Spaces:
Running
on
Zero
Running
on
Zero
prompt PromptWeighting
Browse files- README.md +1 -1
- app.py +83 -55
- gradio_promptweighting-0.0.1-py3-none-any.whl +0 -0
- requirements.txt +6 -3
README.md
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@@ -4,7 +4,7 @@ emoji: ⚡️⚡️⚡️⚡️
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colorFrom: yellow
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colorTo: purple
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sdk: gradio
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sdk_version: 4.
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app_file: app.py
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pinned: false
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disable_embedding: true
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colorFrom: yellow
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colorTo: purple
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sdk: gradio
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sdk_version: 4.25.0
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app_file: app.py
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pinned: false
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disable_embedding: true
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app.py
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from diffusers import (
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StableDiffusionXLPipeline,
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EulerDiscreteScheduler,
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import torch
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import os
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from huggingface_hub import hf_hub_download
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from PIL import Image
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CHECKPOINT = "sdxl_lightning_2step_unet.safetensors"
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taesd_model = "madebyollin/taesdxl"
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# {
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# "1-Step": ["sdxl_lightning_1step_unet_x0.safetensors", 1],
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# "2-Step": ["sdxl_lightning_2step_unet.safetensors", 2],
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# "4-Step": ["sdxl_lightning_4step_unet.safetensors", 4],
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# "8-Step": ["sdxl_lightning_8step_unet.safetensors", 8],
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# }
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SFAST_COMPILE = os.environ.get("SFAST_COMPILE", "0") == "1"
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SAFETY_CHECKER = os.environ.get("SAFETY_CHECKER", "0") == "1"
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pipe = StableDiffusionXLPipeline.from_pretrained(
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BASE, unet=unet, torch_dtype=torch.float16, variant="fp16", safety_checker=False
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).to("cuda")
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if USE_TAESD:
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pipe.vae = AutoencoderTiny.from_pretrained(
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pipe = compile(pipe, config)
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generator = torch.manual_seed(seed)
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last_time = time.time()
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results = pipe(
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generator=generator,
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num_inference_steps=2,
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guidance_scale=
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# width=768,
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# height=768,
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output_type="pil",
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css = """
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#container{
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margin: 0 auto;
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max-width:
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}
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#intro{
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max-width: 100%;
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margin: 0 auto;
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}
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"""
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with gr.Blocks(css=css) as demo:
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with gr.Column(elem_id="container"):
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elem_id="intro",
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)
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with gr.Row():
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with gr.
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inputs = [prompt, seed]
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outputs = [image]
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)
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prompt.input(fn=predict, inputs=inputs, outputs=outputs, show_progress=False)
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seed.change(fn=predict, inputs=inputs, outputs=outputs, show_progress=False)
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demo.queue()
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demo.launch()
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import spaces
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from diffusers import (
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StableDiffusionXLPipeline,
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EulerDiscreteScheduler,
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import torch
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import os
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from huggingface_hub import hf_hub_download
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from compel import Compel, ReturnedEmbeddingsType
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from gradio_promptweighting import PromptWeighting
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from PIL import Image
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CHECKPOINT = "sdxl_lightning_2step_unet.safetensors"
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taesd_model = "madebyollin/taesdxl"
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SFAST_COMPILE = os.environ.get("SFAST_COMPILE", "0") == "1"
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SAFETY_CHECKER = os.environ.get("SAFETY_CHECKER", "0") == "1"
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pipe = StableDiffusionXLPipeline.from_pretrained(
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BASE, unet=unet, torch_dtype=torch.float16, variant="fp16", safety_checker=False
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).to("cuda")
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unet = unet.to(dtype=torch.float16)
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compel = Compel(
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tokenizer=[pipe.tokenizer, pipe.tokenizer_2],
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text_encoder=[pipe.text_encoder, pipe.text_encoder_2],
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returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED,
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requires_pooled=[False, True],
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)
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if USE_TAESD:
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pipe.vae = AutoencoderTiny.from_pretrained(
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pipe = compile(pipe, config)
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@spaces.GPU
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def predict(prompt, prompt_w, guidance_scale, seed=1231231):
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generator = torch.manual_seed(seed)
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last_time = time.time()
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prompt_w = " ".join(
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[f"({p['prompt']}){p['scale']}" for p in prompt_w if p["prompt"]]
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)
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conditioning, pooled = compel([prompt + " " + prompt_w, ""])
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results = pipe(
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prompt_embeds=conditioning[0:1],
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pooled_prompt_embeds=pooled[0:1],
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negative_prompt_embeds=conditioning[1:2],
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negative_pooled_prompt_embeds=pooled[1:2],
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generator=generator,
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num_inference_steps=2,
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guidance_scale=guidance_scale,
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# width=768,
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# height=768,
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output_type="pil",
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css = """
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#container{
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margin: 0 auto;
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max-width: 80rem;
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}
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#intro{
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max-width: 100%;
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margin: 0 auto;
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}
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.generating {
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display: none
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}
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"""
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with gr.Blocks(css=css) as demo:
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with gr.Column(elem_id="container"):
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elem_id="intro",
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)
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with gr.Row():
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with gr.Column():
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with gr.Group():
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prompt = gr.Textbox(
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placeholder="Insert your prompt here:",
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max_lines=1,
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label="Prompt",
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)
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prompt_w = PromptWeighting(
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min=0,
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max=3,
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step=0.005,
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show_label=False,
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)
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with gr.Accordion("Advanced options", open=True):
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seed = gr.Slider(
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minimum=0,
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maximum=12013012031030,
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label="Seed",
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step=1,
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)
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guidance_scale = gr.Slider(
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minimum=0.0,
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maximum=20.0,
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label="Guidance scale",
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value=0.0,
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step=0.1,
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)
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generate_bt = gr.Button("Generate")
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with gr.Column():
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image = gr.Image(type="filepath")
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inputs = [
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prompt,
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prompt_w,
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guidance_scale,
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seed,
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]
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outputs = [image]
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gr.on(
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triggers=[
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prompt.input,
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prompt_w.input,
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generate_bt.click,
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guidance_scale.input,
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seed.input,
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],
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fn=predict,
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inputs=inputs,
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outputs=outputs,
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show_progress="hidden",
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show_api=False,
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trigger_mode="always_last",
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)
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demo.queue(api_open=False)
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demo.launch()
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gradio_promptweighting-0.0.1-py3-none-any.whl
ADDED
Binary file (38.8 kB). View file
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requirements.txt
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@@ -1,6 +1,6 @@
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diffusers==0.
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transformers
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gradio==4.
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torch==2.1.0
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fastapi==0.104.0
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uvicorn==0.23.2
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hf_transfer
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huggingface_hub
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safetensors
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diffusers==0.27.2
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transformers
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gradio==4.25.0
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torch==2.1.0
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fastapi==0.104.0
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uvicorn==0.23.2
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hf_transfer
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huggingface_hub
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safetensors
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compel
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stable_fast @ https://github.com/chengzeyi/stable-fast/releases/download/v1.0.2/stable_fast-1.0.2+torch211cu121-cp310-cp310-manylinux2014_x86_64.whl ; sys_platform != 'darwin' or platform_machine != 'arm64'
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spaces
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gradio_promptweighting @ ./gradio_promptweighting-0.0.1-py3-none-any.whl
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