mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
illustration of two generate alternatives
This commit is contained in:
Lincoln Stein
parent
6e0c6d9cc9
commit
a6efcca78c
212
invokeai/renderer1.py
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212
invokeai/renderer1.py
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'''
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Simple class hierarchy
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'''
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import copy
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import dataclasses
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import diffusers
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import importlib
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import traceback
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from abc import ABCMeta, abstractmethod
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from omegaconf import OmegaConf
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from pathlib import Path
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from PIL import Image
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from typing import List, Type
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from dataclasses import dataclass
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from diffusers.schedulers import SchedulerMixin as Scheduler
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import invokeai.assets as image_assets
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from ldm.invoke.globals import global_config_dir
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from ldm.invoke.conditioning import get_uc_and_c_and_ec
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from ldm.invoke.model_manager import ModelManager
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from ldm.invoke.generator.diffusers_pipeline import StableDiffusionGeneratorPipeline
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from ldm.invoke.devices import choose_torch_device
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@dataclass
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class RendererBasicParams:
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width: int=512
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height: int=512
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cfg_scale: int=7.5
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steps: int=20
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ddim_eta: float=0.0
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model: str='stable-diffusion-1.5'
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scheduler: int='ddim'
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precision: str='float16'
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@dataclass
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class RendererOutput:
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image: Image
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seed: int
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model_name: str
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model_hash: str
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params: RendererBasicParams
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class InvokeAIRenderer(metaclass=ABCMeta):
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scheduler_map = dict(
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ddim=diffusers.DDIMScheduler,
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dpmpp_2=diffusers.DPMSolverMultistepScheduler,
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k_dpm_2=diffusers.KDPM2DiscreteScheduler,
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k_dpm_2_a=diffusers.KDPM2AncestralDiscreteScheduler,
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k_dpmpp_2=diffusers.DPMSolverMultistepScheduler,
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k_euler=diffusers.EulerDiscreteScheduler,
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k_euler_a=diffusers.EulerAncestralDiscreteScheduler,
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k_heun=diffusers.HeunDiscreteScheduler,
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k_lms=diffusers.LMSDiscreteScheduler,
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plms=diffusers.PNDMScheduler,
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)
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def __init__(self,
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model_manager: ModelManager,
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params: RendererBasicParams
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):
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self.model_manager=model_manager
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self.params=params
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def render(self,
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prompt: str='',
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callback: callable=None,
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iterations: int=1,
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step_callback: callable=None,
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**keyword_args,
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)->List[RendererOutput]:
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results = []
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model_name = self.params.model or self.model_manager.current_model
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model_info: dict = self.model_manager.get_model(model_name)
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model:StableDiffusionGeneratorPipeline = model_info['model']
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model_hash = model_info['hash']
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scheduler: Scheduler = self.get_scheduler(
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model=model,
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scheduler_name=self.params.scheduler
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)
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uc, c, extra_conditioning_info = get_uc_and_c_and_ec(prompt,model=model)
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def _wrap_results(image: Image, seed: int, **kwargs):
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nonlocal results
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output = RendererOutput(
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image=image,
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seed=seed,
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model_name = model_name,
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model_hash = model_hash,
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params=copy.copy(self.params)
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)
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if callback:
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callback(output)
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results.append(output)
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generator = self.load_generator(model, self._generator_name())
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generator.generate(prompt,
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conditioning=(uc, c, extra_conditioning_info),
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image_callback=_wrap_results,
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sampler=scheduler,
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iterations=iterations,
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**dataclasses.asdict(self.params),
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**keyword_args
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)
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return results
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def load_generator(self, model: StableDiffusionGeneratorPipeline, class_name: str):
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module_name = f'ldm.invoke.generator.{class_name.lower()}'
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module = importlib.import_module(module_name)
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constructor = getattr(module, class_name)
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return constructor(model, self.params.precision)
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def get_scheduler(self, scheduler_name:str, model: StableDiffusionGeneratorPipeline)->Scheduler:
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scheduler_class = self.scheduler_map.get(scheduler_name,'ddim')
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scheduler = scheduler_class.from_config(model.scheduler.config)
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# hack copied over from generate.py
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if not hasattr(scheduler, 'uses_inpainting_model'):
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scheduler.uses_inpainting_model = lambda: False
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return scheduler
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@abstractmethod
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def _generator_name(self)->str:
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'''
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In derived classes will return the name of the generator to use.
