Spaces:
Running
on
Zero
Running
on
Zero
alfredplpl
commited on
Update app.py
Browse files
app.py
CHANGED
@@ -14,13 +14,38 @@ dtype = torch.float16
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repo = "stabilityai/stable-diffusion-3-medium"
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t2i = StableDiffusion3Pipeline.from_pretrained(repo, torch_dtype=torch.float16, revision="refs/pr/26",token=os.environ["TOKEN"]).to(device)
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MAX_SEED = np.iinfo(np.int32).max
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MAX_IMAGE_SIZE = 1344
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@spaces.GPU
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def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True)):
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-
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if randomize_seed:
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seed = random.randint(0, MAX_SEED)
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repo = "stabilityai/stable-diffusion-3-medium"
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t2i = StableDiffusion3Pipeline.from_pretrained(repo, torch_dtype=torch.float16, revision="refs/pr/26",token=os.environ["TOKEN"]).to(device)
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model = AutoModelForCausalLM.from_pretrained(
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"microsoft/Phi-3-mini-4k-instruct",
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device_map="cuda",
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torch_dtype=torch.bfloat16,
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trust_remote_code=True,
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)
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tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-4k-instruct")
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upsampler = pipeline(
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"text-generation",
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model=model,
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tokenizer=tokenizer,
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)
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generation_args = {
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"max_new_tokens": 300,
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"return_full_text": False,
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"temperature": 0.0,
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"do_sample": False,
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}
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MAX_SEED = np.iinfo(np.int32).max
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MAX_IMAGE_SIZE = 1344
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@spaces.GPU
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def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True)):
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messages = [
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{"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"},
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{"role": "assistant", "content": "Sure! Here are some ways to eat bananas and dragonfruits together: 1. Banana and dragonfruit smoothie: Blend bananas and dragonfruits together with some milk and honey. 2. Banana and dragonfruit salad: Mix sliced bananas and dragonfruits together with some lemon juice and honey."},
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{"role": "user", "content": "What about solving an 2x + 3 = 7 equation?"},
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]
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output = pipe(messages, **generation_args)
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upsampled_prompt=output[0]['generated_text']
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if randomize_seed:
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seed = random.randint(0, MAX_SEED)
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