node_output["images"]: - image_data = get_image( - image["filename"], image["subfolder"], image["type"] - ) - images_output.append(image_data) - output_images.extend(images_output) - if len(output_images) == 0: - return (torch.zeros(0),) - for i, image in enumerate(list(reversed(output_images))): - if i < offset: - continue - if i >= offset + count: - break - # Decode image as tensor - img = Image.open(image) - log.debug(f"Image from history {i} of shape {img.size}") - frames.append(img) - - # Display the shape of the tensor - # print("Tensor shape:", image_tensor.shape) - - # return (output_images,) - - output = pil2tensor( - list(reversed(frames)), - ) - - return (output,) class LoadFilmModel: @@ -247,9 +151,4 @@ def concat_images(self, imageA: torch.Tensor, imageB: torch.Tensor): return (self.concatenate_tensors(imageA, imageB),) -__nodes__ = [ - LoadFilmModel, - FilmInterpolation, - ConcatImages, - GetBatchFromHistory, -] +__nodes__ = [LoadFilmModel, FilmInterpolation, ConcatImages] diff --git a/nodes/image_processing.py b/nodes/image_processing.py index 04f04e1..ac173e4 100644 --- a/nodes/image_processing.py +++ b/nodes/image_processing.py @@ -1,20 +1,16 @@ import torch from skimage.filters import gaussian -from skimage.restoration import denoise_tv_chambolle from skimage.util import compare_images -from skimage.color import rgb2hsv, hsv2rgb import numpy as np -import torchvision.transforms.functional as F -from PIL import Image, ImageChops -from ..utils import tensor2pil, pil2tensor, np2tensor, tensor2np -import cv2 +import torch.nn.functional as F +from PIL import Image +from ..utils import tensor2pil, pil2tensor, tensor2np import torch -from ..log import log import folder_paths from PIL.PngImagePlugin import PngInfo import json import os -import comfy.model_management as model_management +import math # try: @@ -377,7 +373,7 @@ def INPUT_TYPES(cls): "required": { "image": ("IMAGE",), "mask": ("MASK",), - "invert": ("BOOL", {"default": False}), + "invert": ("BOOLEAN", {"default": False}), } } @@ -425,10 +421,18 @@ def INPUT_TYPES(cls): "FLOAT", {"default": 2, "min": 0.01, "max": 16.0, "step": 0.01}, ), - "supersample": ("BOOL", {"default": True}), + "supersample": ("BOOLEAN", {"default": True}), "resampling": ( - ["lanczos", "nearest", "bilinear", "bicubic"], - {"default": "lanczos"}, + [ + "nearest", + "linear", + "bilinear", + "bicubic", + "trilinear", + "area", + "nearest-exact", + ], + {"default": "nearest"}, ), }, "optional": { @@ -440,71 +444,6 @@ def INPUT_TYPES(cls): RETURN_TYPES = ("IMAGE", "MASK") FUNCTION = "resize" - def resize_image( - self, - image: torch.Tensor, - factor: float = 0.5, - supersample=False, - resample="lanczos", - mask=None, - ) -> torch.Tensor: - model_management.throw_exception_if_processing_interrupted() - batch_count = 1 - img = tensor2pil(image) - - if isinstance(img, list): - log.debug("Multiple images detected (list)") - out = [] - for im in img: - im = self.resize_image( - pil2tensor(im), factor, supersample, resample, mask - ) - out.append(im) - return torch.cat(out, dim=0) - elif isinstance(img, torch.Tensor): - if len(image.shape) > 3: - batch_count = image.size(0) - - if batch_count > 1: - log.debug("Multiple images detected (batch count)") - out = [ - self.resize_image(image[i], factor, supersample, resample, mask) - for i in range(batch_count) - ] - return torch.cat(out, dim=0) - - log.debug("Resizing image") - # Get the current width and height of the image - current_width, current_height = img.size - - log.debug(f"Current width: {current_width}, Current height: {current_height}") - - # Calculate the new width and height based on the given mode and parameters - new_width, new_height = int(factor * current_width), int( - factor * current_height - ) - - log.debug(f"New width: {new_width}, New height: {new_height}") - - # Define a dictionary of resampling filters - resample_filters = {"nearest": 0, "bilinear": 2, "bicubic": 3, "lanczos": 1} - - # Apply supersample - if supersample: - super_size = (new_width * 8, new_height * 8) - log.debug(f"Applying supersample: {super_size}") - img = img.resize( - super_size, resample=Image.Resampling(resample_filters[resample]) - ) - - # Resize