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'''
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pass
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# ------------------------------------
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class Txt2Img(InvokeAIRenderer):
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def _generator_name(self)->str:
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return 'Txt2Img'
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# ------------------------------------
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class Img2Img(InvokeAIRenderer):
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def render(self,
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init_image: Image,
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strength: float=0.75,
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**keyword_args
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)->List[RendererOutput]:
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return super().render(init_image=init_image,
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strength=strength,
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**keyword_args
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)
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def _generator_name(self)->str:
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return 'Img2Img'
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class RendererFactory(object):
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def __init__(self,
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model_manager: ModelManager,
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params: RendererBasicParams
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):
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self.model_manager = model_manager
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self.params = params
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def renderer(self, rendererclass: Type[InvokeAIRenderer], **keyword_args)->InvokeAIRenderer:
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return rendererclass(self.model_manager,
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self.params,
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**keyword_args
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)
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# ---- testing ---
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def main():
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config_file = Path(global_config_dir()) / "models.yaml"
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model_manager = ModelManager(OmegaConf.load(config_file),
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precision='float16',
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device_type=choose_torch_device(),
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)
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params = RendererBasicParams(
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model = 'stable-diffusion-1.5',
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steps = 30,
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scheduler = 'k_lms',
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cfg_scale = 8.0,
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height = 640,
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width = 640
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)
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factory = RendererFactory(model_manager, params)
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print ('=== TXT2IMG TEST ===')
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txt2img = factory.renderer(Txt2Img)
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renderer_outputs = txt2img.render(prompt='banana sushi',
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iterations=2,
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callback=lambda outputs: print(f'SUCCESS: got image with seed {outputs.seed}')
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)
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for r in renderer_outputs:
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print(f'image={r.image}, seed={r.seed}, model={r.model_name}, hash={r.model_hash}')
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print ('\n=== IMG2IMG TEST ===')
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img2img = factory.renderer(Img2Img)
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try:
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renderer_outputs = img2img.render(prompt='basket of sushi')
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except Exception as e:
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print(f'SUCCESS: Calling img2img() without required parameter rejected {str(e)}')
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try:
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test_image = Path(__file__,'../../docs/assets/still-life-inpainted.png')
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renderer_outputs = img2img.render(prompt='basket of sushi',
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strength=0.5,
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init_image=Image.open(test_image))
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except Exception as e:
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print(f'FAILURE: {str(e)}')
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print('Image saved as "ugly-sushi.png"')
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renderer_outputs[0].image.save('ugly-sushi.png')
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if __name__=='__main__':
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main()
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187
invokeai/renderer2.py
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187
invokeai/renderer2.py
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@ -0,0 +1,187 @@
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'''
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Simple class hierarchy
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'''
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import copy
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import dataclasses
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import diffusers
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import importlib
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import traceback
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from abc import ABCMeta, abstractmethod
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from omegaconf import OmegaConf
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from pathlib import Path
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from PIL import Image
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from typing import List, Type
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from dataclasses import dataclass
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from diffusers.schedulers import SchedulerMixin as Scheduler
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import invokeai.assets as image_assets
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from ldm.invoke.globals import global_config_dir
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from ldm.invoke.conditioning import get_uc_and_c_and_ec
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from ldm.invoke.model_manager import ModelManager
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from ldm.invoke.generator.diffusers_pipeline import StableDiffusionGeneratorPipeline
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from ldm.invoke.devices import choose_torch_device
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@dataclass
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class RendererBasicParams:
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width: int=512
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height: int=512
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cfg_scale: int=7.5
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steps: int=20
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ddim_eta: float=0.0
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model: str='stable-diffusion-1.5'
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scheduler: int='ddim'
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precision: str='float16'
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@dataclass
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class RendererOutput:
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image: Image
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seed: int
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model_name: str
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model_hash: str
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params: RendererBasicParams
