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app_copy.py

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+import json
+import os
+import random
+import time
+from pathlib import Path
+
+import gradio as gr
+import requests
+
+from PIL import Image
+
+
+BASE_FOLDER = Path("C:/Users/risha/OneDrive/Documents/CourseWork/ComfyUI/ComfyUI_windows_portable/ComfyUI")
+MODELS_FOLDER = BASE_FOLDER / "models/checkpoints"
+UPSCALE_MODELS_FOLDER = BASE_FOLDER / "models/upscale_models"
+INPUT_DIR = BASE_FOLDER / "input"
+OUTPUT_DIR = BASE_FOLDER / "output"
+URL = "http://127.0.0.1:8188/prompt"
+TXT2IMG = "workflow_api_text2img.json"
+IMG2IMG = "workflow_api_img2img.json"
+HIRES = "workflow_api_HiRes.json"
+
+KSAMPLER_NAMES = [
+    "euler",
+    "euler_ancestral",
+    "dpmpp_sde",
+    "dpmpp_2m",
+    "dpmpp_2m_sde",
+    "dpmpp_3m_sde",
+    "ddpm",
+    "lcm",
+]
+
+SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform"]
+
+
+css = """
+.green {
+    border: 3px solid green !important;
+}
+.red {
+    border: 3px solid red !important;
+}
+"""
+
+
+def checkpoint_list():
+    return [model.name for model in MODELS_FOLDER.glob("*")]
+
+
+
+def get_latest_image():
+    image_files = list(OUTPUT_DIR.glob("*.png"))
+    image_files.sort(key=lambda x: os.path.getmtime(x))
+    latest_image = image_files[-1] if image_files else None
+    return latest_image
+
+
+def start_queue(prompt_workflow):
+    p = {"prompt": prompt_workflow}
+    data = json.dumps(p).encode("utf-8")
+    requests.post(URL, data=data)
+
+
+def txt2img_workflow(checkpoint_name, positive_prompt):
+    with open(TXT2IMG, "r") as file_json:
+        prompt = json.load(file_json)
+
+    prompt["1"]["inputs"]["ckpt_name"] = checkpoint_name
+    prompt["2"]["inputs"]["noise_seed"] = random.randint(1, 999999999999)
+    prompt["3"]["inputs"]["text"] = positive_prompt
+
+    print( prompt["2"]["inputs"]["noise_seed"])
+    previous_image = get_latest_image()
+
+    start_queue(prompt)
+
+    while True:
+        latest_image = get_latest_image()
+        if latest_image != previous_image:
+            return latest_image
+
+        time.sleep(1)
+
+
+
+def main():
+    with gr.Blocks(css=css) as demo:
+        models = checkpoint_list()
+
+        with gr.Row():
+            with gr.Column():
+                base_checkpoint = gr.Dropdown(choices=models, label="Stable Diffusion Checkpoint")
+
+        with gr.Tab("txt2img"):
+            workflow = TXT2IMG
+            with gr.Row():
+                with gr.Column(scale=3):
+                    positive = gr.Textbox(lines=4, placeholder="Positive prompt", container=False, elem_classes="green")
+
+                with gr.Column(scale=1):
+                    generate_btn_txt2img = gr.Button("Generate")
+
+            with gr.Row():
+                output_img = gr.Image(label="Output", interactive=False)
+
+        generate_btn_txt2img.click(fn=txt2img_workflow, inputs=[base_checkpoint, positive], outputs=output_img)
+
+    demo.launch()
+
+
+if __name__ == "__main__":
+    main()

workflow_api_text2img.json

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+{
+  "1": {
+    "inputs": {
+      "ckpt_name": "sd_xl_turbo_1.0_fp16.safetensors"
+    },
+    "class_type": "CheckpointLoaderSimple",
+    "_meta": {
+      "title": "Load Checkpoint"
+    }
+  },
+  "2": {
+    "inputs": {
+      "add_noise": true,
+      "noise_seed": 240322318706585,
+      "cfg": 1.1,
+      "model": [
+        "1",
+        0
+      ],
+      "positive": [
+        "3",
+        0
+      ],
+      "negative": [
+        "4",
+        0
+      ],
+      "sampler": [
+        "6",
+        0
+      ],
+      "sigmas": [
+        "7",
+        0
+      ],
+      "latent_image": [
+        "8",
+        0
+      ]
+    },
+    "class_type": "SamplerCustom",
+    "_meta": {
+      "title": "SamplerCustom"
+    }
+  },
+  "3": {
+    "inputs": {
+      "text": "an colorful 3d blender anime portrait of a girl with black long hair and glasses",
+      "clip": [
+        "1",
+        1
+      ]
+    },
+    "class_type": "CLIPTextEncode",
+    "_meta": {
+      "title": "CLIP Text Encode (Prompt)"
+    }
+  },
+  "4": {
+    "inputs": {
+      "text": "",
+      "clip": [
+        "1",
+        1
+      ]
+    },
+    "class_type": "CLIPTextEncode",
+    "_meta": {
+      "title": "CLIP Text Encode (Prompt)"
+    }
+  },
+  "5": {
+    "inputs": {
+      "samples": [
+        "2",
+        0
+      ],
+      "vae": [
+        "1",
+        2
+      ]
+    },
+    "class_type": "VAEDecode",
+    "_meta": {
+      "title": "VAE Decode"
+    }
+  },
+  "6": {
+    "inputs": {
+      "sampler_name": "euler_ancestral"
+    },
+    "class_type": "KSamplerSelect",
+    "_meta": {
+      "title": "KSamplerSelect"
+    }
+  },
+  "7": {
+    "inputs": {
+      "steps": 10,
+      "denoise": 1,
+      "model": [
+        "1",
+        0
+      ]
+    },
+    "class_type": "SDTurboScheduler",
+    "_meta": {
+      "title": "SDTurboScheduler"
+    }
+  },
+  "8": {
+    "inputs": {
+      "width": 512,
+      "height": 512,
+      "batch_size": 1
+    },
+    "class_type": "EmptyLatentImage",
+    "_meta": {
+      "title": "Empty Latent Image"
+    }
+  },
+  "30": {
+    "inputs": {
+      "filename_prefix": "ComfyUI",
+      "images": [
+        "5",
+        0
+      ]
+    },
+    "class_type": "SaveImage",
+    "_meta": {
+      "title": "Save Image"
+    }
+  }
+}