the image using the given resampling filter - resized_image = img.resize( - (new_width, new_height), - resample=Image.Resampling(resample_filters[resample]), - ) - - return pil2tensor(resized_image) - def resize( self, image: torch.Tensor, @@ -513,24 +452,53 @@ def resize( resampling: str, mask=None, ): - log.debug(f"Resizing image with factor {factor} and resampling {resampling}") + # Check if the tensor has the correct dimension + if len(image.shape) not in [3, 4]: # HxWxC or BxHxWxC + raise ValueError("Expected image tensor of shape (H, W, C) or (B, H, W, C)") - batch_count = image.size(0) - log.debug(f"Batch count: {batch_count}") - if batch_count == 1: - log.debug("Batch count is 1, returning single image") - return (self.resize_image(image, factor, supersample, resampling),) + # Transpose to CxHxW or BxCxHxW for PyTorch + if len(image.shape) == 3: + image = image.permute(2, 0, 1).unsqueeze(0) # CxHxW else: - log.debug("Batch count is greater than 1, returning multiple images") - images = [ - self.resize_image(image[i], factor, supersample, resampling) - for i in range(batch_count) - ] - images = torch.cat(images, dim=0) - return (images,) + image = image.permute(0, 3, 1, 2) # BxCxHxW + + # Compute new dimensions + B, C, H, W = image.shape + new_H, new_W = int(H * factor), int(W * factor) + + align_corner_filters = ("linear", "bilinear", "bicubic", "trilinear") + # Resize the image + resized_image = F.interpolate( + image, + size=(new_H, new_W), + mode=resampling, + align_corners=resampling in align_corner_filters, + ) + # Optionally supersample + if supersample: + resized_image = F.interpolate( + resized_image, + scale_factor=2, + mode=resampling, + align_corners=resampling in align_corner_filters, + ) -import math + # Transpose back to the original format: BxHxWxC or HxWxC + if len(image.shape) == 4: + resized_image = resized_image.permute(0, 2, 3, 1) + else: + resized_image = resized_image.squeeze(0).permute(1, 2, 0) + + # Apply mask if provided + if mask is not None: + if len(mask.shape) != len(resized_image.shape): + raise ValueError( + "Mask tensor should have the same dimensions as the image tensor" + ) + resized_image = resized_image * mask + + return (resized_image,) class SaveImageGrid: @@ -546,7 +514,7 @@ def INPUT_TYPES(cls): "required": { "images": ("IMAGE",), "filename_prefix": ("STRING", {"default": "ComfyUI"}), - "save_intermediate": ("BOOL", {"default": False}), + "save_intermediate": ("BOOLEAN", {"default": False}), }, "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, } diff --git a/nodes/io.py b/nodes/io.py index d31d27e..3558e2a 100644 --- a/nodes/io.py +++ b/nodes/io.py @@ -1,4 +1,4 @@ -from ..utils import tensor2np +from ..utils import tensor2np, PIL_FILTER_MAP import uuid import folder_paths from ..log import log @@ -7,10 +7,12 @@ import torch from pathlib import Path import numpy as np +from PIL import Image +from typing import Optional, List -class ExportToProres: - """Export to ProRes 4444 (Experimental)""" +class ExportWithFfmpeg: + """Export with FFmpeg (Experimental)""" @classmethod def INPUT_TYPES(cls): @@ -20,6 +22,11 @@ def INPUT_TYPES(cls): # "frames": ("FRAMES",), "fps": ("FLOAT", {"default": 24, "min": 1}), "prefix": ("STRING", {"default": "export"}), + "format": (["mov", "mp4", "mkv", "avi"], {"default": "mov"}), + "codec": ( + ["prores_ks", "libx264", "libx265"], + {"default": "prores_ks"}, + ), } } @@ -33,13 +40,17 @@ def export_prores( images: torch.Tensor, fps: float, prefix: str, + format: str, + codec: str, ): if images.size(0) == 0: return ("",) output_dir = Path(folder_paths.get_output_directory()) - id = f"{prefix}_{uuid.uuid4()}.mov" + pix_fmt = "rgb48le" if codec == "prores_ks" else "yuv420p" + file_ext = format + file_id = f"{prefix}_{uuid.uuid4()}.