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class InvokeAIRenderer(metaclass=ABCMeta):
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scheduler_map = dict(
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ddim=diffusers.DDIMScheduler,
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dpmpp_2=diffusers.DPMSolverMultistepScheduler,
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k_dpm_2=diffusers.KDPM2DiscreteScheduler,
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k_dpm_2_a=diffusers.KDPM2AncestralDiscreteScheduler,
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k_dpmpp_2=diffusers.DPMSolverMultistepScheduler,
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k_euler=diffusers.EulerDiscreteScheduler,
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k_euler_a=diffusers.EulerAncestralDiscreteScheduler,
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k_heun=diffusers.HeunDiscreteScheduler,
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k_lms=diffusers.LMSDiscreteScheduler,
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plms=diffusers.PNDMScheduler,
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)
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def __init__(self,
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model_manager: ModelManager,
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params: RendererBasicParams
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):
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self.model_manager=model_manager
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self.params=params
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def render(self,
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prompt: str='',
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callback: callable=None,
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step_callback: callable=None,
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**keyword_args,
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)->List[RendererOutput]:
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model_name = self.params.model or self.model_manager.current_model
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model_info: dict = self.model_manager.get_model(model_name)
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model:StableDiffusionGeneratorPipeline = model_info['model']
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model_hash = model_info['hash']
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scheduler: Scheduler = self.get_scheduler(
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model=model,
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scheduler_name=self.params.scheduler
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)
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uc, c, extra_conditioning_info = get_uc_and_c_and_ec(prompt,model=model)
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def _wrap_results(image: Image, seed: int, **kwargs):
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nonlocal results
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results.append(output)
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generator = self.load_generator(model, self._generator_name())
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while True:
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results = generator.generate(prompt,
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conditioning=(uc, c, extra_conditioning_info),
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sampler=scheduler,
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**dataclasses.asdict(self.params),
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**keyword_args
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)
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output = RendererOutput(
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image=results[0][0],
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seed=results[0][1],
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model_name = model_name,
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model_hash = model_hash,
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params=copy.copy(self.params)
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)
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if callback:
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callback(output)
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yield output
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def load_generator(self, model: StableDiffusionGeneratorPipeline, class_name: str):
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module_name = f'ldm.invoke.generator.{class_name.lower()}'
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module = importlib.import_module(module_name)
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constructor = getattr(module, class_name)
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return constructor(model, self.params.precision)
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def get_scheduler(self, scheduler_name:str, model: StableDiffusionGeneratorPipeline)->Scheduler:
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scheduler_class = self.scheduler_map.get(scheduler_name,'ddim')
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scheduler = scheduler_class.from_config(model.scheduler.config)
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# hack copied over from generate.py
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if not hasattr(scheduler, 'uses_inpainting_model'):
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scheduler.uses_inpainting_model = lambda: False
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return scheduler
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@abstractmethod
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def _generator_name(self)->str:
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'''
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In derived classes will return the name of the generator to use.
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'''
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pass
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# ------------------------------------
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class Txt2Img(InvokeAIRenderer):
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def _generator_name(self)->str:
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return 'Txt2Img'
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# ------------------------------------
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class Img2Img(InvokeAIRenderer):
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def render(self,
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init_image: Image,
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strength: float=0.75,
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**keyword_args
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)->List[RendererOutput]:
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return super().render(init_image=init_image,
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strength=strength,
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**keyword_args
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)
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def _generator_name(self)->str:
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return 'Img2Img'
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class RendererFactory(object):
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def __init__(self,
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model_manager: ModelManager,
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params: RendererBasicParams
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):
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self.model_manager = model_manager
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self.params = params
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def renderer(self, rendererclass: Type[InvokeAIRenderer], **keyword_args)->InvokeAIRenderer:
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return rendererclass(self.model_manager,
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self.params,
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**keyword_args
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)
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# ---- testing ---
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def main():
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config_file = Path(global_config_dir()) / "models.yaml"
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model_manager = ModelManager(OmegaConf.load(config_file),
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precision='float16',
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device_type=choose_torch_device(),
