Add files via upload

api_client.py

@@ -0,0 +1,180 @@
+import io
+import json
+import uuid
+import requests
+import websocket
+from typing import Literal
+
+def open_websocket_connection(server_address):
+    try:
+        client_id = str(uuid.uuid4())
+        ws = websocket.WebSocket()
+        ws.connect(f"ws://{server_address}/ws?clientId={client_id}")
+        return ws, client_id
+    except Exception as e:
+        print(f"Error open websocket connection: {e}")
+
+def get_embeddings(server_address):
+    response = requests.get(f"http://{server_address}/embeddings")
+    return response.json()
+
+def get_models_types(server_address):
+    response = requests.get(f"http://{server_address}/models")
+    return response.json()
+
+def get_models_folder(server_address, folder):
+    response = requests.get(f"http://{server_address}/models/{folder}")
+    return response.json()
+
+def get_extensions(server_address):
+    response = requests.get(f"http://{server_address}/extensions")
+    return response.json()
+
+def upload_image_file(server_address, file, subfolder=None, overwrite=False,
+                      type: Literal["input", "temp", "output"] = "input"):
+    try:
+        files = {"image": file}
+        data = {"type": type, "subfolder": subfolder, "overwrite": overwrite}
+        response = requests.post(f"http://{server_address}/upload/image", files=files, data=data)
+        response.raise_for_status()
+        file_info = response.json()
+        file_path = file_info["name"]
+        if file_info["subfolder"]:
+            file_path = file_info["subfolder"] + "/" + file_path
+        return file_path
+    except Exception as e:
+        print(f"Error upload image file: {e}")
+
+def upload_mask_file(server_address, file, subfolder=None, overwrite=False,
+                     type: Literal["input", "temp", "output"] = "input"):
+    try:
+        files = {"image": file}
+        data = {"type": type, "subfolder": subfolder, "overwrite": overwrite}
+        response = requests.post(f"http://{server_address}/upload/mask", files=files, data=data)
+        response.raise_for_status()
+        file_info = response.json()
+        file_path = file_info["name"]
+        if file_info["subfolder"]:
+            file_path = file_info["subfolder"] + "/" + file_path
+        return file_path
+    except Exception as e:
+        print(f"Error upload mask file: {e}")
+
+def get_image_file(server_address, filename, subfolder=None, preview=None, channel=None,
+                   type: Literal["input", "temp", "output"] = "output"):
+    try:
+        params = {"filename": filename, "type": type}
+        if subfolder:
+            params["subfolder"] = subfolder
+        if preview:
+            params["preview"] = preview
+        if channel:
+            params["channel"] = channel
+        response = requests.get(f"http://{server_address}/view", params=params)
+        response.raise_for_status()
+        image_file = io.BytesIO()
+        image_file.name = filename
+        image_file.write(response.content)
+        image_file.seek(0)
+        return image_file
+    except Exception as e:
+        print(f"Error get iamge file: {e}")
+
+def get_images_files(server_address, prompt_id, download_preview=False):
+    hisstory = get_history_prompt(server_address, prompt_id)[prompt_id]
+    images_files = []
+    for node_id in hisstory["outputs"]:
+        node_output = hisstory["outputs"][node_id]
+        if "images" in node_output:
+            for image in node_output["images"]:
+                if image["type"] == "output" or (image["type"] == "temp" and download_preview):
+                    image_file = get_image_file(server_address, image["filename"], image["subfolder"], type=image["type"])
