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Update app.py
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app.py
CHANGED
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@@ -4,12 +4,11 @@ import gradio as gr
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import numpy as np
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import random
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#from diffusers import FluxPipeline
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from huggingface_hub import
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from translatepy import Translator
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#from huggingface_hub import hf_hub_download
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import requests
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import re
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import asyncio
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from PIL import Image
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os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
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@@ -32,8 +31,7 @@ JS = """function () {
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}
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}"""
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-
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client2 = AsyncInferenceClient()
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def enable_lora(lora_in, lora_add):
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if not lora_in and not lora_add:
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@@ -43,7 +41,7 @@ def enable_lora(lora_in, lora_add):
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lora_in = lora_add
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return lora_in
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-
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prompt:str,
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model:str,
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width:int=768,
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@@ -61,7 +59,7 @@ async def generate_image(
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#generator = torch.Generator().manual_seed(seed)
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image1 =
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prompt=text,
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height=height,
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width=width,
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@@ -69,7 +67,7 @@ async def generate_image(
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num_inference_steps=steps,
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model=basemodel,
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)
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image2 =
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prompt=text,
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height=height,
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width=width,
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@@ -79,7 +77,7 @@ async def generate_image(
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)
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return image1, image2, seed
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-
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prompt:str,
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lora_in:str="",
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lora_add:str="",
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@@ -92,7 +90,7 @@ async def gen(
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):
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model = enable_lora(lora_in, lora_add)
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print(model)
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image1, image2, seed =
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return image1, image2, seed
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import numpy as np
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import random
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#from diffusers import FluxPipeline
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from huggingface_hub import InferenceClient
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from translatepy import Translator
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#from huggingface_hub import hf_hub_download
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import requests
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import re
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from PIL import Image
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os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
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}
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}"""
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client = InferenceClient()
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def enable_lora(lora_in, lora_add):
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if not lora_in and not lora_add:
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lora_in = lora_add
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return lora_in
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def generate_image(
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prompt:str,
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model:str,
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width:int=768,
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#generator = torch.Generator().manual_seed(seed)
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image1 = client.text_to_image(
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prompt=text,
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height=height,
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width=width,
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num_inference_steps=steps,
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model=basemodel,
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)
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image2 = client.text_to_image(
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prompt=text,
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height=height,
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width=width,
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)
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return image1, image2, seed
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def gen(
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prompt:str,
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lora_in:str="",
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lora_add:str="",
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):
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model = enable_lora(lora_in, lora_add)
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print(model)
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image1, image2, seed = generate_image(prompt,model,width,height,scales,steps,seed)
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return image1, image2, seed
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