mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
Merge branch 'development' into development
This commit is contained in:
Peter Baylies
GitHub
commit
357e1ad35f
@ -74,7 +74,7 @@ We combine the two variations using `-V` (--with_variations). Again, we must pro
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this to work.
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```
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dream> "prompt" -S3357757885 -V3647897225,0.1;1614299449,0.1
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dream> "prompt" -S3357757885 -V3647897225:0.1,1614299449:0.1
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Outputs:
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./outputs/Xena/000003.1614299449.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.1,1614299449:0.1 -S3357757885
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```
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@ -86,7 +86,7 @@ Here we are providing equal weights (0.1 and 0.1) for both the subseeds. The res
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We could either try combining the images with different weights, or we can generate more variations around the almost-but-not-quite image. We do the latter, using both the `-V` (combining) and `-v` (variation strength) options. Note that we use `-n6` to generate 6 variations:
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```
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dream> "prompt" -S3357757885 -V3647897225,0.1;1614299449,0.1 -v0.05 -n6
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dream> "prompt" -S3357757885 -V3647897225:0.1,1614299449:0.1 -v0.05 -n6
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Outputs:
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./outputs/Xena/000004.3279757577.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.1,1614299449:0.1,3279757577:0.05 -S3357757885
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./outputs/Xena/000004.2853129515.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.1,1614299449:0.1,2853129515:0.05 -S3357757885
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@ -22,6 +22,12 @@ def build_opt(post_data, seed, gfpgan_model_exists):
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setattr(opt, 'invert_mask', 'invert_mask' in post_data)
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setattr(opt, 'cfg_scale', float(post_data['cfg_scale']))
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setattr(opt, 'sampler_name', post_data['sampler_name'])
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# embiggen not practical at this point because we have no way of feeding images back into img2img
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# however, this code is here against that eventuality
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setattr(opt, 'embiggen', None)
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setattr(opt, 'embiggen_tiles', None)
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setattr(opt, 'gfpgan_strength', float(post_data['gfpgan_strength']) if gfpgan_model_exists else 0)
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setattr(opt, 'upscale', [int(post_data['upscale_level']), float(post_data['upscale_strength'])] if post_data['upscale_level'] != '' else None)
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setattr(opt, 'progress_images', 'progress_images' in post_data)
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@ -109,7 +115,7 @@ class DreamServer(BaseHTTPRequestHandler):
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out_dir = os.path.realpath(self.outdir.rstrip('/'))
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if self.path.startswith('/static/dream_web/'):
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path = '.' + self.path
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elif out_dir.endswith(path_dir):
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elif out_dir.replace('\\', '/').endswith(path_dir):
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file = os.path.basename(self.path)
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path = os.path.join(self.outdir,file)
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else:
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@ -157,6 +163,7 @@ class DreamServer(BaseHTTPRequestHandler):
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def image_done(image, seed, upscaled=False):
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name = f'{prefix}.{seed}.png'
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iter_opt = argparse.Namespace(**vars(opt)) # copy
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print(f'iter_opt = {iter_opt}')
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if opt.variation_amount > 0:
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this_variation = [[seed, opt.variation_amount]]
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if opt.with_variations is None:
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25
main.py
25
main.py
@ -40,7 +40,8 @@ def load_model_from_config(config, ckpt, verbose=False):
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print('unexpected keys:')
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print(u)
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model.cuda()
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if torch.cuda.is_available():
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model.cuda()
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return model
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@ -549,23 +550,26 @@ class CUDACallback(Callback):
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# see https://github.com/SeanNaren/minGPT/blob/master/mingpt/callback.py
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def on_train_epoch_start(self, trainer, pl_module):
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# Reset the memory use counter
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torch.cuda.reset_peak_memory_stats(trainer.root_gpu)
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torch.cuda.synchronize(trainer.root_gpu)
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if torch.cuda.is_available():
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torch.cuda.reset_peak_memory_stats(trainer.root_gpu)
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torch.cuda.synchronize(trainer.root_gpu)
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self.start_time = time.time()
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def on_train_epoch_end(self, trainer, pl_module, outputs):
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torch.cuda.synchronize(trainer.root_gpu)
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max_memory = (
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torch.cuda.max_memory_allocated(trainer.root_gpu) / 2**20
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)
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if torch.cuda.is_available():
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torch.cuda.synchronize(trainer.root_gpu)
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epoch_time = time.time() - self.start_time
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try:
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max_memory = trainer.training_type_plugin.reduce(max_memory)
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epoch_time = trainer.training_type_plugin.reduce(epoch_time)
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rank_zero_info(f'Average Epoch time: {epoch_time:.2f} seconds')
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rank_zero_info(f'Average Peak memory {max_memory:.2f}MiB')
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if torch.cuda.is_available():
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max_memory = (
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torch.cuda.max_memory_allocated(trainer.root_gpu) / 2**20
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)
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max_memory = trainer.training_type_plugin.reduce(max_memory)
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rank_zero_info(f'Average Peak memory {max_memory:.2f}MiB')
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except AttributeError:
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pass
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@ -872,7 +876,6 @@ if __name__ == '__main__':
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config.data.params.validation.params.data_root = opt.data_root
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data = instantiate_from_config(config.data)
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data = instantiate_from_config(config.data)
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# NOTE according to https://pytorch-lightning.readthedocs.io/en/latest/datamodules.html
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# calling these ourselves should not be necessary but it is.