+                    images_files.append(image_file)
+    return images_files
+
+def get_metadata(server_address, folder_name, filename=".safetensors"):
+    params = {"filename": filename}
+    response = requests.get(f"http://{server_address}/view_metadata/{folder_name}", params=params)
+    return response.json()
+
+def get_system_stats(server_address):
+    response = requests.get(f"http://{server_address}/system_stats")
+    return response.json()
+
+def get_prompt(server_address):
+    response = requests.get(f"http://{server_address}/prompt")
+    return response.json()
+
+def get_object_info(server_address):
+    response = requests.get(f"http://{server_address}/object_info")
+    return response.json()
+
+def get_object_info_node(server_address, node_class):
+    response = requests.get(f"http://{server_address}/object_info/{node_class}")
+    return response.json()
+
+def get_history(server_address, max_items=None):
+    params = {"max_items": max_items}
+    response = requests.get(f"http://{server_address}/history", params=params)
+    return response.json()
+
+def get_history_prompt(server_address, prompt_id):
+    response = requests.get(f"http://{server_address}/history/{prompt_id}")
+    return response.json()
+
+def get_queue(server_address):
+    response = requests.get(f"http://{server_address}/queue")
+    return response.json()
+
+def queue_prompt(server_address, prompt, client_id=None):
+    json = {"prompt": prompt}
+    if client_id:
+        json["client_id"] = client_id
+    response = requests.post(f"http://{server_address}/prompt", json=json)
+    return response.json()
+
+def queue_clear_or_delete(server_address, clear=False, delete_prompt_id=None):
+    json = {"clear": clear}
+    if delete_prompt_id: # 删除指定队列
+        json["delete"] = delete_prompt_id
+    return requests.post(f"http://{server_address}/queue", json=json)
+
+def queue_interrupt(server_address):
+    return requests.post(f"http://{server_address}/interrupt")
+
+def queue_free(server_address, unload_models=False, free_memory=False):
+    json = {"unload_models": unload_models, "free_memory": free_memory}
+    return requests.post(f"http://{server_address}/free", json=json)
+
+def history_clear_or_delete(server_address, clear=False, delete_prompt_id=None):
+    json = {"clear": clear}
+    if delete_prompt_id: # 删除历史记录
+        json["delete"] = delete_prompt_id
+    return requests.post(f"http://{server_address}/history", json=json)
+
+def track_progress(ws, prompt, prompt_id):
+    node_ids = list(prompt.keys())
+    finished_nodes = []
+    while True:
+        message = ws.recv()
+        if isinstance(message, str):
+            message = json.loads(message)
+            if message["type"] == "progress":
+                step = message["data"]["value"]
+                max_step = message["data"]["max"]
+                print(f"K-Sampler Progress: Step {step} of {max_step}")
+            elif message["type"] == "execution_cached":
+                node_id = message["data"]["nodes"]
+                if node_id not in finished_nodes:
+                    finished_nodes.append(node_id)
+                print(f"Total Progress: Tasks completed {len(finished_nodes)}/{len(node_ids)}")
+                if node_id is None and message["data"]["prompt_id"] == prompt_id:
+                    break
+            elif message["type"] == "executing":
+                node_id = message["data"]["node"]
+                if node_id not in finished_nodes:
+                    finished_nodes.append(node_id)
+                print(f"Total Progress: Tasks completed {len(finished_nodes)}/{len(node_ids)}")
+                if node_id is None and message["data"]["prompt_id"] == prompt_id:
+                    break
+    return

