Upload 3 files

README.md

@@ -4,7 +4,7 @@ emoji: 🌐🌊
 colorFrom: blue
 colorTo: purple
 sdk: gradio
-sdk_version: 4.40.0
+sdk_version: 4.42.0
 app_file: app.py
 short_description: Text-to-Image
 license: mit

app.py

@@ -1,13 +1,12 @@
 import gradio as gr
 from model import models
-from multit2i import (
-    load_models, infer_fn, infer_rand_fn, save_gallery,
+from multit2i import (load_models, infer_fn, infer_rand_fn, save_gallery,
     change_model, warm_model, get_model_info_md, loaded_models,
     get_positive_prefix, get_positive_suffix, get_negative_prefix, get_negative_suffix,
-    get_recom_prompt_type, set_recom_prompt_preset, get_tag_type,
-)
+    get_recom_prompt_type, set_recom_prompt_preset, get_tag_type)
 
 max_images = 8
+MAX_SEED = 2**32-1
 load_models(models)
 
 css = """
@@ -26,10 +25,13 @@ with gr.Blocks(theme="NoCrypt/miku@>=1.2.2", fill_width=True, css=css) as demo:
             prompt = gr.Text(label="Prompt", lines=2, max_lines=8, placeholder="1girl, solo, ...", show_copy_button=True)
             neg_prompt = gr.Text(label="Negative Prompt", lines=1, max_lines=8, placeholder="")
             with gr.Accordion("Advanced options", open=False):
-                width = gr.Number(label="Width", info="If 0, the default value is used.", maximum=1216, step=32, value=0)
-                height = gr.Number(label="Height", info="If 0, the default value is used.", maximum=1216, step=32, value=0)
-                steps = gr.Number(label="Number of inference steps", info="If 0, the default value is used.", maximum=100, step=1, value=0)
-                cfg = gr.Number(label="Guidance scale", info="If 0, the default value is used.", maximum=30.0, step=0.1, value=0)
+                with gr.Row():
+                    width = gr.Number(label="Width", info="If 0, the default value is used.", maximum=1216, step=32, value=0)
+                    height = gr.Number(label="Height", info="If 0, the default value is used.", maximum=1216, step=32, value=0)
+                with gr.Row():
+                    steps = gr.Number(label="Number of inference steps", info="If 0, the default value is used.", maximum=100, step=1, value=0)
+                    cfg = gr.Number(label="Guidance scale", info="If 0, the default value is used.", maximum=30.0, step=0.1, value=0)
+                seed = gr.Slider(label="Seed", info="Randomize Seed if -1.", minimum=-1, maximum=MAX_SEED, step=1, value=-1)
             with gr.Accordion("Recommended Prompt", open=False):
                 recom_prompt_preset = gr.Radio(label="Set Presets", choices=get_recom_prompt_type(), value="Common")
                 with gr.Row():
@@ -81,13 +83,13 @@ with gr.Blocks(theme="NoCrypt/miku@>=1.2.2", fill_width=True, css=css) as demo:
         img_i = gr.Number(i, visible=False)
         image_num.change(lambda i, n: gr.update(visible = (i < n)), [img_i, image_num], o, show_api=False)
         gen_event = gr.on(triggers=[run_button.click, prompt.submit],
-         fn=lambda i, n, m, t1, t2, n1, n2, n3, n4, l1, l2, l3, l4: infer_fn(m, t1, t2, n1, n2, n3, n4, l1, l2, l3, l4) if (i < n) else None,
-         inputs=[img_i, image_num, model_name, prompt, neg_prompt, height, width, steps, cfg,
+         fn=lambda i, n, m, t1, t2, n1, n2, n3, n4, n5, l1, l2, l3, l4: infer_fn(m, t1, t2, n1, n2, n3, n4, n5, l1, l2, l3, l4) if (i < n) else None,
+         inputs=[img_i, image_num, model_name, prompt, neg_prompt, height, width, steps, cfg, seed,
                   positive_prefix, positive_suffix, negative_prefix, negative_suffix],
          outputs=[o], queue=True, show_api=False)
         gen_event2 = gr.on(triggers=[random_button.click],
-         fn=lambda i, n, m, t1, t2, n1, n2, n3, n4, l1, l2, l3, l4: infer_rand_fn(m, t1, t2, n1, n2, n3, n4, l1, l2, l3, l4) if (i < n) else None,
-         inputs=[img_i, image_num, model_name, prompt, neg_prompt, height, width, steps, cfg,
+         fn=lambda i, n, m, t1, t2, n1, n2, n3, n4, n5, l1, l2, l3, l4: infer_rand_fn(m, t1, t2, n1, n2, n3, n4, n5, l1, l2, l3, l4) if (i < n) else None,
+         inputs=[img_i, image_num, model_name, prompt, neg_prompt, height, width, steps, cfg, seed,
                   positive_prefix, positive_suffix, negative_prefix, negative_suffix],
          outputs=[o], queue=True, show_api=False)
         o.change(save_gallery, [o, results], [results, image_files], show_api=False)

multit2i.py

@@ -6,7 +6,7 @@ from huggingface_hub import InferenceClient
 import os
 
 
-HF_TOKEN = os.environ.get("HF_TOKEN") if os.environ.get("HF_TOKEN") else None
+HF_TOKEN = os.environ.get("HF_TOKEN") if os.environ.get("HF_TOKEN") else None # If private or gated models aren't used, ENV setting is unnecessary.
 server_timeout = 600
 inference_timeout = 300
 
