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https://github.com/invoke-ai/InvokeAI
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Merge branch 'development' into development
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commit
c6be8f320d
@ -528,6 +528,12 @@ This will create stable-diffusion folder where you will follow the rest of the s
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After these steps, your command prompt will be prefixed by "(ldm)" as shown above.
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After these steps, your command prompt will be prefixed by "(ldm)" as shown above.
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Note: If necessary, you can update the environment via this command:
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```sh
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(ldm) ~/stable-diffusion$ conda env update --file environment.yaml
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```
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6. Load a couple of small machine-learning models required by stable diffusion:
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6. Load a couple of small machine-learning models required by stable diffusion:
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```
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```
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@ -78,6 +78,4 @@ class PromptFormatter:
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if opt.with_variations:
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if opt.with_variations:
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formatted_variations = ','.join(f'{seed}:{weight}' for seed, weight in opt.with_variations)
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formatted_variations = ','.join(f'{seed}:{weight}' for seed, weight in opt.with_variations)
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switches.append(f'-V{formatted_variations}')
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switches.append(f'-V{formatted_variations}')
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if t2i.full_precision:
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switches.append('-F')
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return ' '.join(switches)
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return ' '.join(switches)
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@ -540,9 +540,6 @@ class Generate:
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sd = pl_sd['state_dict']
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sd = pl_sd['state_dict']
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model = instantiate_from_config(config.model)
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model = instantiate_from_config(config.model)
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m, u = model.load_state_dict(sd, strict=False)
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m, u = model.load_state_dict(sd, strict=False)
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model.to(self.device)
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model.eval()
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if self.full_precision:
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if self.full_precision:
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print(
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print(
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@ -553,6 +550,8 @@ class Generate:
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'>> Using half precision math. Call with --full_precision to use more accurate but VRAM-intensive full precision.'
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'>> Using half precision math. Call with --full_precision to use more accurate but VRAM-intensive full precision.'
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)
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)
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model.half()
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model.half()
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model.to(self.device)
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model.eval()
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# usage statistics
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# usage statistics
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toc = time.time()
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toc = time.time()
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