img2vid.py

@@ -0,0 +1,57 @@
+import io
+import json
+import random
+from api_client import *
+from pydantic import BaseModel
+from fastapi import FastAPI, UploadFile, File, Form, responses
+
+API_URL = "127.0.0.1:8181"
+
+with open("prompt/img2vid.json", "r") as f:
+    prompt = json.load(f)
+
+class Img2vidParams(BaseModel):
+    file: UploadFile = File(...) #  图片文件
+    width: int = 512
+    height: int = 512
+    video_frames: int = 25 # 视频帧数
+    motion_bucket_id: int = 100 # 视频动作量
+    fps: int = 8 # 视频流畅度
+    seed: int = None
+    steps: int = 20
+    cfg: float = 2.0
+    save_fps: int = 8 # 视频帧率
+
+app = FastAPI()
+@app.post("/img2vid")
+async def img2vid(params: Img2vidParams = Form(...)):
+    image_file = io.BytesIO()
+    image_file.name = params.file.filename
+    image_file.write(await params.file.read())
+    image_file.seek(0)
+    image_path = upload_image_file(API_URL, image_file)
+    prompt["4"]["inputs"]["image"] = image_path
+
+    prompt["3"]["inputs"]["width"] = params.width
+    prompt["3"]["inputs"]["height"] = params.height
+    prompt["3"]["inputs"]["video_frames"] = params.video_frames
+    prompt["3"]["inputs"]["motion_bucket_id"] = params.motion_bucket_id
+    prompt["3"]["inputs"]["fps"] = params.fps
+    prompt["5"]["inputs"]["seed"] = random.randint(0, 1e16)
+    if params.seed is not None:
+        prompt["5"]["inputs"]["seed"] = params.seed
+    prompt["5"]["inputs"]["steps"] = params.steps
+    prompt["5"]["inputs"]["cfg"] = params.cfg
+    prompt["7"]["inputs"]["save_fps"] = params.save_fps
+
+    ws, client_id = open_websocket_connection(API_URL)
+    print("client_id: ", client_id)
+    response = queue_prompt(API_URL, prompt, client_id)
+    prompt_id = response["prompt_id"]
+    print("prompt_id: ", prompt_id)
+    track_progress(ws, prompt, prompt_id)
+    outputs = get_images_files(API_URL, prompt_id)
+    return responses.Response(content=outputs[0].read())
+
+import uvicorn
+uvicorn.run(app, host="0.0.0.0", port=8182)

prompt/img2vid.json

@@ -0,0 +1,123 @@
+{
+  "1": {
+    "inputs": {
+      "ckpt_name": "svd_xt_image_decoder.safetensors"
+    },
+    "class_type": "ImageOnlyCheckpointLoader",
+    "_meta": {
+      "title": "Image Only Checkpoint Loader (img2vid model)"
+    }
+  },
+  "2": {
+    "inputs": {
+      "min_cfg": 1,
+      "model": [
+        "1",
+        0
+      ]
+    },
+    "class_type": "VideoLinearCFGGuidance",
+    "_meta": {
+      "title": "VideoLinearCFGGuidance"
+    }
+  },
+  "3": {
+    "inputs": {
+      "width": 512,
+      "height": 512,
+      "video_frames": 25,
+      "motion_bucket_id": 100,
+      "fps": 8,
+      "augmentation_level": 0,
+      "clip_vision": [
+        "1",
+        1
+      ],
+      "init_image": [
+        "4",
+        0
+      ],
+      "vae": [
+        "1",
+        2
+      ]
+    },
+    "class_type": "SVD_img2vid_Conditioning",
+    "_meta": {
+      "title": "SVD_img2vid_Conditioning"
+    }
+  },
+  "4": {
+    "inputs": {
+      "image": "chenglong.png",
+      "upload": "image"
+    },
+    "class_type": "LoadImage",
+    "_meta": {
+      "title": "Load Image"
+    }
+  },
+  "5": {
+    "inputs": {
+      "seed": 670980613132736,
+      "steps": 20,
+      "cfg": 2,
+      "sampler_name": "dpmpp_2m",
+      "scheduler": "karras",
+      "denoise": 1,
+      "model": [
+        "2",
+        0
+      ],
+      "positive": [
+        "3",
+        0
+      ],
+      "negative": [
+        "3",
+        1
+      ],
+      "latent_image": [
+        "3",
+        2
+      ]
+    },
+    "class_type": "KSampler",
+    "_meta": {
+      "title": "KSampler"
+    }
+  },
+  "6": {
+    "inputs": {
+      "samples": [
+        "5",
+        0
+      ],
+      "vae": [
+        "1",
+        2
+      ]
+    },
+    "class_type": "VAEDecode",
+    "_meta": {
+      "title": "VAE Decode"
+    }
+  },
+  "7": {
+    "inputs": {
+      "filename_prefix": "ComfyUI",
+      "fps": 8,
+      "lossless": false,
+      "quality": 80,
+      "method": "default",
+      "images": [
+        "6",
+        0
+      ]
+    },
+    "class_type": "SaveAnimatedWEBP",
+    "_meta": {
+      "title": "SaveAnimatedWEBP"
+    }
+  }
+}