{file_ext}" - log.debug(f"Exporting to {output_dir / id}") + log.debug(f"Exporting to {output_dir / file_id}") frames = tensor2np(images) log.debug(f"Frames type {type(frames[0])}") @@ -49,7 +60,7 @@ def export_prores( height, width, _ = frames[0].shape - out_path = (output_dir / id).as_posix() + out_path = (output_dir / file_id).as_posix() # Prepare the FFmpeg command command = [ @@ -62,17 +73,13 @@ def export_prores( "-s", f"{width}x{height}", "-pix_fmt", - "rgb48le", + pix_fmt, "-r", str(fps), "-i", "-", "-c:v", - "prores_ks", - "-profile:v", - "4", - "-pix_fmt", - "yuva444p10le", + codec, "-r", str(fps), "-y", @@ -91,6 +98,37 @@ def export_prores( return (out_path,) +def prepare_animated_batch( + batch: torch.Tensor, + pingpong=False, + resize_by=1.0, + resample_filter: Optional[Image.Resampling] = None, + image_type=np.uint8, +) -> List[Image.Image]: + images = tensor2np(batch) + images = [frame.astype(image_type) for frame in images] + + height, width, _ = batch[0].shape + + if pingpong: + reversed_frames = images[::-1] + images.extend(reversed_frames) + pil_images = [Image.fromarray(frame) for frame in images] + + # Resize frames if necessary + if abs(resize_by - 1.0) > 1e-6: + new_width = int(width * resize_by) + new_height = int(height * resize_by) + pil_images_resized = [ + frame.resize((new_width, new_height), resample=resample_filter) + for frame in pil_images + ] + pil_images = pil_images_resized + + return pil_images + + +# todo: deprecate for apng class SaveGif: """Save the images from the batch as a GIF""" @@ -101,8 +139,12 @@ def INPUT_TYPES(cls): "image": ("IMAGE",), "fps": ("INT", {"default": 12, "min": 1, "max": 120}), "resize_by": ("FLOAT", {"default": 1.0, "min": 0.1}), - "pingpong": ("BOOL", {"default": False}), - } + "optimize": ("BOOLEAN", {"default": False}), + "pingpong": ("BOOLEAN", {"default": False}), + }, + "optional": { + "resample_filter": (list(PIL_FILTER_MAP.keys()),), + }, } RETURN_TYPES = () @@ -110,59 +152,44 @@ def INPUT_TYPES(cls): CATEGORY = "mtb/IO" FUNCTION = "save_gif" - def save_gif(self, image, fps=12, resize_by=1.0, pingpong=False): + def save_gif( + self, + image, + fps=12, + resize_by=1.0, + optimize=False, + pingpong=False, + resample_filter=None, + ): if image.size(0) == 0: return ("",) - images = tensor2np(image) - images = [frame.astype(np.uint8) for frame in images] - if pingpong: - reversed_frames = images[::-1] - images.extend(reversed_frames) + if resample_filter is not None: + resample_filter = PIL_FILTER_MAP.get(resample_filter) - height, width, _ = image[0].shape + pil_images = prepare_animated_batch( + image, + pingpong, + resize_by, + resample_filter, + ) ruuid = uuid.uuid4() - ruuid = ruuid.hex[:10] - out_path = f"{folder_paths.output_directory}/{ruuid}.gif" - log.debug(f"Saving a gif file {width}x{height} as {ruuid}.gif") - - # Prepare the FFmpeg command - command = [ - "ffmpeg", - "-y", - "-f", - "rawvideo", - "-vcodec", - "rawvideo", - "-s", - f"{width}x{height}", - "-pix_fmt", - "rgb24", # GIF only supports rgb24 - "-r", - str(fps), - "-i", - "-", - "-vf", - f"fps={fps},scale={width * resize_by}:-1", # Set frame rate and resize if necessary - "-y", + # Create the GIF from PIL images + pil_images[0].save( out_path, - ] - - process = subprocess.Popen(command, stdin=subprocess.PIPE) - - for frame in images: - model_management.throw_exception_if_processing_interrupted() - process.stdin.write(frame.tobytes()) - - process.stdin.close() - process.wait() - results = [] - results.append({"filename": f"{ruuid}.gif", "subfolder": "", "type": "output"}) + save_all=True, + append_images=pil_images[1:], + optimize=optimize, + duration=int(1000 / fps), + loop=0, + ) + + results = [{"filename": f"{ruuid}.gif", "subfolder": "", "type": "output"}] return {"ui": {"gif": results}} -__nodes__ = [SaveGif, ExportToProres] +__nodes__ = [SaveGif, ExportWithFfmpeg] diff --git a/nodes/mask.py b/nodes/mask.py index 64e5119..044faab 100644 --- a/nodes/mask.py +++ b/nodes/mask.py @@ -13,7 +13,7 @@ def INPUT_TYPES(cls): "required": { "image": ("IMAGE",), "alpha_matting": ( - "BOOL", + "BOOLEAN", {"default": False}, ), "alpha_matting_foreground_threshold": ( @@ -29,12 +29,12 @@ def INPUT_TYPES(cls): {"default": 10, "min": 0, "max": 255}, ), "post_process_mask": ( - "BOOL", + "BOOLEAN", {"default": False}, ), "bgcolor": ( "COLOR", - {"default": "black"}, + {"default": "#000000"}, ), }, } diff --git a/nodes/number.py b/nodes/number.py index dc1be4b..6881194 100644 --- a/nodes/number.py +++ b/nodes/number.py @@ -14,7 +14,7 @@ def INPUT_TYPES(cls): } } - RETURN_TYPES = ("BOOL",) + RETURN_TYPES = ("BOOLEAN",) FUNCTION = "int_to_bool" CATEGORY = "mtb/number" diff --git a/nodes/transform.py b/nodes/transform.py index 71d6580..da9429c 100644 --- a/nodes/transform.py +++ b/nodes/transform.py @@ -1,5 +1,9 @@ import torch -import torchvision.transforms.functional as F +import torchvision.transforms.functional as TF +from ..utils import log, hex_to_rgb, tensor2pil, pil2tensor +from math import sqrt, ceil +from typing import cast +from PIL import Image class TransformImage: @@ -14,11 +18,19 @@ def INPUT_TYPES(cls): return { "required": { "image": ("IMAGE",), - "x": ("FLOAT", {"default": 0}), - "y": ("FLOAT", {"default": 0}), - "zoom": ("FLOAT", {"default": 1.0, "min": 0.001}), - "angle": ("FLOAT", {"default": 0}), - "shear": ("FLOAT", {"default": 0}), + "x": ("FLOAT", {"default": 0, "step": 1, "min": -4096, "max": 4096}), + "y": ("FLOAT", {"default": 0, "step": 1, "min": -4096, "max": 4096}), + "zoom": ("FLOAT", {"default": 1.0, "min": 0.001, "step": 0.01}), + "angle": ("FLOAT", {"default": 0, "step": 1, "min": -360, "max": 360}), + "shear": ( + "FLOAT", + {"default": 0, "step": 1, "min": -4096, "max": 4096}, + ), + "border_handling": ( + ["edge", "constant", "reflect", "symmetric"], + {"default": "edge"}, + ), + "constant_color": ("COLOR", {"default": "#000000"}), }, } @@ -32,23 +44,67 @@ def transform( x: float, y: float, zoom: float, - angle: int, - shear, + angle: float, + shear: float, + border_handling="edge", + constant_color=None, ): + x = int(x) + y = int(y) + angle = int(angle) + + log.debug(f"Zoom: {zoom} | x: {x}, y: {y}, angle: {angle}, shear: {shear}") + if image.size(0) == 0: return (torch.zeros(0),) transformed_images = [] - for img in image: - img = img.transpose(0, 2) + frames_count, frame_height, frame_width, frame_channel_count = image.size() + + new_height, new_width = int(frame_height * zoom), int(frame_width * zoom) + + log.debug(f"New height: {new_height}, New width: {new_width}") + + # - Calculate diagonal of the original image + diagonal = sqrt(frame_width**2 + frame_height**2) + max_padding = ceil(diagonal * zoom - min(frame_width, frame_height)) + # Calculate padding for zoom + pw = int(frame_width - new_width) + ph = int(frame_height - new_height) + + pw += abs(max_padding) + ph += abs(max_padding) - transformed_image = F.affine( - img, angle=angle, scale=zoom, translate=[int(y), int(x)], shear=shear + padding = [max(0, pw + x), max(0, ph + y), max(0, pw - x), max(0, ph - y)] + + constant_color = hex_to_rgb(constant_color) + log.debug(f"Fill Tuple: {constant_color}") + + for img in tensor2pil(image): + img = TF.pad( + img, # transformed_frame, + padding=padding, + padding_mode=border_handling, + fill=constant_color or 0, + ) + + img = cast( + Image.Image, + TF.affine(img, angle=angle, scale=zoom, translate=[x, y], shear=shear), ) - transformed_image = transformed_image.transpose(2, 0) - transformed_images.append(transformed_image.unsqueeze(0)) + left = abs(padding[0]) + upper = abs(padding[1]) + right = img.width - abs(padding[2]) + bottom = img.height - abs(padding[3]) + + # log.debug("crop is [:,top:bottom, left:right] for tensors") + log.debug("crop is [left, top, right, bottom] for PIL") + log.debug(f"crop is {left}, {upper}, {right}, {bottom}") + img = img.crop((left, upper, right, bottom)) + + transformed_images.append(img) - return (torch.cat(transformed_images, dim=0),) + return (pil2tensor(transformed_images),) __nodes__ = [TransformImage] diff --git a/reqs.txt b/reqs.txt index b0c986f..852259a 100644 --- a/reqs.txt +++ b/reqs.txt @@ -1,11 +1,7 @@ onnxruntime-gpu==1.15.1 qrcode[pil] rembg==2.0.50 -# on windows non WSL 2.10 is the last version with GPU support -tensorflow==2.10.1; platform_system == "Windows" -tb-nightly==2.12.0a20230126; platform_system == "Windows" -tensorflow; platform_system != "Windows" -# the old tf version on windows comes with a breaking protobuf version +tensorflow facexlib==0.3.0 insightface==0.7.3 basicsr==1.4.2 diff --git a/reqs_windows.txt b/reqs_windows.txt new file mode 100644 index 0000000..4c57cac --- /dev/null +++ b/reqs_windows.txt @@ -0,0 +1,18 @@ +https://github.com/melMass/comfy_mtb/releases/download/v0.1.3/pycocotools-2.0.6-cp310-cp310-win_amd64.whl +https://github.com/melMass/comfy_mtb/releases/download/v0.1.3/future-0.18.3-py3-none-any.whl +https://github.com/melMass/comfy_mtb/releases/download/v0.1.3/filterpy-1.4.5-py3-none-any.whl +https://github.com/melMass/comfy_mtb/releases/download/v0.1.3/easydict-1.10-py3-none-any.whl +https://github.com/melMass/comfy_mtb/releases/download/v0.1.3/gdown-4.7.1-py3-none-any.whl +https://github.com/melMass/comfy_mtb/releases/download/v0.1.3/basicsr-1.4.2-py3-none-any.whl +https://github.com/melMass/comfy_mtb/releases/download/v0.1.3/mmcv-2.0.0-py2.py3-none-any.whl +https://github.com/melMass/comfy_mtb/releases/download/v0.1.3/insightface-0.7.3-cp310-cp310-win_amd64.whl + +onnxruntime-gpu==1.15.1 +qrcode[pil] +rembg==2.0.50 +# on windows non WSL 2.10 is the last version with GPU support +tensorflow==2.10.1; +tb-nightly==2.12.0a20230126; platform_system == "Windows" +facexlib==0.3.0 +# the old tf version on windows comes with a breaking protobuf version +protobuf==3.19.6 \ No newline at end of file diff --git a/interpolate_frames.py b/scripts/interpolate_frames.py similarity index 100% rename from interpolate_frames.py rename to scripts/interpolate_frames.py diff --git a/utils.py b/utils.py index 4068c6b..4da4381 100644 --- a/utils.py +++ b/utils.py @@ -4,10 +4,38 @@ from pathlib import Path import sys from typing import List -from .log import log +import signal +from contextlib import suppress +from queue import Queue, Empty +import subprocess +import threading +import os +import math + +try: + from .log import log +except ImportError: + try: + from log import log + + log.warn("Imported log without relative path") + except ImportError: + import logging + + log = logging.getLogger("comfy mtb utils") + log.warn("[comfy mtb] You probably called the file outside a module.") # region MISC Utilities +def hex_to_rgb(hex_color): + try: + hex_color = hex_color.lstrip("#") + return tuple(int(hex_color[i : i + 2], 16) for i in (0, 2, 4)) + except ValueError: + log.error(f"Invalid hex color: {hex_color}") + return (0, 0, 0) + + def add_path(path, prepend=False): if isinstance(path, list): for p in path: @@ -24,6 +52,79 @@ def add_path(path, prepend=False): sys.path.append(path) +def enqueue_output(out, queue): + for line in iter(out.readline, b""): + queue.put(line) + out.close() + + +def run_command(cmd): + if isinstance(cmd, str): + shell_cmd = cmd + elif isinstance(cmd, list): + shell_cmd = "" + for arg in cmd: + if isinstance(arg, Path): + arg = arg.as_posix() + shell_cmd += f"{arg} " + else: + raise ValueError( + "Invalid 'cmd' argument. It must be a string or a list of arguments." + ) + + process = subprocess.Popen( + shell_cmd, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + universal_newlines=True, + shell=True, + ) + + # Create separate threads to read standard output and standard error streams + stdout_queue = Queue() + stderr_queue = Queue() + stdout_thread = threading.Thread( + target=enqueue_output, args=(process.stdout, stdout_queue) + ) + stderr_thread = threading.Thread( + target=enqueue_output, args=(process.stderr, stderr_queue) + ) + stdout_thread.daemon = True + stderr_thread.daemon = True + stdout_thread.start() + stderr_thread.start() + + interrupted = False + + def signal_handler(signum, frame): + nonlocal interrupted + interrupted = True + print("Command execution interrupted.") + + # Register the signal handler for keyboard interrupts (SIGINT) + signal.signal(signal.SIGINT, signal_handler) + + # Process output from both streams until the process completes or interrupted + while not interrupted and ( + process.poll() is None or not stdout_queue.empty() or not stderr_queue.empty() + ): + with suppress(Empty): + stdout_line = stdout_queue.get_nowait() + if stdout_line.strip() != "": + print(stdout_line.strip()) + with suppress(Empty): + stderr_line = stderr_queue.get_nowait() + if stderr_line.strip() != "": + print(stderr_line.strip()) + return_code = process.returncode + + if return_code == 0 and not interrupted: + print("Command