Spaces:
Running
on
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Running
on
Zero
zR
commited on
Commit
•
e1d35f0
1
Parent(s):
4ddbeae
use cogview3 model
Browse files
app.py
CHANGED
@@ -5,25 +5,15 @@ import time
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from datetime import datetime, timedelta
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import gradio as gr
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import numpy as np
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import random
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import spaces # [uncomment to use ZeroGPU]
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from diffusers import
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import torch
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from openai import OpenAI
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device = "cuda" if torch.cuda.is_available() else "cpu"
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model_repo_id = "black-forest-labs/FLUX.1-dev"
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torch_dtype = torch.float16
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else:
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torch_dtype = torch.float32
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pipe = FluxPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
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pipe = pipe.to(device)
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MAX_SEED = np.iinfo(np.int32).max
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def clean_string(s):
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@@ -134,9 +124,7 @@ threading.Thread(target=delete_old_files, daemon=True).start()
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def infer(prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps,
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progress=gr.Progress(track_tqdm=True)):
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if randomize_seed:
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seed = random.randint(0,
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generator = torch.Generator().manual_seed(seed)
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image = pipe(
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prompt=prompt,
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@@ -145,7 +133,7 @@ def infer(prompt, seed, randomize_seed, width, height, guidance_scale, num_infer
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num_inference_steps=num_inference_steps,
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width=width,
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height=height,
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generator=
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).images[0]
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return image, seed
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@@ -207,7 +195,7 @@ with gr.Blocks(css=css) as demo:
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seed = gr.Slider(
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label="Seed",
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minimum=0,
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maximum=
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step=1,
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value=0,
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)
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@@ -220,7 +208,7 @@ with gr.Blocks(css=css) as demo:
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minimum=512,
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maximum=2048,
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step=32,
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value=1024,
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)
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height = gr.Slider(
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from datetime import datetime, timedelta
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import gradio as gr
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import random
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import spaces # [uncomment to use ZeroGPU]
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from diffusers import CogView3PlusPipeline
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import torch
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from openai import OpenAI
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device = "cuda" if torch.cuda.is_available() else "cpu"
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pipe = CogView3PlusPipeline.from_pretrained("THUDM/CogView3-Plus-3B", torch_dtype=torch.bfloat16).to(device)
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def clean_string(s):
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def infer(prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps,
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progress=gr.Progress(track_tqdm=True)):
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if randomize_seed:
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seed = random.randint(0, 65536)
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image = pipe(
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prompt=prompt,
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num_inference_steps=num_inference_steps,
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width=width,
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height=height,
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generator=torch.Generator().manual_seed(seed)
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).images[0]
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return image, seed
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seed = gr.Slider(
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label="Seed",
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minimum=0,
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maximum=65536,
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step=1,
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value=0,
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)
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minimum=512,
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maximum=2048,
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step=32,
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value=1024,
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)
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height = gr.Slider(
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