prompt/text2img.json

@@ -0,0 +1,106 @@
+{
+  "1": {
+    "inputs": {
+      "ckpt_name": "helloyoung25d_V15j.safetensors"
+    },
+    "class_type": "CheckpointLoaderSimple",
+    "_meta": {
+      "title": "Load Checkpoint"
+    }
+  },
+  "2": {
+    "inputs": {
+      "text": "a boy",
+      "clip": [
+        "1",
+        1
+      ]
+    },
+    "class_type": "CLIPTextEncode",
+    "_meta": {
+      "title": "CLIP Text Encode (Prompt)"
+    }
+  },
+  "3": {
+    "inputs": {
+      "text": "text",
+      "clip": [
+        "1",
+        1
+      ]
+    },
+    "class_type": "CLIPTextEncode",
+    "_meta": {
+      "title": "CLIP Text Encode (Prompt)"
+    }
+  },
+  "4": {
+    "inputs": {
+      "seed": 971044526173875,
+      "steps": 20,
+      "cfg": 8,
+      "sampler_name": "dpmpp_2m",
+      "scheduler": "karras",
+      "denoise": 1,
+      "model": [
+        "1",
+        0
+      ],
+      "positive": [
+        "2",
+        0
+      ],
+      "negative": [
+        "3",
+        0
+      ],
+      "latent_image": [
+        "5",
+        0
+      ]
+    },
+    "class_type": "KSampler",
+    "_meta": {
+      "title": "KSampler"
+    }
+  },
+  "5": {
+    "inputs": {
+      "width": 512,
+      "height": 512,
+      "batch_size": 1
+    },
+    "class_type": "EmptyLatentImage",
+    "_meta": {
+      "title": "Empty Latent Image"
+    }
+  },
+  "6": {
+    "inputs": {
+      "samples": [
+        "4",
+        0
+      ],
+      "vae": [
+        "1",
+        2
+      ]
+    },
+    "class_type": "VAEDecode",
+    "_meta": {
+      "title": "VAE Decode"
+    }
+  },
+  "7": {
+    "inputs": {
+      "images": [
+        "6",
+        0
+      ]
+    },
+    "class_type": "PreviewImage",
+    "_meta": {
+      "title": "Preview Image"
+    }
+  }
+}

prompt/text2img_lora.json

@@ -0,0 +1,125 @@
+{
+  "1": {
+    "inputs": {
+      "ckpt_name": "helloyoung25d_V15j.safetensors"
+    },
+    "class_type": "CheckpointLoaderSimple",
+    "_meta": {
+      "title": "Load Checkpoint"
+    }
+  },
+  "2": {
+    "inputs": {
+      "lora_name": "ClayAnimation.safetensors",
+      "strength_model": 1,
+      "strength_clip": 1,
+      "model": [
+        "1",
+        0
+      ],
+      "clip": [
+        "1",
+        1
+      ]
+    },
+    "class_type": "LoraLoader",
+    "_meta": {
+      "title": "Load LoRA"
+    }
+  },
+  "3": {
+    "inputs": {
+      "text": "a boy",
+      "clip": [
+        "2",
+        1
+      ]
+    },
+    "class_type": "CLIPTextEncode",
+    "_meta": {
+      "title": "CLIP Text Encode (Prompt)"
+    }
+  },
+  "4": {
+    "inputs": {
+      "text": "text",
+      "clip": [
+        "2",
+        1
+      ]
+    },
+    "class_type": "CLIPTextEncode",
+    "_meta": {
+      "title": "CLIP Text Encode (Prompt)"
+    }
+  },
+  "5": {
+    "inputs": {
+      "seed": 101018934678671,
+      "steps": 20,
+      "cfg": 8,
+      "sampler_name": "dpmpp_2m",
+      "scheduler": "karras",
+      "denoise": 1,
+      "model": [
+        "2",
+        0
+      ],
+      "positive": [
+        "3",
+        0
+      ],
+      "negative": [
+        "4",
+        0
+      ],
+      "latent_image": [
+        "6",
+        0
+      ]
+    },
+    "class_type": "KSampler",
+    "_meta": {
+      "title": "KSampler"
+    }
+  },
+  "6": {
+    "inputs": {
+      "width": 512,
+      "height": 512,
+      "batch_size": 1
+    },
+    "class_type": "EmptyLatentImage",
+    "_meta": {
+      "title": "Empty Latent Image"
+    }
+  },
+  "7": {
+    "inputs": {
+      "samples": [
+        "5",
+        0
+      ],
+      "vae": [
+        "1",
+        2
+      ]
+    },
+    "class_type": "VAEDecode",
+    "_meta": {
+      "title": "VAE Decode"
+    }
+  },
+  "8": {
+    "inputs": {
+      "images": [
+        "7",
+        0
+      ]
+    },
+    "class_type": "PreviewImage",
+    "_meta": {
+      "title": "Preview Image"
+    }
+  }
+}