@@ -354,13 +354,14 @@ def warm_model(model_name: str):
 # https://huggingface.co/docs/huggingface_hub/package_reference/inference_client
 def infer_body(client: InferenceClient | gr.Interface, prompt: str, neg_prompt: str | None = None,
                height: int | None = None, width: int | None = None,
-               steps: int | None = None, cfg: int | None = None):
+               steps: int | None = None, cfg: int | None = None, seed: int = -1):
     png_path = "image.png"
     kwargs = {}
     if height is not None and height >= 256: kwargs["height"] = height
     if width is not None and width >= 256: kwargs["width"] = width
     if steps is not None and steps >= 1: kwargs["num_inference_steps"] = steps
     if cfg is not None and cfg > 0: cfg = kwargs["guidance_scale"] = cfg
+    if seed >= 0: kwargs["seed"] = seed
     try:
         if isinstance(client, InferenceClient):
             image = client.text_to_image(prompt=prompt, negative_prompt=neg_prompt, **kwargs, token=HF_TOKEN)
@@ -376,17 +377,18 @@ def infer_body(client: InferenceClient | gr.Interface, prompt: str, neg_prompt:
 
 async def infer(model_name: str, prompt: str, neg_prompt: str | None = None,
                height: int | None = None, width: int | None = None,
-               steps: int | None = None, cfg: int | None = None,
+               steps: int | None = None, cfg: int | None = None, seed: int = -1,
                save_path: str | None = None, timeout: float = inference_timeout):
     import random
     noise = ""
-    rand = random.randint(1, 500)
-    for i in range(rand):
-        noise += " "
+    if seed < 0:
+        rand = random.randint(1, 500)
+        for i in range(rand):
+            noise += " "
     model = load_model(model_name)
     if not model: return None
     task = asyncio.create_task(asyncio.to_thread(infer_body, model, f"{prompt} {noise}", neg_prompt,
-                                                 height, width, steps, cfg))
+                                                 height, width, steps, cfg, seed))
     await asyncio.sleep(0)
     try:
         result = await asyncio.wait_for(task, timeout=timeout)
@@ -403,7 +405,7 @@ async def infer(model_name: str, prompt: str, neg_prompt: str | None = None,
 
 
 def infer_fn(model_name: str, prompt: str, neg_prompt: str | None = None, height: int | None = None,
-             width: int | None = None, steps: int | None = None, cfg: int | None = None,
+             width: int | None = None, steps: int | None = None, cfg: int | None = None, seed: int = -1,
              pos_pre: list = [], pos_suf: list = [], neg_pre: list = [], neg_suf: list = [], save_path: str | None = None):
     if model_name == 'NA':
         return None
@@ -411,7 +413,7 @@ def infer_fn(model_name: str, prompt: str, neg_prompt: str | None = None, height
         prompt, neg_prompt = recom_prompt(prompt, neg_prompt, pos_pre, pos_suf, neg_pre, neg_suf)
         loop = asyncio.new_event_loop()
         result = loop.run_until_complete(infer(model_name, prompt, neg_prompt, height, width,
-                                               steps, cfg, save_path, inference_timeout))
+                                               steps, cfg, seed, save_path, inference_timeout))
     except (Exception, asyncio.CancelledError) as e:
         print(e)
         print(f"Task aborted: {model_name}")
@@ -422,7 +424,7 @@ def infer_fn(model_name: str, prompt: str, neg_prompt: str | None = None, height
 
 
 def infer_rand_fn(model_name_dummy: str, prompt: str, neg_prompt: str | None = None, height: int | None = None,
-             width: int | None = None, steps: int | None = None, cfg: int | None = None,
+             width: int | None = None, steps: int | None = None, cfg: int | None = None, seed: int = -1,
              pos_pre: list = [], pos_suf: list = [], neg_pre: list = [], neg_suf: list = [], save_path: str | None = None):
     import random
     if model_name_dummy == 'NA':
@@ -433,7 +435,7 @@ def infer_rand_fn(model_name_dummy: str, prompt: str, neg_prompt: str | None = N
         prompt, neg_prompt = recom_prompt(prompt, neg_prompt, pos_pre, pos_suf, neg_pre, neg_suf)
         loop = asyncio.new_event_loop()
         result = loop.run_until_complete(infer(model_name, prompt, neg_prompt, height, width,
-                                               steps, cfg, save_path, inference_timeout))
+                                               steps, cfg, seed, save_path, inference_timeout))
     except (Exception, asyncio.CancelledError) as e:
         print(e)
         print(f"Task aborted: {model_name}")