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# lightning still takes care of proper multiprocessing though
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@ -620,6 +620,7 @@ def create_cmd_parser():
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)
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parser.add_argument(
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'-embiggen',
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'--embiggen',
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nargs='+',
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default=None,
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type=float,
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@ -627,6 +628,7 @@ def create_cmd_parser():
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)
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parser.add_argument(
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'-embiggen_tiles',
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'--embiggen_tiles',
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nargs='+',
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default=None,
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type=int,
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@ -91,6 +91,7 @@ header h1 {
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}
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#fieldset-config {
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line-height:2em;
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background-color: #F0F0F0;
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}
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input[type="number"] {
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width: 60px;
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@ -122,6 +123,9 @@ label {
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cursor: pointer;
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color: red;
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}
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#basic-parameters {
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background-color: #EEEEEE;
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}
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#txt2img {
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background-color: #DCDCDC;
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}
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@ -129,15 +133,19 @@ label {
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background-color: #EEEEEE;
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}
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#img2img {
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background-color: #F5F5F5;
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background-color: #DCDCDC;
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}
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#gfpgan {
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background-color: #DCDCDC;
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background-color: #EEEEEE;
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}
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#progress-section {
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background-color: #F5F5F5;
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}
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.section-header {
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text-align: left;
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font-weight: bold;
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padding: 0 0 0 0;
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}
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#no-results-message:not(:only-child) {
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display: none;
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}
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@ -25,6 +25,7 @@
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</div>
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</fieldset>
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<fieldset id="fieldset-config">
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<div class="section-header">Basic options</div>
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<label for="iterations">Images to generate:</label>
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<input value="1" type="number" id="iterations" name="iterations" size="4">
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<label for="steps">Steps:</label>
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@ -39,11 +40,11 @@
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<option value="k_dpm_2">KDPM_2</option>
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<option value="k_dpm_2_a">KDPM_2A</option>
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<option value="k_euler">KEULER</option>
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<option value="k_euler_a">KEULER_A</option>
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<option value="k_euler_a">KEULER_A</option>
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<option value="k_heun">KHEUN</option>
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</select>
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<input type="checkbox" name="seamless" id="seamless">
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<label for="seamless">Seamless circular tiling</label>
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<label for="seamless">Seamless circular tiling</label>
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<br>
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<label title="Set to multiple of 64" for="width">Width:</label>
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<select id="width" name="width" value="512">
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@ -79,26 +80,28 @@
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<input value="0" type="number" id="perlin" name="perlin" step="0.01" min="0" max="1">
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<button type="button" id="reset-all">Reset to Defaults</button>
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</div>
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<div id="variations">
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<span id="variations">
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<label title="If > 0, generates variations on the initial seed instead of random seeds per iteration. Must be between 0 and 1. Higher values will be more different." for="variation_amount">Variation amount (0 to disable):</label>
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<input value="0" type="number" id="variation_amount" name="variation_amount" step="0.01" min="0" max="1">
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<label title="list of variations to apply, in the format `seed:weight,seed:weight,..." for="with_variations">With variations (seed:weight,seed:weight,...):</label>
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<input value="" type="text" id="with_variations" name="with_variations">
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</div>
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<div id="img2img">
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<label title="Upload an image to use img2img" for="initimg">Initial image:</label>
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<input type="file" id="initimg" name="initimg" accept=".jpg, .jpeg, .png">
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<button type="button" id="remove-image">Remove Image</button>
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</span>
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</fieldset>
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<fieldset id="img2img">
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<div class="section-header">Image-to-image options</div>
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<label title="Upload an image to use img2img" for="initimg">Initial image:</label>
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<input type="file" id="initimg" name="initimg" accept=".jpg, .jpeg, .png">
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<button type="button" id="remove-image">Remove Image</button>
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<br>
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<label for="strength">Img2Img Strength:</label>
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<input value="0.75" type="number" id="strength" name="strength" step="0.01" min="0" max="1">
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<input type="checkbox" id="fit" name="fit" checked>
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<label title="Rescale image to fit within requested width and height" for="fit">Fit to width/height:</label>
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</div>
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<label title="Rescale image to fit within requested width and height" for="fit">Fit to width/height</label>
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</fieldset>
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<fieldset id="gfpgan">
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<div class="section-header">Post-processing options</div>
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<label title="Strength of the gfpgan (face fixing) algorithm." for="gfpgan_strength">GPFGAN Strength (0 to disable):</label>
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<input value="0.8" min="0" max="1" type="number" id="gfpgan_strength" name="gfpgan_strength" step="0.05">
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<input value="0.0" min="0" max="1" type="number" id="gfpgan_strength" name="gfpgan_strength" step="0.1">
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<label title="Upscaling to perform using ESRGAN." for="upscale_level">Upscaling Level</label>
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<select id="upscale_level" name="upscale_level" value="">
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<option value="" selected>None</option>
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@ -119,7 +122,7 @@
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<div id="scaling-inprocess-message">
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<i><span>Postprocessing...</span><span id="processing_cnt">1/3</span></i>
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</div>
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</div>
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</span>
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</section>
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<div id="results">
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