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)
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params = RendererBasicParams(
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model = 'stable-diffusion-1.5',
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steps = 30,
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scheduler = 'k_lms',
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cfg_scale = 8.0,
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height = 640,
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width = 640
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)
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factory = RendererFactory(model_manager, params)
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print ('=== TXT2IMG TEST ===')
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txt2img = factory.renderer(Txt2Img)
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outputs = txt2img.render(prompt='banana sushi')
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for i in range(3):
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output = next(outputs)
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print(f'image={output.image}, seed={output.seed}, model={output.model_name}, hash={output.model_hash}')
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if __name__=='__main__':
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main()
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191
invokeai/renderer3.py
Normal file
191
invokeai/renderer3.py
Normal file
@ -0,0 +1,191 @@
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'''
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Simple class hierarchy
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'''
|
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import copy
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import dataclasses
|
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import diffusers
|
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import importlib
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import traceback
|
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|
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from abc import ABCMeta, abstractmethod
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from omegaconf import OmegaConf
|
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from pathlib import Path
|
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from PIL import Image
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from typing import List, Type
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from dataclasses import dataclass
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from diffusers.schedulers import SchedulerMixin as Scheduler
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|
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import invokeai.assets as image_assets
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from ldm.invoke.globals import global_config_dir
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from ldm.invoke.conditioning import get_uc_and_c_and_ec
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from ldm.invoke.model_manager2 import ModelManager
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# ^^^^^^^^^^^^^^ note alternative version
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from ldm.invoke.generator.diffusers_pipeline import StableDiffusionGeneratorPipeline
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from ldm.invoke.devices import choose_torch_device
|
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|
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@dataclass
|
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class RendererBasicParams:
|
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width: int=512
|
||||
height: int=512
|
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cfg_scale: int=7.5
|
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steps: int=20
|
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ddim_eta: float=0.0
|
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model: str='stable-diffusion-1.5'
|
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scheduler: int='ddim'
|
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precision: str='float16'
|
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|
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@dataclass
|
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class RendererOutput:
|
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image: Image
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seed: int
|
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model_name: str
|
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model_hash: str
|
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params: RendererBasicParams
|
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|
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class InvokeAIRenderer(metaclass=ABCMeta):
|
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scheduler_map = dict(
|
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ddim=diffusers.DDIMScheduler,
|
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dpmpp_2=diffusers.DPMSolverMultistepScheduler,
|
||||
k_dpm_2=diffusers.KDPM2DiscreteScheduler,
|
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k_dpm_2_a=diffusers.KDPM2AncestralDiscreteScheduler,
|
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k_dpmpp_2=diffusers.DPMSolverMultistepScheduler,
|
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k_euler=diffusers.EulerDiscreteScheduler,
|
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k_euler_a=diffusers.EulerAncestralDiscreteScheduler,
|
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k_heun=diffusers.HeunDiscreteScheduler,
|
||||
k_lms=diffusers.LMSDiscreteScheduler,
|
||||
plms=diffusers.PNDMScheduler,
|
||||
)
|
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|
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def __init__(self,
|
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model_manager: ModelManager,
|
||||
params: RendererBasicParams
|
||||
):
|
||||
self.model_manager=model_manager
|
||||
self.params=params
|
||||
|
||||
def render(self,
|
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prompt: str='',
|
||||
callback: callable=None,
|
||||
iterations: int=1,
|
||||
step_callback: callable=None,
|
||||
**keyword_args,
|
||||
)->List[RendererOutput]:
|
||||
results = []
|
||||
|
||||
# closure
|
||||
def _wrap_results(image: Image, seed: int, **kwargs):
|
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nonlocal results
|
||||
output = RendererOutput(
|
||||
image=image,
|
||||
seed=seed,
|
||||
model_name = model_name,
|
||||
model_hash = model_hash,
|
||||
params=copy.copy(self.params)
|
||||
)
|
||||
if callback:
|
||||
callback(output)
|
||||
results.append(output)
|
||||
|
||||
model_name = self.params.model or self.model_manager.current_model
|
||||
print(f'** OUTSIDE CONTEXT: Reference count for {model_name} = {self.model_manager.refcount(model_name)}**')
|
||||
|
||||
with self.model_manager.get_model(model_name) as model_info:
|
||||
print(f'** INSIDE CONTEXT: Reference count for {model_name} = {self.model_manager.refcount(model_name)} **')
|
||||
|
||||
model:StableDiffusionGeneratorPipeline = model_info['model']
|
||||
model_hash = model_info['hash']
|
||||
scheduler: Scheduler = self.get_scheduler(
|
||||
model=model,
|
||||
scheduler_name=self.params.scheduler
|
||||
)
|
||||
uc, c, extra_conditioning_info = get_uc_and_c_and_ec(prompt,model=model)
|
||||
|
||||
generator = self.load_generator(model, self._generator_name())
|
||||
generator.generate(prompt,
|
||||
conditioning=(uc, c, extra_conditioning_info),
|
||||
image_callback=_wrap_results,
|
||||
sampler=scheduler,
|
||||
iterations=iterations,
|
||||
**dataclasses.asdict(self.params),
|
||||
**keyword_args
|
||||
)
|
||||
|
||||
print(f'AGAIN OUTSIDE CONTEXT: Reference count for {model_name} = {self.model_manager.refcount(model_name)}')
|
||||
return results
|
||||
|
||||
def load_generator(self, model: StableDiffusionGeneratorPipeline, class_name: str):
|
||||
module_name = f'ldm.invoke.generator.{class_name.lower()}'
|
||||
module = importlib.import_module(module_name)
|
||||
constructor = getattr(module, class_name)
|
||||
return constructor(model, self.params.precision)
|
||||
|
||||
def get_scheduler(self, scheduler_name:str, model: StableDiffusionGeneratorPipeline)->Scheduler:
|
||||
scheduler_class = self.scheduler_map.get(scheduler_name,'ddim')
|
||||
scheduler = scheduler_class.from_config(model.scheduler.config)
|
||||
# hack copied over from generate.py
|
||||
if not hasattr(scheduler, 'uses_inpainting_model'):
|
||||
scheduler.uses_inpainting_model = lambda: False
|
||||
return scheduler
|
||||
|
||||
@abstractmethod
|
||||
def _generator_name(self)->str:
|
||||
'''
|
||||
In derived classes will return the name of the generator to use.