test/test.ipynb

@@ -0,0 +1,56 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "data = {\n",
+    "    \"width\": 512, \"height\": 512, \"video_frames\": 25, \"motion_bucket_id\": 100, \"fps\": 8, # 条件\n",
+    "    \"seed\": None, \"steps\": 20, \"cfg\": 2.0, #采样\n",
+    "    \"save_fps\": 8, # 保存\n",
+    "}\n",
+    "files = {\"file\": open(\"input/test.png\", \"rb\")}\n",
+    "response = requests.post(\"http://192.168.13.121:8182/img2vid\", files=files, data=data)\n",
+    "with open(\"output/test.webp\", \"wb\") as f:\n",
+    "    f.write(response.content)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "json = {\"prompt\": \"a cute boy\", \"style\": \"Clay\", \"seed\": None, \"num\": 1}\n",
+    "response = requests.post(\"http://192.168.13.121:8183/text2img_lora\", json=json)\n",
+    "with open(\"output/test.png\", \"wb\") as f:\n",
+    "    f.write(response.content)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "diffusion",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

text2img_lora.py

@@ -0,0 +1,39 @@
+import json
+import random
+from api_client import *
+from typing import Literal
+from pydantic import BaseModel
+from fastapi import FastAPI, Form, responses
+
+API_URL = "127.0.0.1:8181"
+
+with open("prompt/text2img_lora.json", "r") as f:
+    prompt = json.load(f)
+
+class Text2imgParams(BaseModel):
+    prompt: str = "a cute boy"
+    style: Literal["qban", "guofeng", "Clay", "Cyberpunk"] = "Clay"
+    seed: int = None
+    num: int = 1
+
+app = FastAPI()
+@app.post("/text2img_lora")
+async def text2img(params: Text2imgParams = Form(...)):
+    prompt["2"]["inputs"]["lora_name"] = params.style + ".safetensors"
+    prompt["3"]["inputs"]["text"] = params.style + ", " + params.prompt
+    prompt["5"]["inputs"]["seed"] = random.randint(0, 1e16)
+    if params.seed is not None:
+        prompt["5"]["inputs"]["seed"] = params.seed
+    prompt["6"]["inputs"]["batch_size"] = params.num
+
+    ws, client_id = open_websocket_connection(API_URL)
+    print("client_id: ", client_id)
+    response = queue_prompt(API_URL, prompt, client_id)
+    prompt_id = response["prompt_id"]
+    print("prompt_id: ", prompt_id)
+    track_progress(ws, prompt, prompt_id)
+    outputs = get_images_files(API_URL, prompt_id, download_preview=True)
+    return responses.Response(content=outputs[0].read())
+
+import uvicorn
+uvicorn.run(app, host="0.0.0.0", port=8183)