executed successfully!") + else: + if not interrupted: + print(f"Command failed with return code: {return_code}") + + # todo use the requirements library reqs_map = { "onnxruntime": "onnxruntime-gpu==1.15.1", @@ -50,36 +151,51 @@ def import_install(package_name): # endregion + # region GLOBAL VARIABLES -# Get the absolute path of the parent directory of the current script +# - detect mode +comfy_mode = None +if os.environ.get("COLAB_GPU"): + comfy_mode = "colab" +elif "python_embeded" in sys.executable: + comfy_mode = "embeded" +elif ".venv" in sys.executable: + comfy_mode = "venv" + +# - Get the absolute path of the parent directory of the current script here = Path(__file__).parent.resolve() -# Construct the absolute path to the ComfyUI directory +# - Construct the absolute path to the ComfyUI directory comfy_dir = here.parent.parent -# Construct the path to the font file +# - Construct the path to the font file font_path = here / "font.ttf" -# Add extern folder to path +# - Add extern folder to path extern_root = here / "extern" add_path(extern_root) for pth in extern_root.iterdir(): if pth.is_dir(): add_path(pth) - -# Add the ComfyUI directory and custom nodes path to the sys.path list +# - Add the ComfyUI directory and custom nodes path to the sys.path list add_path(comfy_dir) add_path((comfy_dir / "custom_nodes")) + +PIL_FILTER_MAP = { + "nearest": Image.Resampling.NEAREST, + "box": Image.Resampling.BOX, + "bilinear": Image.Resampling.BILINEAR, + "hamming": Image.Resampling.HAMMING, + "bicubic": Image.Resampling.BICUBIC, + "lanczos": Image.Resampling.LANCZOS, +} # endregion # region TENSOR UTILITIES def tensor2pil(image: torch.Tensor) -> List[Image.Image]: - batch_count = 1 - if len(image.shape) > 3: - batch_count = image.size(0) - + batch_count = image.size(0) if len(image.shape) > 3 else 1 if batch_count > 1: out = [] for i in range(batch_count): @@ -108,9 +224,7 @@ def np2tensor(img_np: np.ndarray | List[np.ndarray]) -> torch.Tensor: def tensor2np(tensor: torch.Tensor) -> List[np.ndarray]: - batch_count = 1 - if len(tensor.shape) > 3: - batch_count = tensor.size(0) + batch_count = tensor.size(0) if len(tensor.shape) > 3 else 1 if batch_count > 1: out = [] for i in range(batch_count): @@ -122,6 +236,7 @@ def tensor2np(tensor: torch.Tensor) -> List[np.ndarray]: # endregion + # region MODEL Utilities def download_antelopev2(): antelopev2_url = "https://drive.google.com/uc?id=18wEUfMNohBJ4K3Ly5wpTejPfDzp-8fI8" @@ -161,3 +276,194 @@ def download_antelopev2(): # endregion + + +# region UV Utilities + + +def create_uv_map_tensor(width=512, height=512): + u = torch.linspace(0.0, 1.0, steps=width) + v = torch.linspace(0.0, 1.0, steps=height) + + U, V = torch.meshgrid(u, v) + + uv_map = torch.zeros(height, width, 3, dtype=torch.float32) + uv_map[:, :, 0] = U.t() + uv_map[:, :, 1] = V.t() + + return uv_map.unsqueeze(0) + + +# endregion + + +# region ANIMATION Utilities +def apply_easing(value, easing_type): + if value < 0 or value > 1: + raise ValueError("The value should be between 0 and 1.") + + if easing_type == "Linear": + return value + + # Back easing functions + def easeInBack(t): + s = 1.70158 + return t * t * ((s + 1) * t - s) + + def easeOutBack(t): + s = 1.70158 + return ((t - 1) * t * ((s + 1) * t + s)) + 1 + + def easeInOutBack(t): + s = 1.70158 * 1.525 + if t < 0.5: + return (t * t * (t * (s + 1) - s)) * 2 + return ((t - 2) * t * ((s + 1) * t + s) + 2) * 2 + + # Elastic easing functions + def easeInElastic(t): + if t == 0: + return 0 + if t == 1: + return 1 + p = 0.3 + s = p / 4 + return -(math.pow(2, 10 * (t - 1)) * math.sin((t - 1 - s) * (2 * math.pi) / p)) + + def easeOutElastic(t): + if t == 0: + return 0 + if t == 1: + return 1 + p = 0.3 + s = p / 4 + return math.pow(2, -10 * t) * math.sin((t - s) * (2 * math.pi) / p) + 1 + + def easeInOutElastic(t): + if t == 0: + return 0 + if t == 1: + return 1 + p = 0.3 * 1.5 + s = p / 4 + t = t * 2 + if t < 1: + return -0.5 * ( + math.pow(2, 10 * (t - 1)) * math.sin((t - 1 - s) * (2 * math.pi) / p) + ) + return ( + 0.5 * math.pow(2, -10 * (t - 1)) * math.sin((t - 1 - s) * (2 * math.pi) / p) + + 1 + ) + + # Bounce easing functions + def easeInBounce(t): + return 1 - easeOutBounce(1 - t) + + def easeOutBounce(t): + if t < (1 / 2.75): + return 7.5625 * t * t + elif t < (2 / 2.75): + t -= 1.5 / 2.75 + return 7.5625 * t * t + 0.75 + elif t < (2.5 / 2.75): + t -= 2.25 / 2.75 + return 7.5625 * t * t + 0.9375 + else: + t -= 2.625 / 2.75 + return 7.5625 * t * t + 0.984375 + + def easeInOutBounce(t): + if t < 0.5: + return easeInBounce(t * 2) * 0.5 + return easeOutBounce(t * 2 - 1) * 0.5 + 0.5 + + # Quart easing functions + def easeInQuart(t): + return t * t * t * t + + def easeOutQuart(t): + t -= 1 + return -(t**2 * t * t - 1) + + def easeInOutQuart(t): + t *= 2 + if t < 1: + return 0.5 * t * t * t * t + t -= 2 + return -0.5 * (t**2 * t * t - 2) + + # Cubic easing functions + def easeInCubic(t): + return t * t * t + + def easeOutCubic(t): + t -= 1 + return t**2 * t + 1 + + def easeInOutCubic(t): + t *= 2 + if t < 1: + return 0.5 * t * t * t + t -= 2 + return 0.5 * (t**2 * t + 2) + + # Circ easing functions + def easeInCirc(t): + return -(math.sqrt(1 - t * t) - 1) + + def easeOutCirc(t): + t -= 1 + return math.sqrt(1 - t**2) + + def easeInOutCirc(t): + t *= 2 + if t < 1: + return -0.5 * (math.sqrt(1 - t**2) - 1) + t -= 2 + return 0.5 * (math.sqrt(1 - t**2) + 1) + + # Sine easing functions + def easeInSine(t): + return -math.cos(t * (math.pi / 2)) + 1 + + def easeOutSine(t): + return math.sin(t * (math.pi / 2)) + + def easeInOutSine(t): + return -0.5 * (math.cos(math.pi * t) - 1) + + easing_functions = { + "Sine In": easeInSine, + "Sine Out": easeOutSine, + "Sine In/Out": easeInOutSine, + "Quart In": easeInQuart, + "Quart Out": easeOutQuart, + "Quart In/Out": easeInOutQuart, + "Cubic In": easeInCubic, + "Cubic Out": easeOutCubic, + "Cubic In/Out": easeInOutCubic, + "Circ In": easeInCirc, + "Circ Out": easeOutCirc, + "Circ In/Out": easeInOutCirc, + "Back In": easeInBack, + "Back Out": easeOutBack, + "Back In/Out": easeInOutBack, + "Elastic In": easeInElastic, + "Elastic Out": easeOutElastic, + "Elastic In/Out": easeInOutElastic, + "Bounce In": easeInBounce, + "Bounce Out": easeOutBounce, + "Bounce In/Out": easeInOutBounce, + } + + function_ease = easing_functions.get(easing_type) + if function_ease: + return function_ease(value) + + log.error(f"Unknown easing type: {easing_type}") + log.error(f"Available easing types: {list(easing_functions.keys())}") + raise ValueError(f"Unknown easing type: {easing_type}") + + +# endregion diff --git a/web/comfy_shared.js b/web/comfy_shared.js index 57850ac..f570fac 100644 --- a/web/comfy_shared.js +++ b/web/comfy_shared.js @@ -49,7 +49,7 @@ export function offsetDOMWidget( position: 'absolute', background: !node.color ? '' : node.color, color: !node.color ? '' : 'white', - zIndex: app.graph._nodes.indexOf(node), + zIndex: 5, //app.graph._nodes.indexOf(node), }) } @@ -60,7 +60,7 @@ export function offsetDOMWidget( */ export function getWidgetType(config) { // Special handling for COMBO so we restrict links based on the entries - let type = config[0] + let type = config?.[0] let linkType = type if (type instanceof Array) { type = 'COMBO' diff --git a/web/imageFeed.js b/web/imageFeed.js index 4a2cf4b..45c4973 100644 --- a/web/imageFeed.js +++ b/web/imageFeed.js @@ -31,7 +31,7 @@ const styles = { background: 'none', border: 'none', color: '#fff', - zIndex: 9999999, + zIndex: 1000, fontSize: '30px', cursor: 'pointer', pointerEvents: 'auto', @@ -43,7 +43,7 @@ const styles = { width: '100vw', position: 'absolute', bottom: 0, - zIndex: 9999999, + zIndex: 10, background: '#333', overflow: 'auto', }, diff --git a/web/mtb_widgets.js b/web/mtb_widgets.js index c5f36b4..803d1a4 100644 --- a/web/mtb_widgets.js +++ b/web/mtb_widgets.js @@ -13,7 +13,7 @@ import * as shared from '/extensions/mtb/comfy_shared.js' import { log } from '/extensions/mtb/comfy_shared.js' import { api } from '/scripts/api.js' -const newTypes = ['BOOL', 'COLOR', 'BBOX'] +const newTypes = [, /*'BOOL'*/ 'COLOR', 'BBOX'] export const MtbWidgets = { BBOX: (key, val) => { @@ -204,116 +204,7 @@ export const MtbWidgets = { widget.desc = 'Represents a Bounding Box with x, y, width, and height.' return widget }, - BOOL: (key, val, compute = false) => { - /** @type {import("/types/litegraph").IWidget} */ - const widget = { - name: key, - type: 'BOOL', - options: { default: false }, - y: 0, - - draw: function (ctx, node, widget_width, widgetY, height) { - const hide = this.type !