|
||||
'''
|
||||
pass
|
||||
|
||||
# ------------------------------------
|
||||
class Txt2Img(InvokeAIRenderer):
|
||||
def _generator_name(self)->str:
|
||||
return 'Txt2Img'
|
||||
|
||||
# ------------------------------------
|
||||
class Img2Img(InvokeAIRenderer):
|
||||
def render(self,
|
||||
init_image: Image,
|
||||
strength: float=0.75,
|
||||
**keyword_args
|
||||
)->List[RendererOutput]:
|
||||
return super().render(init_image=init_image,
|
||||
strength=strength,
|
||||
**keyword_args
|
||||
)
|
||||
|
||||
def _generator_name(self)->str:
|
||||
return 'Img2Img'
|
||||
|
||||
class RendererFactory(object):
|
||||
def __init__(self,
|
||||
model_manager: ModelManager,
|
||||
params: RendererBasicParams
|
||||
):
|
||||
self.model_manager = model_manager
|
||||
self.params = params
|
||||
|
||||
def renderer(self, rendererclass: Type[InvokeAIRenderer], **keyword_args)->InvokeAIRenderer:
|
||||
return rendererclass(self.model_manager,
|
||||
self.params,
|
||||
**keyword_args
|
||||
)
|
||||
|
||||
# ---- testing ---
|
||||
def main():
|
||||
config_file = Path(global_config_dir()) / "models.yaml"
|
||||
model_manager = ModelManager(OmegaConf.load(config_file),
|
||||
precision='float16',
|
||||
device_type=choose_torch_device(),
|
||||
)
|
||||
|
||||
params = RendererBasicParams(
|
||||
model = 'stable-diffusion-1.5',
|
||||
steps = 30,
|
||||
scheduler = 'k_lms',
|
||||
cfg_scale = 8.0,
|
||||
height = 640,
|
||||
width = 640
|
||||
)
|
||||
factory = RendererFactory(model_manager, params)
|
||||
outputs = factory.renderer(Txt2Img).render(prompt='banana sushi')
|
||||
|
||||
if __name__=='__main__':
|
||||
main()
|
||||
@ -62,7 +62,7 @@ class Generator:
|
||||
self.variation_amount = variation_amount
|
||||
self.with_variations = with_variations
|
||||
|
||||
def generate(self,prompt,init_image,width,height,sampler, iterations=1,seed=None,
|
||||
def generate(self,prompt,width,height,sampler, init_image=None, iterations=1,seed=None,
|
||||
image_callback=None, step_callback=None, threshold=0.0, perlin=0.0,
|
||||
h_symmetry_time_pct=None, v_symmetry_time_pct=None,
|
||||
safety_checker:dict=None,
|
||||
|
||||
@ -55,7 +55,6 @@ VAE_TO_REPO_ID = { # hack, see note in convert_and_import()
|
||||
"vae-ft-mse-840000-ema-pruned": "stabilityai/sd-vae-ft-mse",
|
||||
}
|
||||
|
||||
|
||||
class ModelManager(object):
|
||||
def __init__(
|
||||
self,
|
||||
@ -79,10 +78,12 @@ class ModelManager(object):
|
||||
self.device = torch.device(device_type)
|
||||
self.max_loaded_models = max_loaded_models
|
||||
self.models = {}
|
||||
self.in_use = {} # ref counts of models in use, for locking some day
|
||||
self.stack = [] # this is an LRU FIFO
|
||||
self.current_model = None
|
||||
self.sequential_offload = sequential_offload
|
||||
|
||||
|
||||
def valid_model(self, model_name: str) -> bool:
|
||||
"""
|
||||
Given a model name, returns True if it is a valid
|
||||
|
||||
1399
ldm/invoke/model_manager2.py
Normal file
1399
ldm/invoke/model_manager2.py
Normal file
File diff suppressed because it is too large
Load Diff
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Reference in New Issue
Block a user