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import gc |
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import datetime |
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import os |
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import re |
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from typing import Literal |
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import streamlit as st |
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import torch |
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from diffusers import ( |
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StableDiffusionPipeline, |
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StableDiffusionControlNetPipeline, |
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ControlNetModel, |
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EulerDiscreteScheduler, |
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DDIMScheduler, |
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) |
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PIPELINES = Literal["txt2img", "sketch2img"] |
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@st.cache_resource(max_entries=1) |
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def get_pipelines( name:PIPELINES, enable_cpu_offload = False, ) -> StableDiffusionPipeline: |
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pipe = None |
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if name == "txt2img": |
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pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16,cache_dir="D:/huggingface/CACHE/") |
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pipe.unet.load_attn_procs("./") |
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pipe.safety_checker = lambda images, **kwargs: (images, [False] * len(images)) |
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elif name == "sketch2img": |
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controlnet = ControlNetModel.from_pretrained("Abhi5ingh/model_dresscode", torch_dtype=torch.float16,cache_dir="D:/huggingface/CACHE/") |
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pipe = StableDiffusionControlNetPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", controlnet = controlnet, torch_dtype = torch.float16,cache_dir="D:/huggingface/CACHE/") |
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pipe.unet.load_attn_procs("./") |
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pipe.safety_checker = lambda images, **kwargs: (images, [False] * len(images)) |
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if pipe is None: |
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raise Exception(f"Pipeline not Found {name}") |
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if enable_cpu_offload: |
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print("Enabling cpu offloading for the given pipeline") |
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pipe.enable_model_cpu_offload() |
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else: |
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pipe = pipe.to("cuda") |
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return pipe |
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def generate( |
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prompt, |
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pipeline_name: PIPELINES, |
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sketch_pil = None, |
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num_inference_steps = 30, |
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negative_prompt = None, |
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width = 512, |
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height = 512, |
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guidance_scale = 7.5, |
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controlnet_conditioning_scale = None, |
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enable_cpu_offload= False): |
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negative_prompt = negative_prompt if negative_prompt else None |
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p = st.progress(0) |
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callback = lambda step,*_: p.progress(step/num_inference_steps) |
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pipe = get_pipelines(pipeline_name,enable_cpu_offload=enable_cpu_offload) |
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torch.cuda.empty_cache() |
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kwargs = dict( |
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prompt = prompt, |
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negative_prompt=negative_prompt, |
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num_inference_steps=num_inference_steps, |
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callback=callback, |
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guidance_scale=guidance_scale, |
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) |
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print("kwargs",kwargs) |
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if pipeline_name =="sketch2img" and sketch_pil: |
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kwargs.update(sketch_pil=sketch_pil,controlnet_conditioning_scale=controlnet_conditioning_scale) |
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elif pipeline_name == "txt2img": |
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kwargs.update(width = width, height = height) |
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else: |
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raise Exception( |
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f"Cannot generate image for pipeline {pipeline_name} and {prompt}") |
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images = pipe(**kwargs).images |
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image = images[0] |
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os.makedirs("outputs", exist_ok=True) |
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filename = ( |
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"outputs/" |
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+ re.sub(r"\s+", "_",prompt)[:30] |
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+ f"_{datetime.datetime.now().timestamp()}" |
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) |
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image.save(f"{filename}.png") |
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with open(f"{filename}.txt", "w") as f: |
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f.write(f"Prompt: {prompt}\n\nNegative Prompt:{negative_prompt}") |
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return image |
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