Spaces:
Running
on
Zero
Running
on
Zero
fix : pydantic error
Browse files- __pycache__/live_preview_helpers.cpython-310.pyc +0 -0
- __pycache__/llm_wrapper.cpython-310.pyc +0 -0
- app.py +1 -7
- llm_wrapper.py +0 -6
__pycache__/live_preview_helpers.cpython-310.pyc
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Binary files a/__pycache__/live_preview_helpers.cpython-310.pyc and b/__pycache__/live_preview_helpers.cpython-310.pyc differ
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__pycache__/llm_wrapper.cpython-310.pyc
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Binary file (3.08 kB). View file
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app.py
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@@ -15,11 +15,6 @@ import subprocess
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subprocess.run("rm -rf /data-nvme/zerogpu-offload/*", env={}, shell=True)
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from pydantic import BaseModel
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class RefinedPrompt(BaseModel):
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prompt: str
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dtype = torch.bfloat16
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device = "cuda" if torch.cuda.is_available() else "cpu"
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@@ -49,11 +44,10 @@ def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidan
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refined_prompt = run_gemini(
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target_prompt=prompt,
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prompt_in_path="prompt.json",
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output_structure=RefinedPrompt,
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)
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for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images(
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prompt=refined_prompt
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guidance_scale=guidance_scale,
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num_inference_steps=num_inference_steps,
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width=width,
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subprocess.run("rm -rf /data-nvme/zerogpu-offload/*", env={}, shell=True)
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dtype = torch.bfloat16
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device = "cuda" if torch.cuda.is_available() else "cpu"
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refined_prompt = run_gemini(
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target_prompt=prompt,
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prompt_in_path="prompt.json",
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)
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for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images(
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prompt=refined_prompt,
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guidance_scale=guidance_scale,
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num_inference_steps=num_inference_steps,
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width=width,
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llm_wrapper.py
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@@ -60,7 +60,6 @@ def encode_image(image_source):
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def run_gemini(
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target_prompt: str,
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prompt_in_path: str,
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output_structure,
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img_in_data: str = None,
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model: str = "gemini-2.0-flash",
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) -> str:
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@@ -89,11 +88,6 @@ def run_gemini(
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chat_completion = client.models.generate_content(
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model=model,
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contents=input_content,
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config={
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"system_instruction": system_prompt,
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"response_mime_type": "application/json",
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"response_schema": output_structure,
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}
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)
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chat_output = chat_completion.parsed
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def run_gemini(
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target_prompt: str,
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prompt_in_path: str,
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img_in_data: str = None,
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model: str = "gemini-2.0-flash",
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) -> str:
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chat_completion = client.models.generate_content(
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model=model,
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contents=input_content,
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)
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chat_output = chat_completion.parsed
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