== 'BOOL' && app.canvas.ds.scale > 0.5 - if (hide) { - return - } - const outline_color = LiteGraph.WIDGET_OUTLINE_COLOR - const background_color = LiteGraph.WIDGET_BGCOLOR - const text_color = LiteGraph.WIDGET_TEXT_COLOR - const H = LiteGraph.NODE_WIDGET_HEIGHT - // const arrowSize = 8 - - let margin = 15 - if (hide) return - - let currentY = widgetY - - ctx.textAlign = 'left' - ctx.strokeStyle = outline_color - ctx.fillStyle = background_color - ctx.beginPath() - // ctx.roundRect(margin, currentY, widget_width - margin * 2, H, [H * 0.5]); - ctx.rect(margin, currentY, H, H) // Draw checkbox square - - ctx.fill() - ctx.stroke() - - ctx.fillStyle = text_color - // ctx.fillText(this.label || this.name, margin * 2 + 5, currentY + H * 0.7); - ctx.fillText( - this.label || this.name, - H + margin * 2, - currentY + H * 0.7 - ) - - // Draw arrow if the value is true - // Draw checkmark if the value is true - if (this.value) { - ctx.fillStyle = text_color - ctx.beginPath() - ctx.moveTo(margin + H * 0.15, currentY + H * 0.5) - ctx.lineTo(margin + H * 0.4, currentY + H * 0.8) - ctx.lineTo(margin + H * 0.85, currentY + H * 0.2) - ctx.stroke() - } - }, - get value() { - return this.inputEl.value === 'true' - }, - set value(x) { - this.inputEl.value = x - }, - computeSize: function (width) { - return [width, 32] - }, - mouse: function (event, pos, node) { - // let x = pos[0] - node.pos[0]; - // let y = pos[1] - node.pos[1]; - // let width = node.size[0]; - // let H = LiteGraph.NODE_WIDGET_HEIGHT; - // let margin = 15; - - // if (event.type == LiteGraph.pointerevents_method + "down") { - // if (x > margin && x < widget_width - margin && y > widgetY && y < widgetY + H) { - // this.value = !this.value; // Toggle checkbox value - // shared.inner_value_change(this, this.value, event); - // app.canvas.setDirty(true); - // } - // } - if (event.type === 'pointerdown') { - // get widgets of type type : "COLOR" - const widgets = node.widgets.filter((w) => w.type === 'BOOL') - - for (const w of widgets) { - // color picker - const rect = [w.last_y, w.last_y + 32] - if (pos[1] > rect[0] && pos[1] < rect[1]) { - // picker.style.position = "absolute"; - // picker.style.left = ( pos[0]) + "px"; - // picker.style.top = ( pos[1]) + "px"; - - // place at screen center - // picker.style.position = "absolute"; - // picker.style.left = (window.innerWidth / 2) + "px"; - // picker.style.top = (window.innerHeight / 2) + "px"; - // picker.style.transform = "translate(-50%, -50%)"; - // picker.style.zIndex = 1000; - - this.value = this.value ? false : true - } - } - } - }, - } - // create a checkbox - widget.inputEl = document.createElement('input') - widget.inputEl.type = 'checkbox' - widget.value = val || false - - document.body.appendChild(widget.inputEl) - return widget - }, COLOR: (key, val, compute = false) => { /** @type {import("/types/litegraph").IWidget} */ const widget = {} @@ -890,6 +781,40 @@ const mtb_widgets = { } break } + case 'Interpolate Clip Sequential (mtb)': { + const onNodeCreated = nodeType.prototype.onNodeCreated + nodeType.prototype.onNodeCreated = function () { + const r = onNodeCreated + ? onNodeCreated.apply(this, arguments) + : undefined + const addReplacement = () => { + const input = this.addInput( + `replacement_${this.widgets.length}`, + 'STRING', + '' + ) + console.log(input) + this.addWidget('STRING', `replacement_${this.widgets.length}`, '') + } + //- add + this.addWidget('button', '+', 'add', function (value, widget, node) { + console.log('Button clicked', value, widget, node) + addReplacement() + }) + //- remove + this.addWidget( + 'button', + '-', + 'remove', + function (value, widget, node) { + console.log(`Button clicked: ${value}`, widget, node) + } + ) + + return r + } + break + } case 'Styles Loader (mtb)': { const origGetExtraMenuOptions = nodeType.prototype.getExtraMenuOptions nodeType.prototype.getExtraMenuOptions = function (_, options) {